1
|
Xu G, Geng G, Wang A, Li Z, Liu Z, Liu Y, Hu J, Wang W, Li X. Three autism subtypes based on single-subject gray matter network revealed by semi-supervised machine learning. Autism Res 2024. [PMID: 38925611 DOI: 10.1002/aur.3183] [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: 12/28/2023] [Accepted: 06/12/2024] [Indexed: 06/28/2024]
Abstract
Autism spectrum disorder (ASD) is a heterogeneous, early-onset neurodevelopmental condition characterized by persistent impairments in social interaction and communication. This study aims to delineate ASD subtypes based on individual gray matter brain networks and provide new insights from a graph theory perspective. In this study, we extracted and normalized single-subject gray matter networks and calculated each network's topological properties. The heterogeneity through discriminative analysis (HYDRA) method was utilized to subtype all patients based on network properties. Next, we explored the differences among ASD subtypes in terms of network properties and clinical measures. Our investigation identified three distinct ASD subtypes. In the case-control study, these subtypes exhibited significant differences, particularly in the precentral gyrus, lingual gyrus, and middle frontal gyrus. In the case analysis, significant differences in global and nodal properties were observed between any two subtypes. Clinically, subtype 1 showed lower VIQ and PIQ compared to subtype 3, but exhibited higher scores in ADOS-Communication and ADOS-Total compared to subtype 2. The results highlight the distinct brain network properties and behaviors among different subtypes of male patients with ASD, providing valuable insights into the neural mechanisms underlying ASD heterogeneity.
Collapse
Affiliation(s)
- Guomei Xu
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Guohong Geng
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Ankang Wang
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
- Department of Neurology, Southwest Hospital, Army Medical University, Chongqing, China
| | - Zhangyong Li
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Zhichao Liu
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yanping Liu
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Jun Hu
- Department of Neurology, Southwest Hospital, Army Medical University, Chongqing, China
| | - Wei Wang
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Xinwei Li
- Chongqing Engineering Research Center of Medical Electronics and Information Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| |
Collapse
|
2
|
Wen J, Antoniades M, Yang Z, Hwang G, Skampardoni I, Wang R, Davatzikos C. Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning. Biol Psychiatry 2024:S0006-3223(24)01286-1. [PMID: 38718880 DOI: 10.1016/j.biopsych.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/29/2024] [Accepted: 04/22/2024] [Indexed: 05/21/2024]
Abstract
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal magnetic resonance imaging to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, and multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes. Dimensional neuroimaging endophenotypes dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes), presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with disease etiology.
Collapse
Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science, University of Southern California, Los Angeles, California.
| | - Mathilde Antoniades
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Gyujoon Hwang
- Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Rongguang Wang
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory, Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
| |
Collapse
|
3
|
Wen J, Antoniades M, Yang Z, Hwang G, Skampardoni I, Wang R, Davatzikos C. Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning. ARXIV 2024:arXiv:2401.09517v1. [PMID: 38313197 PMCID: PMC10836087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes that present significant differences in various brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in transdiagnostic settings. Subsequently, we summarize relevant machine learning methodologies and discuss an emerging paradigm which we call dimensional neuroimaging endophenotype (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into a low-dimensional yet informative, quantitative brain phenotypic representation, serving as a robust intermediate phenotype (i.e., endophenotype) largely reflecting underlying genetics and etiology. Finally, we discuss the potential clinical implications of the current findings and envision future research avenues.
Collapse
Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Mathilde Antoniades
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyujoon Hwang
- Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Watertown Plank Rd, Milwaukee, WI, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rongguang Wang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| |
Collapse
|
4
|
Park BY, Benkarim O, Weber CF, Kebets V, Fett S, Yoo S, Martino AD, Milham MP, Misic B, Valk SL, Hong SJ, Bernhardt BC. Connectome-wide structure-function coupling models implicate polysynaptic alterations in autism. Neuroimage 2024; 285:120481. [PMID: 38043839 DOI: 10.1016/j.neuroimage.2023.120481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 11/29/2023] [Accepted: 12/01/2023] [Indexed: 12/05/2023] Open
Abstract
Autism spectrum disorder (ASD) is one of the most common neurodevelopmental diagnoses. Although incompletely understood, structural and functional network alterations are increasingly recognized to be at the core of the condition. We utilized multimodal imaging and connectivity modeling to study structure-function coupling in ASD and probed mono- and polysynaptic mechanisms on structurally-governed network function. We examined multimodal magnetic resonance imaging data in 80 ASD and 61 neurotypical controls from the Autism Brain Imaging Data Exchange (ABIDE) II initiative. We predicted intrinsic functional connectivity from structural connectivity data in each participant using a Riemannian optimization procedure that varies the times that simulated signals can unfold along tractography-derived personalized connectomes. In both ASD and neurotypical controls, we observed improved structure-function prediction at longer diffusion time scales, indicating better modeling of brain function when polysynaptic mechanisms are accounted for. Prediction accuracy differences (∆prediction accuracy) were marked in transmodal association systems, such as the default mode network, in both neurotypical controls and ASD. Differences were, however, lower in ASD in a polysynaptic regime at higher simulated diffusion times. We compared regional differences in ∆prediction accuracy between both groups to assess the impact of polysynaptic communication on structure-function coupling. This analysis revealed that between-group differences in ∆prediction accuracy followed a sensory-to-transmodal cortical hierarchy, with an increased gap between controls and ASD in transmodal compared to sensory/motor systems. Multivariate associative techniques revealed that structure-function differences reflected inter-individual differences in autistic symptoms and verbal as well as non-verbal intelligence. Our network modeling approach sheds light on atypical structure-function coupling in autism, and suggests that polysynaptic network mechanisms are implicated in the condition and that these can help explain its wide range of associated symptoms.
Collapse
Affiliation(s)
- Bo-Yong Park
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada; Department of Data Science, Inha University, Incheon, South Korea; Department of Statistics and Data Science, Inha University, Incheon, South Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea.
| | - Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Clara F Weber
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Valeria Kebets
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Serena Fett
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Seulki Yoo
- Convergence Research Institute, Sungkyunkwan University, Suwon, South Korea
| | - Adriana Di Martino
- Center for the Developing Brain, Child Mind Institute, New York, United States
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, United States
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Sofie L Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Seok-Jun Hong
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea; Center for the Developing Brain, Child Mind Institute, New York, United States; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| |
Collapse
|
5
|
Choi H, Byeon K, Lee J, Hong S, Park B, Park H. Identifying subgroups of eating behavior traits unrelated to obesity using functional connectivity and feature representation learning. Hum Brain Mapp 2024; 45:e26581. [PMID: 38224537 PMCID: PMC10789215 DOI: 10.1002/hbm.26581] [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: 08/30/2023] [Revised: 12/13/2023] [Accepted: 12/20/2023] [Indexed: 01/17/2024] Open
Abstract
Eating behavior is highly heterogeneous across individuals and cannot be fully explained using only the degree of obesity. We utilized unsupervised machine learning and functional connectivity measures to explore the heterogeneity of eating behaviors measured by a self-assessment instrument using 424 healthy adults (mean ± standard deviation [SD] age = 47.07 ± 18.89 years; 67% female). We generated low-dimensional representations of functional connectivity using resting-state functional magnetic resonance imaging and estimated latent features using the feature representation capabilities of an autoencoder by nonlinearly compressing the functional connectivity information. The clustering approaches applied to latent features identified three distinct subgroups. The subgroups exhibited different levels of hunger traits, while their body mass indices were comparable. The results were replicated in an independent dataset consisting of 212 participants (mean ± SD age = 38.97 ± 19.80 years; 35% female). The model interpretation technique of integrated gradients revealed that the between-group differences in the integrated gradient maps were associated with functional reorganization in heteromodal association and limbic cortices and reward-related subcortical structures such as the accumbens, amygdala, and caudate. The cognitive decoding analysis revealed that these systems are associated with reward- and emotion-related systems. Our findings provide insights into the macroscopic brain organization of eating behavior-related subgroups independent of obesity.
Collapse
Affiliation(s)
- Hyoungshin Choi
- Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwonRepublic of Korea
- Center for Neuroscience Imaging ResearchInstitute for Basic ScienceSuwonRepublic of Korea
| | | | - Jong‐eun Lee
- Department of Electrical and Computer EngineeringSungkyunkwan UniversitySuwonRepublic of Korea
- Center for Neuroscience Imaging ResearchInstitute for Basic ScienceSuwonRepublic of Korea
| | - Seok‐Jun Hong
- Center for Neuroscience Imaging ResearchInstitute for Basic ScienceSuwonRepublic of Korea
- Center for the Developing BrainChild Mind InstituteNew YorkUSA
- Department of Biomedical EngineeringSungkyunkwan UniversitySuwonRepublic of Korea
| | - Bo‐yong Park
- Center for Neuroscience Imaging ResearchInstitute for Basic ScienceSuwonRepublic of Korea
- Department of Data ScienceInha UniversityIncheonRepublic of Korea
- Department of Statistics and Data ScienceInha UniversityIncheonRepublic of Korea
| | - Hyunjin Park
- Center for Neuroscience Imaging ResearchInstitute for Basic ScienceSuwonRepublic of Korea
- School of Electronic and Electrical EngineeringSungkyunkwan UniversitySuwonRepublic of Korea
| |
Collapse
|
6
|
Bedford SA, Lai MC, Lombardo MV, Chakrabarti B, Ruigrok A, Suckling J, Anagnostou E, Lerch JP, Taylor M, Nicolson R, Stelios G, Crosbie J, Schachar R, Kelley E, Jones J, Arnold PD, Courchesne E, Pierce K, Eyler LT, Campbell K, Barnes CC, Seidlitz J, Alexander-Bloch AF, Bullmore ET, Baron-Cohen S, Bethlehem RA. Brain-charting autism and attention deficit hyperactivity disorder reveals distinct and overlapping neurobiology. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.06.23299587. [PMID: 38106166 PMCID: PMC10723556 DOI: 10.1101/2023.12.06.23299587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Background Autism and attention deficit hyperactivity disorder (ADHD) are heterogeneous neurodevelopmental conditions with complex underlying neurobiology. Despite overlapping presentation and sex-biased prevalence, autism and ADHD are rarely studied together, and sex differences are often overlooked. Normative modelling provides a unified framework for studying age-specific and sex-specific divergences in neurodivergent brain development. Methods Here we use normative modelling and a large, multi-site neuroimaging dataset to characterise cortical anatomy associated with autism and ADHD, benchmarked against models of typical brain development based on a sample of over 75,000 individuals. We also examined sex and age differences, relationship with autistic traits, and explored the co-occurrence of autism and ADHD (autism+ADHD). Results We observed robust neuroanatomical signatures of both autism and ADHD. Overall, autistic individuals showed greater cortical thickness and volume localised to the superior temporal cortex, whereas individuals with ADHD showed more global effects of cortical thickness increases but lower cortical volume and surface area across much of the cortex. The autism+ADHD group displayed a unique pattern of widespread increases in cortical thickness, and certain decreases in surface area. We also found evidence that sex modulates the neuroanatomy of autism but not ADHD, and an age-by-diagnosis interaction for ADHD only. Conclusions These results indicate distinct cortical differences in autism and ADHD that are differentially impacted by age, sex, and potentially unique patterns related to their co-occurrence.
Collapse
Affiliation(s)
- Saashi A. Bedford
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei 100229, Taiwan
| | - Michael V. Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Bhismadev Chakrabarti
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Centre for Autism, School of Psychology and Clinical Language Sciences, University of Reading, Reading RG6 6ES, UK
| | - Amber Ruigrok
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Evdokia Anagnostou
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Department of Pediatrics, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Jason P. Lerch
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Margot Taylor
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Rob Nicolson
- Department of Psychiatry, University of Western Ontario, London, Ontario, Canada
| | | | - Jennifer Crosbie
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Genetics & Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Russell Schachar
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Genetics & Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Elizabeth Kelley
- Department of Psychology, Queen’s University, Kingston, ON K7L 3N6 Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, ON K7L 3N6 Canada
- Department of Psychiatry, Queen’s University, Kingston, ON K7L 3N6 Canada
| | - Jessica Jones
- Department of Psychology, Queen’s University, Kingston, ON K7L 3N6 Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, ON K7L 3N6 Canada
- Department of Psychiatry, Queen’s University, Kingston, ON K7L 3N6 Canada
| | - Paul D. Arnold
- The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Departments of Psychiatry and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Eric Courchesne
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Karen Pierce
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Lisa T. Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA
| | - Kathleen Campbell
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Cynthia Carter Barnes
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
| | - Aaron F. Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
| | - Edward T. Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Cambridge Lifetime Autism Spectrum Service (CLASS), Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Richard A.I. Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | | |
Collapse
|
7
|
Guo X, Zhai G, Liu J, Zhang X, Zhang T, Cui D, Zhou R, Gao L. Heterogeneity of dynamic synergetic configurations of salience network in children with autism spectrum disorder. Autism Res 2023; 16:2275-2290. [PMID: 37815146 DOI: 10.1002/aur.3037] [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: 05/17/2023] [Accepted: 09/20/2023] [Indexed: 10/11/2023]
Abstract
Atypical functional connectivity (FC) patterns have been identified in autism spectrum disorders (ASD), especially within salience network (SN) and between SN and default mode network (DMN) and central executive network (CEN). But whether the dynamic configuration of intra-SN and inter-SN (SN with DMN and CEN) FC in ASD is also heterogeneous remains unknown. Based on the resting-state functional magnetic resonance imaging data from 105 ASD and 102 typically-developing controls (TC), we calculated the time-varying FC of intra-SN and inter-SN (SN with DMN and CEN). Then, the joint recurrence features for the time-varying FC were calculated to assess how the SN dynamically recruits different configurations of network segregation and integration in ASD, that is, synergies, from the dynamical systems perspective. We analyzed the differences in synergetic patterns between ASD subtypes obtained by k-means clustering algorithm based on the synergy of SN and TC, and investigated the relationships between synergy of SN and severity of clinical symptoms of ASD for ASD subtypes. Two ASD subtypes were revealed, where the synergy of SN in ASD subtype 1 has lower stability and periodicity compared to the TC, and ASD subtype 2 exhibits the opposite alteration. Synergy of SN for ASD subtype 1 and 2 was found to predict the severity of communication impairments and restricted and repetitive behaviors in ASD, respectively. These results suggest the existence of subtypes with distinct patterns of the synergy of SN in ASD, and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.
Collapse
Affiliation(s)
- Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Guangjin Zhai
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Junfeng Liu
- Department of Neurology, West China Hospital Sichuan University, Chengdu, China
| | - Xia Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Tao Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Dong Cui
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| | - Rongjuan Zhou
- Finance Department, Maternity and Child Health Hospital of Qinhuangdao, Qinhuangdao, China
| | - Le Gao
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, China
| |
Collapse
|
8
|
Ko C, Kang S, Hong SB, Park YR. Protocol for the development of joint attention-based subclassification of autism spectrum disorder and validation using multi-modal data. BMC Psychiatry 2023; 23:589. [PMID: 37582781 PMCID: PMC10426216 DOI: 10.1186/s12888-023-04978-4] [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: 06/02/2023] [Accepted: 06/22/2023] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND Heterogeneity in clinical manifestation and underlying neuro-biological mechanisms are major obstacles to providing personalized interventions for individuals with autism spectrum disorder (ASD). Despite various efforts to unify disparate data modalities and machine learning techniques for subclassification, replicable ASD clusters remain elusive. Our study aims to introduce a novel method, utilizing the objective behavioral biomarker of gaze patterns during joint attention, to subclassify ASD. We will assess whether behavior-based subgrouping yields clinically, genetically, and neurologically distinct ASD groups. METHODS We propose a study involving 60 individuals with ASD recruited from a specialized psychiatric clinic to perform joint attention tasks. Through the examination of gaze patterns in social contexts, we will conduct a semi-supervised clustering analysis, yielding two primary clusters: good gaze response group and poor gaze response group. Subsequent comparison will occur across these clusters, scrutinizing neuroanatomical structure and connectivity using structural as well as functional brain imaging studies, genetic predisposition through single nucleotide polymorphism data, and assorted socio-demographic and clinical information. CONCLUSIONS The aim of the study is to investigate the discriminative properties and the validity of the joint attention-based subclassification of ASD using multi-modality data. TRIAL REGISTRATION Clinical trial, KCT0008530, Registered 16 June 2023, https://cris.nih.go.kr/cris/index/index.do .
Collapse
Affiliation(s)
- Chanyoung Ko
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea
| | - Soyeon Kang
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University Hospital, Seoul, South Korea
| | - Soon-Beom Hong
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University Hospital, Seoul, South Korea.
- Department of Psychiatry and Institute of Human Behavioral Medicine, Seoul National University College of Medicine, Seoul, South Korea.
| | - Yu Rang Park
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea.
| |
Collapse
|
9
|
Zhang S, She S, Qiu Y, Li Z, Wu X, Hu H, Zheng W, Huang R, Wu H. Multi-modal MRI measures reveal sensory abnormalities in major depressive disorder patients: A surface-based study. Neuroimage Clin 2023; 39:103468. [PMID: 37473494 PMCID: PMC10372163 DOI: 10.1016/j.nicl.2023.103468] [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: 03/20/2023] [Revised: 06/17/2023] [Accepted: 07/05/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Multi-modal magnetic resonance imaging (MRI) measures are supposed to be able to capture different brain neurobiological aspects of major depressive disorder (MDD). A fusion analysis of structural and functional modalities may better reveal the disease biomarker specific to the MDD disease. METHODS We recruited 30 MDD patients and 30 matched healthy controls (HC). For each subject, we acquired high-resolution brain structural images and resting-state fMRI (rs-fMRI) data using a 3 T MRI scanner. We first extracted the brain morphometric measures, including the cortical volume (CV), cortical thickness (CT), and surface area (SA), for each subject from the structural images, and then detected the structural clusters showing significant between-group differences in each measure using the surface-based morphology (SBM) analysis. By taking the identified structural clusters as seeds, we performed seed-based functional connectivity (FC) analyses to determine the regions with abnormal FC in the patients. Based on a logistic regression model, we performed a classification analysis by selecting these structural and functional cluster-wise measures as features to distinguish the MDD patients from the HC. RESULTS The MDD patients showed significantly lower CV in a cluster involving the right superior temporal gyrus (STG) and middle temporal gyrus (MTG), and lower SA in three clusters involving the bilateral STG, temporal pole gyrus, and entorhinal cortex, and the left inferior temporal gyrus, and fusiform gyrus, than the controls. No significant difference in CT was detected between the two groups. By taking the above-detected clusters as seeds to perform the seed-based FC analysis, we found that the MDD patients showed significantly lower FC between STG/MTG (CV's cluster) and two clusters located in the bilateral visual cortices than the controls. The logistic regression model based on the structural and functional features reached a classification accuracy of 86.7% (p < 0.001) between MDD and controls. CONCLUSION The present study showed sensory abnormalities in MDD patients using the multi-modal MRI analysis. This finding may act as a disease biomarker distinguishing MDD patients from healthy individuals.
Collapse
Affiliation(s)
- Shufei Zhang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Shenglin She
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Yidan Qiu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Zezhi Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Xiaoyan Wu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Huiqing Hu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Wei Zheng
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Ruiwang Huang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China.
| | - Huawang Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China.
| |
Collapse
|
10
|
Ren P, Bi Q, Pang W, Wang M, Zhou Q, Ye X, Li L, Xiao L. Stratifying ASD and characterizing the functional connectivity of subtypes in resting-state fMRI. Behav Brain Res 2023; 449:114458. [PMID: 37121277 DOI: 10.1016/j.bbr.2023.114458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 04/12/2023] [Accepted: 04/26/2023] [Indexed: 05/02/2023]
Abstract
BACKGROUND Although stratifying autism spectrum disorder (ASD) into different subtypes is a common effort in the research field, few papers have characterized the functional connectivity alterations of ASD subgroups classified by their clinical presentations. METHODS This is a case-control rs-fMRI study, based on large samples of open database (Autism Brain Imaging Data Exchange, ABIDE). The rs-MRI data from n=415 ASD patients (males n=357), and n=574 typical development (TD) controls (males n=410) were included. Clinical features of ASD were extracted and classified using data from each patient's Autism Diagnostic Interview-Revised (ADI-R) evaluation. Each subtype of ASD was characterized by local functional connectivity using regional homogeneity (ReHo) for assessment, remote functional connectivity using voxel-mirrored homotopic connectivity (VMHC) for assessment, the whole-brain functional connectivity, and graph theoretical features. These identified imaging properties from each subtype were integrated to create a machine learning model for classifying ASD patients into the subtypes based on their rs-fMRI data, and an independent dataset was used to validate the model. RESULTS All ASD participants were classified into Cluster-1 (patients with more severe impairment) and Cluster-2 (patients with moderate impairment) according to the dimensional scores of ADI-R. When compared to the TD group, Cluster-1 demonstrated increased local connection and decreased remote connectivity, and widespread hyper- and hypo-connectivity variations in the whole-brain functional connectivity. Cluster-2 was quite similar to the TD group in both local and remote connectivity. But at the level of whole-brain functional connectivity, the MCC-related connections were specifically impaired in Cluster-2. These properties of functional connectivity were fused to build a machine learning model, which achieved ~75% for identifying ASD subtypes (Cluster-1 accuracy = 81.75%; Cluster-2 accuracy = 76.48%). CONCLUSIONS The stratification of ASD by clinical presentations can help to minimize disease heterogeneity and highlight the distinguished properties of brain connectivity in ASD subtypes.
Collapse
Affiliation(s)
- Pengchen Ren
- Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, China; NHC Key Laboratory of Tropical Disease Control, Hainan Medical University, Haikou, China
| | - Qingshang Bi
- Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, China
| | - Wenbin Pang
- Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, China
| | - Meijuan Wang
- Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, China
| | - Qionglin Zhou
- Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, China
| | - Xiaoshan Ye
- Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, China
| | - Ling Li
- Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, China; School of Pediatrics, Hainan Medical University, Haikou, China.
| | - Le Xiao
- Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, China; School of Pediatrics, Hainan Medical University, Haikou, China.
| |
Collapse
|
11
|
Rabot J, Rødgaard EM, Joober R, Dumas G, Bzdok D, Bernhardt B, Jacquemont S, Mottron L. Genesis, modelling and methodological remedies to autism heterogeneity. Neurosci Biobehav Rev 2023; 150:105201. [PMID: 37116771 DOI: 10.1016/j.neubiorev.2023.105201] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 04/22/2023] [Accepted: 04/24/2023] [Indexed: 04/30/2023]
Abstract
Diagnostic criteria used in autism research have undergone a shift towards the inclusion of a larger population, paralleled by increasing, but variable, estimates of autism prevalence across clinical settings and continents. A categorical diagnosis of autism spectrum disorder is now consistent with large variations in language, intelligence, comorbidity, and severity, leading to a heterogeneous sample of individuals, increasingly distant from the initial prototypical descriptions. We review the history of autism diagnosis and subtyping, and the evidence of heterogeneity in autism at the cognitive, neurological, and genetic levels. We describe two strategies to address the problem of heterogeneity: clustering, and truncated-compartmentalized enrollment strategy based on prototype recognition. The advances made using clustering methods have been modest. We present an alternative, new strategy for dissecting autism heterogeneity, emphasizing incorporation of prototypical samples in research cohorts, comparison of subgroups defined by specific ranges of values for the clinical specifiers, and retesting the generality of neurobiological results considered to be acquired from the entire autism spectrum on prototypical cohorts defined by narrow specifiers values.
Collapse
Affiliation(s)
| | - Eya-Mist Rødgaard
- Department of Psychology, Copenhagen University, Copenhagen, Denmark,.
| | - Ridha Joober
- Neurological Institute and Hospital, McGill University, Montreal, Quebec, H4H 1R3, Canada,.
| | - Guillaume Dumas
- Department of Psychiatry & Addictology, University of Montreal, Montreal, QC, H3T 1C5, Canada, Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada,.
| | - Danilo Bzdok
- Mila - Quebec Artificial Intelligence Institute, Montreal, Canada, Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, QC, Canada,.
| | - Boris Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, McGill University, Montreal, QC, H3A 2B4, Canada,.
| | - Sebastien Jacquemont
- Department of Pediatrics, University of Montreal, Montréal, Quebec, H3T 1C5, Canada,.
| | - Laurent Mottron
- Department of Psychiatry & Addictology, University of Montreal, Montreal, QC, H3T 1C5, Canada, CIUSSS-NIM, Research Center, Montréal, QC, H1E 1A4, Canada,.
| |
Collapse
|
12
|
Dhamala E, Yeo BTT, Holmes AJ. One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry. Biol Psychiatry 2023; 93:717-728. [PMID: 36577634 DOI: 10.1016/j.biopsych.2022.09.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 09/07/2022] [Accepted: 09/23/2022] [Indexed: 12/30/2022]
Abstract
Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosis exhibit identical symptom profiles. Over the last several decades, group-level analyses of in vivo neuroimaging data have led to fundamental advances in our understanding of the neurobiology of psychiatric illnesses. More recently, access to computational resources and large, publicly available datasets alongside the rise of predictive modeling and precision medicine approaches have facilitated the study of psychiatric illnesses at an individual level. Data-driven machine learning analyses can be applied to identify disease-relevant biological subtypes, predict individual symptom profiles, and recommend personalized therapeutic interventions. However, when developing these predictive models, methodological choices must be carefully considered to ensure accurate, robust, and interpretable results. Choices pertaining to algorithms, neuroimaging modalities and states, data transformation, phenotypes, parcellations, sample sizes, and populations we are specifically studying can influence model performance. Here, we review applications of neuroimaging-based machine learning models to study psychiatric illnesses and discuss the effects of different methodological choices on model performance. An understanding of these effects is crucial for the proper implementation of predictive models in psychiatry and will facilitate more accurate diagnoses, prognoses, and therapeutics.
Collapse
Affiliation(s)
- Elvisha Dhamala
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut.
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut.
| |
Collapse
|
13
|
Buch AM, Vértes PE, Seidlitz J, Kim SH, Grosenick L, Liston C. Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder. Nat Neurosci 2023; 26:650-663. [PMID: 36894656 DOI: 10.1038/s41593-023-01259-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 01/17/2023] [Indexed: 03/11/2023]
Abstract
The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD) are not well understood. Using a large neuroimaging dataset, we identified three latent dimensions of functional brain network connectivity that predicted individual differences in ASD behaviors and were stable in cross-validation. Clustering along these three dimensions revealed four reproducible ASD subgroups with distinct functional connectivity alterations in ASD-related networks and clinical symptom profiles that were reproducible in an independent sample. By integrating neuroimaging data with normative gene expression data from two independent transcriptomic atlases, we found that within each subgroup, ASD-related functional connectivity was explained by regional differences in the expression of distinct ASD-related gene sets. These gene sets were differentially associated with distinct molecular signaling pathways involving immune and synapse function, G-protein-coupled receptor signaling, protein synthesis and other processes. Collectively, our findings delineate atypical connectivity patterns underlying different forms of ASD that implicate distinct molecular signaling mechanisms.
Collapse
Affiliation(s)
- Amanda M Buch
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - So Hyun Kim
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
- Center for Autism and the Developing Brain, Weill Cornell Medicine, White Plains, NY, USA
- School of Psychology, Korea University, Seoul, South Korea
| | - Logan Grosenick
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
| |
Collapse
|
14
|
Boksha IS, Prokhorova TA, Tereshkina EB, Savushkina OK, Burbaeva GS. Differentiated Approach to Pharmacotherapy of Autism Spectrum Disorders: Biochemical Aspects. BIOCHEMISTRY (MOSCOW) 2023; 88:303-318. [PMID: 37076279 DOI: 10.1134/s0006297923030021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Autism Spectrum Disorders (ASD) are highly heterogeneous neurodevelopmental disorders caused by a complex interaction of numerous genetic and environmental factors and leading to deviations in the nervous system formation at the very early developmental stages. Currently, there are no accepted pharmacological treatments for the so-called core symptoms of ASD, such as social communication disorders and restricted and repetitive behavior patterns. Lack of knowledge about biological basis of ASD, absence of the clinically significant biochemical parameters reflecting abnormalities in the signaling cascades controlling the nervous system development and functioning, and lack of methods for selection of clinically and biologically homogeneous subgroups are considered as causes for the failure of clinical trials of ASD pharmacotherapy. This review considers the possibilities of applying differentiated clinical and biological approaches to the targeted search for ASD pharmacotherapy with emphasis on biochemical markers associated with ASD and attempts to stratify patients by biochemical parameters. The use of such approach as "the target-oriented therapy and assessment of the target status before and during the treatment to identify patients with a positive response to treatment" is discussed using the published results of clinical trials as examples. It is concluded that identification of biochemical parameters for selection of the distinct subgroups among the ASD patients requires research on large samples reflecting clinical and biological diversity of the patients with ASD, and use of unified approaches for such studies. An integrated approach, including clinical observation, clinical-psychological assessment of the patient behavior, study of medical history and description of individual molecular profiles should become a new strategy for stratifying patients with ASD for clinical pharmacotherapeutic trials, as well as for evaluating their efficiency.
Collapse
|
15
|
Hong SJ, Mottron L, Park BY, Benkarim O, Valk SL, Paquola C, Larivière S, Vos de Wael R, Degré-Pelletier J, Soulieres I, Ramphal B, Margolis A, Milham M, Di Martino A, Bernhardt BC. A convergent structure-function substrate of cognitive imbalances in autism. Cereb Cortex 2023; 33:1566-1580. [PMID: 35552620 PMCID: PMC9977381 DOI: 10.1093/cercor/bhac156] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/28/2022] [Accepted: 03/29/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a common neurodevelopmental diagnosis showing substantial phenotypic heterogeneity. A leading example can be found in verbal and nonverbal cognitive skills, which vary from elevated to impaired compared with neurotypical individuals. Moreover, deficits in verbal profiles often coexist with normal or superior performance in the nonverbal domain. METHODS To study brain substrates underlying cognitive imbalance in ASD, we capitalized categorical and dimensional IQ profiling as well as multimodal neuroimaging. RESULTS IQ analyses revealed a marked verbal to nonverbal IQ imbalance in ASD across 2 datasets (Dataset-1: 155 ASD, 151 controls; Dataset-2: 270 ASD, 490 controls). Neuroimaging analysis in Dataset-1 revealed a structure-function substrate of cognitive imbalance, characterized by atypical cortical thickening and altered functional integration of language networks alongside sensory and higher cognitive areas. CONCLUSION Although verbal and nonverbal intelligence have been considered as specifiers unrelated to autism diagnosis, our results indicate that intelligence disparities are accentuated in ASD and reflected by a consistent structure-function substrate affecting multiple brain networks. Our findings motivate the incorporation of cognitive imbalances in future autism research, which may help to parse the phenotypic heterogeneity and inform intervention-oriented subtyping in ASD.
Collapse
Affiliation(s)
- Seok-Jun Hong
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada.,Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Seobu-ro 2066, Jangan-gu, Suwon 16419, South Korea.,Department of Biomedical Engineering, Sungkyunkwan University, Seobu-ro 2066, Jangan-gu, Suwon 16419, South Korea.,Center for the Developing Brain, Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Laurent Mottron
- Centre de Recherche du CIUSSSNIM and Department of Psychiatry and Addictology, Université de Montréal, 7070 boulevard Perras, Montréal, Quebec H1E 1A4, Canada
| | - Bo-Yong Park
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada.,Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Seobu-ro 2066, Jangan-gu, Suwon 16419, South Korea.,Department of Data Science, Inha Univerisity, Incheon 22212, South Korea
| | - Oualid Benkarim
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Sofie L Valk
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada.,Otto Hahn group Cognitive neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraβe 1A. Leipzig D-04103, Germany.,Institute of Neuroscience and Medicine, Research Centre Wilhelm-Johnen-Strasse, Jülich 52425, Germany.,Institute of Systems Neuroscience, Heinrich Heine University, Moorenstr. 5, Düsseldorf 40225, Germany
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada.,Institute of Neuroscience and Medicine, Research Centre Wilhelm-Johnen-Strasse, Jülich 52425, Germany
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Janie Degré-Pelletier
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada.,Department of Psychology, Université du Québec à Montréal, 100 rue Sherbrooke Ouest, Montréal, Québec H2X 3P2, Canada
| | - Isabelle Soulieres
- Department of Psychology, Université du Québec à Montréal, 100 rue Sherbrooke Ouest, Montréal, Québec H2X 3P2, Canada
| | - Bruce Ramphal
- Department of Psychiatry, The New York State Psychiatric Institute and the College of Physicians Surgeons, Columbia University, 1051 Riverside Drive, New York, NY 10032, United States
| | - Amy Margolis
- Department of Psychiatry, The New York State Psychiatric Institute and the College of Physicians Surgeons, Columbia University, 1051 Riverside Drive, New York, NY 10032, United States
| | - Michael Milham
- Center for the Developing Brain, Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States.,Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 140 Old Orangeburg Road, Orangeburg, NY 10962, United States
| | - Adriana Di Martino
- Autism Center, Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| |
Collapse
|
16
|
Aglinskas A, Schwartz E, Anzellotti S. Disentangling disorder-specific variation is key for precision psychiatry in autism. Front Behav Neurosci 2023; 17:1121017. [PMID: 37025108 PMCID: PMC10070721 DOI: 10.3389/fnbeh.2023.1121017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 03/03/2023] [Indexed: 04/08/2023] Open
|
17
|
Inter-individual heterogeneity of functional brain networks in children with autism spectrum disorder. Mol Autism 2022; 13:52. [PMID: 36572935 PMCID: PMC9793594 DOI: 10.1186/s13229-022-00535-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/20/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a neurodevelopmental disorder with considerable clinical heterogeneity. This study aimed to explore the heterogeneity of ASD based on inter-individual heterogeneity of functional brain networks. METHODS Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were used in this study for 105 children with ASD and 102 demographically matched typical controls (TC) children. Functional connectivity (FC) networks were first obtained for ASD and TC groups, and inter-individual deviation of functional connectivity (IDFC) from the TC group was then calculated for each individual with ASD. A k-means clustering algorithm was used to obtain ASD subtypes based on IDFC patterns. The FC patterns were further compared between ASD subtypes and the TC group from the brain region, network, and whole-brain levels. The relationship between IDFC and the severity of clinical symptoms of ASD for ASD subtypes was also analyzed using a support vector regression model. RESULTS Two ASD subtypes were identified based on the IDFC patterns. Compared with the TC group, the ASD subtype 1 group exhibited a hypoconnectivity pattern and the ASD subtype 2 group exhibited a hyperconnectivity pattern. IDFC for ASD subtype 1 and subtype 2 was found to predict the severity of social communication impairments and the severity of restricted and repetitive behaviors in ASD, respectively. LIMITATIONS Only male children were selected for this study, which limits the ability to study the effects of gender and development on ASD heterogeneity. CONCLUSIONS These results suggest the existence of subtypes with different FC patterns in ASD and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.
Collapse
|
18
|
Gagnon D, Zeribi A, Douard É, Courchesne V, Huguet G, Jacquemont S, Loum MA, Mottron L. Using developmental regression to reorganize the clinical importance of autistic atypicalities. Transl Psychiatry 2022; 12:498. [PMID: 36456542 PMCID: PMC9715666 DOI: 10.1038/s41398-022-02263-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 11/16/2022] [Accepted: 11/21/2022] [Indexed: 12/04/2022] Open
Abstract
Early regression (ER) is often reported in autistic children with a prototypical phenotype and has been proposed as a possible pathognomonic sign present in most autistic children. Despite the uncertainties attached to its definition and report, using ER to anchor the autism phenotype could help identify the signs that best contribute to an autism diagnosis. We extracted retrospective data from 1547 autistic children between the ages of 6 and 18 years from the Simons Simplex collection. Logistic regression identified the atypicalities associated with a history of ER. Stepwise variable selection using logistic regression analysis followed by a bootstrap procedure of 1000 iterations identified the cluster of atypicalities best associated with ER. Linear and logistic regressions measured the association between combinations of atypicalities within the identified cluster and adaptative behaviors, diagnostic areas of severity, and other categories. Seven atypicalities significantly increased the likelihood of having experienced ER (OR = 1.73-2.13). Four ("hand leading-ever", "pronominal reversal-ever", "never shakes head at age 4-5" and "stereotypic use of objects or interest in parts of objects-ever"), when grouped together, best characterized the phenotype of verbal autistic children with ER. This clustering of signs was associated with certain persistent language difficulties, higher summary scores on a diagnostic scale for autism, and greater odds of receiving an "autistic disorder" diagnosis instead of another pervasive developmental disorder (PDD) diagnosis. These results raise questions about using language as a clinical specifier, defining cross-sectional signs independent of their relationship with an early developmental trajectory, and relying on polythetic criteria or equivalent weighted autistic atypicalities.
Collapse
Affiliation(s)
- David Gagnon
- grid.414305.70000 0001 0555 2355Research Center of the CIUSSS-NIM, Hôpital Rivière-des-Prairies, 7070 boul. Perras, Montréal, QC H1E 1A4 Canada ,grid.14848.310000 0001 2292 3357Department of Psychiatry and Addictology, University of Montreal, 2900, boul. Édouard-Montpetit, Montreal, QC H3T 1J4 Canada
| | - Abderrahim Zeribi
- grid.14848.310000 0001 2292 3357Department of Pediatrics, University of Montreal, 2900, boul. Édouard-Montpetit, Montreal, QC H3T 1J4 Canada ,Sainte-Justine Research Center, 3175, chemin de la Côte-Sainte-Catherine, Montreal, QC H3T 1C5 Canada
| | - Élise Douard
- grid.14848.310000 0001 2292 3357Psychoeducation School, University of Montreal, C.P. 6128, Succ. Centre-Ville, Montréal, QC H3C 3J7 Canada ,grid.459278.50000 0004 4910 4652Research Center of the CIUSSS-EMTL, 7331, rue Hochelaga, Montréal, QC H1N 3V2 Canada
| | - Valérie Courchesne
- grid.155956.b0000 0000 8793 5925Campbell Family Mental Health Research Institute (CAMH), 1001 Queen Street West, Toronto, M6J 1H4 ON Canada
| | - Guillaume Huguet
- grid.14848.310000 0001 2292 3357Department of Pediatrics, University of Montreal, 2900, boul. Édouard-Montpetit, Montreal, QC H3T 1J4 Canada ,Sainte-Justine Research Center, 3175, chemin de la Côte-Sainte-Catherine, Montreal, QC H3T 1C5 Canada
| | - Sébastien Jacquemont
- grid.14848.310000 0001 2292 3357Department of Pediatrics, University of Montreal, 2900, boul. Édouard-Montpetit, Montreal, QC H3T 1J4 Canada ,Sainte-Justine Research Center, 3175, chemin de la Côte-Sainte-Catherine, Montreal, QC H3T 1C5 Canada
| | - Mor Absa Loum
- grid.14848.310000 0001 2292 3357Department of Pediatrics, University of Montreal, 2900, boul. Édouard-Montpetit, Montreal, QC H3T 1J4 Canada ,Sainte-Justine Research Center, 3175, chemin de la Côte-Sainte-Catherine, Montreal, QC H3T 1C5 Canada
| | - Laurent Mottron
- Research Center of the CIUSSS-NIM, Hôpital Rivière-des-Prairies, 7070 boul. Perras, Montréal, QC, H1E 1A4, Canada. .,Department of Psychiatry and Addictology, University of Montreal, 2900, boul. Édouard-Montpetit, Montreal, QC, H3T 1J4, Canada.
| |
Collapse
|
19
|
Hettwer MD, Larivière S, Park BY, van den Heuvel OA, Schmaal L, Andreassen OA, Ching CRK, Hoogman M, Buitelaar J, van Rooij D, Veltman DJ, Stein DJ, Franke B, van Erp TGM, Jahanshad N, Thompson PM, Thomopoulos SI, Bethlehem RAI, Bernhardt BC, Eickhoff SB, Valk SL. Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders. Nat Commun 2022; 13:6851. [PMID: 36369423 PMCID: PMC9652311 DOI: 10.1038/s41467-022-34367-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 10/24/2022] [Indexed: 11/13/2022] Open
Abstract
Neuropsychiatric disorders are increasingly conceptualized as overlapping spectra sharing multi-level neurobiological alterations. However, whether transdiagnostic cortical alterations covary in a biologically meaningful way is currently unknown. Here, we studied co-alteration networks across six neurodevelopmental and psychiatric disorders, reflecting pathological structural covariance. In 12,024 patients and 18,969 controls from the ENIGMA consortium, we observed that co-alteration patterns followed normative connectome organization and were anchored to prefrontal and temporal disease epicenters. Manifold learning revealed frontal-to-temporal and sensory/limbic-to-occipitoparietal transdiagnostic gradients, differentiating shared illness effects on cortical thickness along these axes. The principal gradient aligned with a normative cortical thickness covariance gradient and established a transcriptomic link to cortico-cerebello-thalamic circuits. Moreover, transdiagnostic gradients segregated functional networks involved in basic sensory, attentional/perceptual, and domain-general cognitive processes, and distinguished between regional cytoarchitectonic profiles. Together, our findings indicate that shared illness effects occur in a synchronized fashion and along multiple levels of hierarchical cortical organization.
Collapse
Affiliation(s)
- M. D. Hettwer
- grid.411327.20000 0001 2176 9917Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany ,grid.419524.f0000 0001 0041 5028Max Planck School of Cognition, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany ,grid.419524.f0000 0001 0041 5028Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - S. Larivière
- grid.416102.00000 0004 0646 3639Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC Canada
| | - B. Y. Park
- grid.416102.00000 0004 0646 3639Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC Canada ,grid.202119.90000 0001 2364 8385Department of Data Science, Inha University, Incheon, Republic of Korea ,grid.410720.00000 0004 1784 4496Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - O. A. van den Heuvel
- grid.484519.5Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neuroscience and Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - L. Schmaal
- grid.1008.90000 0001 2179 088XCentre for Youth Mental Health, The University of Melbourne, Melbourne, VIC Australia ,grid.488501.00000 0004 8032 6923Orygen, Parkville, VIC Australia
| | - O. A. Andreassen
- grid.5510.10000 0004 1936 8921NORMENT Centre, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - C. R. K. Ching
- grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA USA
| | - M. Hoogman
- grid.10417.330000 0004 0444 9382Departments of Psychiatry and Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - J. Buitelaar
- grid.10417.330000 0004 0444 9382Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - D. van Rooij
- grid.10417.330000 0004 0444 9382Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - D. J. Veltman
- grid.484519.5Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neuroscience and Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - D. J. Stein
- grid.7836.a0000 0004 1937 1151South African Medical Research Council Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - B. Franke
- grid.10417.330000 0004 0444 9382Departments of Psychiatry and Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - T. G. M. van Erp
- grid.266093.80000 0001 0668 7243Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine Hall, Irvine, CA USA ,grid.266093.80000 0001 0668 7243Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA USA
| | | | | | | | | | | | | | - N. Jahanshad
- grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA USA
| | - P. M. Thompson
- grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA USA
| | - S. I. Thomopoulos
- grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA USA
| | - R. A. I. Bethlehem
- grid.5335.00000000121885934Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK ,grid.5335.00000000121885934Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - B. C. Bernhardt
- grid.416102.00000 0004 0646 3639Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC Canada
| | - S. B. Eickhoff
- grid.411327.20000 0001 2176 9917Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany
| | - S. L. Valk
- grid.411327.20000 0001 2176 9917Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany ,grid.419524.f0000 0001 0041 5028Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | | | | |
Collapse
|
20
|
Cruces RR, Royer J, Herholz P, Larivière S, Vos de Wael R, Paquola C, Benkarim O, Park BY, Degré-Pelletier J, Nelson MC, DeKraker J, Leppert IR, Tardif C, Poline JB, Concha L, Bernhardt BC. Micapipe: A pipeline for multimodal neuroimaging and connectome analysis. Neuroimage 2022; 263:119612. [PMID: 36070839 PMCID: PMC10697132 DOI: 10.1016/j.neuroimage.2022.119612] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 08/20/2022] [Accepted: 09/03/2022] [Indexed: 11/25/2022] Open
Abstract
Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain microstructure, geometry, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and to consolidate findings across different spatial scales. Here, we present micapipe, an open processing pipeline for multimodal MRI datasets. Based on BIDS-conform input data, micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. The above matrices can be automatically generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations, allowing researchers to replicate findings easily across different spatial scales. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, function, cand connectivity.
Collapse
Affiliation(s)
- Raúl R Cruces
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada; Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
| | - Peer Herholz
- NeuroDataScience - ORIGAMI lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada; Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Oualid Benkarim
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Bo-Yong Park
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada; Department of Data Science, Inha University, Incheon, Republic of Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Janie Degré-Pelletier
- Labo IDEA, Département de Psychologie, Université du Québec à Montréal, Montréal, Québec, Canada
| | - Mark C Nelson
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Jordan DeKraker
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Ilana R Leppert
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Christine Tardif
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Jean-Baptiste Poline
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Luis Concha
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Mexico
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
| |
Collapse
|
21
|
Bel-Bahar TS, Khan AA, Shaik RB, Parvaz MA. A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment. Front Hum Neurosci 2022; 16:995534. [PMID: 36325430 PMCID: PMC9619053 DOI: 10.3389/fnhum.2022.995534] [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: 07/16/2022] [Accepted: 09/20/2022] [Indexed: 11/24/2022] Open
Abstract
Substance use disorders (SUDs) constitute a growing global health crisis, yet many limitations and challenges exist in SUD treatment research, including the lack of objective brain-based markers for tracking treatment outcomes. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity, and although much is known about EEG activity in acute and chronic substance use, knowledge regarding EEG in relation to abstinence and treatment outcomes is sparse. We performed a scoping review of longitudinal and pre-post treatment EEG studies that explored putative changes in brain function associated with abstinence and/or treatment in individuals with SUD. Following PRISMA guidelines, we identified studies published between January 2000 and March 2022 from online databases. Search keywords included EEG, addictive substances (e.g., alcohol, cocaine, methamphetamine), and treatment related terms (e.g., abstinence, relapse). Selected studies used EEG at least at one time point as a predictor of abstinence or other treatment-related outcomes; or examined pre- vs. post-SUD intervention (brain stimulation, pharmacological, behavioral) EEG effects. Studies were also rated on the risk of bias and quality using validated instruments. Forty-four studies met the inclusion criteria. More consistent findings included lower oddball P3 and higher resting beta at baseline predicting negative outcomes, and abstinence-mediated longitudinal decrease in cue-elicited P3 amplitude and resting beta power. Other findings included abstinence or treatment-related changes in late positive potential (LPP) and N2 amplitudes, as well as in delta and theta power. Existing studies were heterogeneous and limited in terms of specific substances of interest, brief times for follow-ups, and inconsistent or sparse results. Encouragingly, in this limited but maturing literature, many studies demonstrated partial associations of EEG markers with abstinence, treatment outcomes, or pre-post treatment-effects. Studies were generally of good quality in terms of risk of bias. More EEG studies are warranted to better understand abstinence- or treatment-mediated neural changes or to predict SUD treatment outcomes. Future research can benefit from prospective large-sample cohorts and the use of standardized methods such as task batteries. EEG markers elucidating the temporal dynamics of changes in brain function related to abstinence and/or treatment may enable evidence-based planning for more effective and targeted treatments, potentially pre-empting relapse or minimizing negative lifespan effects of SUD.
Collapse
Affiliation(s)
- Tarik S. Bel-Bahar
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Anam A. Khan
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Riaz B. Shaik
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Muhammad A. Parvaz
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| |
Collapse
|
22
|
Laidi C, Floris DL, Tillmann J, Elandaloussi Y, Zabihi M, Charman T, Wolfers T, Durston S, Moessnang C, Dell'Acqua F, Ecker C, Loth E, Murphy D, Baron-Cohen S, Buitelaar JK, Marquand AF, Beckmann CF, Frouin V, Leboyer M, Duchesnay E, Coupé P, Houenou J. Cerebellar Atypicalities in Autism? Biol Psychiatry 2022; 92:674-682. [PMID: 36137706 DOI: 10.1016/j.biopsych.2022.05.020] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 04/27/2022] [Accepted: 05/16/2022] [Indexed: 12/11/2022]
Abstract
BACKGROUND The cerebellum contains more than 50% of the brain's neurons and is involved in social cognition. Cerebellar anatomical atypicalities have repeatedly been reported in individuals with autism. However, studies have yielded inconsistent findings, likely because of a lack of statistical power, and did not capture the clinical and neuroanatomical diversity of autism. Our aim was to better understand cerebellar anatomy and its diversity in autism. METHODS We studied cerebellar gray matter morphology in 274 individuals with autism and 219 control subjects of a multicenter European cohort, EU-AIMS LEAP (European Autism Interventions-A Multicentre Study for Developing New Medications; Longitudinal European Autism Project). To ensure the robustness of our results, we conducted lobular parcellation of the cerebellum with 2 different pipelines in addition to voxel-based morphometry. We performed statistical analyses with linear, multivariate (including normative modeling), and meta-analytic approaches to capture the diversity of cerebellar anatomy in individuals with autism and control subjects. Finally, we performed a dimensional analysis of cerebellar anatomy in an independent cohort of 352 individuals with autism-related symptoms. RESULTS We did not find any significant difference in the cerebellum when comparing individuals with autism and control subjects using linear models. In addition, there were no significant deviations in our normative models in the cerebellum in individuals with autism. Finally, we found no evidence of cerebellar atypicalities related to age, IQ, sex, or social functioning in individuals with autism. CONCLUSIONS Despite positive results published in the last decade from relatively small samples, our results suggest that there is no striking difference in cerebellar anatomy of individuals with autism.
Collapse
Affiliation(s)
- Charles Laidi
- Department of Translational Neuro-Psychiatry, Université Paris Est Créteil, Institut National de la Santé et de la Recherche Médicale U955, Créteil, France; Fondation FondaMental, Créteil, France; Département Médico-Universitaire de Psychiatrie et d'Addictologie, Fédération Hospitalo-Universitaire de Médecine de Précision en Psychiatrie, Assistance Publique-Hôpitaux de Paris, Hôpitaux Universitaires Henri Mondor, Créteil, France; Neurospin, CEA, Paris-Saclay University, Gif-sur-Yvette; Center for the Developing Brain, Child Mind Institute, New York, New York.
| | - Dorothea L Floris
- Donders Institute for Brain, Cognition, and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands; Methods of Plasticity Research, Department of Psychology, University of Zürich, Zürich, Switzerland
| | - Julian Tillmann
- Department of Psychology, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
| | - Yannis Elandaloussi
- Department of Translational Neuro-Psychiatry, Université Paris Est Créteil, Institut National de la Santé et de la Recherche Médicale U955, Créteil, France; Fondation FondaMental, Créteil, France; Neurospin, CEA, Paris-Saclay University, Gif-sur-Yvette
| | - Mariam Zabihi
- Donders Institute for Brain, Cognition, and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Tony Charman
- Department of Psychology, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Norwegian Center for Mental Disorders Research, Oslo, Norway
| | - Sarah Durston
- Education Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Carolin Moessnang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany; Department of Applied Psychology, SRH University Heidelberg, Heidelberg
| | - Flavio Dell'Acqua
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
| | - Christine Ecker
- Department of Psychology, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom; Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital Frankfurt am Main, Goethe University, Frankfurt, Germany
| | - Eva Loth
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
| | - Declan Murphy
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
| | - Simon Baron-Cohen
- Donders Institute for Brain, Cognition, and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands; Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Jan K Buitelaar
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands; Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, The Netherlands
| | - Andre F Marquand
- Donders Institute for Brain, Cognition, and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Christian F Beckmann
- Donders Institute for Brain, Cognition, and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | | | - Marion Leboyer
- Department of Translational Neuro-Psychiatry, Université Paris Est Créteil, Institut National de la Santé et de la Recherche Médicale U955, Créteil, France; Fondation FondaMental, Créteil, France; Département Médico-Universitaire de Psychiatrie et d'Addictologie, Fédération Hospitalo-Universitaire de Médecine de Précision en Psychiatrie, Assistance Publique-Hôpitaux de Paris, Hôpitaux Universitaires Henri Mondor, Créteil, France
| | | | - Pierrick Coupé
- Pictura Research Group, Unité Mixte de Recherche Centre National de la Recherche Scientifique (UMR 5800), Laboratoire Bordelais de Recherche en Informatique, Centre National de la Recherche Scientifique, Talence, France; Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands; Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, The Netherlands
| | - Josselin Houenou
- Department of Translational Neuro-Psychiatry, Université Paris Est Créteil, Institut National de la Santé et de la Recherche Médicale U955, Créteil, France; Fondation FondaMental, Créteil, France; Département Médico-Universitaire de Psychiatrie et d'Addictologie, Fédération Hospitalo-Universitaire de Médecine de Précision en Psychiatrie, Assistance Publique-Hôpitaux de Paris, Hôpitaux Universitaires Henri Mondor, Créteil, France; Neurospin, CEA, Paris-Saclay University, Gif-sur-Yvette
| |
Collapse
|
23
|
Horien C, Floris DL, Greene AS, Noble S, Rolison M, Tejavibulya L, O'Connor D, McPartland JC, Scheinost D, Chawarska K, Lake EMR, Constable RT. Functional Connectome-Based Predictive Modeling in Autism. Biol Psychiatry 2022; 92:626-642. [PMID: 35690495 PMCID: PMC10948028 DOI: 10.1016/j.biopsych.2022.04.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 04/14/2022] [Accepted: 04/17/2022] [Indexed: 01/08/2023]
Abstract
Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.
Collapse
Affiliation(s)
- Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; MD-PhD Program, Yale School of Medicine, New Haven, Connecticut.
| | - Dorothea L Floris
- Methods of Plasticity Research, Department of Psychology, University of Zürich, Zurich, Switzerland; Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; MD-PhD Program, Yale School of Medicine, New Haven, Connecticut
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Max Rolison
- Yale Child Study Center, New Haven, Connecticut
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut
| | - David O'Connor
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - James C McPartland
- Department of Psychology, Yale University, New Haven, Connecticut; Yale Child Study Center, New Haven, Connecticut
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut; Yale Child Study Center, New Haven, Connecticut
| | - Katarzyna Chawarska
- Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut; Yale Child Study Center, New Haven, Connecticut
| | - Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, Connecticut; Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut.
| |
Collapse
|
24
|
Multiscale neural gradients reflect transdiagnostic effects of major psychiatric conditions on cortical morphology. Commun Biol 2022; 5:1024. [PMID: 36168040 PMCID: PMC9515219 DOI: 10.1038/s42003-022-03963-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/07/2022] [Indexed: 02/06/2023] Open
Abstract
It is increasingly recognized that multiple psychiatric conditions are underpinned by shared neural pathways, affecting similar brain systems. Here, we carried out a multiscale neural contextualization of shared alterations of cortical morphology across six major psychiatric conditions (autism spectrum disorder, attention deficit/hyperactivity disorder, major depression disorder, obsessive-compulsive disorder, bipolar disorder, and schizophrenia). Our framework cross-referenced shared morphological anomalies with respect to cortical myeloarchitecture and cytoarchitecture, as well as connectome and neurotransmitter organization. Pooling disease-related effects on MRI-based cortical thickness measures across six ENIGMA working groups, including a total of 28,546 participants (12,876 patients and 15,670 controls), we identified a cortex-wide dimension of morphological changes that described a sensory-fugal pattern, with paralimbic regions showing the most consistent alterations across conditions. The shared disease dimension was closely related to cortical gradients of microstructure as well as neurotransmitter axes, specifically cortex-wide variations in serotonin and dopamine. Multiple sensitivity analyses confirmed robustness with respect to slight variations in analytical choices. Our findings embed shared effects of common psychiatric conditions on brain structure in multiple scales of brain organization, and may provide insights into neural mechanisms of transdiagnostic vulnerability.
Collapse
|
25
|
Royer J, Rodríguez-Cruces R, Tavakol S, Larivière S, Herholz P, Li Q, Vos de Wael R, Paquola C, Benkarim O, Park BY, Lowe AJ, Margulies D, Smallwood J, Bernasconi A, Bernasconi N, Frauscher B, Bernhardt BC. An Open MRI Dataset For Multiscale Neuroscience. Sci Data 2022; 9:569. [PMID: 36109562 PMCID: PMC9477866 DOI: 10.1038/s41597-022-01682-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 08/24/2022] [Indexed: 12/17/2022] Open
Abstract
AbstractMultimodal neuroimaging grants a powerful window into the structure and function of the human brain at multiple scales. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends (also referred to as gradients) in brain microstructure and connectivity, offering an integrative framework to study multiscale brain organization. Here, we share a multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired in 50 healthy adults (23 women; 29.54 ± 5.62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusion-weighted MRI, and resting-state functional MRI at 3 Tesla. In addition to raw anonymized MRI data, this release includes brain-wide connectomes derived from (i) resting-state functional imaging, (ii) diffusion tractography, (iii) microstructure covariance analysis, and (iv) geodesic cortical distance, gathered across multiple parcellation scales. Alongside, we share large-scale gradients estimated from each modality and parcellation scale. Our dataset will facilitate future research examining the coupling between brain microstructure, connectivity, and function. MICA-MICs is available on the Canadian Open Neuroscience Platform data portal (https://portal.conp.ca) and the Open Science Framework (https://osf.io/j532r/).
Collapse
|
26
|
Park BY, Paquola C, Bethlehem RAI, Benkarim O, Mišić B, Smallwood J, Bullmore ET, Bernhardt BC. Adolescent development of multiscale structural wiring and functional interactions in the human connectome. Proc Natl Acad Sci U S A 2022; 119:e2116673119. [PMID: 35776541 PMCID: PMC9271154 DOI: 10.1073/pnas.2116673119] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 04/30/2022] [Indexed: 01/03/2023] Open
Abstract
Adolescence is a time of profound changes in the physical wiring and function of the brain. Here, we analyzed structural and functional brain network development in an accelerated longitudinal cohort spanning 14 to 25 y (n = 199). Core to our work was an advanced in vivo model of cortical wiring incorporating MRI features of corticocortical proximity, microstructural similarity, and white matter tractography. Longitudinal analyses assessing age-related changes in cortical wiring identified a continued differentiation of multiple corticocortical structural networks in youth. We then assessed structure-function coupling using resting-state functional MRI measures in the same participants both via cross-sectional analysis at baseline and by studying longitudinal change between baseline and follow-up scans. At baseline, regions with more similar structural wiring were more likely to be functionally coupled. Moreover, correlating longitudinal structural wiring changes with longitudinal functional connectivity reconfigurations, we found that increased structural differentiation, particularly between sensory/unimodal and default mode networks, was reflected by reduced functional interactions. These findings provide insights into adolescent development of human brain structure and function, illustrating how structural wiring interacts with the maturation of macroscale functional hierarchies.
Collapse
Affiliation(s)
- Bo-yong Park
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
- Department of Data Science, Inha University, Incheon, 22212, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, 16419, Republic of Korea
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
- Institute of Neuroscience and Medicine, Forschungszentrum Jülich, Jülich, 52428, Germany
| | - Richard A. I. Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, United Kingdom
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, United Kingdom
| | - Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| | | | - Bratislav Mišić
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Jonathan Smallwood
- Department of Psychology, Queen’s University, Kingston, ON, K7L 3N6, Canada
| | - Edward T. Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, United Kingdom
| | - Boris C. Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| |
Collapse
|
27
|
Shan X, Uddin LQ, Xiao J, He C, Ling Z, Li L, Huang X, Chen H, Duan X. Mapping the Heterogeneous Brain Structural Phenotype of Autism Spectrum Disorder Using the Normative Model. Biol Psychiatry 2022; 91:967-976. [PMID: 35367047 DOI: 10.1016/j.biopsych.2022.01.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 12/28/2021] [Accepted: 01/14/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by substantial clinical and biological heterogeneity. Quantitative and individualized metrics for delineating the heterogeneity of brain structure in ASD are still lacking. Likewise, the extent to which brain structural metrics of ASD deviate from typical development (TD) and whether deviations can be used for parsing brain structural phenotypes of ASD is unclear. METHODS T1-weighted magnetic resonance imaging data from the Autism Brain Imaging Data Exchange (ABIDE) II (nTD = 564) were used to generate a normative model to map brain structure deviations of ABIDE I subjects (nTD = 560, nASD = 496). Voxel-based morphometry was used to compute gray matter volume. Non-negative matrix factorization was employed to decompose the gray matter matrix into 6 factors and weights. These weights were used for normative modeling to estimate the factor deviations. Then, clustering analysis was used to identify ASD subtypes. RESULTS Compared with TD, ASD showed increased weights and deviations in 5 factors. Three subtypes with distinct neuroanatomical deviation patterns were identified. ASD subtype 1 and subtype 3 showed positive deviations, whereas ASD subtype 2 showed negative deviations. Distinct clinical manifestations in social communication deficits were identified among the three subtypes. CONCLUSIONS Our findings suggest that individuals with ASD have heterogeneous deviation patterns in brain structure. The results highlight the need to test for subtypes in neuroimaging studies of ASD. This study also presents a framework for understanding neuroanatomical heterogeneity in this increasingly prevalent neurodevelopmental disorder.
Collapse
Affiliation(s)
- Xiaolong Shan
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Jinming Xiao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Changchun He
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Zihan Ling
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Li
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Xinyue Huang
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Huafu Chen
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Xujun Duan
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Ministry of Education Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China.
| |
Collapse
|
28
|
Biotypes of HIV-associated neurocognitive disorders based on viral and immune pathogenesis. Curr Opin Infect Dis 2022; 35:223-230. [PMID: 35665716 PMCID: PMC9179892 DOI: 10.1097/qco.0000000000000825] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
PURPOSE OF REVIEW HIV-associated neurocognitive disorders (HAND) continues to be prevalent in people living with HIV despite antiretroviral therapy. However, understanding disease mechanisms and identifying therapeutic avenues has been challenging. One of the challenges is that HAND is a heterogeneous disease and that patients identified with similar impairments phenotypically may have very different underlying disease processes. As the NeuroAIDS field is re-evaluating the approaches used to identify patients with HIV-associated neurological impairments, we propose the subtyping of patients into biotypes based on viral and immune pathogenesis. RECENT FINDINGS Here we review the evidence supporting subtyping patients with HIV-associated neurological complications into four biotypes: macrophage-mediated HIV encephalitis, CNS viral escape, T-cell-mediated HIV encephalitis, and HIV protein-associated encephalopathy. SUMMARY Subtyping patients into subgroups based on biotypes has emerged as a useful approach for studying heterogeneous diseases. Understanding biotypes of HIV-associated neurocognitive impairments may therefore enable better understanding of disease mechanisms, allow for the development of prognostic and diagnostic markers, and could ultimately guide therapeutic decisions.
Collapse
|
29
|
Lindenmaier Z, Ellegood J, Stuive M, Easson K, Yee Y, Fernandes D, Foster J, Anagnostou E, Lerch JP. Examining the effect of chronic intranasal oxytocin administration on the neuroanatomy and behavior of three autism-related mouse models. Neuroimage 2022; 257:119243. [PMID: 35508216 DOI: 10.1016/j.neuroimage.2022.119243] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/14/2022] [Accepted: 04/21/2022] [Indexed: 11/28/2022] Open
Abstract
Although initially showing great potential, oxytocin treatment has encountered a translational hurdle in its promise of treating the social deficits of autism. Some debate surrounds the ability of oxytocin to successfully enter the brain, and therefore modify neuroanatomy. Moreover, given the heterogeneous nature of autism, treatment will only amerliorate symptoms in a subset of patients. Therefore, to determine whether oxytocin changes brain circuitry, and whether it does so variably, depending on genotype, we implemented a large randomized, blinded, placebo-controlled, preclinical study on chronic intranasal oxytocin treatment in three different mouse models related to autism with a focus on using neuroanatomical phenotypes to assess and subset treatment response. Intranasal oxytocin (0.6IU) was administered daily, for 28 days, starting at 5 weeks of age to the 16p11.2 deletion, Shank3 (exon 4-9) knockout, and Fmr1 knockout mouse models. Given the sensitivity of structural magnetic resonance imaging (MRI) to the neurological effects of interventions like drugs, along with many other advantages, the mice underwent in vivo longitudinal and high-resolution ex vivo imaging with MRI. The scans included three in vivo T1weighted, 90um isotropic resolution scans and a T2-weighted, 3D fast spin echo with 40um isotropic resolution ex vivo scan to assess the changes in neuroanatomy using established automated image registration and deformation based morphometry approaches in response to oxytocin treatment. The behavior of the mice was assessed in multiple domains, including social behaviours and repetitive behaviours, among others. Treatment effect on the neuroanatomy did not reach significance, although the pattern of trending effects was promising. No significant effect of treatment was found on social behavior in any of the strains, although a significant effect of treatment was found in the Fmr1 mouse, with treatment normalizing a grooming deficit. No other treatment effect on behavior was observed that survived multiple comparisons correction. Overall, chronic treatment with oxytocin had limited effects on the three mouse models related to autism, and no promising pattern of response susceptibility emerged.
Collapse
Affiliation(s)
- Zsuzsa Lindenmaier
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
| | - Jacob Ellegood
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Monique Stuive
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Kaitlyn Easson
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Yohan Yee
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Darren Fernandes
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Jane Foster
- Department of Psychiatry and Behavioral Neurosciences, McMaster University, St.Joseph's Healthcare, Hamilton, Ontario, Canada
| | - Evdokia Anagnostou
- Autism Research Center, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada
| | - Jason P Lerch
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Wellcome Centre for Integrative NeuroImaging, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
30
|
Choi H, Byeon K, Park BY, Lee JE, Valk SL, Bernhardt B, Martino AD, Milham M, Hong SJ, Park H. Diagnosis-informed connectivity subtyping discovers subgroups of autism with reproducible symptom profiles. Neuroimage 2022; 256:119212. [PMID: 35430361 DOI: 10.1016/j.neuroimage.2022.119212] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/28/2022] [Accepted: 04/13/2022] [Indexed: 10/18/2022] Open
Abstract
Clinical heterogeneity has been one of the main barriers to develop effective biomarkers and therapeutic strategies in autism spectrum disorder (ASD). Recognizing this challenge, much effort has been made in recent neuroimaging studies to find biologically more homogeneous subgroups (called 'neurosubtypes') in autism. However, most approaches have rarely evaluated how much the employed features in subtyping represent the core anomalies of ASD, obscuring its utility in actual clinical diagnosis. To address this, we combined two data-driven methods, 'connectome-based gradient' and 'functional random forest', collectively allowing to discover reproducible neurosubtypes based on resting-state functional connectivity profiles that are specific to ASD. Indeed, the former technique provides the features (as input for subtyping) that effectively summarize whole-brain connectome variations in both normal and ASD conditions, while the latter leverages a supervised random forest algorithm to inform diagnostic labels to clustering, which makes neurosubtyping driven by the features of ASD core anomalies. Applying this framework to the open-sharing Autism Brain Imaging Data Exchange repository data (discovery, n = 103/108 for ASD/typically developing [TD]; replication, n = 44/42 for ASD/TD), we found three dominant subtypes of functional gradients in ASD and three subtypes in TD. The subtypes in ASD revealed distinct connectome profiles in multiple brain areas, which are associated with different Neurosynth-derived cognitive functions previously implicated in autism studies. Moreover, these subtypes showed different symptom severity, which degree co-varies with the extent of functional gradient changes observed across the groups. The subtypes in the discovery and replication datasets showed similar symptom profiles in social interaction and communication domains, confirming a largely reproducible brain-behavior relationship. Finally, the connectome gradients in ASD subtypes present both common and distinct patterns compared to those in TD, reflecting their potential overlap and divergence in terms of developmental mechanisms involved in the manifestation of large-scale functional networks. Our study demonstrated a potential of the diagnosis-informed subtyping approach in developing a clinically useful brain-based classification system for future ASD research.
Collapse
Affiliation(s)
- Hyoungshin Choi
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
| | - Kyoungseob Byeon
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
| | - Bo-Yong Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea; Department of Data Science, Inha University, Incheon, South Korea
| | - Jong-Eun Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
| | - Sofie L Valk
- Otto Hahn group, Cognitive neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences; Institute of Neuroscience and Medicine, Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | | | - Michael Milham
- Center for the Developing Brain, Child Mind Institute, New York, United States; Nathan S. Kline Institute for Psychiatric Research, New York, United States
| | - Seok-Jun Hong
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea; Center for the Developing Brain, Child Mind Institute, New York, United States; Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, South Korea.
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea; School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 16419, South Korea.
| |
Collapse
|
31
|
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.
Collapse
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
| |
Collapse
|
32
|
Zerbi V, Pagani M, Markicevic M, Matteoli M, Pozzi D, Fagiolini M, Bozzi Y, Galbusera A, Scattoni ML, Provenzano G, Banerjee A, Helmchen F, Basson MA, Ellegood J, Lerch JP, Rudin M, Gozzi A, Wenderoth N. Brain mapping across 16 autism mouse models reveals a spectrum of functional connectivity subtypes. Mol Psychiatry 2021; 26:7610-7620. [PMID: 34381171 PMCID: PMC8873017 DOI: 10.1038/s41380-021-01245-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 06/30/2021] [Accepted: 07/20/2021] [Indexed: 02/07/2023]
Abstract
Autism Spectrum Disorder (ASD) is characterized by substantial, yet highly heterogeneous abnormalities in functional brain connectivity. However, the origin and significance of this phenomenon remain unclear. To unravel ASD connectopathy and relate it to underlying etiological heterogeneity, we carried out a bi-center cross-etiological investigation of fMRI-based connectivity in the mouse, in which specific ASD-relevant mutations can be isolated and modeled minimizing environmental contributions. By performing brain-wide connectivity mapping across 16 mouse mutants, we show that different ASD-associated etiologies cause a broad spectrum of connectional abnormalities in which diverse, often diverging, connectivity signatures are recognizable. Despite this heterogeneity, the identified connectivity alterations could be classified into four subtypes characterized by discrete signatures of network dysfunction. Our findings show that etiological variability is a key determinant of connectivity heterogeneity in ASD, hence reconciling conflicting findings in clinical populations. The identification of etiologically-relevant connectivity subtypes could improve diagnostic label accuracy in the non-syndromic ASD population and paves the way for personalized treatment approaches.
Collapse
Affiliation(s)
- V Zerbi
- Neural Control of Movement Lab, ETH Zurich, Zurich, Switzerland
| | - M Pagani
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy
| | - M Markicevic
- Neural Control of Movement Lab, ETH Zurich, Zurich, Switzerland
| | - M Matteoli
- Laboratory of Pharmacology and Brain Pathology, Neurocenter, Humanitas Clinical and Research Center - IRCCS, Rozzano, Mi, Italy
- CNR Institute of Neuroscience, Milano, Italy
| | - D Pozzi
- Laboratory of Pharmacology and Brain Pathology, Neurocenter, Humanitas Clinical and Research Center - IRCCS, Rozzano, Mi, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - M Fagiolini
- F.M. Kirby Neurobiology Department, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Y Bozzi
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Rovereto, Italy
| | - A Galbusera
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy
| | - M L Scattoni
- Research Coordination and Support Service, Istituto Superiore di Sanità, Rome, Italy
| | - G Provenzano
- Department of Cellular, Computational and Integrative Biology. (CIBIO), University of Trento, Trento, Italy
| | - A Banerjee
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - F Helmchen
- Brain Research Institute, University of Zurich, Zurich, Switzerland
| | - M A Basson
- Centre for Craniofacial and Regenerative Biology, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College, London, London, UK
| | - J Ellegood
- Mouse Imaging Ctr., Hosp. For Sick Children, Toronto, ON, Canada
| | - J P Lerch
- Mouse Imaging Ctr., Hosp. For Sick Children, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - M Rudin
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - A Gozzi
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy.
| | - N Wenderoth
- Neural Control of Movement Lab, ETH Zurich, Zurich, Switzerland
| |
Collapse
|
33
|
Lee HM, Fadaie F, Gill R, Caldairou B, Sziklas V, Crane J, Hong SJ, Bernhardt BC, Bernasconi A, Bernasconi N. Decomposing MRI phenotypic heterogeneity in epilepsy: a step towards personalized classification. Brain 2021; 145:897-908. [PMID: 34849619 DOI: 10.1093/brain/awab425] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/28/2021] [Accepted: 10/28/2021] [Indexed: 11/14/2022] Open
Abstract
In drug-resistant temporal lobe epilepsy (TLE), precise predictions of drug response, surgical outcome, and cognitive dysfunction at an individual level remain challenging. A possible explanation may lie in the dominant "one-size-fits-all" group-level analytical approaches that does not allow parsing inter-individual variations along the disease spectrum. Conversely, analyzing inter-patient heterogeneity is increasingly recognized as a step towards person-centered care. Here, we utilized unsupervised machine learning to estimate latent relations (or disease factors) from 3 T multimodal MRI features (cortical thickness, hippocampal volume, FLAIR, T1/FLAIR, diffusion parameters) representing whole-brain patterns of structural pathology in 82 TLE patients. We assessed the specificity of our approach against age- and sex-matched healthy individuals and a cohort of frontal lobe epilepsy patients with histologically-verified focal cortical dysplasia. We identified four latent disease factors variably co-expressed within each patient and characterized by ipsilateral hippocampal microstructural alterations, loss of myelin and atrophy (Factor-1), bilateral paralimbic and hippocampal gliosis (Factor-2), bilateral neocortical atrophy (Factor-3), bilateral white matter microstructural alterations (Factor-4). Bootstrap analysis and parameter variations supported high stability and robustness of these factors. Moreover, they were not expressed in healthy controls and only negligibly in disease controls, supporting specificity. Supervised classifiers trained on latent disease factors could predict patient-specific drug-response in 76 ± 3% and postsurgical seizure outcome in 88 ± 2%, outperforming classifiers that did not operate on latent factor information. Latent factor models predicted inter-patient variability in cognitive dysfunction (verbal IQ: r = 0.40 ± 0.03; memory: r = 0.35 ± 0.03; sequential motor tapping: r = 0.36 ± 0.04), again outperforming baseline learners. Data-driven analysis of disease factors provides a novel appraisal of the continuum of interindividual variability, which is likely determined by multiple interacting pathological processes. Incorporating interindividual variability is likely to improve clinical prognostics.
Collapse
Affiliation(s)
- Hyo Min Lee
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Fatemeh Fadaie
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Ravnoor Gill
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Benoit Caldairou
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Viviane Sziklas
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Joelle Crane
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Seok-Jun Hong
- Center for Neuroscience Imaging Research Institute for Basic Science, Department of Biomedical Engineering, Sungkyunkwan University Suwon South Korea
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| |
Collapse
|
34
|
D'Souza NS, Nebel MB, Crocetti D, Robinson J, Wymbs N, Mostofsky SH, Venkataraman A. Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations. Neuroimage 2021; 241:118388. [PMID: 34271159 PMCID: PMC8528511 DOI: 10.1016/j.neuroimage.2021.118388] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 07/05/2021] [Accepted: 07/10/2021] [Indexed: 11/27/2022] Open
Abstract
We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract biomarkers of brain connectivity predictive of behavior. Our framework couples a generative model of the connectomics data with a deep network that predicts behavioral scores. The generative component is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying subject-specific loadings. We use the DTI tractography to regularize this matrix factorization and learn anatomically informed functional connectivity profiles. The deep component of our framework is an LSTM-ANN block, which uses the temporal evolution of the subject-specific sr-DDL loadings to predict multidimensional clinical characterizations. Our joint optimization strategy collectively estimates the basis networks, the subject-specific time-varying loadings, and the neural network weights. We validate our framework on a dataset of neurotypical individuals from the Human Connectome Project (HCP) database to map to cognition and on a separate multi-score prediction task on individuals diagnosed with Autism Spectrum Disorder (ASD) in a five-fold cross validation setting. Our hybrid model outperforms several state-of-the-art approaches at clinical outcome prediction and learns interpretable multimodal neural signatures of brain organization.
Collapse
Affiliation(s)
- N S D'Souza
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA.
| | - M B Nebel
- Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins School of Medicine, USA
| | - D Crocetti
- Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA
| | - J Robinson
- Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA
| | - N Wymbs
- Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins School of Medicine, USA
| | - S H Mostofsky
- Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins School of Medicine, USA; Department of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, USA
| | - A Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| |
Collapse
|
35
|
Park S, Haak KV, Cho HB, Valk SL, Bethlehem RAI, Milham MP, Bernhardt BC, Di Martino A, Hong SJ. Atypical Integration of Sensory-to-Transmodal Functional Systems Mediates Symptom Severity in Autism. Front Psychiatry 2021; 12:699813. [PMID: 34489757 PMCID: PMC8417581 DOI: 10.3389/fpsyt.2021.699813] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 07/16/2021] [Indexed: 12/12/2022] Open
Abstract
A notable characteristic of autism spectrum disorder (ASD) is co-occurring deficits in low-level sensory processing and high-order social interaction. While there is evidence indicating detrimental cascading effects of sensory anomalies on the high-order cognitive functions in ASD, the exact pathological mechanism underlying their atypical functional interaction across the cortical hierarchy has not been systematically investigated. To address this gap, here we assessed the functional organisation of sensory and motor areas in ASD, and their relationship with subcortical and high-order trandmodal systems. In a resting-state fMRI data of 107 ASD and 113 neurotypical individuals, we applied advanced connectopic mapping to probe functional organization of primary sensory/motor areas, together with targeted seed-based intrinsic functional connectivity (iFC) analyses. In ASD, the connectopic mapping revealed topological anomalies (i.e., excessively more segregated iFC) in the motor and visual areas, the former of which patterns showed association with the symptom severity of restricted and repetitive behaviors. Moreover, the seed-based analysis found diverging patterns of ASD-related connectopathies: decreased iFCs within the sensory/motor areas but increased iFCs between sensory and subcortical structures. While decreased iFCs were also found within the higher-order functional systems, the overall proportion of this anomaly tends to increase along the level of cortical hierarchy, suggesting more dysconnectivity in the higher-order functional networks. Finally, we demonstrated that the association between low-level sensory/motor iFCs and clinical symptoms in ASD was mediated by the high-order transmodal systems, suggesting pathogenic functional interactions along the cortical hierarchy. Findings were largely replicated in the independent dataset. These results highlight that atypical integration of sensory-to-high-order systems contributes to the complex ASD symptomatology.
Collapse
Affiliation(s)
- Shinwon Park
- Institute for Basic Science, Center for Neuroscience Imaging Research, Sungkyunkwan University, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Koen V. Haak
- Donders Institute of Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Han Byul Cho
- Institute for Basic Science, Center for Neuroscience Imaging Research, Sungkyunkwan University, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Sofie L. Valk
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-7), Forschungszentrum Jülich, Jülich, Germany
| | - Richard A. I. Bethlehem
- Department of Psychiatry, Autism Research Centre, University of Cambridge, Cambridge, United Kingdom
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Michael P. Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, United States
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, New York, NY, United States
| | - Boris C. Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | | | - Seok-Jun Hong
- Institute for Basic Science, Center for Neuroscience Imaging Research, Sungkyunkwan University, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
- Center for the Developing Brain, Child Mind Institute, New York, NY, United States
| |
Collapse
|
36
|
Reardon AM, Hu XP, Li K, Langley J. Subtyping Autism Spectrum Disorder via Joint Modeling of Clinical and Connectomic Profiles. Brain Connect 2021; 12:193-205. [PMID: 34102874 DOI: 10.1089/brain.2020.0997] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Autism Spectrum Disorder (ASD) is a highly heterogeneous developmental disorder with diverse clinical manifestations. Neuroimaging studies have explored functional connectivity (FC) of ASD through resting-state functional MRI studies, however the findings have remained inconsistent, thus reflecting the possibility of multiple subtypes. Identification of the relationship between clinical symptoms and FC measures may help clarify the inconsistencies in earlier findings and advance our understanding of ASD subtypes. METHODS Canonical correlation analysis was performed on two-hundred and ten ASD subjects from the Autism Brain Imaging Data Exchange to identify significant linear combinations of resting-state connectomic and clinical profiles of ASD. Then, hierarchical clustering defined ASD subtypes based on distinct brain-behavior relationships. Finally, a support vector machine classifier was used to verify that subtypes were comprised of subjects with distinct clinical and connectivity features. RESULTS Three ASD subtypes were identified. Subtype 1 exhibited increased intra-network FC, increased IQ scores and restricted and repetitive behaviors. Subtype 2 was characterized by decreased whole-brain FC and more severe ADI-R and SRS symptoms. Subtype 3 demonstrated mixed FC, low IQ scores, as well as social motivation and verbal deficits. To verify subtype assignment, a multi-class support vector machine using connectomic and clinical profiles yielded an average accuracy of 71.3% and 65.2% respectively for subtype classification, which is significantly higher than chance (33.3%). CONCLUSION The present study demonstrates that combining connectomic and behavioral measures is a powerful approach for disease subtyping and suggests that there are ASD subtypes with distinct connectomic and clinical profiles.
Collapse
Affiliation(s)
- Alexandra M Reardon
- University of California Riverside, 8790, Biomedical Engineering, Riverside, California, United States;
| | - Xiaoping P Hu
- University of California Riverside, 8790, Biomedical Engineering, Riverside, California, United States.,University of California Riverside, 8790, Center for Advanced NeuroImaging, Riverside, California, United States;
| | - Kaiming Li
- University of California Riverside, 8790, Center for Advanced NeuroImaging, Riverside, California, United States;
| | - Jason Langley
- University of California Riverside, 8790, Center for Advanced NeuroImaging, Riverside, California, United States;
| |
Collapse
|
37
|
Cauvet É, Van't Westeinde A, Toro R, Kuja-Halkola R, Neufeld J, Mevel K, Bölte S. The social brain in female autism: a structural imaging study of twins. Soc Cogn Affect Neurosci 2021; 15:423-436. [PMID: 32363404 PMCID: PMC7308659 DOI: 10.1093/scan/nsaa064] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 03/26/2020] [Accepted: 04/27/2020] [Indexed: 12/30/2022] Open
Abstract
A female advantage in social cognition (SoC) might contribute to women's underrepresentation in autism spectrum disorder (ASD). The latter could be underpinned by sex differences in social brain structure. This study investigated the relationship between structural social brain networks and SoC in females and males in relation to ASD and autistic traits in twins. We used a co-twin design in 77 twin pairs (39 female) aged 12.5 to 31.0 years. Twin pairs were discordant or concordant for ASD or autistic traits, discordant or concordant for other neurodevelopmental disorders or concordant for neurotypical development. They underwent structural magnetic resonance imaging and were assessed for SoC using the naturalistic Movie for the Assessment of Social Cognition. Autistic traits predicted reduced SoC capacities predominantly in male twins, despite a comparable extent of autistic traits in each sex, although the association between SoC and autistic traits did not differ significantly between the sexes. Consistently, within-pair associations between SoC and social brain structure revealed that lower SoC ability was associated with increased cortical thickness of several brain regions, particularly in males. Our findings confirm the notion that sex differences in SoC in association with ASD are underpinned by sex differences in brain structure.
Collapse
Affiliation(s)
- Élodie Cauvet
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm Health Care Services, Stockholm 11330, Sweden
| | - Annelies Van't Westeinde
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm Health Care Services, Stockholm 11330, Sweden
| | - Roberto Toro
- Department of Neuroscience, Human Genetics and Cognitive Functions, Institut Pasteur, Paris 75015, France.,CNRS URA 2182 "Genes, synapses and cognition", Pasteur Institute, Paris 75015, France.,Human Genetics and Cognitive Functions, Université Paris Diderot, Sorbonne Paris Cité, Paris 75013, France
| | - Ralf Kuja-Halkola
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17177, Sweden
| | - Janina Neufeld
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm Health Care Services, Stockholm 11330, Sweden
| | - Katell Mevel
- GIP Cyceron, Normandy University, Caen 14074, France
| | - Sven Bölte
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm Health Care Services, Stockholm 11330, Sweden.,Child and Adolescent Psychiatry, Stockholm County Council, Stockholm 11330, Sweden.,School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, Western Australia 6102, Australia
| |
Collapse
|
38
|
Park BY, Hong SJ, Valk SL, Paquola C, Benkarim O, Bethlehem RAI, Di Martino A, Milham MP, Gozzi A, Yeo BTT, Smallwood J, Bernhardt BC. Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism. Nat Commun 2021; 12:2225. [PMID: 33850128 PMCID: PMC8044226 DOI: 10.1038/s41467-021-21732-0] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 02/05/2021] [Indexed: 01/14/2023] Open
Abstract
The pathophysiology of autism has been suggested to involve a combination of both macroscale connectome miswiring and microcircuit anomalies. Here, we combine connectome-wide manifold learning with biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. We studied neuroimaging and phenotypic data in 47 individuals with autism and 37 typically developing controls obtained from the Autism Brain Imaging Data Exchange initiative. Our analysis establishes significant differences in structural connectome organization in individuals with autism relative to controls, with strong between-group effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models reveal that the degree of macroscale anomalies is related to atypical increases of recurrent excitation/inhibition, as well as subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic association analysis based on postmortem datasets identifies genes expressed in cortical and thalamic areas from childhood to young adulthood. Finally, supervised machine learning finds that the macroscale perturbations are associated with symptom severity scores on the Autism Diagnostic Observation Schedule. Together, our analyses suggest that atypical subcortico-cortical interactions are associated with both microcircuit and macroscale connectome differences in autism.
Collapse
Affiliation(s)
- Bo-Yong Park
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
- Department of Data Science, Inha University, Incheon, South Korea.
| | - Seok-Jun Hong
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Center for the Developing Brain, Child Mind Institute, New York City, NY, USA
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Sofie L Valk
- Forschungszentrum, Julich, Germany
- Max Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Richard A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Adriana Di Martino
- Center for the Developing Brain, Child Mind Institute, New York City, NY, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York City, NY, USA
| | - Alessandro Gozzi
- Istituto Italiano di Tecnologia, Centre for Neuroscience and Cognitive Systems @ UNITN, Rovereto, Italy
| | - 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
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - Jonathan Smallwood
- Department of Psychology, York Neuroimaging Centre, University of York, York, UK
- Department of Psychology, Queen's University, Kingston, ON, Canada
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| |
Collapse
|
39
|
Hong SJ, Sisk LM, Caballero C, Mekhanik A, Roy AK, Milham MP, Gee DG. Decomposing complex links between the childhood environment and brain structure in school-aged youth. Dev Cogn Neurosci 2021; 48:100919. [PMID: 33556882 PMCID: PMC7868609 DOI: 10.1016/j.dcn.2021.100919] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 10/26/2020] [Accepted: 01/18/2021] [Indexed: 12/22/2022] Open
Abstract
Childhood experiences play a profound role in conferring risk and resilience for brain and behavioral development. However, how different facets of the environment shape neurodevelopment remains largely unknown. Here we sought to decompose heterogeneous relationships between environmental factors and brain structure in 989 school-aged children from the Adolescent Brain Cognitive Development Study. We applied a cross-modal integration and clustering approach called 'Similarity Network Fusion', which combined two brain morphometrics (i.e., cortical thickness and myelin-surrogate markers), and key environmental factors (i.e., trauma exposure, neighborhood safety, school environment, and family environment) to identify homogeneous subtypes. Depending on the subtyping resolution, results identified two or five subgroups, each characterized by distinct brain structure-environment profiles. Notably, more supportive caregiving and school environments were associated with greater myelination, whereas less supportive caregiving, higher family conflict and psychopathology, and higher perceived neighborhood safety were observed with greater cortical thickness. These subtypes were highly reproducible and predicted externalizing symptoms and overall mental health problems. Our findings support the theory that distinct environmental exposures are differentially associated with alterations in structural neurodevelopment. Delineating more precise associations between risk factors, protective factors, and brain development may inform approaches to enhance risk identification and optimize interventions targeting specific experiences.
Collapse
Affiliation(s)
- Seok-Jun Hong
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Lucinda M Sisk
- Department of Psychology, Yale University, New Haven, CT, USA
| | | | - Anthony Mekhanik
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Amy K Roy
- Department of Psychology, Fordham University, Bronx, NY, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
| | - Dylan G Gee
- Department of Psychology, Yale University, New Haven, CT, USA.
| |
Collapse
|
40
|
Lawrence RM, Bridgeford EW, Myers PE, Arvapalli GC, Ramachandran SC, Pisner DA, Frank PF, Lemmer AD, Nikolaidis A, Vogelstein JT. Standardizing human brain parcellations. Sci Data 2021; 8:78. [PMID: 33686079 PMCID: PMC7940391 DOI: 10.1038/s41597-021-00849-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 01/29/2021] [Indexed: 11/09/2022] Open
Abstract
Using brain atlases to localize regions of interest is a requirement for making neuroscientifically valid statistical inferences. These atlases, represented in volumetric or surface coordinate spaces, can describe brain topology from a variety of perspectives. Although many human brain atlases have circulated the field over the past fifty years, limited effort has been devoted to their standardization. Standardization can facilitate consistency and transparency with respect to orientation, resolution, labeling scheme, file storage format, and coordinate space designation. Our group has worked to consolidate an extensive selection of popular human brain atlases into a single, curated, open-source library, where they are stored following a standardized protocol with accompanying metadata, which can serve as the basis for future atlases. The repository containing the atlases, the specification, as well as relevant transformation functions is available in the neuroparc OSF registered repository or https://github.com/neurodata/neuroparc .
Collapse
|
41
|
Qi S, Morris R, Turner JA, Fu Z, Jiang R, Deramus TP, Zhi D, Calhoun VD, Sui J. Common and unique multimodal covarying patterns in autism spectrum disorder subtypes. Mol Autism 2020; 11:90. [PMID: 33208189 PMCID: PMC7673101 DOI: 10.1186/s13229-020-00397-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 11/05/2020] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND The heterogeneity inherent in autism spectrum disorder (ASD) presents a substantial challenge to diagnosis and precision treatment. Heterogeneity across biological etiologies, genetics, neural systems, neurocognitive attributes and clinical subtypes or phenotypes has been observed across individuals with ASD. METHODS In this study, we aim to investigate the heterogeneity in ASD from a multimodal brain imaging perspective. The Autism Diagnostic Observation Schedule (ADOS) was used as a reference to guide functional and structural MRI fusion. DSM-IV-TR diagnosed Asperger's disorder (n = 79), pervasive developmental disorder-not otherwise specified [PDD-NOS] (n = 58) and Autistic disorder (n = 92) from ABIDE II were used as discovery cohort, and ABIDE I (n = 400) was used for replication. RESULTS Dorsolateral prefrontal cortex and superior/middle temporal cortex are the primary common functional-structural covarying cortical brain areas shared among Asperger's, PDD-NOS and Autistic subgroups. Key differences among the three subtypes are negative functional features within subcortical brain areas, including negative putamen-parahippocampus fractional amplitude of low-frequency fluctuations (fALFF) unique to the Asperger's subtype; negative fALFF in anterior cingulate cortex unique to PDD-NOS subtype; and negative thalamus-amygdala-caudate fALFF unique to the Autistic subtype. Furthermore, each subtype-specific brain pattern is correlated with different ADOS subdomains, with social interaction as the common subdomain. The identified subtype-specific patterns are only predictive for ASD symptoms manifested in the corresponding subtypes, but not the other subtypes. CONCLUSIONS Although ASD has a common neural basis with core deficits linked to social interaction, each ASD subtype is strongly linked to unique brain systems and subdomain symptoms, which may help to better understand the underlying mechanisms of ASD heterogeneity from a multimodal neuroimaging perspective. LIMITATIONS This study is male based, which cannot be generalized to the female or the general ASD population.
Collapse
Affiliation(s)
- Shile Qi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Robin Morris
- Department of Psychology and Neuroscience Institute, Georgia State University, Atlanta, GA, 30302, USA
| | - Jessica A Turner
- Department of Psychology and Neuroscience Institute, Georgia State University, Atlanta, GA, 30302, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Thomas P Deramus
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Dongmei Zhi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA.
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA.
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- University of Chinese Academy of Sciences, Beijing, 100190, China.
- Institute of Automation, Chinese Academy of Sciences Center for Excellence in Brain Science, Beijing, 100190, China.
| |
Collapse
|
42
|
Zabihi M, Floris DL, Kia SM, Wolfers T, Tillmann J, Arenas AL, Moessnang C, Banaschewski T, Holt R, Baron-Cohen S, Loth E, Charman T, Bourgeron T, Murphy D, Ecker C, Buitelaar JK, Beckmann CF, Marquand A. Fractionating autism based on neuroanatomical normative modeling. Transl Psychiatry 2020; 10:384. [PMID: 33159037 PMCID: PMC7648836 DOI: 10.1038/s41398-020-01057-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 09/29/2020] [Accepted: 10/19/2020] [Indexed: 12/25/2022] Open
Abstract
Autism is a complex neurodevelopmental condition with substantial phenotypic, biological, and etiologic heterogeneity. It remains a challenge to identify biomarkers to stratify autism into replicable cognitive or biological subtypes. Here, we aim to introduce a novel methodological framework for parsing neuroanatomical subtypes within a large cohort of individuals with autism. We used cortical thickness (CT) in a large and well-characterized sample of 316 participants with autism (88 female, age mean: 17.2 ± 5.7) and 206 with neurotypical development (79 female, age mean: 17.5 ± 6.1) aged 6-31 years across six sites from the EU-AIMS multi-center Longitudinal European Autism Project. Five biologically based putative subtypes were derived using normative modeling of CT and spectral clustering. Three of these clusters showed relatively widespread decreased CT and two showed relatively increased CT. These subtypes showed morphometric differences from one another, providing a potential explanation for inconsistent case-control findings in autism, and loaded differentially and more strongly onto symptoms and polygenic risk, indicating a dilution of clinical effects across heterogeneous cohorts. Our results provide an important step towards parsing the heterogeneous neurobiology of autism.
Collapse
Affiliation(s)
- Mariam Zabihi
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands. .,Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands.
| | - Dorothea L. Floris
- grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands ,grid.10417.330000 0004 0444 9382Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Seyed Mostafa Kia
- grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands ,grid.10417.330000 0004 0444 9382Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Thomas Wolfers
- grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands ,grid.5510.10000 0004 1936 8921Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), University of Oslo & Oslo University Hospital, Oslo, Norway ,grid.5510.10000 0004 1936 8921Department of Psychology, University of Oslo, Oslo, Norway
| | - Julian Tillmann
- grid.13097.3c0000 0001 2322 6764Department of Psychology, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London, London, UK ,grid.10420.370000 0001 2286 1424Department of Applied Psychology: Health, Development, Enhancement, and Intervention, University of Vienna, Vienna, Austria
| | - Alberto Llera Arenas
- grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands ,grid.10417.330000 0004 0444 9382Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Carolin Moessnang
- grid.7700.00000 0001 2190 4373Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Tobias Banaschewski
- grid.7700.00000 0001 2190 4373Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Rosemary Holt
- grid.5335.00000000121885934Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Simon Baron-Cohen
- grid.5335.00000000121885934Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Eva Loth
- grid.13097.3c0000 0001 2322 6764Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK ,grid.13097.3c0000 0001 2322 6764Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Tony Charman
- grid.13097.3c0000 0001 2322 6764Department of Psychology, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London, London, UK
| | - Thomas Bourgeron
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, France
| | - Declan Murphy
- grid.13097.3c0000 0001 2322 6764Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK ,grid.13097.3c0000 0001 2322 6764Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Christine Ecker
- grid.13097.3c0000 0001 2322 6764Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK ,Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt am Main, Goethe University, Frankfurt, Germany
| | - Jan K. Buitelaar
- grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands ,grid.10417.330000 0004 0444 9382Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands ,grid.461871.d0000 0004 0624 8031Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, The Netherlands
| | - Christian F. Beckmann
- grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands ,grid.10417.330000 0004 0444 9382Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands ,grid.4991.50000 0004 1936 8948Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK
| | - Andre Marquand
- grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands ,grid.10417.330000 0004 0444 9382Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands ,grid.13097.3c0000 0001 2322 6764Department of Neuroimaging, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London, London, UK
| | | |
Collapse
|
43
|
Paquola C, Seidlitz J, Benkarim O, Royer J, Klimes P, Bethlehem RAI, Larivière S, Vos de Wael R, Rodríguez-Cruces R, Hall JA, Frauscher B, Smallwood J, Bernhardt BC. A multi-scale cortical wiring space links cellular architecture and functional dynamics in the human brain. PLoS Biol 2020; 18:e3000979. [PMID: 33253185 PMCID: PMC7728398 DOI: 10.1371/journal.pbio.3000979] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 12/10/2020] [Accepted: 11/02/2020] [Indexed: 12/11/2022] Open
Abstract
The vast net of fibres within and underneath the cortex is optimised to support the convergence of different levels of brain organisation. Here, we propose a novel coordinate system of the human cortex based on an advanced model of its connectivity. Our approach is inspired by seminal, but so far largely neglected models of cortico-cortical wiring established by postmortem anatomical studies and capitalises on cutting-edge in vivo neuroimaging and machine learning. The new model expands the currently prevailing diffusion magnetic resonance imaging (MRI) tractography approach by incorporation of additional features of cortical microstructure and cortico-cortical proximity. Studying several datasets and different parcellation schemes, we could show that our coordinate system robustly recapitulates established sensory-limbic and anterior-posterior dimensions of brain organisation. A series of validation experiments showed that the new wiring space reflects cortical microcircuit features (including pyramidal neuron depth and glial expression) and allowed for competitive simulations of functional connectivity and dynamics based on resting-state functional magnetic resonance imaging (rs-fMRI) and human intracranial electroencephalography (EEG) coherence. Our results advance our understanding of how cell-specific neurobiological gradients produce a hierarchical cortical wiring scheme that is concordant with increasing functional sophistication of human brain organisation. Our evaluations demonstrate the cortical wiring space bridges across scales of neural organisation and can be easily translated to single individuals.
Collapse
Affiliation(s)
- Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, Maryland, United States of America
| | - Oualid Benkarim
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Petr Klimes
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | | | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Raul Rodríguez-Cruces
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jeffery A. Hall
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | | | - Boris C. Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
44
|
Bletsch A, Schäfer T, Mann C, Andrews DS, Daly E, Gudbrandsen M, Ruigrok ANV, Dallyn R, Romero-Garcia R, Lai MC, Lombardo MV, Craig MC, Suckling J, Bullmore ET, Baron-Cohen S, Murphy DGM, Dell'Acqua F, Ecker C. Atypical measures of diffusion at the gray-white matter boundary in autism spectrum disorder in adulthood. Hum Brain Mapp 2020; 42:467-484. [PMID: 33094897 PMCID: PMC7775996 DOI: 10.1002/hbm.25237] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 09/08/2020] [Accepted: 09/30/2020] [Indexed: 01/07/2023] Open
Abstract
Autism spectrum disorder (ASD) is a highly complex neurodevelopmental condition that is accompanied by neuroanatomical differences on the macroscopic and microscopic level. Findings from histological, genetic, and more recently in vivo neuroimaging studies converge in suggesting that neuroanatomical abnormalities, specifically around the gray‐white matter (GWM) boundary, represent a crucial feature of ASD. However, no research has yet characterized the GWM boundary in ASD based on measures of diffusion. Here, we registered diffusion tensor imaging data to the structural T1‐weighted images of 92 adults with ASD and 92 matched neurotypical controls in order to examine between‐group differences and group‐by‐sex interactions in fractional anisotropy and mean diffusivity sampled at the GWM boundary, and at different sampling depths within the superficial white and into the gray matter. As hypothesized, we observed atypical diffusion at and around the GWM boundary in ASD, with between‐group differences and group‐by‐sex interactions depending on tissue class and sampling depth. Furthermore, we identified that altered diffusion at the GWM boundary partially (i.e., ~50%) overlapped with atypical gray‐white matter tissue contrast in ASD. Our study thus replicates and extends previous work highlighting the GWM boundary as a crucial target of neuropathology in ASD, and guides future work elucidating etiological mechanisms.
Collapse
Affiliation(s)
- Anke Bletsch
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Tim Schäfer
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Caroline Mann
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany
| | - Derek S Andrews
- Department of Psychiatry and Behavioral Sciences at the M.I.N.D. Institute, University of California, Davis, California, USA
| | - Eileen Daly
- Department of Forensic and Neurodevelopmental Sciences, and the Sackler Institute for Translational Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Maria Gudbrandsen
- Department of Forensic and Neurodevelopmental Sciences, and the Sackler Institute for Translational Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Amber N V Ruigrok
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Robert Dallyn
- Department of Forensic and Neurodevelopmental Sciences, and the Sackler Institute for Translational Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Rafael Romero-Garcia
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Centre for Addiction and Mental Health and The Hospital for Sick Children, Department of Psychiatry, University of Toronto, Toronto, Canada.,Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Michael V Lombardo
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.,Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Michael C Craig
- Department of Forensic and Neurodevelopmental Sciences, and the Sackler Institute for Translational Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK.,National Autism Unit, Bethlem Royal Hospital, London, UK
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Edward T Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | | | - Declan G M Murphy
- Department of Forensic and Neurodevelopmental Sciences, and the Sackler Institute for Translational Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Flavio Dell'Acqua
- Department of Forensic and Neurodevelopmental Sciences, and the Sackler Institute for Translational Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Christine Ecker
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe University, Frankfurt, Germany.,Department of Forensic and Neurodevelopmental Sciences, and the Sackler Institute for Translational Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| |
Collapse
|
45
|
Hong SJ, Hyung B, Paquola C, Bernhardt BC. The Superficial White Matter in Autism and Its Role in Connectivity Anomalies and Symptom Severity. Cereb Cortex 2020; 29:4415-4425. [PMID: 30566613 DOI: 10.1093/cercor/bhy321] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 01/11/2018] [Accepted: 11/26/2018] [Indexed: 12/29/2022] Open
Abstract
In autism spectrum disorders (ASDs), the majority of neuroimaging studies have focused on the analysis of cortical morphology. White matter changes remain less understood, particularly their association to cortical structure and function. Here, we focused on region that has gained only little attention in ASD neuroimaging: the superficial white matter (SWM) immediately beneath the cortical interface, a compartment playing a prominent role in corticogenesis that incorporates long- and short-range fibers implicated in corticocortical connectivity. Studying a multicentric dataset of ASD and neurotypical controls, we harnessed surface-based techniques to aggregate microstructural SWM diffusion features. Multivariate analysis revealed SWM anomalies in ASD compared with controls in medial parietal and temporoparietal regions. Effects were similar in children and adolescents/adults and consistent across sites. Although SWM anomalies were more confined when correcting for cortical thickness and surface area, findings were overall robust. Diffusion anomalies modulated functional connectivity reductions in ASD and related to symptom severity. Furthermore, mediation models indicated a link between SWM changes, functional connectivity, and symptom load. Analyses targeting the SWM offer a novel perspective on the interplay between structural and functional network perturbations in ASD, highlighting a potentially important neurobiological substrate contributing to its diverse behavioral phenotype.
Collapse
Affiliation(s)
- Seok-Jun Hong
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada.,Center for the Developing Brain, Child Mind Institute, 101 E 56th St, New York, NY 10022, USA
| | - Brian Hyung
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| |
Collapse
|
46
|
Park BY, Vos de Wael R, Paquola C, Larivière S, Benkarim O, Royer J, Tavakol S, Cruces RR, Li Q, Valk SL, Margulies DS, Mišić B, Bzdok D, Smallwood J, Bernhardt BC. Signal diffusion along connectome gradients and inter-hub routing differentially contribute to dynamic human brain function. Neuroimage 2020; 224:117429. [PMID: 33038538 DOI: 10.1016/j.neuroimage.2020.117429] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 09/13/2020] [Accepted: 09/30/2020] [Indexed: 12/14/2022] Open
Abstract
Human cognition is dynamic, alternating over time between externally-focused states and more abstract, often self-generated, patterns of thought. Although cognitive neuroscience has documented how networks anchor particular modes of brain function, mechanisms that describe transitions between distinct functional states remain poorly understood. Here, we examined how time-varying changes in brain function emerge within the constraints imposed by macroscale structural network organization. Studying a large cohort of healthy adults (n = 326), we capitalized on manifold learning techniques that identify low dimensional representations of structural connectome organization and we decomposed neurophysiological activity into distinct functional states and their transition patterns using Hidden Markov Models. Structural connectome organization predicted dynamic transitions anchored in sensorimotor systems and those between sensorimotor and transmodal states. Connectome topology analyses revealed that transitions involving sensorimotor states traversed short and intermediary distances and adhered strongly to communication mechanisms of network diffusion. Conversely, transitions between transmodal states involved spatially distributed hubs and increasingly engaged long-range routing. These findings establish that the structure of the cortex is optimized to allow neural states the freedom to vary between distinct modes of processing, and so provides a key insight into the neural mechanisms that give rise to the flexibility of human cognition.
Collapse
Affiliation(s)
- Bo-Yong Park
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Oualid Benkarim
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Raul R Cruces
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Qiongling Li
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Sofie L Valk
- Institute of Neuroscience and Medicine (INM-7: Brain & Behaviour), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Daniel S Margulies
- Frontlab, Institut du Cerveau et de la Moelle épinière, UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Bratislav Mišić
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Danilo Bzdok
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Mila - Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
| | - Jonathan Smallwood
- Department of Psychology, York Neuroimaging Centre, University of York, New York, United Kingdom
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| |
Collapse
|
47
|
Lee HM, Gill RS, Fadaie F, Cho KH, Guiot MC, Hong SJ, Bernasconi N, Bernasconi A. Unsupervised machine learning reveals lesional variability in focal cortical dysplasia at mesoscopic scale. NEUROIMAGE-CLINICAL 2020; 28:102438. [PMID: 32987299 PMCID: PMC7520429 DOI: 10.1016/j.nicl.2020.102438] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 09/10/2020] [Accepted: 09/14/2020] [Indexed: 02/03/2023]
Abstract
Consensus clustering of MRI contrasts maps focal cortical dysplasia lesional variability. Lesions were parcellated into four classes with distinct structural profiles. FCD classes reflected typical functional and histopathological characteristics. Class membership was replicated in two independent datasets. Class-informed detection algorithm outperformed a class-naïve paradigm.
Objective Focal cortical dysplasia (FCD) is the most common epileptogenic developmental malformation and a prevalent cause of surgically amenable epilepsy. While cellular and molecular biology data suggest that FCD lesional characteristics lie along a spectrum, this notion remains to be verified in vivo. We tested the hypothesis that machine learning applied to MRI captures FCD lesional variability at a mesoscopic scale. Methods We studied 46 patients with histologically verified FCD Type II and 35 age- and sex-matched healthy controls. We applied consensus clustering, an unsupervised learning technique that identifies stable clusters based on bootstrap-aggregation, to 3 T multicontrast MRI (T1-weighted MRI and FLAIR) features of FCD normalized with respect to distributions in controls. Results Lesions were parcellated into four classes with distinct structural profiles variably expressed within and across patients: Class-1 with isolated white matter (WM) damage; Class-2 combining grey matter (GM) and WM alterations; Class-3 with isolated GM damage; Class-4 with GM-WM interface anomalies. Class membership was replicated in two independent datasets. Classes with GM anomalies impacted local function (resting-state fMRI derived ALFF), while those with abnormal WM affected large-scale connectivity (assessed by degree centrality). Overall, MRI classes reflected typical histopathological FCD characteristics: Class-1 was associated with severe WM gliosis and interface blurring, Class-2 with severe GM dyslamination and moderate WM gliosis, Class-3 with moderate GM gliosis, Class-4 with mild interface blurring. A detection algorithm trained on class-informed data outperformed a class-naïve paradigm. Significance Machine learning applied to widely available MRI contrasts uncovers FCD Type II variability at a mesoscopic scale and identifies tissue classes with distinct structural dimensions, functional and histopathological profiles. Integrating in vivo staging of FCD traits with automated lesion detection is likely to inform the development of novel personalized treatments.
Collapse
Affiliation(s)
- Hyo M Lee
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Ravnoor S Gill
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Fatemeh Fadaie
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Kyoo H Cho
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Marie C Guiot
- Department of Pathology, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Seok-Jun Hong
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| |
Collapse
|
48
|
Bethlehem RAI, Seidlitz J, Romero-Garcia R, Trakoshis S, Dumas G, Lombardo MV. A normative modelling approach reveals age-atypical cortical thickness in a subgroup of males with autism spectrum disorder. Commun Biol 2020; 3:486. [PMID: 32887930 PMCID: PMC7474067 DOI: 10.1038/s42003-020-01212-9] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 08/05/2020] [Indexed: 12/27/2022] Open
Abstract
Understanding heterogeneity is an important goal on the path to precision medicine for autism spectrum disorders (ASD). We examined how cortical thickness (CT) in ASD can be parameterized as an individualized metric of atypicality relative to typically-developing (TD) age-related norms. Across a large sample (n = 870 per group) and wide age range (5-40 years), we applied normative modelling resulting in individualized whole-brain maps of age-related CT atypicality in ASD and isolating a small subgroup with highly age-atypical CT. Age-normed CT scores also highlights on-average differentiation, and associations with behavioural symptomatology that is separate from insights gleaned from traditional case-control approaches. This work showcases an individualized approach for understanding ASD heterogeneity that could potentially further prioritize work on a subset of individuals with cortical pathophysiology represented in age-related CT atypicality. Only a small subset of ASD individuals are actually highly atypical relative to age-norms. driving small on-average case-control differences.
Collapse
Affiliation(s)
- Richard A I Bethlehem
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK. .,Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK.
| | - Jakob Seidlitz
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK.,Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Rafael Romero-Garcia
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Stavros Trakoshis
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy.,Department of Psychology, University of Cyprus, Nicosia, Cyprus
| | - Guillaume Dumas
- Human Genetics and Cognitive Functions Unit, Institut Pasteur, Paris, France.,CNRS UMR3571 Genes, Synapses and Cognition, Institut Pasteur, Paris, France.,Human Genetics and Cognitive Functions Unit, University Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Michael V Lombardo
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK.,Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| |
Collapse
|
49
|
Hong SJ, Vogelstein JT, Gozzi A, Bernhardt BC, Yeo BTT, Milham MP, Di Martino A. Toward Neurosubtypes in Autism. Biol Psychiatry 2020; 88:111-128. [PMID: 32553193 DOI: 10.1016/j.biopsych.2020.03.022] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 03/25/2020] [Accepted: 03/28/2020] [Indexed: 12/22/2022]
Abstract
There is a consensus that substantial heterogeneity underlies the neurobiology of autism spectrum disorder (ASD). As such, it has become increasingly clear that a dissection of variation at the molecular, cellular, and brain network domains is a prerequisite for identifying biomarkers. Neuroimaging has been widely used to characterize atypical brain patterns in ASD, although findings have varied across studies. This is due, at least in part, to a failure to account for neurobiological heterogeneity. Here, we summarize emerging data-driven efforts to delineate more homogeneous ASD subgroups at the level of brain structure and function-that is, neurosubtyping. We break this pursuit into key methodological steps: the selection of diagnostic samples, neuroimaging features, algorithms, and validation approaches. Although preliminary and methodologically diverse, current studies generally agree that at least 2 to 4 distinct ASD neurosubtypes may exist. Their identification improved symptom prediction and diagnostic label accuracy above and beyond group average comparisons. Yet, this nascent literature has shed light onto challenges and gaps. These include 1) the need for wider and more deeply transdiagnostic samples collected while minimizing artifacts (e.g., head motion), 2) quantitative and unbiased methods for feature selection and multimodal fusion, 3) greater emphasis on algorithms' ability to capture hybrid dimensional and categorical models of ASD, and 4) systematic independent replications and validations that integrate different units of analyses across multiple scales. Solutions aimed to address these challenges and gaps are discussed for future avenues leading toward a comprehensive understanding of the mechanisms underlying ASD heterogeneity.
Collapse
Affiliation(s)
- Seok-Jun Hong
- Center for the Developing Brain, Child Mind Institute, New York
| | - Joshua T Vogelstein
- Department of Biomedical Engineering Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, Maryland
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - B T Thomas Yeo
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Department of Electrical and Computer Engineering, Center for Sleep and Cognition, Clinical Imaging Research Centre, N.1 Institute for Health, National University of Singapore; NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore; Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York
| | | |
Collapse
|
50
|
Nikolaidis A, Solon Heinsfeld A, Xu T, Bellec P, Vogelstein J, Milham M. Bagging improves reproducibility of functional parcellation of the human brain. Neuroimage 2020; 214:116678. [PMID: 32119986 PMCID: PMC7302537 DOI: 10.1016/j.neuroimage.2020.116678] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 02/22/2020] [Accepted: 02/23/2020] [Indexed: 12/21/2022] Open
Abstract
Increasing the reproducibility of neuroimaging measurement addresses a central impediment to the advancement of human neuroscience and its clinical applications. Recent efforts demonstrating variance in functional brain organization within and between individuals shows a need for improving reproducibility of functional parcellations without long scan times. We apply bootstrap aggregation, or bagging, to the problem of improving reproducibility in functional parcellation. We use two large datasets to demonstrate that compared to a standard clustering framework, bagging improves the reproducibility and test-retest reliability of both cortical and subcortical functional parcellations across a range of sites, scanners, samples, scan lengths, clustering algorithms, and clustering parameters (e.g., number of clusters, spatial constraints). With as little as 6 min of scan time, bagging creates more reproducible group and individual level parcellations than standard approaches with twice as much data. This suggests that regardless of the specific parcellation strategy employed, bagging may be a key method for improving functional parcellation and bringing functional neuroimaging-based measurement closer to clinical impact.
Collapse
Affiliation(s)
- Aki Nikolaidis
- The Child Mind Institute, 101 East 56th Street, New York, NY, 10022, USA.
| | | | - Ting Xu
- The Child Mind Institute, 101 East 56th Street, New York, NY, 10022, USA
| | - Pierre Bellec
- University of Montreal, PO Box 6128 Downtown STN Montreal QC, H3C 3J7, Canada
| | - Joshua Vogelstein
- Department of Biomedical Engineering, Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, 3400 N. Charles St Baltimore, MD, 21218, USA
| | - Michael Milham
- The Child Mind Institute, 101 East 56th Street, New York, NY, 10022, USA
| |
Collapse
|