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Li M, Izumoto M, Wang Y, Kato Y, Iwatani Y, Hirata I, Mizuno Y, Tachibana M, Mohri I, Kagitani-Shimono K. Altered white matter connectivity of ventral language networks in autism spectrum disorder: An automated fiber quantification analysis with multi-site datasets. Neuroimage 2024; 297:120731. [PMID: 39002786 DOI: 10.1016/j.neuroimage.2024.120731] [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: 02/27/2024] [Revised: 06/28/2024] [Accepted: 07/11/2024] [Indexed: 07/15/2024] Open
Abstract
Comprehension and pragmatic deficits are prevalent in autism spectrum disorder (ASD) and are potentially linked to altered connectivity in the ventral language networks. However, previous magnetic resonance imaging studies have not sufficiently explored the microstructural abnormalities in the ventral fiber tracts underlying comprehension dysfunction in ASD. Additionally, the precise locations of white matter (WM) changes in the long tracts of patients with ASD remain poorly understood. In the current study, we applied the automated fiber-tract quantification (AFQ) method to investigate the fine-grained WM properties of the ventral language pathway and their relationships with comprehension and symptom manifestation in ASD. The analysis included diffusion/T1 weighted imaging data of 83 individuals with ASD and 83 age-matched typically developing (TD) controls. Case-control comparisons were performed on the diffusion metrics of the ventral tracts at both the global and point-wise levels. We also explored correlations between diffusion metrics, comprehension performance, and ASD traits, and conducted subgroup analyses based on age range to examine developmental moderating effects. Individuals with ASD exhibited remarkable hypoconnectivity in the ventral tracts, particularly in the temporal portions of the left inferior longitudinal fasciculus (ILF) and the inferior fronto-occipital fasciculus (IFOF). These WM abnormalities were associated with poor comprehension and more severe ASD symptoms. Furthermore, WM alterations in the ventral tract and their correlation with comprehension dysfunction were more prominent in younger children with ASD than in adolescents. These findings indicate that WM disruptions in the temporal portions of the left ILF/IFOF are most notable in ASD, potentially constituting the core neurological underpinnings of comprehension and communication deficits in autism. Moreover, impaired WM connectivity and comprehension ability in patients with ASD appear to improve with age.
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Affiliation(s)
- Min Li
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan
| | - Maya Izumoto
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan
| | - Yide Wang
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan
| | - Yoko Kato
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan
| | - Yoshiko Iwatani
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan
| | - Ikuko Hirata
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan
| | - Yoshifumi Mizuno
- Research Center for Child Mental Development, University of Fukui, Fukui, Japan
| | - Masaya Tachibana
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan
| | - Ikuko Mohri
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan
| | - Kuriko Kagitani-Shimono
- Osaka University, Department of Child Development, United Graduate School of Child Development, Suita, Osaka, Japan.
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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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3
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Andrews DS, Diers K, Lee JK, Harvey DJ, Heath B, Cordero D, Rogers SJ, Reuter M, Solomon M, Amaral DG, Nordahl CW. Sex differences in trajectories of cortical development in autistic children from 2-13 years of age. Mol Psychiatry 2024:10.1038/s41380-024-02592-8. [PMID: 38755243 DOI: 10.1038/s41380-024-02592-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 04/30/2024] [Accepted: 05/02/2024] [Indexed: 05/18/2024]
Abstract
Previous studies have reported alterations in cortical thickness in autism. However, few have included enough autistic females to determine if there are sex specific differences in cortical structure in autism. This longitudinal study aimed to investigate autistic sex differences in cortical thickness and trajectory of cortical thinning across childhood. Participants included 290 autistic (88 females) and 139 nonautistic (60 females) individuals assessed at up to 4 timepoints spanning ~2-13 years of age (918 total MRI timepoints). Estimates of cortical thickness in early and late childhood as well as the trajectory of cortical thinning were modeled using spatiotemporal linear mixed effects models of age-by-sex-by-diagnosis. Additionally, the spatial correspondence between cortical maps of sex-by-diagnosis differences and neurotypical sex differences were evaluated. Relative to their nonautistic peers, autistic females had more extensive cortical differences than autistic males. These differences involved multiple functional networks, and were mainly characterized by thicker cortex at ~3 years of age and faster cortical thinning in autistic females. Cortical regions in which autistic alterations were different between the sexes significantly overlapped with regions that differed by sex in neurotypical development. Autistic females and males demonstrated some shared differences in cortical thickness and rate of cortical thinning across childhood relative to their nonautistic peers, however these areas were relatively small compared to the widespread differences observed across the sexes. These results support evidence of sex-specific neurobiology in autism and suggest that processes that regulate sex differentiation in the neurotypical brain contribute to sex differences in the etiology of autism.
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Affiliation(s)
- Derek S Andrews
- Department of Psychiatry & Behavioral Sciences, the MIND Institute, University of California, Davis, CA, USA.
| | - Kersten Diers
- AI in Medical Imaging, German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Joshua K Lee
- Department of Psychiatry & Behavioral Sciences, the MIND Institute, University of California, Davis, CA, USA
| | - Danielle J Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, University of California, Davis, CA, USA
| | - Brianna Heath
- Department of Psychiatry & Behavioral Sciences, the MIND Institute, University of California, Davis, CA, USA
| | - Devani Cordero
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Sally J Rogers
- Department of Psychiatry & Behavioral Sciences, the MIND Institute, University of California, Davis, CA, USA
| | - Martin Reuter
- AI in Medical Imaging, German Center for Neurodegenerative Diseases, Bonn, Germany
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Marjorie Solomon
- Department of Psychiatry & Behavioral Sciences, the MIND Institute, University of California, Davis, CA, USA
| | - David G Amaral
- Department of Psychiatry & Behavioral Sciences, the MIND Institute, University of California, Davis, CA, USA
| | - Christine Wu Nordahl
- Department of Psychiatry & Behavioral Sciences, the MIND Institute, University of California, Davis, CA, USA
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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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Liloia D, Manuello J, Costa T, Keller R, Nani A, Cauda F. Atypical local brain connectivity in pediatric autism spectrum disorder? A coordinate-based meta-analysis of regional homogeneity studies. Eur Arch Psychiatry Clin Neurosci 2024; 274:3-18. [PMID: 36599959 PMCID: PMC10787009 DOI: 10.1007/s00406-022-01541-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 12/16/2022] [Indexed: 01/05/2023]
Abstract
Despite decades of massive neuroimaging research, the comprehensive characterization of short-range functional connectivity in autism spectrum disorder (ASD) remains a major challenge for scientific advances and clinical translation. From the theoretical point of view, it has been suggested a generalized local over-connectivity that would characterize ASD. This stance is known as the general local over-connectivity theory. However, there is little empirical evidence supporting such hypothesis, especially with regard to pediatric individuals with ASD (age [Formula: see text] 18 years old). To explore this issue, we performed a coordinate-based meta-analysis of regional homogeneity studies to identify significant changes of local connectivity. Our analyses revealed local functional under-connectivity patterns in the bilateral posterior cingulate cortex and superior frontal gyrus (key components of the default mode network) and in the bilateral paracentral lobule (a part of the sensorimotor network). We also performed a functional association analysis of the identified areas, whose dysfunction is clinically consistent with the well-known deficits affecting individuals with ASD. Importantly, we did not find relevant clusters of local hyper-connectivity, which is contrary to the hypothesis that ASD may be characterized by generalized local over-connectivity. If confirmed, our result will provide a valuable insight into the understanding of the complex ASD pathophysiology.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI Research Group, Koelliker Hospital and Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Jordi Manuello
- GCS-fMRI Research Group, Koelliker Hospital and Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- GCS-fMRI Research Group, Koelliker Hospital and Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, 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.
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Andrea Nani
- Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI Research Group, Koelliker Hospital and Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, 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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6
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Faraji R, Ganji Z, Khandan Khadem Z, Akbari-Lalimi H, Eidy F, Zare H. Volume-based and Surface-Based Methods in Autism Compared with Healthy Controls Are Free surfer and CAT12 in Agreement? IRANIAN JOURNAL OF CHILD NEUROLOGY 2024; 18:93-118. [PMID: 38375127 PMCID: PMC10874516 DOI: 10.22037/ijcn.v18i1.43294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/07/2023] [Indexed: 02/21/2024]
Abstract
Objectives Autism Spectrum Disorder (ASD) encompasses a range of neurodevelopmental disorders, and early detection is crucial. This study aims to identify the Regions of Interest (ROIs) with significant differences between healthy controls and individuals with autism, as well as evaluate the agreement between FreeSurfer 6 (FS6) and Computational Anatomy Toolbox (CAT12) methods. Materials & Methods Surface-based and volume-based features were extracted from FS software and CAT12 toolbox for Statistical Parametric Mapping (SPM) software to estimate ROI-wise biomarkers. These biomarkers were compared between 18 males Typically Developing Controls (TDCs) and 40 male subjects with ASD to assess group differences for each method. Finally, agreement and regression analyses were performed between the two methods for TDCs and ASD groups. Results Both methods revealed ROIs with significant differences for each parameter. The Analysis of Covariance (ANCOVA) showed that both TDCs and ASD groups indicated a significant relationship between the two methods (p<0.001). The R2 values for TDCs and ASD groups were 0.692 and 0.680, respectively, demonstrating a moderate correlation between CAT12 and FS6. Bland-Altman graphs showed a moderate level of agreement between the two methods. Conclusion The moderate correlation and agreement between CAT12 and FS6 suggest that while some consistency is observed in the results, CAT12 is not a superior substitute for FS6 software. Further research is needed to identify a potential replacement for this method.
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Affiliation(s)
- Reyhane Faraji
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zohreh Ganji
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zahra Khandan Khadem
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hossein Akbari-Lalimi
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fereshteh Eidy
- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Hoda Zare
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
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Liloia D, Cauda F, Uddin LQ, Manuello J, Mancuso L, Keller R, Nani A, Costa T. Revealing the Selectivity of Neuroanatomical Alteration in Autism Spectrum Disorder via Reverse Inference. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1075-1083. [PMID: 35131520 DOI: 10.1016/j.bpsc.2022.01.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 12/30/2021] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Although neuroimaging research has identified atypical neuroanatomical substrates in individuals with autism spectrum disorder (ASD), it is at present unclear whether and to what extent disorder-selective gray matter alterations occur in this spectrum of conditions. In fact, a growing body of evidence shows a substantial overlap between the pathomorphological changes across different brain diseases, which may complicate identification of reliable neural markers and differentiation of the anatomical substrates of distinct psychopathologies. METHODS Using a novel data-driven and Bayesian methodology with published voxel-based morphometry data (849 peer-reviewed experiments and 22,304 clinical subjects), this study performs the first reverse inference investigation to explore the selective structural brain alteration profile of ASD. RESULTS We found that specific brain areas exhibit a >90% probability of gray matter alteration selectivity for ASD: the bilateral precuneus (Brodmann area 7), right inferior occipital gyrus (Brodmann area 18), left cerebellar lobule IX and Crus II, right cerebellar lobule VIIIA, and right Crus I. Of note, many brain voxels that are selective for ASD include areas that are posterior components of the default mode network. CONCLUSIONS The identification of these spatial gray matter alteration patterns offers new insights into understanding the complex neurobiological underpinnings of ASD and opens attractive prospects for future neuroimaging-based interventions.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin, Turin, Italy
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Jordi Manuello
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lorenzo Mancuso
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Andrea Nani
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin, Turin, Italy
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Ali MT, Gebreil A, ElNakieb Y, Elnakib A, Shalaby A, Mahmoud A, Sleman A, Giridharan GA, Barnes G, Elbaz AS. A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework. Sci Rep 2023; 13:17048. [PMID: 37813914 PMCID: PMC10562430 DOI: 10.1038/s41598-023-43478-z] [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: 03/09/2023] [Accepted: 09/25/2023] [Indexed: 10/11/2023] Open
Abstract
Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical presentations. In this study, we dissect Autism Spectrum Disorder (ASD) into its behavioral components, mirroring the diagnostic process used in clinical settings. Morphological features are extracted from magnetic resonance imaging (MRI) scans, found in the publicly available dataset ABIDE II, identifying the most discriminative features that differentiate ASD within various behavioral domains. Then, each subject is categorized as having severe, moderate, or mild ASD, or typical neurodevelopment (TD), based on the behavioral domains of the Social Responsiveness Scale (SRS). Through this study, multiple artificial intelligence (AI) models are utilized for feature selection and classifying each ASD severity and behavioural group. A multivariate feature selection algorithm, investigating four different classifiers with linear and non-linear hypotheses, is applied iteratively while shuffling the training-validation subjects to find the set of cortical regions with statistically significant association with ASD. A set of six classifiers are optimized and trained on the selected set of features using 5-fold cross-validation for the purpose of severity classification for each behavioural group. Our AI-based model achieved an average accuracy of 96%, computed as the mean accuracy across the top-performing AI models for feature selection and severity classification across the different behavioral groups. The proposed AI model has the ability to accurately differentiate between the functionalities of specific brain regions, such as the left and right caudal middle frontal regions. We propose an AI-based model that dissects ASD into behavioral components. For each behavioral component, the AI-based model is capable of identifying the brain regions which are associated with ASD as well as utilizing those regions for diagnosis. The proposed system can increase the speed and accuracy of the diagnostic process and result in improved outcomes for individuals with ASD, highlighting the potential of AI in this area.
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Affiliation(s)
- Mohamed T Ali
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
- UT Southwestern Medical Center, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ahmad Gebreil
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | - Yaser ElNakieb
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
- UT Southwestern Medical Center, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ahmed Elnakib
- Electrical and Computer Engineering, Penn State Erie-The Behrend College, Erie, PA, 16563, USA
| | - Ahmed Shalaby
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | - Ahmed Sleman
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | | | - Gregory Barnes
- Department of Neurology and Pediatric Research Institute, University of Louisville, Louisville, KY, 40202, USA
| | - Ayman S Elbaz
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA.
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9
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Wang M, Xu D, Zhang L, Jiang H. Application of Multimodal MRI in the Early Diagnosis of Autism Spectrum Disorders: A Review. Diagnostics (Basel) 2023; 13:3027. [PMID: 37835770 PMCID: PMC10571992 DOI: 10.3390/diagnostics13193027] [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: 08/17/2023] [Revised: 09/13/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder in children. Early diagnosis and intervention can remodel the neural structure of the brain and improve quality of life but may be inaccurate if based solely on clinical symptoms and assessment scales. Therefore, we aimed to analyze multimodal magnetic resonance imaging (MRI) data from the existing literature and review the abnormal changes in brain structural-functional networks, perfusion, neuronal metabolism, and the glymphatic system in children with ASD, which could help in early diagnosis and precise intervention. Structural MRI revealed morphological differences, abnormal developmental trajectories, and network connectivity changes in the brain at different ages. Functional MRI revealed disruption of functional networks, abnormal perfusion, and neurovascular decoupling associated with core ASD symptoms. Proton magnetic resonance spectroscopy revealed abnormal changes in the neuronal metabolites during different periods. Decreased diffusion tensor imaging signals along the perivascular space index reflected impaired glymphatic system function in children with ASD. Differences in age, subtype, degree of brain damage, and remodeling in children with ASD led to heterogeneity in research results. Multimodal MRI is expected to further assist in early and accurate clinical diagnosis of ASD through deep learning combined with genomics and artificial intelligence.
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Affiliation(s)
- Miaoyan Wang
- Department of Radiology, Affiliated Children’s Hospital of Jiangnan University, Wuxi 214000, China; (M.W.); (D.X.)
| | - Dandan Xu
- Department of Radiology, Affiliated Children’s Hospital of Jiangnan University, Wuxi 214000, China; (M.W.); (D.X.)
| | - Lili Zhang
- Department of Child Health Care, Affiliated Children’s Hospital of Jiangnan University, Wuxi 214000, China
| | - Haoxiang Jiang
- Department of Radiology, Affiliated Children’s Hospital of Jiangnan University, Wuxi 214000, China; (M.W.); (D.X.)
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10
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Alves CL, Toutain TGLDO, de Carvalho Aguiar P, Pineda AM, Roster K, Thielemann C, Porto JAM, Rodrigues FA. Diagnosis of autism spectrum disorder based on functional brain networks and machine learning. Sci Rep 2023; 13:8072. [PMID: 37202411 DOI: 10.1038/s41598-023-34650-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 05/04/2023] [Indexed: 05/20/2023] Open
Abstract
Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets.
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Affiliation(s)
- Caroline L Alves
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil.
- BioMEMS Lab, Aschaffenburg University of Applied Sciences, Aschaffenburg, Germany.
| | | | - Patricia de Carvalho Aguiar
- Hospital Israelita Albert Einstein, São Paulo, Brazil
- Department of Neurology and Neurosurgery, Federal University of São Paulo, São Paulo, Brazil
| | - Aruane M Pineda
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil
| | - Kirstin Roster
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil
| | | | | | - Francisco A Rodrigues
- Institute of Mathematical and Computer Sciences (ICMC), University of São Paulo (USP), São Paulo, Brazil
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11
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Tsompanidis A, Warrier V, Baron-Cohen S. The genetics of autism and steroid-related traits in prenatal and postnatal life. Front Endocrinol (Lausanne) 2023; 14:1126036. [PMID: 37223033 PMCID: PMC10200920 DOI: 10.3389/fendo.2023.1126036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 04/18/2023] [Indexed: 05/25/2023] Open
Abstract
Background Autism likelihood is a largely heritable trait. Autism prevalence has a skewed sex ratio, with males being diagnosed more often than females. Steroid hormones play a mediating role in this, as indicated by studies of both prenatal biology and postnatal medical conditions in autistic men and women. It is currently unclear if the genetics of steroid regulation or production interact with the genetic liability for autism. Methods To address this, two studies were conducted using publicly available datasets, which focused respectively on rare genetic variants linked to autism and neurodevelopmental conditions (study 1) and common genetic variants (study 2) for autism. In Study 1 an enrichment analysis was conducted, between autism-related genes (SFARI database) and genes that are differentially expressed (FDR<0.1) between male and female placentas, in 1st trimester chorionic villi samples of viable pregnancies (n=39). In Study 2 summary statistics of genome wide association studies (GWAS) were used to investigate the genetic correlation between autism and bioactive testosterone, estradiol and postnatal PlGF levels, as well as steroid-related conditions such as polycystic ovaries syndrome (PCOS), age of menarche, and androgenic alopecia. Genetic correlation was calculated based on LD Score regression and results were corrected for multiple testing with FDR. Results In Study 1, there was significant enrichment of X-linked autism genes in male-biased placental genes, independently of gene length (n=5 genes, p<0.001). In Study 2, common genetic variance associated with autism did not correlate to the genetics for the postnatal levels of testosterone, estradiol or PlGF, but was associated with the genotypes associated with early age of menarche in females (b=-0.109, FDR-q=0.004) and protection from androgenic alopecia for males (b=-0.135, FDR-q=0.007). Conclusion The rare genetic variants associated with autism appear to interact with placental sex differences, while the common genetic variants associated with autism appear to be involved in the regulation of steroid-related traits. These lines of evidence indicate that the likelihood for autism is partly linked to factors mediating physiological sex differences throughout development.
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Affiliation(s)
- Alex Tsompanidis
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
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12
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Kaur P, Kaur A. Review of Progress in Diagnostic Studies of Autism Spectrum Disorder Using Neuroimaging. Interdiscip Sci 2023; 15:111-130. [PMID: 36633792 DOI: 10.1007/s12539-022-00548-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 12/27/2022] [Accepted: 12/27/2022] [Indexed: 01/13/2023]
Abstract
This review article summarizes the recent advances in the diagnostic studies of autism spectrum disorders (ASDs) considering some of the most influential research articles from the last two decades. ASD is a heterogeneous neurodevelopmental disorder characterized by abnormalities in social interaction, communication, and behavioral patterns as well as some unique strengths and differences. The current diagnosis systems are based on autism diagnostic observation schedule (ADOS) or autism diagnostic interview-revised (ADI-R), but biological markers are also important for an effective diagnosis of ASDs. The amalgamation of neuroimaging techniques, such as structural and functional magnetic resonance imaging (sMRI and fMRI), with machine-learning and deep-learning approaches helps throw new light on typical biological markers of ASDs at the early stage of life. To assess the performance of a deep neural network, we develop a light-weighted CNN model for ASD classification. The overall accuracy, precision, and F1-score of the proposed model are 99.92%, 99.93% and 99.92%, respectively. All the neuroimaging studies we have reviewed can be divided into 3 categories, viz. thickness, volume and functional connectivity-based studies. We conclude with a discussion of the major findings of considered studies and promising directions for future research in this field.
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Affiliation(s)
- Palwinder Kaur
- Department of Computer Science and Technology, Central University of Punjab, Bathinda, Punjab, 151001, India
| | - Amandeep Kaur
- Department of Computer Science and Technology, Central University of Punjab, Bathinda, Punjab, 151001, India.
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13
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Arutiunian V, Gomozova M, Minnigulova A, Davydova E, Pereverzeva D, Sorokin A, Tyushkevich S, Mamokhina U, Danilina K, Dragoy O. Structural brain abnormalities and their association with language impairment in school-aged children with Autism Spectrum Disorder. Sci Rep 2023; 13:1172. [PMID: 36670149 PMCID: PMC9860052 DOI: 10.1038/s41598-023-28463-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 01/18/2023] [Indexed: 01/22/2023] Open
Abstract
Language impairment is comorbid in most children with Autism Spectrum Disorder (ASD) but its neural basis is poorly understood. Using structural magnetic resonance imaging (MRI), the present study provides the whole-brain comparison of both volume- and surface-based characteristics between groups of children with and without ASD and investigates the relationships between these characteristics in language-related areas and the language abilities of children with ASD measured with standardized tools. A total of 36 school-aged children participated in the study: 18 children with ASD and 18 age- and sex-matched typically developing controls. The results revealed that multiple regions differed between groups of children in gray matter volume, gray matter thickness, gyrification, and cortical complexity (fractal dimension). White matter volume and sulcus depth did not differ between groups of children in any region. Importantly, gray matter thickness and gyrification of language-related areas were related to language functioning in children with ASD. Thus, the results of the present study shed some light on the structural brain abnormalities associated with language impairment in ASD.
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Affiliation(s)
- Vardan Arutiunian
- Center for Child Health, Behavior and Development, Seattle Children's Research Institute, 1920 Terry Ave., Seattle, WA, 98101, USA.
| | | | | | - Elizaveta Davydova
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia.,Chair of Differential Psychology and Psychophysiology, Moscow State University of Psychology and Education, Moscow, Russia
| | - Darya Pereverzeva
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Alexander Sorokin
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia.,Haskins Laboratories, New Haven, CT, USA
| | - Svetlana Tyushkevich
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Uliana Mamokhina
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Kamilla Danilina
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Olga Dragoy
- Center for Language and Brain, HSE University, Moscow, Russia.,Institute of Linguistics, Russian Academy of Sciences, Moscow, Russia
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14
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DiPiero MA, Surgent OJ, Travers BG, Alexander AL, Lainhart JE, Dean Iii DC. Gray matter microstructure differences in autistic males: A gray matter based spatial statistics study. Neuroimage Clin 2022; 37:103306. [PMID: 36587584 PMCID: PMC9817031 DOI: 10.1016/j.nicl.2022.103306] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/29/2022] [Accepted: 12/24/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a complex neurodevelopmental condition. Understanding the brain's microstructure and its relationship to clinical characteristics is important to advance our understanding of the neural supports underlying ASD. In the current work, we implemented Gray-Matter Based Spatial Statistics (GBSS) to examine and characterize cortical microstructure and assess differences between typically developing (TD) and autistic males. METHODS A multi-shell diffusion MRI (dMRI) protocol was acquired from 83 TD and 70 autistic males (5-to-21-years) and fit to the DTI and NODDI models. GBSS was performed for voxelwise analysis of cortical gray matter (GM). General linear models were used to investigate group differences, while age-by-group interactions assessed age-related differences between groups. Within the ASD group, relationships between cortical microstructure and measures of autistic symptoms were investigated. RESULTS All dMRI measures were significantly associated with age across the GM skeleton. Group differences and age-by-group interactions are reported. Group-wise increases in neurite density in autistic individuals were observed across frontal, temporal, and occipital regions of the right hemisphere. Significant age-by-group interactions of neurite density were observed within the middle frontal gyrus, precentral gyrus, and frontal pole. Negative relationships between neurite dispersion and the ADOS-2 Calibrated Severity Scores (CSS) were observed within the ASD group. DISCUSSION Findings demonstrate group and age-related differences between groups in neurite density in ASD across right-hemisphere brain regions supporting cognitive processes. Results provide evidence of altered neurodevelopmental processes affecting GM microstructure in autistic males with implications for the role of cortical microstructure in the level of autistic symptoms. CONCLUSION Using dMRI and GBSS, our findings provide new insights into group and age-related differences of the GM microstructure in autistic males. Defining where and when these cortical GM differences arise will contribute to our understanding of brain-behavior relationships of ASD and may aid in the development and monitoring of targeted and individualized interventions.
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Affiliation(s)
- Marissa A DiPiero
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA
| | - Olivia J Surgent
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA
| | - Brittany G Travers
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Occupational Therapy Program, University of Wisconsin-Madison, Madison, WI, USA; Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Andrew L Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | - Janet E Lainhart
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Douglas C Dean Iii
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, USA.
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15
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Wang J, Weatheritt R, Voineagu I. Alu-minating the Mechanisms Underlying Primate Cortex Evolution. Biol Psychiatry 2022; 92:760-771. [PMID: 35981906 DOI: 10.1016/j.biopsych.2022.04.021] [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: 10/26/2021] [Revised: 04/04/2022] [Accepted: 04/28/2022] [Indexed: 11/02/2022]
Abstract
The higher-order cognitive functions observed in primates correlate with the evolutionary enhancement of cortical volume and folding, which in turn are driven by the primate-specific expansion of cellular diversity in the developing cortex. Underlying these changes is the diversification of molecular features including the creation of human and/or primate-specific genes, the activation of specific molecular pathways, and the interplay of diverse layers of gene regulation. We review and discuss evidence for connections between Alu elements and primate brain evolution, the evolutionary milestones of which are known to coincide along primate lineages. Alus are repetitive elements that contribute extensively to the acquisition of novel genes and the expansion of diverse gene regulatory layers, including enhancers, alternative splicing, RNA editing, and microRNA pathways. By reviewing the impact of Alus on molecular features linked to cortical expansions or gyrification or implications in cognitive deficits, we suggest that future research focusing on the role of Alu-derived molecular events in the context of brain development may greatly advance our understanding of higher-order cognitive functions and neurologic disorders.
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Affiliation(s)
- Juli Wang
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia.
| | - Robert Weatheritt
- St Vincent Clinical School, University of New South Wales, Sydney, Australia; Garvan Institute of Medical Research, EMBL Australia, Sydney, New South Wales, Australia
| | - Irina Voineagu
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia; Cellular Genomics Futures Institute, University of New South Wales, Sydney, Australia.
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16
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Beopoulos A, Géa M, Fasano A, Iris F. Autism spectrum disorders pathogenesis: Toward a comprehensive model based on neuroanatomic and neurodevelopment considerations. Front Neurosci 2022; 16:988735. [PMID: 36408388 PMCID: PMC9671112 DOI: 10.3389/fnins.2022.988735] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 10/10/2022] [Indexed: 11/26/2023] Open
Abstract
Autism spectrum disorder (ASD) involves alterations in neural connectivity affecting cortical network organization and excitation to inhibition ratio. It is characterized by an early increase in brain volume mediated by abnormal cortical overgrowth patterns and by increases in size, spine density, and neuron population in the amygdala and surrounding nuclei. Neuronal expansion is followed by a rapid decline from adolescence to middle age. Since no known neurobiological mechanism in human postnatal life is capable of generating large excesses of frontocortical neurons, this likely occurs due to a dysregulation of layer formation and layer-specific neuronal migration during key early stages of prenatal cerebral cortex development. This leads to the dysregulation of post-natal synaptic pruning and results in a huge variety of forms and degrees of signal-over-noise discrimination losses, accounting for ASD clinical heterogeneities, including autonomic nervous system abnormalities and comorbidities. We postulate that sudden changes in environmental conditions linked to serotonin/kynurenine supply to the developing fetus, throughout the critical GW7 - GW20 (Gestational Week) developmental window, are likely to promote ASD pathogenesis during fetal brain development. This appears to be driven by discrete alterations in differentiation and patterning mechanisms arising from in utero RNA editing, favoring vulnerability outcomes over plasticity outcomes. This paper attempts to provide a comprehensive model of the pathogenesis and progression of ASD neurodevelopmental disorders.
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Affiliation(s)
| | | | - Alessio Fasano
- Division of Pediatric Gastroenterology and Nutrition, Mucosal Immunology and Biology Research Center, Massachusetts General Hospital for Children, Boston, MA, United States
- Division of Pediatric Gastroenterology and Nutrition, Center for Celiac Research and Treatment, Massachusetts General Hospital for Children, Boston, MA, United States
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17
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Fuhrmann D, Madsen KS, Johansen LB, Baaré WFC, Kievit RA. The midpoint of cortical thinning between late childhood and early adulthood differs between individuals and brain regions: Evidence from longitudinal modelling in a 12-wave neuroimaging sample. Neuroimage 2022; 261:119507. [PMID: 35882270 DOI: 10.1016/j.neuroimage.2022.119507] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 07/19/2022] [Accepted: 07/21/2022] [Indexed: 11/16/2022] Open
Abstract
Charting human brain maturation between childhood and adulthood is a fundamental prerequisite for understanding the rapid biological and psychological changes during human development. Two barriers have precluded the quantification of maturational trajectories: demands on data and demands on estimation. Using high-temporal resolution neuroimaging data of up to 12-waves in the HUBU cohort (N = 90, aged 7-21 years) we investigate changes in apparent cortical thickness across childhood and adolescence. Fitting a four-parameter logistic nonlinear random effects mixed model, we quantified the characteristic, s-shaped, trajectory of cortical thinning in adolescence. This approach yields biologically meaningful parameters, including the midpoint of cortical thinning (MCT), which corresponds to the age at which the cortex shows most rapid thinning - in our sample occurring, on average, at 14 years of age. These results show that, given suitable data and models, cortical maturation can be quantified with precision for each individual and brain region.
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Affiliation(s)
- D Fuhrmann
- Department of Psychology, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.
| | - K S Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Kettegaard Allé 30, DK-2650, Hvidovre, Denmark; Radiography, Department of Technology, University College Copenhagen, Sigurdsgade 26, DK-2200, Copenhagen N., Denmark
| | - L B Johansen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Kettegaard Allé 30, DK-2650, Hvidovre, Denmark
| | - W F C Baaré
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Kettegaard Allé 30, DK-2650, Hvidovre, Denmark
| | - R A Kievit
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
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18
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Benkarim O, Paquola C, Park BY, Kebets V, Hong SJ, Vos de Wael R, Zhang S, Yeo BTT, Eickenberg M, Ge T, Poline JB, Bernhardt BC, Bzdok D. Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging. PLoS Biol 2022; 20:e3001627. [PMID: 35486643 PMCID: PMC9094526 DOI: 10.1371/journal.pbio.3001627] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 05/11/2022] [Accepted: 04/11/2022] [Indexed: 12/18/2022] Open
Abstract
Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.
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Affiliation(s)
- Oualid Benkarim
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Casey Paquola
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Bo-yong Park
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
- Department of Data Science, Inha University, Incheon, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea
| | - Valeria Kebets
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Seok-Jun Hong
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea
- Center for the Developing Brain, Child Mind Institute, New York, New York, United States of America
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Shaoshi Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | | | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Jean-Baptiste Poline
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Boris C. Bernhardt
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Danilo Bzdok
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Canada
- School of Computer Science, McGill University, Montreal, Canada
- Mila—Quebec Artificial Intelligence Institute, Montreal, Canada
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19
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Wang Y, Hu D, Wu Z, Wang L, Huang W, Li G. Developmental abnormalities of structural covariance networks of cortical thickness and surface area in autistic infants within the first 2 years. Cereb Cortex 2022; 32:3786-3798. [PMID: 35034115 PMCID: PMC9433424 DOI: 10.1093/cercor/bhab448] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 10/18/2021] [Accepted: 11/01/2021] [Indexed: 01/19/2023] Open
Abstract
Converging evidence supports that a collection of brain regions is functionally or anatomically abnormal in autistic subjects. Structural covariance networks (SCNs) representing patterns of coordinated regional maturation are widely used to study abnormalities associated with neurodisorders. However, the possible developmental changes of SCNs in autistic individuals during the first 2 postnatal years, which features dynamic development and can potentially serve as biomarkers, remain unexplored. To fill this gap, for the first time, SCNs of cortical thickness and surface area were constructed and investigated in infants at high familial risk for autism and typically developing infants in this study. Group differences of SCNs emerge at 12 months of age in surface area. By 24 months of age, the autism group shows significantly increased integration, decreased segregation, and decreased small-worldness, compared with controls. The SCNs of surface area are deteriorated and shifted toward randomness in autistic infants. The abnormal brain regions changed during development, and the group differences of the left lateral occipital cortex become more prominent with age. These results indicate that autism has more significant influences on coordinated development of surface area than that of cortical thickness and the occipital cortex maybe an important biomarker of autism during infancy.
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Affiliation(s)
- Ya Wang
- National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China,Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dan Hu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Wenhua Huang
- Address correspondence to Wenhua Huang, National Key Discipline of Human Anatomy, School of Basic Medical Sciences, 11th floor, Southern Medical University, Guangzhou 510515, China. ; Gang Li, The University of North Carolina at Chapel Hill, Bioinformatics Building #3104, Chapel Hill, NC 27599.
| | - Gang Li
- Address correspondence to Wenhua Huang, National Key Discipline of Human Anatomy, School of Basic Medical Sciences, 11th floor, Southern Medical University, Guangzhou 510515, China. ; Gang Li, The University of North Carolina at Chapel Hill, Bioinformatics Building #3104, Chapel Hill, NC 27599.
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20
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Ali MT, ElNakieb Y, Elnakib A, Shalaby A, Mahmoud A, Ghazal M, Yousaf J, Abu Khalifeh H, Casanova M, Barnes G, El-Baz A. The Role of Structure MRI in Diagnosing Autism. Diagnostics (Basel) 2022; 12:165. [PMID: 35054330 PMCID: PMC8774643 DOI: 10.3390/diagnostics12010165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 12/30/2021] [Accepted: 01/05/2022] [Indexed: 12/30/2022] Open
Abstract
This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.
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Affiliation(s)
- Mohamed T. Ali
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Yaser ElNakieb
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Ahmed Shalaby
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Mohammed Ghazal
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.); (J.Y.); (H.A.K.)
| | - Jawad Yousaf
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.); (J.Y.); (H.A.K.)
| | - Hadil Abu Khalifeh
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.); (J.Y.); (H.A.K.)
| | - Manuel Casanova
- Department of Biomedical Sciences, School of Medicine Greenville, University of South Carolina, Greenville, SC 29425, USA;
| | - Gregory Barnes
- Department of Neurology, Norton Children’s Autism Center, University of Louisville, Louisville, KY 40208, USA;
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
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21
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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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22
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Chien YL, Chen YC, Chiu YN, Tsai WC, Gau SSF. A translational exploration of the effects of WNT2 variants on altered cortical structures in autism spectrum disorder. J Psychiatry Neurosci 2021; 46:E647-E658. [PMID: 34862305 PMCID: PMC8648347 DOI: 10.1503/jpn.210022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 06/19/2021] [Accepted: 07/28/2021] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Evidence suggests that cortical anatomy may be aytpical in autism spectrum disorder. The wingless-type MMTV integration site family, member 2 (WNT2), a candidate gene for autism spectrum disorder, may regulate cortical development. However, it is unclear whether WNT2 variants are associated with altered cortical thickness in autism spectrum disorder. METHODS In a sample of 118 people with autism spectrum disorder and 122 typically developing controls, we investigated cortical thickness using FreeSurfer software. We then examined the main effects of the WNT2 variants and the interactions of group × SNP and age × SNP for each hemisphere and brain region that was altered in people with autism spectrum disorder. RESULTS Compared to neurotypical controls, people with autism spectrum disorder showed reduced mean cortical thickness in both hemispheres and 9 cortical regions after false discovery rate correction, including the right cingulate gyrus, the orbital gyrus, the insula, the inferior frontal gyrus (orbital part and triangular part), the lateral occipitotemporal gyrus, the posterior transverse collateral sulcus, the lateral sulcus and the superior temporal sulcus. In the full sample, 2 SNPs of WNT2 (rs6950765 and rs2896218) showed age × SNP interactions for the mean cortical thickness of both hemispheres, the middle-posterior cingulate cortex and the superior temporal cortex. LIMITATIONS We examined the genetic effect for each hemisphere and the 9 regions that were altered in autism spectrum disorder. The age effect we found in this cross-sectional study needs to be examined in longitudinal studies. CONCLUSION Based on neuroimaging and genetic data, our findings suggest that WNT2 variants might be associated with altered cortical thickness in autism spectrum disorder. Whether and how these WNT2 variants might involve cortical thinning requires further investigation. TRIAL REGISTRATION ClinicalTrials.gov no. NCT01582256. PROTOCOL REGISTRATION National Institutes of Health no. NCT00494754.
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Affiliation(s)
| | | | | | | | - Susan Shur-Fen Gau
- From the Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan (Chien, Chen, Chiu, Tsai, Gau); and the Graduate Institute of Clinical Medicine, and Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan (Chen, Gau)
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23
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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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