Literature DB >> 32632150

Atypical age-related changes in cortical thickness in autism spectrum disorder.

Adonay S Nunes1, Vasily A Vakorin2,3, Nataliia Kozhemiako2, Nicholas Peatfield2, Urs Ribary3,4,5,6, Sam M Doesburg2,3.   

Abstract

Recent longitudinal neuroimaging and neurophysiological studies have shown that tracking relative age-related changes in neural signals, rather than a static snapshot of a neural measure, could offer higher sensitivity for discriminating typically developing (TD) individuals from those with autism spectrum disorder (ASD). It is not clear, however, which aspects of age-related changes (trajectories) would be optimal for identifying atypical brain development in ASD. Using a large cross-sectional data set (Autism Brain Imaging Data Exchange [ABIDE] repository; releases I and II), we aimed to explore age-related changes in cortical thickness (CT) in TD and ASD populations (age range 6-30 years old). Cortical thickness was estimated from T1-weighted MRI images at three scales of spatial coarseness (three parcellations with different numbers of regions of interest). For each parcellation, three polynomial models of age-related changes in CT were tested. Specifically, to characterize alterations in CT trajectories, we compared the linear slope, curvature, and aberrancy of CT trajectories across experimental groups, which was estimated using linear, quadratic, and cubic polynomial models, respectively. Also, we explored associations between age-related changes with ASD symptomatology quantified as the Autism Diagnostic Observation Schedule (ADOS) scores. While no overall group differences in cortical thickness were observed across the entire age range, ASD and TD populations were different in terms of age-related changes, which were located primarily in frontal and tempo-parietal areas. These atypical age-related changes were also associated with ADOS scores in the ASD group and used to predict ASD from TD development. These results indicate that the curvature is the most reliable feature for localizing brain areas developmentally atypical in ASD with a more pronounced effect with symptomatology and is the most sensitive in predicting ASD development.

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Year:  2020        PMID: 32632150      PMCID: PMC7338512          DOI: 10.1038/s41598-020-67507-3

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  63 in total

1.  An MRI study of increased cortical thickness in autism.

Authors:  Antonio Y Hardan; Sri Muddasani; Madhuri Vemulapalli; Matcheri S Keshavan; Nancy J Minshew
Journal:  Am J Psychiatry       Date:  2006-07       Impact factor: 18.112

2.  A clinicopathological study of autism.

Authors:  A Bailey; P Luthert; A Dean; B Harding; I Janota; M Montgomery; M Rutter; P Lantos
Journal:  Brain       Date:  1998-05       Impact factor: 13.501

3.  Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age.

Authors:  Robert W Emerson; Chloe Adams; Tomoyuki Nishino; Heather Cody Hazlett; Jason J Wolff; Lonnie Zwaigenbaum; John N Constantino; Mark D Shen; Meghan R Swanson; Jed T Elison; Sridhar Kandala; Annette M Estes; Kelly N Botteron; Louis Collins; Stephen R Dager; Alan C Evans; Guido Gerig; Hongbin Gu; Robert C McKinstry; Sarah Paterson; Robert T Schultz; Martin Styner; Bradley L Schlaggar; John R Pruett; Joseph Piven
Journal:  Sci Transl Med       Date:  2017-06-07       Impact factor: 17.956

4.  Early brain enlargement and elevated extra-axial fluid in infants who develop autism spectrum disorder.

Authors:  Mark D Shen; Christine W Nordahl; Gregory S Young; Sandra L Wootton-Gorges; Aaron Lee; Sarah E Liston; Kayla R Harrington; Sally Ozonoff; David G Amaral
Journal:  Brain       Date:  2013-07-09       Impact factor: 13.501

5.  Cortical thickness analysis in autism with heat kernel smoothing.

Authors:  Moo K Chung; Steven M Robbins; Kim M Dalton; Richard J Davidson; Andrew L Alexander; Alan C Evans
Journal:  Neuroimage       Date:  2005-05-01       Impact factor: 6.556

Review 6.  Brain anatomy and development in autism: review of structural MRI studies.

Authors:  Paolo Brambilla; Antonio Hardan; Stefania Ucelli di Nemi; Jorge Perez; Jair C Soares; Francesco Barale
Journal:  Brain Res Bull       Date:  2003-10-15       Impact factor: 4.077

Review 7.  Neuroanatomy of autism.

Authors:  David G Amaral; Cynthia Mills Schumann; Christine Wu Nordahl
Journal:  Trends Neurosci       Date:  2008-02-06       Impact factor: 13.837

8.  Developmental changes of cortical white-gray contrast as predictors of autism diagnosis and severity.

Authors:  Gleb Bezgin; John D Lewis; Alan C Evans
Journal:  Transl Psychiatry       Date:  2018-11-16       Impact factor: 6.222

Review 9.  Towards a neuroanatomy of autism: a systematic review and meta-analysis of structural magnetic resonance imaging studies.

Authors:  Andrew C Stanfield; Andrew M McIntosh; Michael D Spencer; Ruth Philip; Sonia Gaur; Stephen M Lawrie
Journal:  Eur Psychiatry       Date:  2007-08-31       Impact factor: 5.361

10.  EEG Analytics for Early Detection of Autism Spectrum Disorder: A data-driven approach.

Authors:  William J Bosl; Helen Tager-Flusberg; Charles A Nelson
Journal:  Sci Rep       Date:  2018-05-01       Impact factor: 4.379

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  6 in total

1.  Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses.

Authors:  Nikhil Bhagwat; Amadou Barry; Erin W Dickie; Shawn T Brown; Gabriel A Devenyi; Koji Hatano; Elizabeth DuPre; Alain Dagher; Mallar Chakravarty; Celia M T Greenwood; Bratislav Misic; David N Kennedy; Jean-Baptiste Poline
Journal:  Gigascience       Date:  2021-01-22       Impact factor: 6.524

2.  Developmental abnormalities of structural covariance networks of cortical thickness and surface area in autistic infants within the first 2 years.

Authors:  Ya Wang; Dan Hu; Zhengwang Wu; Li Wang; Wenhua Huang; Gang Li
Journal:  Cereb Cortex       Date:  2022-08-22       Impact factor: 4.861

3.  Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging.

Authors:  Oualid Benkarim; Casey Paquola; Bo-Yong Park; Valeria Kebets; Seok-Jun Hong; Reinder Vos de Wael; Shaoshi Zhang; B T Thomas Yeo; Michael Eickenberg; Tian Ge; Jean-Baptiste Poline; Boris C Bernhardt; Danilo Bzdok
Journal:  PLoS Biol       Date:  2022-04-29       Impact factor: 9.593

4.  A translational exploration of the effects of WNT2 variants on altered cortical structures in autism spectrum disorder.

Authors:  Yi-Ling Chien; Yu-Chieh Chen; Yen-Nan Chiu; Wen-Che Tsai; Susan Shur-Fen Gau
Journal:  J Psychiatry Neurosci       Date:  2021-12-03       Impact factor: 6.186

5.  The Role of Structure MRI in Diagnosing Autism.

Authors:  Mohamed T Ali; Yaser ElNakieb; Ahmed Elnakib; Ahmed Shalaby; Ali Mahmoud; Mohammed Ghazal; Jawad Yousaf; Hadil Abu Khalifeh; Manuel Casanova; Gregory Barnes; Ayman El-Baz
Journal:  Diagnostics (Basel)       Date:  2022-01-11

6.  Longitudinal Changes in Cortical Thickness in Adolescents with Autism Spectrum Disorder and Their Association with Restricted and Repetitive Behaviors.

Authors:  Valentina Bieneck; Anke Bletsch; Caroline Mann; Tim Schäfer; Hanna Seelemeyer; Njål Herøy; Jennifer Zimmermann; Charlotte Marie Pretzsch; Elke Hattingen; Christine Ecker
Journal:  Genes (Basel)       Date:  2021-12-20       Impact factor: 4.096

  6 in total

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