期刊
NEUROIMAGE
卷 224, 期 -, 页码 -出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2020.117429
关键词
structural connectome; gradients; functional dynamics; Hidden Markov Model; multimodal imaging; diffusion MRI
资金
- National Research Foundation of Korea [NRF-2020R1A6A3A03037088]
- Molson Neuro-Engineering fellowship by Montreal Neurological Institute and Hospital (MNI)
- Fonds de la Recherche du Quebec -Sante(FRQ-S)
- Savoy Foundation
- FRQ-S
- Canadian Institutes of Health Research (CIHR)
- Healthy Brains for Healthy Lives (HBHL) postdoctoral fellowship
- Canadian Open Neuroscience Platform (CONP) fellowship
- CIHR
- Healthy Brains Healthy Lives initiative (Canada First Research Excellence fund)
- CIFAR Artificial Intelligence Chairs program (Canada Institute for Advanced Research)
- the European Research Council [WANDERINGMINDS-ERC646927]
- National Science and Engineering Research Council of Canada [NSERC Discovery-1304413]
- Canadian Institutes of Health Research [CIHR FDN-154298]
- SickKids Foundation [NI17039]
- Azrieli Center for Autism Research (ACAR-TACC)
- BrainCanada
- Tier-2 Canada Research Chairs program
- MNI-Cambridge collaborative award
- [1U54MH091657]
The study shows that the structure of the cortex can predict dynamic changes in neural states and is optimized to allow neural states to vary between different processing modes, providing insight into the flexibility of human cognition.
Human cognition is dynamic, alternating over time between externally-focused states and more abstract, often self-generated, patterns of thought. Although cognitive neuroscience has documented how networks anchor particular modes of brain function, mechanisms that describe transitions between distinct functional states remain poorly understood. Here, we examined how time-varying changes in brain function emerge within the constraints imposed by macroscale structural network organization. Studying a large cohort of healthy adults (n = 326), we capitalized on manifold learning techniques that identify low dimensional representations of structural connectome organization and we decomposed neurophysiological activity into distinct functional states and their transition patterns using Hidden Markov Models. Structural connectome organization predicted dynamic transitions anchored in sensorimotor systems and those between sensorimotor and transmodal states. Connectome topology analyses revealed that transitions involving sensorimotor states traversed short and intermediary distances and adhered strongly to communication mechanisms of network diffusion. Conversely, transitions between transmodal states involved spatially distributed hubs and increasingly engaged long-range routing. These findings establish that the structure of the cortex is optimized to allow neural states the freedom to vary between distinct modes of processing, and so provides a key insight into the neural mechanisms that give rise to the flexibility of human cognition.
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