Cytoarchitecture of the Anterior Cingulate Cortex in Patients with Schizophrenia, Bipolar Disorder and Major Depression

Mohamad Hakim Abbass

A thesis submitted in conformity with the requirements for the degree of Master of Science, Institute of Medical Science University of Toronto

Mohamad Hakim Abbass

Master of Science

Institute of Medical Science University of Toronto

2014 Abstract

There is evidence for impaired neuron migration during development in patients with schizophrenia and bipolar disorder, and we hypothesized to find evidence for this in post-mortem cortical tissue. We used layer-specific markers and novel automated cell counting methods to investigate large areas of the anterior cingulate cortex in patients with major depression, bipolar disorder and schizophrenia. We did not find profound differences, suggesting that any cytoarchitectural abnormalities are subtle. Almost no differences were seen in major depression, except for a smaller cell size in one region. Bipolar patients had increased densities and smaller cells in lower layers, and decreased densities in upper layers. This may support our hypothesis, or it could be explained by increased cell death in upper layers or decreased neuropil in lower layers. Patients with schizophrenia had non-consistent changes, which did not support our hypothesis. Thus, schizophrenia is potentially a particularly heterogeneous disease with multiple pathophysiological abnormalities.

Acknowledgments

Firstly, I would like to thank my supervisor and mentor Dr. Albert Wong. I am grateful for this learning opportunity and the skills he helped me gain, which I will carry on to my future career. In addition, he has been a tremendously supportive mentor by consistently offering me advice and helping me achieve my future goals and aspirations. I would also like to thank my committee members, Dr. Ramsey and Dr. Kish for continuously providing feedback and helping me with my project.

I would also like to thank my lab mates: Laura Feldcamp, Joel Kosowan and James

Samson. I appreciate all help and guidance Laura has provided me with throughout my degree.

Beginning my degree at the same time as Joel, it was helpful to have a friend to adjust to graduate school with. I am also grateful to Frankie Lee, who first taught me many of the lab techniques I know when I first began as an undergraduate student, and continued to provide me with advice during my degree. I am also thankful to Mawahib Semeralul and John Zawadski, who maintained the lab, and ensured I had what I needed to perform my experiments. I would also like to thank David Long and Jialun Chen for their continuous help with this project. I am especially thankful to Anton Semechko, who developed a MATLAB algorithm that improved the efficiency, accuracy and quality of this project.

I am grateful to all my friends who helped keep me grounded during times of stress. I would like to thank my parents, Nahaya Shareef and Hakim Abbass who have been extremely supportive through and through. I am fortunate to have such an involved support system.

Finally, I would like to extend my gratitude to Dr. Maree Webster, the Stanley Brain

Research Laboratory and Brain Collection, and the individuals who generously donated their organs for research.

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Contributions

The conception and planning of this study were completed by me and Dr. Albert Wong.

In addition, Dr. Wong also contributed to the interpretation of the results. Dr. Amy Ramsey and

Dr. Stephen Kish also helped contribute to the planning and interpretation of this work.

David Long helped with image analysis, data analysis and interpretation of the results.

Jialun Chen helped with image analysis. Anton Semechko developed the extensive MATLAB algorithm which was used to calculate cortex width, relative width of each layer, cell densities, and relative distance measures.

Dr. Maree Webster and the Stanley Brain Research Laboratory and Brain Collection provided us with cortical samples on slides. In addition, Dr. Maree Webster helped answer questions about our methodology and histological procedures.

Table of Contents

Title Page i

Abstract ii

Acknowledgements iii

Contributions iv

Table of Contents v-vii

List of Tables viii

List of Figures ix-x

List of Appendices xi

List of Abbreviations xii-xiv

Chapter 1: Introduction and Literature Review 1

1.1 Cortical Development 1

1.1.1 Developmental Brain Anatomy 1

1.1.2 Corticogenesis 2

1.1.3 Cell-Fate Determination 4

1.1.4 Cortical Neuron Migration 9

1.1.4.1 Radial Migration 9

1.1.4.2 Tangential Migration 10

1.1.4.3 Molecular Basis of Neuronal Migration 11

1.2 Cerebral Cortex 13

1.2.1 Overview of the Cerebral Cortex 13

1.2.2 Cytoarchitecture 14

1.2.3 Neurons of the Cortex 16

1.2.3.1 Pyramidal Neurons 16

1.2.3.2 Non-Pyramidal Neurons 17

1.3 Anterior Cingulate Cortex 19

1.3.1 Anterior Cingulate Anatomy 19

1.3.2 Function of the Anterior Cingulate Cortex 21

4.1 Psychiatric Disorders 23

1.4.1 Schizophrenia 23

1.4.1.1 Neurochemical Pathologies 24

1.4.1.2 Gross Anatomical Pathologies 25

1.4.1.3 Histological Studies 26

1.4.1.4 Histology in Schizophrenia 27

1.4.1.5 Anterior Cingulate Cortex in Schizophrenia 29

1.4.1.6 Genetic and Molecular Pathologies 31

1.4.2 Bipolar Disorder 35

1.4.3 Major Depression 38

Chapter 2: Research Aims and Hypotheses 40

Chapter 3: Methods 44

3.1 Tissue Samples 44

3.2 Immunohistochemistry 44

3.3 Image Analysis 46

3.3.1 Regional and Laminar Delineation 46

3.3.2 Automatic Cell Segmentation 48

3.4 Automatic Data Generation 52

3.4.1 Cell Density 54

3.4.2 Cell area 56

3.4.3 Distance from Pia 56

3.5 Statistical Analysis 57

Chapter 4: Results 58

4.1 Tissue Staining 59

4.2 Region 24a 61

4.3 Region 24b 66

4.4 Region 24c 70

4.5 Region 32 75

Chapter 5: Discussion 80

5.1 Labeled Cell Population 80

5.2 Summary of Findings 82

5.3 Schizophrenia 82

5.4 Bipolar Disorder 86

5.5 Major Depression 92

5.6 Limitations 94

5.7 Conclusion 97

Chapter 6: Future Directions 98

Chapter 7: References 101

Chapter 8: Appendix 130

8.1 Appendix 1 130

8.2 Appendix 2 131

vii

List of Tables

Table 1. Summary of histological studies of the ACC in schizophrenia 32

Table 2. Demographic information for sample populations 45

viii

List of Figures

Figure 1. Schematic of the developing cortex at roughly 30 weeks 5

Figure 2. Schematic of the formation of pyramidal neurons 7

Figure 3. Labeled pregenual and subgenual anterior cingulate cortex and mid- cingulate cortex on a sagittal section 20

Figure 4. Enlarged corononal section delineating areas 24a, 24b, 24c and 32

and the corresponding areas on a sagittal section 22

Figure 5. Stained images of CUX2, ZNF312 and DAPI 47

Figure 6. Entire cortex stained with CUX2, ZNF312 and DAPI, with areas

24a, 24b, 24c and 32 delineated 48

Figure 7. A step-by-step illustration of our segmentation process 50

Figure 8. Illustration of our segmentation for the entire cortex 51

Figure 9. Example of the raw-images inputted into our image-processing algorithm and an output illustrating the boundaries 53

Figure 10. CUX2 positive ZNF312 negative cell population shown on a cortical section 54

Figure 11. Delaunay triangulation and area parameterization onto a trapezoid of the cortex for distance measures 58

Figure 12. Staining intensity for CUX2 and ZNF312 cells across layers 59

Figure 13. Cortical width and relative layer widths for area 24a 61

Figure 14. Absolute and relative CUX2 densities in each layer for area 24a 62

Figure 15. Absolute and relative ZNF312 densities in each layer for area 24a 63

Figure 16. Absolute and relative CUX2 positive ZNF312 negative densities in each layer for area 24a 64

Figure 17. ZNF312 cell areas in area 24a 65

Figure 18. Relative mean distances for cells from the pia in area 24a 65

Figure 19. Cortical width and relative layer widths for area 24b 66

Figure 20. Absolute and relative CUX2 densities in each layer for area 24b 67

Figure 21. Absolute and relative ZNF312 densities in each layer for area 24b 68

Figure 22. Absolute and relative CUX2 positive ZNF312 negative densities in each layer for area 24b 68

Figure 23. ZNF312 cell areas in area 24b 69

Figure 24. Relative mean distances for cells from the pia in area 24b 69

Figure 25. Cortical width and relative layer widths for area 24c 70

Figure 26. Absolute and relative CUX2 densities in each layer for area 24c 71

Figure 27. Absolute and relative ZNF312 densities in each layer for area 24c 73

Figure 28. Absolute and relative CUX2 positive ZNF312 negative densities in each layer for area 24c 73

Figure 29. ZNF312 cell areas in area 24c 74

Figure 30. Relative mean distances for cells from the pia in area 24c 74

Figure 31. Cortical width and relative layer widths for area 32 75

Figure 32. Absolute and relative CUX2 densities in each layer for area 32 76

Figure 33. Absolute and relative ZNF312 densities in each layer for area 32 76

Figure 34. Absolute and relative CUX2 positive ZNF312 negative densities in each layer for area 32 77

Figure 35. ZNF312 cell areas in area 32 78

Figure 36. Relative mean distances for cells from the pia in area 32 79

List of Appendices

Appendix 1. Derivation of the Abercrombie Correction Factor 130

Appendix 2. Derivation of the Nuclear Size Correction Formula 131

List of Abbreviations

ACC: Anterior cingulate cortex aMCC: Anterior mid-cingulate cortex

BDNF: Brain-derived neurotropic factor

BMP: Bone morphogenic protein

BOLD: Blood-oxygen-level dependent

CAMKII: Calmodulin-dependent protein kinase II

CB: Calbindin

CFPN: Corticofugal projection neurons

CP: Cortical plate

CPN: Callosal projection neurons

CR: Calretinin

CRz: Cajal-Retzius

CS: Cingulate Sulcus

CSs: Superior cingulate sulcus

CRK: CT10 regulator of kinase

CTPN: Corticothalamic projection neurons

CUX2: Cut-like homeobox 2 dACC: Dorsal Anterior Cingulate Cortex

DBS: Deep brain stimulation

DCX: Doublecortin

CDK5: Cyclin-dependent kinase 5

DSM: Diagnostic and statistics manual of mental disorders

DAB1: Disabled-1

xii

DAPI: 4',6-diamidino-2-phenylindole

DISC1: Disrupted in schizophrenia 1

DTI: Diffusion tensor imaging

ECM: Extra-cellular matrix

FBS: Fetal bovine serum

FEZF2: Forebrain Embryonic Zinc Finger Protein 2 (also known as ZNF312)

FFT: Fast-fourier transformation fMRI: Functional Magnetic Resonance Imaging

GABA: Gamma aminobutyric acid

GAD67: glutamic acid decarboxylase 67

GE: Ganglionic emience

GW: Gestational week

GWAS: Genome-wide association study

ICD: International Statistical Classification of Diseases and Related Health Problems

IHC: Immunohistochemistry

IZ: Intermediate zone

MCC: Mid-cingulate cortex

MT: Microtubule

MZ: Marginal zone

NDEL1: Nuclear distribution protein nudE-like 1

NeuN: Neuronal Nuclei

NMDA: N-methyl-D-aspartate receptor

NRG1: Neuregulin 1

LIS1: Lissencephaly 1 protein oRGC: Outer radial glial cell

xiii pACC: Pregenual anterior cingulate cortex

PAX6: Paired box protein 6

PBS: Phosphate buffered saline

PCC: Posterior cingulate cortex

PP: Preplate

PV: Parvalbumin

RAP1: Ras-proximate-1

RELN: Reelin

RGC: Radial glial cell sACC: Subgenual anterior cingulate cortex

SCPN: Subcortical projection neurons

SNP: Single nuclear polymorphism

SP: Subplate

SUN1/1: Sad1 and Unc-84 Domain Protein 1/2

SVZ: Subventricular zone

TBR1: T-box brain 1

TH: Tyrosine Hydroxylase

VZ: Ventricular zone

ZNF312: Zinc finger protein 312

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CHAPTER ONE: INTRODUCTION AND LITERATURE REVIEW

1.1 CORTICAL DEVELOPMENT

1.1.1 Developmental Brain Anatomy

O’Rahilly and Müller (2007) document the anatomy of the developing human nervous system. Within the first two gestational weeks (GW) of fertilization, a human embryo has progressed from a single fertilized cell to a three layered gastrula. The three germ-layers that define the gastrula are the endoderm, mesoderm and ectoderm. On GW3, the medial ectoderm receives Bone Morphogenic Protein (BMP) inhibitor signals, allowing this region to differentiate into the neuroectoderm, which then thickens into the neural plate. Through neurulation, the neural plate folds to form a neural tube running down the rostro-caudal axis of the embryo.

During GW4-5 neuroepithelial cells in different locations of the neural tube begin to proliferate at different rates, forming three vesicles: prosencepelon (forebrain), mesencephalon (midbrain), and the rhombencephalon (hindbrain) vesicles. The mesencephalon and rhombenphalon later form the brainstem and cerebellum. The prosencephalon is further divided into the diaencephalon and telencephalon. The telencephalon goes on to form the cerebral cortex, olfactory bulbs, basal ganglia and the two lateral ventricles.

By GW6, the telencephalon is a bilateral evagination forming two lateral out-pockets.

The tissue encapsulating the dorsal aspect of each outpocket is termed the pallium, which can be distinguished into a medial, dorsal and lateral separation based on the regional histology. The lateroventral region of the telencephalon, adjacent to the lateral pallium, is termed the subpallium. The medial, dorsal and lateral pallia will develop into the future archicortex,

1 neocortex and paleocortex respectively. The subpallium will form the basal ganglia, as well as the transitory ganglionic eminence (GE). The GE generates tangentially migrating interneurons destined for the cortex and olfactory bulbs.

1.1.2 Corticogenesis

On day 30, the neural tube has just closed rostrally and a single layer of neuroepithelial cells surround the recently evaginated lateral ventricles, termed the ventricular zone (VZ)

(Brystron 2008). VZ neuroepithelia connect to the pial and ventricular surfaces through two radial processes. When these cells symmetrically divide, their nuclei move between the pial and ventricular surfaces during different cell-cycle stages through a process called interkinetic nuclear movement. On day 32, a new layer adjacent to the pial surface, termed the preplate, emerges. This layer contains a heterogeneous population of early, transient neurons which have migrated from extra-cortical areas (Bystron, Rakic et al. 2006). Immunohistochemical anaylsis of the preplate shows that this area is subcompartmentalized into a reelin positive region composed of Cajal-Retzius (CRz) cells closer to the pial surface, and a reelin negative subcompartment underneath (Marín-Padilla 1998). On day 33, VZ neuroepithelia begin to switch from symmetric to asymmetric cell division, predominantly producing radial glial cells (RGCs) (Noctor, Flint et al. 2001). Shortly after, intermediate progenitors appear above the VZ neuroepithelia, delineating a region called the subventicular zone (SVZ). SVZ intermediate progenitors are the result of asymmetric division of RGCs (Noctor, Martinez-Cerdeno et al. 2004). While RCGs generate both glia and neurons directly, the more restricted intermediate progenitors in the SVZ only generate neurons (Tarabykin, Stoykova et al. 2001). Recent studies have reported that some

RGCs move towards the outer SVZ during GW14, and are therefore termed outer RGCs

(oRGCs) (Hansen, Lui et al. 2010). Like RGCS, oRGCs also self-renew and produce neurons.

Beginning on day 50, the first radially migrating neurons from the VZ and SVZ appear.

These neurons constitute the cortical plate (CP), destined to become layers II to VI of the neocortex. The layers in the CP accumulate one layer at a time in an inside-out fashion: the earliest neurons form layer VI, and the latest neurons migrate past layers VI-III to become layer

II (Angevine, Sidman 1961). These layers appear in waves, with condensations observed between 10-12 weeks, 13-15 weeks, 16-24 weeks and by the 32nd week, the six neocortical layers can be distinguished (Kostovic 1990). All future pyramidal neurons originate from the

SVZ and VZ before radially migrating to their prospective layers. Interneurons appear at the same time as pyramidal neurons while each layer is formed; however, rodent studies suggest that almost all interneurons originate in the ganglionic eminences and migrate tangentially to the CP

(Xu 2004). The origin of interneurons in humans remains unclear, with studies reporting differing contributions from the VZ/SVZ (Letinic, Zoncu et al. 2002, David V Hansen, Jan H Lui et al. 2013).

The first radially migrating neurons stop just before reaching the reelin-positive subcompartment of the preplate. This splits the preplate into an upper layer termed the marginal zone (MZ; later forming layer I) and the subplate (SP) (Marín-Padilla 1998). Neurons are likely continuously added to the MZ through tangential migration until the 25th week. The MZ remains a complex and heterogeneous region during development; however, a large number of neurons in the MZ disappear. The mature layer I remaining in a newborn’s brain contains a small number of neurons (CR cells) and the dendritic arborizations of the pyramidal neurons underneath. The SP houses transient pyramidal and interneurons that appear throughout the development of the cortex (Kostovic, Rakic 1990). Many SP neurons form transient connections with neurons in the

CP, and extend descending axons which form a scaffold for future thalamocortical axons

(Bystron, Blakemore et al. 2008). Thalamic afferents generally wait in the SP before invading layer IV in the overlying CP. Therefore, the SP is particularly large underneath future sensory and association cortices. The SP disappears by birth, and its remnants include white-matter interstitial cells.

Finally, a layer termed the intermediate zone (IZ) can be distinguished in the developing cortex by GW8. It is difficult to distinguish the IZ from the SP without using SP specific markers, and controversy remains about where to draw the line between these different regions.

In concordance with the definition outlined by the Boulder Committee, the IZ is the space between the germinal layers (VZ/SVZ) and postmigratory cells (SP) (Bystron, Blakemore et al.

2008). Therefore, the neurons found in the IZ are either tangentially or radially migrating through the area. Axon tracts also course through the IZ, and this region grows as more cortical pyramidal neurons extend their axons through it. Future myelination of axons in the IZ transforms this area into the underlying white matter. A schematic summarizing all of the compartments and cells described can be seen in figure 1.

1.1.3 Cell-Fate Determination

The developing cortex goes from being populated solely by neuroepithelial cells to all the subtypes of neurons. This diversity of neuronal subtypes is brought on by differential expression of transcription factors; it is the specific combination of transcription factors a cell expresses that determine the specific type of neuron it will become (Wen, Li et al. 2009). These transcription factors influence things such as: the layer a neuron will migrate to, the neurotransmitters it produces and is receptive to, and its hodology. In addition, many transcription factors associated with neuronal progenitors either promote or inhibit cell-cycle exit, thus regulating whether a cell

SVZ

Figure 1. A simplified schematic of the developing cortex at roughly 30 weeks. The Ventricular Zone (VZ), Subventricular Zone (SVZ), Intermediate Zone (IZ), Subplate (SP), Cortical Plate (CP) and Marginal Zone

(MZ) are labeled. Radial Glial Cell (RGC) bodies can be seen in blue in the VZ. Intermediate Progenitor Cells (IPCs) are multipolar cells in the lower SVZ (green cells), and an outer RGC (oRGC) can be seen in blue in the upper SVZ. White-matter tracks are seen in the IZ, and multipolar cells are illustrated in the SP in purple. A radially migrating pyramidal neuron can be seen in orange. Post-migratory red pyramidal neurons are visualized in the CP; older lower layer pyramidal neurons have had a chance to grow more neurites. Finally, bipolar cells (including Cajal Retizus) cells are visualized in yellow in the MZ.

5 will proliferate or differentiate (Dehay, Kennedy 2007). Therefore, a single transcription factor is likely insufficient to define a particular subtype of neurons because a given transcription factor may be expressed in many different neuronal subtypes. To study cell fate determination, a

Boolean process for transcription factors has been described (Greig, Woodworth et al. 2013).

This suggests that cell fate is determined by the mutual possession and exclusion of certain transcription factors.

Before the molecular determinants of a neuronal sub-type can be studied, it is necessary to decide how to categorize neurons. For instance, the neurotransmitters a neuron produces, the layer it resides in, the afferents it receives, or its site of projection are all legitimate end-stages that can be traced back to a particular lineage. One commonly used phenotypic characteristic is the type of projection a pyramidal neuron has (Greig, Woodworth et al. 2013). Cortical projection neurons can be divided into two main groups: corticofugal projection neurons (CFPN; projecting away from the cortex) and callosal projection neurons (CPN; projecting to the cortex).

CFPN can be further subdivided into corticothalamic and subcortical projection neurons (CTPN and SCPN respectively). Given that, in general, CTPNs are found in layer VI, SCPNs in layer V, and CPNs in layers II/III, these different subgroups are produced at different times in development. However, the precise point in development at which progenitors become fate- restricted remains unknown.

There are a number of transcription factors that influence various stages of neuronal progenitor fate-restriction. PAX6 is a well-characterized transcription factor in RGCs (Bystron,

Blakemore et al. 2008). It is a cell-cycle exit inhibitor, and thus it maintains the progenitor pool

(Estivill-Torrus, Pearson et al. 2002). Its decreased expression in daughter cells is typically indicative of differentiation. If a RGC daughter cell increases expression of TBR1 while

6 decreasing PAX6 it is likely to become an intermediate progenitor (Bayatti, Sarma et al. 2008,

Englund, Fink et al. 2005). RGC or intermediate progenitor daughter cells that increase TBR2 expression are likely destined to become pyramidal neurons (Bayatti, Sarma et al. 2008,

Englund, Fink et al. 2005). Projection neurons are then separated into at least two lineages:

FEZF2 positive progenitors which will become CFPNs and CUX2 positive progenitors destined to become CPNs (Greig, Woodworth et al. 2013). A simplified schematic of the development of neurons in different layers from a population of RGCs can be seen in figure 2.

Figure 2. A simplified schematic of the formation of pyramidal neurons in different layers. Earlier in development Radial Glial Cells (RGCs) in blue asymmetrically divide into another RGC and ZNF312 positive neurons (red) destined for lower layers. Later on, RGCs typically asymmetrically divide into Intermediate Progenitor Cells (IPCs; green multipolar cell) that

reside in the Subventricular Zone (SVZ). IPCs typically symmetrically divide into CUX2 positive neurons (green neurons) destined for the upper, more superficial layers.

Fezf2 is a zinc-finger transcription factor (also known as ZNF312) expressed by some

VZ progenitors during deep cortical layer formation, and also by post-mitotic CFPNs (Arlotta,

Molyneaux et al. 2005). Fezf2 is expressed highly in SCPNs in layer V, and to a lesser extent in

CTPNs in layer VI. Fezf2 null mice are missing SCPNs characteristic in layer V, including all

SCPN-specific genes (Chen, Schaevitz et al. 2005, Molyneaux, Arlotta et al. 2005). These mice have no subcortical projections to the brainstem and spinal cord. CTPNs in layer VI are present but disorganized. Therefore, Fezf2 appears to be a master regulator of SCPNs in layer V and also contributes to CTPN development in layer VI. In utero electroporation of Fezf2 in layer II/III

CPN redirects their axons to the thalamus, brainstem and spinal cord (Rouaux, Arlotta 2013).

Therefore Fezf2 is sufficient to instruct CFPN identity. Zhu et al. demonstrate that FEZF2 is highly expressed in layers V/VI in most of the developing fetal cortex (Zhu, Yang et al. 2010). In adults, FEZF2 remains more highly expressed in layers V/VI of the prefrontal cortex (Arion,

Unger et al. 2007). FEZF2 will be referred to as ZNF312 from this point.

Cux2 is a cut-like homeobox transcription factor expressed in the SVZ during layer II/III neurogenesis (Nieto, Monuki et al. 2004). Cux2 positive progenitors are also observed while deep layer CFPNs are being formed. They are mostly proliferating at this point rather than producing neurons, although they do produce a small number of neurons (Franco, Gil-Sanz et al.

2012). These neurons are reported to become deep-layer CPNs, however they may also contribute to interneurons given that a large number of Cux2 positive neurons in the deep layers are interneurons. This finding also suggests that different progenitors are fate-restricted to produce certain subtypes (CPNs or CFPNs). Cux2 seems to promote cell cycle exit in SVZ, but inhibit cell-cycle exit in the VZ (Cubelos, Sebastian-Serrano et al. 2007, Franco, Gil-Sanz et al.

2012). Therefore, proliferation is increased or decreased based on which cell population Cux2 is

8 expressed in. Additionally, in post-mitotic CPNs, it appears to coordinate dendritic growth and synapse formation (Cubelos, Sebastián-Serrano et al. 2010). CUX2 appears to be preferentially expressed in the upper layers (II-IV) in human prefrontal cortex (Arion, Unger et al. 2007).

However, in situ hybridization for CUX2 in multiple cortical areas suggest that, while expressed more strongly in the superficial layers, CUX2 appears to be variably expressed throughout all the layers (Zeng, Shen et al. 2012).

1.1.4 Cortical Neuron Migration

1.1.4.1 Radial Migration

Newly generated neurons from proliferative zones reach the CP though two modes of migration: continuous somal translocation or saltatory glial-guided locomotion (Rakic 1972,

Nadarajah, Brunstrom et al. 2001). Somal translocation is a faster mode of migration because it is continuous, and occurs predominates early in corticogenesis when the cortical wall is thin

(Nadarajah, Brunstrom et al. 2001). Therefore, only neurons destined for layer VI solely undergo this mode of migration. Neurons using this mode of migration are polarized with a long leading process attached to the pia, and a short trailing process oriented towards the ventricular surface.

The nucleus moves towards the leading process through a process called nucleokinesis, the leading process remains attached to the pial surface, and the trailing process retracts.

Later born neurons destined for glial-guided locomotion undergo a circuitous path before migrating to the CP. By observing real-time migration in organotypic embryonic rat slices,

Noctor, Martinez-Cerdeno et al. (2004) organized the migration of these cells into four phases. In the first phase, newly generated cells from RGCs in the VZ quickly migrate to the SVZ while attached to a RGC. In phase two, the cells pause in the SVZ and adopt a multipolar morphology

9 for at least 24 hours. Multipolar cells exhibit dynamic behaviour including: extending/retracting processes, changing orientation and tangentially moving across the SVZ from one RGC to another. This movement was termed multipolar migration (Tabata, Nakajima 2003). Most cells then move on to phase three, termed retrograde migration. This involves multipolar cells extending a leading process that contacts the ventricular surface, followed by the cell body moving towards the VZ. However, other neurons proceed straight from phase two to phase four, and some cells reside in the SVZ after phase two and become SVZ intermediate progenitors.

After intermediate progenitors generate neurons, they can either proceed to phase three or phase four directly. During phase four, the neuron assumes a bipolar morphology and continues to the

CP through glial-guided locomotion.

Neurons undergoing glial-guided locomotion also have leading and trailing processes; however, the leading process is not attached to the pial surface (Rakic 1972). These neurons move through multiple iterations of a three step cycle, which is repeated until they reach their final destination (Nadarajah, Parnavelas 2002). The first step is to extend the leading process, the second step is nucleokinesis towards the leading process, and the final step is to eliminate the trailing process. Unlike in somal translocation, neurons using glial-mediated locomotion use radial glial cells as a scaffold when migrating. Once the neurons reach the MZ, they undergo somal translocation over a short distance to their final location.

1.1.4.2 Tangential Migration

GABAergic interneurons produced in the GEs must migrate a relatively large distance to the CP, tangential to the layers of the developing cortex. Tangentially migrating neurons can take one of three pathways towards the pallium: through the MZ, SP or IZ/SVZ border (Anderson,

Eisenstat et al. 1997). Once the migrating neurons reach their destination, they switch from tangential to radial migration to enter the CP (Polleux, Whitford et al. 2002). Neurons migrating through the SP or IZ/SVZ reach their appropriate layers by migrating upwards using RGCs as a substrate, while neurons migrating through the MZ descend down their appropriate layers.

Interneurons migrating through the IZ/SVZ have also been reported to migrate down the VZ before radially migrating towards their appropriate layer, perhaps to acquire positional information (Nadarajah, Alifragis et al. 2002).

As reviewed by Métin, Baudoin et al. (2006), tangentially migrating interneurons move similarly to radially migrating neurons in that they extend leading processes, undergo nucleokinesis and then retract trailing processes. However, tangentially migrating interneurons extend multiple branches from their leading process, each with a growth-cone-like structure at the distal end. This growth-cone structure senses the environment for chemoattractants, such as neuregulin, and chemorepellents, such as semaphorin and ephrin. The process closer to attractants and further from repellents becomes established as the leading process. The nucleus moves towards the leading process through nucleokinesis, while the other processes retract. This cycle is repeated until the interneuron reaches its destination.

1.1.4.3 Molecular Basis of Neuronal Migration

The sequential molecular mechanisms for all of the previously discusses steps of migration remain to be elucidated. However, it is thought that the multitude of extrinsic cues and internal signaling ultimately converge on the cytoskeleton and its related proteins (Heng, Chariot et al. 2010). There are common pathways and processes regarding the types of neuronal migration previously discussed, such as nucleokinesis and the retraction of the trailing process.

However, unique molecular pathways also mediate different types of migration in different neuronal populations.

The molecular processes of the leading process remain largely unknown; however, F- actin appears to be critical in this stage (He, Zhang et al. 2010). The polymerization of F-actin pushes at the edge of the leading process to extend it, and remains anchored by contacting membrane-bound laminin receptors (Li, Han et al. 2014). Laminin receptors bind to extracellular matrix (ECM) molecules, anchoring the leading process to the substrate it is migrating on. Once the leading process has advanced, the cell’s centrosome moves forward (He, Zhang et al. 2010). Microtubules (MT) extend out of the centrosome and envelope the nucleus in a cage- like structure. Attached to the MT is a motor protein called dynein that moves towards its negative end (towards the centrosome). Numerous proteins directly influence dynein’s interaction with MTs and its activity, including LIS1, NDEL1, DISC1 and DCX (Evsyukova,

Plestant et al. 2013). This aggregate of proteins attaches to nuclear membrane proteins SUN1/2, thereby attaching the nucleus to the cytoskeletal system (Zhang, Lei et al. 2009). Dynein moves towards the centrosome, which is located closer to the leading edge, and thus provides a force for nucleokinesis. Regulation of the dynein-MT complex provides a method for regulating nucleokinesis. For instance, numerous signaling pathways converge on CDK5, which phosphorylates NDEL1 to activate it (Niethammer, Smith et al. 2000). In addition, part of the force for nucleokinesis comes from an aggregate of actomysoin behind the nucleus (Schaar,

McConnell et al. 2005). Myosin II moves F-actin towards the leading process, with the nucleus attached to F-actin via SUN1/2 (Luxton, Gomes et al. 2011).

A cell’s microenvironment is composed of extracellular molecules and cells, and it plays a critical role in almost all aspects of the cell’s migratory process. One particularly important

ECM molecule for migration is the glycoprotein Reelin, produced and released from CRz cells in the MZ. Reelin can diffuse throughout the cortical wall to influence neurons many stages of radial migration (Jossin, Goffinet 2007). Through the Dab1-Crk/CrkL-C3G pathway, Reelin activates the small GTPases RAP1 (Ballif, Arnaud et al. 2004). When RAP1 is activated in multipolar neurons in the SVZ (in phase two as previously described), it increases N-cadherin levels (Jossin, Cooper 2011). N-cadherin allows multipolar neurons to sense environmental cues to properly polarize into a bipolar neuron. Reelin is also important in triggering terminal somal translocation once the migrating neuron has reached its appropriate layer underneath the MZ

(Franco, Martinez-Garay et al. 2011). Once the migrating neuron reaches the bottom of the MZ,

Reelin activates RAP1 which in turn activates integrin α5β1 in the leading process, which can bind to fibronectin found in the MZ (Sekine, Kawauchi et al. 2012). The neuron can then travel using somal translocation to its target location.

1.2 CEREBRAL CORTEX

1.2.1 Overview of the Cerebral Cortex

Zilles (2004) reviews the organization of the human cerebral cortex, a 2-4mm thick sheet of tissue convoluted into the cranium. In brief: it is composed of the archicortex, paleocortex and neocortex. The archicortex is phylogenetically the oldest part of the cortex. It consists of three layers of cells, and is found in the hippocampus and the primary olfactory cortex in human.

These areas are important in memory formation and olfaction, respectively. The paleocortex is phylogenetically newer, and consists of four to five layers of cells. Paleocortex is mostly found in the piriform cortex and other olfactory association areas. The neocortex is evolutionarily the newest region of the cortex, and consists of six cortical layers. It forms the majority of the human

13 cerebral cortex. Regions of the neocortex are typically separated into sensory, motor and association areas, with association areas being the most abundant in humans. All in all, the cortex has a very versatile role, and is central to perception, cognition and intentional motor control.

Sanides (1969) further sub-categorized the cortex, especially at the borders of the previously discussed regions. The periallocortex is a transition cortex between the neocortex and the archi/paleocortex. The periarchcortex is at the transition of the archicortex and neocortex. It includes areas close to the hippocampus, such as the entorhinal, perirhinal, and retrosplenial cortices, in addition to a small part of the cingulate cortex. The peripaleocortex is at the transition of the neocortex (the insula in this case) and paleocortex, and is therefore found in the anterior insula. The neocortex (also known as the isocortex) can be subdivided into the true isocortex and the proisocortex. The true isocortex is largest division in humans, and found in the frontal, temporal, parietal and occipital lobes, as well as the insula and parts of the cingulate cortex. The proisocortex is the transition between the neocortex and the periallocortex. It is mostly found in limbic areas such as parts of the cingulate gyrus, insula, parahippocampal gyrus and subcallosal area.

1.2.2 Cytoarchitecture

In 1909 Brodmann performed Nissl stains over the entire human and monkey cerebral corticies, and noted all the cytoarchitectural patterns (Brodmann, Gary et al. 2006). Although cortical areas had a similar basic plan, he described 52 distinct areas, referred to as Brodmann areas. While his original classifications have been reassessed and refined, they remain frequently cited. Interestingly, many these areas are correlated with specific cortical functions (Annese

2009). Despite these differences across the cortex, the cortex has the same basic cytoarchitectural

14 pattern. In general, the neocortex contains the same six layers, with mostly the same cell types in each layer, organized in a similar way. Differences such as relative layer width, cell density and a small number of unique cells differentiate Brodmann areas in the neocortex. This similar basic plan is used as evidence to suggest that the general method of computation in the cortex is universal, but it is the different inputs and outputs that define an area’s unique function (Bond

2004).

Creutzfeldt (1995) reviewed the typical cytoarchitecture observed in each of the six layers in the neocortex. Briefly, the first layer, also known as the marginal zone, is a relatively neuron-sparse layer that contains Cajal-Retzius cells, spiny stellate neurons, and the apical tufts of pyramidal neurons underneath. Layer I receives input from the cortex and thalamic matrix neurons; cortical input to apical tufts is thought to play a role in feedback interactions important in cognitive processes such as attention (Rubio-Garrido, Perez-de-Manzo et al. 2009, Cauller

1995). Layer II, also called the external granular layer, contains small pyramidal neurons that generally project to ipsilateral cortical areas (via association fibres). Layer II also receives input from other cortical areas. Layer III is called the external pyramidal layer, and it contains small/medium pyramidal neurons. It mostly projects to contralateral cortical areas, but also has some ipsilateral association fibres. Layer IV, called the internal granular layer, mostly contains spiny stellate neurons. It primarily receives input from the thalamus, and mostly projects to layer

II/III neurons (Jones 1998). Layer V is called the internal pyramidal layer, and contains the largest pyramidal neurons in the cortex. These pyramidal neurons project out of the cortex to the striatum, brainstem and spinal cord. Layer VI, or the multiform layer, contains large pyramidal neurons and small spindle-like pyramidal neurons. It provides the main source of cortical projection fibres to the thalamus (Lam, Sherman 2009).

Neurons in these cortical layers extensively communicate vertically with each other over a small area, forming mini-columns of 80-100 neurons (Buxhoeveden, Casanova 2002,

Mountcastle 1997). Groups of mini-columns (roughly 50-100) interact with each other horizontally, forming a functional module called a column. Each column is reported to have

1000 to 10,000 neurons, suggesting there are 100,000 cortical columns in the human cortex. In brief, the underlying connections within a cortical mini-column involve: extracortical inputs to layer IV, which then projects to layers II/III, which project to layers V/VI, which project to extracortical areas (Buxhoeveden, Casanova 2002). These columns act as functional units in the cortex, communicating with other cortical columns and brain areas (Sporns, Tononi et al. 2005).

1.2.3. Neurons of the Cortex

1.2.3.1 Pyramidal Neurons

The cortex is composed of two main classes of neurons: pyramidal neurons and interneurons. As reviewed by (Spruston 2008), pyramidal neurons are the excitatory glutamatergic projection neurons of the cortex, and account for about 75% its neurons. Named after the triangular shape of their soma, they are oriented so that one apex of their triangular shape faces the pia, while the base on the opposite side faces the white matter. Their axons exit the base and join the white matter underneath. They have two types of dendrites, a single large apical dendrite and smaller basal dendrites. The apical dendrite emerges from the apex facing the pia, and typically extends to the marginal zone. Multiple basal dendrites emerge from the base facing the white matter. These dendrites have dendritic spines, which are small mushroom- shaped protrusions of plasma membrane (Nimchinsky, Sabatini et al. 2002). Dendritic spines are the post-synaptic receptive regions of incoming excitatory signals. They are not static, changing

16 their size and receptor concentration through long-term potentiation and depression. In fact, these properties suggest that dendritic spines underlie much of the molecular basis of neural plasticity, and therefore learning and memory (Segal 2005).

Even though pyramidal neurons have some similar characteristics, they have been classified in subgroups based on numerous characteristics such as sites of projection, dendritic morphology, laminar position, firing patterns and more recently, molecular composition (Hevner,

Daza et al. 2003, Molnár, Cheung 2006, Toyama, Matsunami et al. 1974, Molyneaux, Arlotta et al. 2007). Traditionally, pyramidal neurons are characterized based on their layer. In fact, many other phenotypes such as somal size, hodology and dendritic morphology correlate with the layer a pyramidal neuron is found in; however, even within a layer there is heterogeneity in pyramidal neuron composition. For instance, small numbers of CPNs have been found in layer V (Koester,

O'Leary 1993).

Pyramidal neurons can also be sub-classified based on their electrophysiological properties. The major classifications include bursting neurons with no adaptation, regularly spiking neurons with adaptation, and regularly spiking neurons without adaptation

(Franceschetti, Sancini et al. 1998). All of these characteristics presumably have underlying molecular bases. Therefore, molecular approaches have been proposed to provide a more objective classification of pyramidal neurons. Recent studies have attempted provide “molecular profiles” that distinguish different pyramidal neuron subclasses (Molyneaux, Arlotta et al. 2007,

Hevner, Daza et al. 2003, Molnár, Cheung 2006). This has been a difficult endeavour with much remaining to be elucidated.

1.2.3.2 Non-Pyramidal Neurons

Non-pyramidal neurons can be subdivided into two major groups: spiny non-pyramidal

(or stellate) neurons and aspiny non-pyramidal cells (DeFelipe and Jones, 1988). Stellate neurons have a multipolar dendritic morphology with dendritic spines, and are excitatory glutamatergic neurons. These neurons are commonly found in layer IV of the cortex and project locally, as previously discussed (Schubert, Kotter et al. 2003). In addition, non-pyramidal glutamatergic spiny neurons have been reported in layer VI (Andjelic, Gallopin et al. 2008).

The vast majority of non-pyramidal neurons are aspiny GABAergic interneurons that form local circuits in the cortex to modulate processing, as reviewed by (Thomson, Lamy 2007).

In fact, the term “interneuron” is typically used to refer to this population of cells. GABAergic interneurons encompass a diverse population of cells. Like with pyramidal neurons, interneurons can be classified based on morphology, electrophysiology or molecular markers (Gupta 2000).

Unlike pyramidal neurons, interneurons can be more easily separated in a number of classes which significantly differ from each other with respect to morphology, electrophysiology and molecular composition (Toledo-Rodriguez 2005). Interneurons typically express one of three markers, calbindin (CB), calretinin (CR) and parvalbumin (PV) (Raghanti, Spocter et al. 2010).

Over 90% of cortical interneurons express one of these markers, with very little overlap

(DeFelipe 1997). PV interneurons include large multipolar basket or chandelier interneurons that target pyramidal neurons of other cortical columns. CR interneurons include bipolar, double bouquet and Cajal-Retzius cells. Bipolar and double bouquet cells target pyramidal neurons belonging to different layers in the same cortical column. In the primate cortex, CB interneurons are typically double-bouquet cells.

1.3 ANTERIOR CINGULATE CORTEX

Vogt (2005) provides an in depth review of the cingulate gyrus. Briefly, the cingulate cortex is the largest part of the limbic lobe, laterally surrounding the corpus callosum. It is separated into anterior, middle and posterior regions along the rostrocaudal axis. The posterior cingulate cortex (PCC), with its close proximity to the parietal cortex, is involved in visuospatial orientation, particularly orienting the body towards noxious stimuli. The mid-cingulate cortex

(MCC) was commonly referred to as the caudal anterior cingulate cortex (ACC); however, these regions are cytoarchitecturally and neurochemically distinct, with different connections. The

MCC has significant skeletomotor influences, projecting to cortical and subcortical motor areas, especially in relation to reward and pain. It is also important in response selection outside of pain/reward decisions, even in tasks that do not require movement. The ACC is further separated into a subgenual region (sACC) ventral to the genu of the corpus callosum, and a pre-genual region (pACC) located more dorsally. The pACC plays a more important role in emotion and autonomic integration, and the sACC is important in autonomic control. Figure 3 illustrates these sub-divisions on a sagittal section.

1.3.1 Anterior Cingulate Cortex Anatomy

Cingulate cortex anatomy is variable in humans, with different gyral and sulcal patterns between individuals. Vogt, Nimchinsky et al. (1995) observed two distinct general patterns in the

ACC: single cingulate sulcus (CS) or double parallel CS patterns. In the single CS pattern, the cingulate gyrus is located between the callosal sulcus (immediately superior to the corpus callosum) and a single CS superior to the callosal sulcus. Within the cingulate gyrus there are horizontal dimples, or shallow depressions, parallel to the cingulate and callosal sulci.

Individuals with the double parallel CS have two CS parallel to each other. The inferior sulcus is termed CS and the superior one is termed superior CS (CSs). Therefore, there must also be two cingulate gyri; these are termed cingulate gyrus and superior cingulate gyrus.

Figure 3. A schematic of a sagittal delineating the pregenual anterior cingulate cortex (pACC) in green, the anterior portion of the mid-cingulate cortex (a-MCC) in blue and the subgenual ACC (sACC) in red. Image is adapted from Vogt (1995).

The pACC contains three Brodmann areas: area 33, 24 and 32 (Vogt, Nimchinsky et al.

1995). Moving laterally, from the callosal sulcus to the CS (or CSs in the double parallel CS case), the pACC progresses from area 33 to 24 to 32. Area 33 is a narrow band of periallocortex adjacent to the corpus callosum with minimal laminar organization. Area 24 has three subdivisions: 24a, 24b and 24c. Area 24 is aganular (lacking a layer IV), with the rest of the layers intact. Its layer V is separated into two sections, Va with a very dense band of pyramidal neurons and Vb with clumps of pyramidal neurons. Area 32 is the most lateral aspect of the pACC, and is termed the cingulofrontal transition area. It contains a mixture of characteristic features observed in area 24 underneath, and the frontal/parietal cortex around it. Areas 24 and

32 are considered proisocorticies. However, it has been suggested that they are phylogentically

20 newer more specialized areas of the neocortex, rather than evolutionarily primitive areas

(Allman, Hakeem et al. 2001). Figure 4 shows the separation of Broadmann areas in the ACC, from the view of an enlarged coronal section to a gross saggital section.

1.3.2 Function of the Anterior Cingulate Cortex

In general, the ACC is heavily associated with affective experiences and their engagement with the autonomic nervous system (Stevens, Hurley et al. 2011). Retrograde tracer injections in the rhesus monkey provide evidence that the amygdala, an area important for emotions such as fear, projects to areas 25, 24 and 32 (Vogt, Pandya et al. 1987). Both the sACC and pACC also project back to the amygdala (Ghashghaei, Hilgetag et al. 2007). Area 25 in the sACC has direct projections to many autonomic nuclei, such as the periaqueductal gray, dorsal motor nucleus of vagus, nucleus of the solitary tract and the lateral hypothalamus (Chiba,

Kayahara et al. 2001). The sACC is therefore regarded as an autonomic control centre. In contrast with the sACC, the pACC has more cortical projections and fewer connections with the amygdala and autonomic centres. The pACC has connections with the lateral prefrontal cortex, orbitofrontal cortex, and also receives some pain input from thalamic nuclei (Stevens, Hurley et al. 2011). The pACC therefore plays more of a role in affect, including pain-related affect, and its integration to the autonomic nervous system. Different areas in the cingulate cortex are also reciprocally connected to each other, and tend to reciprocally inhibit adjacent areas when active

(Bush, Luu et al. 2000).

Electrical stimulation and functional imaging studies support conclusions generated from the neuroanatomical connections of the ACC. Stimulation of areas 24 and 25 produced reports of

A Superior Cingulate Sulcus

Cingulate Sulcus

Horizontal Dip

Callosal Sulcus Callosal Sulcus

Single CS Double-Parallel CS

Figure 4. A. A schematic of a coronal section of the cingulate cortex, with labeled sulci. Two types of anatomical organizations are typically seen (Single Cingulate Sulcus or Double Parallel Cingualte

Sulci). 24a is visualized in red, 24b in blue, 24c in yellow and 32 in green. B. Represents sagittal sections of the two types of anatomical organizations, with the colour-codes from 4.A. conserved. 24c (yellow) cannot be observed here because it is found within a sulcus in both cases. Image is adapted from Vogt (1995)

22 fear and changes in respiration, cardiac rate, blood pressure, piloerection, facial flushing and gastrointestinal responses (Talairach, Bancaud et al. 1973, Hyam, Kringelbach et al. 2012).

Functional Magnetic Resonance Imaging (fMRI) show increased Blood Oxygen-Level

Dependent (BOLD) signals in the pACC during visceral pain (Strigo, Duncan et al. 2003,

Binkofski, Schnitzler et al. 1998). Listening to or recalling emotional memories also activated the ACC. More specifically, happiness increases activity in the pACC while sadness increases sACC activity (Vogt 2005). The ACC is also plays a role in cognitive tasks, particularly in relation to emotion (Etkin, Egner et al. 2011). Studies that employed an emotional conflict task, asking subjects to categorize facial emotions while ignoring emotion word labels such as

“happy”, found increased activation in the sACC (Etkin, Egner et al. 2006). The ACC is also activated during conscious regulation of emotion. sACC is one of the cortical areas activated during reappraisal, a cognitive technique focusing on reinterpreting a stimulus to elicit a different emotion response (Kalisch 2009, Etkin, Egner et al. 2011). In this case, the sACC was hypothesized to be a mediator between prefrontal areas and the amygdala. Additionally,

Beauregard, Levesque et al. (2001) demonstrated activation in the pACC of males who were instructed to inhibit their sexual arousal while watching an erotic film. All in all, the ACC may potentially be a conduit between cognitive and conscious influences on emotion.

1.4 PSYCHIATRIC DISORDERS

1.4.1 Schizophrenia

Schizophrenia is a complex psychiatric disease, characterized by positive symptoms such as hallucinations, delusions and thought disorder; negative symptoms such as anhedonia and social withdrawal; as well as cognitive impairments in various domains such as working memory

23 and attention (Kelly, Sharkey et al. 2000, Weickert, Goldberg et al. 2000, Wong, Van Tol 2003).

Diagnosis of schizophrenia is based on DSM or ICD diagnostic criteria (DSM-IV 4th ed. 1994,

WHO 1992). Based on these criteria, individuals with little overlap in symptoms can both have a diagnosis of schizophrenia, which is therefore a clinically-heterogeneous disease. The DSM-IV attempts to sub-classify schizophrenia based on different symptoms; however, the DSM-5 proposes dropping these sub-classifications (Tandon, Gaebel et al. 2013).

Studying the neuropathology of schizophrenia is difficult for a number of reasons. Firstly, the underlying neurological bases for many of the normal cognitive functions disrupted in schizophrenia remain to be elucidated. Therefore, it is difficult to attribute behavioural abnormalities to the neural abnormalities observed. Secondly, it is unlikely that there is a common pathology among all individuals with schizophrenia. It may be the case that there are multiple etiologies and pathophysiological pathways that lead to a similar clinical presentation.

In addition, the heterogeneity of the symptoms of schizophrenia makes this even more likely.

Therefore, studies investigating the pathology of schizophrenia may be investigating multiple pathologies within a single group, potentially masking each separate pathology.

1.4.1.1 Neurochemical Pathologies

Multiple lines of evidence suggest that schizophrenia may have neurochemical bases.

Both typical and atypical antipsychotics partly target dopamine D2 receptors, and typical antipsychotic potency is correlated with D2 affinity (Creese, Burt et al. 1976, Seeman 2013).

Additionally, imaging studies showed increased dopamine D2 receptor concentrations in the striatum of patients with schizophrenia (Laruelle 1998). It is hypothesized that increased D2 stimulation in the mesolimbic system leads to positive symptoms while decreased D1 stimulation

24 in the mesocortical system leads to the cognitive and negative systems in schizophrenia (Knable,

Weinberger 1997, Seeman 2013). There is also a glutamate hypothesis of schizophrenia, suggesting a hypofunction of NMDA signalling. NMDA antagonists produce behaviours similar to the positive and negative symptoms in schizophrenia, and glutamate agonists are reported to reduce the negative symptoms in schizophrenia (Halberstadt 1995, Lahti, Koffel et al. 1995,

Tuominen, Tiihonen et al. 2005). The glutamate and dopamine hypotheses are not mutually exclusive, given that glutamate modules dopamine activity (Coyle, Tsai et al. 2003).

1.4.1.2 Gross Anatomical Pathologies

Some consistent gross volumetric changes in the brains of patients with schizophrenia have been observed. Post-mortem studies suggest that patients with schizophrenia have enlarged ventricles (especially the lateral ventricles), decreased brain weight and size, and brain length

(Brown, Colter et al. 1986, Bruton, Crow et al. 1990, Crow, Ball et al., 1989, Pakkenberg 1987).

More specifically, decreased temporal lobe and thalamic volumes, and increased basal ganglia volumes have been reported (Brown, Colter et al., 1986, Pakkenberg 1992, Heckers, Heinsen et al. 1991). However, some post-mortem studies reveal negative findings for all of the parameters previously discussed (Bogerts, Falkai et al. 1990, Dwork 1997, Rosenthal, Bigelow 1972). More recently, structural imaging studies have revealed more consistent findings (Shenton, Dickey et al. 2001). CT and MRI studies have looked at more subjects, without confounds associated with post-mortem tissue acquisition and processing.

Meta-analyses of imaging studies investigating gross brain changes have shown enlarged lateral and third ventricles with volumetric reductions in the: whole brain, temporal lobes, hippocampus, amygdala, thalamus and ACC (Ellison-Wright, Glahn et al. 2008, Steen, Mull et

25 al. 2006, Vita, De Peri et al. 2006, Shenton, Dickey et al. 2001). Enlarged ventricles and decreased whole brain, hippocampal, basal ganglia and thalamic volumes are observed in first- episode schizophrenia patients (Ellison-Wright, Glahn et al. 2008, Steen, Mull et al. 2006).

Longitudinally, patients with schizophrenia have progressive gray-matter cortical volume loss, especially in the temporal and frontal cortices (Hulshoff, Kahn 2008, Mathalon, Sullivan et al.

2001, van Haren, Hulshoff Pol et al. 2007). However, it is unclear if this is a result of schizophrenia, drug treatment, co-morbidities or non-specific lifestyle or environmental factors.

Meta-analyses of Diffusion Tensor Imaging (DTI) studies investigating white matter tracts have shown abnormalities in the prefrontal and temporal cortices, cingulum (running beneath the cingulate cortex) and corpus callosum (Kubicki, McCarley et al. 2007).

1.4.1.3 Histological Studies

Unlike gross anatomical changes, cytoarchitectural analyses in patients with schizophrenia have solely relied on post-mortem tissue, and are therefore subject to the confounds associated with such studies (Harrison 2011). Tissue quality in post-mortem studies is an important issue. Since human tissue is only collected naturalistically, variability exists among samples. Factors such as hypoxia and hyperpyrexia at the time of death, post-mortem interval, pH of tissue, and freezing/thawing have been reported to degrade RNA in tissue (Barton, Pearson et al. 1993, Stan, Ghose et al. 2006, Harrison, Heath et al. 1995). Protein quality is reported to be much more stable, with no correlations observed between protein level and post-mortem interval,

RNA degradation and pH level (Stan, Ghose et al. 2006). However, protein degradation has been reported at very high post-mortem intervals (greater than 40 hours) (Liu, Brun 1995, Stan, Ghose et al. 2006). In addition to tissue quality, using very thin histological cross-sections to measure three dimensional cytoarchitecture leads to inaccuracies. For instance, counting the number of

26 objects in a defined area leads to an overestimation depending on the size of the object and the thickness of the section, because even though the object’s centre could be outside the plane of the section, part of it could still intersect with this plane (Abercrombie 1946). Corrections for some of these biases exist, and some papers employ stereological techniques to correct for them

(Schmitz, Hof 2005, Clarke 1993, Mouton 2013). Despite these confounds, histological analysis of post-mortem cortical tissue remains the only way to investigate cortical cytoarchitecture in patients with schizophrenia. If these confounds are properly controlled for, these studies can provide important information that is otherwise unobtainable.

1.4.1.4 Histology in Schizophrenia

Histological studies looking at cytoarchitecture in patients with schizophrenia have not produced consistent findings, and there is not as much consensus as with gross anatomical findings. The major areas of focus include those thought to contribute to the symptoms of schizophrenia, such as the medial and superior temporal lobe, dorsolateral prefrontal cortex, cingulate cortex, thalamus and basal ganglia. With regards to subcortical areas, the thalamus has been reported to have decreased overall neuron counts, and decreased PV positive interneuron counts (Pakkenberg B 1990, Danos, Baumann et al. 1998). The following observations have been made in the basal ganglia: decreased neuron density in the substantia nigra; fewer neurons in the nucleus accumbens; increased neuron number in the striatum; and no changes in globus pallidus and ventral pallidum (Arendt, Bigl et al. 1983, Bogerts 1982, Pakkenberg B 1990).

A number of studies investigating the entorhinal cortex found that neurons were disorganized, misplaced and misshappen; these findings were most prominent in layer II

(Arnold, Hyman, et al. 1991, Arnold, Franz et al. 1995, Arnold, Ruscheinsky et al. 1997, Falkai,

Schneider-Axmann et al. 2000, Jakob H 1986). However, Jakob H (1986) did not use a control group, and the other studies did not take into account the shifting of external landmarks in patients with schizophrenia (Akil, Lewis 1997, Harrison 1999). Studies that were more rigorous in their choices of a region of interest found no differences in the entorhinal cortices of patients who had schizophrenia (Akil, Lewis 1997, Krimer, Herman et al. 1997).

The orientation of neurons in the hippocampus has been reported to be variable, or even reversed in some cases (Kovelman 1984, Altshuler, Conrad et al. 1987, Conrad, Abebe, et al.

1991, Jönsson, Luts et al. 1997, Zaidel, Esiri et al. 1997). However, studies that showed this effect reported it in different sub-regions of the hippocampus. Additionally, some studies failed to find aberrant orientation of neurons in the hippocampus (Christison, Casanova et al. 1989,

Benes, Sorensen et al. 1991, Arnold, Franz et al. 1995). Interstitial white matter neurons left from the developing subplate have been found to be distributed more deeply in the white matter of patients with schizophrenia under the frontal, temporal and cingulate cortices (Akbarian,

Bunney ,et al. 1993, Akbarian Viñuela et al. 1993, Akbarian, Kim et al. 1996, Eastwood,

Harrison 2003, Rioux, Nissanov et al. 2003, Eastwood, Harrison 2005, Connor, Guo et al. 2009).

Even though few studies failed to observe a difference in interstitial neurons under the frontal cortex, this finding is more robust compared to other histological findings in schizophrenia

(Beasley, Cotter et al. 2002, Connor, Crawford et al. 2011).

Studies of cortical and hippocampal neuron density in schizophrenia have produced conflicting findings. Many studies report no overall difference in hippocampal and temporal cortical neuron density (Kovelman 1984, Falkai, Bogerts 1988, Benes, Sorensen et al. 1991,

Heckers, Heinsen et al. 1991, Arnold, Franz et al. 1995). However, (Jeste 1989, Jönsson, Luts et al. 1997) reported decreased hippocampal neuron density, while Zaidel, Esiri et al. (1997)

28 reported increased hippocampal neuron density. The frontal cortex has had more reports of overall increased neuron density (Selemon, Rajkowska et al. 1995, Selemon, Rajkowska et al.

1998, Rajkowska, Selemon et al. 1998). One hypothesis for the increased density suggests that it is increased due to decreased neuropil surrounding the neurons (Selemon, Goldman-Rakic 1999).

This is supported by findings of decreased pyramidal neuron dendritic length and complexity in patients with schizophrenia, contributing to the surrounding neuropil (Garey, Ong et al. 1998,

Glantz 2000, Black, Kodish et al. 2004). Studies have also consistently found smaller neurons in the hippocampus, and temporal and frontal cortices of patients with schizophrenia (Benes,

Sorensen et al. 1991, Arnold, Franz et al. 1995, Zaidel, Esiri et al. 1997, Rajkowska, Selemon et al. 1998). Decreased neuron size along with less neuropil may contribute to more neuron packing, supporting the hypothesis for increased overall neuron density. Looking at layer specific differences, few studies have found decreased neuron densities, or thinning in layer II, along with increased neuron densities in the lower layers (Benes, McSparren et al. 1991, Arnold,

Ruscheinsky et al. 1997, Selemon, Rajkowska et al. 1998). This has been observed in the entorhinal and frontal cortices.

1.4.1.5 Anterior Cingulate Cortex in Schizophrenia

Multiple lines of evidence implicate abnormalities in ACC gross anatomy, function and histology in schizophrenia. MRI studies suggest patients with schizophrenia have reduced gray matter in the pACC and MCC, with minimal changes in the sACC (Goldstein, Goodman et al.

1999, Suzuki, Zhou et al. 2005, Qiu, Younes et al. 2007, Wang, Hosakere et al. 2007, Fornito,

Yücel et al. 2009). In addition, longitudinal studies looking at individuals at a higher risk for developing schizophrenia found pre-onset reductions in ACC gray matter prior to psychosis onset (Pantelis, Velakoulis et al. 2003, Borgwardt, Riecher-Rossler et al. 2007, Fornito, Yücel et

29 al. 2009). Neuroimaging studies have found both hyperactivity and hypoactivity in the ACC.

Most ACC-dependent tasks show hypoactivation in the ACC of patients with schizophrenia compared to controls (Adams, David 2007). However, the ACC has been shown to be hyperactive during auditory verbal hallucinations in schizophrenics (Silbersweig, Stern et al.

1995, Copolov, Seal et al. 2003). Given that the ACC receives both internal and external sensory input while also potentially playing a role in conscious control of actions, it is hypothesized to form a connection between self and external objects (Damasio 1999, Adams, David 2007).

Therefore, aberrant functioning in the ACC could lead to a disconnect between the external and internal experiences in psychosis.

Histological studies of the ACC in schizophrenia are heterogeneous in both their methods and findings. For instance, some studies investigate area 24 without subdividing it and without differentiating between the pACC, sACC and the MCC; however, other studies do make these distinctions (Fornito, Yücel et al. 2009). Therefore, it is difficult to directly compare findings between these studies. Regardless, cortical thickness is reported to be decreased in area 24 of the

MCC and sACC, specifically layers II, V and VI of the sACC (Bouras, Kovari et al. 2001,

Kreczmanski, Schmidt-Kastner et al. 2005). However, no differences in laminar thickness have been observed in the pACC (Benes, Vincent et al. 2001). Studies estimating total neuron counts in areas 24 and 32 of the sACC and pACC found no differences in patients with schizophrenia

(Ongur, Drevets et al. 1998, Stark, Uylings et al. 2004). One study has observed increased neuron density in layers V and VI of area 24 of the dACC (Chana, Landau et al. 2003), with three studies reporting no change in schizophrenia (Ongur, Drevets et al. 1998, Bouras, Kovari et al. 2001, Cotter, Mackay et al. 2001, Höistad, Heinsen et al. 2013). No changes in neuron density have been observed in area 24 of the sACC in patients with schizophrenia (Ongur, Drevets et al.

1998, Bouras, Kovari et al. 2001). A meta-analysis of three studies looking at area 24 in the pACC found layer specific decreased pyramidal and non-pyramidal neuron densities in patients with schizophrenia (Benes, McSparren et al. 1991, Benes, Todtenkopf et al. 1997, Benes,

Vincent et al. 2001, Todtenkopf, Vincent et al. 2005). Non-pyramidal neuron density was decreased in layer II and pyramidal neuron density was decreased in layers IV, V and VI. No differences in neuron density have been observed in area 32 in the pACC (Jones, Johnson et al.

2002, Stark, Uylings et al. 2004). Finally, increased neuron density has been observed in the white matter underneath the ACC in patients with schizophrenia (Connor, Guo et al. 2009).

Table 1 summarizes histological studies in the ACC of patients with schizophrenia.

1.4.1.6 Genetic and Molecular Pathologies

While the etiology of schizophrenia remains to be elucidated, it appears to have a significant genetic contribution. Concordance for schizophrenia is reported to be between 41-

65% between monozygotic twins, and 0-28% in dizygotic twins (Cardno, Gottesman 2000,

Sullivan, Kendler et al. 2003). Based on these values, heritability, a measure of the phenotypic variation explained by genetic variation, is estimated to be 64% to 81% depending on which patient records are used (Cardno, Gottesman 2000, Sullivan, Kendler et al. 2003, Cannon,

Lichtenstein et al. 2009, Wray, Gottesman 2012). Genetic studies suggest that schizophrenia is highly polygenetic, with multiple variants conferring risk (Kim, Zerwas et al. 2011, Sullivan,

Daly et al. 2012, Visscher, Goddard et al. 2012). Genome-Wide Association Studies (GWAS) are finding numerous significant genetic associations with schizophrenia (Allen, Bagade et al.

2008, Stefansson, Ophoff et al. 2009, “Schizophrenia GWAS Consoritum” 2011, Ripke,

Group Area Sample Stain Findings (SZ:CN) Bouras, Kovari et al. 2001 24a/b*, sACC 44:55 Nissl Decreased thickness of layers II/III/V/VI, unchanged densities Smaller Pyramidal neurons in layer V 24a/b*, pACC 44:55 Nissl Decreased thickness of layers II/V/VI Unchanged densities, Smaller Pyramidal neurons in layer VI Kreczmanski, Kastner et al. 2005 24', aMCC 13:13 Cresyl Violet Decreased thickness over whole cortex (layers I- VI) Benes, Vincent et al. 2001 24, pACC 11:12 Cresyl Violet No difference in laminar areas, decreased pyramidal density in layer IV (decrease in layers IV/VI after Abercrombie correction), no difference in non-pyramidal neuron density, no difference in pyramidal/non-pyramidal neuron size Ongur, Drevets et al. 1998 24, sACC 11:11 Nissl More “smaller” cells and less “larger” cells, no difference in overall neuron density/number Stark, Uylings et al. 2004 24, pACC 12:14 Giemsa stain No difference in total neuron count or density

32, pACC 12:15 Giemsa stain No difference in total neuron count or density

Chana, Landau et al. 2003 24c, dACC 15:15 Cresyl Violet Smaller neurons in layer V (trend for smaller neurons in layer III p=0.038), Trends for increased density in layers V/VI (p=0.028/0.017), No change in neuron clustering Cotter, Mackay et al. 2001 24b, dACC 15:15 Cresyl Violet No differences in neuron size, density or cortical height Höistad, Heinsen et al. 2013 24, dACC 13:13 Nissl No difference in neuron density and number in layers II-III and V-VI, No difference in cortical volume Benes, McSparren et al. 1991 24, pACC 18:12 Cresyl Violet No difference in pyramidal neuron density, lower density of small neurons in layers II, III, IV, V and VI in schizophrenia + affective syndromes only Benes, Todtenkopf et al. 1997 24, pACC 10:15 IHC for TH Same number of TH-IR varicosities in pyramidal/non-pyramidal neurons, but less in neuropil of layers V/VI Jones, Johnson et al. 2002 32, pACC 8:8 Nissl No difference in pyramidal neuron density in layers III/V Connor, Guo et al. 2009 24, aMCC 22:45 IHC for NeuN Greater density of neurons in white-matter underneath layer VI

Table 1. A summary of all histology studies in the ACC of patients with schizophrenia. Area, sample size (SZ is schizophrenia, CN is control), the stain used and findings are summarized. If no subregion of area 24 was indicated (i.e. 24a, 24b, 24c), then this was not specified in the study. IHC = Immunohistochemistry, TH = Tyrosine Hydroxylase. *regions 24a/b were combined in this study.

O'Dushlaine et al. 2013). It is estimated that there may be thousands of genetic variants conferring risk to schizophrenia with different levels of penetrance. These variants also likely interact with the individual’s genetic background and environment, adding to the complexity of understanding the genetic determinants of schizophrenia.

Many risk genes for schizophrenia are involved in neurodevelopmental processes such as neuronal proliferation and migration, axonal outgrowth and synapse development (Fatemi and

Folsom, 2009). Migration-related risk genes in schizophrenia include DISC1, RELN, and

NRG1. In fact, cell migration has been explored in humans with schizophrenia. NRG1 induced cell migration in B lymphocytes was reported to be decreased in patients with schizophrenia

(Sei, Ren-Patterson et al. 2007). Another study differentiated fibroblast derived human induced pluripotent stem cells into neural progenitor cells in patients with schizophrenia (Brennand,

Savas et al. 2014). Cells derived from patients with schizophrenia were reported to migrate a shorter distance over 48 hours. Another group investigated migration in neural stem cells from the olfactory mucosa from patients with schizophrenia (Fan, Abrahamsen et al. 2013). This group actually found increased motility in cells from schizophrenia patients because of impaired focal adhesion to the fibronectin coated wells. It is difficult to extrapolate from in vivo migration studies with cells derived from schizophrenia patients, because in vivo neurons migrate in a highly-structured three-dimensional environment rich with signaling cues and cell-cell interactions.

RELN is a commonly-reported risk gene for schizophrenia, with multiple studies reporting an association between RELN variants and schizophrenia (Li, Song et al. 2011, Li, Luo

33 et al. 2013). Hypermethylation of the RELN promoter has been observed in patients with schizophrenia, in addition to 50% reductions in RELN mRNA expression in post-mortem brain tissue (Fatemi, Earle et al. 2000, Grayson 2005, Guidotti, Auta et al 2000, Impagnatiello,

Guidotti et al. 1998, Ruzicka, Zhubi et al. 2007). Reelin knockout mice have an ataxic and

“reeling” gait, and their cortical layers are inverted (Fatemi 2001). This finding is expected given the role of Reelin in neuron migration, as previously discussed. Reelin knockdown mouse models, mimicking what is observed in schizophrenia more accurately than complete knockouts, exhibit schizophrenia endophenotypes such as sensiomotor gating, working memory and executive function deficits (Brigman, Padukiewicz et al. 2006, Barr, Fish et al. 2008, Rogers,

Zhao et al. 2013). Neuronal changes in these mice are more subtle, and include decreased pyramidal spine density, increased neuron density in layers III-VI but not layer II (Liu, Pesold et al. 2001). This may suggest that minor deficits in radial migration resulted in layer II-destined neurons to not fully reach their layer. However, increased neuron density in layers III-VI can also be explained by the decreased cortical thickness observed in these mice.

DISC1 is another well-characterized risk-gene for schizophrenia. It was first identified in a large Scottish family with a translocation that correlated with a number of psychiatric disorders, including schizophrenia, bipolar disorder and major depression (Millar, Wilson-Annan et al. 2000). 18 of the 29 translocation carriers developed mental illness, while none of the 38 non-translocation carriers developed any mental health problems. While the translocation is unique to the Scottish family, other unique variants have been observed in schizophrenia patients

(Callicott, Straub et al. 2005, Allen, Bagade et al. 2008, Song, Li et al. 2008, Green, Grozeva et al. 2011, Moens, Rijk et al. 2011).

Numerous mouse models with altered Disc1 expression or sequence consistently showed behavioural changes relevant to schizophrenia or depression (Koike, Arguello et al. 2006,

Hikida, Jaaro-Peled et al. 2007, Li, Zhou et al. 2007, Clapcote, Lipina et al. 2007, Shen, Lang et al. 2008, Pletnikov, Ayhan et al. 2008). Models that could temporally alter Disc1 suggest that deficits were only observed when Disc1 was altered pre- and peri-natally (Li, Zhou et al. 2007,

Niwa, Kamiya et al. 2010). Adults exhibited morphological abnormalities such as enlarged ventricles, smaller corticies and aberrant dendritic formation. One study specifically investigated neuron migration in a Disc1 mouse model with an L100P point mutation (Lee, Fadel et al. 2011).

Immunohistochemistry targeting upper layer pyramidal neurons was performed, and these neurons extended to lower layers in the L100P mutants. Therefore, it was hypothesized that minor deficits in migration would have a larger impact on upper layer neurons, which need to migrate a longer distance. These neurons may have not made it to their target layers, and would therefore be observed in lower layers.

1.4.2 Bipolar Disorder

Bipolar disorder is a psychiatric condition characterized by periods of elevated mood and depression (DSM-IV. 4th ed. 1994, Muller-Oerlinghausen, Berghofer et al. 2002). Periods of elevated mood can be characterized as mania or hypomania depending on the severity, and patients with mania can experience psychosis. Given the overlap of these symptoms with schizophrenia, it has been suggested that these disorders may be on a continuum (Moller 2003,

Maier, Zobel et al. 2006, Post 2010, Keshavan, Morris et al. 2011). Consistent with the spectrum concept, schizoaffective disorder is a condition characterized by symptoms present in both schizophrenia and mood disorders. However, this hypothesis of a psychosis continuum remains a

35 topic of debate, and the relationship between these disorders remains unclear (Lawrie, Hall et al.

2010).

With regards to pathophysiology, gross anatomical and histological changes have been observed in bipolar disorder (Harrison 2002). Patients with bipolar disorder are reported to have enlarged lateral ventricles, decreased corpus callosum area, whole brain volume reductions

(particularly the prefrontal lobe) and increased global pallidus volume (McDonald, Zanelli et al.

2004, Kempton, Geddes et al. 2008, Arnone, Cavanagh et al. 2009, Houenou, Frommberger et al.

2011). MRI studies investigating cortical gray matter changes have been inconsistent, with varying reports of decreased cortical thickness in the medial prefrontal, temporal and parietal corticies (Lyoo, Kim et al. 2004, Farrow, Whitford et al. 2005, Lyoo, Sung et al. 2006, Janssen,

Reig et al. 2008). Other studies have found increased gray matter in temporal and parietal regions (Adler, DelBello et al. 2007, Bearden, Thompson et al. 2007, Kempton, Geddes et al.

2008). While some studies have reported reduced gray matter in the sACC, others have reported increases in this area (only in males) in addition to the rest of the cingulate cortex (Hirayasu,

Shenton et al. 1999, Adler, DelBello et al. 2007, Koo, Levitt et al. 2008, Fornito, Yucel et al.

2009). Longitudinal studies suggest that gray matter in the sACC and pACC decreases following onset of bipolar disorder, and continues to decrease over the next 2-3 years (Farrow, Whitford et al. 2005, Gogtay, Ordonez et al. 2007, Koo, Levitt et al. 2008).

Neuron density is reported to generally be decreased in bipolar disorder (Harrison 2002).

Pyramidal neuron density in layers II, III and V was reported to be decreased in the dorsolateral

PFC in one study (Rajkowska, Halaris et al. 2001). Another study investigating total neuron density only found reductions in layer V in this area (Cotter, Mackay et al. 2002). Reductions in neuron density have also been observed in the hippocampus, including GABAergic interneurons

(Benes, Kwok et al. 1998, Heckers, Stone et al. 2002). Cytoarchitectural studies in area 24 of the

ACC have not produced consistent results in bipolar disorder patients. Three studies report no changes in neuron density or size in the pACC and sACC (Ongur, Drevets et al. 1998, Benes,

Todtenkopf et al. 2000, Cotter, Mackay et al. 2001). Benes, Vincent et al. (2001) reported reduced density of non-pyramidal neurons in layer II, larger non-pyramidal neurons in layers

II/III and larger pyramidal neurons in layer II in the pACC. Another study reported reduced neuron density in layers III, V and VI in area 24 of the sACC but no difference in the pACC

(Bouras, Kovari et al. 2001). Potentially decreased CB interneurons have been observed in layer

II, and increased clustering of PV interneurons in area 24 of the pACC (Cotter, Landau et al.

2002). Decreased neuron clustering in 24c of the MCC has also been observed, with decreased neuron size in layer V and increased neuron density in layer VI (Chana, Landau et al. 2003). A meta-analysis of area 24 in the pACC reported decreased density of non-pyramidal neurons in layer II in bipolar disorder (Todtenkopf, Vincent et al. 2005).

Heritability for bipolar disorder, estimated from twin studies, has been reported to be

71%-83%, and therefore has a significant genetic contribution (McGuffin, Rijsdijk et al. 2003,

Edvardsen, Torgersen et al. 2008). In fact, a significant number of risk genes for bipolar disorder are also risk genes for schizophrenia. The common genetic variation between schizophrenia and bipolar disorder contributing to their heritability was found to be 15% at a large genome-wide study looking at SNPs (Cross-Disorder Group of the Psychiatric Genomics Consortium et al.

2013). However, the genetic variation contributing to schizophrenia and bipolar disorder that was assessed in this study was 23% and 25% respectively. This is used as evidence to support the continuum hypothesis of psychosis in bipolar disorder and schizophrenia. Like with schizophrenia, multiple genes associated with neuron migration have been implicated with

37 bipolar disorder, including DISC1 and RELN (Millar, Wilson-Annan et al. 2000, Hennah,

Thomson et al. 2009, Ovadia, Shifman 2011).

1.4.3 Major Depression

Major Depressive Disorder is a unipolar mood disorder characterized by decreased mood, anhedonia and physiological changes such as sleep and appetite disturbances (DSM-IV 4th ed.

1994, Belmaker, Agam 2008). The boundary between major depression and bipolar disorder is not clear-cut, and it has been suggested that these disorders lie on a continuum referred to as a mood spectrum, existing on a different dimension to the psychosis continuum bipolar disorder and schizophrenia lie on (Cassano, Rucci et al. 2004, Akiskal, Benazzi 2006). However, more recently it has been suggested that mania and depression may be on separate dimensions rather than the same one, creating three-dimensions along with the psychosis axis (K R Merikangas, L

Cui et al. 2014).

Neuropathological studies have reported some anatomical and histological changes associated with major depression. A meta-analysis of structural imaging studies reported volume reductions in the ACC, orbitofrontal cortex, lateral prefrontal cortex, hippocampus and striatum

(Koolschijn, van Haren et al. 2009). Volume reduction in the ACC in patients with major depression was the strongest and most robust finding, particularly in the left ACC. The sACC in particular is strongly associated with major depression (Hamani, Mayberg et al. 2011). As previously discussed, sACC activity increases during feelings of sadness. Patients with major depression have increased sACC activity, and various forms of treatment reduced activity in this region (Drevets, Price et al. 1997, Mayberg, Silva et al. 2002, Mayberg, Liotti et al. 1999,

Hamani, Mayberg et al. 2011). In fact, high frequency deep brain stimulation (DBS) has been

38 used on the sACC of patients with major depression (Lozano, Mayberg et al. 2008). Patients reported improved mood during stimulation, and six months after surgery 60% of patients responded positively, and 35% met criteria for remission.

Cytroarchitectural studies report some differences in areas associated with major depression, although findings are inconsistent as with bipolar disorder and schizophrenia

(Harrison 2002). One study found decreased neuron size in layers III and IV and decreased density of neurons in layers II, III and IV of the dorsolateral PFC (Rajkowska, Miguel-Hidalgo et al. 1999); however, another study investigating this area only found decreased neuron size in layer VI (Cotter, Mackay et al. 2002). Lewis, Cruz et al. (2001) found no changes in PV interneurons in the dorsolateral PFC in major depression patients. Misplaced cells have been reported in the right entorhinal cortex, and an increased number of cells positive for an apoptotic marker have been reported in the hippocampus (Bernstein, Krell et al. 1998, Lucassen, Müller et al. 2001). Cotter, Mackay et al. (2001) only reported decreased neuron size in layer VI of area 24 in the sACC in major depression patients. Other studies have not found changes in area 24 of the sACC or pACC in major depression patients (Ongur, Drevets et al. 1998, Bouras, Kovari et al.

2001, Cotter, Landau et al. 2002).

Heritability for major depression is estimated to be 37% based on twin and family studies

(Sullivan, Neale et al. 2000). Major depression therefore also has a genetic component, although to a lesser extent than schizophrenia and bipolar disorder. There is a significant amount of overlap in risk genes between major depression and bipolar disorder and schizophrenia. The large genome-wide study previously mentioned found that about 10% of the genetic variation in

SNPs contributed to both major depression and bipolar disorder, and 9% for major depression and schizophrenia (Cross-Disorder Group of the Psychiatric Genomics Consortium et al. 2013).

The genetic variation explored in this study that contributed to major depression was reported to be 21%. Major risk-genes associated with major depression are typically involved in neurotransmission, such as serotonin receptors (Flint, Kendler 2014). There is less evidence to suggest that genes associated with neuronal migration confer risk to developing major depression; however, polymorphisms in DISC1 and RELN have been linked to major depression

(Hashimoto, Numakawa et al. 2006, Fatemi 2011).

CHAPTER TWO: RESEARCH AIMS AND HYPOTHESES

Multiple lines of evidence suggest that schizophrenia is a neurodevelopmental disorder, and more specifically, is associated with impaired neuron migration. Histological studies consistently report increased interstitial white-matter neuron densities. This potentially suggests impaired neuronal migration towards the cortical plate during development, and that more neurons remained in the subplate of patient with schizophrenia. The findings of decreased neuron densities in superficial layers along with increased densities in lower layers may also be explained by migration deficits in neurons that did not reach the top layers. Genetic studies further support the hypothesis of impaired neuron migration, with polymorphisms in genes such as RELN, DISC1, and NRG1 being associated with schizophrenia. Schizophrenia mouse models support this hypothesis as well. Finally, alterations in neuron migration have been directly observed from patients with schizophrenia in vitro.

We hypothesize that schizophrenia is associated with deficits in neuron migration, and that the result of this can be observed in post-mortem cortical samples from adult patients. We hypothesize that deficits in neuron migration are more subtle in schizophrenia as compared to disorders such as lissencephaly, and therefore histological evidence will also be more subtle.

Given that upper cortical layer neurons have the longest distance to migrate during development, we suggest that these neurons will be the most affected by deficits in migration. Neurons destined for the upper layers may be less likely to reach those layers, and we suggest that they may be found ectopically in lower layers. We therefore expect to find greater densities of neurons destined for upper layers to be found in lower layers in post-mortem cortical tissue from schizophrenia patients compared to unaffected controls.

We also plan to investigate post-mortem cortical samples of patients with bipolar disorder and major depression. There is some evidence to suggest there is impaired neuron migration in bipolar, and to a lesser extent in major depression. We expect a less pronounced, or potentially negligible effect in bipolar disorder and major depression. We are interested in exploring to what extent this neuronal disturbance would be found in mental illness. In other words, is it unique to schizophrenia, or illnesses on the psychosis continuum, or does it extend to mental illnesses on the mood spectrum as well?

We plan to take advantage of layer-specific neuronal molecular markers to identify different populations of neurons. Specifically, we plan to use antibodies against CUX2 to identify upper layer II/III neurons, and ZNF312 antibodies for lower layer V/VI neurons. We plan to exclude double-labelled CUX2 and ZNF312 neurons, leaving a population of

CUX2+ve/ZNF312-be neurons (also referred to as CUX-ZNF neurons) that have an upper layer identity. We plan to then measure the relative distance of these neurons from the pial surface, and we hypothesize that this measure will be increased in schizophrenia. No other study has investigated the distances from cortical neurons to the pia in mental illness.

We plan to test this hypothesis on post-mortem cortical tissue samples of the pACC, specifically looking at Brodmann areas 24a, 24b, 24c and 32. To our knowledge, there have not been any reports investigating laminar cortical cytoarchitecture using molecular markers in schizophrenia. Our methods have the advantage of objectively defining neuron subtypes in different layers rather than subjective morphological criteria. In addition, we plan to investigate larger cortical areas than previous studies. While histology studies select regions of interest

1000um wide at most, we plan to investigate the entire available coronal area on a slide. We are able to perform these measurements by fully automating the segmentation and counting of cells,

42 taking advantage of computationally demanding tasks that may have been more difficult to achieve in the past.

Since we are investigating the cytoarchitecture of larger cortical areas in a more objective manner, we plan to also perform measurements of neuron density and size. These measurements do not test our hypothesis; however, given that the literature finds conflicting results in these measures, we hope that our larger sample sizes and more objective methods will help to advance our understanding of these measures in mental illness. In addition, these measures have not been explored in the specific neuron populations our markers are targeting.

CHAPTER THREE: METHODS

3.1 Tissue Samples

Frozen cortical tissue was provided from the Stanley Brain Research Laboratory and

Brain Collection’s Neuropathology Consortium. Fresh frozen 14um thick sections mounted on

3.81cm x 7.62cm glass slides were provided from the anterior cingulate cortex. Samples were collected from just above the most rostral aspect of the genu of the corpus callosum, making our sections part of the pACC in accordance with figure 3. These were coronal cross-sections in which Broadmann areas 24a, 24b, 24c and 32 could be identified. 60 samples were provided, 15 from control individuals, 15 from schizophrenia patients, 15 from bipolar disorder patients and

15 from patients with major depression. We eliminated 7 samples due to damaged tissue. The demographic information for our sample of 53 individuals can be seen in table 2. Some samples had damage to specific areas of the tissue, so not all sub-divisions of the ACC could be used. We remained blinded to the demographic information until we completed all of our analyses.

3.2 Immunohistochemistry

Immunohistochemistry on slide-mounted cortical tissue with CUX2 and ZNF312 markers were performed as described by Waldvogel, Curtis et al. (2007). Staining was performed on the mounted slides provided. On day one, the frozen sections were first fixed by being submersed in4% paraformaldehyde for 10 minutes at room temperature. The sections were then placed in

Phosphate Buffered Saline (PBS) with 0.2% Triton-X (PBS-triton) overnight at 4ºC. On the second day, the sections were then placed in a solution of 0.1M sodium citrate buffer at a pH of

4.5 overnight at 4ºC. The sections were then transferred to a new solution of 0.1M sodium citrate buffer (pH 4.5) on the third day. 10 slides at a time were placed in a 200mL of solution which

Control Major Depression Bipolar Disorder Schizophrenia Number of Cases 13 13 14 13 Male:Female 7:6 8:5 8:6 8:5 Age 46.9 (29-61) 45.8 (30-65) 43.2 (25-61) 43.0 (25-62) PMI (hours) 23.2 (8-42) 26.7 (7-47) 32.6 (13-62) 33.5 (12-61) pH 6.31 (6-7) 6.15 (6-7) 6.14 (6-7) 6.23 (6-7) Days in Freezer 351 (30.9-774) 488 (104-931) 615 (224-836) 659 (404-938) Side of Brain 7L 6R 6L 7R 7L 7R 5L 7R Weight of Brain (g) 1487 1394 1448 1484 Substance Abusea 13:0:0:0:0 9:1:0:0:1:2 6:0:0:5:1:2 8:0:1:1:2:1 Alcohol Abuseb 5:5:1:2:0:0 4:4:0:1:1:3 2:4:1:1:3:3 3:3:2:2:2:1 Medicationsc 7:9:8:3 0:2:10:1 0:0:0:0 10:3:4:3 Suicide 0 7 9 4 Number of 24a 13 11 13 12 Number of 24b 13 11 12 12 Number of 24c 11 12 13 11 Number of 32 13 12 11 11

Table 2. Demographic information for Stanley Brain Research Laboratory and Brain Collection’s a/b Neuropathology Consortium. Ratios denote are counts for Little/none : social : moderate use (past) : moderate use (present) : heavy use (past) : heavy use (present) c ratios denote Antipsychotics : Mood Stabilizers : Antidepressants : Anticholingergics

was then placed in the centre of a 650W microwave for 90 seconds. Once the solution cooled to

room temperature, the slides were washed with PBS-triton three times for 15 minutes each time

at room temperature. The slides were then incubated with a mouse anti-CUX2 monoclonal

antibody (1:600, Abnova), diluted in solution of PBS-triton with 1:100 Fetal Bovine Serum

(FBS), for three days at 4ºC. On the sixth day, the slides were washed with PBS-triton three

times for 15 minutes each time at room temperature. They were then incubated an Alexa Fluor®

488 Goat Anti-Mouse IgG antibody, diluted in solution of PBS-triton with 1:100 Fetal Bovine

Serum (FBS), overnight at 4ºC. On day seven, the slides were washed with PBS-triton three

times for 15 minutes each time at room temperature. They were then incubated with a rabbit anti-

ZNF312B polyclonal antibody (1:600, abcam), diluted in solution of PBS-triton with 1:100 Fetal

Bovine Serum (FBS), for three days at 4ºC. On the tenth day, the slides were washed with PBS-

45 triton three times for 15 minutes each time at room temperature. They were then incubated an

Alexa Fluor® 594 Goat Anti-Rabbit IgG antibody, diluted in PBS-triton with 1:100 Fetal Bovine

Serum (FBS), overnight at 4ºC. On the eleventh and last day, the slides were washed with PBS- triton three times for 15 minutes each time at room temperature. Coverslips were then placed with the addition of ProLong® Gold Antifade Mountant with DAPI.

3.3 Image Analysis

Images were obtained using a Zeiss Epifluorescence Microscope at 10x magnification.

The whole cortex on the slide was imaged at this magnification through the use of a motorized stage; images were obtained with 15% overlap and then stitched using Volocity® 3D Image

Analysis Software to produce a single image of the whole section on the slide. Three images were produced, one for CUX2, ZNF312 and DAPI. An image overlapped with all three stains is illustrated in figure 5.

3.3.1 Region and Laminar Delineation

Photoshop CS6 was used to crop Brodmann areas 24a, 24b, 24c and 32 and trace lines delineating the pia, and boundaries of layers I, II, III, Va, Vb and VI in area 24a, 24b, 24c and layers I, II, III, IV, V and VI in area 32. Determination of the Brodmann areas were based on gross anatomical landmarks as well as cytoarchitectural differences as defined by Vogt (1995).

Briefly, 24a is the closest to the callosal sulcus and extends roughly to the horizontal dip (if present). 24b is a relatively straight gyral section, and 24c is found in the cingulate sulcus. Area

24a’s layers II/III are not as differentiated as in other regions, it has a thin but prominent layer

Va, and its layer Vb contains a high density of spindle pyramidal neurons. Area 24b has the thickest layer Va in the cingulate cortex, and also a thick layer III and Vb. Area 24c has thicker

CUX2 ZNF312 DAPI Overlap

Figure 5. Gray-scale images of CUX2, ZNF312 and DAPI along with an overlapped, artificially coloured image with all three stains. DAPI was coloured in blue, CUX2 in red and ZNF312 in green. The length of the bar is

200um.

layers II/III (typically thicker than layers V/VI), and a prominent layer Va with smaller

pyramidal neurons. Area 32 contains a sparse and thin layer IV (and is therefore dysgranular),

and a thick layer III with larger pyramidal neurons in the bottom part of it, termed layer IIIc. Due

to the subjectivity of defining these areas and layers, these steps were performed by one person.

Figure 6 shows an example of a section segmented by layer and separated into the four sub-areas

of the pACC.

I II III Va Vb VI 24c

24a

24b VI

V IV 32 III II I

Figure 6. An entire cortical section scanned with three overlapped markers. CUX2 is labeled in red, ZNF312 is labeled in green and DAPI is labeled in blue. The section is separated into areas 24a, 24b, 24c and 32 as labeled. Areas 24 are segmented into layers I, II, III, Va, Vb and VI while area 32 is segmented into layers I, II, III, IV,

V, and VI. The length of the bar is 1000um.

3.3.2 Automatic Cell Segmentation

Segmentation of CUX2 and ZNF312 cells was adapted from the protocol described by

Woeffler-Maucler, Beghin, et al. (2014). ImageJ (http://rsb.info.nih.gov/ij/) was used to segment

outlines for cells. Images were converted to 8-bit grayscale images. Pixels with intensities

greater than 200 were removed as outliers. A fast-fourier transformation (FFT) was performed on

the images, with a bandpass filter applied to the transform to filter out objects greater than 61.2

48 um and smaller than 4.6 um. The FFT-bandpass filter enhances the relative intensity of the cells, removing fluctuations in intensity over large distances (potentially caused by shading differences) and fluctuations in intensity over very small distances (caused by small structures or artifacts). Images were autoscaled (putting the lowest and highest intensities to 0 and 255 respectively), and saturated to conserve the differences in intensity. The image was then dichromatized using an intermodes automatic threshold. This step places an intensity threshold on the image, and pixels with intensities above the threshold are made black while pixels under it are made white. Therefore, a binary image is created, with black filled cells on a white background. The intensity threshold is based on an intermodes thresholdng algorithm, which places the threshold in the middle of a bimodally distributed intensity, with one local bright distribution representing the cells and one darker distribution representing the background

(Prewitt and Mendelsohn, 1966). A watershed function is then applied to the image, which separates two adjacent cells that may be connected and therefore viewed as a single object.

Finally, outlines were automatically drawn over all the segmented objects. The progression of an image through these steps is illustrated in figure 7, and the segmentation performed on an entire cortical sample can be seen in figure 8.

Given that DAPI-stained nuclei are smaller, and the staining appears slightly different, different criteria for segmentation were used for DAPI images. With regards to the differences, the FFT-bandpass filter was applied to objects greater than 45.9 um and smaller than 3.1 um. In addition the image was segmented using an Otsu autothreshold. Rather than finding the midpoint between a bimodal distribution of pixel intensities, this method minimizes the variance between those two clusters.

ii CUX2 ZNF312 DAPI

Figure 7. A. The step-by-step images generated during the segmentation process are shown for CUX2 (i) and ZNF312 (ii). Going from left to right, the first image shows the raw gray-scale image. The second image is what is obtained after removing outliers and performing the Fast-Fourier Transform function with a bandpass filter while autoscaling the image. The next image represents the application of the intermodes autothreshold, with the pixels exceeding the threshold (i.e. the labeled cells) represented in red. The final image is obtained after applying the watershed function and outlining all cells. B. A contrast between the outlines automatically segmented and the raw images artificially coloured (i) and in gray-scale for each marker (ii) for CUX2, ZNF312

and DAPI. CUX2 is shown in red, ZNF312 in green and DAPI in blue. 50

B A

Figure 8. Illustration of the segmentation of cells for the entire cortex (A) and an enlarged section of the cortex (B). The outlines generated were coloured blue for DAPI outlines, red for CUX2 outlines and green for ZNF312 outlines. Only the segmented outlines are shown on a black background. Bar is 1000um in A and 100um in B.

In order to assess the accuracy of this automated counting procedure, random samples of

100 um by 100 um square cortical sections were counted manually. Out of 148 CUX2 cells

counted manually, this process segmented 142 (96%) of those cells, and counted 8 extra cells. In

total, the density counted manually was 296 cells/mm2, and automatically was 300 cell/mm2. Out

of the 121 ZNF312 cells counted manually, 120 (99%) were segmented manually, and 13 extra

cells were counted. In total, the density manually counted was 242 cell/mm2, and the

51 automatically calculated density was 266 cells/mm2. We also found the pixel intensity within the segmented cells in each of the different layers throughout area 24 in a control test section. We looked for the pixel intensity in the raw images we obtained from the Ziess Epifluorescent microscope. We then compared the intensity of our markers across the different layers to look at the specificity of our markers.

3.4 Automatic Data Generation

The images containing the outlines of the segmented cells along with the drawn lines of the cortex and layers were subsequently processed with algorithms produced in MATLAB. An example of an image inputted into MATLAB can be seen in figure 9A. Firstly, the drawn lines were enclosed by joining two perpendicular lines at the ends of the white-matter and pia lines, producing an enclosed space containing all layers in that region, as illustrated in figure 9B. The algorithm differentiated the pia from the white-matter border as having the highest curvature, and this designation was visually confirmed for each image. The area of each layer and the entire cortical region was then computed. The width of each layer was estimated by dividing the area of each layer by the mean length of the lines enclosing that layer. The cortex width was measured manually by finding the mean distance across roughly 200um sections in a test control sample. In comparison to the manually measured width of 1677um, the computed width was 1667um.

The outlined ZNF312 and CUX2 cells were only analyzed if the centroid of the DAPI stain was found within their contours; ZNF312 and CUX2 outlined cells not encompassing the centre of a DAPI cell were eliminated. Firstly, this increases the probability that the outline being segmented is actually a cell, since it is concurrently stained with DAPI. Secondly this helps

A B

White-matter and Lines for Layers Pia Lines

DAPI Outlines ZNF312 Outlines CUX2 Outlines

Figure 9A. The five images that are directly inputted into MATLAB for further analysis. This is an example image of area 24a. The bar is 500 um in width. B. Output file after the algorithm determines the pia and white-matter lines (which are then visually inspected) and defines the shape by drawing two perpendicular lines from the pia to white-matter lines.

alleviate some inherent problems in two-dimensional histological analyses, which will be

discussed below. After this elimination step, any CUX2 outlined cell that overlaps with a

ZNF312 outline was eliminated from the CUX2 outlines. This only leaves behind cells that are

CUX2 positive and ZNF312 negative. This step is performed to delineate upper layer neurons

that are strongly stained with CUX2, and removes the CUX2 neurons in the lower layers where

ZNF312 is more strongly stained. Figure 10 shows an example of the CUX+ve ZNF-ve cells

delineating upper layer neurons.

VI V IV III II I

Figure 10. A schematic of cells remaining after eliminating ZNF312 positive cells from CUX2 positive cells. Remaining cells are marked in white, most of which are found in layer II. The bar is 1000 um in width.

3.4.1 Cell Density

Cell density was measured in each cortical layer in sections 24a, 24b, 24c, and 32 for

CUX2 +ve, ZNF312 +ve and CUX+ve/ZNF-ve cells. The raw cell density measurement (DM)

was obtained by counting the number of centroids over the area investigated. However DM is a

biased measure because cell centroids that are found outside the thickness of the slide (T; 14um

in this case) could still extend into the slide and be counted. This leads to an overestimation of

cells within the region, which increases with increasing cell size. This is corrected by multiplying

DM by the Abercrombie correction factor (F) which is defined by the following formula (with DT

being the true cell density and H being the average height of each cell):

푇 퐹 = ∴ 퐷 = 퐹 × 퐷 푇 + 퐻 푇 푀

Equation 1: Abercrombie’s Correction. Factor (F) by which the true density (DT) differs from the measured density (DM) is calculated by the ratio of the thickness of the section (T) to the sum of T and the mean height of the cells (H)

The derivation of this equation can be found in the appendix. It is apparent from F that a larger T and smaller H would result in the least biased measured density (DM). This is partly why only cells overlapped with DAPI were analyzed, since DAPI only stains nuclei and therefore results in a lower H.

H is a measure that is orthogonal to the plane of the slide being investigated, and therefore is not available to us. We make the assumption that cell nuclei are spherical, and that the outlined DAPI cells are therefore circular. This is a better assumption for cell nuclei compared to neuronal cell bodies, which is another reason we only analyzed cells overlapped with DAPI. The height of the nucleus orthogonal to the plane would therefore be equal to the diameter of the nucleus in the plane. The average measured diameter (dM) of the DAPI cells in each layer was calculated from the average area (A) of DAPI cells in each layer with the following equation:

퐴 푑 = 2√ 푀 휋

Equation 2: Calculated diameter measured (dM) for each cell from the area measured (A), assuming a circular shape

However, dM underestimates the true diameter (dT) of DAPI nuclei because the nucleus would be cut at the edges of the section. This results in smaller cell diameters being observed.

This underestimation is corrected with the following formula:

2(푑 − 푇 + √(푇 − 푑 )2 + 휋푑 푇) 푑 = 푀 푀 푀 푇 휋

Equation 3: True diameter (dT) calculated based on the measured diameter (dM) and thickness of the section (T), assuming a spherical cell

The derivation of this formula is provided in the appendix. Given that a spherical shape for nuclei is assumed, the dT calculated here will be substituted for H in the Abercrombie correction factor (F) above.

3.4.2 Cell Area

Measurements for cell area were also investigated in areas 24a, 24b, 24c and 32. Given that CUX2 strongly stains the nucleus and the cell body to a lesser extent, this marker could not be used as a good measure for cell-body area. However, ZNF312 stains the cell bodies more strongly, and therefore only ZNF312 +ve neurons had their cell areas measured. This was only performed in layers III, Va, Vb and VI in areas 24 and III, V, and VI in area 32 since these are the layers where ZNF312 cells are predominantly stained.

3.4.3 Distance from Pia

The distance of each cell to the pial surface relative to the cortex width was calculated in areas 24a, 24b, 24c and 32 for all CUX+ve, ZNF312+ve and CUX+ve/ZNF-ve cells. Firstly, the

56 entire cortex was segmented into triangles through Delaunay triangulation. This is a method of connecting a set of points into triangles so that the circumference surrounding a given triangle contains no other point. In this case, it is simply used as a method to split the cortex into a set of triangles in a consistent and replicable manner. The triangles constituting the cortex were then mapped onto a trapezoid using area preserving parameterization. This maps the cortex onto a trapezoid, conserving the relative distances of the pia and white-matter line, as well as its area and the area of each triangle. The centroid of each cell is mapped onto each triangle, and its relative position is maintained during the area preserving parameterization onto a trapezoid. This process is illustrated in figure 11. A given cell’s relative distance across the cortex is therefore measured as its position on the x-axis of the trapezoid, which represents the relative distance from zero to one across the cortex. The relative distance of 11 random cells was determined manually, and on average relative distance across the pia was measured to be 0.55 and automatically calculated to be 0.54. In fact, the average difference between our manual and automatic measures was 0.95%, and the maximum difference was 2.11% (manually and automatically measured at 0.519 and 0.508 respectively). Because we are interested in upper layer neurons, and hypothesize that they are found in lower layers, we are also measuring the relative distance of each cell from the pia to the mid-point of the cortex. This measure eliminates the variation from lower layers that may mask the effect we are looking for.

3.5 Statistical Analysis

We decided to perform three planned comparisons between controls and patients for all of our measures using t-tests. We opted to use this method firstly because we had a priori hypotheses, and secondly because we are only interested in differences we see compared to control groups. We used Pearson’s r correlation to determine correlations between PMI, pH,

A B

Figure 11.A. Delaunay triangulation applied on the cortex and then transformed into a trapezoid with area preserving parameterization (B). The image is colour-coded from red to yellow to show the areas of the cortex transformed into the trapezoid. The positions of the pia line (blue) and white-matter line (green) are also shown on the trapezoid. To find the relative distance of a neuron, its position on the cortex is transformed onto the trapezoid, and its relative distance is found on the x-axis of B. The Y and X axes are relative, and therefore unitless.

days in freezer and our measures. For any differences we found, we included these variables as

covariates in our general linear model, for each planned comparison. For significant findings

(p<0.05), we report both the p-values we obtained using covariates as well as the p-values

obtained without the use of covariates in order to assess how robust our findings are. We also

assessed the impact gender, age, and cerebral hemipshere had on our findings. In order to control

for multiple comparisons, we used Bonferroni correction. For layer-specific widths, cell areas

and densities, we divided the p-value for significance (0.05) by the number of layers

investigated. We divided our p-value for significance by two for distance from the pia measures

to account for the two measures (distance from pia to mid-point and to white-matter). Normality

was assessed with the Shapiro-Wilk normality test. All statistical analysis was performed on

IBM SPSS software version 19, and graphs were drawn using GraphPad Prism 6.

CHAPTER FOUR: RESULTS

4.1 Tissue Staining

The neuronal markers were relatively consistent in their staining pattern across the regions investigated and patient/control groups. CUX2 was consistently expressed in more layer

II neurons, but there was substantial expression in the other layers as well. ZNF312 had more layer V neurons stained relative to CUX2, but was not completely specific to that layer. Layer II neurons were largely stained with ZNF312 as well. Neurons that were positive for CUX2 but negative for ZNF312 were consistently confined to layer II, with very few cells in other layers.

With regards to staining intensity, figure 12 shows the intensity of cells stained for CUX2 and

ZNF312. Out of the neurons stained with CUX2, layer II neurons were the most intensely stained, but there was significant staining in other layers. For ZNF312, neurons in layer Va and

Vb were the most intensely stained, but other neurons were also significantly stained.

S ta in in g In te n s ity

e 1 5 0 x

i C U X 2

i Z N F 3 12

- 8

) 1 0 0

(

t y

e t

n 5 0

n I

r e

v 0 A I II II a b I r I I V V e r r V r y e e r r e a y y e e y L a a y y a L L a a L L L

Figure 12 Mean Intensity of staining + standard error across the different layers in area 24 for

CUX2 and ZNF312.

Looking across regions 24a, 24b and 24c, 41 out of 56 measures were significantly different (p<0.05) across these regions (36 with a p<0.01) after performing ANOVAs, solidifying that these regions have cytoarchitectural differences. Measures that were not significantly different across groups include: CUX2 density in layers I and VI, relative CUX2 density in layer II, relative ZNF312 density in layers II and Va, CUX+ve/ZNF-ve overall density and cell areas in layers Vb and VI. As a result of the large number of differences across regions, we decided to look at these regions independently rather than combine our results to look at area

24 as a whole. The Shapiro-Wilk normality test indicated that all our measures followed a normal distribution across these regions (p<0.05).

We found that the PMI, pH and storage time of the tissue correlated with many of our measures. Using Pearson’s correlation to look for significance (p<0.05) between these variables and our measures, we found pH was positively correlated with relative width of layer I, relative distance measures, and ZNF area, and negatively correlated with measures of density. PMI was positively correlated for relative width of layer III and our density measures, and negatively correlated with cell area, relative width of layer VI, and relative distance measures. Days in freezer was negatively correlated with measures of density and positively correlated for relative distance of CUX2. We therefore used PMI, pH and storage time as covariates in our model for these measures. We report both p-values obtained with and without covariates, and in almost all cases our significant findings and trends do not change. A t-test for gender-specific changes revealed few changes. Females had a greater overall CUX2 density in area 24a, and higher

ZNF312 and CUX+ve/ZNF-ve densities in layer Vb in area 32. These are likely chance finding, but regardless none of our significant findings or trends changed after including gender in our model as a fixed variable. With regards to the hemisphere, a t-test showed only one difference:

left hemisphere had an overall higher density in CUX+ve/ZNF-ve neurons in area 24c, likely a

chance finding. Regardless, including this in our model did not change any results. Very few of

our measures correlated with age. Age correlated positively with ZNF312 density in layer VI in

areas 24b and 24c, CUX+ve/ZNF-ve density in layer II in 24c and negatively with cell area in

area 32. Given this inconsistency, these are likely chance findings, but once again our significant

results and trends did not change by including age in our model.

4.2 Region 24a

Figure 13 shows cortex width and relative width of each layer across the different groups.

Cortex width did not differ in patients compared to controls, however patients with schizophrenia

proportionately had a wider layer Va compared to controls (p=0.008). This finding survives the

Bonferroni correction, and is robust without including covariates (p=0.015). However, without

covariates it does not survive corrections for multiple-comparisons. Otherwise, the other layers

are proportionately the same widths. C o rtic a l W id th b y L a y e r

A C o rte x W id th B 0 .3 C o n tro l

4 0 0 0 M a jo r D e p re s s io n h

t B ip o la r D is o rd e r d i S c h iz o p h re n ia

3 0 0 0 W 0 .2

)

m u

o ** (

t h

2 0 0 0 r

d i

p 0 .1 W

o r

1 0 0 0 P

0 0 .0 n r l e ia o io I I II I r d n I I a b t s r e r r V V V n s o r e e r r e s h y e r r e o r i y y e e C p a a y p D o L a y y a e r z L L a a D a i L L l h L r o o c j ip S a B M Figure 13 Cortical width in area 24a. A. Mean cortical width + standard error. B. Mean proportion of each cortical layer + standard error. Layer Va is proportionately wider in patients with schizophrenia (p=0.008) * denotes p-value <0.05 with covariates, and ** denotes p-value < 0.0083 with covariates

compared to controls. 0.0083 is the adjusted cutoff for significance after the Bonferroni post-hoc correction. 61

CUX2 density overall and per layer is shown in figure 14a, and the relative densities in

each layer can be seen in figure 14b. Absolute CUX2 densities did not differ in any patient group

compared to control. The relative density of CUX2 in layer III had a trend to be lower in

schizophrenia (p=0.016), but this does not survive the Bonferroni correction. This trend remains

without including covariates (p=0.043), but still does not survive our multiple-comparison

correction. Relative density of CUX2 in layer Vb had a trend to be higher in bipolar disorder

(p=0.020), but this did not survive post-hoc testing. This trend remained without covariates being

included (p=0.030). Otherwise, no other differences were found in relative CUX2 density.

A B R e la tiv e C U X 2 D e n s ity C U X 2 D e n s ity 2 .0 2 0 0 C o n tro l M a jo r D e p re s s io n

2 B ip o la r D is o rd e r

y m t 1 .5 S c h iz o p h re n ia

1 5 0 i

e l

e * D *

1 .0 e

f 1 0 0

r a

e m

5 0 R 0 .5 u

0 0 .0 I I I I I I I I y I II a b I I a b it r r V V r I V e r V r r V V s y e e r r e r r n y e e y e e r r a a y e y y e e e L a y y a a y e y D L a a a a y y L L L L a a ll L L L a L a L L r e v O

Figure 14 CUX2 +ve density measures in area 24a. A. Mean absolute density + standard error. B. Mean density relative to overall density + standard error. There are trends for lower relative density in layer III in schizophrenia (p=0.016) and higher relative density in layer Vb in bipolar disorder (p=0.043) * denotes p-value <0.05 with covariates, and ** denotes p-value < 0.0083 with covariates compared to controls. 0.0083 is the adjusted cutoff for significance after the Bonferroni post-hoc correction.

Absolute ZNF density along with relative ZNF density can be seen in figure 15. No

differences were found in absolute ZNF densities in any patient group compared to controls.

ZNF relative densities were also comparable in patient groups compared to controls in all layers.

There was a trend for decreased relative density of ZNF cells in schizophrenia in layer III

(p=0.016); however this does not survive post-hoc testing. This trend remains without covariates

(p=0.020) but does not survive post-hoc testing.

A B R e la tiv e Z N F D e n s ity

Z N F D e n s ity 2 .0 C o n tro l 1 5 0 M a jo r D e p re s s io n

B ip o la r D is o rd e r

2 y

t 1 .5 S c h iz o p h re n ia

n s 1 0 0 e

l *

C 1 .0 e

l e

5 0 e b

R 0 .5

u N

0 0 .0 I I I I I I I I a b y I I I a b I r I V t r I V r V V i e r r V V e e r r s e r r y e r r e y y e r e y e e n a y e e y a a y y e a a y y L a y y L L a a L a a D L a L L a L ll L L L L a r e v O Figure 15 ZNF +ve density measures in area 24a. A. Mean absolute density + standard error. B. Mean density relative to overall density + standard error. There is a trend for lower density in layer III in patients with schizophrenia (p=0.016) * denotes p-value <0.05 with covariates, and ** denotes p-value

< 0.0083 with covariates compared to controls. 0.0083 is the adjusted cutoff for significance after the Bonferroni post-hoc correction.

CUX-ZNF absolute and relative densities are reported in figure 16. No differences in

absolute densities were observed in patient groups compared to controls. CUX-ZNF relative

density in layer Vb had a trend to be higher in patients with bipolar disorder (p=0.022), but this

does not survive post-hoc corrections. This finding survives testing without covariates (p=0.002)

and also survives post-hoc corrections.

ZNF cell area for patients and control groups is reported in figure 17. A trend for lower

cell area is seen in layer VI for patients with bipolar disorder (p=0.013), but this does not survive

post-hoc testing. This finding is also found without including covariates (p=0.007), and this does

survive post-hoc testing. In addition, bipolar disorder cell area in layer Vb had a trend of being

decreased without covariates (p=0.029), but this finding was not significant after including

covariates. Otherwise, no differences in cell area were observed in other groups and layers.

A B R e la tiv e C U X -Z N F D e n s ity C U X -Z N F D e n s ity 3 C o n tro l 8 0 M a jo r D e p re s s io n

B ip o la r D is o rd e r

2 y

t S c h iz o p h re n ia

i m

m 6 0 2

C e

f 4 0

a l

e 1 * e

b R

m 2 0 u N

0 0 I I I I I I I I ty I II a b I I a b i r r V V r I V s e r V r r r V V e e r r e e r n y y e e y e r r e e a y e y y y e e L a a y y a a y D L a a a L a y y a l L L L L a a l L L L L a L r e v O Figure 16 CUX-ZNF density measures in area 24a. A. Mean absolute density + standard error. B. Mean density relative to overall density + standard error. There is a trend for higher density in layer Va in patients with bipolar disorder (p=0.022) * denotes p-value <0.05 with covariates, and ** denotes p- value < 0.0083 with covariates compared to controls. 0.0083 is the adjusted cutoff for significance after the Bonferroni post-hoc correction.

Relative distances of cells from the pia to the white matter and from the pia to the mid-

point of the cortex can be seen in figure 18. No significant differences or trends were observed

for the mean relative distances for each marker in the patient groups compared to controls.

Z N F C e ll A re a

3 0 0 C o n tro l M a jo r D e p re s s io n B ip o la r D is o rd e r

) S c h iz o p h re n ia 2

m 2 0 0 *

u (

r A

l 1 0 0 e

0 I I II a b V V r V r e r r e y e e y a y y a L a a L L L Figure 17 Mean ZNF cell size + standard error in area 24a. There is a trend for smaller cell size in layer VI in patients with bipolar disorder* denotes p-value <0.05 with covariates, and ** denotes p- value < 0.0125 with covariates compared to controls. 0.0125 is the adjusted cutoff for significance after the Bonferroni post-hoc correction.

A B R e la tiv e D is ta n c e R e la tiv e D is ta n c e to M id -P o in t 0 .6

0 .3 5

a i

a C o n tro l

m M a jo r D e p re s s io n m

o 0 .5 r

o 0 .3 0

f r

B ip o la r D is o rd e r

e c

c S c h iz o p h re n ia

n n

a 0 .4 a

t 0 .2 5 t

v i

0 .3 i

t 0 .2 0

R R 0 .2

e e e .5 .5 .5 c c c 0 0 n n n 0 a a a e e e t t t c c c s s is n n n i i a a D D D ta t t s s 2 F F is i i X N N D D D U Z -Z 2 F F C X X N N U U Z -Z C C X U C

Figure 18 Cell distance measures in area 24a. A. Box and whisker plot of relative mean distances from pia to white-matter. B. Box and whisker plots of relative distances from pia to the mid-point of the cortex. There are no significant differences or trends between controls and patients.

4.3 Region 24b

Cortex width and relative width of each layer across the different groups are shown in

figure 19. No significant differences or trends were observed for cortex width or the widths of

each layer in patient groups compared to controls.

Absolute CUX2 density and the relative densities in each layer can be seen in figure 20.

Neither absolute nor relative CUX2 densities significantly differed in any patient group

compared to control, and no trends were observed. Absolute and relative ZNF densities are

reported in figure 21. Again no significant differences or trends were seen between patients and

controls. Finally, CUX-ZNF absolute and relative densities are reported in figure 22. No

significant differences or trends were observed between patients and controls.

A B C o rtic a l W id th b y L a y e r C o rte x W id th 0 .3 5 0 0 0 C o n tro l

M a jo r D e p re s s io n h

t B ip o la r D is o rd e r

4 0 0 0 d

i S c h iz o p h re n ia

0 .2

)

m 3 0 0 0

(

r d

i 2 0 0 0

o W

p 0 .1 o 1 0 0 0 r

0 0 .0 l n r a o o e i tr i d n s r e I II II I n r I a b o s o r r V V V e is h e e r r C r p y e r r e p D o y e e e r a a y y iz L a y y a D la h L L a a r o c L L o L j ip S a B M

Figure 19 Cortical width in area 24b. A. Mean cortical width + standard error. B. Mean proportion of each cortical layer + standard error. There are no significant differences or trends between controls and patients.

A B R e la tiv e C U X 2 D e n s ity C U X 2 D e n s ity 2 .0 2 5 0 C o n tro l M a jo r D e p re s s io n

2 B ip o la r D is o rd e r

y m

2 0 0 t 1 .5 S c h iz o p h re n ia

m s

e l

e 1 5 0

1 .0 e

r 1 0 0

l b

e m

R 0 .5

u 5 0 N

0 0 .0 I I I I I I I y I II a b I I I a b it r r V V r I V e r V r r V V s y e e r r e r r n y e e y e e r r a a y e y y e e e L a y y a a y e y D L a a a a y y L L L L a a ll L L L a L a L L r e v O

Figure 20 CUX2 +ve density measures in area 24b A. Mean absolute density + standard error. B. Mean density relative to overall density + standard error. There are no significant differences or trends

between controls and patients.

A B R e la tiv e Z N F D e n s ity Z N F D e n s ity 1 .5 1 5 0 C o n tro l M a jo r D e p re s s io n

2 B ip o la r D is o rd e r

y t

m S c h iz o p h re n ia

s m

/ 1 .0

n s

l 1 0 0

a e l 0 .5

5 0 b

m u N

0 0 .0 I I I I I I y I I I a b I I I a b t r I V r I V i e r r V V r r V V s e r r e e r y y e r e y e r r e n a y e e y y e e e a a y y a a y y L L a a L a y y a D L a L L L a a ll L L L L L a r e v O

Figure 21 ZNF312 +ve density measures in area 24b A. Mean absolute density + standard error. B. Mean density relative to overall density + standard error. There are no significant differences or trends between controls and patients.

A B R e la tiv e C U X -Z N F D e n s ity

C U X -Z N F D e n s ity 4 C o n tro l 1 0 0 M a jo r D e p re s s io n

B ip o la r D is o rd e r

2 y

t 3 S c h iz o p h re n ia m

8 0 i

s e

D e

6 0

C 2

v i

t r

4 0 a

e b

R 1 m

u 2 0 N

0 0 y I II II a b I I II II a b I it r I V r I V r r V V r V V s e e r r e e r r n y y e r e y e r r e e a y e e y a y y e e L a a y y a y D L a a a L a y y a L L L L a a ll L L L L a L r e v O Figure 22 CUX-ZNF density measures in area 24b A. Mean absolute density + standard error. B. Mean

density relative to overall density + standard error. There are no significant differences or trends between controls and patients.

ZNF cell areas are reported in figure 23. A trend for smaller cells was seen in bipolar

disorder in layers Vb and VI (p=0.029 and 0.021 respectively). This trend was not significant

after the Bonferroni post hoc correction. These findings were observed without the use of

covariates (p=0.011 for layer Vb and p=0.008 for layer VI), and both of these results achieved

significance with post hoc corrections. Otherwise, no significant findings or trends were

observed for cell size.

Relative distances of cells from the pia to the white matter and from the pia to the

mid-point of the cortex can be seen in figure 24. Patients with schizophrenia had a trend towards

larger relative distance to mid-point for CUX2 (p=0.040); however, this does not achieve

significance with post-hoc testing and is not observed without the use of covariates.

Z N F c e ll a re a

3 0 0 C o n tro l M a jo r D e p re s s io n B ip o la r D is o rd e r

) * S c h iz o p h re n ia 2

m 2 0 0 *

u (

l l 1 0 0

e C

0 I I II a b V V r V r e r r e y e e y a y y a L a a L L L

Figure 23 Mean ZNF cell size + standard error in area 24b. There is a trend for decreased cell size in

layers Vb (p=0.029) and VI (p=0.021) in patients with bipolar disorder. * denotes p-value <0.05 with covariates, and ** denotes p-value < 0.0125 with covariates compared to controls. 0.0125 is the adjusted cutoff for significance after the Bonferroni post-hoc correction.

R e la tiv e D is ta n c e A B R e la tiv e D is ta n c e to M id -P o in t 0 .6

0 .3 5 a i C o n tro l

M a jo r D e p re s s io n

m m

o 0 .5 r

o 0 .3 0 f

r B ip o la r D is o rd e r

e e

c S c h iz o p h re n ia

n n

a 0 .4 t

a 0 .2 5 t

v i

0 .3 i

t 0 .2 0

R R 0 .2 e e e 5 5 5 c c c . . . n n n 0 0 0 a a a e e e t t t c c c is is is n n n a a D D D ta t t s 2 F F is is i X N N D D D U Z -Z 2 F F C X X N N U U Z -Z C C X U C Figure 24 Cell distance measures in area 24b A. Box and whisker plot of relative mean distances from pia to white-matter. B. Box and whisker plots of relative distances from pia to the mid-point of the cortex. There is a trend for increased distance of CUX2 cells from the pia to the middle of the cortex in patients with schizophrenia (p=0.040). * denotes p-value <0.05 with covariates, and ** denotes p- value < 0.025 with covariates compared to controls. 0.025 is the adjusted cutoff for significance after the Bonferroni post-hoc correction.

4.4 Region 24c

Cortex width and relative width of each layer across the different groups are shown in

figure 25. No significant differences or trends were observed for cortex width or the widths of

each layer in patient groups compared to controls.

Absolute CUX2 density and the relative densities in each layer can be seen in figure 26.

Patients with bipolar disorder had a lower absolute density of CUX2 in layer I (p=0.014), but this

does not survive post-hoc testing. This trend is observed without covariates (p=0.022) but does

not survive post-hoc testing. CUX2 density is also relatively lower in layer I in patients with

bipolar disorder (p=0.005). This finding survives post-hoc testing, and is also found without

covariates (p=0.004) and also survives post-hoc testing. Patients with schizophrenia had lower

CUX2 relative density in layers I and VI (p=0.018 and p=0.042 respectively), but these findings

A B C o rtic a l W id th b y L a y e r

C o rte x W id th 0 .4 C o n tro l

5 0 0 0 M a jo r D e p re s s io n h

t B ip o la r D is o rd e r

0 .3 d

4 0 0 0 i S c h iz o p h re n ia

) n

m 3 0 0 0

o (

i 0 .2

r t

d o

i 2 0 0 0

W o

r 0 .1

1 0 0 0 P

0 0 .0 l n r a I I I I o o e i I I a b tr i d n r I V s r e r V V n r e e r r o s o y e r r e e is h a y y e e C r p a y y y p D o L a a e r z L L a a i L L L D la h r o c o j ip S a B M Figure 25 Cortical width in area 24c. A. Mean cortical width + standard error. B. Mean proportion of

each cortical layer + standard error. There are no significant differences or trends between controls and patients.

A B R e la tiv e C U X 2 D e n s ity

C U X 2 D e n s ity 2 .0 C o n tro l 2 5 0

M a jo r D e p re s s io n 2

y B ip o la r D is o rd e r

t m 2 0 0 i 1 .5

s S c h iz o p h re n ia

e l

D e

1 5 0

C 1 .0

v o

r 1 0 0 *

l b

e 0 .5 * m 5 0 R **

u * N

0 0 .0 I I I I I I I I y I II a b I I a b it r r V V r I V e r V r r V V s y e e r r e e r r n y e e y e r r e a a y e y y e e e L a y y a a a y y D L L a a L a y y a l L L L L L a a l L L a L r e v O Figure 26 CUX2 +ve density measures in area 24c A. Mean absolute density + standard error. B. Mean density relative to overall density + standard error. Relative layer I density is significantly lower in patients with bipolar disorder (p=0.005), and there are trends for decreased absolute layer I density in patients with bipolar disorder (p=0.014), and decreased relative density in layers I (p = 0.018) and VI (p=0.042) in patients with schizophrenia. * denotes p-value <0.05 with covariates, and ** denotes p- value < 0.0083 with covariates compared to controls. 0.0083 is the adjusted cutoff for significance after the Bonferroni post-hoc correction.

do not survive post-hoc corrections. They are also observed without covariates (p=0.012 in layer

I and p=0.018 in layer VI), but they are not significant with post-hoc corrections. Absolute and

relative ZNF densities are reported in figure 27. No significant differences or trends were seen

between patients and controls.

CUX-ZNF abosulte and relative densities are reported in figure 28. Absolute and relative

CUX-ZNF density in layer I was decreased in bipolar disorder (p=0.014 and p=0.005

respectively). While relative CUX-ZNF density in layer I was significant after post-hoc

corrections, absolute density in this layer was not. Both these results were found without

covariates and both survived post-hoc corrections (p=0.003 for absolute density and p<0.001 for

relative density). CUX-ZNF relative density was increased in layer III for patients with bipolar

71 disorder (p=0.008), and this survives post-hoc corrections and remains significant without covariates (p=0.001). In addition, patients with bipolar disorder had a trend for increased CUX-

ZNF density in layer Vb (p=0.030) which does not survive post-hoc testing. This trend is also observed without the use of covariates (p=0.010), but still does not survive post-hoc testing. In schizophrenia, patients had a decreased relative density of layer I CUX-ZNF cells (0.009), but this is not significant following post-hoc corrections. However, without the use of covariates there is a significant decrease in layer I CUX-ZNF relative density (p<0.001).

ZNF cell areas are reported in figure 29. There was a trend for smaller cells in layer VI in patients with bipolar disorder (0.036) which is not significant after post-hoc corrections. The same trend is seen without the use of covariates (p=0.023). A trend for smaller cells was also observed in patients with major depression in layer Vb (p=0.033), which is not significant following post-hoc testing. This trend remains without the use of covariates (p=0.025).

Otherwise, no other significant findings or trends were observed for cell size.

Relative distances of cells from the pia to the white matter and from the pia to the mid- point of the cortex can be seen in figure 30. No significant differences or trends were observed for the mean relative distances for each marker in the patient groups.

A B R e la tiv e Z N F D e n s ity

Z N F D e n s ity 2 .0 C o n tro l 2 0 0

M a jo r D e p re s s io n 2

y B ip o la r D is o rd e r

t i m 1 .5

s S c h iz o p h re n ia m

/ 1 5 0

s e

1 .0

f 1 0 0

v i

e e b 0 .5

5 0 R

u N

0 0 .0 I I I I I I y I I I a b I I II a b t r I V r r V i e r r V V e r V V s e r r e r r y y e r e y y e r e n a y e e y a y e e e a a y y a y y y L L a a L L a a D L a L L a a L ll L L L L a r e v O Figure 27 ZNF312 +ve density measures in area 24c A. Mean absolute density + standard error. B. Mean density relative to overall density + standard error. There are no significant differences or trends between controls and patients.

A B R e la tiv e C U X -Z N F D e n s ity

C U X -Z N F D e n s ity 4 C o n tro l 1 5 0

M a jo r D e p re s s io n 2

y B ip o la r D is o rd e r

t m i 3

s S c h iz o p h re n ia

m /

s e

l 1 0 0

C 2

t r

a ** e

5 0 l b

e 1 * m R * u * ** N 0 0 I I I I I I I I y I II a b I I a b it r r V V r I V e r V r r r V V s y e e r r e e r n y e e y e r r e a a y e y a y y e e e L a y y a a y y D L L a a L a y a L L L L a a ll L L L L a r e v O

Figure 28 CUX-ZNF density measures in area 24c A. Mean absolute density + standard error. B. Mean density relative to overall density + standard error. Patients with bipolar disorder had significantly

lower relative density of layer I (p=0.005) and layer III (p=0.008) neurons. There were trends for increased decreased absolute density in layer I in patients with bipolar disorder (p=0.014), decreased relative density in layer I in patients with schizophrenia (p=0.009) and increased relative density in layer Vb in patients with bipolar disorder. * denotes p-value <0.05 with covariates, and ** denotes p- value < 0.0083 with covariates compared to controls. 0.0083 is the adjusted cutoff for significance after the Bonferroni post-hoc correction.

Z N F c e ll a re a

3 0 0 C o n tro l M a jo r D e p re s s io n

) B ip o la r D is o rd e r

2 * S c h iz o p h re n ia

m 2 0 0 *

u (

e r

l 1 0 0 e

0 I I II a b V V r V r e r r e y e e y a y y a L a a L L L

Figure 29 Mean ZNF cell size + standard error in area 24c. There were trends for decreased cell size in layer Vb in patients with major depression (p=0.033) and decreased cell size in layer VI in patients

with bipolar disorder (p=0.023). * denotes p-value <0.05 with covariates, and ** denotes p-value < 0.0125 with covariates compared to controls. 0.0125 is the adjusted cutoff for significance after the Bonferroni post-hoc correction.

A B R e la tiv e D is ta n c e s R e la tiv e D is ta n c e to M id -P o in t

0 .6 0 .3 5

a C o n tro l

i i

M a jo r D e p re s s io n

m o

0 .5 o 0 .3 0 r

r B ip o la r D is o rd e r

e S c h iz o p h re n ia

c n

n a 0 .4 a

t 0 .2 5

v i

0 .3 i 0 .2 0 t

R R 0 .2

e e e .5 .5 .5 c c c 0 n n n 0 0 a a a e e e t t t c c c is is is n n n a a a D D D t t t 2 F F is is is X N N D D D U Z -Z 2 F F C X X N N U U Z -Z C C X U C

Figure 30 Cell distance measures in area 24a. A. Box and whisker plot of relative mean distances from pia to white-matter. B. Box and whisker plots of relative distances from pia to the mid-point of the cortex. There are no significant differences or trends between controls and patients.

4.5 Region 32

Cortex width and relative width of each layer across the different groups are shown in

figure 31. No significant differences or trends were observed for cortex width or the widths of

each layer in patient groups compared to controls.

CUX2 absolute and relative cell densities are reported in figure 32. Relative density for

CUX2 in layer I had a trend of being lower in patients with bipolar disorder (p=0.036), but this

does not achieve significance with post-hoc corrections, and this trend was not observed without

the use of covariates. Otherwise no other differences were observed for CUX2 densities in

patient groups. Absolute and relative ZNF densities can be seen in figure 33. Relative density of

ZNF cells in layer III was significantly lower in patients with bipolar disorder (p=0.008),

achieving significance after post-hoc corrections. A trend in this direction was seen without

covariates (p=0.033) but this does not achieve significance after post-hoc corrections. Otherwise

no differences were observed for ZNF densities.

A B C o rtic a l W id th b y L a y e r

C o rte x W id th 0 .4 C o n tro l 5 0 0 0 M a jo r D e p re s s io n

h B ip o la r D is o rd e r

t 0 .3 d

4 0 0 0 i S c h iz o p h re n ia

) n

m 3 0 0 0

u i

( 0 .2

o d

i 2 0 0 0

p W

r 0 .1

1 0 0 0 P

0 0 .0 l r o n a I I I I r o e i I II V V t i d n r r I V n s r e e r r r r o s o r y e e e e s h y e y e C r i p a a y y y p D o L a a a e L L a r iz L L L D la h r o c o j ip S a B M Figure 31 Cortical width in area 32 A. Mean cortical width + standard error. B. Mean proportion of each cortical layer + standard error. There are no significant differences or trends between controls and patients. 75

A B R e la tiv e C U X 2 D e n s ity

C U X 2 D e n s ity 2 .5 C o n tro l 2 5 0

M a jo r D e p re s s io n 2 y 2 .0

t B ip o la r D is o rd e r m 2 0 0 i

s S c h iz o p h re n ia

/ n

e l

l 1 .5

D e

1 5 0

o i

1 .0 t

r 1 0 0

e m 5 0 R 0 .5

u * N 0 0 .0 I II II I I I I I y r I V I I V it r r V r I V s e e r r r r y e e e e e r r n a y y y y e e e e a a y a y y y L L a a a a y D L L L L L a a ll L L L a r e v O Figure 32 CUX2 +ve density measures in area 32 A. Mean absolute density + standard error. B. Mean density relative to overall density + standard error. There was a trend for a lower relative density in layer I in patients with bipolar disorder (p=0.036). * denotes p-value <0.05 with covariates, and ** denotes p- value < 0.01 with covariates compared to controls. 0.01 is the adjusted cutoff for significance after the

Bonferroni post-hoc correction.

A B R e la tiv e Z N F D e n s ity Z N F D e n s ity 2 .0 1 5 0 C o n tro l

M a jo r D e p re s s io n

y t

m B ip o la r D is o rd e r

i 1 .5 s

m S c h iz o p h re n ia

s l 1 0 0 e

l **

C 1 .0

a e

5 0 l

e 0 .5

R u

0 0 .0 I I I y I I V I t r I V I II II I i e r r r I V V s e e r r r y y e e e r r r n a y y y y e e e e L a a a y y e L L a a a y y D L L L a a a ll L L L I a r e v O

Figure 33 ZNF312 +ve density measures in area 32 A. Mean absolute density + standard error. B. Mean density relative to overall density + standard error. Patients with bipolar disorder had a significantly lower relative density in layer III (p=0.008). * denotes p-value <0.05 with covariates, and ** denotes p-

value < 0.01 with covariates compared to controls. 0.01 is the adjusted cutoff for significance after the Bonferroni post -hoc correction.

CUX+ve/ZNF-ve absolute and relative cell densities can be seen in figure 34. Trends for

lower absolute CUX-ZNF cell densities overall (p=0.016), and more specifically in layers II

(0.011) and V (0.035) can be seen in patients with schizophrenia. However, none of these

findings achieve significance after Bonferroni corrections. Without covariates, all of these

findings are significantly different with post-hoc corrections (p=0.002 for overall density,

p=0.003 for layer II and p=0.006 for layer V). Absolute CUX-ZNF density was lower in bipolar

disorder compared to controls (p=0.023) but this is not significant after post-hoc corrections. The

same trend is seen without covariates (p=0.011) but remains non-significant with Bonferroni

corrections. Finally, the relative density of CUX-ZNF cells was increased in patients with bipolar

disorder in layer V (p=0.040), but this is not significant following post-hoc corrections and this

trend is not seen without covariates.

A B R e la tiv e C U X -Z N F D e n s ity

C U X -Z N F D e n s ity 4 C o n tro l 1 5 0

M a jo r D e p re s s io n 2

y B ip o la r D is o rd e r

t i m 3

s S c h iz o p h re n ia

s e

l 1 0 0

l D

e *

C 2 e

t r

a e

5 0 l b

e 1 * R m *

u * N

0 0 I I I I I I I y I I V I I I V t r I V r I V i e r r r r r s e e r e e r r y y e e y e e e n a y y y a y y y e L a a a a y L L a L a a a D L L L L L ll L a r e v O Figure 34 CUX-ZNF density measures in area 32 A. Mean absolute density + standard error. B. Mean

density relative to overall density + standard error. There are trends for decreased absolute overall density (p=0.016), density in layers II (p=0.011) and V (p=0.035) in patients with schizophrenia, decreased absolute density in layer II of patients with bipolar disorder (p=0.023), and increased relative density in layer V of patients with bipolar disorder (p=0.040). * denotes p-value <0.05 with covariates, and ** denotes p-value < 0.01 with covariates compared to controls. 0.01 is the adjusted cutoff for significance after the Bonferroni post-hoc correction. 77

ZNF312 cell areas are reported in figure 35, and no significant differences or trends were

observed in the patient groups compared to controls. Relative distances of cells from the pia to

the white matter and from the pia to the mid-point of the cortex can be seen in figure 36. No

significant differences or trends were observed for the mean relative distances for each marker in

the patient groups.

Z N F c e ll a re a 3 0 0 C o n tro l M a jo r D e p re s s io n B ip o la r D is o rd e r S c h iz o p h re n ia 2 0 0

1 0 0

0 I I II V V r r r e e e y y y a a a L L L

Figure 35 Mean ZNF cell size + standard error in area 32. There are no significant differences or trends between controls and patients.

A B R e la tiv e D is ta n c e R e la tiv e D is ta n c e to M id -P o in t 0 .6

0 .3 5

a i

a C o n tro l i

M a jo r D e p re s s io n

m m

o 0 .5

o 0 .3 0

r r

f B ip o la r D is o rd e r

e S c h iz o p h re n ia

n a

0 .4 a

t 0 .2 5

s i

v i

0 .3 i 0 .2 0

t a

R R 0 .2 e e e .5 .5 .5 c c c 0 0 0 n n n a e e e ta ta t c c c s s s n n n i i i a a a D D D t t t is is is 2 F F D X N N D D 2 F F U Z -Z C X N N X U Z Z U - C X C U C

Figure 36 Cell distance measures in area 32. A. Box and whisker plot of relative mean distances from pia to white-matter. B. Box and whisker plots of relative distances from pia to the mid-point of the cor tex. There are no significant differences or trends between controls and patients.

CHAPTER FIVE: DISCUSSION

5.1 Labeled Cell Population

This is one of few studies that uses neuron-specific markers to investigate cortical cytoarchitecture in psychiatric diseases, and the only study that attempted to investigate layer- specific neurons. After visual inspection of marker-stained cells in comparison to cells stained with DAPI (all neurons and glial cells), we concluded that our markers were in fact specific to neurons. Both our markers were not completely specific to any particular layer. Few studies have characterized layer-specific markers in the human cortex, and few markers are been reported to be layer-specific in humans while also staining for pyramidal neurons. CUX2 and ZNF312 are two consistently reported layer-specific markers that also stain pyramidal neurons in humans; however, no study has investigated their expression in the ACC (Arion, Unger et al. 2007, Zhu,

Yang et al. 2010, Zeng, Shen et al. 2012). This is consistent with their hypothesized roles in cortical development, although much of the evidence for this hypothesis comes from rodent studies (Greig, Woodworth et al. 2013). After visually examining the sections, it appeared that

ZNF312 stained mostly pyramidal neurons while CUX2 stained both pyramidal and non- pyramidal neurons. This is also consistent with their reported expression in rodent cortical development studies (Franco, Gil-Sanz et al. 2012).

CUX2 and ZNF312 were not confined to specific layers. CUX2 was expressed in more layer II neurons, but also had substantial expression in lower layers. ZNF312 had significant layer V expression, but also had a large number of neurons in layer II stained. Looking at the intensity of staining, neurons in layer II were more intensely stained with CUX2 than neurons in lower layers, and neurons in layer V were more intensely stained with ZNF312 than layer II

80 neurons. Therefore, these markers appear to be binding to proteins in neurons belonging to the layers they were targeting more so than any other layer. The neurons that were less intensely stained were still counted because they are still clearly visible, and choosing an arbitrary intensity threshold to not include neurons in the count would have added to the subjectivity of this study.

The fact that neurons outside of layers II/III for CUX2 and outside of layers V/VI for

ZNF312 were observed has two possible explanations. Firstly, this could be the result of non- specific binding of our markers. However, cells positive for our markers in their non-targeted layers were visually identified as neurons compared to DAPI stained cells. Therefore, the non- specific target would have to be neuron-specific. Alternatively, a more likely explanation may be that CUX2 and ZNF312 are expressed to a lesser extent in neurons belonging to their non- targeted layers. While these markers are well characterized during cortical development in rodents where they appear to be expressed in different cell populations that are destined for different layers, their expression is not as well elucidated in humans and adult tissue (especially in the ACC). The Allen Brain Atlas provides an atlas of in-situ hybridization of many genes in multiple regions of the cortex. Visually inspecting the samples on their website for CUX2 stains in the cingulate cortex reveals the same pattern that we observed (Allen Human Brain Atlas).

Investigating neurons that are positive for CUX2 but negative for ZNF312 resulted in a more layer specific neuron population, mostly belonging to layer II. It is difficult to characterize this population of neurons, and the only conclusion that can be made about them is that they are preferentially found in layer II. Given that ZNF312 appeared to mostly stain for pyramidal neurons and CUX2 appeared to stain both pyramidal and non-pyramidal neurons, this

CUX+ve/ZNF-ve population may potentially represent a proportionately greater population of non-pyramidal neurons.

5.2 Summary of Findings

Our findings suggest that there are minimal differences in the cytoarchitecture of neurons positive for our markers in patients with schizophrenia, bipolar disorder and major depression.

The differences we did find were subtle. This is consistent with the view that there are no pronounced cytoarchitectural differences in these diseases. We found almost no differences in patients with major depression, with the only potential finding being decreased ZNF312 cell area in layer Vb. We had more findings in schizophrenia; however these were not consistent across regions. The statistically strongest finding was a relatively larger layer Va in area 24 in patients with schizophrenia. A consistent trend was the decreased CUX+ve/ZNF-ve densities in multiple layers in area 32. Otherwise, the other trends observed in schizophrenia were not consistent across the regions investigated. Our findings in patients with bipolar disorder were interestingly more consistent across regions. Relative density of CUX+ve/ZNF-ve neurons in layer Vb for areas 24a and 24c and layer V for area 32 had a trend to be increased. In addition, neurons positive for ZNF312 had a trend to be smaller in layer VI in areas 24a, 24b and 24c. Finally, density of CUX2 neurons in layer I was decreased in area 24c and 32.

5.3 Schizophrenia

We hypothesized that neurons in individuals with schizophrenia would have impaired migration during development, and that we would see evidence for this in post-mortem cortical samples from these individuals. Specifically, we expected greater densities of neurons in lower layers V/VI and lower densities in the superficial layers II/III. We would also potentially expect

82 a wider layer II, and greater mean distances of neurons from the pia. We found no evidence for a relatively thicker layer II in schizophrenia, no evidence for a relatively decreased layer II/III density with increased layer V/VI density in any of our markers, and little evidence for a greater mean distance of each neuron from the pia in any of our markers. The only evidence was a greater distance for CUX2 neurons from the pia to the mid-point of the cortex in area 24b; however, this was not a significant finding after correcting for multiple comparisons. Therefore, we are unable to support our hypothesis with this study.

A lack of evidence for our hypothesis can have multiple potential explanations. Firstly, it could be that migration through the cortical plate is not abnormal enough in schizophrenia to have a significant impact on neuron position. Radial glial cell mediated migration could be less interrupted than other forms of migration in patients with schizophrenia. Migration of neurons on radial glial cells in schizophrenia is something that has not been directly investigated in the past.

A related explanation is that the misposition of neurons is subtle enough for our methods to not find any differences. Cytoarchitectural differences in schizophrenia are likely to be subtle given a lack of pronounced findings in histological studies.

Alternatively, it could be that the effect we expect is minimal in the ACC, but could be more pronounced in other cortical areas. The ACC is largely part of the proisocortex, and this type of cortex could be less impacted than neocortical areas. Perhaps, thicker neocortical areas would have a greater impact due to a longer distance of required for neurons. Finally, schizophrenia is a heterogeneous disease and could potentially have multiple etiologies. It could be that a migration deficit in some individuals could lead to symptoms of schizophrenia, but that these symptoms are reached through other pathophysiological processes in other individuals.

Therefore, grouping a heterogeneous population could result in a lack of significant findings.

The most robust finding in schizophrenia was an increase in the relative width of layer

Va in area 24a. A trend towards a wider Va was not observed in individuals with schizophrenia in other areas. No other findings in schizophrenia remained significant after the stringent

Bonferroni correction, but there were other differences without this correction. The most consistent trend was a decreased CUX+ve/ZNF-ve density only in area 32. Another some-what consistent trend was the decrease in relative density of CUX2 in layer I in areas 24c and 32.

Even though these trends are not significant after correcting for multiple comparisons, their consistency between and within regions increases the likelihood that they are real effects rather than statistical anomalies. Other trends in schizophrenia such as a higher relative density of

CUX2 and ZNF312 in layer III of area 24a, a larger relative average distance to mid-point for

CUX2 cells in area 24b and a decreased relative density of CUX2 in layer VI in area 24c do not survive corrections for multiple comparisons. The fact that these findings are not consistent may suggest that they are more likely to be chance findings after multiple testing rather than real effects.

Other studies investigating the cytoarchitecture in the ACC have similarly found inconsistent results. However, it is difficult to compare our findings with other cytoarchitectural studies. Firstly, some studies do not specify which subregion of area 24 they investigated, and potentially may have grouped areas 24a, 24b and 24c together. Secondly, studies typically use

Nissl or Cresyl Violet stains which mark all neurons. While some studies do differentiate between pyramidal and non-pyramidal neurons, we are likely looking at a different subpopulation of neurons with our markers. Therefore, any different findings in our study could only be attributed to our subpopulation of neurons rather than the entire population other studies have looked at.

Increased thickness in layer Va has never been reported in the pACC before in patients with schizophrenia. Decreased thickness of layers II, III, V and VI have been reported in areas

24a/b analyzed as a whole (Bouras, Kovari et al. 2001). However, they did not look at areas 24a and 24b separately, layer V was not separated into Va and Vb, and they investigated absolute thickness while we looked at relative thickness. It is possible that absolute thickness could be decreased, but relatively Va is larger in individuals with schizophrenia. However, this is unlikely given we found cortical thickness was unchanged in patients with schizophrenia. Benes, Vincent et al. (2001) found no difference in laminar areas in region 24, but the specific subregion of area

24 used was not indicated.

Density of CUX+ve/ZNF-ve cells had a consistent trend of being decreased in patients with schizophrenia in area 32. While this finding was not significant for each layer after correcting for multiple testing, it may be a real effect given that density overall was different, and density in layers II and V had a trend of being decreased. Very few studies have looked at area

32 in schizophrenia; two studies that looked at total neuron density and pyramidal neuron densities found no differences. As previously discussed, the population of CUX+ve/ZNF-ve neurons may mostly represent a non-pyramidal neuron population. In fact, cytoarchitecture studies in other cortical areas have reported decreased densities of interneurons. Interestingly, this was not observed in areas 24a, 24b and 24c. A possible explanation could be that this population of neurons is decreased in specific cortical regions. This could be a result of decreased neurogenesis during development or increased post-natal cell death. In fact, markers for apoptotic dysregulation have been observed in cortical samples from patients with schizophrenia, and interneurons in particular appear vulnerable to apoptosis (Jarskog, Glantz et

85 al. 2005, Catts & Weickert 2012). These findings have been used to explain gray matter reductions in cortical areas after the onset of schizophrenia.

Other findings in schizophrenia were not consistent within a region or across different regions, and likely do not have a relationship to each other. Given the lack of consistency in these findings, it is possible that they are chance findings after multiple comparisons.

Interestingly, our findings with major depression resulted in only a single trend being found and our trends in bipolar disorder were more consistent with a lack of inconsistent trends. This begs the question of why we see multiple unrelated trends in schizophrenia but not in depression or bipolar disorder. In fact, findings in the literature are inconsistent with each other as well, as demonstrated in table 1. While all studies we are aware of only investigate one region, we have the advantage of looking at multiple regions to assess consistency. Of course, it is possible that none of these trends are real differences and are only chance findings; however, it may also be possible that there is a larger heterogeneity in the ACC among patients with schizophrenia. The possibility of heterogeneity in the underlying pathophysiology of patients with schizophrenia was previously discussed, and it may be the case that there are multiple potential etiologies of schizophrenia. Therefore, these inconsistent findings could be from grouping a heterogeneous population of individuals with different cytoarchitectural differences in different cortical regions.

5.4 Bipolar Disorder

We had a number of consistent findings in patients with bipolar disorder. Relatively, layer Vb had an increased density of CUX+ve/ZNF-ve neurons in areas 24a, 24c and 32. Figure

22 shows a larger mean relative density of CUX+ve/ZNF-ve density in layer 24b as well, although this finding did not achieve significance. These findings did not survive significance in

86 post-hoc corrections for multiple comparisons, but their consistency across regions increases the likelihood that they are real differences rather than chance findings. Another consistent finding was a decrease in ZNF312 cell size in layer VI in areas 24a, 24b and 24c. Looking at figures 17,

23, 29 and 35, cell size in bipolar disorder is consistently lower in all regions in layers Vb and

VI, even though significance for this finding was not always achieved for layer Vb. Again, the consistency of this finding suggests that it is a real effect. Layer I CUX2 neurons had a trend of being decreased, particularly in area 24c and 32. This was also seen in CUX+ve/ZNF-ve neurons in area 24c. Looking at graphs 14, 16, 20, 22, 26, 28, 32 and 34, CUX2 and CUX+ve/ZNF312 densities in layer I in bipolar disorder appeared to have lower means across all regions investigated, even though this finding was not always significant. Other findings include an increased relative CUX2 density in layer Vb in area 24a, increased CUX+ve/ZNF-ve relative density in layer III in area 24c and a decreased absolute density of CUX+ve/ZNF-ve neurons in layer II in area 32. All of these findings are consistent with each other and also consistent across regions. Unlike with schizophrenia patients, we have minimal unrelated and inconsistent findings in patients with bipolar disorder. Perhaps this suggests that the ACC is a less heterogeneous area in our sample of patients with bipolar disorder compared to our sample of patients with schizophrenia.

As previously discussed, it is difficult to place our findings in perspective of previous findings of the cytoarchitecture in the ACC of patients with bipolar disorder given our methodology and staining, and in some cases, a lack of information about the specific area investigated. Our finding of decreased cell size has been reported in patients with bipolar disorder in the ACC. One study found decreased neuron size in layer V in area 24c (Chana,

Landau et al. 2003), but multiple other studies found no difference in neuron size in this layer

(Ongur, Drevets et al. 1998, Benes, Todtenkopf et al. 2000, Cotter, Mackay et al. 2001). Chana,

Landau et al. (2003) did not differentiate between pyramidal and non-pyramidal neurons, and it is unclear which neuron population was smaller in their study. Our ZNF312 population of neurons likely represents more pyramidal neurons, and so this finding is also true of this population. Other studies investigating the size of pyramidal neurons in bipolar disorder have found reductions in size throughout many cortical regions (Cotter, Mackay et al. 2002, Cotter,

Hudson et al. 2005). Therefore, this finding is likely not specific to the ACC.

We also found a consistent change in the relative density of CUX+ve/ZNF-ve neurons.

Relatively, patients with bipolar disorder tended to have more CUX+ve/ZNF-ve neurons in layer

Vb throughout the regions we investigated. This was accompanied with a relatively lower density of this population in layer I in areas 24c and 32, and a similar trend in the other two areas. These findings are particularly difficult to compare with previous findings because measures of cortical relative density have not previously been investigated in bipolar disorder.

Like us, most studies investigating absolute neuron density in the ACC of patients with bipolar disorder have not found any differences. However, Benes, Vincent et al. (2001) have reported reduced densities of non-pyramidal neurons in layer II. Cotter, Landau et al. (2002) also found decreased CB interneuron densities in layer II, while Chana, Landau et al. (2003) found increased neuron density in layer VI in area 24c. Like most studies, we see little evidence for changes in absolute density of neurons in bipolar disorder, but our findings with relative density could explain the consistent results seen in other studies.

Looking at the relative densities of CUX+ve/ZNF-ve neurons in figures 16, 22, 28 and

34, the most consistent trends are an increased density in layer Vb and a decreased density in layer I. Another trend seen in some regions is a decreased relative density in layer II. Therefore,

88 there appears to be a change in the distribution of neurons, with fewer layer I/II neurons and more layer Vb neurons. While we find fewer differences in the absolute densities of these neurons, we do see a decrease in absolute density in layer II in area 32 and layer I in area 24c.

Part of the reason we do not see the same number of differences in our absolute cell densities is that we likely lack the power the detect them. Normalizing densities in each layer removes some variation associated with differences in overall density. We do indeed see smaller standard errors with our relative densities compared to our absolute density. A related idea is that any insignificant change in overall density could mask relative changes. An example of this can be seen in figure 16, where the mean overall density is lower in patients with bipolar disorder. This masks the effect of layer Vb, which is relatively higher in patients with bipolar disorder, but does not appear different in terms of absolute densities.

Therefore, we argue that there are subtle effects seen with regards to density differences, and that we lack the power to detect all these differences (although we do detect some). By normalizing to the overall density, we eliminate random variation related to density measures, and are better able to detect these changes. This potentially explains why many studies do not find layer-specific differences in bipolar disorder, as they may lack the power to pick up on these subtle differences. The few studies that do find changes in the pACC of patients with bipolar disorder report decreased non-pyramidal neuron density in layer II and increased density in layer

VI (Benes, Vincent et al. 2001, Cotter, Landau et al. 2002, Chana, Landau et al. 2003) This is similar to our findings of higher density in layer Vb and less density in layers I and II in

CUX+ve/ZNF-ve neurons. In addition we find these differences in the CUX+ve/ZNF-ve population of neurons, which likely represents a non-pyramidal population of neurons. One finding that is not consistent with these studies is the decrease in layer I neuron density.

However, differences have been found in layer I in patients with bipolar disorder. Reports of significantly decreased RELN positive cells (mostly layer I and II interneuons) have been reported in bipolar disorder and schizophrenia in the frontal cortex (Guidotti, Auta et al. 2000).

These findings have many potential physiological explanations. Relatively higher densities of layer Vb neurons in conjunction with relatively lower densities of II neurons may suggest impaired neuron migration in bipolar disorder, as we previously hypothesized. Neurons destined for upper layers may have been failed to migrate to that level. It is unclear why only this population of neurons would have this finding. Perhaps this population of neurons is particularly susceptible to deficits in radial migration, although there is no evidence for this. A more likely explanation may be that since these neurons tend to be found in upper layers, a small difference in their density in lower layers is more likely to have a significant effect. Although no significance was seen in the mean distance from pia measures, looking at the distribution of means in figures 18, 24, 30 and 36 suggests a shift in distribution in mean distance to be lower for CUX+ve/ZNF-ve neurons. This further supports the hypothesis that there is a deficit in migration. As previously discussed, there is genetic evidence for migration deficits in bipolar disorder. This finding however does not explain the decreased density of layer I CUX+ve/ZNF- ve neurons since these neurons do not radially migrate to their layer, but are found as part of the preplate before the underlying cortical plate begins to form.

Increased neuronal death in patients with bipolar disorder could also explain these findings. If there is a greater extent of cell death in layers I/II compared with layer Vb, this would result in a lower relative density in these layers compared to a higher relative density of these neurons in layer Vb. In fact, there is evidence for neuronal apotosis in patients with bipolar disorder. Patients with bipolar disorder have increased markers for oxidative stress, and

90 expression of genes involved in apoptosis is increased in these patients (Benes, Matzilevich et al.

2006, Andreazza, Kauer-Sant’Anna et al. 2008). Specifically, apoptosis has been attributed more to interneurons rather than pyramidal neurons (Benes, McSparren et al. 1991). Authors of studies reporting reduced densities of interneurons in the ACC have attributed this to apoptosis in bipolar disorder. Our results are consistent with this, and suggest that cell death is more prominent in the top layers.

Finally, another explanation is consistent with the reduced neuropil hypothesis. Increased density of neurons in the lower layers in the ACC of patients with bipolar disorder along with reduced size of neurons has been observed before (Chana, Landau et al. 2003). Smaller neurons are believed to correlate with less dendritic arborizations (Selemon and Goldman-Rakic, 1995).

Therefore, this would reduce the neuropil surrounding neurons in these layers. Since this effect of neuron size is most pronounced in the lower layers, neuropil could be more decreased in these layers. Less neuropil would increase the density of neurons in that layer (Selemon and Goldman-

Rakic, 1995). This could explain the higher relative density of CUX+ve/ZNF-ve neurons in layer

Vb, and as a result the lower relative density in layers I/II. However, this is not as likely given that the most pronounced decrease in size was seen in layer VI, and the most prominent relative increase in density was seen in layer Vb. In addition, we have evidence for a decrease in absolute density in layer I/II. Therefore, the relative decrease in density in layers I/II is likely not solely driven by the increased relative density observed in layer Vb.

These potential explanations are not mutually exclusive, and they could all play a role in our observation. Given our study it is difficult to differentiate between these potential causes, and it would generally be difficult to do so with post-mortem histological studies. It also appears that these changes are specifically targeting CUX+ve/ZNF-ve neurons. Given that these neurons

91 likely represent an interneuron population, this finding is consistent with studies that report interneurons in particular are aberrant in bipolar disorder. Fewer differences are reported with all

CUX2 positive neurons, and we find almost no differences with ZNF312 neurons. Since ZNF312 densities are the same, CUX2 neurons should show the same patterns as CUX+ve/ZNF-ve neurons. We do see some evidence for this (increased CUX2 relative density in layer Vb in area

24a, and decreased CUX2 relative density in layer I in areas 24c and 32). A similar pattern remains in CUX2 neurons, but it is not as pronounced. This is likely because CUX2 positive cells contain multiple groups of neurons, and the CUX+ve/ZNF-ve group in particular is changed. Therefore, there may be less of an effect on the whole group of CUX2 positive neurons as compared with CUX+ve/ZNF-ve neurons. We likely lack the power to detect this smaller change in the entire CUX2 population of neurons.

5.5 Major Depression

We had almost no findings in patients with major depression; the only trend we found was a lower cell size in layer Vb in area 24c, which did not achieve significance after corrections for multiple testing. However, despite consistently having a smaller mean, cell size in other layers in this region were not close to being significantly different. In addition, this finding was not consistent across different regions. While there may be a subtle decrease in cell size in major depression, there is a reasonable likelihood that this is a chance finding. Remarkably, other than this one trend, there were no other trends in patients with major depression, even without correcting for multiple comparisons. This potentially suggests that our methodology can provide consistent results.

Our findings are consistent with the literature. Most studies have not found cytoarchitectural differences in the pACC of patients with major depression (Ongur, Drevets et al. 1998, Bouras, Kovari et al. 2001, Cotter, Landau et al. 2002). Cotter, Mackay et al. (2001) reported decreased neuron size in layer VI of area 24 in the sACC. In addition, Chana, Landau et al. (2003) reported decreased cell sizes in layer I and V in area 24c. Consistent with our findings, no differences in density have been reported. These findings are quite consistent with what we have seen. In light of the previous findings of decreased cell size, particularly in layer V of area

24c by Chana, Landau et al (2003), our finding of decreased cell size in layer Vb in area 24c may be a true finding rather than a chance finding. However, we are not investigating the same population of neurons as Chana et al. (2003). They looked at all neurons, while we are only investigating ZNF312 positive cells, most likely pyramidal neurons. Decreased cell size has also been reported in other cortical regions, such as the frontal cortex and hippocampus, in patients with major depression (Rajkowska, Michuel-Hidalgo 1999, Cotter, Mackay et al. 2002,

Stockmeier, Mahajan et al. 2004). In fact, decreased dendritic complexity in conjunction with decreased neurotropic factors (such as BDNF) is a robust finding in major depression (Duman and Li 2010). Given that cell size is not static, and is dependent on dendritic complexity, a smaller cell size is expected in patients with major depression. However, factors such stress, malnutrition or impoverished environments also lead to decreased dendritic complexity and cell size (Fiala, Spacek et al. 2002). Therefore, it is unclear if smaller cell size is directly related to major depression or if it is caused by other factors patients with depression are more likely experience.

Other than this minor change, this study, along with other studies find very few cytoarchitectural changes in the ACC of patients with major depression. This is interesting given

93 that the ACC is heavily involved in affect, with the sACC specifically responding to sad emotions and the pACC to happy emotions (Vogt 2005). In fact, DBS is specifically targeted to the sACC in patients with major depression (Lozano, Mayberg et al. 2008). While there is a clear relationship between these regions and major depression, problems with these regions seem to not be cytoarchitectural in nature. Perhaps there are aberrant connections in these regions without any difference in neuron organization. DTI studies do show reduced fractional anisotropy between the pACC and frontal cortical areas and the amygdala (Pizzagalli 2011). Given the role of the ACC, this perhaps suggests a diminished ability to modulate emotions.

5.6 Limitations

This study has a number of limitations, some found in most histological studies and some unique to this particular project. With regards to the methodology, we decided to use a two- dimensional approach for our measurements. This gave us the advantage of using a very large amount of cortical tissue, increasing our sample size and potentially giving us more representative results. However, looking and measuring three-dimensional objects in two- dimensional images leads to a number of inaccuracies. For instance, density is generally overestimated because of cell centroids outside the thin tissue section. We attempted to remove these effects using the Abercrombie correction factor. However, we are unable to know the height of DAPI cells in our section and we made the assumption that they are spherical. This may not be true, and could influence the densities we reported. However, DAPI size was unchanged between groups, minimizing the impact this assumption would have on our findings.

In addition, the Abercrombie correction does not account for an effect referred to as the lost-caps problem. When slicing the tissue, some of the cells partly outside the section are removed completely rather than being cut. The Abercrombie correction assumes this does not happen, and

94 reduces the density observed assuming all small cell sections are completely cut. Therefore, it overcorrects what is on the slide, and the true cell density is likely slightly higher than what is reported. An issue arises when one group has larger cells, and would have a larger lost-caps effect, and a lower cell density would be reported. We attempted to minimize this by only including cells that overlapped with DAPI stained nuclei, minimizing the proportion of a cell that needs to be out of the plane. In addition, the fact that the DAPI size was the same across all groups means that this effect would not have a significant impact on our findings.

The cell size we reported is likely underestimated due to the fact that cells outside the plane of the slide would be cut, resulting in smaller portions being seen on the section. We could not use equation 3 to correct for cell area like we did with DAPI because this equation makes the assumption that the object is spherical. This is a more appropriate assumption with cell nuclei rather than for neuronal cell bodies (particularly pyramidal cells). Assuming a pyramidal shape, it is impossible to develop a correction formula without knowing the height to width ratio of the shape. Therefore, we did not attempt to correct this problem. Only including cell areas of neurons that overlapped with DAPI helps to minimize this bias, as it minimizes the number of neurons outside of the section we measure. The fact that DAPI sizes are the same across groups, the underestimation would be proportional across groups.

Another limitation is that tissue may not be sectioned completely perpendicular to the layers of the cortex. The convoluted shape of the cortex makes this difficult to achieve, and we did notice sections that may have been slightly slanted with respect to the cortex. However, it is impossible to be certain whether a section is in fact slanted on a two-dimensional section. If a section is slanted, the cortex would appear to be thicker than if it was cut completely perpendicularly. This could also affect cell density, depending on the density of neurons

95 perpendicularly compared to tangentially in the cortex. If neurons are more densely packed perpendicularly down the cortex than tangentially, a slanted cut would decrease absolute density.

This is partly why we decided to also investigate relative density of neurons in each layer. This factor partly explains the larger variance seen in absolute density compared to relative density.

Using immunohistochemical stains on specific proteins to count cells also limits our ability to be certain of changes in our measures. In the current study, it is not possible to determine whether the density of a population of cells is decreased or expression of CUX2 and

ZNF312 are decreased. Our cell segmentation methods attempt to minimize differences in intensity levels by autoscaling the intensity of our images following FFT and selecting a threshold based on relative difference in background to cell intensity. Another study segmenting cells stained with NeuN in the mouse cortex in a similar way reported minimal decreases in the number of cells segmented following a large decrease in the intensity of their stain (Woeffler-

Maucler, Beghin, et al. 2014). We similarly saw no change in cell segmentation after artificially lowering the intensity of our images, regardless of how severely we did so. However, it is still possible that a certain group could have negligible expression of our markers in a sub-population of cells. The cells labeled would still have the same intensity, but the apparent density would be decreased. However, there is no reason to suspect such a specific change in expression of our markers, and there is no evidence linking our markers with the diseases we are investigating.

Limitations unrelated to methodology include confounds that could not be controlled for.

This study is ultimately a correlational one and we cannot be cannot be certain of the causative factors of our findings. We controlled for potentially tissue changing factors such as PMI, pH of the tissue and days in storage, and we investigated the effects gender, age and side of the cortex had on our measures. However, other factors potentially affecting cortical cytoarchitecture could

96 not be assessed. Patients with these psychiatric conditions may experience less social interaction, more stress and less cognitive stimulation. These factors could be attributed to the cytoarchitectural differences we observed. In fact, these factors have been reported to alter neuronal shape and dendritic complexity.

5.7 Conclusion

We investigated neuron cytoarchitecture of four structurally distinct regions of the pACC in patients with schizophrenia, bipolar disorder and major depression. We studied distinct cell populations, staining for markers that are more specific to certain cortical layers. Neurons positive for these markers have not been investigated in these psychiatric disorders. In addition, we implemented an automatic counting method to study significantly larger areas of the cortex.

As a result, we sampled over ten times the number of cells as the largest reported histological studies for these disorders. We attempted to test the hypothesis that neuron migration was impaired during corticogenesis, particularly in schizophrenia and potentially bipolar disorder. As a result, we expected neurons destined for upper layers to be found in lower layers.

We did not find consistent evidence to support our hypothesis in schizophrenia. However, we did see a trend for lower densities in upper layers and higher densities in lower layers of a particular population of neurons (likely interneurons) in patients with bipolar disorder. In addition, measures of mean cortical distance of this population of cells had a more skewed distribution in patients with bipolar disorder. One explanation for these consistent findings is that there was a deficit in migration in these patients during development. However, other potential explanations include decreased neuropil in lower layers increasing density observed here. This has previous evidence, and we did find consistently smaller cells in lower layers, providing

97 further evidence that dendritic complexity is decreased here. Another explanation includes increased cell death in upper layer neurons. In fact, apoptosis, particularly in interneurons, has been reported in patients with bipolar disorder. Decreased densities of interneurons have also been reported. It is unclear which of these hypotheses would have resulted in our findings; however, they are not mutually exclusive.

We found almost no differences in major depression, only decreased cell size in one layer in one region. There is past evidence for this, so this finding may be a real effect rather than a chance finding. Decreased dendritic complexity and neurotropic factors reported in major depression could be the reason for decreased cell size. In contrast, we found multiple unrelated findings in schizophrenia. While they may be chance findings due to multiple comparisons, it is unclear why this many chance findings were not observed with major depression and bipolar disorder. Perhaps these findings suggest that schizophrenia is a particularly heterogeneous disease, and our group contains patients with multiple pathophysiological abnormalities.

CHAPTER SIX: FUTURE DIRECTIONS

We plan to investigate how robust our findings are in other cortical regions. We plan to perform the same analyses on the orbitofrontal and superior temporal cortices. These areas have been implicated in the disorders we are investigating. This will allow us to determine if our findings are specific to certain areas of the cortex. Our initial hypothesis of alternations in neuronal migration suggests that these effects should not be unique to one particular cortical region; disruptions in proteins like DISC1 and RELN should affect migration in all areas.

However, perhaps certain cortical areas are more susceptible. Wider neocortical areas could be more susceptible to deficits in migration than the proisocortex we investigated. If we do see differences, differences in the expression of genetic markers for migration, corticogenesis and cell death between these areas may provide hints as to why certain areas are more susceptible to others.

We also plan to better characterize the neuron populations we investigated. Co-localizing our stained neurons with NeuN markers would better validate that we are in fact looking at neurons. In addition, using pyramidal specific markers such as CAMKII could help us more precisely determine which of our cell populations are pyramidal neurons. Using GAD67 would tell us how many interneurons we are investigating, while staining with CB, PV and CR would give us a better idea of the subpopulation of interneurons we are investigating. In addition, we could obtain cortical tissue samples to look at protein levels of CUX2 and ZNF312 between groups. This would help determine if our findings were skewed by different expression levels of this protein between groups.

To better assess the likelihood of our interpretations and have better evidence for the underlying pathophysiology of what we observed, we can correlate our findings with genetic data. Specifically, the Stanley Neuropathology Consortium has an integrative database containing genome-wide SNP data as well as microarray expression data in multiple brain regions. Given that we have a small sample of individuals in each group, we can investigate target genes involved in cell death, neurogenesis and cell migration. This will help us determine which of our proposed explanations for our findings in bipolar disorder are more likely: neuron death in upper layers, neuron migration deficits or decreased neuropil in lower layers. In addition, we suggested that patients with schizophrenia had varying underlying pathophysiologies. Using genetic markers, we may be better able to separate this group, although this will likely be difficult given our sample sizes.

In order to eliminate many of the limitations we have from performing two-dimensional analyses, we can perform large-scale measures of cytoarchitecture on cubic samples of cortical tissue using novel methods of making the tissue transparent through methods such as CLARITY or SCALE (Chung, Wallace et al. 2013, Ke, Fujimoto, et al. 2013). This removes most confounds associated with assessing cytoarchitecture in very thin sections, and would provide us with measures that are as close to true densities as possible. There would be no more need for assumptions regarding Abercrombie corrections or corrections for cell size. We could use our automatic segmentation protocol to analyze large portions of the cortex, and could potentially analyze the cytoarchitecure of an individual’s entire pACC. This would be a completely novel project, as no one has attempted to use these novel transparency techniques to investigate the cortex of patients with psychiatric disorders.

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CHAPTER EIGHT: APPENDIX

11.1 Appendix 1

T T+H

Figure A – 1. A cross-section of a slice of tissue with thickness T, and circular nuclei with radii r. If the centre of a nucleus is outside the tissue section but within a distance of its radius (i.e. in the shaded region), it will be included in the count. Therefore, a nucleus will be included in the count if it is within T + H, where H is its height, or 2r.

The figure above represents a cross-section of a slice of tissue being analyzed. The

thickness of the tissue is represented by T, and the circles represent the nuclei being investigated.

We wish to find the number of cells within the thickness of our section, but it is possible for the

centre of a cell to be outside our slice and still be included. The green cell’s centre is within the

section, and it is included in our count as expected. The red cell’s centre is outside our section,

but it is still counted because part of it enters the section. The distance outside the section at

which a cell’s centre is counted is equal to the radius of the cell. Therefore, the centre of the blue

cell lies outside of this radius, and therefore does not penetrate the section. Therefore, if a cell is

within (T+H), where H is equal to 2r and represents the diameter (or height) of the cell, it will be

130 counted. To estimate the number of cells within the thickness T, we simply multiply our measured number by the ratio of T to (T+H).

11.2 Appendix 2

Figure A-1 also shows how cell area can be underestimated by looking at a two- dimensional image from a section. The green cell’s centre is fully within the section, and so its diameter and cell area would appear at its maximum. However, the red cell is cut, and so its area appears smaller than it would be in reality. If a cell’s centre is within the section, its area will not be minimized; however, if its centre is within a distance of its radius outside the section (i.e. the shaded part of figure A-1), it will be cut and its area will appear smaller. The mean cross-

휋 sectional diameter of a circle sectioned randomly is equal to multiplied by its diameter. 4

Therefore, if a cell is within the section, its measured diameter (dM) will equal its true diameter

휋 (dT). However, if a cell is within the shaded part of figure A-1, its dM will equal ( ) dT. 4

Therefore, the diameter that we measure equals the true diameter of the cells within the thickness

푇 휋 of the section (i.e. ) added to multiplied by the measured diameter of the cells outside of the 푇+퐻 4

퐻 section (i.e. ). Given that H is also the true diameter of the cell (dT), the following equation 푇+퐻 was derived:

푇 푑푇 휋 푑푀 = 푑푇 + 푑푇 푇 + 푑푇 푇 + 푑푇 4

휋 푑 (푇 + 푑 ) = (푑 ) (푇 + 푑 ) 푀 푇 푇 푇 4

휋 푇푑 + 푑 푑 = 푇푑 + 푑 2 푀 푀 푇 푇 4 푇

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휋 2 푑 + 푇푑 − 푑 푑 − 푇푑 = 0 4 푇 푇 푀 푇 푀

휋 2 푑 + 푑 (푇 − 푑 ) − 푇푑 = 0 4 푇 푇 푀 푀

휋 √ 2 −(푇 − 푑푀) ± (푇 − 푑푀) − 4(4)(−푇푑푀) 푑푇 = 휋 2(4)

2 푑푀 − T ± √(푇 − 푑푀) + π푇푑푀 푑푇 = 휋 (2)

Given that subtracting the square root term gives a nonsensical answer, we must add it.

2(푑 − T) + √(푇 − 푑 )2 + π푇푑 ∴ 푑 = 푀 푀 푀 푇 π

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