Advance Access published May 1, 2015 doi:10.1093/brain/awv110 BRAIN 2015: Page 1 of 12 | 1

Wider minicolumns in autism: a neural basis for altered processing?

Rebecca McKavanagh,1,2 Eleanor Buckley1 and Steven A. Chance1

Previous studies have found alterations in the columnar organization of the cortex in autism spectrum disorders. Such changes have been suggested to be limited to higher order association areas and to spare primary sensory areas. In addition, evidence from gene- expression studies have suggested that there may be an attenuation of cortical differentiation in autism spectrum disorders. The present study specifically assessed the minicolumns of cells that span the depth of the cortex in a larger sample of autism spectrum disorder cases than have been studied previously, and across a broad age range. The cortical regions to be investigated were carefully chosen to enable hypotheses about cortical differentiation and the vulnerability of association cortex to be tested. Measures of the minicolumnar arrangement of the cortex (minicolumn width, spacing and width of the associated bundles) were made in four regions of cortex (primary auditory cortex, auditory association cortex, orbital frontal cortex and inferior Downloaded from parietal lobe) for 28 subjects with autism spectrum disorder and 25 typically developing control subjects. The present study found wider minicolumns in autism spectrum disorder [F(1,28) = 8.098, P = 0.008], which was particularly pronounced at younger ages, providing evidence for an altered developmental trajectory at the microstructural level. In addition, altered minicolumn width was not restricted to higher order association areas, but was also seen in the primary sensory region investigated. Finally, this study by guest on May 6, 2015 found evidence that cortical regional differentiation was still present in autism spectrum disorder [F(3,39) = 5.486, P = 0.003], although attenuated compared to typically developing subjects [F(3,45) = 18.615, P 5 0.001]. It is suggested that wider spacing of the minicolumns may relate to the enhanced discrimination seen in some individuals with autism spectrum disorders.

1 Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK 2 Neuroscience and Mental Health Research Institute, University of Cardiff, CF24 4HQ, UK Correspondence to: Dr Steven Chance Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK, E-mail: [email protected]. Keywords: autism; minicolumns; primary sensory cortex; brain development; enhanced discrimination Abbreviations: ASD = autism spectrum disorders; BA = Brodmann area

(Casanova et al., 2002b, c, 2006a, b; Buxhoeveden et al., Introduction 2006). Minicolumns consist of a vertical string of approxi- Autism spectrum disorders (ASD) are a cluster of pervasive mately 80–100 stretching between cortical layers developmental disorders diagnosed on the basis of impair- II–VI, with associated dendrites and myelinated axon bun- ments in a triad of domains: social interaction, verbal and dles, and form the fundamental structural unit of the cortex non-verbal communication, and the presence of restricted (Mountcastle, 1997; Buxhoeveden and Casanova, 2002; and repetitive behaviours (DSM-IV-TR; APA 2000) for Casanova et al., 2008). The core area of the minicolumn, which the neural basis is uncertain. Previous research has containing the cell bodies, apical dendrites and unmyeli- reported a reduction in cortical minicolumn width in ASD nated , is surrounded by a cell sparse peripheral

Received December 11, 2014. Revised February 23, 2015. Accepted February 28, 2015. ß The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: [email protected] 2 | BRAIN 2015: Page 2 of 12 R. McKavanagh et al. neuropil space (Casanova et al., 2002b). Multiple individ- unchanged (Buxhoeveden et al., 2006). Brodmann area ual axons group together, forming bundles as they descend (BA) 41, primary auditory cortex, was used as an example from layers III to VI within, or closely adjacent to, the core of primary sensory cortex, which has a clear minicolumnar of the minicolumn (Casanova et al., 2002b). The edges of structure identified in normal development and ageing the minicolumn core contain GABAergic interneurons, (Chance et al., 2011). It is notable that there is a high which are thought to provide inhibition between neigh- reported rate of auditory abnormalities in ASD (Klintwall bouring minicolumns, thereby reinforcing functional segre- et al., 2011). The planum temporale was included as an gation (Favorov and Kelly, 1994). Minicolumn width, example of unimodal association cortex as well as for its defined as the centre-to-centre spacing of the minicolumns, role in language, which is known to be affected in ASD. has been shown to be sensitive to regional variation within The final two regions comprise heteromodal association the brain (Chance et al., 2011) as well as both ageing cortex; BA11 (orbital frontal cortex) and BA40 (inferior (Chance et al., 2006, 2011; Di Rosa et al., 2009) and path- parietal cortex), both of which have been identified as con- ology (Casanova et al., 2002b; Chance et al., 2008; Di tributing to theory of mind (Sabbagh, 2004; Vo¨ llm et al., Rosa et al., 2009). 2006; Carrington and Bailey, 2009; Abu-Akel and Shamay- Several studies have reported that it is specifically the Tsoory, 2011; Brink et al., 2011). This range of cortical peripheral neuropil space component of the minicolumn regions enabled us to address the suggestion that there is that is reduced in ASD (Casanova et al., 2002b; reduced cortical differentiation in ASD (Voineagu et al., Buxhoeveden et al., 2006). In these studies, peripheral 2011; Ziats and Rennert, 2013). The present study also neuropil space is calculated as the difference between the included participants between 4 and 88 years enabling centre-to-centre spacing of the minicolumns and the width comment on whether differences between ASD and typic- of the core region (the core region being defined as the part ally developing cases are observed at all ages, or whether of the column containing 90% of the cells) (Casanova they might be due to altered developmental trajectories in et al., 2002b). Therefore, it has been suggested that reduc- the two groups. tion of the peripheral neuropil space corresponds to loss of inhibitory GABAergic cells at the edge of the core region Downloaded from (Casanova, 2006). It has been suggested that this would Materials and methods affect the excitation-to-inhibition ratio during cortical

development and the increased excitation may relate to by guest on May 6, 2015 the high rate of seizures seen in ASD (Casanova et al., Subjects 2002a). However, computational models of minicolumnar Fixed tissue was obtained from 28 ASD and 25 typic- organization suggest the alternative scenario; that decreased ally developing control subjects, matched as far as possible for lateral inhibition would lead to the formation of wider age (age range 4–88 years, although due to the scarcity of minicolumns (Gustafsson, 1997, 2004). Wider minicolumns tissue and missing data cases could not be additionally are thought to indicate less overlapping dendritic trees and matched on sex and brain weight), sampling regions approx- so more discrete functioning of individual minicolumns imating to Brodmann areas 11, 40, 41 and planum temporale. (Chance et al., 2012; Chance, 2014), which is consistent Tissue was obtained through the Autism Tissue Program from the NICHD Brain and Tissue Bank, University of Maryland; with the preserved or even enhanced featural processing the Harvard Brain Tissue Resource Centre and the UK Brain seen in ASD (Happe´, 1999). Bank for Autism, and the Thomas Willis Oxford Brain A study by Buxhoeveden et al. (2006) found narrower Collection. Where available, information on brain weight minicolumns in ASD in prefrontal regions and not primary and clinical characteristics were also recorded (Table 1 and , and so hypothesized that the alterations in Supplementary Table 1). ASD may be selective to higher order association areas while primary sensory cortices are spared (Buxhoeveden et al., 2006). In addition, more differences were reported Neurohistological sampling in adults than in children with ASD. Given the small Brains were sectioned coronally and blocks of size sample size included in the study by Buxhoeveden et al. 25 mm  25 mm  10 mm were sampled for the four regions (2006) it was not possible to draw firm conclusions from from one hemisphere per brain. Blocks within the surrounding the study, but the data suggested that hierarchical regional, tissue were photographed using an Olympus C-5050 digital and age-related, differences may deserve further camera for reference. BA41 is bordered medially by the investigation. insula cortex and laterally by the planum temporale. The an- terior boundary is marked by the first transverse sulcus and Therefore, the aim of the present study was to investigate the anterior boundary by Heschl’s sulcus. The planum tempor- potential minicolumnar differences in ASD, with a focus on ale is located on the lower bank of the Sylvian fissure and regional and age-related patterns of difference. Four differ- bounded posteriorly by its ascending ramus. The anterior ent cortical regions were chosen for investigation, repre- boundary is formed by Heschl’s gyrus. BA11 blocks were senting both association cortices and a primary sensory sampled from the gyrus rectus, bounded medially by the mid- region, directly testing the suggestion that association line, posteriorly by BA25, separated laterally from BA10 by cortices are affected while primary sensory cortices are the olfactory sulcus and dorsally from BA12 by the superior Altered minicolumns in autism BRAIN 2015: Page 3 of 12 | 3

Table 1 Demographic means A sample line of standard length (590 mm; determined by the size of the image view) was superimposed, perpendicular to the Age Brain ADI-A ADI-B ADI-C ADI-D bundle direction, and bundles intersecting this line were mea- (years) weight (g) sured. Most vertically oriented axons were members of bun- ASD 23.7 (16.1) 1405 (226) 24 (6) 14 (3) 6 (3) 4 (1) dles and single axons or pairs of axons crossing the line were TD 34.0 (25.4) 1405 (142) - - - - not considered to constitute axon bundles for the purposes of this analysis. All bundles (42 axons) intersecting the line were Means (with standard deviations) for age, brain weight and ADI-R scores for subjects identified and bundle spacing measurements were made from included in this study. the centre of each bundle to the centre of the adjacent bundle. TD = typically developing; ADI = Autism Diagnostic Interview. The width of each axon bundle was also measured. For the width measurements, the edges of the bundles were marked at orbital sulcus. BA40 was defined as the supramarginal gyrus, the point where they intersected the line, and the bundle width which is bounded superiorly by the intraparietal sulcus, infer- was determined as the distance between these two points. iorly by the Sylvian fissure, anteriorly by the postcentral sulcus Edges of axon bundles were distinguished by the change in and posteriorly by the Jensen sulcus. Identical macroscopic intensity of staining from the background. Pilot data revealed landmarks were used for identification of regions across high reliability of this method when comparing measurements tissue banks and region selection was confirmed cytoarchitec- of the same photographs made on different occasions (for turally in accordance with Von Economo and Koskinas bundle width intraclass correlation coefficient = 0.81, (1925). P 5 0.001; for bundle spacing intraclass correlation coeffi- All tissue was sucrose protected (30% w/v) prior to freezing, cient = 0.98, P 5 0.001). The values from the three photo- to minimize artefacts. Tissue was frozen as this has been graphs were then averaged to give a single value for bundle shown to improve analysis with respect to variable shrinkage spacing and a single value for bundle width for each region of in the z-direction in comparison with paraffin embedding interest. (Hatton and Von Bartheld, 1999; Gardella et al., 2003). Four 30-mm sections were cut from each block. Two non- Statistical analysis contiguous sections were Nissl stained with cresyl violet

(ThermoFisher Scientific) for visualization of neurons and All data were analysed using SPSS v19 for Windows. Repeated Downloaded from two non-contiguous sections were stained with Sudan black measures ANOVAs with cortical region as the within subjects for visualization of axon bundles. factor and diagnosis as the between subjects factor were used to investigate differences in minicolumnar width, axon bundle width and axon bundle spacing. The effect of including age by guest on May 6, 2015 Minicolumn analysis and brain weight as covariates was also investigated. Group differences in age and brain weight were investigated Minicolumn width based on cell bodies was assessed using a using independent samples t-tests. Pearson’s correlation coeffi- semi-automated procedure that has been described in detail cient was used to investigate correlations. previously (see Buxhoeveden et al., 2001; Casanova and Switala, 2005 for descriptions and illustrations). This proced- ure gives a value for the minicolumn width consisting of the cell dense core region plus the associated peripheral neuropil Results space surrounding it. For each region of interest, four digital micrographs were taken across the two slides (usually two Regional and diagnostic differences pictures from each slide), each containing a region of 1 mm2. Image locations were selected using a random number A repeated measures ANOVA with cortical region as the generator, excluding areas of high curvature, which have been within subjects factor and diagnosis as the between subjects shown to affect cell distribution (Chance et al., 2004). As factor revealed a significant effect of diagnosis minicolumns are clearest in layer III, images were centred on [F(1,28) = 8.098, P = 0.008], due to wider minicolumns in that layer and obtained through a Â4 objective lens, with an ASD, as well as a significant effect of region Olympus BX40 microscope. Values calculated from the four [F(3,84) = 17.417, P 5 0.001]. Including brain weight, photomicrographs were averaged to give a single value for post-mortem interval and age as covariates in the model each region (Chance et al., 2004; Casanova and Switala, reduced the effect of cortical region (apparently affected 2005; Di Rosa et al., 2009). by a significant interaction between cortical region and brain weight) [F(3,45) = 2.954, P = 0.042], which could be Axon bundle measurements due to the presence of a relationship between brain weight and minicolumn width for BA11 (r = 0.465, P = 0.002), For each region three images were obtained through a Â10 BA40 (r = 0.488, P = 0.001) and BA41 (r = 0.359, objective lens with an Olympus BX40 microscope, centred around layer V as the axon bundles were clearest there. P = 0.029) but not for planum temporale (r = 0.182, Areas of extreme curvature were avoided, as for the minicol- P = 0.355), although the effect of diagnosis remained umn measurements. Measurements of axonal bundle centre- [F(1,15) = 7.225, P = 0.017]. to-centre spacing, and the width of the bundles themselves Post hoc investigation revealed wider minicolumns in were made in Axiovision, using the manual measurement ASD in all regions, particularly in BA11 [t(28) = 2.942, tools (Fig. 1). P = 0.006] and planum temporale [t(28) = 3.144, 4 | BRAIN 2015: Page 4 of 12 R. McKavanagh et al.

Figure 1 Sudan black stained section for axon bundle measurement. Illustrating (A) cortical layers (III, IV and V), (B) tissue type and

(C) measurements of axonal bundle width (a) and axonal bundle spacing (b), as indicated by the white arrows. GM = grey matter; WM = white Downloaded from matter.

P = 0.004] and a trend in BA41 [t(28) = 1.846, P = 0.076] P = 0.013), BA41 (r = 0.525, P = 0.002) and planum tem- by guest on May 6, 2015 (Table 2). Post hoc investigation of the effect of region porale (r = 0.474, P = 0.012), but not BA11 (r = 0.205, found significant differences between all pairs of regions P = 0.277). (all P 5 0.008) except between BA11 and planum tempor- Although the finding of no interaction between diagnosis ale (Fig. 2). and cortical region indicated a similar pattern of regional Performing the same analysis on peripheral neuropil differences in both ASD and typically developing controls, space revealed a significant main effect of region due to our a priori interest in whether regional differenti- [F(3,84) = 3.772, P = 0.014], as well as a trend towards ation is attenuated in ASD, we addressed this question an effect of diagnosis [F(1,28) = 3.988, P = 0.056]. explicitly by conducting an analysis of each group separ- Post hoc investigation of the main effects revealed larger ately. A significant main effect of cortical region on mini- peripheral neuropil space in ASD cases, and significantly column width was found for both ASD [F(3,39) = 5.486, larger peripheral neuropil space in BA40 than BA41 P = 0.003] and typically developing [F(3,45) = 18.615, (P = 0.013). However, including age, post-mortem interval P 5 0.001] groups separately (repeated measures and brain weight as covariates cancelled the effect of region ANOVA). Post hoc analysis revealed that while significant [F(3,45) = 1.101, P = 0.359] and reduced the effect of diag- differences were seen in typically developing controls be- nosis [F(1,15) = 3.444, P = 0.083]. tween all regions (all t 4 2.4, P 5 0.03), except between A similarly structured repeated measures ANOVA for planum temporale and BA11 (t = À0.577, P = 0.573), the axon bundle width found a significant main effect of ASD group showed no differences between cortical regions region on bundle width [F(3,69) = 7.17, P 5 0.001]. except that BA41 had narrower minicolumns than all other Repeating this analysis for axon bundle spacing revealed regions (all t 4 2.6, P 5 0.02). no significant effects. Post hoc investigation determined that axon bundle Relationship with demographic widths in planum temporale were significantly smaller than in all other regions (Fig. 2B). Inclusion of age and measures brain weight as covariates cancelled the effect of region, No differences were seen between diagnostic groups for which may be due to the presence of an interaction either age or overall brain weight (P 4 0.05) (although between age and cortical region [F(3,39) = 3.270, brain weight was not available for four ASD and six typ- P = 0.031], whereby a positive correlation is seen between ically developing cases). To test for an effect of medication age and axon bundle width for BA40 (r = 0.429, or the presence of seizures within the ASD group, separate Altered minicolumns in autism BRAIN 2015: Page 5 of 12 | 5

Table 2 Histology data

Diagnosis ASD Typically developing BA11 BA40 BA41 PT BA11 BA40 BA41 PT Minicolumn width (mm) 35.7 (4.1) 37.3 (4.6) 32.7 (3.3) 35.6 (2.9) 32.1 (2.6) 35.1 (3.7) 30.6 (2.9) 32.4 (2.6) Axon bundle spacing (mm) 54.1 (9.0) 54.8 (6.3) 50.6 (2.4) 54.1 (4.9) 54.1 (7.8) 55.0 (11.1) 55.7 (4.3) 55.3 (13.5) Axon bundle width (mm) 6.7 (0.5) 7.5 (0.8) 6.8 (1.3) 6.3 (1.1) 7.2 (0.9) 7.4 (1.4) 7.1 (1.3) 6.1 (1.5)

Means (standard deviations) for minicolumn width and width and spacing of axonal bundles for each cortical region. PT = planum temporale. Downloaded from by guest on May 6, 2015

Figure 2 Regional and diagnostic differences.(A) Regional and diagnostic differences in minicolumn width. (B) Regional differences in axon bundle width. *Significance at the P 5 0.05 level; **significance at the P 5 0.01 level. PT = planum temporale. repeated measures ANOVAs, with region as the within sub- A trend towards a negative correlation was observed jects factor and either medication or the presence of seiz- between minicolumn width and age for ASD (r = À0.509, ures as the between subjects factor, were conducted for the P = 0.063) and for typically developing cases (r = À0.456, histological measures. No effect of presence of seizures on P = 0.088). For peripheral neuropil space, a negative cor- minicolumn width, bundle spacing or bundle width was relation was seen with age for typically developing found (all F 5 0.65, P 4 0.44). In contrast, although no (r = À0.602, P = 0.014) but not ASD cases (r = À0.335, effect of medication was found on minicolumn width or P = 0.242). bundle spacing (both F 5 1.94, P 4 0.36), a main effect For axon bundle spacing, a positive correlation with age of medication on bundle width was seen [F(1,8) = 12.639, was observed in typically developing controls (r = 0.594, P = 0.007], with wider axon bundles being seen in those P = 0.042), which was not significant in ASD cases cases with a history of medication. However, the distribu- (r = 0.378, P = 0.203) (Fig. 3A). In contrast, ASD cases tion of cases with a history of medication versus those showed a significant positive correlation between axon without, mirrored the distribution of old versus young bundle width and age (r = 0.650, P = 0.012), which was cases, where older cases tended to have a history of medi- not significant in typically developing control subjects cation and wider axon bundles (discussed below), and so (r = 0.381, P = 0.145) (Fig. 3B). this finding may just reflect an age-related difference. In Pre-fixation brain weight was not available for all cases, addition, it may be difficult to draw firm conclusions re- and as post-fixation brain weight is undesirable for com- garding medication as a number of cases were classified as parisons, investigation of correlations between histology having no history of medication on the basis of an absence and total brain weight were restricted to those cases for of evidence, which sometimes depends on very sparse clin- which pre-fixation weights were available. No correlations ical notes rather than direct confirmatory evidence. were seen between total brain weight and minicolumn A mean value was calculated across the different regions width, peripheral neuropil space or aspects of axon for each case to test correlations between measured vari- bundle structure. No relationships were seen with measures ables and demographic variables. of autism severity. 6 | BRAIN 2015: Page 6 of 12 R. McKavanagh et al.

No correlations were seen between post-mortem interval including BA41, with implications for altered processing and any histology measures. in ASD. This is the largest study to date and includes cases across a wide age range, providing some evidence Investigation of age group effects of an altered developmental trajectory in ASD. To investigate whether the diagnostic differences in the Interpreting diagnostic differences measured variables were more pronounced in adulthood than childhood, as suggested by Buxhoeveden et al. Even though they differ from some other studies of mini- (2006), the cases in the present study were split into two columns, which mostly investigated different brain regions groups: those 16 years and below, and those over 16. Age on substantially smaller sample sizes, the present findings 16 was chosen as the cut-off point because Courchesne sit comfortably within the broader literature in several et al. (2011) found increased numbers in children respects. A number of studies and theories (Happe´ and with ASD up to and including 16 years of age, and this age Frith, 2006; Mottron et al., 2006) support the idea of a has social and legal significance in many countries, includ- bias towards a local, rather than global, processing style in ing with regard to healthcare. ASD. This suggests that when presented with a stimulus the A significant effect of diagnosis [F(1,26) = 7.856, features of that stimulus are processed individually rather P = 0.009] and region [F(2.213,57.544) = 15.911, than holistically as typically seen in typically developing P 5 0.001], as well as a trend towards an effect of age controls. Wider spacing of minicolumns, as in the present group [F(1,26) = 3.807, P = 0.062] on minicolumn width study, has been shown to result in less overlap of the den- were found (repeated measures ANOVA with cortical dritic trees associated with the constituent neurons (Seldon, region as the within subjects factor and diagnosis and age 1981). This in turn has been suggested to result in less group as the between subjects factors). Post hoc analysis of co-activation of neighbouring minicolumns allowing them these effects revealed significant differences between all to function more independently and so facilitate the pro- pairs of regions (all P 5 0.02) except BA11 and planum cessing of individual features as seen in ASD (Harasty temporale (P = 0.140). Wider minicolumns were found in et al., 2003; Chance et al., 2012). In contrast, narrower Downloaded from ASD cases compared to typically developing (as described minicolumns are more likely to be co-activated and so fa- above), while narrower minicolumns were found in the cilitate holistic processing of stimuli. There is evidence from

over 16 age group compared to those under 16. This age the neuroimaging literature to support a reduction in den- by guest on May 6, 2015 effect appeared to be largely driven by several elderly cases dritic density consistent with wider spacing, MRI studies aged over 60 years at death. have reported alterations in grey matter brain chemistry Age effects on axon bundle width were similarly investi- which are consistent with a reduced number or density of gated using a repeated measures ANOVA. An effect of dendrites in ASD (Friedman et al., 2003, 2006). both cortical region [F(3,63) = 7.66, P 5 0.001] and age In addition, a number of theories have suggested that group [F(1,21) = 19.02, P 5 0.001] along with a significant there may be disruption of the excitation-inhibition ratio interaction between cortical region and age group in ASD towards a relatively increased level of excitation [F(3,63) = 3.63, P = 0.018] and an interaction between (Rubenstein and Merzenich, 2003; Polleux and Lauder, diagnosis and age group [F(1,21) = 4.31, P = 0.050] were 2004; Pizzarelli and Cherubini, 2011); a disruption seen. No significant effects were seen for axon bundle which, if occurring as the primary change, has been spacing. shown to bias towards the formation of wider minicolumns Post hoc investigation of the main effects revealed a pat- (Gustafsson, 1997, 2004). However, it has also been tern where axon bundle widths were larger in ASD cases in suggested that a primary alteration in the minicolumnar the younger age group (Fig. 4A), but larger in the typically structure could itself lead to an alteration in the excita- developing cases in the older age group (Fig. 4B). tion-inhibition balance (Casanova et al., 2003; Casanova, Narrower axon bundles were found in planum temporale 2006). compared to all other regions. Regarding the interaction This is the first study to examine aspects of axon bundle between cortical region and age, narrower axon bundles organization in the cortex in ASD, and these axon bundles were seen in the young age group for Brodmann areas are generally continuous with the white matter below the 40, 41 and planum temporale, but not for BA11 (Fig. 4B). cortex, which has been investigated in ASD in several stu- dies (Herbert et al., 2004; Zikopoulos and Barbas, 2010). Although we did not find any diagnostic differences in Discussion axon bundle spacing, this is consistent with other published data indicating no difference (in ASD compared to typically The current study finds wider minicolumns in ASD, in con- developing controls) in overall axon density in the white trast to some previous findings (Casanova et al., 2002b, matter immediately underlying the grey matter in orbital 2006a; Buxhoeveden et al., 2006). The effect did not frontal cortex (BA11) and lateral prefrontal cortex appear to be restricted to higher order association areas (BA46) (Zikopoulos and Barbas, 2010). Future work may as a 6–10% difference was seen across all regions, determine which parameters of the axon bundles are Altered minicolumns in autism BRAIN 2015: Page 7 of 12 | 7

Figure 3 Relationships with demographic measures. Least squares regression line for the relationship between age and (A) minicolumn width, (B) axon bundle spacing and (C) axon bundle width for ASD and typically developing cases. Downloaded from by guest on May 6, 2015

Figure 4 Age-related differences. Diagnostic differences in axon bundle width for each cortical region in (A) cases aged 16 years and under and (B) cases aged over 16. Error bars indicate the standard error of the mean. PT = planum temporale. altered to give rise to narrower axon bundles (e.g. changes narrower axon bundles. In contrast, the finding of wider in axonal density, changes in the number of axons or axon bundles in ASD in the younger age group suggests changes in the size of individual axons, and changes in either enlargement of individual axons or increased num- myelination). Zikopoulos and Barbas (2010) found bers of axons. Increased numbers of small axons mediating region-specific reductions in the number of large axons connections between neighbouring areas could lead to an and an increase in the number of small axons in adult increase in the volume of white matter immediately under- ASD cases, suggesting a shift towards smaller axons in lying the cortex, and this has been found to show enlarge- ASD which could in turn lead to narrower axonal bundles. ments at early ages (Herbert et al., 2004). Smaller axons mediate short range connections (Zikopoulos and Barbas, 2013) and a bias towards smaller Age axons is compatible with evidence suggesting a reduction in long range connectivity, while short range connectivity is The findings here suggest an effect of age on the difference preserved or even increased (Courchesne and Pierce, 2005). in minicolumn width between ASD and typically develop- Future work should clarify whether this is also true in the ing cases. Although a negative correlation was seen white matter underlying the regions examined in the pre- between minicolumn width and age for both ASD and typ- sent study, and whether reduced axon diameter is seen in ically developing subjects, there appeared to be a greater 8 | BRAIN 2015: Page 8 of 12 R. McKavanagh et al. rate of decline in ASD compared to typically developing study that has investigated axon bundle width in BA41 subjects (Fig. 3A). Despite the limited age overlap between found slightly higher values than those found in the present ASD and typically developing groups, there was a trend for study (Seldon, 1981), although that may reflect differences minicolumn widths in ASD and typically developing sub- in tissue processing [celloidin embedded as in Seldon (1981) jects to become more similar with age. versus frozen tissue in the present study] and the wider age In addition, when the cases were split above and below range included in the present study [5–50 years in the pre- age 16, differences in minicolumn width appeared more sent study versus 74–89 years in Seldon (1981)]. As one pronounced in the younger age group (Supplementary might expect, the areas with the widest minicolumns also Fig. 1). This pattern of larger differences at younger ages had the widest axon bundles and vice versa. It is plausible fits with other whole brain volume data in ASD, which that if individual axons are wider this would be accompa- shows larger brains in ASD at young ages with reduced nied by an increase in neuronal size (Vinters and difference in adulthood (Courchesne et al., 2011). Kleinschmidt-DeMasters, 2008) both of which would there- However, in contrast to total brain volume data where fore contribute to an increase in minicolumn width. diagnostic differences disappear largely due to typically Although the present study found a significant effect of developing controls ‘catching up’, the diagnostic differences region for both ASD and typically developing cases, post in minicolumn width appeared to reduce through reduction hoc testing revealed fewer regional differences in ASD than of minicolumnar width with age in ASD cases only. in typically developing controls, suggesting that cortical Increased minicolumnar width could contribute to the differentiation is not absent, but may be attenuated. It is increases seen in grey matter at early ages, particularly known that typically developing controls show a reduction increases in total surface area (Mak-Fan et al., 2012) in cortical differentiation with age, with higher order asso- (in addition to a large white matter contribution) ciation regions showing a reduction in minicolumnar width (Herbert et al., 2004). One recent study found larger after 65 years of age, which is not seen in primary sensory brain surface areas in young children with ASD which regions (Chance et al., 2006). Additionally, subjects with decreased with age until they reached a similar value to ASD are known to show an altered developmental trajec- that seen in typically developing subjects at around 9 tory, with a possibly accelerated onset of degeneration Downloaded from years of age (Mak-Fan et al., 2012). However, we saw (Courchesne et al., 2011). Unfortunately the present data differences in minicolumnar width at later ages than this, cannot distinguish between a scenario where ASD is char-

and Mak-Fan et al. (2012) found that some regions were acterized by reduced cortical differentiation from child- by guest on May 6, 2015 still showing increases in surface area in ASD at later ages. hood, and one where ASD is characterized by an earlier It is not clear whether the specific age range over which onset of reduction in minicolumn width, resulting in appar- surface area enlargements are seen corresponds to that over ently reduced cortical differentiation on average across the which wider minicolumns are seen in the same regions. age range. Visual inspection of the data in Supplementary However, the pattern of higher values in ASD, which Fig. 1 seems to support the latter explanation and future then decrease with age, becoming more similar to typically studies should investigate this formally. developing controls, is similar to the pattern observed here Although the difference in minicolumn width is not in the minicolumn data. Increased minicolumn width in significant for all regions, it does not follow the pattern ASD may bias towards a local processing style at early of differences being present in ‘higher order’ associative ages, such that, despite diminishing diagnostic differences regions rather than primary sensory regions as suggested in total brain volume and minicolumn width, the bias in by Buxhoeveden et al. (2006). BA41, a primary sensory processing style has already been established. region, contributed towards the evidence of wider minicol- umns in ASD in the present study, whereas BA40, a hetero- Regional differences modal association area, differed little in minicolumn width between ASD cases and typically developing controls. The present study found regional differences in both mini- column width and axon bundle width, with BA40 having Comparison with previous studies the widest minicolumns and axon bundles, in both typically developing and ASD cases, and BA41 having the narrow- As the present study finds wider minicolumns, rather est. This is consistent with previous reports that minicol- than the previously reported narrower minicolumns, in umn width relates to cortical hierarchy, with the narrowest ASD, it is useful to consider the comparison with previous minicolumns being seen in primary sensory regions (in this studies. case BA41) and the widest minicolumns being seen in het- eromodal sensory regions (of which BA40 is an example) Components of minicolumn investigated (Chance, 2006). Although it is well established that cortical Minicolumnar abnormalities in ASD were originally regions differ in their myeloarchitecture as well as cyto- reported to affect both the centre-to-centre minicolumn architecture (DeFelipe, 2005), the former has generally width and the neuropil component of that distance specif- been overlooked in the anatomical literature and so ically (Casanova et al., 2002b). Peripheral neuropil space is regional differences are not well characterized. The one the space between the column cores (the part of the column Altered minicolumns in autism BRAIN 2015: Page 9 of 12 | 9

Figure 5 Comparison with previous work. Difference in minicolumn width between ASD and control cases across the age range. Positive values indicate wider minicolumns in ASD. Data from (A) Casanova et al. (2006),(B) the present study. Different regions are represented by different colours. Only data from Casanova et al. (2006) are illustrated here, but includes a greater number of regions. Data shown for Casanova et al. (2006) are taken from lamina III for greatest comparability with the data from the present study. Downloaded from Table 3 Minicolumn measurements across studies

Study BA3 BA4 BA11 BA9 BA10 BA44 BA17 BA40 BA41 PT BA43 BA24 by guest on May 6, 2015 ASD CTL ASD CTL ASD CTL ASD CTL ASD CTL ASD CTL ASD CTL ASD CTL ASD CTL ASD CTL ASD CTL ASD CTL

Minicolumn width (km) Casanova et al. (2006)b 25.8 27.0 27.5 28.2 22.0 23.4 h 26.5 29.4 23.0 24.1 Buxhoeveden et al. (2006)c 40.2 46.9f ASD = 42.0; CTL = 47.3d 31.5 32.7 49.3 50.4g Present study 34.8* 32.5 36.2 35.1 32.1 30.4 35.6* 32.6 Core width (km) Casanova et al. (2010)a,b 9.5 12.5 9.5 12.5 9 11.5 8.5 12 9.5 12 8 13 9.5 12 9 13 10 11.5 Present study 27.1 25.7 28.2 27.4 25.8* 24.6 27.7 26.5 Neuropil space (km) Casanova et al. (2006) 11.1 11.1 11.9 11.2 11.5 12.0 11.2 11.9 11.7* 11.2 12.0 12.7 9.1 9.8 12.1 12.3 12.4* 11.0 Present study 7.7 6.7 8.0 7.7 6.3 5.8 7.9* 6.0

Average values for minicolumn width, core width and peripheral neuropil space in all cortical regions examined to date. *Statistically significant differences between ASD and control groups (P 5 0.05). PT = planum temporal; CTL = control. aValues calculated from the figure provided in the paper. bNo pairwise comparisons reported in the paper to assess statistical differences between diagnoses within cortical regions. cNo pairwise comparisons reported. A significant effect of diagnosis was seen for frontal regions but not BA17. dValue provided for dorsal frontal cortex. fValue provided for orbital frontal cortex. gValue provided for mesial frontal cortex. hValue calculated from the data provided through the Autism Tissue Program portal (www.atpportal.org).

containing the cell bodies). Although the original study in ASD [F(1,28) = 8.563, P = 0.007] along with a trend to- reported differences in both minicolumn width and periph- wards greater peripheral neuropil spacing [F(1,28) = 3.988, eral neuropil space (Casanova et al., 2002b), other studies P = 0.056]. This suggests that the difference in the present have reported differences in just width of the column core study does not reflect the decision to look specifically at (Casanova et al., 2010) or peripheral neuropil space one particular aspect of the minicolumn. (Casanova et al., 2006b), with Casanova et al. (2006b) finding increased peripheral neuropil space in ASD com- Age pared to typically developing cases in BA10 and BA24. Differences in minicolumn width have previously been The present study found significantly wider column cores reported to be more pronounced in adults than children 10 | BRAIN 2015: Page 10 of 12 R. McKavanagh et al.

(Buxhoeveden et al., 2006). It may be possible therefore, have compared minicolumn width between ASD and con- that contrasting findings, including the wider minicolumns trol cases in BA40, 41 or planum temporale makes it dif- found in the present study, are due to a difference in ficult to determine if there is a discrepancy between the the distribution of ages represented between studies. present findings and previous studies, or whether ASD Indeed a greater age range of cases was included in the might in fact be characterized by areas of both increased present study (4–88 years) than in previous studies and decreased minicolumn width. This raises the interesting (Supplementary Table 2). possibility of different effects on connectivity and process- The data from the study by Casanova et al. (2006b) are ing in different regions of the cortex. However, there does available through the Autism Tissue Program portal (www. not, as of yet, seem to be a unifying characteristic of those atpportal.org). Therefore, these data were analysed along- regions showing wider minicolumns in contrast to those side the data from the current study to investigate showing narrower minicolumns (i.e. there does not age-related variation in the magnitude of difference in mini- appear to be a consistent difference between primary sen- columnar measures between ASD cases and typically sory and association cortices, or cortical areas involved in developing controls. For comparison, the set of cases social processes and those involved in non-social processes). from the present study, which could be age matched to It would be interesting for future work to consider a within 1 year of each other, were used. The difference in broader range of cortical regions, within a developmental minicolumn width between each age-matched ASD and context, to try to further disentangle the effects of age and typically developing control case was then calculated and cortical region on differences in minicolumn width. converted to z-scores to allow comparison between studies. The data in the present study showed larger differences in favour of wider minicolumns in ASD at younger ages, but Conclusion little difference at older ages (Fig. 5B). A similar pattern of results was seen in the data from Casanova et al. (2006b) This is the largest study to date to investigate minicolumn (Fig. 5A). widths in ASD and finds that contrary to previous reports, Investigation of how z-score changes with age revealed minicolumn width is increased in some areas of the brain. Downloaded from significant correlations between age and the z-score for dif- In addition, both primary sensory and higher order asso- ference in minicolumn width between ASD and typically ciative areas are affected, though there does seem to be

developing control cases, in BA10 (r = À 0.829, evidence for reduced cortical differentiation which may by guest on May 6, 2015 P = 0.042), BA24 (r = À0.914, P = 0.011) and BA3 reflect an altered developmental trajectory. Although this (r = À0.906, P = 0.013) with a trend towards a relationship study was not formally able to address the issue of age, in BA44 (r = À0.746, P = 0.088), in the data from the results indicated that the differences between ASD and Casanova et al. (2006b). control cases are more pronounced at earlier ages. Interestingly, the same direction of relationship is seen in The present finding of wider minicolumns has important both the data from Casanova et al. (2006b) and the data implications for understanding the neural basis of ASD, as from the present study, suggesting wider minicolumns in it may contribute to the bias towards a more local, feature- ASD at young ages, but that this difference reduces with oriented processing style. To further validate this conclu- age. This suggests that although minicolumnar widths may sion, future work should investigate markers of minicolum- eventually become narrower in ASD, which might suggest a nar organization that can be associated with cognitive more holistic processing style (Harasty et al., 2003; Chance processing style in life. et al., 2012), the presence of wider minicolumns at early stages of development may establish an early bias in cor- tical connectivity and preference for feature processing, Acknowledgements which remains throughout life, despite later changes in minicolumn width in ASD. We would like to thank Charity Emin for technical sup- The main difference between the studies seemed to arise port. We gratefully acknowledge the NICHD Brain and from the fact that Casanova et al. (2006b) found wider Tissue Bank, University of Maryland; the Harvard minicolumns in typically developing controls by about Brain Tissue Resource Center; and the Autism Speaks’ age 20, whereas the present study found little difference Autism Tissue Program for providing brain tissues for between groups at that age. However, the evidence alto- this study. We acknowledge the Oxford Brain Bank, sup- gether suggests that age may play a role in the direction ported by the Medical Research Council (MRC), Brains for of difference seen between studies, and that age should be Dementia Research (BDR) (Alzheimer Society and taken into account in future studies investigating differences Alzheimer Research UK), Autistica UK and the NIHR in minicolumnar organization. Oxford Biomedical Research Centre. We would like to that the donors and their families and their contribution Cortical region investigated to this research. There is considerable variability in the cortical regions We would also like to acknowledge the legacy of investigated (Table 3) and the fact that no previous studies Vernon Mountcastle, who died earlier this year, for his Altered minicolumns in autism BRAIN 2015: Page 11 of 12 | 11 pioneering work on the columnar organization of the cere- comparison study with pyramidal cell arrays. J Neurosci Methods bral cortex. 2008; 168: 367–72. Casanova MF, Switala AE. Minicolumnar Morphometry: Computerized Image Analysis. In: Casanova MF, editor. Neocortical Modularity and the Cell Minicolumn. New York: Nova Biomedical; 2005. p. 161–80. Funding Casanova MF, van Kooten I, Switala AE, van Engeland H, Heinsen H, This research was funded by a project grant from Autism Steinbusch HWM, et al. Abnormalities of cortical minicolumnar organization in the prefrontal lobes of autistic patients. Clin Speaks #6026 (to S.A.C.). S.A.C. is also supported by a Neurosci Res 2006b; 6: 127–33. grant from the Shirley Foundation UK, and the Chance S, Casanova M, Switala A, Crow T, Esiri M. Minicolumn Simons Foundation, USA (SFARI ID:307098). R.M. is sup- thinning in temporal lobe association cortex but not primary audi- ported by funding from the Waterloo Foundation. tory cortex in normal human ageing. Acta Neuropathol 2006; 111: 459–64. Chance S, Sawyer E, Clover L, Wicinski B, Hof P, Crow T. Hemispheric asymmetry in the fusiform gyrus distinguishes Homo Supplementary material sapiens from chimpanzees. Brain Struct Func 2012: 1–15. Chance SA. Subtle changes in the ageing human brain. Nutr Health Supplementary material is available at Brain online. 2006; 18: 217–24. Chance SA. The cortical microstructural basis of lateralised cognition: a review. Front Psychol 2014; 5: 820. Chance SA, Casanova MF, Switala AE, Crow TJ. 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