Cortical electrophysiological evidence for individual‐specific temporal organization of brain functional networks

• Published in: Human brain mapping Vol. 41; no. 8; pp. 2160 - 2172
• Main Authors: Shu, Su, Qin, Lang, Yin, Yayan, Han, Meizhen, Cui, Wei, Gao, Jia‐Hong
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2020
• Subjects: Brain; Brain architecture; Cognitive ability; default mode network; dynamic functional connectivity; Electrophysiology; Functional morphology; large‐scale network; Magnetoencephalography; Markov chains; Nervous system; Networks; Occupancy; static functional connectivity; Substrates; temporal organization; Transition probabilities; dynamic functional connectivity; default mode network; static functional connectivity; temporal organization; large-scale network; magnetoencephalography
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.24937

The human brain has been demonstrated to rapidly and continuously form and dissolve networks on a subsecond timescale, offering effective and essential substrates for cognitive processes. Understanding how the dynamic organization of brain functional networks on a subsecond level varies across individuals is, therefore, of great interest for personalized neuroscience. However, it remains unclear whether features of such rapid network organization are reliably unique and stable in single subjects and, therefore, can be used in characterizing individual networks. Here, we used two sets of 5‐min magnetoencephalography (MEG) resting data from 39 healthy subjects over two consecutive days and modeled the spontaneous brain activity as recurring networks fast shifting between each other in a coordinated manner. MEG cortical maps were obtained through source reconstruction using the beamformer method and subjects' temporal structure of recurring networks was obtained via the Hidden Markov Model. Individual organization of dynamic brain activity was quantified with the features of the network‐switching pattern (i.e., transition probability and mean interval time) and the time‐allocation mode (i.e., fractional occupancy and mean lifetime). Using these features, we were able to identify subjects from the group with significant accuracies (~40% on average in 0.5–48 Hz). Notably, the default mode network displayed a distinguishable pattern, being the least frequently visited network with the longest duration for each visit. Together, we provide initial evidence suggesting that the rapid dynamic temporal organization of brain networks achieved in electrophysiology is intrinsic and subject specific.