Linking Vestibular Function and Cortical and Sub

Home , Schaffer collateral

LINKING VESTIBULAR FUNCTION AND

CORTICAL AND SUB-CORTICAL ALTERATIONS IN

AN AGING POPULATION

Athira Jane Jacob

A thesis submitted to Johns Hopkins University in conformity with the re-

quirements for the degree of Master of Science in Engineering

Baltimore, Maryland

May 2018

ABSTRACT

It is well known that vestibular system is responsible for maintaining balance, posture and coordination.

However, there is increasing evidence that the vestibular system also plays an important role in cognition.

It is involved in spatial learning and navigation, mental imagery, bodily self-consciousness and self-motion perception, object recognition etc. Thus, it comes as no surprise that vestibular decline has been linked to cognitive loss, especially in the elderly. Like every other system, vestibular system undergoes degeneration with aging. However, it is unclear if vestibular loss precedes and causes cognitive loss, and if so, the extent of contribution of vestibular loss to cognitive loss, as compared to general effects of aging. In this cross-sectional study, we seek to identify relationships between vestibular function, and morphometric changes in brain structures from neuroimages.

We use a subset of participants from the Baltimore Longitudinal Study of Aging (BLSA), who had both brain MRI and vestibular physiological data taken at the same visit. Vestibular function was evaluated through the cervical vestibular-evoked myogenic potential (cVEMP) to assess saccular function, the ocular VEMP (oVEMP) to assess utricular function, and the video head-impulse test (VHIT) to assess semicircular canal function based on vestibular ocular reflex (VOR). For these subjects, we analyze the hippocampus, amygdala, thalamus, caudate nucleus, putamen, insula, entorhinal cortex (ERC), trans-entorhinal cortex (TEC) and the perirhinal cortex, as these structures comprise the putative “vestibular cortex”. We model the volume and shape of these structures with each of the vestibular variables. We found a positive correlation in volumes of the hippocampus and the ERC with cVEMP. In addition, we also found significant relationships between the shape of the hippocampus, amygdala, thalamus, caudate nucleus, putamen, insula and ERC -TEC complex, with the cVEMP. VOR gain showed significant localized relationships with caudate nucleus, putamen and the collateral sulcus. oVEMP showed no significant results.

Primary Reader: Tilak Ratnanather

Secondary Readers: Yuri Agrawal, Siamak Ardekani

ACKNOWLEDGEMENTS

I would first like to thank my thesis advisor Dr. Tilak Ratnanather, for steering me in the right direction at every stage of this research. I would also like to thank Dr. Yuri Agrawal and Dr. Susan Resnick for their valuable feedback and encouragement throughout my work. I am indebted to Timothy Brown for getting me started on this project and for the periodic guidance whenever I needed it. I would also like to express my sincere thanks to Dr. Daniel Tward and Dr. Rebecca Kamil for our very fruitful discussions. Finally, I thank Dr. Siamak Ardekani for reviewing my thesis, inspite of his very busy schedule.

Finally, I must express my profound gratitude to my parents for providing me with unfailing support and continuous encouragement throughout this Master’s program and through the process of researching and writing this thesis. This accomplishment would not have been possible without them.

Thank you.

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CONTENTS

Abstract ...... ii Acknowledgements ...... iii Contents ...... iv List of tables ...... vi List of Figures ...... vii Introduction ...... 1 1.1 Background ...... 1 1.2 Problem Statement ...... 3 1.3 Motivation ...... 4 Literature Review ...... 6 2.1 Large Deformation Diffeomorphic Metric Mapping (LDDMM) ...... 6 2.2 Vestibular pathways in the brain ...... 7 2.3 Hypothesis testing and multiple hypothesis problem ...... 13 Methods ...... 16 3.1 Data ...... 16 3.1.1 MRI Testing ...... 16 3.1.2 Vestibular data ...... 17 3.2 Pipeline ...... 21 3.2.1 Segmentation and 3D Reconstruction ...... 22 3.2.2 Volume Analyses ...... 23 3.2.3 Shape analyses ...... 24 3.3 Statistical Testing ...... 26 Results ...... 28 4.1 Volume analyses ...... 28 4.1.1 cVEMP ...... 28 4.1.2 VOR gain ...... 28 4.2 Shape analyses ...... 29 4.2.1 cVEMP ...... 29 4.2.2 VOR Mean ...... 40 Conclusion ...... 45

5.1 Discussion ...... 45 5.2 Limitations ...... 48 5.3 Future work ...... 49 Appendix ...... 51 A. Amygdala and Hippocampus Sub-field Atlas ...... 51 B. Thalamus sub-field atlas ...... 52 C. Test Statistic: Ratio of Max Square Errors ...... 54 D. Inter observer reliability ...... 56 E. File locations ...... 58 F. Pipeline Steps ...... 60 Bibliography ...... 65 Biography ...... 77

LIST OF TABLES

Table 1 Main vestibular cortices and their locations (Lopez, 2015) ...... 8 Table 2 Summary of volume results with cVEMP as the vestibular variable ...... 28 Table 3 Summary of volume results with VOR gain as the vestibular variable ...... 29 Table 4 Summary of significant results for cVEMP ...... 31 Table 5 Summary of significant results with VOR gain ...... 41 Table 6 Sub-fields of the thalamus and corresponding labels/file names ...... 52 Table 7 Intra- and Inter-reliability scores (before and after editing): Kappa scores ...... 56 Table 8 File locations ...... 58

LIST OF FIGURES

Figure 1 The human vestibular system. Adapted from “Vestibular Rehabilitation,” 2016 (http://www.trimetricsphysio.com/vestibular-rehabilitation/) ...... 2 Figure 2 Four main vestibular pathways to hippocampus (Hitier et al., 2014). Structures investigated in our study are marked...... 4 Figure 3 Pathway from vestibular nucleus to striatum (Stiles et al., 2015). Structures investigated in this study are marked...... 5 Figure 4 Ontology of thalamic nuclei ...... 10 Figure 5 a) Sample experimental setup to measure cVEMP b) Distribution of cVEMP values measured ...... 17 Figure 6 a) Sample experimental setup to measure oVEMP b) Distribution of oVEMP values measured ...... 19 Figure 7 a) Sample experimental setup to measure VOR gain b) Distribution of VOR gain values measured ...... 20 Figure 8 General overview of the analyses pipeline ...... 22 Figure 9 a) Structure of interest is marked in the MRI images b) Binary segmented volume c) Reconstructed surface ...... 22 Figure 10 Schematic of the volume analyses ...... 23 Figure 11 Correlations between volumes calculated from 3D binary volumes and 3D surfaces are close to 1...... 24 Figure 12 Schematic of shape analyses ...... 25 Figure 13 Example of clusters in putamen (k = 14) and hippocampus (k = 20). Each cluster is assigned one jacobian value, as opposed to jacobian values for each individual vertex ...... 26 Figure 14 All structures with significant regions shown in color-View from rostral, ventral side. Colors represent correlation with cVEMP values (red- positive, blue - negative). Coordinate system on right depicts relative directions ...... 30 Figure 15 All structures with significant regions shown in color-View from right, rostral side. Colors represent correlation with cVEMP values (red- positive, blue - negative). Coordinate system on right depicts relative directions ...... 30 Figure 16 All structures with significant regions shown in color-View from right, caudal side. Colors represent correlation with cVEMP values (red- positive, blue - negative). Coordinate system on right depicts relative directions ...... 31 Figure 17 Different views of the hippocampus template, showing the significant regions ...... 33 Figure 18 High field atlas showing CA1 (yellow), CA2 (purple) and CA3 (blue) ...... 33

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Figure 19 Amygdala template, showing significant regions (View from rostral side) ...... 34 Figure 20 a) Amygdala subfields b) Close-up of left amygdala showing significant regions ...... 34 Figure 21 Different views of the caudate template, showing significant regions (View from caudal side) 35 Figure 22 Putamen template, showing significant region (View from right, rostral side) ...... 35 Figure 23 Thalamus template, showing significant region (View from caudal side) ...... 36 Figure 24 Thalamus sub-field atlas with a few nuclei labelled. (a) and (b) show views from lateral side and (c) shows view from medial side. LGN: Lateral Geniculate nucleus. MGN: Medial geniculate nucleus, VLN: Ventral lateral nucleus, LDN: Lateral dorsal nucleus, RN: Reticular nucleus ...... 37 Figure 25 Insula template, showing significant regions (View from right, caudal side) ...... 38 Figure 26 ERC+TEC template, showing significant results (View from rostral side) ...... 38 Figure 27 ERC template, showing significant results (View from rostral side) ...... 39 Figure 28 All structures showing significant results with VOR gain ...... 40 Figure 29 ERC template, showing significant results (View from rostral side) ...... 42 Figure 30 ERC+TEC template showing significant regions (View from rostral side) ...... 42 Figure 31 Caudate template, showing significant regions. Posterior view from right ...... 43 Figure 32 Putamen template, showing significant regions. Posterior view from right ...... 44 Figure 33 Square error over subjects and vertices for different structures showing their long tail distribution ...... 55 Figure 34 Sample case where manual editing improved segmentations. MRICloud segmentation often included the choroid plexus, which had to be edited out...... 57

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CHAPTER 1

INTRODUCTION

1.1 BACKGROUND

All living organisms monitor their environment. This involves not only monitoring external factors, but also relative position and orientation of the self with respect to the environment. This includes sensing subjective motion and spatial orientation of the head, generating muscular activity for maintaining balance, posture, eye fixation etc. The vestibular system performs many of these important tasks. It is involved in many pathways that are responsible for making compensatory moments in muscles. It also projects to the integrative centers located in the cerebellum, brainstem and cortex, to provide perceptions of gravity, movement and orientation (Purves et al., 2001).

Anatomically, the vestibular system consists of 5 organs: 3 fluid filled semicircular canals, for detecting angular accelerations and the two otolith organs, saccule and utricle, for detecting linear accelerations

(See Figure 1). The semicircular canals are arranged in three mutually perpendicular directions. One canal

(the horizontal) is located in a plane that is about 30° from horizontal. The other two canals (the posterior and superior) are in vertical planes, orientated at approximately 45° between frontal and sagittal planes.

The movement of fluid within the canals pushes on a structure called the cupula, which contains hair cells that transduce the mechanical movement to electrical signals. The horizontal semicircular canal detects rotations around the vertical axis while the anterior and posterior semicircular canals detect rotations of the head in the sagittal plane, and in the frontal plane.

Figure 1 The human vestibular system. Adapted from “Vestibular Rehabilitation,” 2016 (http://www.trimetricsphysio.com/vestibular-rehabilitation/)

While the semicircular canals detect rotations, the otolithic organs respond to linear accelerations. They consist of the utricle and saccule, both of which contain some crystals of calcium carbonate called oto- liths, which play a major role in their excitation. The utricle senses motion in the horizontal plane while the saccule senses motion in the vertical plane. The sensors of the vestibular system can detect a change in orientation of the head of 0.5° from the upright position, a change of 5° from the horizontal position, or a change of 15° from the upside-down position.

While the role of vestibular system in balance and coordination has been well known, recent research increasingly suggests that the vestibular system, being one of the oldest sensory systems, has shaped the brain in important ways. Research in both animals and humans has revealed the role of the vestibular system in cognition. Vestibular system has been found to be an important contributor to spatial learning and navigation (Smith et al., 2013; Cullen, 2014; Yoder et al., 2014). There is also evidence that vestibular system is involved in mental imagery, self-other discrimination, bodily self-consciousness and self-motion perception, including its influences on emotional aspects and mood (Tremblay et al., 2013; Mast et al., 2014; Pfeiffer et al., 2014). Vestibular input has also been found to be important for object recognition (Brown et al., 2007) and numerical cognition (Smith, 2012).

There is increasing evidence that vestibular loss may contribute to cognitive loss. Mast et al. (2014) also reports how vestibular disorders and some psychiatric symptoms may be entangled. Like every other sen-

2 sory system, vestibular system is also affected by aging. Previc et al. (2014) point out how vestibular degeneration may contribute to spatial memory impairments, and raises the question of the consequences of progressive sensory loss in the elderly. While there are many epidemiological studies that indicate that vestibular dysfunction is significantly associated with cognitive deficits in the elderly (Bigelow et al.,

2016; Semenov et al., 2016), the exact pathways through which the loss of vestibular system affects cognition in the elderly is unknown. Hitier et al. (2014), provide a comprehensive review of the pathways running from the vestibular apparatus to the cortex, in animals and humans. In our work, we seek to investigate the relationship between vestibular loss and sub-cortical and cortical structures of the brain in the elderly. Specifically, we use MRI images and vestibular data, to detect volume and shape changes associated with vestibular function.

1.2 PROBLEM STATEMENT

We plan to use MRI data to reconstruct the structure of interest as a 3D surface, and then model volume and shape in relation to vestibular function. The following structures are investigated:

a. Hippocampus d. Thalamus

b. Entorhinal cortex (ERC) e. Putamen

c. Perirhinal cortex (PRC) f. Caudate nucleus

d. Trans-entorhinal cortex (TEC) g. Amygdala

e. Insula

1.3 MOTIVATION

Vestibular dysfunction is being increasingly implicated as a major reason for poor health. 35.4% and 85% of people aged 40 years and above, and 80 years and above, respectively, suffer from balance disorders due to vestibular dysfunction (Agrawal et al., 2009). Patients with bilateral vestibular deficiency have re- ported a significantly reduced quality of life, including emotional impairment, resulting in a substantial economic burden in terms of visits to the doctor and lost productivity due to missed work days (Sun et al.,

2014). In addition, there is growing evidence that vestibular defects may be a risk factor for dementia in the elderly (Previc, 2013). Conversely, studies have shown many cerebral benefits to vestibular stimulation, including improved recovery in stroke patients, in psychiatric disorders etc. (Mast et al., 2014; Palla et al., 2014; Wilkinson et al., 2014). This raises the possibility that vestibular sensory therapy is a viable approach to mitigate cognitive defects due to vestibular dysfunction and the risk of dementia in the elderly.

Figure 2 Four main vestibular pathways to hippocampus (Hitier et al., 2014). Structures investigated in our study are marked.

The motivation for the specific structures investigated comes from previous research. Hitier et al. (2014), investigates vestibular pathways involved in cognition. Four major pathways were hypothesized that con- nects the vestibular system to the vestibular cortices. More about these pathways are described in Section

2.2. From these pathways we identify few of the important structures involved: the hippocampus, medial entorhinal cortex (MEC or medial ERC), perirhinal cortex, and thalamus (Figure 2). The insula consti- tutes the major part of the Parieto-Insular Vestibular Cortex (PIVC), which is usually considered the primary vestibular cortex, as a third of its neurons are sensitive to vestibular stimulation. In addition, a fifth pathway is hypothesized to the basal ganglia. Stiles et al. (2015) corroborates this by proposing a pathway to the striatum through the thalamus. The striatum (putamen and caudate nucleus) is considered to be the main input center for vestibular signals in the basal ganglia (Figure 3).

Figure 3 Pathway from vestibular nucleus to striatum (Stiles et al., 2015). Structures investigated in this study are marked.

CHAPTER 2

LITERATURE REVIEW

2.1 LARGE DEFORMATION DIFFEOMORPHIC METRIC MAPPING (LDDMM)

Many studies seeking to investigate sub-cortical structures involved with neuropathologies have focused on changes in volumes of these structures with the disease (Fox et al., 1996; Chetelat et al., 2003). While this is definitely a useful quantitative measure, often the differences are not large enough to be captured by a global measure like volume. In addition, this misses regional changes in shape, like atrophy or expansion that might be an important early indicator for the disease. Thus, being able to find local shape changes is a useful tool to find correlations between regional atrophy and clinical features of the disease, which can lead to better diagnostic tools. Computational anatomy is the study of statistical variability in anatomical structures. A common procedure is to geometrically transform the population of anatomies to a reference structure (or template), and the transformations encode information about variability in shapes. Statistical inference can be done on the information from these transformations to extract significant patterns.

While there are many ways to calculate these transformations, Large Deformation Diffeomorphic Metric

Mapping (LDDMM) is considered the reference method to perform diffeomorphic registration of anatomies to the template (Dupuis et al., 1998; Beg et al., 2005). A diffeomorphism is a differentiable map with differentiable inverse. In LDDMM, diffeomorphism is represented as evolution of images through paths, parametrized by smooth, time varying velocity fields. For two images 퐼0 and 퐼1 in a collection of

−1 anatomies, there exists a diffeomorphism 휙푡 such that 퐼0(휙푡 ) = 퐼1. The map is constructed as a flow of

6 ordinary differential equations 푑휙푡⁄푑푡 = 푣푡(휙푡), 푡 ∈ [0,1] where 푣푡 ∈ 푉, 푡 ∈ [0,1] is a smooth time-dependent vector field. The optimal transformation between the two images is obtained from the vector field satisfying the variational problem

1 2 −1 2 푣̂ = arg min (∫ || 푣푡||푉 푑푡 + || 퐼0(휙 ) − 퐼1||푅3) 푣:푑휙푡⁄푑푡=푣푡(휙푡) 0

LDDMM can be used to register 3D volumes to multi-atlases for segmentation (Tang et al., 2013). It can also be formulated to register surfaces, with some modification to the metric (See Vaillant et al., 2007).

Surface LDDMM enables us to register template to population surfaces to obtain shape statistics (Vaillant et al., 2007; Qiu et al., 2008 a). This approach to shape analyses has been used to study sub-cortical atrophy associated with dementia and Alzheimer’s disease (Qiu et al., 2009 a; Miller et al., 2012; Younes et al., 2014), Huntington’s disease (Faria et al., 2016), ADHD (attention deficit hyperactivity disorder) (Qiu et al., 2009 b), schizophrenia (Qiu et al., 2008 b, 2010 a), Parkinson’s disease (Garg et al., 2015), autism spectrum disorder (Qiu et al., 2010 b) , cerebral palsy (Faria et al., 2011) etc. We follow a pipeline similar to the one in Faria et al. (2016) for our work.

Both the segmentation and surface LDDMM are performed on MRICloud (Mori et al., 2016), an online neuroinformatics platform that provides tools for automatic brain parcellation and surface registration

(https://www.mricloud.org/). The brain parcellation of MPRAGE images is based on Multiple-Atlas Like- lihood Fusion (MALF) algorithm (Tang et al., 2013, 2015), with 286 defined structures.

2.2 VESTIBULAR PATHWAYS IN THE BRAIN

An early study using Positron Emission Tomography (PET) showed that vestibular stimulation (VS) in- duced activation in the contraletral temporoparietal junction, in the posterior insula, in the putamen, and

7 in the anterior cingulate cortex, as well as in the right primary sensory cortex (Bottini et al., 1994). In another study, VS produced activations in the dorsomedial region of the ipsilateral caudate and dorsolateral region of the contralateral caudate (Potegal et al., 1971). Other studies using fMRI showed activations in insular gyrus, intraparietal sulcus, superior temporal gyrus, hippocampus, cingulate gyrus, and thalamus

(Suzuki et al., 2001).

Neuroimaging studies have identified several distributed cortical areas that respond to vestibular stimulation. These areas were divided into seven broad areas (see review in Lopez, 2015), which are summarized in Table 1.

Table 1 Main vestibular cortices and their locations (Lopez, 2015)

No. Cortical area Regions Included

a. Parietoinsular Vestibular Posterior & anterior insula, the parietal operculum, tem-

Cortex poro-parietal junction – including the superior temporal gy-

rus and inferior parietal lobule.

b. Primary somatosensory cor- Anterior part of intraparietal sulcus close to postcentral gy-

tex rus

c. Posterior parietal cortex Posterior part of intraparietal sulcus, ventral & medial intra-

parietal area, lateral superior parietal cortex, precuneus

d. Frontal cortex Dorsolateral prefrontal cortex, middle/superior frontal gyri

e. Extrastriate visual cortex Medial temporal gyrus, medial superior temporal complex

f. Cingulate cortex Anterior and posterior part

g. Hippocampus Hippocampus, subiculum, parahippocampal gyrus (entorhi-

nal, perirhinal and postrhinal cortices)

Hitier et al. (2014) describes four major pathways hypothesized to carry information from vestibular system to the vestibular cortices (See Figure 2), and additionally hypothesizes a fifth pathway:

a. the vestibulo-thalamo-cortical pathway, which transmits spatial information about the environ-

ment via the parietal, entorhinal and perirhinal cortices to the hippocampus, and is associated with

spatial representation and self-versus object motion distinctions;

b. the pathway from the dorsal tegmental nucleus via the lateral mammillary nucleus, the anterodor-

sal nucleus of the thalamus to the entorhinal cortex, which transmits information for estimations

of head direction

c. the pathway via the nucleus reticularis pontis oralis, the supramammillary nucleus and the medial

septum to the hippocampus, which transmits information supporting hippocampal theta rhythm

and memory

d. a pathway via the cerebellum, and the ventral lateral nucleus of the thalamus (perhaps to the pari-

etal cortex), which transmits information for spatial learning

e. Via basal ganglia (striatum) to hippocampus, potentially involved in spatial learning and spatial

memory

Vestibular input seems to be very important for place and head-direction (HD) cells. Place cells have activity that is correlated with location of the subject in the environment (O’Keefe, 1976). Ekstrom et al.

(2003), estimated that approximately 11% of the recorded cells responded to place but not view and these were most common in the hippocampus. HD cells, on the other hand, show high activity when the head is facing a narrow range of directions. They have been found in numerous cortical locations including the

PoS and CA1 of the hippocampus, and also in several subcortical nuclei (especially in pathway (b) above). In humans, functional imaging during vestibular stimulation demonstrates activation or inactivation of the hippocampal and parahippocampal areas (Suzuki et al., 2001; Deutschländer et al., 2002; Fa-

9 sold et al., 2002). In addition, patients with chronic bilateral vestibular deficits show bilateral hippocampal atrophy and spatial memory impairment (Brandt et al., 2005). Jacob et al. (2014), suggest that vestibular information is necessary for entorhinal cortex activity.

Thalamus is the primary site of relay for all the sensory pathways except olfaction on their way to the cerebral cortex (See ‘https://www.dartmouth.edu/~rswenson/NeuroSci/chapter_10.html’ for more details).

The thalamus is divided into three regions that are anatomically defined by a "Y" shaped bundle of nerve fibers termed the internal medullary lamina. Broadly, there is an anterior, lateral and medial subdivision of the thalamus, of which further sub-divisions are shown in Figure 4.

Dorsal (MD/DM) Medial Midline nuclei Medial (VPM) Anterior Anterior nucleus Posterior (VP) Thalamus Lateral (VPL) Ventral Lateral (VL)

Anterior (VA) Lateral Pulvinar

Lateral posterior Dorsal (LP)

Lateral dorsal (LD)

Figure 4 Ontology of thalamic nuclei

(Adapted from ‘Chapter 10: The Thalamus’, ‘https://www.dartmouth.edu/~rswenson/NeuroSci/chapter_10.html’)

The VPL and VPM nuclei are part of the somatosensory system. The VPL relays medial lemniscal and spinothalamic connections to the cerebral cortex. The VPM receives trigeminothalamic input and relays to the inferior portion of the postcentral gyrus. The VL receives input from the cerebellum, mainly from

10 the dentate nucleus. There is a small input from the basal ganglia to the rostral part of the VL, as well. The VA nucleus receives most of its input from the basal ganglia especially the medial globus palli- dus and substantia nigra, parts reticulata. The pulvinar receives afferent projections from the superior colliculus as well as from the association cortex. The dorsomedial nucleus (DM; also known as the mediodorsal nucleus MD) receives projections from the superior colliculus, olfactory cortex and the ventral pallidum. This is involved in controlling eye movements and attending to visual stimuli but it also plays a role in emotional "tone". The anterior nucleus of the thalamus has connections similar to the LD nucleus.

It receives input from the hippocampus via the mamillary bodies and projects to the posterior cingulate cortex.

Five pathways are described that transmit vestibular inputs from vestibular nuclei to the thalamus (Zwer- gal et al., 2009):

a. Medial longitudinal fasciculus: This is involved in the vestibular-perception network (Zwergal et

al., 2008). Neuronal tracer studies demonstrate that the medial longitudinal fasciculus links: (1)

the superior vestibular nucleus to the ipsilateral central lateral nucleus, bilateral ventro-postero-

lateral and bilateral ventro-lateral thalamic nuclei; (2) the medial vestibular nucleus to the bilat-

eral ventro- postero-lateral, and the contralateral central lateral thalamic nuclei; and (3) the de-

scending vestibular nuclei to the contralateral medial geniculate nucleus (Kotchabhakdi et al.,

1980; Nagata, 1986; Shiroyama et al., 1999).

b. The ascending tract of Deiter: This links the superior vestibular nucleus and medial vestibular nu-

cleus to the central-lateral, ventral-posterior-lateral and ventral-lateral thalamic nuclei in rats and

cats (Kotchabhakdi et al., 1980; Nagata, 1986; Shiroyama et al., 1999). No role in vestibular cog-

nition has been demonstrated yet (Zwergal et al., 2009).

c. The crossing ventral tegmental tract: This vestibulo-oculomotor pathway transmits the anterior

canal inputs through the superior vestibular nucleus and Y-group to the contralateral oculomotor

nucleus (III), and also the thalamus (anatomical studies in monkey) (Zwergal et al., 2009). No

cognitive role is proven.

d. The ipsilateral vestibulo-thalamic tract: This is a fast pathway for vestibular inputs to the thala-

mus and vestibular cortices, which provides it to the cortical multisensory network for the percep-

tion of body motion and spatial orientation. It ascends from the Y-group and probably transmits

otolithic signals to the postero-lateral thalamus (Zwergal et al., 2009).

e. From the bilateral medial vestibular nuclei and the ipsilateral superior and descending vestibular

nucleus to the parafascicular nucleus (PFN) of the thalamus. The central lateral and paracentral

nuclei also receive vestibular inputs. It is also likely that the other intralaminar nuclei (ILN) neu-

rons also receive vestibular input (see review in Lopez et al., 2011).

Thus, the superior vestibular nucleus and the medial vestibular nucleus project to the thalamic ventral posterior complex: i.e., ventral- posterior-lateral nucleus, nucleus ventralis intermedius, ventral posterior medial nucleus or ventral posterior inferior nucleus (Lopez et al., 2011). The superior, medial, lateral and descending vestibular nuclei project to the medial geniculate, the lateral geniculate and the suprageniculate nuclei (Kotchabhakdi et al., 1980; Nagata, 1986; Shiroyama et al., 1999). Among the thalamic nuclei, some neurons are responsive only to vestibular stimulation (i.e., first order relay), for example, the ventral posterior complex (Marlinski et al., 2008). Other vestibular thalamic nuclei are higher order relays, which receive somatosensory inputs (e.g., ventral-posterior-lateral, ventral-posterior-medial, ventral-posterior- inferior nuclei) or visual inputs (e.g., lateral geniculate nucleus) (Reichova et al., 2004; Sherman, 2007;

Sherman et al., 2011). Vestibular pathways as well as the thalamic nuclei involved are summarized in

Figure 2.

2.3 HYPOTHESIS TESTING AND MULTIPLE HYPOTHESIS PROBLEM

In the case of a single hypothesis, we evaluate an alternate hypothesis against a null hypothesis based on some test statistic and reject the null hypothesis if the test statistic falls in a certain rejection region. There are two types of errors possible in such a situation:

a. When the null hypothesis is true but incorrectly rejected. This is a Type I error or a false positive.

b. When the alternate hypothesis is true but we do not reject the null hypothesis. This is a Type II

error or a false negative.

Usually the rejection region is chosen to control the probability of Type I error to some level 훼. How- ever, if the same rule is followed for multiple hypotheses tested together, the probability of making at least one Type I error is much higher than alpha, especially when the number of hypotheses is large. For m independent tests, if 훼 is the rejection level for each p-value, then this probability becomes (1 − 훼)푚.

Because 0 < 훼 < 푚, it follows that

(1 − 훼)푚 < (1 − 훼)

So, the probability of making no Type I errors in 푚 > 1 tests is much smaller than in the case of one test.

For e.g., if 훼 = 0.05, then the probability of at least one Type I error for 100 independent tests is is 0.99.

This is called the multiple hypothesis testing problem. It is an increasingly important area of research, with wide applications in genomics and image analyses. There are different approaches to control Type I errors. Most methods try to adjust 훼 in some way, so that the probability of observing at least one Type I error remains below the desired significance level.

The earliest multiple hypothesis adjustment methods focused on controlling the family-wise error rate

(FWER). The FWER is defined as the probability of making at least one false rejection when all null hypotheses are true. Instead of controlling the probability of a Type I error at a set level for each test, these

13 methods control the overall FWER at a given level. The trade-off, however, is that they are often overly conservative, resulting in low-power tests. One example is the Bonferroni correction, which sets the significance level for each individual test at 훼/푛, where 푛 is the number of tests (Abdi, 2007). A major assumption here is that all the tests are independent of each other, which is often not the case in practical applications. Hence Bonferroni correction tends to be extremely conservative, with a high rate of false negatives. Hochberg’s procedure (Hochberg, 1988) is a popular and more powerful method used to control for FWER. A marginally more difficult, but more powerful technique than the Hochberg’s procedure is the Hommel’s procedure (Hommel, 1988).

Often, in large scale multiple testing, controlling the false discovery rate (FDR) might offer better power.

The FDR is defined as the expected percentage or proportion of rejected hypotheses that have been wrongly rejected (Benjamini et al., 1995). Benjamini and Hochberg procedure (Benjamini et al., 1995) is one of the most commonly used technique for controlling FDR. It ranks p-values in an ascending order, multiplies them by the number of features, and divides them by their corresponding rank. It has been shown that when the test statistics are continuous and independent, this procedure controls for FDR at the given level. Benjamini and Liu procedure (Benjamini et al., 1999) and Storey’s procedure (Storey, 2002) are other methods to control for FDR.

However, there is an assumption of independence among tests in all these methods. This is a strong assumption for most practical cases. Most of the common tests mentioned above can still be used under dependent conditions, with some modifications or under certain restricted cases (Sarkar et al., 1997; Benja- mini et al., 2001; Guo et al., 2011). Permutation tests are non-parametric methods that incorporate dependencies among tests while controlling for FWER. They try to infer statistical significance directly from the data being analyzed, rather than the number of tests being performed. Permutation tests build an empirical estimate of the distribution of the test statistic under the null hypothesis, by permuting/resampling data (Belmonte et al., 2001; Nakagawa, 2004; Kimmel et al., 2007). A general procedure to perform a permutation test is as follows:

a. Choose a test statistic.

b. Calculate the test statistic based on original labelling of data.

c. Under null hypothesis, the assumption is that all data has come from the same distribution. So,

shuffle the data multiple times and calculate the test statistic each time. This gives us a distribu-

tion of the test statistic under null hypothesis.

d. Calculate p value by seeing where the test statistic from original data lies with respect to this null

distribution. For e.g., p value could be the proportion of test statistics that are higher than the

original test statistic.

Thus, permutation testing is a natural choice when the null/alternative hypothesis and what to permute are clear. To this date permutation testing is widely accepted and recommended in multiple testing (Dudoit et al., 2002; Nakagawa, 2004).

CHAPTER 3

METHODS

3.1 DATA

Baltimore Longitudinal Study of Aging (BLSA) is a long running study of aging, among community dwelling adults started in 1958 ("Baltimore Longitudinal Study of Aging", 2018). While there are more than 1100 participants in the study, we choose a subset where the participants are >= 60 years old and un- derwent both brain MRI scans and vestibular testing in the same study visit between 2013 and 2015. All participants provided written informed consent, and the BLSA study protocol was approved by the Na- tional Institute of Environmental Health Sciences Institutional Review Board.

3.1.1 MRI Testing

MRI scans were performed using a 3T Philips Achieva scanner at the National Institute on Aging (NIA)

Clinical Research Unit. Sequences included a T-1 volumetric scan magnetization prepared rapid acquisi- tion with gradient echo (MPRAGE; TR=6.5ms, TE 3.1ms, flip angle = 8 degrees, 256x256 image matrix,

170 slices, voxel size = 1.0x1.0mm, slice thickness=1.2mm, FOV=256x240mm). T1-weighted volumetric scan images were aligned in parallel with the anterior-posterior commissure plane.

3.1.2 Vestibular data

Vestibular functioning was evaluated through three measures: cVEMP to assess saccular function, oVEMP to assess utricular function and VOR gain to assess semicircular canal functioning. Each test is described below:

Vestibular Evoked Myogenic Potentials (VEMP): VEMPs are myogenic potentials generated due to high intensity auditory or vibratory signals. These can be used to determine the function of the otolithic organs

(Nguyen et al., 2010; Li et al., 2014). A commercial electromyographic system (software version 14.1,

Carefusion Synergy, Dublin, OH) was used to record the VEMP signals. Electromyogram signals were recorded with disposable, pre-gelled Ag/AgCl electrodes with 40-inch safety lead wires from GN Otomet- rics (Schaumburg, IL). Signals were amplified and band-pass filtered using 20-2000 Hz for cVEMP and

3-500 Hz for oVEMP.

a. Cervical VEMP (cVEMP): cVEMP is generated in the sternocleidomastoid (SCM) muscle

through sound stimulus and used to measure saccular function (See Figure 5). Participants sat on

a chair inclined to 30 degrees and qualified examiners placed electromyographic (EMG) elec-

a) b)

Figure 5 a) Sample experimental setup to measure cVEMP b) Distribution of cVEMP values measured

trodes on the sternocleidomastoid (SCM) muscle and sternoclavicular junction bilaterally (Ngu-

yen et al., 2010; Li et al., 2014, 2015 a; Harun et al., 2016). A ground electrode was placed on the

manubrium. Sound stimuli of frequency 500 Hz and 125 dB were given in bursts monoaurally

through headphones. Myogenic potentials recorded were normalized for background EMG activ-

ity collected 10 ms before the onset of sound stimulus. The higher cVEMP from either ear was

used in the analyses. An absent response was defined by a response below a threshold level per

published guidelines (Nguyen et al., 2010; Li et al., 2015 a) and the assessment is repeated for

confirmation. 80 participants (78% of the cohort) had cVEMPs measured during the study pe-

riod.

cVEMPs are used in three forms in our model:

Form Explanation Range

As it is (N = 80) Use cvemp values as they are. All 0-3.22

'Absent' values are read as 0.

Binary (N = 80) Threshold at 1. cvemp<1 :0, 0/1

cvemp>=1 : 1

Ignore <1 (N = 35) Remove all subjects with 1-3.22

cvemp<1

18 b. Ocular VEMP (oVEMP): oVEMP is measured through electrodes placed on the cheek, below

the eyes, in response to vibration (See Figure 6) and is used to measure utricular function. The

patient is seated similar to as in measuring cVEMP, and a non-inverting electrode is placed on the

cheek inferior to the pupil approximately 3mm below the orbit (Nguyen et al., 2010; Li et al.,

2014; Harun et al., 2016). Another inverting electrode was placed 2cm below the non-inverting

electrode and lastly, a grounding electrode was placed on the manubrium. Before testing, partici-

pants were asked to perform several 20-degree vertical saccades to confirm bilateral signals were

symmetric. New electrodes were applied if signals revealed more than 25% asymmetry. During

oVEMP testing, participants were asked to continue a 20-degree up gaze. Head taps were per-

a) b)

Figure 6 a) Sample experimental setup to measure oVEMP b) Distribution of oVEMP values measured

formed using a reflex hammer in the midline of the face at the hairline and approximately one

third of the space between the inion and nasion. The best response from either ear was used in

this analysis. If the response was below threshold levels, an absent response was recorded (Ngu-

yen et al., 2010; Harun et al., 2016) and was repeated for confirmation. 76 participants (74% of

the cohort) had oVEMPs measured during the study period.

oVEMPs are used in three forms in our model:

Form Explanation Range

As it is (N = 69) Use ovemp values as they are. All 'Ab- 0-52.7

sent' values are read as 0.

Binary (N = 69) Threshold at 10. ovemp<10 :0, 0/1

ovemp>=10: 1

Ignore <10 (N = 42) Remove all subjects with ovemp<10 10-52.7

c. VOR gain: Video head impulse testing (VHIT) was used to measure the horizontal vestibular-

ocular reflex (VOR) (Harun et al., 2016). The EyeSeeCam system (Interacoustics, Eden Prarie,

a) b)

Figure 7 a) Sample experimental setup to measure VOR gain b) Distribution of VOR gain values measured

MN) was used in the same plane as the right and left horizontal semicircular canals to determine

VOR gain (Schneider et al., 2009) The participant’s head was slanted down 30 degrees from the

horizontal axis to place the horizontal canals in the correct plane of stimulation. Participants were

directed to fix their gaze on a wall target 1.5 meters away. The participant’s head was moved 5-

15 degrees with high speed (approximately 150-250 degrees per second) in the horizontal plane at

least 10 times toward the right side and at least 10 times toward left side (See Figure 7). The di-

rection of head movement was randomized so as to be unpredictable. The EyeSeeCam system

measured eye and head velocity, and the VOR gain was calculated by dividing the eye velocity

by the head velocity. A normal eye and head velocity should be equal therefore a normal VOR

gain should equal 1.0. A VOR gain less than 0.8 with clear refixation saccades suggests periph-

eral vestibular hypofunction (Weber et al., 2009; Li et al., 2015 b). 90 participants (87% of the

cohort) had VOR gain measured during the study period.

VOR gain is used in the following forms:

Form Explanation Range

As it is (N = 91) Use VOR mean values as they are 0.485 – 1.41

Ternary (N = 91) Threshold at 0.8 and 1.2, and in- 0/1, 2 variables

troduce two variables: VOR_I and

VOR_II. VOR_I = 1 if vor>=0.8

and <1.2 and 0 otherwise. VOR_II

= 1 if VOR>1.2 and 0 otherwise

abs(1-VOR) (N = 91) Deviation of VOR mean from 1, 0-0.52

as 1 is normal

3.2 PIPELINE

An outline of the procedure is shown in Figure 8. The structure of interest is segmented from the MRI images of the brain to obtain a binary segmentation volume. The binary volume is then triangulated to get the 3D surface. Further statistical analysis is done through regression, with the vestibular variables as independent variables, and volume/shape descriptors as the dependent variables. Volumes are obtained

from the 3D segmentations or surfaces. Shape descriptors are obtained as a ‘deviation’ in a particular

shape from a mean shape. Each step is further explained in the following subsections.

Binary Neuroimages 3D Model Shape Segmentation Analysis

Volume Analysis

Figure 8 General overview of the analyses pipeline

3.2.1 Segmentation and 3D Reconstruction

a) b) c)

Figure 9 a) Structure of interest is marked in the MRI images b) Binary segmented volume c) Reconstructed surface

T1 MRI images of the brain are automatically segmented by registering them to multi atlas, using

LDDMM (Dupuis et al., 1998; Beg et al., 2005). The brain parcellation of MPRAGE images is based on

Multiple-Atlas Likelihood Fusion (MALF) algorithm (Tang et al., 2013, 2015), with 286 defined struc-

tures. The parcellation process is done through MRICloud (Mori et al., 2016)(See Section 2.1 for more

22 details). Surfaces are created using Restricted Delaunay triangulation (Amenta, Choi, Dey, & Leekha,

2000; Cazals & Giesen, 2006; Cohen-Steiner & Morvan, 2003, p.). Since the results critically depend on the quality of segmentations as well as the surface reconstruction, we perform quality control at every state. The segmentations are manually edited if they are inaccurate. (See Appendix D). The procedure is depicted in Figure 9.

3.2.2 Volume Analyses

Segmentations/Surfaces Volumes (left/right) Vest. Variable (Eg. cVEMP)

Patient 1 3589 푚푚3 0.2 휇푉

3 Patient 2 4019 푚푚 3 휇푉

Regress

Figure 10 Schematic of the volume analyses

A schematic of the volume analyses is shown in Figure 10. The volumes of the structures are obtained from the binary segmentations by counting voxels and multiplying with the voxel dimensions. This shows good correlation with volumes calculated from surfaces, through a triple product (Figure 11). The model used is:

퐻1: 푣표푙 = 푐푣푉푒푠푡 + 푐푖퐼퐶푉 + 푐푎퐴푔푒 + 푐푠푆푒푥 + 푐0 (1)

Where, 푉푒푠푡 refers to the vestibular variable investigated (cVEMP, oVEMP or VOR Mean), and ICV or

Intracranial volume refers to the total volume within the skull, including left and right hemispheres, brainstem, cerebellum and CSF (cerebral spinal fluid). These volumes are read from the volume report file, ac- companying the parcellations from MRI Cloud. Age and sex of the patient is read from the demographics sheet. 푐푣, 푐푖, 푐푎, 푐푠 are coefficients for vestibular variable, ICV, age and sex respectively, and 푐0 is the constant term. The left and right brain structures are processed separately.

Figure 11 Correlations between volumes calculated from 3D binary volumes and 3D surfaces are close to 1.

3.2.3 Shape analyses

A schematic of the shape analysis is shown in Figure 12. The population of surfaces is rigidly aligned and used to create a ‘mean shape’, which is called a surface template. The template is generated based on a generative probability model over the entire population in which the observed surfaces are modeled as random deformations of the template (Ma et al., 2010). We create templates separately for left and right sides, for each structure. The resulting template acts as a coordinate system that represents the population,

24 and is blind to labels/groups. Each subject surface is then registered to this template through surface mapping, first rigidly, and then using non-rigid deformation (surface LDDMM, see Section 2.1 for details).

The registration provides deformation that maps the mean shape to individual subject's subcortical shapes.

These deformations can be used to estimate determinant of surface Jacobian at each vertex, which acts as a shape descriptor at the vertex. A positive surface jacobian value implies expansion of the template around that vertex to fit the subject, while a negative value implies regional atrophy. We fit a model to each vertex, thus giving 푁 models for each subject, where 푁 is the number of vertices in the population template. The null hypothesis is:

퐻0: 푗푎푐 = 푐푖퐼퐶푉 + 푐푎퐴푔푒 + 푐푠푆푒푥 + 푐0 (2)

And the alternate hypothesis is:

퐻1: 푗푎푐 = 푐푣푉푒푠푡 + 푐푖퐼퐶푉 + 푐푎퐴푔푒 + 푐푠푆푒푥 + 푐0 (3)

Where, the variables have the same definitions as defined in the previous section. We calculate the significance level or p-value through permutation testing (See next section).

Population ‘Mean’ shape Population Surface jacobians Vest. Variable

-0.3,0.2,0.5,-0.5.. 0.2 휇푉 0.01,0.1, -0.5, 3 휇푉 …0.5,0.3… 0.2,0.2,0.3,-0.6.. 1.5 휇푉

Regress

Figure 12 Schematic of shape analyses

To increase the power of the analyses, as well as to further reduce the number of models being tested sim-

ultaneously, we divide all vertices (푁 ≈ 600) to 푘 (10-20) clusters, through spectral clustering (as in

(Faria et al., 2016)). This method only depends on the surface geometry of the shape. The maximum sur-

face jacobian in each segment is assigned to that cluster, thus creating 푘 super-vertices. In Figure 13 be-

low, each colored segment represents one cluster/super-vertex. Subsequently, the number of models per

structure is reduced from 푁 (number of vertices, ~600), to 푘 (number of clusters, 10-20).

Figure 13 Example of clusters in putamen (k = 14) and hippocampus (k = 20). Each cluster is assigned one jacobian value, as opposed to jacobian values for each individual vertex

3.3 STATISTICAL TESTING

Fitting a linear model to each vertex/cluster gives rise to the multiple hypothesis problem described in

Section 2.3. We use permutation testing to control for Family-Wise Error Rate (FWER), as FWER con-

trols error at the desired level for each experiment (as opposed to expected value of error over multiple

experiments as in False Discovery Rate). Permutation testing generates a distribution of a test statistic un-

der null hypothesis, usually by permuting the group labels/variable of interest. The test statistic from the

real data is then compared to this distribution, and the p-value is the fraction of the population greater

than the real test statistic. The full procedure is described below:

26 a. Fit the null hypothesis 퐻0 to the real data, and find error 퐸0 as the maximum square error across

subjects.

2 퐸 = max ( 푌 − 푌푟푒푎푙 ) 0 ( 푡푟푢푒 푝푟푒푑,퐻0)

b. Fit the alternate hypothesis 퐻1, and find error 퐸1,푟푒푎푙 as the maximum square error across sub-

jects.

2 퐸 = max ( 푌 − 푌푟푒푎푙 ) 1,푟푒푎푙 ( 푡푟푢푒 푝푟푒푑,퐻1)

c. Find test statistic of the actual data, 푡푟푒푎푙 from our data, as a ratio of error under null hypothesis

to error under alternate hypothesis.

퐸0 푡푟푒푎푙 = 퐸1,푟푒푎푙 d. Find the max test statistic across all vertices.

푚푎푥 푡푟푒푎푙 = max(푡푟푒푎푙) e. Permute values of our variable of interest across subjects, in this case, the vestibular variable, and

fit model 퐻1 to this simulated data. f. Find error 퐸1,푠푖푚from this simulated data, under 퐻1, as the max square error across subjects.

2 퐸 = max ( 푌 − 푌푠푖푚 ) 1,푠푖푚 ( 푡푟푢푒 푝푟푒푑,퐻1)

퐸0 푚푎푥 g. Find simulated test statistic 푡푠푖푚 = , and the maximum test statistic, 푡푠푖푚 = max(푡푠푖푚). 퐸1,푠푖푚 h. Repeat steps e-g to get a distribution of max test statistic over simulated data

푚푎푥 푚푎푥 푚푎푥 i. Compare our real test statistic 푡푟푒푎푙 with 푡푠푖푚 . p-value is calculated as the fraction of 푡푠푖푚

푚푎푥 greater than 푡푟푒푎푙 .

CHAPTER 4

RESULTS

4.1 VOLUME ANALYSES

We find significant relationships with cVEMP and VOR gain, but not oVEMP. They are summarized below. Coefficient refers to 푐푣 in Equation (1) and (3) (Section 3.2.2 and 3.2.3, respectively).

4.1.1 cVEMP

Subjects with cVEMP values less than 1 are ignored, and the rest are used as they are.

Table 2 Summary of volume results with cVEMP as the vestibular variable

Structure Side Coefficient p-value

(Left+right)/2 314.9 0.006

Hippocampus Left 289.9 0.014

Right 339.9 0.003

ERC left 56.8 0.034

Both the hippocampus and ERC volumes show a positive correlation with cVEMP values.

4.1.2 VOR gain

Deviation from 1 is used in the model, in the form of abs(1-VOR).

Table 3 Summary of volume results with VOR gain as the vestibular variable

Structure Side Coefficient (풄풗) p-value

ERC-TEC left 627.7 0.02

left 337.9 0.002 ERC (Left+right)/2 216 0.02

Entorhinal cortex (ERC) as well as ERC-Trans-entorhinal cortex (TEC) complex show a positive cor-

relation with deviation from normal VOR mean. The increase is seen in the anterior part (See Section

4.2.2).

4.2 SHAPE ANALYSES

Overall, we see significant results in relation with cVEMP and VOR Mean while oVEMP fails to give any. The results for relationships between shape of the structures investigated and cVEMP and VOR

Mean are presented in Section 4.2.1 and Section 4.2.2 respectively. A point to be noted is that, in addition to ERC, we analyze ERC and TEC as a single structure (collateral sulcus) since the boundaries between them are often ambiguous. TEC is not analyzed on its own.

4.2.1 cVEMP

We see significant results in the hippocampus, amygdala, caudate nucleus, putamen, thalamus, insula,

ERC and ERC-TEC. PRC does not show any significant results. An overview of the significant results is shown as figures below (Figure 15,Figure 16,Figure 14) and in Table 4. Wherever available, high field atlases are used to zero in on specific sub-fields, as described in the following sections. The hippocampus

and amygdala atlases were created from manual segmentations by expert anatomists (See Appendix A),

while the thalamus atlas was created from the high resolution images published by Allen Institute for-

Brain Sciences (Ding et al., 2016). See Appendix B for more details. Multiple coefficients refer to multi-

ple significant clusters.

Overview

Caudate

Insula

Dorsal

Caudal Left

Rostral Right Amygdala Ventral Hippocampus ERC+TEC

Figure 15 All structures with significant regions shown in color-View from right, rostral side. Colors represent correlation with cVEMP values (red- positive, blue - negative). Coordinate system on right depicts relative directions

Insula Caudate

Dorsal Caudal Left Thalamus Putamen Right

Amygdala Rostral Ventral

ERC+TEC

Figure 14 All structures with significant regions shown in color-View30 from rostral, ventral side. Colors represent correlation with cVEMP values (red- positive, blue - negative). Coordinate system on right depicts relative directions Caudate Insula Thalamus

Putamen Dorsal Rostral Thalamus Left

Caudal Right

Ventral Hippocampus

ERC+TEC

Figure 16 All structures with significant regions shown in color-View from right, caudal side. Colors represent correlation with cVEMP values (red- positive, blue - negative). Coordinate system on right depicts relative directions

Table 4 Summary of significant results for cVEMP

Structure Coefficient (풄풗) Image p-value

Hippocampus -0.124, -0.154 0.0008

Amygdala -0.057 0.01

Caudate nu- 0.0995 0.002 cleus

Putamen 0.058 0.02

Thalamus 0.062 0.008

Insula 0.056 0.03

ERC+TEC -0.172 0.008

ERC -0.109 0.011

Individual Structures

Result for each structure is described in detail below.

a) Hippocampus

Figure 17 Different views of the hippocampus template, showing the significant regions

Side Left

p-value 0.0008

Dorsal segment: -0.1239 Mostly CA2, with some CA3 Coefficient (풄풗) Ventral segment: -0.1542 Almost all CA1

Vestibular variable form Binary cVEMP

No. of clusters 20

A high field atlas (See Appendix A), created from expert segmentations, containing CA1, CA2 and CA3 marked (See Figure 18) was registered to the population template to identify the significant regions as mostly CA1 and then CA2.

Figure 18 High field atlas showing CA1 (yellow), CA2 (purple) and CA3 (blue)

b) Amygdala

Figure 19 Amygdala template, showing significant regions (View from rostral side)

Side Left - Basolateral & basomedial nuclei

p-value 0.011

Coefficient (풄풗) -0.057

Vestibular variable form Binary cVEMP

No. of clusters 14

Similar to the hippocampus, we used a high-resolution atlas of the amygdala (See Figure 20, Appendix A) to further identify the significant regions as lying in basolateral and basomedial nuclei.

Centromedial

Basomedial

Lateral Basolateral

Figure 20 a) Amygdala subfields b) Close-up of left amygdala showing significant regions

34 c) Caudate nucleus

Figure 21 Different views of the caudate template, showing significant regions (View from caudal side)

Side Left

p-value 0.002

Coefficient (풄풗) 0.0995

Vestibular variable form Binary cVEMP

No. of clusters 16

d) Putamen

Figure 22 Putamen template, showing significant region (View from right, rostral side)

Side Right

p-value 0.02

Coefficient (풄풗) 0.058

Vestibular variable form Remove cVEMP<1

No. of clusters 14

e) Thalamus

Figure 23 Thalamus template, showing significant region (View from caudal side)

Side Right, mostly ventral lateral nucleus, and some parts of

reticular nucleus

p-value 0.008

Coefficient (풄풗) 0.062

Vestibular variable form Binary cVEMP

No. of clusters 10

An atlas containing sub-fields of the thalamus was created from the high resolution digital images provided by Allen Institute for Brain Sciences (Ding et al., 2016). More details on the atlas creation can be

36 found in Appendix B. The significant regions were identified to be mostly ventral lateral nucleus, and some parts of reticular nucleus (See Figure 24).

Posterior

Anterior RN

Posterior

LGN LGN

MGN Anterior

VLN LDN

MGN

Anterior Posterior

Figure 24 Thalamus sub-field atlas with a few nuclei labelled. (a) and (b) show views from lateral side and (c) shows view from medial side. LGN: Lateral Geniculate nucleus. MGN: Medial geniculate nucleus, VLN: Ventral lateral nucleus, LDN: Lateral dorsal nucleus, RN: Reticular nucleus

f) Insula

Figure 25 Insula template, showing significant regions (View from right, caudal side)

Side Right

p-value 0.03

Coefficient (풄풗) 0.056

Vestibular variable form Binary cVEMP

No. of clusters 16

g) Entorhinal cortex + Trans-entorhinal cortex (ERC+TEC)

Figure 26 ERC+TEC template, showing significant results (View from rostral side)

Side Left

p-value 0.008

Coefficient (풄풗) -0.172

Vestibular variable form Binary cVEMP

No. of clusters 13

h) Entorhinal cortex(ERC)

Figure 27 ERC template, showing significant results (View from rostral side)

Side Left

p-value 0.011

Coefficient (풄풗) -0.109

Vestibular variable form Binary cVEMP

No. of clusters 6

4.2.2 VOR Mean

Overview

Caudate Caudate

Putamen Caudate

Putamen Putamen

ERC+TEC

Dorsal Dorsal Caudal Rostral Left Left

Right Caudal Right Rostral Ventral Ventral

Dorsal Caudal

Left

Right Rostral Ventral ERC+TEC

Figure 28 All structures showing significant results with VOR gain

Structure Coefficient (풄풗) Image p-value

ERC 0.592 - 0.699 0.006

ERC + TEC 0.493 – 0.806 0.003

Caudate nu- -0.251 0.04

cleus

Putamen -0.178 0.01

Table 5 Summary of significant results with VOR gain

Individual structures

a) Entorhinal cortex

Figure 29 ERC template, showing significant results (View from rostral side)

Side Left

p-value 0.006

Coefficient (풄풗) 0.678, 0.699, 0.592

Vestibular variable form abs(1-VOR)

No. of clusters 6

b) Entorhinal cortex + Trans-entorhinal cortex

Figure 30 ERC+TEC template showing significant regions (View from rostral side)

Side Left

p-value 0.003

Coefficient (풄풗) 0.806, 0.493, 0.640, 0.670

Vestibular variable form abs(1-VOR)

No. of clusters 13

c) Caudate nucleus

Figure 31 Caudate template, showing significant regions. Posterior view from right

Side Left

p-value 0.04

Coefficient (풄풗) -0.251

Vestibular variable form VOR Mean as it is

No. of clusters 16

43 d) Putamen

Figure 32 Putamen template, showing significant regions. Posterior view from right

Side Left

p-value 0.01

Coefficient (풄풗) -0.178

Vestibular variable form abs(1-vor)

No. of clusters 14

CHAPTER 5

CONCLUSION

5.1 DISCUSSION

In this study, we combined volume analyses and shape diffeomorphometry to better understand the relationship between vestibular function and brain atrophy in an aging population. We use MRI images from the BLSA study. Vestibular function is assessed through three measurements: cVEMP, oVEMP and VOR gain. In our study, we specifically look at the volume and shape metrics of 9 structures, that are considered important in vestibular pathways: hippocampus, amygdala, thalamus, caudate nucleus, putamen, insula, ERC, ERC-TEC and PRC. In the first part of our study (Kamil et al., 2018), we found increasing hippocampal atrophy with decreasing vestibular function. Specifically, 1μV amplitude increase of cVEMP was associated with an increase of 31.9 cm3 (p=0.003) in mean hippocampal volume. Functional imaging studies have previously found vestibular projections to the insula, thalamus, ERC, PRC and basal ganglia. However, to the best of our knowledge, there have been no volumetric or shape studies involv- ing these structures.

In our study, we find that only cVEMP and VOR Mean seem to have significant correlation with the volume/shape of these structures. Only hippocampus and ERC show volumetric relation to vestibular function, both showing atrophy with decreasing vestibular function, as assessed by cVEMP. TEC shows a great deal of ambiguity in shape, which could explain the lack of similar volume results in ERC+TEC complex. In case of shape analyses, most structures show a positive correlation with cVEMP locally, except for hippocampus, ERC-TEC and amygdala, which show negative correlation locally. Interestingly,

45 these three structures are known to have extensive interconnections. Most of the significant regions in case of the hippocampus seem to lie in CA1, followed by CA2. Zheng et al. (2003) found that, following a unilateral vestibular lesion in rats, CA1 neurons exhibited a marked decrease in electrical excitability in response to stimulation of the Schaffer collateral pathway. Stackman et al. (2002) show that temporary inactivation of the vestibular system led to the disruption of location‐specific firing in hippocampal place cells and direction‐specific discharge of postsubicular HD cells. Place cells are found in the hippocampus, subiculum and ERC (Ekstrom et al., 2003; Fyhn et al., 2004), while HD cells are found in CA1 of hippocampus and subiculum (Ekstrom et al., 2003). In addition, ERC projects strongly to CA3 and CA1 of the hippocampus (Brun et al., 2002). Curiously, both hippocampus and ERC show a positive correlation in case of volume, but negative correlation in case of shape. This might be due to limitations of the shape study, in terms of resolution, loss of information from inside structure, biases in the population subjects

(which will affect the template) etc. More investigation is required.

In case of the amygdala, the significant region was localized to basolateral and basomedial nuclei. Many studies report a strong connection between basal nuclei of the amygdala and CA1, CA3 of the hippocampus and ERC. The most widespread projections from the amygdala to the hippocampal formation origi- nate in the basal nucleus, which projects substantially to the entorhinal cortex (EC), CA3 and CA1 fields of the hippocampus, the subiculum, and the parasubiculum, according to a study in macaque monkeys

(Pitkänen et al., 2002) . The accessory basal-nucleus projects substantially to three components of the hippocampal formation: the ERC, the CA1 field, and the parasubiculum (Pitkänen et al., 2002). The main projections from the lateral nucleus are directed to the ERC and the parasubiculum (Pitkänen et al., 2000).

The amygdala-hippocampal connection is bidirectional, with the CA1 and ERC strongly projecting to basolateral nucleus (Swanson et al., 1986; Canteras et al., 1992; Mello et al., 1992). Another study reports substantial inputs to the amygdala, specifically the lateral, basal, accessory basal, and central nuclei, from the rostral half of the entorhinal cortex and the temporal (ventral) end of the CA1 subfield (Pitkänen et al.,

2000). The same study reports heavy projections from lateral, basal, accessory basal, and posterior cortical nuclei, to rostral half of the entorhinal cortex, the temporal end of the CA3 and CA1 subfields.

We also find a positive correlation of the ventral lateral nucleus of the thalamus, and a small part of the reticular nucleus, with cVEMP values. Many studies report that the superior vestibular nucleus and the medial vestibular nucleus project to the thalamic ventral- posterior-lateral nucleus, nucleus ventralis intermedius, ventral posterior medial nucleus or ventral posterior inferior nucleus (Lopez et al., 2011). A study on feline thalamus has also identified the reticular nucleus as a vestibular thalamic area (Magnin et al., 1978). In addition, many studies also implicate posterior insula as an important vestibular cortical area

(Bottini et al., 1994; Bucher et al., 1998; Flynn, 1999; Fasold et al., 2002), and sometimes with a right hemispheric dominance (Fasold et al., 2002). In addition, vestibular information arriving in the posterior insula is thought to be projected from the vestibular nuclei through the ventral-posterior-inferior nucleus of the thalamus (Deecke et al., 1974).

The striatum, both caudate nucleus and putamen shows positive correlation with cVEMP, in the ventral and ventrolateral regions respectively. Many studies indicate vestibular projections to the caudate and putamen (Bottini et al., 1994; Stiles et al., 2015). A few studies report a vestibulo-thalamo-striatal pathway, in which the dorsolateral and ventrolateral regions of the striatum receive inputs from ventral and dorsal regions, respectively, of the parafascicular thalamic nucleus, which in turn receives input from the vestibular nuclei (Tsumori et al., 1998; Lai et al., 2000). Moreover, it has been suggested that the representation of the head/face is located in the ventrolateral parts of the striatum (Pisa, 1988; McGeorge et al., 1989;

Carelli et al., 1991).

VOR gain, or deviation from normal VOR gain, seems to have a negative correlation, locally, with the striatum. The caudate nucleus and putamen are activated by optokinetic stimulation (Dieterich et al.,

1998), suggesting their role in visual–vestibular interaction. They have been implicated as parts of the ef- ferent ocular motor pathways (the basal ganglia–thalamocortical motor loop proposed in by Alexander et

47 al. (1986). A previous study on fixation suppression of caloric nystagmus has revealed simultaneous inactivation of the caudate nucleus (Naito et al., 2003). Interestingly, ERC (as well as ERC-TEC) shows a positive correlation with deviation of VOR gain from normal (i.e. 1) in our study. Specifically, the more abnormal the VOR mean, the larger the rostral part of ERC-TEC complex is. This is also reflected in volume relationships, which show increasing volume with increasing deviation.

5.2 LIMITATIONS

A key limitation of the study is that observable changes are limited by the resolution of the MRI images.

In addition, the results critically depend on the quality of the segmentations. While this is relatively straightforward for sub-cortical structures like the hippocampus, cortical structures like entorhinal cortex, trans-entorhinal cortex and perirhinal cortex are notoriously difficult to segment automatically, due to faint and ambiguous grey matter-white matter boundaries. Moreover, there is a great deal of diversity in cortical shapes and structures resulting in high inter-observer variability, thus requiring expert manual segmentation. Lack of consistent and commonly accepted protocol makes the process even more difficult.

While we have performed quality control for individual structures, the same hasn’t been done for intracranial volume as we assume that, volume changes due to inaccuracies in segmentation will be small relative to its large magnitude.

In this study, we have used spectral clustering to group vertices into clusters and treat these as ‘super-vertices’ for the analyses. While averaging in a cluster over a region of strong signal could increase the power, such a region could also get split into multiple clusters, thus reducing power. Furthermore, it re- duces resolution of our results, limiting our ability to localize to regions smaller than a cluster size. There

48 is an additional localization problem inherent in our shape analyses, in it being limited to only surface regions. Hence atrophy within a structure, like in the intralaminar thalamic nuclei, is likely to go unde- tected.

The test statistic we use is a ratio between maximum squared errors of null and alternate hypotheses. Max square error generally is less robust than sum square error. However, it is observed that the square errors across subjects follow a long tail distribution (See Appendix C). In such a case, max square error could give higher power than sum square error.

Another limitation is that, in case of cVEMP and oVEMP, the best value from either ear is used. Thus, we lose side specificity, if any. This might also confound results, as there might be contralateral or ipsilateral dependence of volume/shape on vestibular function, which is lost when we do not control for side of measurement. In addition, we lack information on confounding factors among subjects, like handedness, medication etc. This might introduce biases into the population, which is then subsequently reflected as a biased template.

5.3 FUTURE WORK

In this study, we have investigated the relationships between vestibular function and the volume and shape of structures in the brain. While such a cross sectional study is useful to observe relationships within many structures, it does not provide definite information about cause-effect relationships. A next step would be a longitudinal study, where the hypothesis that vestibular loss precedes structural atrophy can be tested. Statistical techniques like structural equation modelling can be used to investigate simultaneous or bidirectional relationships between vestibular function and structure. For cortical structures like

ERC and the insula, changes in thickness might also offer relevant insights, and can be similarly mod- elled.

In addition, the analyses can be done by controlling for side of vestibular measurement, to check for contralateral/ipsilateral dependencies. The results can also be further improved by improving segmentations, either through better algorithms or by expert manual segmentations.

We have used simple linear models for the hypothesis testing, while controlling for multiple comparisons through permutation testing. Another approach would be to use Multiscale Graph Correlation (Shen et al.,

2016). MGC tests can reveal a wide variety of dependence structures, in many dimensionalities, typically requiring far fewer samples than existing methods. This could potentially reveal non-linear relationships in our data, while controlling for false positive rates.

APPENDIX

A. AMYGDALA AND HIPPOCAMPUS SUB-FIELD ATLAS

An isotropic 7T MRI scan of resolution 0.8 mm was used to reconstruct high-field parcellation of the population amygdala and hippocampus. The 7T subject is a 42 year old male who is healthy by self-report. The subject was scanned using a standard MPRAGE protocol in a Philips Achieva 7.0T scanner (TR

=4.3 ms, TE=1.95ms, flip=7, FOV=220×220×180). The amygdala was subdivided into four nuclei: lateral, basolateral, basomedial and centromedial nuclei using definitions based on the Paxino Atlas of the

Human Brain (Mai et al., 2004) and illustrated in detail at https://caportal.cis.jhu.edu/protocols/. Simi- larly, the hippocampus is divided into CA1, CA2 and CA3, based on the same atlas.

B. THALAMUS SUB-FIELD ATLAS

An isotropic, whole brain digital atlas with resolution 1 휇m was used to used to reconstruct high-field parcellation of the population thalamus (Ding et al., 2016). The atlas incorporates neuroimaging, high-resolution histology, and chemoarchitecture across a complete adult female brain, consisting of magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and 1,356 large-format cellular resolution

(1 µm/pixel) Nissl and immunohistochemistry anatomical plates. Anatomical delineation was done by the authors on a sub-set of the Nissl plates, with sampling intervals varying from 0.4 to 3.4 mm across the full anterior–posterior (A‐P) extent of the entire left hemisphere. Thus, 862 structures were delineated, and digitally scanned.

We segmented out 27 sub-fields of the thalamus from the digital whole brain atlas. The slices were rigidly registered together to compensate for inter-slice misalignment. Since the slices were sampled at different intervals ranging from 0.4 to 3.4 mm, they were then interpolated and resliced using LDDMM to get uni- formly spaced slices. Sub-field structures were then reconstructed in 3D using Restricted Delauney triangulation. See Appendix F viii for the steps, codes to run, file locations etc.

The thalamus was divided into 27 sub-fields. Sub-fields delineated are shown in blue in the following structure ontology:

Table 6 Sub-fields of the thalamus and corresponding labels/file names

Part Label/File name Dorsal thalamus Anterior nuclear complex of thalamus Anterodorsal nucleus of thalamus 1 Anteromedial nucleus of thalamus 2 Anteroventral nucleus of thalamus 3 Lateral dorsal nucleus of thalamus 4

Medial nuclear complex of thalamus Mediodorsal nucleus of thalamus 5 Reuniens nucleus (medioventral nucleus) of 6 thalamus Parataenial nucleus of thalamus 7 Lateral nuclear complex of thalamus Dorsal group of lateral nucleus Lateral posterior nucleus of thalamus 8 Pulvinar of thalamus 9 Ventral group of lateral nucleus Ventral anterior nucleus of thalamus 10 Ventral lateral nucleus of thalamus 11 Ventral posterior nucleus of thalamus 12 Ventral medial nucleus of thalamus 13 Posterior nuclear complex of thalamus Lateral geniculate nucleus 14 Medial geniculate nuclei 15 Posterior nucleus of thalamus 16 Limitans/suprageniculate nucleus 17 Intralaminar nuclear complex Anterior group of intralaminar nuclei Central lateral nucleus of the thalamus 25 Central medial nucleus of thalamus 26 Paracentral nucleus of thalamus 27 Central dorsal nucleus of thalamus 28 Posterior group of intralaminar nuclei 19 Fasciculosus nucleus of thalamus 20 Midline nuclear complex 21 Epithalamus 22 Ventral thalamus 23 Reticular nucleus of thalamus 24

C. TEST STATISTIC: RATIO OF MAX SQUARE ERRORS

As given in Section 0, the test statistic is chosen as a ratio of maximum square errors in null hypothesis to maximum square error in alternate hypothesis. Max square error is used because the square errors follow a long tail distribution, and we believe the tail of the distribution is important for discriminating power.

The distributions of square error across subjects and vertices for all structures are shown in Figure 33 below.

Figure 33 Square error over subjects and vertices for different structures showing their long tail distribution

D. INTER OBSERVER RELIABILITY

The results of the shape and volume analyses depend significantly on the quality of segmentations. In case of the hippocampus, ERC, TEC and PRC, we perform quality control by editing the segmentations from MRICloud to follow established protocol ( https://caportal.cis.jhu.edu/protocols/). The consistency of the segmentations is judged through intra- and inter-observer reliability scores (See Table 7). Intra-observer reliability score refers to how well different segmentations of the same structure by the same observer/person agree with each other (Case 1 in Table 7). Ten subjects from the BLSA study are used as test cases, and the structure of interest is segmented twice in each of them to calculate an average Kappa score (Cohen, 1960). Inter-observer reliability refers to how segmentations of the same structure by two different observers match. Here, the two different observers refer to MRICloud and the investigator who is manually editing the segmentations later. For the same ten test cases, we calculate the kappa score between outputs from MRICloud and manual segmentations by the investigator (Case 2 in Table 7). To assess the improvement in segmentations after manual quality control, we also calculate an inter-observer reliability score between manual segmentation and edited outputs from MRICloud (Case 3 in Table 7).

Table 7 Intra- and Inter-reliability scores (before and after editing): Kappa scores

Case 1: Intra-Reliabil- Case 2: Inter-reliability: Case 3: Inter-reliability: Structure ity scores Before editing After editing Left Right Left Right Left Right Hippocampus 91% 92% 84% 84% 92% 92% ERC 76% 77% 58% 56% 73% 75% TEC 79% 80% 64% 57% 75% 76% PRC 79% 81% 55% 54% 75% 73%

We observe that manual quality control greatly improves the reliability of the segmentations by increasing the kappa score. Some of the cases where manual editing helped improve hippocampus segmentations are given in Figure 34.

Figure 34 Sample case where manual editing improved segmentations. MRICloud segmentation often included the choroid plexus, which had to be edited out.

E. FILE LOCATIONS

The following gives the general locations of the codes, data etc. used, with the main files specified.

Table 8 File locations

Item Location Codes /cis/project/vestibular/codes/ Volume analysis /cis/project/vestibular/codes/vol_analyze.m Shape analysis (all vertices) /cis/project/vestibular/codes/surfJac_analyze.m Shape analysis (clusters) /cis/project/vestibular/codes/surfJac_clusters.m High field atlas creation-amyg, hippo /cis/project/vestibular/codes/map_highField.m High field atlas creation-thalamus /cis/project/vestibular/codes/read_atlas.m Save visualization .vtk files /cis/project/vestibular/codes/visualize_all.m Retriangulate template /cis/project/vestibular/codes/daniel/vtk/retriangu- lateTemplate.m Calculate intra-observer reliability /cis/project/vestibular/codes/similarity.m Calculate inter-observer reliability /cis/project/vestibular/codes/inter_obs.m Rigidly align slices (Thalamus HFT) /cis/project/vestibular/codes/kwame/process_struc- tures_together_katze.m Re-slice at uniform intervals and cre- /cis/project/vestibular/codes/kawme/join_segs_ana- ate surfaces (Thalamus HFT) lyze_v02_multi_katze.m Convert to surface /cis/project/vestibular/codes/daniel/seg2Surf/seg2Surf.m Save masks from MRI Cloud outputs /cis/project/vestibular/codes/mriCloud2masks.m Save masks from session files /cis/project/vestibular/codes/daniel/vtk/session2Ana- lyze.m

Raw data /cis/project/vestibular/data/rawdata/

MRICloud outputs (segmentations) /cis/project/vestibular/data/MRICloud_seg/ Raw outputs /cis/project/vestibular/data/MRICloud_seg/MRICloud_raw/

Separated structure segmentations /cis/project/vestibular/data/MRICloud_seg/MRICloud_separate/ Modified segmentations /cis/project/vestibular/data/MRICloud_seg/MRICloud_modified/

Surfaces /cis/project/vestibular/data/surfaces/

MRICloud outputs (surfaces) /cis/project/vestibular/data/MRICloud_surf/ Surface templates /cis/project/vestibular/data/MRICloud_surf/STE_1537/ Template matching to population /cis/project/vestibular/data/MRICloud_surf//

Significant regions, coefficients, saved as .mat /cis/project/vestibular/data/codes/clusters_*.mat files

Results visualization /cis/project/vestibular/data/visualization/ Individual structures /cis/project/vestibular/data/visualization/*.vtk Paraview state files /cis/project/vestibular/data/visualization/*.pvsm

High field atlases Hippocampus /cis/project/vestibular/data/high_field_hippo/ Amygdala /cis/project/vestibular/data/high_field_amyg/ Thalamus /cis/project/vestibular/data/thalamus_subfields/

Participants vestibular data /cis/project/vestibular/data/Participants_new.xlsx Participants TBV, ICV, hippocampal volumes /cis/project/vestibular/data/volumes_all.xlsx

Segmentation practice on test data /cis/project/vestibular/data_test/

Figures used in this document /cis/project/vestibular/Pictures/

The daily log containing detailed results, procedures, etc. is in: https://wiki.cis.jhu.edu/Athira/log

F. PIPELINE STEPS

i. Segmentation on MRICloud

a. Open MRICloud

b. Click ‘Segmentation’ -> ‘T1-MultiAtlas Batch’

c. Upload zip file containing all Analyze images

d. Select ‘Sagittal data converted to Axial’, ‘’BIOCARD3T_297Labels_10at-

lases_am_hi_erc_M2_V1’

e. Click Submit

f. Download results from ‘My Job Status’

ii. Surface creation:

Command to run in Terminal:

/cis/home/dtward/.local/bin/seg2Surf.sh 0.95

iii. Surface template creation:

a. Open MRICloud.

b. Click ‘Create Surface Template from Population’

c. Upload hypertemplate (.byu) and zip file containing population files (.byu).

d. Enable Rigid Registration and specify left and right identifiers (*_l_* and *_r_*).

e. Click Submit.

f. Download template from ‘My Job Status’.

g. Retriangulate template if needed.

iv. Population LDDMM:

a. Open MRICloud

b. Click ‘Map Surface Template to Population’.

c. Upload template (.byu) and zip file containing population files (.byu).

d. Enable Rigid Registration and specify left and right identifiers (*_l_* and *_r_*).

e. Click Submit.

f. Download results from ‘My Job Status’.

v. Volume analyses

File to run: /cis/project/vestibular/codes/vol_analyze.m

Inputs:

a. Which part: Eg- ‘hippo’, ‘thalamus’ etc.

b. Volumes: /cis/project/vestibular/codes/vols_other.mat, /cis/project/vestibular/data/vol-

umes_all.xlsx

c. Subject data: /cis/project/vestibular/data/Participants_new.xlsx

vi. Shape analyses – All vertices

File to run: /cis/project/vestibular/codes/surfJac_analyze.m

Inputs:

a. Which part: Eg- ‘hippo’, ‘thalamus’ etc.

b. Surface jacobian values from MRICloud: /cis/project/vestibular/data/MRICloud_surf//

c. Subject data: /cis/project/vestibular/data/Participants_new.xlsx

d. ICV, TBV etc: /cis/project/vestibular/data/volumes_all.xlsx

To save results: save(, ‘clusters’,’clust_ind’,’A1’);

To write .vtk files which can be read by Paraview: /cis/project/vestibular/codes/visualize_all.m

Inputs:

a. Which part: ‘hippo’, ‘thalamus’ etc.

b. Surface and list of values associated with each vertex: Both saved by the save command earlier.

vii. Shape analyses – Clusters

File to run: /cis/project/vestibular/codes/surfJac_clusters.m

Inputs:

a. Which part: Eg- ‘hippo’, ‘thalamus’ etc.

b. Surface jacobian values from MRICloud: /cis/project/vestibular/data/MRICloud_surf//

c. Subject data: /cis/project/vestibular/data/Participants_new.xlsx

d. number of clusters: / cis/project/vestibular/data/codes/clusters_.mat

e. ICV, TBV etc: /cis/project/vestibular/data/volumes_all.xlsx

viii. High-fields Atlas Creation and Usage

File to run: /cis/project/vestibular/codes/map_highField.m

Inputs:

a. Sub-fields as Analyze volumes

Procedure:

For amygdala and hippocampus:

a. Create complete volume using all sub-fields (PART 1 of script)

b. Create surface from volume. (PART 2 of script)

c. Rigidly align high field surface (HFS) to structure template. (PART 3 of script)

d. Use LDDMM surface to deformably register HFS to structure template: Terminal command to be

run on io21: lddmm-surface 5 2

e. Find vertices in HFS that are closest to each vertex in structure template, and check which are the

closest sub-fields to those vertices in HFS, creating a list. Thus, we have a sub-field associated

with each vertex in the structure template. (PART 4 of script)

f. Use this list to check significant vertices of structure template.

For thalamus:

Main script: /cis/project/vestibular/codes/read_atlas.m

a. Download required slices as .png files from online atlas. (Location: /cis/project/vestibu-

lar/data/thalamus_subfields/jpg_imgs/)

b. Combine all slices into one Analyze volume, so as to be read in Seg3D. (PART 1 in script)

Location: /cis/project/vestibular/data/thalamus_subfields/thalamus.img

c. Segment out required sub-fields in Seg3D, and save as separate Analyze volumes.

Location: /cis/project/vestibular/data/thalamus_subfields/sub_fields)

d. Down-sample sub-fields to reduce memory load (PART 2 in script)

Location: /cis/project/vestibular/data/thalamus_subfields/sub_fields_down

e. Rigidly align slices.

Code to run: /cis/project/vestibular/codes/process_structures_together_katze.m

Inputs: Sub-field volumes f. Create surfaces, taking into account different slice thicknesses. Slice locations are estimated from

Figure 16 in Ding et al. (2016), using PART 3 in script.

Code to run: /cis/project/vestibular/codes/kwame/join_segs_analyze_v02_multi_katze.m

Inputs: Slice locations (/cis/project/vestibular/codes/thalamus-z_pos.mat), origin

Output location: /cis/project/vestibular/data/kwame/thalamus_subfields/surfaces_down/ g. Continue from Step c in the previous list.

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BIOGRAPHY

Athira Jane Jacob was born in India in 1993.

Athira did her Bachelors and Masters in Engineering Design, at Indian Institute of Technology, Madras,

India. During her undergraduate studies, she studied biomedical product design, as well as conducted research on medical image processing. She was also a teaching assistant for two classes, Analog and Digital

Circuits, and Design of Surgical Devices. She also interned at the Medical Imaging Lab at GE Corporate

Research at Bangalore, India for a period of six months.

Athira joined the Master’s program in Biomedical Engineering, with a focus on Imaging, at Johns Hop- kins University in 2016.