Multiscale Functional Connectivity: Exploring the brain functional connectivity at different timescales
Manuel Morante, Kristian Fr{\o}lich, Naveed ur Rehman

TL;DR
This paper introduces a novel multiscale functional connectivity method for fMRI data that decomposes signals into intrinsic modes, capturing brain dynamics across multiple timescales while accounting for nonlinear and nonstationary properties.
Contribution
The study presents a new multiscale analysis approach for fMRI data that improves the separation of neurophysiological signals at different timescales and enhances the understanding of brain connectivity.
Findings
Effective separation of fMRI data into multiple timescales.
Identification of reliable functional connectivity patterns.
Insights into how FC spans different timescales across experiments.
Abstract
Human brains exhibit highly organized multiscale neurophysiological dynamics. Understanding those dynamic changes and the neuronal networks involved is critical for understanding how the brain functions in health and disease. Functional Magnetic Resonance Imaging (fMRI) is a prevalent neuroimaging technique for studying these complex interactions. However, analyzing fMRI data poses several challenges. Furthermore, most approaches for analyzing Functional Connectivity (FC) still rely on preprocessing or conventional methods, often built upon oversimplified assumptions. On top of that, those approaches often ignore frequency-related information despite evidence showing that fMRI data contain rich information that spans multiple timescales. This study introduces a novel methodology, Multiscale Functional Connectivity (MFC), to analyze fMRI data by decomposing the fMRI into their intrinsic…
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Taxonomy
TopicsFunctional Brain Connectivity Studies
