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Bezgin G, Lewis JD, Fonov VS, Collins DL, Evans AC. Atypical co-development of the thalamus and cortex in autism: Evidence from age-related white-gray contrast change. Hum Brain Mapp 2024; 45:e26584. [PMID: 38533724 DOI: 10.1002/hbm.26584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 12/05/2023] [Accepted: 12/17/2023] [Indexed: 03/28/2024] Open
Abstract
Recent studies have shown that white-gray contrast (WGC) of either cortical or subcortical gray matter provides for accurate predictions of age in typically developing (TD) children, and that, at least for the cortex, it changes differently with age in subjects with autism spectrum disorder (ASD) compared to their TD peers. Our previous study showed different patterns of contrast change between ASD and TD in sensorimotor and association cortices. While that study was confined to the cortex, we hypothesized that subcortical structures, particularly the thalamus, were involved in the observed cortical dichotomy between lower and higher processing. The current paper investigates that hypothesis using the WGC measures from the thalamus in addition to those from the cortex. We compared age-related WGC changes in the thalamus to those in the cortex. To capture the simultaneity of this change across the two structures, we devised a metric capturing the co-development of the thalamus and cortex (CoDevTC), proportional to the magnitude of cortical and thalamic age-related WGC change. We calculated this metric for each of the subjects in a large homogeneous sample taken from the Autism Brain Imaging Data Exchange (ABIDE) (N = 434). We used structural MRI data from the largest high-quality cross-sectional sample (NYU) as well as two other large high-quality sites, GU and OHSU, all three using Siemens 3T scanners. We observed that the co-development features in ASD and TD exhibit contrasting patterns; specifically, some higher-order thalamic nuclei, such as the lateral dorsal nucleus, exhibited reduction in codevelopment with most of the cortex in ASD compared to TD. Moreover, this difference in the CoDevTC pattern correlates with a number of behavioral measures across multiple cognitive and physiological domains. The results support previous notions of altered connectivity in autism, but add more specific evidence about the heterogeneity in thalamocortical development that elucidates the mechanisms underlying the clinical features of ASD.
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Affiliation(s)
- Gleb Bezgin
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - John D Lewis
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | - Vladimir S Fonov
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - D Louis Collins
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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2
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Chen K, Zhuang W, Zhang Y, Yin S, Liu Y, Chen Y, Kang X, Ma H, Zhang T. Alteration of the large-scale white-matter functional networks in autism spectrum disorder. Cereb Cortex 2023; 33:11582-11593. [PMID: 37851712 DOI: 10.1093/cercor/bhad392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/02/2023] [Accepted: 10/03/2023] [Indexed: 10/20/2023] Open
Abstract
Autism spectrum disorder is a neurodevelopmental disorder whose core deficit is social dysfunction. Previous studies have indicated that structural changes in white matter are associated with autism spectrum disorder. However, few studies have explored the alteration of the large-scale white-matter functional networks in autism spectrum disorder. Here, we identified ten white-matter functional networks on resting-state functional magnetic resonance imaging data using the K-means clustering algorithm. Compared with the white matter and white-matter functional network connectivity of the healthy controls group, we found significantly decreased white matter and white-matter functional network connectivity mainly located within the Occipital network, Middle temporo-frontal network, and Deep network in autism spectrum disorder. Compared with healthy controls, findings from white-matter gray-matter functional network connectivity showed the decreased white-matter gray-matter functional network connectivity mainly distributing in the Occipital network and Deep network. Moreover, we compared the spontaneous activity of white-matter functional networks between the two groups. We found that the spontaneous activity of Middle temporo-frontal and Deep network was significantly decreased in autism spectrum disorder. Finally, the correlation analysis showed that the white matter and white-matter functional network connectivity between the Middle temporo-frontal network and others networks and the spontaneous activity of the Deep network were significantly correlated with the Social Responsiveness Scale scores of autism spectrum disorder. Together, our findings indicate that changes in the white-matter functional networks are associated behavioral deficits in autism spectrum disorder.
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Affiliation(s)
- Kai Chen
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
| | - Wenwen Zhuang
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
| | - Yanfang Zhang
- Department of Ultrasonic Medicine, Baiyun Branch, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou City, Guangdong Province, China
| | - Shunjie Yin
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
| | - Yinghua Liu
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
| | - Yuan Chen
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
| | - Xiaodong Kang
- The Department of Sichuan 81 Rehabilitation Center, Chengdu University of TCM, No. 81 Bayi Road, Yongning Street, Wenjiang District, Chengdu City 610075, China
| | - Hailin Ma
- Plateau Brain Science Research Center, Tibet University, 10 Zangda East Road, Lhasa City 510631, China
| | - Tao Zhang
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
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A critical role of brain network architecture in a continuum model of autism spectrum disorders spanning from healthy individuals with genetic liability to individuals with ASD. Mol Psychiatry 2023; 28:1210-1218. [PMID: 36575304 PMCID: PMC10005951 DOI: 10.1038/s41380-022-01916-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/21/2022] [Accepted: 12/09/2022] [Indexed: 12/28/2022]
Abstract
Studies have shown cortical alterations in individuals with autism spectrum disorders (ASD) as well as in individuals with high polygenic risk for ASD. An important addition to the study of altered cortical anatomy is the investigation of the underlying brain network architecture that may reveal brain-wide mechanisms in ASD and in polygenic risk for ASD. Such an approach has been proven useful in other psychiatric disorders by revealing that brain network architecture shapes (to an extent) the disorder-related cortical alterations. This study uses data from a clinical dataset-560 male subjects (266 individuals with ASD and 294 healthy individuals, CTL, mean age at 17.2 years) from the Autism Brain Imaging Data Exchange database, and data of 391 healthy individuals (207 males, mean age at 12.1 years) from the Pediatric Imaging, Neurocognition and Genetics database. ASD-related cortical alterations (group difference, ASD-CTL, in cortical thickness) and cortical correlates of polygenic risk for ASD were assessed, and then statistically compared with structural connectome-based network measures (such as hubs) using spin permutation tests. Next, we investigated whether polygenic risk for ASD could be predicted by network architecture by building machine-learning based prediction models, and whether the top predictors of the model were identified as disease epicenters of ASD. We observed that ASD-related cortical alterations as well as cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. We also observed that age progression of ASD-related cortical alterations and cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. Further investigation revealed that structural connectomes predicted polygenic risk for ASD (r = 0.30, p < 0.0001), and two brain regions (the left inferior parietal and left suparmarginal) with top predictive connections were identified as disease epicenters of ASD. Our study highlights a critical role of network architecture in a continuum model of ASD spanning from healthy individuals with genetic risk to individuals with ASD. Our study also highlights the strength of investigating polygenic risk scores in addition to multi-modal neuroimaging measures to better understand the interplay between genetic risk and brain alterations associated with ASD.
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Lopez DA, Christensen ZP, Foxe JJ, Ziemer LR, Nicklas PR, Freedman EG. Association between mild traumatic brain injury, brain structure, and mental health outcomes in the Adolescent Brain Cognitive Development Study. Neuroimage 2022; 263:119626. [PMID: 36103956 DOI: 10.1016/j.neuroimage.2022.119626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/20/2022] [Accepted: 09/10/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Children that experience a mild traumatic brain injury (mTBI) are at an increased risk of neural alterations that can deteriorate mental health. We test the hypothesis that mTBI is associated with psychopathology and that structural brain metrics (e.g., volume, area) meaningfully mediate the relation in an adolescent population. METHODS We analyzed behavioral and brain MRI data from 11,876 children who participated in the Adolescent Brain Cognitive Development (ABCD) Study. Mixed-effects models were used to examine the longitudinal association between mTBI and mental health outcomes. Bayesian methods were used to investigate brain regions that are intermediate between mTBI and symptoms of poor mental health. RESULTS There were 199 children with mTBI and 527 with possible mTBI across the three ABCD Study visits. There was a 7% (IRR = 1.07, 95% CI: 1.01, 1.13) and 15% (IRR = 1.16, 95% CI: 1.05, 1.26) increased risk of emotional or behavioral problems in children that experienced possible mTBI or mTBI, respectively. Possible mTBI was associated with a 17% (IRR: 1.17, 95% CI: 0.99, 1.40) increased risk of experiencing distress following a psychotic-like experience. We did not find any brain regions that meaningfully mediated the relationship between mTBI and mental health outcomes. Analysis of volumetric measures found that approximately 2% to 5% of the total effect of mTBI on mental health outcomes operated through total cortical volume. Image intensity measure analyses determined that approximately 2% to 5% of the total effect was mediated through the left-hemisphere of the dorsolateral prefrontal cortex. CONCLUSION Results indicate an increased risk of emotional and behavioral problems in children that experienced possible mTBI or mTBI. Mediation analyses did not elucidate the mechanisms underlying the association between mTBI and mental health outcomes.
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Affiliation(s)
- Daniel A Lopez
- Department of Neuroscience, The Frederick J. and Marion A. Schindler Cognitive Neurophysiology Laboratory, The Ernest J. Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA; Department of Public Health Sciences, Division of Epidemiology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Zachary P Christensen
- Department of Neuroscience, The Frederick J. and Marion A. Schindler Cognitive Neurophysiology Laboratory, The Ernest J. Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA
| | - John J Foxe
- Department of Neuroscience, The Frederick J. and Marion A. Schindler Cognitive Neurophysiology Laboratory, The Ernest J. Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA
| | - Laura R Ziemer
- Department of Neuroscience, The Frederick J. and Marion A. Schindler Cognitive Neurophysiology Laboratory, The Ernest J. Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA
| | - Paige R Nicklas
- Department of Neuroscience, The Frederick J. and Marion A. Schindler Cognitive Neurophysiology Laboratory, The Ernest J. Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA
| | - Edward G Freedman
- Department of Neuroscience, The Frederick J. and Marion A. Schindler Cognitive Neurophysiology Laboratory, The Ernest J. Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA.
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Zhang Y, Zhang S, Chen B, Jiang L, Li Y, Dong L, Feng R, Yao D, Li F, Xu P. Predicting the Symptom Severity in Autism Spectrum Disorder Based on EEG Metrics. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1898-1907. [PMID: 35788457 DOI: 10.1109/tnsre.2022.3188564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Autism spectrum disorder (ASD) is associated with the impaired integrating and segregating of related information that is expanded within the large-scale brain network. The varying ASD symptom severities have been explored, relying on their behaviors and related brain activity, but how to effectively predict ASD symptom severity needs further exploration. In this study, we aim to investigate whether the ASD symptom severity could be predicted with electroencephalography (EEG) metrics. Based on a publicly available dataset, the EEG brain networks were constructed, and four types of EEG metrics were calculated. Then, we statistically compared the brain network differences among ASD children with varying severities, i.e., high/low autism diagnostic observation schedule (ADOS) scores, as well as with the typically developing (TD) children. Thereafter, the EEG metrics were utilized to validate whether they could facilitate the prediction of the ASD symptom severity. The results demonstrated that both high- and low-scoring ASD children showed the decreased long-range frontal-occipital connectivity, increased anterior frontal connectivity and altered network properties. Furthermore, we found that the four types of EEG metrics are significantly correlated with the ADOS scores, and their combination can serve as the features to effectively predict the ASD symptom severity. The current findings will expand our knowledge of network dysfunction in ASD children and provide new EEG metrics for predicting the symptom severity.
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Demirci N, Holland MA. Cortical thickness systematically varies with curvature and depth in healthy human brains. Hum Brain Mapp 2022; 43:2064-2084. [PMID: 35098606 PMCID: PMC8933257 DOI: 10.1002/hbm.25776] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 11/30/2021] [Accepted: 01/05/2022] [Indexed: 12/30/2022] Open
Abstract
Cortical thickness varies throughout the cortex in a systematic way. However, it is challenging to investigate the patterns of cortical thickness due to the intricate geometry of the cortex. The cortex has a folded nature both in radial and tangential directions which forms not only gyri and sulci but also tangential folds and intersections. In this article, cortical curvature and depth are used to characterize the spatial distribution of the cortical thickness with much higher resolution than conventional regional atlases. To do this, a computational pipeline was developed that is capable of calculating a variety of quantitative measures such as surface area, cortical thickness, curvature (mean curvature, Gaussian curvature, shape index, intrinsic curvature index, and folding index), and sulcal depth. By analyzing 501 neurotypical adult human subjects from the ABIDE-I dataset, we show that cortex has a very organized structure and cortical thickness is strongly correlated with local shape. Our results indicate that cortical thickness consistently increases along the gyral-sulcal spectrum from concave to convex shape, encompassing the saddle shape along the way. Additionally, tangential folds influence cortical thickness in a similar way as gyral and sulcal folds; outer folds are consistently thicker than inner.
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Affiliation(s)
- Nagehan Demirci
- Bioengineering Graduate ProgramUniversity of Notre DameNotre DameIndianaUSA
| | - Maria A. Holland
- Bioengineering Graduate ProgramUniversity of Notre DameNotre DameIndianaUSA
- Department of Aerospace and Mechanical EngineeringUniversity of Notre DameNotre DameIndianaUSA
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7
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Growth charts of brain morphometry for preschool children. Neuroimage 2022; 255:119178. [PMID: 35430358 DOI: 10.1016/j.neuroimage.2022.119178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/11/2022] [Accepted: 04/03/2022] [Indexed: 11/23/2022] Open
Abstract
Brain development from 1 to 6 years of age anchors a wide range of functional capabilities and carries early signs of neurodevelopmental disorders. However, quantitative models for depicting brain morphology changes and making individualized inferences are lacking, preventing the identification of early brain atypicality during this period. With a total sample size of 285, we characterized the age-dependence of the cortical thickness and subcortical volume in neurologically normal children and constructed quantitative growth charts of all brain regions for preschool children. While the cortical thickness of most brain regions decreased with age, the entorhinal and parahippocampal regions displayed an inverted-U shape of age-dependence. Compared to the cortical thickness, the normalized volume of subcortical regions exhibited more divergent trends, with some regions increasing, some decreasing, and some displaying inverted-U-shaped trends. The growth curve models for all brain regions demonstrated utilities in identifying brain atypicality. The percentile measures derived from the growth curves facilitate the identification of children with developmental speech and language disorders with an accuracy of 0.875 (area under the receiver operating characteristic curve: 0.943). Our results fill the knowledge gap in brain morphometrics in a critical development period and provide an avenue for individualized brain developmental status evaluation with demonstrated sensitivity.
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8
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Drakulich S, Thiffault AC, Olafson E, Parent O, Labbe A, Albaugh MD, Khundrakpam B, Ducharme S, Evans A, Chakravarty MM, Karama S. Maturational trajectories of pericortical contrast in typical brain development. Neuroimage 2021; 235:117974. [PMID: 33766753 PMCID: PMC8278832 DOI: 10.1016/j.neuroimage.2021.117974] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 02/27/2021] [Accepted: 03/17/2021] [Indexed: 11/16/2022] Open
Abstract
In the last few years, a significant amount of work has aimed to characterize maturational trajectories of cortical development. The role of pericortical microstructure putatively characterized as the gray-white matter contrast (GWC) at the pericortical gray-white matter boundary and its relationship to more traditional morphological measures of cortical morphometry has emerged as a means to examine finer grained neuroanatomical underpinnings of cortical changes. In this work, we characterize the GWC developmental trajectories in a representative sample (n = 394) of children and adolescents (~4 to ~22 years of age), with repeated scans (1-3 scans per subject, total scans n = 819). We tested whether linear, quadratic, or cubic trajectories of contrast development best described changes in GWC. A best-fit model was identified vertex-wise across the whole cortex via the Akaike Information Criterion (AIC). GWC across nearly the whole brain was found to significantly change with age. Cubic trajectories were likeliest for 63% of vertices, quadratic trajectories were likeliest for 20% of vertices, and linear trajectories were likeliest for 16% of vertices. A main effect of sex was observed in some regions, where males had a higher GWC than females. However, no sex by age interactions were found on GWC. In summary, our results suggest a progressive decrease in GWC at the pericortical boundary throughout childhood and adolescence. This work contributes to efforts seeking to characterize typical, healthy brain development and, by extension, can help elucidate aberrant developmental trajectories.
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Affiliation(s)
- Stefan Drakulich
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montréal, QC H3A 2B4, Canada
| | - Anne-Charlotte Thiffault
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montréal, QC H3A 2B4, Canada
| | - Emily Olafson
- Douglas Institute, McGill University, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada
| | - Olivier Parent
- Douglas Institute, McGill University, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada
| | - Aurelie Labbe
- HEC Montréal, 3000, chemin de la Côte-Sainte-Catherine, Montreal, QC H3T 2A7, Canada
| | - Matthew D Albaugh
- Department of Psychiatry, Larnier College of Medicine, University of Vermont, United States
| | - Budhachandra Khundrakpam
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montréal, QC H3A 2B4, Canada
| | - Simon Ducharme
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montréal, QC H3A 2B4, Canada
| | - Alan Evans
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montréal, QC H3A 2B4, Canada
| | - Mallar M Chakravarty
- Douglas Institute, McGill University, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada.
| | - Sherif Karama
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montréal, QC H3A 2B4, Canada; Douglas Institute, McGill University, 6875 Boulevard LaSalle, Verdun, QC H4H 1R3, Canada.
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Olafson E, Bedford SA, Devenyi GA, Patel R, Tullo S, Park MTM, Parent O, Anagnostou E, Baron-Cohen S, Bullmore ET, Chura LR, Craig MC, Ecker C, Floris DL, Holt RJ, Lenroot R, Lerch JP, Lombardo MV, Murphy DGM, Raznahan A, Ruigrok ANV, Spencer MD, Suckling J, Taylor MJ, Lai MC, Chakravarty MM. Examining the Boundary Sharpness Coefficient as an Index of Cortical Microstructure in Autism Spectrum Disorder. Cereb Cortex 2021; 31:3338-3352. [PMID: 33693614 PMCID: PMC8196259 DOI: 10.1093/cercor/bhab015] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 12/06/2020] [Accepted: 01/15/2021] [Indexed: 12/27/2022] Open
Abstract
Autism spectrum disorder (ASD) is associated with atypical brain development. However, the phenotype of regionally specific increased cortical thickness observed in ASD may be driven by several independent biological processes that influence the gray/white matter boundary, such as synaptic pruning, myelination, or atypical migration. Here, we propose to use the boundary sharpness coefficient (BSC), a proxy for alterations in microstructure at the cortical gray/white matter boundary, to investigate brain differences in individuals with ASD, including factors that may influence ASD-related heterogeneity (age, sex, and intelligence quotient). Using a vertex-based meta-analysis and a large multicenter structural magnetic resonance imaging (MRI) dataset, with a total of 1136 individuals, 415 with ASD (112 female; 303 male), and 721 controls (283 female; 438 male), we observed that individuals with ASD had significantly greater BSC in the bilateral superior temporal gyrus and left inferior frontal gyrus indicating an abrupt transition (high contrast) between white matter and cortical intensities. Individuals with ASD under 18 had significantly greater BSC in the bilateral superior temporal gyrus and right postcentral gyrus; individuals with ASD over 18 had significantly increased BSC in the bilateral precuneus and superior temporal gyrus. Increases were observed in different brain regions in males and females, with larger effect sizes in females. BSC correlated with ADOS-2 Calibrated Severity Score in individuals with ASD in the right medial temporal pole. Importantly, there was a significant spatial overlap between maps of the effect of diagnosis on BSC when compared with cortical thickness. These results invite studies to use BSC as a possible new measure of cortical development in ASD and to further examine the microstructural underpinnings of BSC-related differences and their impact on measures of cortical morphology.
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Affiliation(s)
- Emily Olafson
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal H4H 1R3, Canada
- Department of Neuroscience, Weill Cornell Graduate School of Medical Sciences, New York City, NY 10021, USA
| | - Saashi A Bedford
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal H3A 2B4, Canada
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - Gabriel A Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montreal H3A 2B4, Canada
| | - Raihaan Patel
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal H4H 1R3, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal H3A 2B4, Canada
| | - Stephanie Tullo
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal H3A 2B4, Canada
| | - Min Tae M Park
- Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London N6A 3K7, ON, Canada
| | - Olivier Parent
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal H4H 1R3, Canada
- Departement de Psychologie, Universite de Montreal, Montreal, QC, Canada
| | - Evdokia Anagnostou
- Holland Bloorview Kids Rehabilitation Hospital, Toronto M4G 1R8, Canada
- Department of Pediatrics, University of Toronto, Toronto, ON, Canada
| | - Simon Baron-Cohen
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - Edward T Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Lindsay R Chura
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - Michael C Craig
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- National Autism Unit, Bethlem Royal Hospital, London BR3 3BX, UK
| | - Christine Ecker
- Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital of the Goethe University, Frankfurt am Main 60528, Germany
| | - Dorothea L Floris
- Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen 6525 HR, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen 02.275, The Netherlands
| | - Rosemary J Holt
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - Rhoshel Lenroot
- Department of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia
| | - Jason P Lerch
- Department of Medical Biophysics, The University of Toronto, Toronto, ON M5G 1L7, Canada
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK
| | - Michael V Lombardo
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, @UniTn, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy
| | - Declan G M Murphy
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD 20892-9663, USA
| | - Amber N V Ruigrok
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - Michael D Spencer
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - John Suckling
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Margot J Taylor
- Diagnostic Imaging, The Hospital for Sick Children, Toronto M5G 1X8, Canada
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto M5G 1X8, Canada
- Department of Medical Imaging, University of Toronto, Toronto M5G 1X8, Canada
| | | | - Meng-Chuan Lai
- Autism Research Center, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto M6J 1H4, Canada
- Department of Psychiatry, University of Toronto, Toronto M5T 1R8, Canada
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei 100229, Taiwan
- Department of Psychiatry, The Hospital for Sick Children, Toronto M5G 1X8, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal H3A 2B4, Canada
- Department of Psychiatry, McGill University, Montreal H3A 2B4, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal H3A 2B4, Canada
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Bhagwat N, Barry A, Dickie EW, Brown ST, Devenyi GA, Hatano K, DuPre E, Dagher A, Chakravarty M, Greenwood CMT, Misic B, Kennedy DN, Poline JB. Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses. Gigascience 2021; 10:giaa155. [PMID: 33481004 PMCID: PMC7821710 DOI: 10.1093/gigascience/giaa155] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 11/01/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MRI data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance. METHODS Here we use an open structural MRI dataset (ABIDE), supplemented with the Human Connectome Project, to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of (i) software tool (e.g., ANTS, CIVET, FreeSurfer), (ii) cortical parcellation (Desikan-Killiany-Tourville, Destrieux, Glasser), and (iii) quality control procedure (manual, automatic). We divide our statistical analyses by (i) method type, i.e., task-free (unsupervised) versus task-driven (supervised); and (ii) inference objective, i.e., neurobiological group differences versus individual prediction. RESULTS Results show that software, parcellation, and quality control significantly affect task-driven neurobiological inference. Additionally, software selection strongly affects neurobiological (i.e. group) and individual task-free analyses, and quality control alters the performance for the individual-centric prediction tasks. CONCLUSIONS This comparative performance evaluation partially explains the source of inconsistencies in neuroimaging findings. Furthermore, it underscores the need for more rigorous scientific workflows and accessible informatics resources to replicate and compare preprocessing pipelines to address the compounding problem of reproducibility in the age of large-scale, data-driven computational neuroscience.
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Affiliation(s)
- Nikhil Bhagwat
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - Amadou Barry
- Lady Davis Institute for Medical Research, McGill University, Montreal, QC, Canada
| | - Erin W Dickie
- Kimel Family Translational Imaging-Genetics Research Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Shawn T Brown
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - Gabriel A Devenyi
- Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, Verdun, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Koji Hatano
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - Elizabeth DuPre
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - Alain Dagher
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - Mallar Chakravarty
- Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, Verdun, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, McGill University, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
- Gerald Bronfman Department of Oncology; Department of Epidemiology, Biostatistics & Occupational Health Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Bratislav Misic
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - David N Kennedy
- Child and Adolescent Neurodevelopment Initiative, University of Massachusetts, Worcester, MA, USA
| | - Jean-Baptiste Poline
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
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11
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Bletsch A, Schäfer T, Mann C, Andrews DS, Daly E, Gudbrandsen M, Ruigrok ANV, Dallyn R, Romero-Garcia R, Lai MC, Lombardo MV, Craig MC, Suckling J, Bullmore ET, Baron-Cohen S, Murphy DGM, Dell'Acqua F, Ecker C. Atypical measures of diffusion at the gray-white matter boundary in autism spectrum disorder in adulthood. Hum Brain Mapp 2020; 42:467-484. [PMID: 33094897 PMCID: PMC7775996 DOI: 10.1002/hbm.25237] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 09/08/2020] [Accepted: 09/30/2020] [Indexed: 01/07/2023] Open
Abstract
Autism spectrum disorder (ASD) is a highly complex neurodevelopmental condition that is accompanied by neuroanatomical differences on the macroscopic and microscopic level. Findings from histological, genetic, and more recently in vivo neuroimaging studies converge in suggesting that neuroanatomical abnormalities, specifically around the gray‐white matter (GWM) boundary, represent a crucial feature of ASD. However, no research has yet characterized the GWM boundary in ASD based on measures of diffusion. Here, we registered diffusion tensor imaging data to the structural T1‐weighted images of 92 adults with ASD and 92 matched neurotypical controls in order to examine between‐group differences and group‐by‐sex interactions in fractional anisotropy and mean diffusivity sampled at the GWM boundary, and at different sampling depths within the superficial white and into the gray matter. As hypothesized, we observed atypical diffusion at and around the GWM boundary in ASD, with between‐group differences and group‐by‐sex interactions depending on tissue class and sampling depth. Furthermore, we identified that altered diffusion at the GWM boundary partially (i.e., ~50%) overlapped with atypical gray‐white matter tissue contrast in ASD. Our study thus replicates and extends previous work highlighting the GWM boundary as a crucial target of neuropathology in ASD, and guides future work elucidating etiological mechanisms.
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Affiliation(s)
- Anke Bletsch
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Tim Schäfer
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Caroline Mann
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Derek S Andrews
- Department of Psychiatry and Behavioral Sciences at the M.I.N.D. Institute, University of California, Davis, California, USA
| | - Eileen Daly
- Department of Forensic and Neurodevelopmental Sciences, and the Sackler Institute for Translational Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Maria Gudbrandsen
- Department of Forensic and Neurodevelopmental Sciences, and the Sackler Institute for Translational Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Amber N V Ruigrok
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Robert Dallyn
- Department of Forensic and Neurodevelopmental Sciences, and the Sackler Institute for Translational Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Rafael Romero-Garcia
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Centre for Addiction and Mental Health and The Hospital for Sick Children, Department of Psychiatry, University of Toronto, Toronto, Canada.,Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Michael V Lombardo
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Michael C Craig
- Department of Forensic and Neurodevelopmental Sciences, and the Sackler Institute for Translational Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK.,National Autism Unit, Bethlem Royal Hospital, London, UK
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Edward T Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | | | - Declan G M Murphy
- Department of Forensic and Neurodevelopmental Sciences, and the Sackler Institute for Translational Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Flavio Dell'Acqua
- Department of Forensic and Neurodevelopmental Sciences, and the Sackler Institute for Translational Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Christine Ecker
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany.,Department of Forensic and Neurodevelopmental Sciences, and the Sackler Institute for Translational Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
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12
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Nunes AS, Vakorin VA, Kozhemiako N, Peatfield N, Ribary U, Doesburg SM. Atypical age-related changes in cortical thickness in autism spectrum disorder. Sci Rep 2020; 10:11067. [PMID: 32632150 PMCID: PMC7338512 DOI: 10.1038/s41598-020-67507-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 06/08/2020] [Indexed: 01/17/2023] Open
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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Affiliation(s)
- Adonay S Nunes
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada.
| | - Vasily A Vakorin
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada.,Behavioral & Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, Canada
| | - Nataliia Kozhemiako
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada
| | - Nicholas Peatfield
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada
| | - Urs Ribary
- Behavioral & Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, Canada.,Department Pediatrics and Psychiatry, University of British Columbia, Vancouver, Canada.,B.C. Children's Hospital Research Institute, Vancouver, Canada.,Department Psychology, Simon Fraser University, Burnaby, Canada
| | - Sam M Doesburg
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada.,Behavioral & Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, Canada
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13
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Yildiz GY, Vilsten JS, Millard AS, Chouinard PA. Grey-Matter Thickness of the Left But Not the Right Primary Visual Area Correlates with Autism Traits in Typically Developing Adults. J Autism Dev Disord 2020; 51:405-417. [PMID: 32472248 DOI: 10.1007/s10803-020-04553-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We examined whether functional and structural variability in the primary visual area (V1) correlated with autism traits. Twenty-nine participants (16 males; MAge = 26.4 years, SDAge = 4.0 years) completed the autism-spectrum quotient (AQ) questionnaire prior to a magnetic resonance imaging session. The total AQ scores was used to assess the degree of self-reported autism traits. The average functional activation in V1 to visual stimulation and its average grey-matter thickness were calculated. There were no correlations between functional activation in V1 and autism traits. Conversely, grey-matter thickness of the left but not the right V1 correlated with autism traits. We conclude that structural changes in the left V1 could be a marker for the presence of autism traits.
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Affiliation(s)
- Gizem Y Yildiz
- Department of Psychology and Counselling, School of Psychology and Public Health, La Trobe University, Melbourne, VIC, Australia
| | - Julian S Vilsten
- Department of Psychology and Counselling, School of Psychology and Public Health, La Trobe University, Melbourne, VIC, Australia
| | - Amy Siobhan Millard
- Department of Psychology and Counselling, School of Psychology and Public Health, La Trobe University, Melbourne, VIC, Australia
| | - Philippe A Chouinard
- Department of Psychology and Counselling, School of Psychology and Public Health, La Trobe University, Melbourne, VIC, Australia.
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14
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Bedford SA, Park MTM, Devenyi GA, Tullo S, Germann J, Patel R, Anagnostou E, Baron-Cohen S, Bullmore ET, Chura LR, Craig MC, Ecker C, Floris DL, Holt RJ, Lenroot R, Lerch JP, Lombardo MV, Murphy DGM, Raznahan A, Ruigrok ANV, Smith E, Spencer MD, Suckling J, Taylor MJ, Thurm A, Lai MC, Chakravarty MM. Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder. Mol Psychiatry 2020; 25:614-628. [PMID: 31028290 DOI: 10.1038/s41380-019-0420-6] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 03/25/2019] [Accepted: 03/26/2019] [Indexed: 01/29/2023]
Abstract
Significant heterogeneity across aetiologies, neurobiology and clinical phenotypes have been observed in individuals with autism spectrum disorder (ASD). Neuroimaging-based neuroanatomical studies of ASD have often reported inconsistent findings which may, in part, be attributable to an insufficient understanding of the relationship between factors influencing clinical heterogeneity and their relationship to brain anatomy. To this end, we performed a large-scale examination of cortical morphometry in ASD, with a specific focus on the impact of three potential sources of heterogeneity: sex, age and full-scale intelligence (FIQ). To examine these potentially subtle relationships, we amassed a large multi-site dataset that was carefully quality controlled (yielding a final sample of 1327 from the initial dataset of 3145 magnetic resonance images; 491 individuals with ASD). Using a meta-analytic technique to account for inter-site differences, we identified greater cortical thickness in individuals with ASD relative to controls, in regions previously implicated in ASD, including the superior temporal gyrus and inferior frontal sulcus. Greater cortical thickness was observed in sex specific regions; further, cortical thickness differences were observed to be greater in younger individuals and in those with lower FIQ, and to be related to overall clinical severity. This work serves as an important step towards parsing factors that influence neuroanatomical heterogeneity in ASD and is a potential step towards establishing individual-specific biomarkers.
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Affiliation(s)
- Saashi A Bedford
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC, Canada.
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada.
| | - Min Tae M Park
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC, Canada
- Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Gabriel A Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Stephanie Tullo
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Jurgen Germann
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC, Canada
| | - Raihaan Patel
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | | | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Edward T Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Lindsay R Chura
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Michael C Craig
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- National Autism Unit, Bethlem Royal Hospital, London, UK
| | - Christine Ecker
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Goethe University, Frankfurt am Main, Germany
| | - Dorothea L Floris
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Hassenfeld Children's Hospital at NYU Langone Department of Child and Adolescent Psychiatry, Child Study Center, New York City, NY, USA
| | - Rosemary J Holt
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Rhoshel Lenroot
- Department of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Jason P Lerch
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Michael V Lombardo
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cyprus, Nicosia, Cyprus
| | - Declan G M Murphy
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Armin Raznahan
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Amber N V Ruigrok
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Elizabeth Smith
- Section on Behavioral Pediatrics, National Institute of Mental Health, Bethesda, MD, USA
| | - Michael D Spencer
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Margot J Taylor
- Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada
| | - Audrey Thurm
- Section on Behavioral Pediatrics, National Institute of Mental Health, Bethesda, MD, USA
| | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC, Canada.
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada.
- Department of Psychiatry, McGill University, Montreal, QC, Canada.
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada.
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15
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Makowski C, Lewis JD, Lepage C, Malla AK, Joober R, Lepage M, Evans AC. Structural Associations of Cortical Contrast and Thickness in First Episode Psychosis. Cereb Cortex 2019; 29:5009-5021. [PMID: 30844050 PMCID: PMC6918925 DOI: 10.1093/cercor/bhz040] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 01/22/2019] [Indexed: 01/22/2023] Open
Abstract
There is growing evidence that psychosis is characterized by brain network abnormalities. Analyzing morphological abnormalities with T1-weighted structural MRI may be limited in discovering the extent of deviations in cortical associations. We assess whether structural associations of either cortical white-gray contrast (WGC) or cortical thickness (CT) allow for a better understanding of brain structural relationships in first episode of psychosis (FEP) patients. Principal component and structural covariance analyses were applied to WGC and CT derived from T1-weighted MRI for 116 patients and 88 controls, to explore sets of brain regions that showed group differences, and associations with symptom severity and cognitive ability in patients. We focused on 2 principal components: one encompassed primary somatomotor regions, which showed trend-like group differences in WGC, and the second included heteromodal cortices. Patients' component scores were related to general psychopathology for WGC, but not CT. Structural covariance analyses with WGC revealed group differences in pairwise correlations across widespread brain regions, mirroring areas derived from PCA. More group differences were uncovered with WGC compared with CT. WGC holds potential as a proxy measure of myelin from commonly acquired T1-weighted MRI and may be sensitive in detecting systems-level aberrations in early psychosis, and relationships with clinical/cognitive profiles.
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Affiliation(s)
- Carolina Makowski
- McGill Centre for Integrative Neuroscience, McGill University, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
- Department of Psychiatry, McGill University, Verdun, Canada
| | - John D Lewis
- McGill Centre for Integrative Neuroscience, McGill University, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
| | - Claude Lepage
- McGill Centre for Integrative Neuroscience, McGill University, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
| | - Ashok K Malla
- Department of Psychiatry, McGill University, Verdun, Canada
- Prevention and Early Intervention Program for Psychosis, Douglas Mental Health University Institute, Verdun, Canada
| | - Ridha Joober
- Department of Psychiatry, McGill University, Verdun, Canada
- Prevention and Early Intervention Program for Psychosis, Douglas Mental Health University Institute, Verdun, Canada
| | - Martin Lepage
- Department of Psychiatry, McGill University, Verdun, Canada
- Prevention and Early Intervention Program for Psychosis, Douglas Mental Health University Institute, Verdun, Canada
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, McGill University, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada
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16
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Gender Related Changes in Gene Expression Induced by Valproic Acid in A Mouse Model of Autism and the Correction by S-adenosyl Methionine. Does It Explain the Gender Differences in Autistic Like Behavior? Int J Mol Sci 2019; 20:ijms20215278. [PMID: 31652960 PMCID: PMC6862653 DOI: 10.3390/ijms20215278] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 10/21/2019] [Accepted: 10/22/2019] [Indexed: 12/14/2022] Open
Abstract
In previous studies we produced autism like behavioral changes in mice by Valproic acid (VPA) with significant differences between genders. S-adenosine methionine (SAM) prevented the autism like behavior in both genders. The expression of 770 genes of pathways involved in neurophysiology and neuropathology was studied in the prefrontal cortex of 60 days old male and female mice using the NanoString nCounter. In females, VPA induced statistically significant changes in the expression of 146 genes; 71 genes were upregulated and 75 downregulated. In males, VPA changed the expression of only 19 genes, 16 were upregulated and 3 downregulated. Eight genes were similarly changed in both genders. When considering only the genes that were changed by at least 50%, VPA changed the expression of 15 genes in females and 3 in males. Only Nts was similarly downregulated in both genders. SAM normalized the expression of most changed genes in both genders. We presume that genes that are involved in autism like behavior in our model were similarly changed in both genders and corrected by SAM. The behavioral and other differences between genders may be related to genes that were differently affected by VPA in males and females and/or differently affected by SAM.
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17
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Quantifying individual differences in brain morphometry underlying symptom severity in Autism Spectrum Disorders. Sci Rep 2019; 9:9898. [PMID: 31289283 PMCID: PMC6617442 DOI: 10.1038/s41598-019-45774-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 06/14/2019] [Indexed: 01/12/2023] Open
Abstract
The neurobiology of heterogeneous neurodevelopmental disorders such as autism spectrum disorders (ASD) are still unclear. Despite extensive efforts, most findings are difficult to reproduce due to high levels of individual variance in phenotypic expression. To quantify individual differences in brain morphometry in ASD, we implemented a novel subject-level, distance-based method on subject-specific attributes. In a large multi-cohort sample, each subject with ASD (n = 100; n = 84 males; mean age: 11.43 years; mean IQ: 110.58) was strictly matched to a control participant (n = 100; n = 84 males; mean age: 11.43 years; mean IQ: 110.70). Intrapair Euclidean distance of MRI brain morphometry and symptom severity measures (Social Responsiveness Scale) were entered into a regularised machine learning pipeline for feature selection, with rigorous out-of-sample validation and permutation testing. Subject-specific structural morphometry features significantly predicted individual variation in ASD symptom severity (19 cortical thickness features, p = 0.01, n = 5000 permutations; 10 surface area features, p = 0.006, n = 5000 permutations). Findings remained robust across subjects and were replicated in validation samples. Identified cortical regions implicate key hubs of the salience and default mode networks as neuroanatomical features of social impairment in ASD. Present results highlight the importance of subject-level markers in ASD, and offer an important step forward in understanding the neurobiology of heterogeneous disorders.
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