Multifractal long-range dependence pattern of functional magnetic resonance imaging in the human brain at rest

Document Type

Article

Publication Date

12-15-2023

Abstract

Long-range dependence is a prevalent phenomenon in various biological systems that characterizes the long-memory effect of temporal f luctuations. While recent research suggests that functional magnetic resonance imaging signal has fractal property, it remains unknown about the multifractal long-range dependence pattern of resting-state functional magnetic resonance imaging signals. The current study adopted the multifractal detrended f luctuation analysis on highly sampled resting-state functional magnetic resonance imaging scans to investigate long-range dependence profile associated with the whole-brain voxels as specific functional networks. Our findings revealed the long-range dependence’s multifractal properties. Moreover, long-term persistent f luctuations are found for all stations with stronger persistency in whole-brain regions. Subsets with large f luctuations contribute more to the multifractal spectrum in the whole brain. Additionally, we found that the preprocessing with band-pass filtering provided significantly higher reliability for estimating long-range dependence. Our validation analysis confirmed that the optimal pipeline of long-range dependence analysis should include band-pass filtering and removal of daily temporal dependence. Furthermore, multifractal long-range dependence characteristics in healthy control and schizophrenia are different significantly. This work has provided an analytical pipeline for the multifractal long-range dependence in the resting-state functional magnetic resonance imaging signal. The findings suggest differential long-memory effects in the intrinsic functional networks, which may offer a neural marker finding for understanding brain function and pathology.

Identifier

85180012286 (Scopus)

Publication Title

Cerebral Cortex

External Full Text Location

https://doi.org/10.1093/cercor/bhad393

e-ISSN

14602199

ISSN

10473211

PubMed ID

37851793

First Page

11594

Last Page

11608

Issue

24

Volume

33

Grant

23NSFSC2916

Fund Ref

Sichuan Province Science and Technology Support Program

This document is currently not available here.

Share

COinS