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In NeuroImage ; h5-index 117.0

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 the macroscale structural brain organization. Studying a large cohort of healthy adults (n = 326), we capitalized on machine learning techniques that identify low dimensional representations of structural connectome organization and decomposed neurophysiological activity into distinct functional states and their transition patterns. 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.

Park Bo-Yong, de Wael Reinder Vos, Paquola Casey, Larivière Sara, Benkarim Oualid, Royer Jessica, Tavakol Shahin, Cruces Raul R, Li Qiongling, Valk Sofie L, Margulies Daniel S, Mišić Bratislav, Bzdok Danilo, Smallwood Jonathan, Bernhardt Boris C

2020-Oct-07

Functional dynamics, Gradients, Hidden Markov Model, Multimodal imaging, Structural connectome