Construction and Multiple Feature Classification Based on a High-Order Functional Hypernetwork on fMRI Data.

ABSTRACT: Resting-state functional connectivity hypernetworks, in which multiple nodes can be connected, are an effective technique for diagnosing brain disease and performing classification research. Conventional functional hypernetworks can characterize the complex interactions within the human brain in a static form. However, an increasing body of evidence demonstrates that even in a resting state, neural activity in the brain still exhibits transient and subtle dynamics. These dynamic changes are essential for understanding the basic characteristics underlying brain organization and may correlate significantly with the pathological mechanisms of brain diseases. Therefore, considering the dynamic changes of functional connections in the resting state, we proposed methodology to construct resting

SUBMITTER: Li Y 

PROVIDER: S-EPMC9043754 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature