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Dong L, Yang R, Xie A, Wang X, Feng Z, Li F, Ren J, Li J, Yao D. Transforming of scalp EEGs with different channel locations by REST for comparative study. Brain Res Bull 2024; 217:111064. [PMID: 39232993 DOI: 10.1016/j.brainresbull.2024.111064] [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: 05/20/2024] [Revised: 08/11/2024] [Accepted: 08/30/2024] [Indexed: 09/06/2024]
Abstract
OBJECTIVE The diversity of electrode placement systems brought the problem of channel location harmonization in large-scale electroencephalography (EEG) applications to the forefront. Therefore, our goal was to resolve this problem by introducing and assessing the reference electrode standardization technique (REST) to transform EEGs into a common electrode distribution with computational zero reference at infinity offline. METHODS Simulation and eye-closed resting-state EEG datasets were used to investigate the performance of REST for EEG signals and power configurations. RESULTS REST produced small errors (the root mean square error (RMSE): 0.2936-0.4583; absolute errors: 0.2343-0.3657) and high correlations (>0.9) between the estimated signals and true ones. The comparison of configuration similarities in power among various electrode distributions revealed that REST induced infinity reference could maintain a perfect performance similar (>0.9) to that of true one. CONCLUSION These results demonstrated that REST transformation could be adopted to resolve the channel location harmonization problem in large-scale EEG applications.
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Affiliation(s)
- Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China; Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
| | - Runchen Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ao Xie
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xinrui Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zongwen Feng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Junru Ren
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
| | - Jianfu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China; Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China.
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2
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Leachman C, Nichols ES, Al-Saoud S, Duerden EG. Anxiety in children and adolescents with autism spectrum disorder: behavioural phenotypes and environmental factors. BMC Psychol 2024; 12:534. [PMID: 39369261 PMCID: PMC11452981 DOI: 10.1186/s40359-024-02044-6] [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: 07/23/2024] [Accepted: 09/26/2024] [Indexed: 10/07/2024] Open
Abstract
BACKGROUND Anxiety is the most prevalent comorbidity among children and adolescents with autism spectrum disorder (ASD), yet little is known about the associated risk factors. METHODS In a heterogenous cohort of children aged 5-18 years old (n = 262, 42% ASD), participants and their parents completed standardized questionnaires to assess anxiety, ASD symptom severity, inattention/hyperactivity, emotional problems, depressive symptoms, parental styles and stress, and demographic factors. RESULTS An artificial neural network analysis using a self-organizing map, a statistical technique used to cluster large datasets, revealed 3 distinct anxiety profiles: low (n = 114, 5% ASD), moderate (n = 70, 64% ASD) and high (n = 78, 96% ASD) anxiety. A recursive feature elimination analysis revealed that depression and peer problems contributed the most to differences between the anxiety profiles. Difficulties with peers in individuals with ASD who experience anxiety may be related to challenges with social competence and this may heighten depressive symptoms. CONCLUSION Findings highlight the importance of assessing depressive symptoms in children and adolescents with ASD who experience anxiety. Identifying anxiety profiles among children and adolescents with ASD may prove beneficial in clinical practice by facilitating the development of tailored interventions that aid in managing anxiety and depressive symptoms. Furthermore, strengthening social communication skills may improve peer relationships and could aid in managing depressive symptoms among children and adolescents with ASD who experience anxiety.
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Affiliation(s)
- Caitlin Leachman
- Applied Psychology, Faculty of Education, Western University, 1137 Western Rd, London, ON, N6G 1G7, Canada
| | - Emily S Nichols
- Applied Psychology, Faculty of Education, Western University, 1137 Western Rd, London, ON, N6G 1G7, Canada
- Western Institute for Neuroscience, Western University, London, Canada
| | - Sarah Al-Saoud
- Applied Psychology, Faculty of Education, Western University, 1137 Western Rd, London, ON, N6G 1G7, Canada
| | - Emma G Duerden
- Applied Psychology, Faculty of Education, Western University, 1137 Western Rd, London, ON, N6G 1G7, Canada.
- Western Institute for Neuroscience, Western University, London, Canada.
- Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Canada.
- Psychiatry, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada.
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3
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Pho B, Stevenson RA, Saljoughi S, Mohsenzadeh Y, Stojanoski B. Identifying developmental changes in functional brain connectivity associated with cognitive functioning in children and adolescents with ADHD. Dev Cogn Neurosci 2024; 69:101439. [PMID: 39182418 PMCID: PMC11385464 DOI: 10.1016/j.dcn.2024.101439] [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: 01/18/2024] [Revised: 08/14/2024] [Accepted: 08/20/2024] [Indexed: 08/27/2024] Open
Abstract
Youth diagnosed with Attention-Deficit/Hyperactivity Disorder (ADHD) often show deficits in various measures of higher-level cognition, such as, executive functioning. Poorer cognitive functioning in children with ADHD has been associated with differences in functional connectivity across the brain. However, little is known about the developmental changes to the brain's functional properties linked to different cognitive abilities in this cohort. To characterize these changes, we analyzed fMRI data (ADHD = 373, NT = 106) collected while youth between the ages of 6 and 16 watched a short movie-clip. We applied machine learning models to identify patterns of network connectivity in response to movie-watching that differentially predict cognitive abilities in our cohort. Using out-of-sample cross validation, our models successfully predicted IQ, visual spatial, verbal comprehension, and fluid reasoning in children (ages 6 - 11), but not in adolescents with ADHD (ages 12-16). Connections with the default mode, memory retrieval, and dorsal attention were driving prediction during early and middle childhood, but connections with the somatomotor, cingulo-opercular, and frontoparietal networks were more important in middle childhood. This work demonstrated that machine learning approaches can identify distinct functional connectivity profiles associated with cognitive abilities at different developmental stages in children and adolescents with ADHD.
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Affiliation(s)
- Brian Pho
- Program in Neuroscience, University of Western Ontario, London, ON, Canada
| | - Ryan Andrew Stevenson
- Program in Neuroscience, University of Western Ontario, London, ON, Canada; Brain and Mind Institute, University of Western Ontario, London, ON, Canada; Department of Psychology, University of Western Ontario, London, ON, Canada; Western Institute for Neuroscience, University of Western Ontario, London, ON, Canada
| | - Sara Saljoughi
- Faculty of Social Science and Humanities, Ontario Tech University, Oshawa, ON, Canada
| | - Yalda Mohsenzadeh
- Program in Neuroscience, University of Western Ontario, London, ON, Canada; Brain and Mind Institute, University of Western Ontario, London, ON, Canada; Department of Computer Science, Western University, London, ON N6A 5B7, Canada; Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Bobby Stojanoski
- Program in Neuroscience, University of Western Ontario, London, ON, Canada; Brain and Mind Institute, University of Western Ontario, London, ON, Canada; Department of Psychology, University of Western Ontario, London, ON, Canada.
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4
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Tanaka SC, Kasai K, Okamoto Y, Koike S, Hayashi T, Yamashita A, Yamashita O, Johnstone T, Pestilli F, Doya K, Okada G, Shinzato H, Itai E, Takahara Y, Takamiya A, Nakamura M, Itahashi T, Aoki R, Koizumi Y, Shimizu M, Miyata J, Son S, Aki M, Okada N, Morita S, Sawamoto N, Abe M, Oi Y, Sajima K, Kamagata K, Hirose M, Aoshima Y, Hamatani S, Nohara N, Funaba M, Noda T, Inoue K, Hirano J, Mimura M, Takahashi H, Hattori N, Sekiguchi A, Kawato M, Hanakawa T. The status of MRI databases across the world focused on psychiatric and neurological disorders. Psychiatry Clin Neurosci 2024; 78:563-579. [PMID: 39162256 DOI: 10.1111/pcn.13717] [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: 01/22/2024] [Revised: 05/13/2024] [Accepted: 07/02/2024] [Indexed: 08/21/2024]
Abstract
Neuroimaging databases for neuro-psychiatric disorders enable researchers to implement data-driven research approaches by providing access to rich data that can be used to study disease, build and validate machine learning models, and even redefine disease spectra. The importance of sharing large, multi-center, multi-disorder databases has gradually been recognized in order to truly translate brain imaging knowledge into real-world clinical practice. Here, we review MRI databases that share data globally to serve multiple psychiatric or neurological disorders. We found 42 datasets consisting of 23,293 samples from patients with psychiatry and neurological disorders and healthy controls; 1245 samples from mood disorders (major depressive disorder and bipolar disorder), 2015 samples from developmental disorders (autism spectrum disorder, attention-deficit hyperactivity disorder), 675 samples from schizophrenia, 1194 samples from Parkinson's disease, 5865 samples from dementia (including Alzheimer's disease), We recognize that large, multi-center databases should include governance processes that allow data to be shared across national boundaries. Addressing technical and regulatory issues of existing databases can lead to better design and implementation and improve data access for the research community. The current trend toward the development of shareable MRI databases will contribute to a better understanding of the pathophysiology, diagnosis and assessment, and development of early interventions for neuropsychiatric disorders.
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Affiliation(s)
- Saori C Tanaka
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Division of Information Science, Nara Institute of Science and Technology, Nara, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan
- Center for Brain Imaging in Health and Diseases (CBHD), The University of Tokyo Hospital, Tokyo, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan
| | - Shinsuke Koike
- The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Hyogo, Japan
- Department of Brain Connectomics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Ayumu Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Okito Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
| | - Tom Johnstone
- School of Health Sciences, Swinburne University of Technology, Melbourne, Victoria, Australia
| | - Franco Pestilli
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, The University of Texas at Austin, Austin, Texas, USA
| | - Kenji Doya
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan
| | - Hotaka Shinzato
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan
| | - Eri Itai
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan
| | - Yuji Takahara
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Biomarker R&D department, SHIONOGI & CO., Ltd, Osaka, Japan
| | - Akihiro Takamiya
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
- Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine, Tokyo, Japan
- Neuropsychiatry, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Geriatric Psychiatry, University Psychiatric Center KU Leuven, Leuven, Belgium
| | - Motoaki Nakamura
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Takashi Itahashi
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Ryuta Aoki
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
- Graduate School of Humanities, Tokyo Metropolitan University, Tokyo, Japan
| | - Yukiaki Koizumi
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
- Department of Psychiatry, Haryugaoka Hospital, Fukushima, Japan
| | - Masaaki Shimizu
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Jun Miyata
- Department of Psychiatry, Aichi Medical University, Aichi, Japan
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shuraku Son
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Morio Aki
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Naohiro Okada
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
| | - Susumu Morita
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Nobukatsu Sawamoto
- Department of Human Health Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Mitsunari Abe
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Yuki Oi
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kazuaki Sajima
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Masakazu Hirose
- Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yohei Aoshima
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Sayo Hamatani
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
- Research Center for Child Mental Development, University of Fukui, Fukui, Japan
| | - Nobuhiro Nohara
- Department of Stress Sciences and Psychosomatic Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Misako Funaba
- Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
- Student Counseling Center, Meiji Gakuin University, Tokyo, Japan
| | - Tomomi Noda
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kana Inoue
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Jinichi Hirano
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Masaru Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
- Center for Brain Integration Research, Tokyo Medical and Dental University, Tokyo, Japan
| | - Nobutaka Hattori
- Department of Neurology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Neurodegenerative Disorders Collaborative Laboratory, RIKEN Center for Brain Science, Saitama, Japan
| | - Atsushi Sekiguchi
- Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Takashi Hanakawa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
- Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, Japan
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5
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Itahashi T, Yamashita A, Takahara Y, Yahata N, Aoki YY, Fujino J, Yoshihara Y, Nakamura M, Aoki R, Okimura T, Ohta H, Sakai Y, Takamura M, Ichikawa N, Okada G, Okada N, Kasai K, Tanaka SC, Imamizu H, Kato N, Okamoto Y, Takahashi H, Kawato M, Yamashita O, Hashimoto RI. Generalizable and transportable resting-state neural signatures characterized by functional networks, neurotransmitters, and clinical symptoms in autism. Mol Psychiatry 2024:10.1038/s41380-024-02759-3. [PMID: 39342041 DOI: 10.1038/s41380-024-02759-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 09/10/2024] [Accepted: 09/19/2024] [Indexed: 10/01/2024]
Abstract
Autism spectrum disorder (ASD) is a lifelong condition with elusive biological mechanisms. The complexity of factors, including inter-site and developmental differences, hinders the development of a generalizable neuroimaging classifier for ASD. Here, we developed a classifier for ASD using a large-scale, multisite resting-state fMRI dataset of 730 Japanese adults, aiming to capture neural signatures that reflect pathophysiology at the functional network level, neurotransmitters, and clinical symptoms of the autistic brain. Our adult ASD classifier was successfully generalized to adults in the United States, Belgium, and Japan. The classifier further demonstrated its successful transportability to children and adolescents. The classifier contained 141 functional connections (FCs) that were important for discriminating individuals with ASD from typically developing controls. These FCs and their terminal brain regions were associated with difficulties in social interaction and dopamine and serotonin, respectively. Finally, we mapped attention-deficit/hyperactivity disorder (ADHD), schizophrenia (SCZ), and major depressive disorder (MDD) onto the biological axis defined by the ASD classifier. ADHD and SCZ, but not MDD, were located proximate to ASD on the biological dimensions. Our results revealed functional signatures of the ASD brain, grounded in molecular characteristics and clinical symptoms, achieving generalizability and transportability applicable to the evaluation of the biological continuity of related diseases.
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Affiliation(s)
- Takashi Itahashi
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Ayumu Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Yuji Takahara
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Drug Discovery Research Division, Shionogi & Co., Ltd., Osaka, Japan
| | - Noriaki Yahata
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Institute for Quantum Life Science, National Institutes for Quantum Science and Technology, Chiba, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Quantum Life Science, Graduate School of Science and Engineering, Chiba University, Chiba, Japan
| | - Yuta Y Aoki
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
- Department of Psychiatry, Aoki Clinic, Tokyo, Japan
| | - Junya Fujino
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yujiro Yoshihara
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Motoaki Nakamura
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Ryuta Aoki
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
- Department of Language Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Tsukasa Okimura
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Haruhisa Ohta
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Yuki Sakai
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
- XNef, Inc., Kyoto, Japan
| | - Masahiro Takamura
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
- Department of Neurology, Shimane University, Shimane, Japan
| | - Naho Ichikawa
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Naohiro Okada
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN) at The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN) at The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan
- UTokyo Institute for Diversity and Adaptation of Human Mind (UTIDAHM), The University of Tokyo, Tokyo, Japan
| | - Saori C Tanaka
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Division of Information Science, Nara Institute of Science and Technology, Nara, Japan
| | - Hiroshi Imamizu
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Department of Psychology, Graduate School of Humanities and Sociology, The University of Tokyo, Tokyo, Japan
| | - Nobumasa Kato
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Center for Brain Integration Research, Tokyo Medical and Dental University, Tokyo, Japan
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
- XNef, Inc., Kyoto, Japan
| | - Okito Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
| | - Ryu-Ichiro Hashimoto
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan.
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan.
- Department of Language Sciences, Tokyo Metropolitan University, Tokyo, Japan.
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6
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Corrigan NM, Rokem A, Kuhl PK. COVID-19 lockdown effects on adolescent brain structure suggest accelerated maturation that is more pronounced in females than in males. Proc Natl Acad Sci U S A 2024; 121:e2403200121. [PMID: 39250666 PMCID: PMC11420155 DOI: 10.1073/pnas.2403200121] [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: 02/27/2024] [Accepted: 07/26/2024] [Indexed: 09/11/2024] Open
Abstract
Adolescence is a period of substantial social-emotional development, accompanied by dramatic changes to brain structure and function. Social isolation due to lockdowns that were imposed because of the COVID-19 pandemic had a detrimental impact on adolescent mental health, with the mental health of females more affected than males. We assessed the impact of the COVID-19 pandemic lockdowns on adolescent brain structure with a focus on sex differences. We collected MRI structural data longitudinally from adolescents prior to and after the pandemic lockdowns. The pre-COVID data were used to create a normative model of cortical thickness change with age during typical adolescent development. Cortical thickness values in the post-COVID data were compared to this normative model. The analysis revealed accelerated cortical thinning in the post-COVID brain, which was more widespread throughout the brain and greater in magnitude in females than in males. When measured in terms of equivalent years of development, the mean acceleration was found to be 4.2 y in females and 1.4 y in males. Accelerated brain maturation as a result of chronic stress or adversity during development has been well documented. These findings suggest that the lifestyle disruptions associated with the COVID-19 pandemic lockdowns caused changes in brain biology and had a more severe impact on the female than the male brain.
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Affiliation(s)
- Neva M. Corrigan
- Institute for Learning & Brain Sciences, University of Washington, Seattle, WA98195
- Institute on Human Development and Disability, University of Washington, Seattle, WA98195
| | - Ariel Rokem
- Institute on Human Development and Disability, University of Washington, Seattle, WA98195
- Department of Psychology, University of Washington, Seattle, WA98195
- eScience Institute, University of Washington, Seattle, WA98195
| | - Patricia K. Kuhl
- Institute for Learning & Brain Sciences, University of Washington, Seattle, WA98195
- Department of Speech and Hearing Sciences, University of Washington, Seattle, WA98195
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7
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Jiang Y, Mu Y, Xu Z, Liu Q, Wang S, Wang H, Feng J. Identifying individual brain development using multimodality brain network. Commun Biol 2024; 7:1163. [PMID: 39289448 PMCID: PMC11408623 DOI: 10.1038/s42003-024-06876-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 09/10/2024] [Indexed: 09/19/2024] Open
Abstract
The cortical development of our brains is in a hierarchical manner and promotes the emergence of large-scale functional hierarchy. However, under interindividual heterogenicity, how the spatiotemporal features of brain networks reflect brain development and mental health remains unclear. Here we collect both resting-state electroencephalography and functional magnetic resonance imaging data from the Child Mind Institute Biobank to demonstrate that during brain growth, the global dynamic patterns of brain states become more active and the dominant networks shift from sensory to higher-level networks; the individual functional network patterns become more similar to that of adults and their spatial coupling tends to be invariable. Furthermore, the properties of multimodality brain networks are sufficiently robust to identify healthy brain age and mental disorders at specific ages. Therefore, multimodality brain networks provide new insights into the functional development of the brain and a more reliable and reasonable approach for age prediction and individual diagnosis.
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Affiliation(s)
- Yuwei Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
| | - Yangjiayi Mu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Zhao Xu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Qingyang Liu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Shouyan Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
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8
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Ye J, Tejavibulya L, Dai W, Cope LM, Hardee JE, Heitzeg MM, Lichenstein S, Yip SW, Banaschewski T, Baker GJ, Bokde ALW, Brühl R, Desrivières S, Flor H, Gowland P, Grigis A, Heinz A, Martinot JL, Paillère Martinot ML, Artiges E, Nees F, Orfanos DP, Poustka L, Hohmann S, Holz N, Baeuchl C, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Garavan H, Chaarani B, Gee DG, Baskin-Sommers A, Casey BJ, Scheinost D. Variation in moment-to-moment brain state engagement changes across development and contributes to individual differences in executive function. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.06.611627. [PMID: 39314397 PMCID: PMC11419067 DOI: 10.1101/2024.09.06.611627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Neural variability, or variation in brain signals, facilitates dynamic brain responses to ongoing demands. This flexibility is important during development from childhood to young adulthood, a period characterized by rapid changes in experience. However, little is known about how variability in the engagement of recurring brain states changes during development. Such investigations would require the continuous assessment of multiple brain states concurrently. Here, we leverage a new computational framework to study state engagement variability (SEV) during development. A consistent pattern of SEV changing with age was identified across cross-sectional and longitudinal datasets (N>3000). SEV developmental trajectories stabilize around mid-adolescence, with timing varying by sex and brain state. SEV successfully predicts executive function (EF) in youths from an independent dataset. Worse EF is further linked to alterations in SEV development. These converging findings suggest SEV changes over development, allowing individuals to flexibly recruit various brain states to meet evolving needs.
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Affiliation(s)
- Jean Ye
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Wei Dai
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
| | - Lora M Cope
- Department of Psychiatry and Addiction Center, University of Michigan, Ann Arbor, Michigan
| | - Jillian E Hardee
- Department of Psychiatry and Addiction Center, University of Michigan, Ann Arbor, Michigan
| | - Mary M Heitzeg
- Department of Psychiatry and Addiction Center, University of Michigan, Ann Arbor, Michigan
| | - Sarah Lichenstein
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
| | - Sarah W Yip
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
- Child Study Center, Yale School of Medicine, New Haven, Connecticut
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany; German Center for Mental Health (DZPG), partner site Mannheim-Heidelberg-Ulm, Germany
| | - Gareth J Baker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Sylvane Desrivières
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- German Center for Mental Health (DZPG), site Berlin-Potsdam
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales & psychiatrie", University Paris-Saclay, Ecole Normale Supérieure Paris-Saclay, CNRS; Centre Borelli, Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales & psychiatrie", University Paris-Saclay, Ecole Normale Supérieure Paris-Saclay, CNRS; Centre Borelli, Gif-sur-Yvette, France
- AP-HP. Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales & psychiatrie", University Paris-Saclay, Ecole Normale Supérieure Paris-Saclay, CNRS; Centre Borelli, Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes; France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany; German Center for Mental Health (DZPG), partner site Mannheim-Heidelberg-Ulm, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | | | - Luise Poustka
- Department of Child and Adolescent Psychiatry, Center for Psychosocial Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany; German Center for Mental Health (DZPG), partner site Mannheim-Heidelberg-Ulm, Germany
| | - Nathalie Holz
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany; German Center for Mental Health (DZPG), partner site Mannheim-Heidelberg-Ulm, Germany
| | - Christian Baeuchl
- Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Michael N Smolka
- Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Nilakshi Vaidya
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Gunter Schumann
- German Center for Mental Health (DZPG), site Berlin-Potsdam
- Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont, Burlington, Vermont
- Department of Psychology, University of Vermont, Burlington, Vermont
| | - Bader Chaarani
- Department of Psychiatry, University of Vermont, Burlington, Vermont
| | - Dylan G Gee
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
- Child Study Center, Yale School of Medicine, New Haven, Connecticut
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Arielle Baskin-Sommers
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
- Department of Psychology, Yale University, New Haven, Connecticut
| | - B J Casey
- Department of Neuroscience and Behavior, Barnard College-Columbia University, New York, New York
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
- Child Study Center, Yale School of Medicine, New Haven, Connecticut
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
- Department of Statistics & Data Science, Yale University, New Haven, Connecticut
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9
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Hearne LJ, Yeo BTT, Webb L, Zalesky A, Fitzgerald PB, Murphy OW, Tian Y, Breakspear M, Hall CV, Choi S, Kim M, Kwon JS, Cocchi L. Distinct cognitive and functional connectivity features from healthy cohorts can identify clinical obsessive-compulsive disorder. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.02.24312960. [PMID: 39281735 PMCID: PMC11398446 DOI: 10.1101/2024.09.02.24312960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/18/2024]
Abstract
Improving diagnostic accuracy of obsessive-compulsive disorder (OCD) using models of brain imaging data is a key goal of the field, but this objective is challenging due to the limited size and phenotypic depth of clinical datasets. Leveraging the phenotypic diversity in large non-clinical datasets such as the UK Biobank (UKBB), offers a potential solution to this problem. Nevertheless, it remains unclear whether classification models trained on non-clinical populations will generalise to individuals with clinical OCD. This question is also relevant for the conceptualisation of OCD; specifically, whether the symptomology of OCD exists on a continuum from normal to pathological. Here, we examined a recently published "meta-matching" model trained on functional connectivity data from five large normative datasets (N=45,507) to predict cognitive, health and demographic variables. Specifically, we tested whether this model could classify OCD status in three independent clinical datasets (N=345). We found that the model could identify out-of-sample OCD individuals. Notably, the most predictive functional connectivity features mapped onto known cortico-striatal abnormalities in OCD and correlated with genetic brain expression maps previously implicated in the disorder. Further, the meta-matching model relied upon estimates of cognitive functions, such as cognitive flexibility and inhibition, to successfully predict OCD. These findings suggest that variability in non-clinical brain and behavioural features can discriminate clinical OCD status. These results support a dimensional and transdiagnostic conceptualisation of the brain and behavioural basis of OCD, with implications for research approaches and treatment targets.
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Affiliation(s)
- Luke J Hearne
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - 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, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, USA
| | - Lachlan Webb
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Melbourne, Australia
| | - Paul B Fitzgerald
- School of Medicine and Psychology, Australian National University, Canberra, Australia
| | - Oscar W Murphy
- Central Clinical School, Monash University, Clayton, Australia
- Bionics Institute, East Melbourne, Australia
| | - Ye Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Melbourne, Australia
| | - Michael Breakspear
- School of Psychological Sciences, College of Engineering Science and Environment, University of Newcastle, Callaghan, Australia
- School of Medicine and Public Health, College of Health and Medicine, University of Newcastle, Callaghan, Australia
- Program of Neuromodulation, Hunter Medical Research Institute, New Lambton, Australia
| | - Caitlin V Hall
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Sunah Choi
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Republic of Korea
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Republic of Korea
| | - Jun Soo Kwon
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Republic of Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Republic of Korea
| | - Luca Cocchi
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Faculty of Medicine, School of Biomedical Sciences, University of Queensland, Brisbane, Australia
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10
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Huang X, Gao L, Xiao J, Li L, Shan X, Chen H, Chai X, Duan X. Family Environment Modulates Linkage of Transdiagnostic Psychiatric Phenotypes and Dissociable Brain Features in the Developing Brain. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:928-938. [PMID: 38537777 DOI: 10.1016/j.bpsc.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 02/26/2024] [Accepted: 03/16/2024] [Indexed: 06/04/2024]
Abstract
BACKGROUND Family environment has long been known for shaping brain function and psychiatric phenotypes, especially during childhood and adolescence. Accumulating neuroimaging evidence suggests that across different psychiatric disorders, common phenotypes may share common neural bases, indicating latent brain-behavior relationships beyond diagnostic categories. However, the influence of family environment on the brain-behavior relationship from a transdiagnostic perspective remains unknown. METHODS We included a community-based sample of 699 participants (ages 5-22 years) and applied partial least squares regression analysis to determine latent brain-behavior relationships from whole-brain functional connectivity and comprehensive phenotypic measures. Comparisons were made between diagnostic and nondiagnostic groups to help interpret the latent brain-behavior relationships. A moderation model was introduced to examine the potential moderating role of family factors in the estimated brain-behavior associations. RESULTS Four significant latent brain-behavior pairs were identified that reflected the relationship of dissociable brain network and general behavioral problems, cognitive and language skills, externalizing problems, and social dysfunction, respectively. The group comparisons exhibited interpretable variations across different diagnostic groups. A warm family environment was found to moderate the brain-behavior relationship of core symptoms in internalizing disorders. However, in neurodevelopmental disorders, family factors were not found to moderate the brain-behavior relationship of core symptoms, but they were found to affect the brain-behavior relationship in other domains. CONCLUSIONS Our findings leveraged a transdiagnostic analysis to investigate the moderating effects of family factors on brain-behavior associations, emphasizing the different roles that family factors play during this developmental period across distinct diagnostic groups.
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Affiliation(s)
- Xinyue Huang
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Leying Gao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Jinming Xiao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, 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, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xiaolong Shan
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, 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, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xiaoqian Chai
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.
| | - Xujun Duan
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
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11
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Yu H, Chen Y, Bao Z, Luo J, Liu Q, Qin P, Wang C, Qu J, Wang W, Cai L, Gong G. Behavioral and brain morphological changes before and after hemispherotomy. Hum Brain Mapp 2024; 45:e70020. [PMID: 39225128 PMCID: PMC11369683 DOI: 10.1002/hbm.70020] [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: 04/29/2024] [Revised: 08/12/2024] [Accepted: 08/21/2024] [Indexed: 09/04/2024] Open
Abstract
Hemispherotomy is an effective surgery for treating refractory epilepsy from diffuse unihemispheric lesions. To date, postsurgery neuroplastic changes supporting behavioral recovery after left or right hemispherotomy remain unclear. In the present study, we systematically investigated changes in gray matter volume (GMV) before and after surgery and further analyzed their relationships with behavioral scores in two large groups of pediatric patients with left and right hemispherotomy (29 left and 28 right). To control for the dramatic developmental effect during this stage, age-adjusted GMV within unaffected brain regions was derived voxel by voxel using a normative modeling approach with an age-matched reference cohort of 2115 healthy children. Widespread GMV increases in the contralateral cerebrum and ipsilateral cerebellum and GMV decreases in the contralateral cerebellum were consistently observed in both patient groups, but only the left hemispherotomy patients showed GMV decreases in the contralateral cingulate gyrus. Intriguingly, the GMV decrease in the contralateral cerebellum was significantly correlated with improvement in behavioral scores in the right but not the left hemispherotomy patients. Importantly, the preoperative voxelwise GMV features can be used to significantly predict postoperative behavioral scores in both patient groups. These findings indicate an important role of the contralateral cerebellum in the behavioral recovery following right hemispherotomy and highlight the predictive potential of preoperative imaging features in postoperative behavioral performance.
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Affiliation(s)
- Hao Yu
- Pediatric Epilepsy CenterPeking University First HospitalBeijingChina
| | - Yijun Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Ziyu Bao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Junhao Luo
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Qingzhu Liu
- Pediatric Epilepsy CenterPeking University First HospitalBeijingChina
| | - Peipei Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Changtong Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Jingli Qu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Wei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - Lixin Cai
- Pediatric Epilepsy CenterPeking University First HospitalBeijingChina
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
- Chinese Institute for Brain ResearchBeijingChina
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12
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Adams RA, Zor C, Mihalik A, Tsirlis K, Brudfors M, Chapman J, Ashburner J, Paulus MP, Mourão-Miranda J. Voxelwise Multivariate Analysis of Brain-Psychosocial Associations in Adolescents Reveals 6 Latent Dimensions of Cognition and Psychopathology. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:915-927. [PMID: 38588854 DOI: 10.1016/j.bpsc.2024.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 03/15/2024] [Accepted: 03/28/2024] [Indexed: 04/10/2024]
Abstract
BACKGROUND Adolescence heralds the onset of considerable psychopathology, which may be conceptualized as an emergence of altered covariation between symptoms and brain measures. Multivariate methods can detect such modes of covariation or latent dimensions, but none specifically relating to psychopathology have yet been found using population-level structural brain data. Using voxelwise (instead of parcellated) brain data may strengthen latent dimensions' brain-psychosocial relationships, but this creates computational challenges. METHODS We obtained voxelwise gray matter density and psychosocial variables from the baseline (ages 9-10 years) Adolescent Brain Cognitive Development (ABCD) Study cohort (N = 11,288) and employed a state-of-the-art segmentation method, sparse partial least squares, and a rigorous machine learning framework to prevent overfitting. RESULTS We found 6 latent dimensions, 4 of which pertain specifically to mental health. The mental health dimensions were related to overeating, anorexia/internalizing, oppositional symptoms (all ps < .002) and attention-deficit/hyperactivity disorder symptoms (p = .03). Attention-deficit/hyperactivity disorder was related to increased and internalizing symptoms related to decreased gray matter density in dopaminergic and serotonergic midbrain areas, whereas oppositional symptoms were related to increased gray matter in a noradrenergic nucleus. Internalizing symptoms were related to increased and oppositional symptoms to reduced gray matter density in the insular, cingulate, and auditory cortices. Striatal regions featured strongly, with reduced caudate nucleus gray matter in attention-deficit/hyperactivity disorder and reduced putamen gray matter in oppositional/conduct problems. Voxelwise gray matter density generated stronger brain-psychosocial correlations than brain parcellations. CONCLUSIONS Voxelwise brain data strengthen latent dimensions of brain-psychosocial covariation, and sparse multivariate methods increase their psychopathological specificity. Internalizing and externalizing symptoms are associated with opposite gray matter changes in similar cortical and subcortical areas.
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Affiliation(s)
- Rick A Adams
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Max Planck Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom.
| | - Cemre Zor
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Agoston Mihalik
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Max Planck Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom; Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Konstantinos Tsirlis
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Max Planck Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| | - Mikael Brudfors
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - James Chapman
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Max Planck Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| | - John Ashburner
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
| | | | - Janaina Mourão-Miranda
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Max Planck Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
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13
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Castellanos FX. Back to the future: Some similarities and many differences between autism spectrum disorder and early onset schizophrenia. Clues to pathophysiology? Sci Bull (Beijing) 2024; 69:2476-2477. [PMID: 38969537 DOI: 10.1016/j.scib.2024.05.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 05/03/2024] [Accepted: 05/27/2024] [Indexed: 07/07/2024]
Affiliation(s)
- Francisco Xavier Castellanos
- New York University Grossman School of Medicine, New York NY 10016, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg NY 10962, USA.
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14
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Kliemann D, Galdi P, Van De Water AL, Egger B, Jarecka D, Adolphs R, Ghosh SS. Resting-State Functional Connectivity of the Amygdala in Autism: A Preregistered Large-Scale Study. Am J Psychiatry 2024:appiajp20230249. [PMID: 39205507 DOI: 10.1176/appi.ajp.20230249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
OBJECTIVE Three leading neurobiological hypotheses about autism spectrum disorder (ASD) propose underconnectivity between brain regions, atypical function of the amygdala, and generally higher variability between individuals with ASD than between neurotypical individuals. Past work has often failed to generalize, because of small sample sizes, unquantified data quality, and analytic flexibility. This study addressed these limitations while testing the above three hypotheses, applied to amygdala functional connectivity. METHODS In a comprehensive preregistered study, the three hypotheses were tested in a subset (N=488 after exclusions; N=212 with ASD) of the Autism Brain Imaging Data Exchange data sets. The authors analyzed resting-state functional connectivity (FC) from functional MRI data from two anatomically defined amygdala subdivisions, in three hypotheses with respect to magnitude, pattern similarity, and variability, across different anatomical scales ranging from whole brain to specific regions and networks. RESULTS A Bayesian approach to hypothesis evaluation produced inconsistent evidence in ASD for atypical amygdala FC magnitude, strong evidence that the multivariate pattern of FC was typical, and no consistent evidence of increased interindividual variability in FC. The results strongly depended on analytic choices, including preprocessing pipeline for the neuroimaging data, anatomical specificity, and subject exclusions. CONCLUSIONS A preregistered set of analyses found no reliable evidence for atypical functional connectivity of the amygdala in autism, contrary to leading hypotheses. Future studies should test an expanded set of hypotheses across multiple processing pipelines, collect deeper data per individual, and include a greater diversity of participants to ensure robust generalizability of findings on amygdala FC in ASD.
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Affiliation(s)
- Dorit Kliemann
- Department of Psychological and Brain Sciences (Kliemann, Van De Water, Egger), Department of Psychiatry (Kliemann), and Iowa Neuroscience Institute (Kliemann, Van De Water), University of Iowa, Iowa City; Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena (Kliemann, Adolphs); School of Informatics, University of Edinburgh, Edinburgh (Galdi); McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Mass. (Jarecka, Ghosh); Division of Biology and Biological Engineering and Chen Neuroscience Institute, California Institute of Technology, Pasadena (Adolphs); Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston (Ghosh)
| | - Paola Galdi
- Department of Psychological and Brain Sciences (Kliemann, Van De Water, Egger), Department of Psychiatry (Kliemann), and Iowa Neuroscience Institute (Kliemann, Van De Water), University of Iowa, Iowa City; Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena (Kliemann, Adolphs); School of Informatics, University of Edinburgh, Edinburgh (Galdi); McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Mass. (Jarecka, Ghosh); Division of Biology and Biological Engineering and Chen Neuroscience Institute, California Institute of Technology, Pasadena (Adolphs); Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston (Ghosh)
| | - Avery L Van De Water
- Department of Psychological and Brain Sciences (Kliemann, Van De Water, Egger), Department of Psychiatry (Kliemann), and Iowa Neuroscience Institute (Kliemann, Van De Water), University of Iowa, Iowa City; Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena (Kliemann, Adolphs); School of Informatics, University of Edinburgh, Edinburgh (Galdi); McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Mass. (Jarecka, Ghosh); Division of Biology and Biological Engineering and Chen Neuroscience Institute, California Institute of Technology, Pasadena (Adolphs); Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston (Ghosh)
| | - Brandon Egger
- Department of Psychological and Brain Sciences (Kliemann, Van De Water, Egger), Department of Psychiatry (Kliemann), and Iowa Neuroscience Institute (Kliemann, Van De Water), University of Iowa, Iowa City; Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena (Kliemann, Adolphs); School of Informatics, University of Edinburgh, Edinburgh (Galdi); McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Mass. (Jarecka, Ghosh); Division of Biology and Biological Engineering and Chen Neuroscience Institute, California Institute of Technology, Pasadena (Adolphs); Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston (Ghosh)
| | - Dorota Jarecka
- Department of Psychological and Brain Sciences (Kliemann, Van De Water, Egger), Department of Psychiatry (Kliemann), and Iowa Neuroscience Institute (Kliemann, Van De Water), University of Iowa, Iowa City; Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena (Kliemann, Adolphs); School of Informatics, University of Edinburgh, Edinburgh (Galdi); McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Mass. (Jarecka, Ghosh); Division of Biology and Biological Engineering and Chen Neuroscience Institute, California Institute of Technology, Pasadena (Adolphs); Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston (Ghosh)
| | - Ralph Adolphs
- Department of Psychological and Brain Sciences (Kliemann, Van De Water, Egger), Department of Psychiatry (Kliemann), and Iowa Neuroscience Institute (Kliemann, Van De Water), University of Iowa, Iowa City; Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena (Kliemann, Adolphs); School of Informatics, University of Edinburgh, Edinburgh (Galdi); McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Mass. (Jarecka, Ghosh); Division of Biology and Biological Engineering and Chen Neuroscience Institute, California Institute of Technology, Pasadena (Adolphs); Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston (Ghosh)
| | - Satrajit S Ghosh
- Department of Psychological and Brain Sciences (Kliemann, Van De Water, Egger), Department of Psychiatry (Kliemann), and Iowa Neuroscience Institute (Kliemann, Van De Water), University of Iowa, Iowa City; Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena (Kliemann, Adolphs); School of Informatics, University of Edinburgh, Edinburgh (Galdi); McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Mass. (Jarecka, Ghosh); Division of Biology and Biological Engineering and Chen Neuroscience Institute, California Institute of Technology, Pasadena (Adolphs); Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston (Ghosh)
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15
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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 RAI. Brain-Charting Autism and Attention-Deficit/Hyperactivity Disorder Reveals Distinct and Overlapping Neurobiology. Biol Psychiatry 2024:S0006-3223(24)01513-0. [PMID: 39128574 DOI: 10.1016/j.biopsych.2024.07.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 05/30/2024] [Accepted: 07/11/2024] [Indexed: 08/13/2024]
Abstract
BACKGROUND Autism and attention-deficit/hyperactivity disorder (ADHD) are heterogeneous neurodevelopmental conditions with complex underlying neurobiology that is still poorly understood. Despite overlapping presentation and sex-biased prevalence, autism and ADHD are rarely studied together and sex differences are often overlooked. Population modeling, often referred to as normative modeling, provides a unified framework for studying age-specific and sex-specific divergences in brain development. METHODS Here, we used population modeling and a large, multisite neuroimaging dataset (N = 4255 after quality control) to characterize cortical anatomy associated with autism and ADHD, benchmarked against models of average brain development based on a sample of more than 75,000 individuals. We also examined sex and age differences and relationship with autistic traits and explored the co-occurrence of autism and ADHD. RESULTS We observed robust neuroanatomical signatures of both autism and ADHD. Overall, autistic individuals showed greater cortical thickness and volume that was localized to the superior temporal cortex, whereas individuals with ADHD showed more global increases in cortical thickness but lower cortical volume and surface area across much of the cortex. The co-occurring autism+ADHD group showed a unique pattern of widespread increases in cortical thickness and certain decreases in surface area. We also found that sex modulated the neuroanatomy of autism but not ADHD, and there was an age-by-diagnosis interaction for ADHD only. CONCLUSIONS These results indicate distinct cortical differences in autism and ADHD that are differentially affected by age and sex as well as potentially unique patterns related to their co-occurrence.
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Affiliation(s)
- Saashi A Bedford
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
| | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; 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, Ontario, Canada; Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, 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, United Kingdom; Centre for Autism, School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom
| | - Amber Ruigrok
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, Canada
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Evdokia Anagnostou
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada; Department of Pediatrics, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Jason P Lerch
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada; Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Margot Taylor
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada; Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, 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, Ontario, Canada; Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, Ontario, 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, Ontario, Canada; Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada; Genetics & Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Elizabeth Kelley
- Department of Psychology, Queen's University, Kingston, Ontario, Canada; Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada; Department of Psychiatry, Queen's University, Kingston, Ontario, Canada
| | - Jessica Jones
- Department of Psychology, Queen's University, Kingston, Ontario, Canada; Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada; Department of Psychiatry, Queen's University, Kingston, Ontario, Canada
| | - Paul D Arnold
- 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
| | - Karen Pierce
- Department of Neurosciences, University of California San Diego, La Jolla, California
| | - Lisa T Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, California
| | - Kathleen Campbell
- Department of Neurosciences, University of California San Diego, La Jolla, California
| | - Cynthia Carter Barnes
- Department of Neurosciences, University of California San Diego, La Jolla, California
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, Pennsylvania
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, Pennsylvania
| | - Edward T Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridge Lifetime Autism Spectrum Service, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom
| | - Richard A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Department of Psychology, University of Cambridge, Cambridge, United Kingdom
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16
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Dugré JR, Potvin S. Investigating the impact of lumping heterogenous conduct problems: aggression and rule-breaking rely on distinct spontaneous brain activity. Eur Child Adolesc Psychiatry 2024:10.1007/s00787-024-02557-w. [PMID: 39143190 DOI: 10.1007/s00787-024-02557-w] [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: 04/27/2024] [Accepted: 08/06/2024] [Indexed: 08/16/2024]
Abstract
Accumulating evidence suggests that aggression and rule-breaking may have distinct origins. However, grouping these heterogeneous behaviors into a single dimension labelled Conduct Problems (CP) has become the norm rather than the exception. Yet, the neurobiological features that differentiate aggression and rule-breaking remain largely unexplored. Using a large sample of children and adolescents (n = 1360, 6-18 years old), we examined the common and specific brain activity between CP, aggression, and rule-breaking behaviors. Analyses were conducted using fMRI resting-state data from a 10-minute session to explore the correlations between low frequency fluctuations and both broad and fine-grained CP dimensions. The broad CP dimension was associated with deficits in the precentral gyrus, superior temporal gyrus, and tempo-parietal junction. However, only the superior temporal gyrus was shared between aggression and rule-breaking. Activity of the precentral gyrus was mainly associated with rule-breaking, and the temporo-parietal cortex with aggression. More importantly, voxel-wise analyses on fine-grained dimensions revealed additional specific effects that were initially obscured when using a broad CP dimension. Finally, we showed that the findings specific to aggression and rule-breaking may be related to distinct brain networks and mental functions, especially ventral attention/sensorimotor processes and default mode network/social cognitions, respectively. The current study highlights that aggression and rule-breaking may be related to distinct local and distributed neurobiological markers. Overall, using fine-grained dimensions may provide a clearer picture of the role of neurobiological correlates in CP and their invariance across measurement levels. We advocate for adopting a more thorough examination of the lumping/splitting effect across neuroimaging studies on CP.
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Affiliation(s)
- Jules Roger Dugré
- School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham, B15 2TT, United Kingdom.
| | - Stéphane Potvin
- Department of Psychiatry and Addiction, Faculty of medicine, University of Montreal, Montreal, Canada.
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, 7331, Hochelaga, Montreal, H1N 3V2, Canada.
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17
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Li X, Bianchini Esper N, Ai L, Giavasis S, Jin H, Feczko E, Xu T, Clucas J, Franco A, Sólon Heinsfeld A, Adebimpe A, Vogelstein JT, Yan CG, Esteban O, Poldrack RA, Craddock C, Fair D, Satterthwaite T, Kiar G, Milham MP. Moving beyond processing- and analysis-related variation in resting-state functional brain imaging. Nat Hum Behav 2024:10.1038/s41562-024-01942-4. [PMID: 39103610 DOI: 10.1038/s41562-024-01942-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 07/02/2024] [Indexed: 08/07/2024]
Abstract
When fields lack consensus standard methods and accessible ground truths, reproducibility can be more of an ideal than a reality. Such has been the case for functional neuroimaging, where there exists a sprawling space of tools and processing pipelines. We provide a critical evaluation of the impact of differences across five independently developed minimal preprocessing pipelines for functional magnetic resonance imaging. We show that, even when handling identical data, interpipeline agreement was only moderate, critically shedding light on a factor that limits cross-study reproducibility. We show that low interpipeline agreement can go unrecognized until the reliability of the underlying data is high, which is increasingly the case as the field progresses. Crucially we show that, when interpipeline agreement is compromised, so too is the consistency of insights from brain-wide association studies. We highlight the importance of comparing analytic configurations, because both widely discussed and commonly overlooked decisions can lead to marked variation.
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Affiliation(s)
- Xinhui Li
- Child Mind Institute, New York, NY, USA
| | | | - Lei Ai
- Child Mind Institute, New York, NY, USA
| | | | | | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Ting Xu
- Child Mind Institute, New York, NY, USA
| | | | - Alexandre Franco
- Child Mind Institute, New York, NY, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
| | | | - Azeez Adebimpe
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joshua T Vogelstein
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- International Big-Data Center for Depression Research, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Oscar Esteban
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Psychology, Stanford University, San Francisco, CA, USA
| | | | - Cameron Craddock
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA
| | - Damien Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Theodore Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Michael P Milham
- Child Mind Institute, New York, NY, USA.
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA.
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18
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Bègue I, Elandaloussi Y, Delavari F, Cao H, Moussa-Tooks A, Roser M, Coupé P, Leboyer M, Kaiser S, Houenou J, Brady R, Laidi C. The Cerebellum and Cognitive Function: Anatomical Evidence from a Transdiagnostic Sample. CEREBELLUM (LONDON, ENGLAND) 2024; 23:1399-1410. [PMID: 38151675 PMCID: PMC11269336 DOI: 10.1007/s12311-023-01645-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/30/2023] [Indexed: 12/29/2023]
Abstract
Multiple lines of evidence across human functional, lesion, and animal data point to a cerebellar role, in particular of crus I, crus II, and lobule VIIB, in cognitive function. However, a mapping of distinct facets of cognitive function to cerebellar structure is missing. We analyzed structural neuroimaging data from the Healthy Brain Network (HBN). Cerebellar parcellation was performed with a validated automated segmentation pipeline (CERES) and stringent visual quality check (n = 662 subjects retained from initial n = 1452). Canonical correlation analyses (CCA) examined regional gray matter volumetric (GMV) differences in association to cognitive function (quantified with NIH Toolbox Cognition domain, NIH-TB), accounting for psychopathology severity, age, sex, scan location, and intracranial volume. Multivariate CCA uncovered a significant correlation between two components entailing a latent cognitive canonical (NIH-TB subscales) and a brain canonical variate (cerebellar GMV and intracranial volume, ICV), surviving bootstrapping and permutation procedures. The components correspond to partly shared cerebellar-cognitive function relationship with a first map encompassing cognitive flexibility (r = 0.89), speed of processing (r = 0.65), and working memory (r = 0.52) associated with regional GMV in crus II (r = 0.57) and lobule X (r = 0.59) and a second map including the crus I (r = 0.49) and lobule VI (r = 0.49) associated with working memory (r = 0.51). We show evidence for a structural subspecialization of the cerebellum topography for cognitive function in a transdiagnostic sample.
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Affiliation(s)
- Indrit Bègue
- Department of Psychiatry, Beth Israel Deaconess Medical School & Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, McLean Hospital & Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, University Hospitals of Geneva & University of Geneva, Geneva, Switzerland.
| | - Yannis Elandaloussi
- INSERM U955, Institut Mondor de La Recherche Biomédicale (IRMB), Univ. Paris Est Créteil, Equipe 15 Neuropsychiatrie Translationnelle, Créteil, France
- La Fondation Fondamental, Créteil, France
- NeuroSpin, Neuroimaging Platform, CEA, UNIACT Lab, PsyBrain Team, Saclay, France
| | - Farnaz Delavari
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Hengyi Cao
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Queens, NY, USA
| | - Alexandra Moussa-Tooks
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mathilde Roser
- INSERM U955, Institut Mondor de La Recherche Biomédicale (IRMB), Univ. Paris Est Créteil, Equipe 15 Neuropsychiatrie Translationnelle, Créteil, France
- La Fondation Fondamental, Créteil, France
- NeuroSpin, Neuroimaging Platform, CEA, UNIACT Lab, PsyBrain Team, Saclay, France
| | - Pierrick Coupé
- LABRI UMR 5800, CNRS, Univ. Bordeaux, Bordeaux INPTalence, France
| | - Marion Leboyer
- INSERM U955, Institut Mondor de La Recherche Biomédicale (IRMB), Univ. Paris Est Créteil, Equipe 15 Neuropsychiatrie Translationnelle, Créteil, France
- La Fondation Fondamental, Créteil, France
| | - Stefan Kaiser
- Department of Psychiatry, University Hospitals of Geneva & University of Geneva, Geneva, Switzerland
| | - Josselin Houenou
- INSERM U955, Institut Mondor de La Recherche Biomédicale (IRMB), Univ. Paris Est Créteil, Equipe 15 Neuropsychiatrie Translationnelle, Créteil, France
- La Fondation Fondamental, Créteil, France
- NeuroSpin, Neuroimaging Platform, CEA, UNIACT Lab, PsyBrain Team, Saclay, France
| | - Roscoe Brady
- Department of Psychiatry, Beth Israel Deaconess Medical School & Harvard Medical School, Boston, MA, USA
| | - Charles Laidi
- INSERM U955, Institut Mondor de La Recherche Biomédicale (IRMB), Univ. Paris Est Créteil, Equipe 15 Neuropsychiatrie Translationnelle, Créteil, France.
- La Fondation Fondamental, Créteil, France.
- NeuroSpin, Neuroimaging Platform, CEA, UNIACT Lab, PsyBrain Team, Saclay, France.
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19
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Chen J, Bornstein AM. The causal structure and computational value of narratives. Trends Cogn Sci 2024; 28:769-781. [PMID: 38734531 PMCID: PMC11305923 DOI: 10.1016/j.tics.2024.04.003] [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: 12/08/2023] [Revised: 04/03/2024] [Accepted: 04/08/2024] [Indexed: 05/13/2024]
Abstract
Many human behavioral and brain imaging studies have used narratively structured stimuli (e.g., written, audio, or audiovisual stories) to better emulate real-world experience in the laboratory. However, narratives are a special class of real-world experience, largely defined by their causal connections across time. Much contemporary neuroscience research does not consider this key property. We review behavioral and neuroscientific work that speaks to how causal structure shapes comprehension of and memory for narratives. We further draw connections between this work and reinforcement learning, highlighting how narratives help link causes to outcomes in complex environments. By incorporating the plausibility of causal connections between classes of actions and outcomes, reinforcement learning models may become more ecologically valid, while simultaneously elucidating the value of narratives.
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Affiliation(s)
- Janice Chen
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA.
| | - Aaron M Bornstein
- Department of Cognitive Sciences, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
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20
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Mochalski LN, Friedrich P, Li X, Kröll J, Eickhoff SB, Weis S. Inter- and intra-subject similarity in network functional connectivity across a full narrative movie. Hum Brain Mapp 2024; 45:e26802. [PMID: 39086203 PMCID: PMC11291869 DOI: 10.1002/hbm.26802] [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: 06/27/2023] [Revised: 07/11/2024] [Accepted: 07/17/2024] [Indexed: 08/02/2024] Open
Abstract
Naturalistic paradigms, such as watching movies during functional magnetic resonance imaging, are thought to prompt the emotional and cognitive processes typically elicited in real life situations. Therefore, naturalistic viewing (NV) holds great potential for studying individual differences. Previous studies have primarily focused on using shorter movie clips, geared toward eliciting specific and often isolated emotions, while the potential behind using full narratives depicted in commercial movies as a proxy for real-life experiences has barely been explored. Here, we offer preliminary evidence that a full narrative movie (FNM), that is, a movie covering a complete narrative arc, can capture complex socio-affective dynamics and their links to individual differences. Using the studyforrest dataset, we investigated inter- and intra-subject similarity in network functional connectivity (NFC) of 14 meta-analytically defined networks across a full narrative, audio-visual movie split into eight consecutive movie segments. We characterized the movie segments by valence and arousal portrayed within the sequences, before utilizing a linear mixed model to analyze which factors explain inter- and intra-subject similarity. Our results show that the model best explaining inter-subject similarity comprised network, movie segment, valence and a movie segment by valence interaction. Intra-subject similarity was influenced significantly by the same factors and an additional three-way interaction between movie segment, valence and arousal. Overall, inter- and intra-subject similarity in NFC were sensitive to the ongoing narrative and emotions in the movie. We conclude that FNMs offer complex content and dynamics that might be particularly valuable for studying individual differences. Further characterization of movie features, such as the overarching narratives, that enhance individual differences is needed for advancing the potential of NV research.
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Affiliation(s)
- Lisa N. Mochalski
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Centre JülichJülichGermany
- Institute of Systems Neuroscience, Medical FacultyHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Patrick Friedrich
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Centre JülichJülichGermany
| | - Xuan Li
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Centre JülichJülichGermany
| | - Jean‐Philippe Kröll
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Centre JülichJülichGermany
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Centre JülichJülichGermany
- Institute of Systems Neuroscience, Medical FacultyHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Susanne Weis
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Centre JülichJülichGermany
- Institute of Systems Neuroscience, Medical FacultyHeinrich Heine University DüsseldorfDüsseldorfGermany
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21
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Rosenblatt M, Tejavibulya L, Sun H, Camp CC, Khaitova M, Adkinson BD, Jiang R, Westwater ML, Noble S, Scheinost D. Power and reproducibility in the external validation of brain-phenotype predictions. Nat Hum Behav 2024:10.1038/s41562-024-01931-7. [PMID: 39085406 DOI: 10.1038/s41562-024-01931-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 06/18/2024] [Indexed: 08/02/2024]
Abstract
Brain-phenotype predictive models seek to identify reproducible and generalizable brain-phenotype associations. External validation, or the evaluation of a model in external datasets, is the gold standard in evaluating the generalizability of models in neuroimaging. Unlike typical studies, external validation involves two sample sizes: the training and the external sample sizes. Thus, traditional power calculations may not be appropriate. Here we ran over 900 million resampling-based simulations in functional and structural connectivity data to investigate the relationship between training sample size, external sample size, phenotype effect size, theoretical power and simulated power. Our analysis included a wide range of datasets: the Healthy Brain Network, the Adolescent Brain Cognitive Development Study, the Human Connectome Project (Development and Young Adult), the Philadelphia Neurodevelopmental Cohort, the Queensland Twin Adolescent Brain Project, and the Chinese Human Connectome Project; and phenotypes: age, body mass index, matrix reasoning, working memory, attention problems, anxiety/depression symptoms and relational processing. High effect size predictions achieved adequate power with training and external sample sizes of a few hundred individuals, whereas low and medium effect size predictions required hundreds to thousands of training and external samples. In addition, most previous external validation studies used sample sizes prone to low power, and theoretical power curves should be adjusted for the training sample size. Furthermore, model performance in internal validation often informed subsequent external validation performance (Pearson's r difference <0.2), particularly for well-harmonized datasets. These results could help decide how to power future external validation studies.
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Affiliation(s)
- Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Chris C Camp
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Milana Khaitova
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Margaret L Westwater
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
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22
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Parkes L, Kim JZ, Stiso J, Brynildsen JK, Cieslak M, Covitz S, Gur RE, Gur RC, Pasqualetti F, Shinohara RT, Zhou D, Satterthwaite TD, Bassett DS. A network control theory pipeline for studying the dynamics of the structural connectome. Nat Protoc 2024:10.1038/s41596-024-01023-w. [PMID: 39075309 DOI: 10.1038/s41596-024-01023-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 05/16/2024] [Indexed: 07/31/2024]
Abstract
Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.
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Affiliation(s)
- Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY, USA
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia K Brynildsen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, PA, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dale Zhou
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
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23
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Bernardo D, Xie X, Verma P, Kim J, Liu V, Numis AL, Wu Y, Glass HC, Yap PT, Nagarajan SS, Raj A. Simulation-based Inference of Developmental EEG Maturation with the Spectral Graph Model. ARXIV 2024:arXiv:2405.02524v3. [PMID: 39040639 PMCID: PMC11261974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown. Here, we assess the developmental maturation of electroencephalogram spectra via Bayesian model inversion of the spectral graph model, a parsimonious whole-brain model of spatiospectral neural activity derived from linearized neural field models coupled by the structural connectome. Simulation-based inference was used to estimate age-varying spectral graph model parameter posterior distributions from electroencephalogram spectra spanning the developmental period. This model-fitting approach accurately captures observed developmental electroencephalogram spectral maturation via a neurobiologically consistent progression of key neural parameters: long-range coupling, axonal conduction speed, and excitatory:inhibitory balance. These results suggest that the spectral maturation of macroscopic neural activity observed during typical development is supported by age-dependent functional adaptations in localized neural dynamics and their long-range coupling across the macroscopic structural network.
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Affiliation(s)
- Danilo Bernardo
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Xihe Xie
- Department of Neuroscience, Weill Cornell Medicine, New York, NY, USA
| | - Parul Verma
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Jonathan Kim
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Virginia Liu
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Adam L. Numis
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Hannah C. Glass
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Srikantan S. Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
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24
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Kong Y, Roser M, Bègue I, Elandaloussi Y, Neu N, Grigis A, Duchesnay E, Leboyer M, Houenou J, Laidi C. Cerebellum and social abilities: A structural and functional connectivity study in a transdiagnostic sample. Hum Brain Mapp 2024; 45:e26749. [PMID: 38989605 PMCID: PMC11237877 DOI: 10.1002/hbm.26749] [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: 10/12/2023] [Revised: 05/13/2024] [Accepted: 05/21/2024] [Indexed: 07/12/2024] Open
Abstract
The cerebellum has been involved in social abilities and autism. Given that the cerebellum is connected to the cortex via the cerebello-thalamo-cortical loop, the connectivity between the cerebellum and cortical regions involved in social interactions, that is, the right temporo-parietal junction (rTPJ) has been studied in individuals with autism, who suffer from prototypical deficits in social abilities. However, existing studies with small samples of categorical, case-control comparisons have yielded inconsistent results due to the inherent heterogeneity of autism, suggesting that investigating how clinical dimensions are related to cerebellar-rTPJ functional connectivity might be more relevant. Therefore, our objective was to study the functional connectivity between the cerebellum and rTPJ, focusing on its association with social abilities from a dimensional perspective in a transdiagnostic sample. We analyzed structural magnetic resonance imaging (MRI) and functional MRI (fMRI) scans obtained during naturalistic films watching from a large transdiagnostic dataset, the Healthy Brain Network (HBN), and examined the association between cerebellum-rTPJ functional connectivity and social abilities measured with the social responsiveness scale (SRS). We conducted univariate seed-to-voxel analysis, multivariate canonical correlation analysis (CCA), and predictive support vector regression (SVR). We included 1404 subjects in the structural analysis (age: 10.516 ± 3.034, range: 5.822-21.820, 506 females) and 414 subjects in the functional analysis (age: 11.260 ± 3.318 years, range: 6.020-21.820, 161 females). Our CCA model revealed a significant association between cerebellum-rTPJ functional connectivity, full-scale IQ (FSIQ) and SRS scores. However, this effect was primarily driven by FSIQ as suggested by SVR and univariate seed-to-voxel analysis. We also demonstrated the specificity of the rTPJ and the influence of structural anatomy in this association. Our results suggest that there is a complex relationship between cerebellum-rTPJ connectivity, social performance and IQ. This relationship is specific to the cerebellum-rTPJ connectivity, and is largely related to structural anatomy in these two regions. PRACTITIONER POINTS: We analyzed cerebellum-right temporoparietal junction (rTPJ) connectivity in a pediatric transdiagnostic sample. We found a complex relationship between cerebellum and rTPJ connectivity, social performance and IQ. Cerebellum and rTPJ functional connectivity is related to structural anatomy in these two regions.
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Affiliation(s)
- Yue Kong
- INSERM U955, Institut Mondor de la Recherche Biomédicale (IMRB), Univ. Paris East Créteil, Team 15 Translational Neuropsychiatry, DMU IMPACT, Henri Mondor - AP-HP Paris University Hospitals, Créteil, France
- NeuroSpin, CEA, Paris-Saclay University, France, Saclay, France
| | - Mathilde Roser
- INSERM U955, Institut Mondor de la Recherche Biomédicale (IMRB), Univ. Paris East Créteil, Team 15 Translational Neuropsychiatry, DMU IMPACT, Henri Mondor - AP-HP Paris University Hospitals, Créteil, France
- NeuroSpin, CEA, Paris-Saclay University, France, Saclay, France
- Fondation Fondamental, Créteil, France
| | - Indrit Bègue
- Department of Psychiatry, Beth Israel Deaconess Medical School and Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Neuroimaging and Translational Psychiatry lab, Synapsy Center for Neuroscience and Mental Health Research, Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Yannis Elandaloussi
- INSERM U955, Institut Mondor de la Recherche Biomédicale (IMRB), Univ. Paris East Créteil, Team 15 Translational Neuropsychiatry, DMU IMPACT, Henri Mondor - AP-HP Paris University Hospitals, Créteil, France
- NeuroSpin, CEA, Paris-Saclay University, France, Saclay, France
| | - Nathan Neu
- INSERM U955, Institut Mondor de la Recherche Biomédicale (IMRB), Univ. Paris East Créteil, Team 15 Translational Neuropsychiatry, DMU IMPACT, Henri Mondor - AP-HP Paris University Hospitals, Créteil, France
- NeuroSpin, CEA, Paris-Saclay University, France, Saclay, France
| | - Antoine Grigis
- NeuroSpin, CEA, Paris-Saclay University, France, Saclay, France
| | | | - Marion Leboyer
- INSERM U955, Institut Mondor de la Recherche Biomédicale (IMRB), Univ. Paris East Créteil, Team 15 Translational Neuropsychiatry, DMU IMPACT, Henri Mondor - AP-HP Paris University Hospitals, Créteil, France
- Fondation Fondamental, Créteil, France
| | - Josselin Houenou
- INSERM U955, Institut Mondor de la Recherche Biomédicale (IMRB), Univ. Paris East Créteil, Team 15 Translational Neuropsychiatry, DMU IMPACT, Henri Mondor - AP-HP Paris University Hospitals, Créteil, France
- NeuroSpin, CEA, Paris-Saclay University, France, Saclay, France
- Fondation Fondamental, Créteil, France
| | - Charles Laidi
- INSERM U955, Institut Mondor de la Recherche Biomédicale (IMRB), Univ. Paris East Créteil, Team 15 Translational Neuropsychiatry, DMU IMPACT, Henri Mondor - AP-HP Paris University Hospitals, Créteil, France
- NeuroSpin, CEA, Paris-Saclay University, France, Saclay, France
- Fondation Fondamental, Créteil, France
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25
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Kim W, Kim MJ. Adaptive-to-maladaptive gradient of emotion regulation tendencies are embedded in the functional-structural hybrid connectome. Psychol Med 2024; 54:2299-2311. [PMID: 38533787 DOI: 10.1017/s0033291724000473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
BACKGROUND Emotion regulation tendencies are well-known transdiagnostic markers of psychopathology, but their neurobiological foundations have mostly been examined within the theoretical framework of cortical-subcortical interactions. METHODS We explored the connectome-wide neural correlates of emotion regulation tendencies using functional and diffusion magnetic resonance images of healthy young adults (N = 99; age 20-30; 28 females). We first tested the importance of considering both the functional and structural connectome through intersubject representational similarity analyses. Then, we employed a canonical correlation analysis between the functional-structural hybrid connectome and 23 emotion regulation strategies. Lastly, we sought to externally validate the results on a transdiagnostic adolescent sample (N = 93; age 11-19; 34 females). RESULTS First, interindividual similarity of emotion regulation profiles was significantly correlated with interindividual similarity of the functional-structural hybrid connectome, more so than either the functional or structural connectome. Canonical correlation analysis revealed that an adaptive-to-maladaptive gradient of emotion regulation tendencies mapped onto a specific configuration of covariance within the functional-structural hybrid connectome, which primarily involved functional connections in the motor network and the visual networks as well as structural connections in the default mode network and the subcortical-cerebellar network. In the transdiagnostic adolescent dataset, stronger functional signatures of the found network were associated with higher general positive affect through more frequent use of adaptive coping strategies. CONCLUSIONS Taken together, our study illustrates a gradient of emotion regulation tendencies that is best captured when simultaneously considering the functional and structural connections across the whole brain.
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Affiliation(s)
- Wonyoung Kim
- Department of Psychology, Emory University, Atlanta, GA, USA
- Department of Psychology, Sungkyunkwan University, Seoul, South Korea
| | - M Justin Kim
- Department of Psychology, Sungkyunkwan University, Seoul, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
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26
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Tansey R, Graff K, Rai S, Merrikh D, Godfrey KJ, Vanderwal T, Bray S. Development of human visual cortical function: A scoping review of task- and naturalistic-fMRI studies through the interactive specialization and maturational frameworks. Neurosci Biobehav Rev 2024; 162:105729. [PMID: 38763178 DOI: 10.1016/j.neubiorev.2024.105729] [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: 03/07/2024] [Revised: 05/12/2024] [Accepted: 05/14/2024] [Indexed: 05/21/2024]
Abstract
Overarching theories such as the interactive specialization and maturational frameworks have been proposed to describe human functional brain development. However, these frameworks have not yet been systematically examined across the fMRI literature. Visual processing is one of the most well-studied fields in neuroimaging, and research in this area has recently expanded to include naturalistic paradigms that facilitate study in younger age ranges, allowing for an in-depth critical appraisal of these frameworks across childhood. To this end, we conducted a scoping review of 94 developmental visual fMRI studies, including both traditional experimental task and naturalistic studies, across multiple sub-domains (early visual processing, category-specific higher order processing, naturalistic visual processing). We found that across domains, many studies reported progressive development, but few studies describe regressive or emergent changes necessary to fit the maturational or interactive specialization frameworks. Our findings suggest a need for the expansion of developmental frameworks and clearer reporting of both progressive and regressive changes, along with well-powered, longitudinal studies.
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Affiliation(s)
- Ryann Tansey
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
| | - Kirk Graff
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Shefali Rai
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Daria Merrikh
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Kate J Godfrey
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Tamara Vanderwal
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada; BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Signe Bray
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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27
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Takemura H, Kruper JA, Miyata T, Rokem A. Tractometry of Human Visual White Matter Pathways in Health and Disease. Magn Reson Med Sci 2024; 23:316-340. [PMID: 38866532 PMCID: PMC11234945 DOI: 10.2463/mrms.rev.2024-0007] [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] [Indexed: 06/14/2024] Open
Abstract
Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.
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Affiliation(s)
- Hiromasa Takemura
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Graduate Institute for Advanced Studies, SOKENDAI, Hayama, Kanagawa, Japan
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Osaka, Japan
| | - John A Kruper
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Toshikazu Miyata
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Osaka, Japan
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
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28
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Kang K, Seidlitz J, Bethlehem RA, Xiong J, Jones MT, Mehta K, Keller AS, Tao R, Randolph A, Larsen B, Tervo-Clemmens B, Feczko E, Miranda Dominguez O, Nelson S, Schildcrout J, Fair D, Satterthwaite TD, Alexander-Bloch A, Vandekar S. Study design features increase replicability in cross-sectional and longitudinal brain-wide association studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.29.542742. [PMID: 37398345 PMCID: PMC10312450 DOI: 10.1101/2023.05.29.542742] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Brain-wide association studies (BWAS) are a fundamental tool in discovering brain-behavior associations. Several recent studies showed that thousands of study participants are required for good replicability of BWAS because the standardized effect sizes (ESs) are much smaller than the reported standardized ESs in smaller studies. Here, we perform analyses and meta-analyses of a robust effect size index using 63 longitudinal and cross-sectional magnetic resonance imaging studies from the Lifespan Brain Chart Consortium (77,695 total scans) to demonstrate that optimizing study design is critical for increasing standardized ESs and replicability in BWAS. A meta-analysis of brain volume associations with age indicates that BWAS with larger variability in covariate have larger reported standardized ES. In addition, the longitudinal studies we examined reported systematically larger standardized ES than cross-sectional studies. Analyzing age effects on global and regional brain measures from the United Kingdom Biobank and the Alzheimer's Disease Neuroimaging Initiative, we show that modifying longitudinal study design through sampling schemes improves the standardized ESs and replicability. Sampling schemes that improve standardized ESs and replicability include increasing between-subject age variability in the sample and adding a single additional longitudinal measurement per subject. To ensure that our results are generalizable, we further evaluate these longitudinal sampling schemes on cognitive, psychopathology, and demographic associations with structural and functional brain outcome measures in the Adolescent Brain and Cognitive Development dataset. We demonstrate that commonly used longitudinal models can, counterintuitively, reduce standardized ESs and replicability. The benefit of conducting longitudinal studies depends on the strengths of the between- versus within-subject associations of the brain and non-brain measures. Explicitly modeling between- versus within-subject effects avoids averaging the effects and allows optimizing the standardized ESs for each separately. Together, these results provide guidance for study designs that improve the replicability of BWAS.
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Affiliation(s)
- Kaidi Kang
- Department of Biostatistics, Vanderbilt University Medical Center
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children’s Hospital of Philadelphia
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
| | | | - Jiangmei Xiong
- Department of Biostatistics, Vanderbilt University Medical Center
| | - Megan T. Jones
- Department of Biostatistics, Vanderbilt University Medical Center
| | - Kahini Mehta
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania
| | - Arielle S. Keller
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center
| | - Anita Randolph
- Department of Pediatrics, University of Minnesota Medical School
| | - Bart Larsen
- Department of Pediatrics, University of Minnesota Medical School
| | - Brenden Tervo-Clemmens
- Department of Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota Medical School
| | | | - Steve Nelson
- Department of Pediatrics, University of Minnesota Medical School
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Damien Fair
- Department of Pediatrics, University of Minnesota Medical School
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania
| | - Aaron Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children’s Hospital of Philadelphia
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center
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29
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Makowski C, Nichols TE, Dale AM. Quality over quantity: powering neuroimaging samples in psychiatry. Neuropsychopharmacology 2024:10.1038/s41386-024-01893-4. [PMID: 38902353 DOI: 10.1038/s41386-024-01893-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 05/06/2024] [Accepted: 05/24/2024] [Indexed: 06/22/2024]
Abstract
Neuroimaging has been widely adopted in psychiatric research, with hopes that these non-invasive methods will provide important clues to the underpinnings and prediction of various mental health symptoms and outcomes. However, the translational impact of neuroimaging has not yet reached its promise, despite the plethora of computational methods, tools, and datasets at our disposal. Some have lamented that too many psychiatric neuroimaging studies have been underpowered with respect to sample size. In this review, we encourage this discourse to shift from a focus on sheer increases in sample size to more thoughtful choices surrounding experimental study designs. We propose considerations at multiple decision points throughout the study design, data modeling and analysis process that may help researchers working in psychiatric neuroimaging boost power for their research questions of interest without necessarily increasing sample size. We also provide suggestions for leveraging multiple datasets to inform each other and strengthen our confidence in the generalization of findings to both population-level and clinical samples. Through a greater emphasis on improving the quality of brain-based and clinical measures rather than merely quantity, meaningful and potentially translational clinical associations with neuroimaging measures can be achieved with more modest sample sizes in psychiatry.
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Affiliation(s)
- Carolina Makowski
- Department of Radiology, University of California San Diego, San Diego, CA, USA.
| | - Thomas E Nichols
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Anders M Dale
- Departments of Radiology and Neurosciences, University of California San Diego, San Diego, CA, USA
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Sydnor VJ, Bagautdinova J, Larsen B, Arcaro MJ, Barch DM, Bassett DS, Alexander-Bloch AF, Cook PA, Covitz S, Franco AR, Gur RE, Gur RC, Mackey AP, Mehta K, Meisler SL, Milham MP, Moore TM, Müller EJ, Roalf DR, Salo T, Schubiner G, Seidlitz J, Shinohara RT, Shine JM, Yeh FC, Cieslak M, Satterthwaite TD. A sensorimotor-association axis of thalamocortical connection development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.13.598749. [PMID: 38915591 PMCID: PMC11195180 DOI: 10.1101/2024.06.13.598749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Human cortical development follows a sensorimotor-to-association sequence during childhood and adolescence1-6. The brain's capacity to enact this sequence over decades indicates that it relies on intrinsic mechanisms to regulate inter-regional differences in the timing of cortical maturation, yet regulators of human developmental chronology are not well understood. Given evidence from animal models that thalamic axons modulate windows of cortical plasticity7-12, here we evaluate the overarching hypothesis that structural connections between the thalamus and cortex help to coordinate cortical maturational heterochronicity during youth. We first introduce, cortically annotate, and anatomically validate a new atlas of human thalamocortical connections using diffusion tractography. By applying this atlas to three independent youth datasets (ages 8-23 years; total N = 2,676), we reproducibly demonstrate that thalamocortical connections develop along a maturational gradient that aligns with the cortex's sensorimotor-association axis. Associative cortical regions with thalamic connections that take longest to mature exhibit protracted expression of neurochemical, structural, and functional markers indicative of higher circuit plasticity as well as heightened environmental sensitivity. This work highlights a central role for the thalamus in the orchestration of hierarchically organized and environmentally sensitive windows of cortical developmental malleability.
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Affiliation(s)
- Valerie J. Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joëlle Bagautdinova
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Michael J. Arcaro
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Deanna M. Barch
- Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, Missouri, USA
- Department of Psychological & Brain Sciences, Washington University in St Louis, St Louis, Missouri, USA
| | - Dani S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA
- The Santa Fe Institute, Santa Fe, NM, USA
| | - Aaron F. Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Philip A. Cook
- Penn Image Computing and Science Lab (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Covitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, Schools of Engineering and Medicine, Stanford University, Stanford, CA, USA
| | - Alexandre R. Franco
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Strategic Data Initiatives, Child Mind Institute, New York, NY, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | - Raquel E. Gur
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C. Gur
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Brain Behavior Laboratory, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Allyson P. Mackey
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Kahini Mehta
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Steven L. Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Division of Medical Sciences, Cambridge, MA, USA
| | - Michael P. Milham
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Tyler M. Moore
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Brain Behavior Laboratory, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Eli J. Müller
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - David R. Roalf
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Brain Behavior Laboratory, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Taylor Salo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jakob Seidlitz
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - James M. Shine
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Theodore D. Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute (LiBI), Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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Kruper J, Hagen MP, Rheault F, Crane I, Gilmore A, Narayan M, Motwani K, Lila E, Rorden C, Yeatman JD, Rokem A. Tractometry of the Human Connectome Project: resources and insights. Front Neurosci 2024; 18:1389680. [PMID: 38933816 PMCID: PMC11199395 DOI: 10.3389/fnins.2024.1389680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 05/15/2024] [Indexed: 06/28/2024] Open
Abstract
Introduction The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data. Methods We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines. Results We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry-"Tractoscope" (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.
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Affiliation(s)
- John Kruper
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - McKenzie P. Hagen
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - François Rheault
- Department of Computer Science, Universitè de Sherbrooke, Sherbrooke, QC, Canada
| | - Isaac Crane
- Department of Psychology, University of Chicago, Chicago, IL, United States
| | - Asa Gilmore
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - Manjari Narayan
- Graduate School of Education, Stanford University, Stanford, CA, United States
| | - Keshav Motwani
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Eardi Lila
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Chris Rorden
- Department of Psychology, University of South Carolina, Columbia, SC, United States
| | - Jason D. Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, United States
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA, United States
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Parks KMA, Cardy JO, Joanisse MF. Language and reading in attention-deficit/hyperactivity disorder and comorbid attention-deficit/hyperactivity disorder + developmental language disorder. JCPP ADVANCES 2024; 4:e12218. [PMID: 38827980 PMCID: PMC11143959 DOI: 10.1002/jcv2.12218] [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: 04/04/2023] [Accepted: 11/20/2023] [Indexed: 06/05/2024] Open
Abstract
Background The current study sought to examine whether psycholinguistic assessments could discriminate children and adolescents with developmental language disorder (DLD) from those with attention-deficit/hyperactivity disorder (ADHD; combined or inattentive subtype) and comorbid DLD + ADHD. Methods The Clinical Evaluation of Language Fundamentals-Screening Test (CELFST; Wiig et al., 2013), the Comprehensive Test of Phonological Processing (nonword repetition subtest; Wagner et al., 2013), and the Test of Word Reading Efficiency (sight word and phonemic decoding subtests; Torgesen et al., 2012) were examined in 441 children and adolescents between 6 and 16 years of age. Results The presence of a language disorder (with or without ADHD) predicted poor performance across tasks. Children and adolescents with ADHD (combined vs. inattentive) only significantly differed in sight word reading, in favor of those with combined type. Measures of reading efficiency could distinguish between the two types of ADHD, but not between other groups. Interestingly, scores on the standard language screener were no worse for children with ADHD + DLD than children with DLD only. Conclusions The combination of comorbid ADHD + DLD did not appear to be associated with lower language abilities, sight word reading, or phonemic decoding relative to DLD alone. Reading efficiency was effective in discriminating between ADHD subtypes. These findings offer valuable insights into differential diagnosis and the identification of comorbidity.
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Affiliation(s)
- Kaitlyn M. A. Parks
- Department of PsychologyWestern UniversityLondonOntarioCanada
- Brain and Mind InstituteWestern UniversityLondonOntarioCanada
| | - Janis Oram Cardy
- Brain and Mind InstituteWestern UniversityLondonOntarioCanada
- School of Communication Sciences and DisordersWestern UniversityLondonOntarioCanada
| | - Marc F. Joanisse
- Department of PsychologyWestern UniversityLondonOntarioCanada
- Brain and Mind InstituteWestern UniversityLondonOntarioCanada
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Roy E, Van Rinsveld A, Nedelec P, Richie-Halford A, Rauschecker AM, Sugrue LP, Rokem A, McCandliss BD, Yeatman JD. Differences in educational opportunity predict white matter development. Dev Cogn Neurosci 2024; 67:101386. [PMID: 38676989 DOI: 10.1016/j.dcn.2024.101386] [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: 10/22/2023] [Revised: 02/05/2024] [Accepted: 04/15/2024] [Indexed: 04/29/2024] Open
Abstract
Coarse measures of socioeconomic status, such as parental income or parental education, have been linked to differences in white matter development. However, these measures do not provide insight into specific aspects of an individual's environment and how they relate to brain development. On the other hand, educational intervention studies have shown that changes in an individual's educational context can drive measurable changes in their white matter. These studies, however, rarely consider socioeconomic factors in their results. In the present study, we examined the unique relationship between educational opportunity and white matter development, when controlling other known socioeconomic factors. To explore this question, we leveraged the rich demographic and neuroimaging data available in the ABCD study, as well the unique data-crosswalk between ABCD and the Stanford Education Data Archive (SEDA). We find that educational opportunity is related to accelerated white matter development, even when accounting for other socioeconomic factors, and that this relationship is most pronounced in white matter tracts associated with academic skills. These results suggest that the school a child attends has a measurable relationship with brain development for years to come.
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Affiliation(s)
- Ethan Roy
- Graduate School of Education, Stanford University, Stanford, CA, USA.
| | | | - Pierre Nedelec
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Adam Richie-Halford
- Graduate School of Education, Stanford University, Stanford, CA, USA; Division of Developmental-Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - Andreas M Rauschecker
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Leo P Sugrue
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | | | - Jason D Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, USA; Division of Developmental-Behavioral Pediatrics, Stanford University, Stanford, CA, USA
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Norman LJ, Sudre G, Price J, Shaw P. Subcortico-Cortical Dysconnectivity in ADHD: A Voxel-Wise Mega-Analysis Across Multiple Cohorts. Am J Psychiatry 2024; 181:553-562. [PMID: 38476041 DOI: 10.1176/appi.ajp.20230026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
OBJECTIVE A large body of functional MRI research has examined a potential role for subcortico-cortical loops in the pathogenesis of attention deficit hyperactivity disorder (ADHD), but has produced inconsistent findings. The authors performed a mega-analysis of six neuroimaging data sets to examine associations between ADHD diagnosis and traits and subcortico-cortical connectivity. METHODS Group differences were examined in the functional connectivity of four subcortical seeds in 1,696 youths with ADHD diagnoses (66.39% males; mean age, 10.83 years [SD=2.17]) and 6,737 unaffected control subjects (47.05% males; mean age, 10.33 years [SD=1.30]). The authors examined associations between functional connectivity and ADHD traits (total N=9,890; 50.3% males; mean age, 10.77 years [SD=1.96]). Sensitivity analyses were used to examine specificity relative to commonly comorbid internalizing and non-ADHD externalizing problems. The authors further examined results within motion-matched subsamples, and after adjusting for estimated intelligence. RESULTS In the group comparison, youths with ADHD showed greater connectivity between striatal seeds and temporal, fronto-insular, and supplementary motor regions, as well as between the amygdala and dorsal anterior cingulate cortex, compared with control subjects. Similar findings emerged when ADHD traits were considered and when alternative seed definitions were adopted. Dominant associations centered on the connectivity of the caudate bilaterally. Findings were not driven by in-scanner motion and were not shared with commonly comorbid internalizing and externalizing problems. Effect sizes were small (largest peak d, 0.15). CONCLUSIONS The findings from this large-scale mega-analysis support established links with subcortico-cortical circuits, which were robust to potential confounders. However, effect sizes were small, and it seems likely that resting-state subcortico-cortical connectivity can capture only a fraction of the complex pathophysiology of ADHD.
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Affiliation(s)
- Luke J Norman
- Office of the Clinical Director, NIMH, Bethesda, Md. (Norman, Shaw); Section on Neurobehavioral and Clinical Research, Social and Behavioral Research Branch, National Human Genome Research Institute, NIH, Bethesda, Md. (Sudre, Price, Shaw)
| | - Gustavo Sudre
- Office of the Clinical Director, NIMH, Bethesda, Md. (Norman, Shaw); Section on Neurobehavioral and Clinical Research, Social and Behavioral Research Branch, National Human Genome Research Institute, NIH, Bethesda, Md. (Sudre, Price, Shaw)
| | - Jolie Price
- Office of the Clinical Director, NIMH, Bethesda, Md. (Norman, Shaw); Section on Neurobehavioral and Clinical Research, Social and Behavioral Research Branch, National Human Genome Research Institute, NIH, Bethesda, Md. (Sudre, Price, Shaw)
| | - Philip Shaw
- Office of the Clinical Director, NIMH, Bethesda, Md. (Norman, Shaw); Section on Neurobehavioral and Clinical Research, Social and Behavioral Research Branch, National Human Genome Research Institute, NIH, Bethesda, Md. (Sudre, Price, Shaw)
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Kember J, Patenaude P, Sweatman H, Van Schaik L, Tabuenca Z, Chai XJ. Specialization of anterior and posterior hippocampal functional connectivity differs in autism. Autism Res 2024; 17:1126-1139. [PMID: 38770780 DOI: 10.1002/aur.3170] [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: 01/11/2024] [Accepted: 05/10/2024] [Indexed: 05/22/2024]
Abstract
Structural and functional differences in the hippocampus have been related to the episodic memory and social impairments observed in autism spectrum disorder (ASD). In neurotypical individuals, hippocampal-cortical functional connectivity systematically varies between anterior and posterior hippocampus, with changes observed during typical development. It remains unknown whether this specialization of anterior-posterior hippocampal connectivity is disrupted in ASD, and whether age-related differences in this specialization exist in ASD. We examined connectivity of the anterior and posterior hippocampus in an ASD (N = 139) and non-autistic comparison group (N = 133) aged 5-21 using resting-state functional magnetic resonance imaging (MRI) data from the Healthy Brain Network (HBN). Consistent with previous results, we observed lower connectivity between the whole hippocampus and medial prefrontal cortex in ASD. Moreover, preferential connectivity of the posterior relative to the anterior hippocampus for memory-sensitive regions in posterior parietal cortex was reduced in ASD, demonstrating a weaker anterior-posterior specialization of hippocampal-cortical connectivity. Finally, connectivity between the posterior hippocampus and precuneus negatively correlated with age in the ASD group but remained stable in the comparison group, suggesting an altered developmental specialization. Together, these differences in hippocampal-cortical connectivity may help us understand the neurobiological basis of the memory and social impairments found in ASD.
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Affiliation(s)
- J Kember
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - P Patenaude
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - H Sweatman
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - L Van Schaik
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - Z Tabuenca
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
- Department of Statistics, University of Zaragoza, Zaragoza, Spain
| | - X J Chai
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
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Simarro J, Meyer MI, Van Eyndhoven S, Phan TV, Billiet T, Sima DM, Ortibus E. A deep learning model for brain segmentation across pediatric and adult populations. Sci Rep 2024; 14:11735. [PMID: 38778071 PMCID: PMC11111768 DOI: 10.1038/s41598-024-61798-6] [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: 01/08/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024] Open
Abstract
Automated quantification of brain tissues on MR images has greatly contributed to the diagnosis and follow-up of neurological pathologies across various life stages. However, existing solutions are specifically designed for certain age ranges, limiting their applicability in monitoring brain development from infancy to late adulthood. This retrospective study aims to develop and validate a brain segmentation model across pediatric and adult populations. First, we trained a deep learning model to segment tissues and brain structures using T1-weighted MR images from 390 patients (age range: 2-81 years) across four different datasets. Subsequently, the model was validated on a cohort of 280 patients from six distinct test datasets (age range: 4-90 years). In the initial experiment, the proposed deep learning-based pipeline, icobrain-dl, demonstrated segmentation accuracy comparable to both pediatric and adult-specific models across diverse age groups. Subsequently, we evaluated intra- and inter-scanner variability in measurements of various tissues and structures in both pediatric and adult populations computed by icobrain-dl. Results demonstrated significantly higher reproducibility compared to similar brain quantification tools, including childmetrix, FastSurfer, and the medical device icobrain v5.9 (p-value< 0.01). Finally, we explored the potential clinical applications of icobrain-dl measurements in diagnosing pediatric patients with Cerebral Visual Impairment and adult patients with Alzheimer's Disease.
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Affiliation(s)
- Jaime Simarro
- icometrix, Leuven, Belgium.
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium.
| | | | | | | | | | | | - Els Ortibus
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Pediatric Neurology, UZ Leuven, Leuven, Belgium
- Child and Youth Institute, KU Leuven, Leuven, Belgium
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37
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Dafflon J, Moraczewski D, Earl E, Nielson DM, Loewinger G, McClure P, Thomas AG, Pereira F. Reliability and predictability of phenotype information from functional connectivity in large imaging datasets. ARXIV 2024:arXiv:2405.00255v1. [PMID: 38745697 PMCID: PMC11092871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
One of the central objectives of contemporary neuroimaging research is to create predictive models that can disentangle the connection between patterns of functional connectivity across the entire brain and various behavioral traits. Previous studies have shown that models trained to predict behavioral features from the individual's functional connectivity have modest to poor performance. In this study, we trained models that predict observable individual traits (phenotypes) and their corresponding singular value decomposition (SVD) representations - herein referred to as latent phenotypes from resting state functional connectivity. For this task, we predicted phenotypes in two large neuroimaging datasets: the Human Connectome Project (HCP) and the Philadelphia Neurodevelopmental Cohort (PNC). We illustrate the importance of regressing out confounds, which could significantly influence phenotype prediction. Our findings reveal that both phenotypes and their corresponding latent phenotypes yield similar predictive performance. Interestingly, only the first five latent phenotypes were reliably identified, and using just these reliable phenotypes for predicting phenotypes yielded a similar performance to using all latent phenotypes. This suggests that the predictable information is present in the first latent phenotypes, allowing the remainder to be filtered out without any harm in performance. This study sheds light on the intricate relationship between functional connectivity and the predictability and reliability of phenotypic information, with potential implications for enhancing predictive modeling in the realm of neuroimaging research.
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Affiliation(s)
- Jessica Dafflon
- Data Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
- Machine Learning Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Dustin Moraczewski
- Data Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Eric Earl
- Data Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Dylan M Nielson
- Machine Learning Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Gabriel Loewinger
- Machine Learning Team, National Institute of Mental Health, Bethesda, MD, USA
| | | | - Adam G Thomas
- Data Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Francisco Pereira
- Machine Learning Team, National Institute of Mental Health, Bethesda, MD, USA
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38
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Chung CF, Dugré JR, Potvin S. Dysconnectivity of the Nucleus Accumbens and Amygdala in Youths with Thought Problems: A Dimensional Approach. Brain Connect 2024; 14:226-238. [PMID: 38526373 DOI: 10.1089/brain.2023.0082] [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] [Indexed: 03/26/2024] Open
Abstract
Background: Youths with thought problems (TP) are at risk to develop psychosis and obsessive-compulsive disorder (OCD). Yet, the pathophysiological mechanisms underpinning TP are still unclear. Functional magnetic resonance imaging (fMRI) studies have shown that striatal and limbic alterations are associated with psychosis-like and obsessive-like symptoms in individuals at clinical risk for psychosis, schizophrenia, and OCD. More specifically, nucleus accumbens (NAcc) and amygdala are mainly involved in these associations. The current study aims to investigate the neural correlates of TP in youth populations using a dimensional approach and explore potential cognitive functions and neurotransmitters associated with it. Methods: Seed-to-voxels functional connectivity analyses using NAcc and amygdala as regions-of-interest were conducted with resting-state fMRI data obtained from 1360 young individuals, and potential confounders related to TP such as anxiety and cognitive functions were included as covariates in multiple regression analyses. Replicability was tested in using an adult cohort. In addition, functional decoding and neurochemical correlation analyses were performed to identify the associated cognitive functions and neurotransmitters. Results: The altered functional connectivities between the right NAcc and posterior parahippocampal gyrus, between the right amygdala and lateral prefrontal cortex, and between the left amygdala and the secondary visual area were the best predictors of TP in multiple regression model. These functional connections are mainly involved in social cognition and reward processing. Conclusions: The results show that alterations in the functional connectivity of the NAcc and the amygdala in neural pathways involved in social cognition and reward processing are associated with severity of TP in youths.
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Affiliation(s)
- Chen-Fang Chung
- Centre de Recherche de l'Institut, Universitaire en Santé Mentale de Montréal, Montreal, Canada
- Department of Psychiatry and Addiction, Faculty of medicine, University of Montreal, Montreal, Canada
| | - Jules R Dugré
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Stéphane Potvin
- Centre de Recherche de l'Institut, Universitaire en Santé Mentale de Montréal, Montreal, Canada
- Department of Psychiatry and Addiction, Faculty of medicine, University of Montreal, Montreal, Canada
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Hayashi S, Caron BA, Heinsfeld AS, Vinci-Booher S, McPherson B, Bullock DN, Bertò G, Niso G, Hanekamp S, Levitas D, Ray K, MacKenzie A, Avesani P, Kitchell L, Leong JK, Nascimento-Silva F, Koudoro S, Willis H, Jolly JK, Pisner D, Zuidema TR, Kurzawski JW, Mikellidou K, Bussalb A, Chaumon M, George N, Rorden C, Victory C, Bhatia D, Aydogan DB, Yeh FCF, Delogu F, Guaje J, Veraart J, Fischer J, Faskowitz J, Fabrega R, Hunt D, McKee S, Brown ST, Heyman S, Iacovella V, Mejia AF, Marinazzo D, Craddock RC, Olivetti E, Hanson JL, Garyfallidis E, Stanzione D, Carson J, Henschel R, Hancock DY, Stewart CA, Schnyer D, Eke DO, Poldrack RA, Bollmann S, Stewart A, Bridge H, Sani I, Freiwald WA, Puce A, Port NL, Pestilli F. brainlife.io: a decentralized and open-source cloud platform to support neuroscience research. Nat Methods 2024; 21:809-813. [PMID: 38605111 PMCID: PMC11093740 DOI: 10.1038/s41592-024-02237-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 03/05/2024] [Indexed: 04/13/2024]
Abstract
Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.
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Affiliation(s)
| | - Bradley A Caron
- Indiana University, Bloomington, IN, USA
- The University of Texas, Austin, TX, USA
| | | | - Sophia Vinci-Booher
- Indiana University, Bloomington, IN, USA
- Vanderbilt University, Nashville, TN, USA
| | - Brent McPherson
- Indiana University, Bloomington, IN, USA
- McGill University, Montréal, Quebec, Canada
| | | | | | - Guiomar Niso
- Indiana University, Bloomington, IN, USA
- Cajal Institute, CSIC, Madrid, Spain
| | | | - Daniel Levitas
- Indiana University, Bloomington, IN, USA
- The University of Texas, Austin, TX, USA
| | | | | | | | - Lindsey Kitchell
- Indiana University, Bloomington, IN, USA
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA
| | - Josiah K Leong
- Indiana University, Bloomington, IN, USA
- University of Arkansas, Fayetteville, AR, USA
| | | | | | | | | | | | | | | | - Kyriaki Mikellidou
- University of Limassol, Nicosia, Cyprus
- University of Cyprus, Nicosia, Cyprus
| | - Aurore Bussalb
- Institut du Cerveau, CNRS, Sorbonne Université, Paris, France
| | | | - Nathalie George
- Institut du Cerveau, CNRS, Sorbonne Université, Paris, France
| | | | | | | | - Dogu Baran Aydogan
- University of Eastern Finland, Kuopio, Finland
- Aalto University School of Science, Espoo, Finland
| | | | - Franco Delogu
- Lawrence Technological University, Southfield, MI, USA
| | | | | | | | | | | | - David Hunt
- Indiana University, Bloomington, IN, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ashley Stewart
- University of Queensland, St Lucia, Queensland, Australia
| | | | - Ilaria Sani
- The Rockefeller University, New York, NY, USA
- University of Geneva, Geneva, Switzerland
| | | | - Aina Puce
- Indiana University, Bloomington, IN, USA
| | | | - Franco Pestilli
- Indiana University, Bloomington, IN, USA.
- The University of Texas, Austin, TX, USA.
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Camacho MC, Balser DH, Furtado EJ, Rogers CE, Schwarzlose RF, Sylvester CM, Barch DM. Higher Intersubject Variability in Neural Response to Narrative Social Stimuli Among Youth With Higher Social Anxiety. J Am Acad Child Adolesc Psychiatry 2024; 63:549-560. [PMID: 38070872 DOI: 10.1016/j.jaac.2023.08.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/10/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023]
Abstract
OBJECTIVE Social anxiety is associated with alterations in socioemotional processing, but the pathophysiology remains poorly understood. Movies present an opportunity to examine more naturalistic socioemotional processing by providing narrative and sensory context to emotion cues. This study aimed to characterize associations between neural response to contextualized social cues and social anxiety symptoms in children. METHOD Data from the Healthy Brain Network (final N = 740; age range 5-15 years) were split into discovery and replication samples to maximize generalizability of findings. Associations of parent- and self-reported social anxiety (Screen for Child Anxiety-related Emotional Disorders) with mean differences and person-to-person variability in functional magnetic resonance imaging-measured activation to 2 emotionally dynamic movies were characterized. RESULTS Though no evidence was found to indicate social anxiety symptoms were associated with mean differences in neural activity to emotional content (fit Spearman rs < 0.09), children with high social anxiety symptoms had higher intersubject activation variability in the posterior cingulate, supramarginal gyrus, and inferior frontal gyrus (Bonferroni familywise error-corrected ps < .05)-regions associated with attention, alertness, and emotion cue processing. Identified regions varied by age group and informant. Across ages, these effects were enhanced for scenes containing greater sensory intensity (brighter, louder, more motion, more vibrance). CONCLUSION These results provide evidence that children with high social anxiety symptoms show high person-to-person variability in the neural processing of sensory aspects of emotional content. These data indicate that children with high social anxiety may require personalized interventions for sensory and emotional difficulties, as the underlying neurology differs from child to child. DIVERSITY & INCLUSION STATEMENT One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list.
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Benavidez SM, Abaryan Z, Kim GS, Laltoo E, McCracken JT, Thompson PM, Lawrence KE. Sex Differences in the Brain's White Matter Microstructure during Development assessed using Advanced Diffusion MRI Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.02.578712. [PMID: 38352346 PMCID: PMC10862784 DOI: 10.1101/2024.02.02.578712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Typical sex differences in white matter (WM) microstructure during development are incompletely understood. Here we evaluated sex differences in WM microstructure during typical brain development using a sample of neurotypical individuals across a wide developmental age (N=239, aged 5-22 years). We used the conventional diffusion-weighted MRI (dMRI) model, diffusion tensor imaging (DTI), and two advanced dMRI models, the tensor distribution function (TDF) and neurite orientation dispersion density imaging (NODDI) to assess WM microstructure. WM microstructure exhibited significant, regionally consistent sex differences across the brain during typical development. Additionally, the TDF model was most sensitive in detecting sex differences. These findings highlight the importance of considering sex in neurodevelopmental research and underscore the value of the advanced TDF model.
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Affiliation(s)
- Sebastian M Benavidez
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Zvart Abaryan
- Children's Hospital of Los Angeles, Los Angeles, CA, USA
| | - Gaon S Kim
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Emily Laltoo
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - James T McCracken
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Katherine E Lawrence
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA
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Luo AC, Sydnor VJ, Pines A, Larsen B, Alexander-Bloch AF, Cieslak M, Covitz S, Chen AA, Esper NB, Feczko E, Franco AR, Gur RE, Gur RC, Houghton A, Hu F, Keller AS, Kiar G, Mehta K, Salum GA, Tapera T, Xu T, Zhao C, Salo T, Fair DA, Shinohara RT, Milham MP, Satterthwaite TD. Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy. Nat Commun 2024; 15:3511. [PMID: 38664387 PMCID: PMC11045762 DOI: 10.1038/s41467-024-47748-w] [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: 08/28/2023] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.
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Affiliation(s)
- Audrey C Luo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sydney Covitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Andrew A Chen
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA
| | | | - Eric Feczko
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Alexandre R Franco
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Fengling Hu
- Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Gregory Kiar
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Kahini Mehta
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Giovanni A Salum
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Tinashe Tapera
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, 02115, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Chenying Zhao
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Taylor Salo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
- Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Kang JH, Bae JH, Jeon YJ. Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review. Bioengineering (Basel) 2024; 11:418. [PMID: 38790286 PMCID: PMC11118246 DOI: 10.3390/bioengineering11050418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/18/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024] Open
Abstract
The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.
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Affiliation(s)
- Jae-Hwan Kang
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Jang-Han Bae
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Young-Ju Jeon
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
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Areces-Gonzalez A, Paz-Linares D, Riaz U, Wang Y, Li M, Razzaq FA, Bosch-Bayard JF, Gonzalez-Moreira E, Ontivero-Ortega M, Galan-Garcia L, Martínez-Montes E, Minati L, Valdes-Sosa MJ, Bringas-Vega ML, Valdes-Sosa PA. CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics. Front Neurosci 2024; 18:1237245. [PMID: 38680452 PMCID: PMC11047451 DOI: 10.3389/fnins.2024.1237245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 02/22/2024] [Indexed: 05/01/2024] Open
Abstract
We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.
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Affiliation(s)
- Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Technical Sciences, University “Hermanos Saiz Montes de Oca” of Pinar del Río, Pinar del Rio, Cuba
| | - Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | - Usama Riaz
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Hangzhou Dianzi University, Hangzhou, Zhejiang, China
| | - Fuleah A. Razzaq
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jorge F. Bosch-Bayard
- McGill Centre for Integrative Neurosciences MCIN, LudmerCentre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Eduardo Gonzalez-Moreira
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | | | | | | | - Marlis Ontivero-Ortega
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | | | | | - Ludovico Minati
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
| | | | - Maria L. Bringas-Vega
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | - Pedro A. Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
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Iyer KK, Roberts JA, Waak M, Vogrin SJ, Kevat A, Chawla J, Haataja LM, Lauronen L, Vanhatalo S, Stevenson NJ. A growth chart of brain function from infancy to adolescence based on EEG. EBioMedicine 2024; 102:105061. [PMID: 38537603 PMCID: PMC11026939 DOI: 10.1016/j.ebiom.2024.105061] [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: 07/28/2023] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND In children, objective, quantitative tools that determine functional neurodevelopment are scarce and rarely scalable for clinical use. Direct recordings of cortical activity using routinely acquired electroencephalography (EEG) offer reliable measures of brain function. METHODS We developed and validated a measure of functional brain age (FBA) using a residual neural network-based interpretation of the paediatric EEG. In this cross-sectional study, we included 1056 children with typical development ranging in age from 1 month to 18 years. We analysed a 10- to 15-min segment of 18-channel EEG recorded during light sleep (N1 and N2 states). FINDINGS The FBA had a weighted mean absolute error (wMAE) of 0.85 years (95% CI: 0.69-1.02; n = 1056). A two-channel version of the FBA had a wMAE of 1.51 years (95% CI: 1.30-1.73; n = 1056) and was validated on an independent set of EEG recordings (wMAE = 2.27 years, 95% CI: 1.90-2.65; n = 723). Group-level maturational delays were also detected in a small cohort of children with Trisomy 21 (Cohen's d = 0.36, p = 0.028). INTERPRETATION A FBA, based on EEG, is an accurate, practical and scalable automated tool to track brain function maturation throughout childhood with accuracy comparable to widely used physical growth charts. FUNDING This research was supported by the National Health and Medical Research Council, Australia, Helsinki University Diagnostic Center Research Funds, Finnish Academy, Finnish Paediatric Foundation, and Sigrid Juselius Foundation.
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Affiliation(s)
- Kartik K Iyer
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Faculty of Medicine, The University of Queensland, Brisbane, Australia.
| | - James A Roberts
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Michaela Waak
- Faculty of Medicine, The University of Queensland, Brisbane, Australia; Queensland Children's Hospital, Brisbane, Australia
| | | | - Ajay Kevat
- Faculty of Medicine, The University of Queensland, Brisbane, Australia; Queensland Children's Hospital, Brisbane, Australia
| | - Jasneek Chawla
- Faculty of Medicine, The University of Queensland, Brisbane, Australia; Queensland Children's Hospital, Brisbane, Australia
| | - Leena M Haataja
- Departments of Physiology and Clinical Neurophysiology, BABA Center, Paediatric Research Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Leena Lauronen
- Departments of Physiology and Clinical Neurophysiology, BABA Center, Paediatric Research Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Sampsa Vanhatalo
- Departments of Physiology and Clinical Neurophysiology, BABA Center, Paediatric Research Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Nathan J Stevenson
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
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Loram G, Silk T, Ling M, Sciberras E. Examining the associations between attention-deficit/hyperactivity disorder, sleep problems, and other mental health conditions in adolescents. J Sleep Res 2024; 33:e13830. [PMID: 36907830 DOI: 10.1111/jsr.13830] [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: 08/16/2022] [Revised: 11/27/2022] [Accepted: 01/04/2023] [Indexed: 03/14/2023]
Abstract
Adolescents with attention-deficit/hyperactivity disorder (ADHD) often experience greater sleep difficulties compared to those without. However, findings are mixed, and other mental health conditions are often overlooked. This study aimed to examine the relationship between sleep problems, ADHD, and other mental health conditions in a sample of adolescents. Data from 373 adolescents aged 10-19 years was used as part of the wider 'Healthy Brain Network' study, which targets children and adolescents experiencing mental health and neurodevelopmental difficulties. Mental health conditions were assessed via a comprehensive assessment. Sleep was measured by self- and parent-report, as well as via up to a month of actigraphy data. Actigraphy data were analysed using mixed-methods modelling, while subjective sleep data were analysed using multiple regression. Subjectively-reported sleep was generally worse in adolescents who had ADHD and other mental health conditions compared to those with ADHD but no other conditions. There were no associations between ADHD status and objective sleep measures or self-reported measures, but a significant association was found between ADHD status and parent-reported sleep difficulties, even when accounting for other conditions. Parent-reported sleep problems were associated not only with ADHD, but also with anxiety, depression, and externalising disorders. The strength of association between ADHD and sleep problems is potentially not as strong as previously thought when considering the role of other mental health conditions. Clinicians should consider the role of other mental health conditions when sleep problems are present, and vice versa. The study also highlights the importance of comprehensive, multi-informant assessment of mental health conditions, including sleep.
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Affiliation(s)
- George Loram
- School of Psychology, Deakin University, Burwood, Victoria, Australia
| | - Tim Silk
- School of Psychology, Deakin University, Burwood, Victoria, Australia
- Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - Mathew Ling
- School of Psychology, Deakin University, Burwood, Victoria, Australia
- NEAMI National, Preston, Victoria, Australia
| | - Emma Sciberras
- School of Psychology, Deakin University, Burwood, Victoria, Australia
- Murdoch Children's Research Institute, Parkville, Victoria, Australia
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47
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Quilty LC, Tempelaar W, Andrade BF, Kidd SA, Lunsky Y, Chen S, Wang W, Wong JKY, Lau C, Sedrak AB, Kelly R, Sivakumar H, Jani M, Ameis SH, Cleverley K, Goldstein BI, Felsky D, Dickie EW, Foussias G, Kozloff N, Nikolova YS, Polillo A, Diaconescu AO, Wheeler AL, Courtney DB, Hawke LD, Rotenberg M, Voineskos AN. Cognition and Educational Achievement in the Toronto Adolescent and Youth Cohort Study: Rationale, Methods, and Early Data. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:265-274. [PMID: 37979945 DOI: 10.1016/j.bpsc.2023.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 10/11/2023] [Accepted: 10/30/2023] [Indexed: 11/20/2023]
Abstract
BACKGROUND Both cognition and educational achievement in youths are linked to psychosis risk. One major aim of the Toronto Adolescent and Youth (TAY) Cohort Study is to characterize how cognitive and educational achievement trajectories inform the course of psychosis spectrum symptoms (PSSs), functioning, and suicidality. Here, we describe the protocol for the cognitive and educational data and early baseline data. METHODS The cognitive assessment design is consistent with youth population cohort studies, including the NIH Toolbox, Rey Auditory Verbal Learning Test, Wechsler Matrix Reasoning Task, and Little Man Task. Participants complete an educational achievement questionnaire, and report cards are requested. Completion rates, descriptive data, and differences across PSS status are reported for the first participants (N = 417) ages 11 to 24 years, who were recruited between May 4, 2021, and February 2, 2023. RESULTS Nearly 84% of the sample completed cognitive testing, and 88.2% completed the educational questionnaire, whereas report cards were collected for only 40.3%. Modifications to workflows were implemented to improve data collection. Participants who met criteria for PSSs demonstrated lower performance than those who did not on numerous key cognitive indices (p < .05) and also had more academic/educational problems. CONCLUSIONS Following youths longitudinally enabled trajectory mapping and prediction based on cognitive and educational performance in relation to PSSs in treatment-seeking youths. Youths with PSSs had lower cognitive performance and worse educational outcomes than youths without PSSs. Results show the feasibility of collecting data on cognitive and educational outcomes in a cohort of youths seeking treatment related to mental illness and substance use.
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Affiliation(s)
- Lena C Quilty
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Wanda Tempelaar
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Brendan F Andrade
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Sean A Kidd
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Yona Lunsky
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Sheng Chen
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Wei Wang
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Jimmy K Y Wong
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Chloe Lau
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychology, Western University, London, Ontario, Canada
| | - Andrew B Sedrak
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Rachel Kelly
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Harijah Sivakumar
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Melanie Jani
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Kristin Cleverley
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin I Goldstein
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Daniel Felsky
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - George Foussias
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Nicole Kozloff
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Yuliya S Nikolova
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Alexia Polillo
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Andreea O Diaconescu
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Anne L Wheeler
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Physiology, University of Toronto, Toronto, Ontario, Canada; Hospital for Sick Children, Neurosciences and Mental Health Program, Toronto, Ontario, Canada
| | - Darren B Courtney
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Lisa D Hawke
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Martin Rotenberg
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
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48
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Tong X, Xie H, Fonzo GA, Zhao K, Satterthwaite TD, Carlisle NB, Zhang Y. Symptom dimensions of resting-state electroencephalographic functional connectivity in autism. NATURE. MENTAL HEALTH 2024; 2:287-298. [PMID: 39219688 PMCID: PMC11361313 DOI: 10.1038/s44220-023-00195-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 12/12/2023] [Indexed: 09/04/2024]
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by social and communication deficits (SCDs), restricted and repetitive behaviors (RRBs) and fixated interests. Despite its prevalence, development of effective therapy for ASD is hindered by its symptomatic and neurophysiological heterogeneities. To comprehensively explore these heterogeneities, we developed a new analytical framework combining contrastive learning and sparse canonical correlation analysis that identifies symptom-linked resting-state electroencephalographic connectivity dimensions within 392 ASD samples. We present two dimensions with multivariate connectivity basis exhibiting significant correlations with SCD and RRB, confirm their robustness through cross-validation and demonstrate their conceptual generalizability using an independent dataset (n = 222). Specifically, the right inferior parietal lobe is the core region for RRB, while connectivity between the left angular gyrus and the right middle temporal gyrus show key contribution to SCD. These findings provide a promising avenue to parse ASD heterogeneity with high clinical translatability, paving the way for ASD treatment development and precision medicine.
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Affiliation(s)
- Xiaoyu Tong
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Center for Neuroscience Research, Children’s National Hospital, Washington, DC, USA
| | - Gregory A. Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA
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49
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Hoffmann MS, Moore TM, Axelrud LK, Tottenham N, Pan PM, Miguel EC, Rohde LA, Milham MP, Satterthwaite TD, Salum GA. An Evaluation of Item Harmonization Strategies Between Assessment Tools of Psychopathology in Children and Adolescents. Assessment 2024; 31:502-517. [PMID: 37042304 DOI: 10.1177/10731911231163136] [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] [Indexed: 04/13/2023]
Abstract
Data aggregation in mental health is complicated by using different questionnaires, and little is known about the impact of item harmonization strategies on measurement precision. Therefore, we aimed to assess the impact of various item harmonization strategies for a target and proxy questionnaire using correlated and bifactor models. Data were obtained from the Brazilian High-Risk Study for Mental Conditions (BHRCS) and the Healthy Brain Network (HBN; N = 6,140, ages 5-22 years, 39.6% females). We tested six item-wise harmonization strategies and compared them based on several indices. The one-by-one (1:1) expert-based semantic item harmonization presented the best strategy as it was the only that resulted in scalar-invariant models for both samples and factor models. The between-questionnaires factor correlation, reliability, and factor score difference in using a proxy instead of a target measure improved little when all other harmonization strategies were compared with a completely at-random strategy. However, for bifactor models, between-questionnaire specific factor correlation increased from 0.05-0.19 (random item harmonization) to 0.43-0.60 (expert-based 1:1 semantic harmonization) in BHRCS and HBN samples, respectively. Therefore, item harmonization strategies are relevant for specific factors from bifactor models and had little impact on p-factors and first-order correlated factors when the child behavior checklist (CBCL) and strengths and difficulties questionnaire (SDQ) were harmonized.
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Affiliation(s)
- Maurício Scopel Hoffmann
- Universidade Federal de Santa Maria, Brazil
- Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- London School of Economics and Political Science, UK
| | | | | | | | - Pedro Mario Pan
- Universidade Federal de São Paulo, Brazil
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, Brazil
| | - Eurípedes Constantino Miguel
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, Brazil
- Universidade de São Paulo, Brazil
| | - Luis Augusto Rohde
- Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, Brazil
| | - Michael Peter Milham
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Child Mind Institute, New York, NY, USA
| | | | - Giovanni Abrahão Salum
- Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, Brazil
- Child Mind Institute, New York, NY, USA
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50
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Yablonski M, Karipidis II, Kubota E, Yeatman JD. The transition from vision to language: Distinct patterns of functional connectivity for subregions of the visual word form area. Hum Brain Mapp 2024; 45:e26655. [PMID: 38488471 PMCID: PMC10941549 DOI: 10.1002/hbm.26655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 12/22/2023] [Accepted: 02/26/2024] [Indexed: 03/18/2024] Open
Abstract
Reading entails transforming visual symbols to sound and meaning. This process depends on specialized circuitry in the visual cortex, the visual word form area (VWFA). Recent findings suggest that this text-selective cortex comprises at least two distinct subregions: the more posterior VWFA-1 is sensitive to visual features, while the more anterior VWFA-2 processes higher level language information. Here, we explore whether these two subregions also exhibit different patterns of functional connectivity. To this end, we capitalize on two complementary datasets: Using the Natural Scenes Dataset (NSD), we identify text-selective responses in high-quality 7T adult data (N = 8), and investigate functional connectivity patterns of VWFA-1 and VWFA-2 at the individual level. We then turn to the Healthy Brain Network (HBN) database to assess whether these patterns replicate in a large developmental sample (N = 224; age 6-20 years), and whether they relate to reading development. In both datasets, we find that VWFA-1 is primarily correlated with bilateral visual regions. In contrast, VWFA-2 is more strongly correlated with language regions in the frontal and lateral parietal lobes, particularly the bilateral inferior frontal gyrus. Critically, these patterns do not generalize to adjacent face-selective regions, suggesting a specific relationship between VWFA-2 and the frontal language network. No correlations were observed between functional connectivity and reading ability. Together, our findings support the distinction between subregions of the VWFA, and suggest that functional connectivity patterns in the ventral temporal cortex are consistent over a wide range of reading skills.
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Affiliation(s)
- Maya Yablonski
- Division of Developmental‐Behavioral Pediatrics, Department of PediatricsStanford University School of MedicineStanfordCaliforniaUSA
- Stanford University Graduate School of EducationStanfordCaliforniaUSA
| | - Iliana I. Karipidis
- Department of Psychiatry and Behavioral SciencesStanford School of MedicineStanfordCaliforniaUSA
- Department of Child and Adolescent Psychiatry and PsychotherapyUniversity Hospital of Psychiatry Zurich, University of ZurichZürichSwitzerland
- Neuroscience Center ZurichUniversity of Zurich and ETHZurichSwitzerland
| | - Emily Kubota
- Psychology DepartmentStanford UniversityStanfordCaliforniaUSA
| | - Jason D. Yeatman
- Division of Developmental‐Behavioral Pediatrics, Department of PediatricsStanford University School of MedicineStanfordCaliforniaUSA
- Stanford University Graduate School of EducationStanfordCaliforniaUSA
- Psychology DepartmentStanford UniversityStanfordCaliforniaUSA
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