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Liloia D, Zamfira DA, Tanaka M, Manuello J, Crocetta A, Keller R, Cozzolino M, Duca S, Cauda F, Costa T. Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research. Neurosci Biobehav Rev 2024; 164:105791. [PMID: 38960075 DOI: 10.1016/j.neubiorev.2024.105791] [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: 10/26/2023] [Revised: 05/22/2024] [Accepted: 06/27/2024] [Indexed: 07/05/2024]
Abstract
Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Denisa Adina Zamfira
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Masaru Tanaka
- HUN-REN-SZTE Neuroscience Research Group, Hungarian Research Network, University of Szeged (HUN-REN-SZTE), Danube Neuroscience Research Laboratory, Szeged, Hungary
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Annachiara Crocetta
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Mauro Cozzolino
- Department of Humanities, Philosophical and Educational Sciences, University of Salerno, Fisciano, Italy
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy
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Ramos Benitez J, Kannan S, Hastings WL, Parker BJ, Willbrand EH, Weiner KS. Ventral temporal and posteromedial sulcal morphology in autism spectrum disorder. Neuropsychologia 2024; 195:108786. [PMID: 38181845 DOI: 10.1016/j.neuropsychologia.2024.108786] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 12/14/2023] [Accepted: 01/02/2024] [Indexed: 01/07/2024]
Abstract
Two parallel research tracks link the morphology of small and shallow indentations, or sulci, of the cerebral cortex with functional features of the cortex and human cognition, respectively. The first track identified a relationship between the mid-fusiform sulcus (MFS) in ventral temporal cortex (VTC) and cognition in individuals with Autism Spectrum Disorder (ASD). The second track identified a new sulcus, the inframarginal sulcus (IFRMS), that serves as a tripartite landmark within the posteromedial cortex (PMC). As VTC and PMC are structurally and functionally different in ASD, here, we integrated these two tracks and tested if there are morphological differences in VTC and PMC sulci in a sample of young (5-17 years old) male participants (50 participants with ASD and 50 neurotypical controls). Our approach replicates and extends recent findings in four ways. First, regarding replication, the standard deviation (STD) of MFS cortical thickness (CT) was increased in ASD. Second, MFS length was shorter in ASD. Third, the CT STD effect extended to other VTC and to PMC sulci. Fourth, additional morphological features of VTC sulci (depth, surface area, gray matter volume) and PMC sulci (mean CT) were decreased in ASD, including putative tertiary sulci, which emerge last in gestation and continue to develop after birth. To our knowledge, this study is the most extensive comparison of the sulcal landscape (including putative tertiary sulci) in multiple cortical expanses between individuals with ASD and NTs based on manually defined sulci at the level of individual hemispheres, providing novel targets for future studies of neurodevelopmental disorders more broadly.
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Affiliation(s)
- Javier Ramos Benitez
- Neuroscience Graduate Program, University of Washington School of Medicine, Seattle, WA, USA
| | - Sandhya Kannan
- Department of Radiology, University of California San Francisco, San Francisco, CA, USA
| | - William L Hastings
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Benjamin J Parker
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Ethan H Willbrand
- Medical Scientist Training Program, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Kevin S Weiner
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA; Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA.
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3
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Xu K, Sun Z, Qiao Z, Chen A. Diagnosing autism severity associated with physical fitness and gray matter volume in children with autism spectrum disorder: Explainable machine learning method. Complement Ther Clin Pract 2024; 54:101825. [PMID: 38169278 DOI: 10.1016/j.ctcp.2023.101825] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/18/2023] [Accepted: 12/29/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE This study aimed to investigate the relationship between physical fitness, gray matter volume (GMV), and autism severity in children with autism spectrum disorder (ASD). Besides, we sought to diagnose autism severity associated with physical fitness and GMV using machine learning methods. METHODS Ninety children diagnosed with ASD underwent physical fitness tests, magnetic resonance imaging scans, and autism severity assessments. Diagnosis models were established using extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), and decision tree (DT) algorithms. Hyperparameters were optimized through the grid search cross-validation method. The shapley additive explanation (SHAP) method was employed to explain the diagnosis results. RESULTS Our study revealed associations between muscular strength in physical fitness and GMV in specific brain regions (left paracentral lobule, bilateral thalamus, left inferior temporal gyrus, and cerebellar vermis I-II) with autism severity in children with ASD. The accuracy (95 % confidence interval) of the XGB, RF, SVM, and DT models were 77.9 % (77.3, 78.6 %), 72.4 % (71.7, 73.2 %), 71.9 % (71.1, 72.6 %), and 66.9 % (66.2, 67.7 %), respectively. SHAP analysis revealed that muscular strength and thalamic GMV significantly influenced the decision-making process of the XGB model. CONCLUSION Machine learning methods can effectively diagnose autism severity associated with physical fitness and GMV in children with ASD. In this respect, the XGB model demonstrated excellent performance across various indicators, suggesting its potential for diagnosing autism severity.
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Affiliation(s)
- Keyun Xu
- College of Physical Education, Yangzhou University, Yangzhou, 225127, China
| | - Zhiyuan Sun
- College of Physical Education, Yangzhou University, Yangzhou, 225127, China
| | - Zhiyuan Qiao
- College of Physical Education, Yangzhou University, Yangzhou, 225127, China
| | - Aiguo Chen
- Nanjing Sport Institute, Nanjing, 210014, China.
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Li X, Jiang M, Zhao L, Yang K, Lu T, Zhang D, Li J, Wang L. Relationship between autism and brain cortex surface area: genetic correlation and a two-sample Mendelian randomization study. BMC Psychiatry 2024; 24:69. [PMID: 38263034 PMCID: PMC10807092 DOI: 10.1186/s12888-024-05514-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 01/10/2024] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Alterations in surface area (SA) in specific regions of the cortex have been reported in many individuals with autism spectrum disorder (ASD), however, the genetic background between ASD and SA is still unclear. This study estimated the genetic correlation and causal effect of ASD and cortical SA. METHODS Summarized data of genome-wide association studies (GWAS) were separately downloaded from the Psychiatric Genomics Consortium (18,381 cases of ASD, and 27,969 controls) and the Enhancing Neuroimaging Genetics through Meta-Analysis Consortium (33,992 participants of Europeans). We used Linkage disequilibrium score regression (LDSC) and Heritability Estimation from Summary Statistics (HESS) to calculate the heritability of each trait. As for the genetic correlation between ASD and SA, LDSC was used for global correlation and HESS was used to examine the local genetic covariance further. We used three Mendelian randomization (MR) methods, Inverse-variance weighted, MR-Egger, and weighted median to estimate the causal relationship. RESULTS LDSC observed a nominal significant genetic correlation (rg = 0.1229, P-value = 0.0346) between ASD and SA of the rostral anterior cingulate gyrus whereas analysis through HESS did not reveal any significant loci having genetic covariance. Based on MR results, statistically meaningful estimations were found in the following areas, postcentral cortex (β (SE) = 21.82 (7.84) mm, 95% CI: 6.46 to 37.19 mm, PIVW = 5.38 × 10- 3, PFDR = 3.09 × 10- 2), posterior cingulate gyrus (β (SE) = 6.23 (2.69) mm, 95% CI: 0.96 to 11.49 mm, PIVW = 2.05 × 10- 2, PFDR = 4.26 × 10- 2), supramarginal gyrus (β (SE) = 19.25 (8.43) mm, 95% CI: 29.29 to 35.77 mm, PIVW = 2.24 × 10- 2, PFDR = 4.31 × 10- 2). CONCLUSION Our results provided genetic evidence to support the opinion that individuals with ASD tend to develop differences in cortical SA of special areas. The findings contributed to understanding the genetic relationship between ASD and cortical SA.
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Grants
- 2019B030335001 Key-Area Research and Development Program of Guangdong Province
- 2019B030335001 Key-Area Research and Development Program of Guangdong Province
- 2019B030335001 Key-Area Research and Development Program of Guangdong Province
- 2019B030335001 Key-Area Research and Development Program of Guangdong Province
- 2019B030335001 Key-Area Research and Development Program of Guangdong Province
- 2019B030335001 Key-Area Research and Development Program of Guangdong Province
- 2019B030335001 Key-Area Research and Development Program of Guangdong Province
- 2019B030335001 Key-Area Research and Development Program of Guangdong Province
- 81971283, 82171537, 82071541, 81671363, and 81730037 National Natural Science Foundation of China
- 81971283, 82171537, 82071541, 81671363, and 81730037 National Natural Science Foundation of China
- 81971283, 82171537, 82071541, 81671363, and 81730037 National Natural Science Foundation of China
- 81971283, 82171537, 82071541, 81671363, and 81730037 National Natural Science Foundation of China
- 81971283, 82171537, 82071541, 81671363, and 81730037 National Natural Science Foundation of China
- 81971283, 82171537, 82071541, 81671363, and 81730037 National Natural Science Foundation of China
- 81971283, 82171537, 82071541, 81671363, and 81730037 National Natural Science Foundation of China
- 81971283, 82171537, 82071541, 81671363, and 81730037 National Natural Science Foundation of China
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Affiliation(s)
- Xianjing Li
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital, Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
| | - Miaomiao Jiang
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital, Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
| | - Liyang Zhao
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital, Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
| | - Kang Yang
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital, Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
| | - Tianlan Lu
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital, Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
| | - Dai Zhang
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital, Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Institute for Brain Research and Rehabilitation (IBRR), South China Normal University, Guangzhou, China
| | - Jun Li
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital, Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China.
| | - Lifang Wang
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital, Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China.
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Rasero J, Jimenez-Marin A, Diez I, Toro R, Hasan MT, Cortes JM. The Neurogenetics of Functional Connectivity Alterations in Autism: Insights From Subtyping in 657 Individuals. Biol Psychiatry 2023; 94:804-813. [PMID: 37088169 DOI: 10.1016/j.biopsych.2023.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 03/24/2023] [Accepted: 04/14/2023] [Indexed: 04/25/2023]
Abstract
BACKGROUND There is little consensus and controversial evidence on anatomical alterations in the brains of people with autism spectrum disorder (ASD), due in part to the large heterogeneity present in ASD, which in turn is a major drawback for developing therapies. One strategy to characterize this heterogeneity in ASD is to cluster large-scale functional brain connectivity profiles. METHODS A subtyping approach based on consensus clustering of functional brain connectivity patterns was applied to a population of 657 autistic individuals with quality-assured neuroimaging data. We then used high-resolution gene transcriptomic data to characterize the molecular mechanism behind each subtype by performing enrichment analysis of the set of genes showing a high spatial similarity with the profiles of functional connectivity alterations between each subtype and a group of typically developing control participants. RESULTS Two major stable subtypes were found: subtype 1 exhibited hypoconnectivity (less average connectivity than typically developing control participants) and subtype 2, hyperconnectivity. The 2 subtypes did not differ in structural imaging metrics in any of the analyzed regions (68 cortical and 14 subcortical) or in any of the behavioral scores (including IQ, Autism Diagnostic Interview, and Autism Diagnostic Observation Schedule). Finally, only subtype 2, comprising about 43% of ASD participants, led to significant enrichments after multiple testing corrections. Notably, the dominant enrichment corresponded to excitation/inhibition imbalance, a leading well-known primary mechanism in the pathophysiology of ASD. CONCLUSIONS Our results support a link between excitation/inhibition imbalance and functional connectivity alterations, but only in one ASD subtype, overall characterized by brain hyperconnectivity and major alterations in somatomotor and default mode networks.
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Affiliation(s)
- Javier Rasero
- Cognitive Axon Laboratory, Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania.
| | - Antonio Jimenez-Marin
- Computational Neuroimaging Laboratory, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain; Biomedical Research Doctorate Program, University of the Basque Country, Leioa, Spain
| | - Ibai Diez
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Roberto Toro
- Institut Pasteur, Université de Paris, Département de neuroscience, Paris, France
| | - Mazahir T Hasan
- Laboratory of Brain Circuits Therapeutics, Achucarro Basque Center for Neuroscience, Leioa, Spain; Ikerbasque, The Basque Foundation for Science, Bilbao, Spain
| | - Jesus M Cortes
- Computational Neuroimaging Laboratory, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain; Ikerbasque, The Basque Foundation for Science, Bilbao, Spain; Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
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Kwon H, Kim JI, Son SY, Jang YH, Kim BN, Lee HJ, Lee JM. Sparse Hierarchical Representation Learning on Functional Brain Networks for Prediction of Autism Severity Levels. Front Neurosci 2022; 16:935431. [PMID: 35873817 PMCID: PMC9301472 DOI: 10.3389/fnins.2022.935431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
Machine learning algorithms have been widely applied in diagnostic tools for autism spectrum disorder (ASD), revealing an altered brain connectivity. However, little is known about whether an magnetic resonance imaging (MRI)-based brain network is related to the severity of ASD symptoms in a large-scale cohort. We propose a graph convolution neural network-based framework that can generate sparse hierarchical graph representations for functional brain connectivity. Instead of assigning initial features for each node, we utilized a feature extractor to derive node features and the extracted representations can be fed to a hierarchical graph self-attention framework to effectively represent the entire graph. By incorporating connectivity embeddings in the feature extractor, we propose adjacency embedding networks to characterize the heterogeneous representations of the brain connectivity. Our proposed model variants outperform the benchmarking model with different configurations of adjacency embedding networks and types of functional connectivity matrices. Using this approach with the best configuration (SHEN atlas for node definition, Tikhonov correlation for connectivity estimation, and identity-adjacency embedding), we were able to predict individual ASD severity levels with a meaningful accuracy: the mean absolute error (MAE) and correlation between predicted and observed ASD severity scores resulted in 0.96, and r = 0.61 (P < 0.0001), respectively. To obtain a better understanding on how to generate better representations, we investigate the relationships between the extracted feature embeddings and the graph theory-based nodal measurements using canonical correlation analysis. Finally, we visualized the model to identify the most contributive functional connections for predicting ASD severity scores.
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Affiliation(s)
- Hyeokjin Kwon
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Johanna Inhyang Kim
- Department of Psychiatry, Hanyang University Medical Center, Seoul, South Korea
| | - Seung-Yeon Son
- Department of Artificial Intelligence, Hanyang University, Seoul, South Korea
| | - Yong Hun Jang
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, South Korea
| | - Bung-Nyun Kim
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyun Ju Lee
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
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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] [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 Program University of Notre Dame Notre Dame Indiana USA
| | - Maria A. Holland
- Bioengineering Graduate Program University of Notre Dame Notre Dame Indiana USA
- Department of Aerospace and Mechanical Engineering University of Notre Dame Notre Dame Indiana USA
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8
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Longitudinal Changes in Cortical Thickness in Adolescents with Autism Spectrum Disorder and Their Association with Restricted and Repetitive Behaviors. Genes (Basel) 2021; 12:genes12122024. [PMID: 34946972 PMCID: PMC8701312 DOI: 10.3390/genes12122024] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 12/06/2021] [Accepted: 12/17/2021] [Indexed: 01/01/2023] Open
Abstract
The neuroanatomy of autism spectrum disorder (ASD) shows highly heterogeneous developmental trajectories across individuals. Mapping atypical brain development onto clinical phenotypes, and establishing their molecular underpinnings, is therefore crucial for patient stratification and subtyping. In this longitudinal study we examined intra- and inter-individual differences in the developmental trajectory of cortical thickness (CT) in childhood and adolescence, and their genomic underpinnings, in 33 individuals with ASD and 37 typically developing controls (aged 11–18 years). Moreover, we aimed to link regional atypical CT development to intra-individual variations in restricted and repetitive behavior (RRB) over a two-year time period. Individuals with ASD showed significantly reduced cortical thinning in several of the brain regions functionally related to wider autism symptoms and traits (e.g., fronto-temporal and cingulate cortices). The spatial patterns of the neuroanatomical differences in CT were enriched for genes known to be associated with ASD at a genetic and transcriptomic level. Further, intra-individual differences in CT correlated with within-subject variability in the severity of RRBs. Our findings represent an important step towards characterizing the neuroanatomical underpinnings of ASD across development based upon measures of CT. Moreover, our findings provide important novel insights into the link between microscopic and macroscopic pathology in ASD, as well as their relationship with different clinical ASD phenotypes.
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9
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Pua EPK, Thomson P, Yang JYM, Craig JM, Ball G, Seal M. Individual Differences in Intrinsic Brain Networks Predict Symptom Severity in Autism Spectrum Disorders. Cereb Cortex 2021; 31:681-693. [PMID: 32959054 DOI: 10.1093/cercor/bhaa252] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 08/06/2020] [Accepted: 08/07/2020] [Indexed: 12/18/2022] Open
Abstract
The neurobiology of heterogeneous neurodevelopmental disorders such as Autism Spectrum Disorders (ASD) is still unknown. We hypothesized that differences in subject-level properties of intrinsic brain networks were important features that could predict individual variation in ASD symptom severity. We matched cases and controls from a large multicohort ASD dataset (ABIDE-II) on age, sex, IQ, and image acquisition site. Subjects were matched at the individual level (rather than at group level) to improve homogeneity within matched case-control pairs (ASD: n = 100, mean age = 11.43 years, IQ = 110.58; controls: n = 100, mean age = 11.43 years, IQ = 110.70). Using task-free functional magnetic resonance imaging, we extracted intrinsic functional brain networks using projective non-negative matrix factorization. Intrapair differences in strength in subnetworks related to the salience network (SN) and the occipital-temporal face perception network were robustly associated with individual differences in social impairment severity (T = 2.206, P = 0.0301). Findings were further replicated and validated in an independent validation cohort of monozygotic twins (n = 12; 3 pairs concordant and 3 pairs discordant for ASD). Individual differences in the SN and face-perception network are centrally implicated in the neural mechanisms of social deficits related to ASD.
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Affiliation(s)
- Emmanuel Peng Kiat Pua
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville VIC 3010, Australia.,Developmental Imaging, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Medicine, Austin Health, University of Melbourne, Parkville VIC 3010, Australia
| | - Phoebe Thomson
- Developmental Imaging, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Parkville VIC 3010, Australia
| | - Joseph Yuan-Mou Yang
- Developmental Imaging, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Parkville VIC 3010, Australia.,Neuroscience Research, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Neurosurgery, Neuroscience Advanced Clinical Imaging Suite (NACIS), The Royal Children's Hospital, Parkville VIC 3052, Australia
| | - Jeffrey M Craig
- Department of Paediatrics, University of Melbourne, Parkville VIC 3010, Australia.,Molecular Epidemiology, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Centre for Molecular and Medical Research, School of Medicine, Deakin University, Geelong VIC 3220, Australia
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Parkville VIC 3010, Australia
| | - Marc Seal
- Developmental Imaging, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Parkville VIC 3010, Australia
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10
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Trevis KJ, Brown NJ, Green CC, Lockhart PJ, Desai T, Vick T, Anderson V, Pua EPK, Bahlo M, Delatycki MB, Scheffer IE, Wilson SJ. Tracing Autism Traits in Large Multiplex Families to Identify Endophenotypes of the Broader Autism Phenotype. Int J Mol Sci 2020; 21:E7965. [PMID: 33120939 PMCID: PMC7663259 DOI: 10.3390/ijms21217965] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 10/14/2020] [Accepted: 10/21/2020] [Indexed: 12/11/2022] Open
Abstract
Families comprising many individuals with Autism Spectrum Disorders (ASD) may carry a dominant predisposing mutation. We implemented rigorous phenotyping of the "Broader Autism Phenotype" (BAP) in large multiplex ASD families using a novel endophenotype approach for the identification and characterisation of distinct BAP endophenotypes. We evaluated ASD/BAP features using standardised tests and a semi-structured interview to assess social, intellectual, executive and adaptive functioning in 110 individuals, including two large multiplex families (Family A: 30; Family B: 35) and an independent sample of small families (n = 45). Our protocol identified four distinct psychological endophenotypes of the BAP that were evident across these independent samples, and showed high sensitivity (97%) and specificity (82%) for individuals classified with the BAP. Patterns of inheritance of identified endophenotypes varied between the two large multiplex families, supporting their utility for identifying genes in ASD.
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Affiliation(s)
- Krysta J. Trevis
- Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, VIC 3084, Australia; (K.J.T.); (C.C.G.); (T.D.); (E.P.K.P.); (I.E.S.)
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC 3010, Australia;
| | - Natasha J. Brown
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia; (N.J.B.); (M.B.D.)
- Barwon Health, Geelong, VIC 3220, Australia;
| | - Cherie C. Green
- Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, VIC 3084, Australia; (K.J.T.); (C.C.G.); (T.D.); (E.P.K.P.); (I.E.S.)
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC 3010, Australia;
- Department of Psychology and Counselling, School of Psychology and Public Health, La Trobe University, Bundoora, VIC 3086, Australia
| | - Paul J. Lockhart
- Bruce Lefroy Centre for Genetic Health Research, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia;
- Department of Paediatrics, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Tarishi Desai
- Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, VIC 3084, Australia; (K.J.T.); (C.C.G.); (T.D.); (E.P.K.P.); (I.E.S.)
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC 3010, Australia;
| | - Tanya Vick
- Barwon Health, Geelong, VIC 3220, Australia;
| | - Vicki Anderson
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC 3010, Australia;
- Psychological Service, The Royal Children’s Hospital, Parkville, VIC 3052, Australia
- Clinical Sciences Research, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia
| | - Emmanuel P. K. Pua
- Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, VIC 3084, Australia; (K.J.T.); (C.C.G.); (T.D.); (E.P.K.P.); (I.E.S.)
| | - Melanie Bahlo
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia;
- Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Martin B. Delatycki
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia; (N.J.B.); (M.B.D.)
- Bruce Lefroy Centre for Genetic Health Research, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia;
- Department of Paediatrics, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Ingrid E. Scheffer
- Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, VIC 3084, Australia; (K.J.T.); (C.C.G.); (T.D.); (E.P.K.P.); (I.E.S.)
- Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia; (N.J.B.); (M.B.D.)
- Clinical Sciences Research, Murdoch Children’s Research Institute, Parkville, VIC 3052, Australia
- The Florey Institute of Neuroscience and Mental Health, Parkville, VIC 3052, Australia
| | - Sarah J. Wilson
- Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, VIC 3084, Australia; (K.J.T.); (C.C.G.); (T.D.); (E.P.K.P.); (I.E.S.)
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC 3010, Australia;
- The Florey Institute of Neuroscience and Mental Health, Parkville, VIC 3052, Australia
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