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NeuroMark-DyFICA: NeuroMark dynamic frequency-informed ICA with high-frequency spatial filtering for windowed fMRI analysis.

Created on 30 Jul 2026

Authors

Neda Behzadfar, Armin Iraji, Najme Soleimani, Tulay Adali, Vince Calhoun

Published in

Network neuroscience (Cambridge, Mass.). Volume 10. Issue 3. Pages 567-593. Epub Jul 20, 2026.

Abstract

Fine-scale spatial dynamics within functional brain networks manifest as high spatial-frequency variations that conventional independent component analysis (ICA) methods fail to capture. These subtle changes may carry critical information about transient connectivity and disordered brain function. We developed NeuroMark-DyFICA (dynamic frequency-informed ICA), a novel framework to enhance detection of spatiotemporal variability in fMRI data. It integrates three stages: dynamic NeuroMark ICA across sliding windows to estimate time-varying, spatially constrained networks; high-pass spatial filtering to emphasize fine-scale spatial features; and group-level ICA to extract refined dynamic components with subject-specific mixing weights. Unlike prior NeuroMark applications or conventional dynamic ICA, NeuroMark-DyFICA establishes a reproducible latent space of high-frequency dynamics, uniquely capturing transient, fine-scale reconfigurations of network topography. Validation using a controlled 2D simulation demonstrated reliable detection of subtle spatial shifts mimicking pathology, which conventional ICA failed to recover. Applying to resting-state fMRI from schizophrenia patients and healthy controls, multiple networks were estimated. We highlight six representative systems (thalamus, auditory, visual/fusiform, middle frontal, default mode, cerebellum). Results revealed two complementary abnormalities in schizophrenia: an imbalance between inactive and hyper-engaged states and altered convergence among dynamic states. NeuroMark-DyFICA reveals fine-grained spatiotemporal disruptions in brain networks, offering mechanistic insights and potential biomarkers for psychiatric disorders.

PMID:
42529646
Bibliographic data and abstract were imported from PubMed on 30 Jul 2026.

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