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Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion

Kong, Ru ; Li, Jingwei ; Orban, Csaba ; Sabuncu, Mert R ; Liu, Hesheng ; Schaefer, Alexander ; Sun, Nanbo ; Zuo, Xi-Nian ; Holmes, Avram J ; Eickhoff, Simon B ; Yeo, B T Thomas

Cerebral cortex (New York, N.Y. 1991), 2019-06, Vol.29 (6), p.2533-2551 [Periódico revisado por pares]

United States: Oxford University Press

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Título:
Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion
Autor: Kong, Ru ; Li, Jingwei ; Orban, Csaba ; Sabuncu, Mert R ; Liu, Hesheng ; Schaefer, Alexander ; Sun, Nanbo ; Zuo, Xi-Nian ; Holmes, Avram J ; Eickhoff, Simon B ; Yeo, B T Thomas
Assuntos: Adult ; Cerebral Cortex - anatomy & histology ; Cerebral Cortex - physiology ; Cognition - physiology ; Connectome - methods ; Emotions - physiology ; Female ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging - methods ; Male ; Models, Neurological ; Neural Pathways - anatomy & histology ; Neural Pathways - physiology ; Original ; Personality - physiology
É parte de: Cerebral cortex (New York, N.Y. 1991), 2019-06, Vol.29 (6), p.2533-2551
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição: Abstract Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.
Editor: United States: Oxford University Press
Idioma: Inglês

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Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion

Kong, Ru ; Li, Jingwei ; Orban, Csaba ; Sabuncu, Mert R ; Liu, Hesheng ; Schaefer, Alexander ; Sun, Nanbo ; Zuo, Xi-Nian ; Holmes, Avram J ; Eickhoff, Simon B ; Yeo, B T Thomas

United States: Oxford University Press

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