Interindividual Differences in Cortical Thickness and Their Genomic Underpinnings in Autism Spectrum Disorder

2021 ◽  
pp. appi.ajp.2021.2
Author(s):  
Christine Ecker ◽  
Charlotte M. Pretzsch ◽  
Anke Bletsch ◽  
Caroline Mann ◽  
Tim Schaefer ◽  
...  
2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Budhachandra Khundrakpam ◽  
Uku Vainik ◽  
Jinnan Gong ◽  
Noor Al-Sharif ◽  
Neha Bhutani ◽  
...  

Abstract Autism spectrum disorder is a highly prevalent and highly heritable neurodevelopmental condition, but studies have mostly taken traditional categorical diagnosis approach (yes/no for autism spectrum disorder). In contrast, an emerging notion suggests a continuum model of autism spectrum disorder with a normal distribution of autistic tendencies in the general population, where a full diagnosis is at the severe tail of the distribution. We set out to investigate such a viewpoint by investigating the interaction of polygenic risk scores for autism spectrum disorder and Age2 on neuroimaging measures (cortical thickness and white matter connectivity) in a general population (n = 391, with age ranging from 3 to 21 years from the Pediatric Imaging, Neurocognition and Genetics study). We observed that children with higher polygenic risk for autism spectrum disorder exhibited greater cortical thickness for a large age span starting from 3 years up to ∼14 years in several cortical regions localized in bilateral precentral gyri and the left hemispheric postcentral gyrus and precuneus. In an independent case–control dataset from the Autism Brain Imaging Data Exchange (n = 560), we observed a similar pattern: children with autism spectrum disorder exhibited greater cortical thickness starting from 6 years onwards till ∼14 years in wide-spread cortical regions including (the ones identified using the general population). We also observed statistically significant regional overlap between the two maps, suggesting that some of the cortical abnormalities associated with autism spectrum disorder overlapped with brain changes associated with genetic vulnerability for autism spectrum disorder in healthy individuals. Lastly, we observed that white matter connectivity between the frontal and parietal regions showed significant association with polygenic risk for autism spectrum disorder, indicating that not only the brain structure, but the white matter connectivity might also show a predisposition for the risk of autism spectrum disorder. Our findings showed that the fronto-parietal thickness and connectivity are dimensionally related to genetic risk for autism spectrum disorder in general population and are also part of the cortical abnormalities associated with autism spectrum disorder. This highlights the necessity of considering continuum models in studying the aetiology of autism spectrum disorder using polygenic risk scores and multimodal neuroimaging.


BJPsych Open ◽  
2020 ◽  
Vol 6 (5) ◽  
Author(s):  
Flavia Venetucci Gouveia ◽  
Jürgen Germann ◽  
Gabriel A. Devenyi ◽  
Rosa M. C. B. Morais ◽  
Ana Paula M. Santos ◽  
...  

Aggressive behaviour is a highly prevalent and devastating condition in autism spectrum disorder resulting in impoverished quality of life. Gold-standard therapies are ineffective in about 30% of patients leading to greater suffering. We investigated cortical thickness in individuals with autism spectrum disorder with pharmacological-treatment-refractory aggressive behaviour compared with those with non-refractory aggressive behaviour and observed a brain-wide pattern of local increased thickness in key areas related to emotional control and overall decreased cortical thickness in those with refractory aggressive behaviour, suggesting refractoriness could be related to specific morphological patterns. Elucidating the neurobiology of refractory aggressive behaviour is crucial to provide insights and potential avenues for new interventions.


2007 ◽  
Vol 38 (5) ◽  
pp. 848-856 ◽  
Author(s):  
Mary L. Hediger ◽  
Lucinda J. England ◽  
Cynthia A. Molloy ◽  
Kai F. Yu ◽  
Patricia Manning-Courtney ◽  
...  

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Adonay S. Nunes ◽  
Vasily A. Vakorin ◽  
Nataliia Kozhemiako ◽  
Nicholas Peatfield ◽  
Urs Ribary ◽  
...  

2019 ◽  
Vol 25 (10) ◽  
pp. 2556-2566 ◽  
Author(s):  
John P. Hegarty ◽  
Luiz F. L. Pegoraro ◽  
Laura C. Lazzeroni ◽  
Mira M. Raman ◽  
Joachim F. Hallmayer ◽  
...  

Abstract Atypical growth patterns of the brain have been previously reported in autism spectrum disorder (ASD) but these alterations are heterogeneous across individuals, which may be associated with the variable effects of genetic and environmental influences on brain development. Monozygotic (MZ) and dizygotic (DZ) twin pairs with and without ASD (aged 6–15 years) were recruited to participate in this study. T1-weighted MRIs (n = 164) were processed with FreeSurfer to evaluate structural brain measures. Intra-class correlations were examined within twin pairs and compared across diagnostic groups. ACE modeling was also completed. Structural brain measures, including cerebral and cerebellar gray matter (GM) and white matter (WM) volume, surface area, and cortical thickness, were primarily influenced by genetic factors in TD twins; however, mean curvature appeared to be primarily influenced by environmental factors. Similarly, genetic factors accounted for the majority of variation in brain size in twins with ASD, potentially to a larger extent regarding curvature and subcortical GM; however, there were also more environmental contributions in twins with ASD on some structural brain measures, such that cortical thickness and cerebellar WM volume were primarily influenced by environmental factors. These findings indicate potential neurobiological outcomes of the genetic and environmental risk factors that have been previously associated with ASD and, although preliminary, may help account for some of the previously outlined neurobiological heterogeneity across affected individuals. This is especially relevant regarding the role of genetic and environmental factors in the development of ASD, in which certain brain structures may be more sensitive to specific influences.


2018 ◽  
Vol 48 (10) ◽  
pp. 3319-3329 ◽  
Author(s):  
Molly B. D. Prigge ◽  
Erin D. Bigler ◽  
Brittany G. Travers ◽  
Alyson Froehlich ◽  
Tracy Abildskov ◽  
...  

2015 ◽  
Vol 234 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Julia Richter ◽  
Romy Henze ◽  
Kilian Vomstein ◽  
Bram Stieltjes ◽  
Peter Parzer ◽  
...  

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