Article

Brain surface anatomy in adults with autism: The relationship between surface area, cortical thickness, and autistic symptoms

Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, King's College London, London, UK.
JAMA Psychiatry (Impact Factor: 12.01). 01/2013; 70(1):59-70. DOI: 10.1001/jamapsychiatry.2013.265
Source: PubMed

ABSTRACT Neuroimaging studies of brain anatomy in autism spectrum disorder (ASD) have mostly been based on measures of cortical volume (CV). However, CV is a product of 2 distinct parameters, cortical thickness (CT) and surface area (SA), that in turn have distinct genetic and developmental origins.
To investigate regional differences in CV, SA, and CT as well as their relationship in a large and well-characterized sample of men with ASD and matched controls.
Multicenter case-control design using quantitative magnetic resonance imaging.
Medical Research Council UK Autism Imaging Multicentre Study.
A total of 168 men, 84 diagnosed as having ASD and 84 controls who did not differ significantly in mean (SD) age (26 [7] years vs 28 [6] years, respectively) or full-scale IQ (110 [14] vs 114 [12], respectively).
Between-group differences in CV, SA, and CT investigated using a spatially unbiased vertex-based approach; the degree of spatial overlap between the differences in CT and SA; and their relative contribution to differences in regional CV.
Individuals with ASD differed from controls in all 3 parameters. These mainly consisted of significantly increased CT within frontal lobe regions and reduced SA in the orbitofrontal cortex and posterior cingulum. These differences in CT and SA were paralleled by commensurate differences in CV. The spatially distributed patterns for CT and SA were largely nonoverlapping and shared only about 3% of all significantly different locations on the cerebral surface.
Individuals with ASD have significant differences in CV, but these may be underpinned by (separable) variations in its 2 components, CT and SA. This is of importance because both measures result from distinct developmental pathways that are likely modulated by different neurobiological mechanisms. This finding may provide novel targets for future studies into the etiology of the condition and a new way to fractionate the disorder.

Download full-text

Full-text

Available from: Eileen Daly, Aug 23, 2015
4 Followers
 · 
180 Views
  • Source
    • "area and thickness contribute independently to gray matter volume (GMV), and each is believed to reflect on distinct neurobiologic and genetic mechanisms (Panizzon et al. 2009). Previous morphometric studies in other brain-related conditions demonstrated that alterations in cerebral cortex GMV relate to abnormalities in either thickness or surface area, with minimal spatial overlap between thickness and surface area changes (Ecker et al. 2013). In keeping with this, the present study demonstrates that the atrophy in the anterio-medial temporal cortex in MTLE+HS is attributed largely to cerebral cortex surface area contractions . "
    [Show abstract] [Hide abstract]
    ABSTRACT: Temporal cortex abnormalities are common in patients with mesial temporal lobe epilepsy due to hippocampal sclerosis (MTLE+HS) and believed to be relevant to the underlying mechanisms. In the present study, we set out to determine the familiarity of temporal cortex morphologic alterations in a cohort of MTLE+HS patients and their asymptomatic siblings. A surface-based morphometry (SBM) method was applied to process MRI data acquired from 140 individuals (50 patients with unilateral MTLE+HS, 50 asymptomatic siblings of patients, and 40 healthy controls). Using a region-of-interest approach, alterations in temporal cortex morphology were determined in patients and their asymptomatic siblings by comparing with the controls. Alterations in temporal cortex morphology were identified in MTLE+HS patients ipsilaterally within the anterio-medial regions, including the entorhinal cortex, parahippocampal gyrus, and temporal pole. Subtle but similar pattern of morphology changes with a medium effect size were also noted in the asymptomatic siblings. These localized alterations were related to volume loss that appeared driven by shared contractions in cerebral cortex surface area. These findings indicate that temporal cortex morphologic alterations are common to patients and their asymptomatic siblings and suggest that such localized traits are possibly heritable. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
    Cerebral Cortex 01/2015; DOI:10.1093/cercor/bhu315 · 8.67 Impact Factor
  • Source
    • "This is concordant with the suggestion that cognitive deficits in schizophrenia are associated with neuroanatomical changes that are largely qualitatively similar, but differ in magnitude (Cobia et al., 2011). However, it is possible that greater accuracy would be obtained using other feature sets such as cortical thickness, curvature or area (Ecker et al., 2013; Oliveira et al., 2010; Panizzon et al., 2009; Rimol et al., 2012), or alternative classification approaches such as those that incorporate feature automatic feature selection methods and non-linear kernel methods; notably, our initial investigation here focussed on WM/GM volumetric differences as well established features of schizophrenia (Shepherd et al., 2012). While a priori selection of regions of interest may also improve classification, future whole-brain classification studies appear likely to yield similar results to the present study; non-linear kernel methods offer little advantage in the context of the large number of features in whole-brain datasets, and automatic feature selection methods do not appear to increase classification accuracy when applied to brain volume data (Chu et al., 2012). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Heterogeneity in the structural brain abnormalities associated with schizophrenia has made identification of reliable neuroanatomical markers of the disease difficult. The use of more homogenous clinical phenotypes may improve the accuracy of predicting psychotic disorder/s on the basis of observable brain disturbances. Here we investigate the utility of cognitive subtypes of schizophrenia – ‘cognitive deficit’ and ‘cognitively spared’ – in determining whether multivariate patterns of volumetric brain differences can accurately discriminate these clinical subtypes from healthy controls, and from each other. We applied support vector machine classification to grey- and white-matter volume data from 126 schizophrenia patients previously allocated to the cognitive spared subtype, 74 cognitive deficit schizophrenia patients, and 134 healthy controls. Using this method, cognitive subtypes were distinguished from healthy controls with up to 72% accuracy. Cross-validation analyses between subtypes achieved an accuracy of 71%, suggesting that some common neuroanatomical patterns distinguish both subtypes from healthy controls. Notably, cognitive subtypes were best distinguished from one another when the sample was stratified by sex prior to classification analysis: cognitive subtype classification accuracy was relatively low (<60%) without stratification, and increased to 83% for females with sex stratification. Distinct neuroanatomical patterns predicted cognitive subtype status in each sex: sex-specific multivariate patterns did not predict cognitive subtype status in the other sex above chance, and weight map analyses demonstrated negative correlations between the spatial patterns of weights underlying classification for each sex. These results suggest that in typical mixed-sex samples of schizophrenia patients, the volumetric brain differences between cognitive subtypes are relatively minor in contrast to the large common disease-associated changes. Volumetric differences that distinguish between cognitive subtypes on a case-by-case basis appear to occur in a sex-specific manner that is consistent with previous evidence of disrupted relationships between brain structure and cognition in male, but not female, schizophrenia patients. Consideration of sex-specific differences in brain organization is thus likely to assist future attempts to distinguish subgroups of schizophrenia patients on the basis of neuroanatomical features.
    Clinical neuroimaging 12/2014; 6. DOI:10.1016/j.nicl.2014.09.009 · 2.53 Impact Factor
  • Source
    • "In the current study, we analyzed anatomical data acquired from individuals older than 6 years of age for whom MRI scans are available in the Autism Brain Imaging Data Exchange (ABIDE) database. Previous studies of individuals in this age range have reported that, in comparison to controls, ASD individuals exhibit numerous abnormalities including larger gray matter (Lotspeich et al. 2004; Hazlett et al. 2006; Ecker et al. 2013), white matter (Hazlett et al. 2006), amygdala (Bellani et al. 2013a), and hippocampus (Groen et al. 2010) volumes, smaller cerebellum (Scott et al. 2009; Fatemi et al. 2012) and corpus callosum (CC; Bellani et al. 2013b) volumes, and abnormal cortical thickness (Raznahan et al. 2010; Wallace et al. 2010). These findings have been interpreted as supporting evidence for different theories of ASD including, for example, the " amygdala theory of autism " (Baron-Cohen et al. 2000) and the " underconnectivity " theory of ASD (Just et al. 2007). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Substantial controversy exists regarding the presence and significance of anatomical abnormalities in autism spectrum disorders (ASD). The release of the Autism Brain Imaging Data Exchange (∼1000 participants, age 6-65 years) offers an unprecedented opportunity to conduct large-scale comparisons of anatomical MRI scans across groups and to resolve many of the outstanding questions. Comprehensive univariate analyses using volumetric, thickness, and surface area measures of over 180 anatomically defined brain areas, revealed significantly larger ventricular volumes, smaller corpus callosum volume (central segment only), and several cortical areas with increased thickness in the ASD group. Previously reported anatomical abnormalities in ASD including larger intracranial volumes, smaller cerebellar volumes, and larger amygdala volumes were not substantiated by the current study. In addition, multivariate classification analyses yielded modest decoding accuracies of individuals' group identity (<60%), suggesting that the examined anatomical measures are of limited diagnostic utility for ASD. While anatomical abnormalities may be present in distinct subgroups of ASD individuals, the current findings show that many previously reported anatomical measures are likely to be of low clinical and scientific significance for understanding ASD neuropathology as a whole in individuals 6-35 years old.
    Cerebral Cortex 10/2014; DOI:10.1093/cercor/bhu242 · 8.67 Impact Factor
Show more