Integrated and segregated frequency architecture of the human brain network.

Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 101282001 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1863-2661 (Electronic) Linking ISSN: 18632653 NLM ISO Abbreviation: Brain Struct Funct Subsets: MEDLINE

Original Publication: Berlin : Springer-Verlag, c2007-

Connectome*; Brain/*diagnostic imaging ; Default Mode Network/*diagnostic imaging ; Nerve Net/*diagnostic imaging; Adult ; Female ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Male ; Young Adult

The frequency of brain activity modulates the relationship between the brain and human behavior. Insufficient understanding of frequency-specific features may thus lead to inconsistent explanations of human behavior. However, to date, the frequency-specific features of the human brain functional network at the whole-brain level remain poorly understood. Here, we used resting-state fMRI data and graph-theory analyses to investigate the frequency-specific characteristics of fMRI signals in 12 frequency bands (frequency range 0.01-0.7 Hz) in 75 healthy participants. We found that brain regions with higher level and more complex functions had a more variable functional connectivity pattern but engaged less in higher frequency ranges. Moreover, brain regions that engaged in fewer frequency bands played more integrated roles (i.e., higher network participation coefficient and lower within-module degree) in the functional network, whereas regions that engaged in broader frequency ranges exhibited more segregated functions (i.e., lower network participation coefficient and higher within-module degree). Finally, behavioral analyses revealed that regional frequency variability was associated with a spectrum of behavioral functions from sensorimotor functions to complex cognitive and social functions. Taken together, our results showed that segregated functions are executed in wide frequency ranges, whereas integrated functions are executed mainly in lower frequency ranges. These frequency-specific features of brain networks provided crucial insights into the frequency mechanism of fMRI signals, suggesting that signals in higher frequency ranges should be considered for their relation to cognitive functions.

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19wkzd20 Fundamental Research Funds for the Central Universities; 20wkzd11 Fundamental Research Funds for the Central Universities; 81601559 National Natural Science Foundation of China; 61772569 National Natural Science Foundation of China; 2016A030310233 Natural Science Foundation of Guangdong Province (CN); 2019A1515012148 Guangdong Basic and Applied Basic Research Foundation

Keywords: Connectome; Frequency; Functional connectivity; Graph; Resting-state fMRI

Date Created: 20210103 Date Completed: 20211025 Latest Revision: 20211025

20231215

10.1007/s00429-020-02174-8

33389041