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Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application

Wei-Xing Li1, Qiu-Hua Lin1, Bin-Hua Zhao1

  • 1School of Information and Communication Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China.

Journal of Neuroscience Methods
|December 27, 2023
PubMed
Summary

New dynamic spatial functional network connectivity (dsFNC) using complex-valued fMRI data captures brain phase information, improving schizophrenia detection and identifying potential biomarkers.

Keywords:
Complex-valued fMRI dataDynamic functional network connectivity (dFNC)Markov chainsSchizophreniaSpatial source phase

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Area of Science:

  • Neuroimaging
  • Functional MRI Analysis
  • Psychiatric Disorders

Background:

  • Dynamic spatial functional network connectivity (dsFNC) typically uses magnitude-only fMRI data.
  • Complete fMRI data contains valuable complex-valued phase information often overlooked.
  • Mental disorders can impact functional brain alterations detectable by dsFNC.

Purpose of the Study:

  • To introduce a novel dsFNC method utilizing spatial source phase (SSP) maps from complex-valued fMRI data.
  • To assess the ability of this new method, SSP-dsFNC, to capture dynamic functional connectivity.
  • To differentiate between individuals with schizophrenia (SZs) and healthy controls (HCs) using SSP-dsFNC.

Main Methods:

  • Derived SSP maps from complex-valued fMRI data to create SSP-dsFNC.
  • Quantified connectivity using mutual information.
  • Employed statistical analysis and Markov chains to evaluate dynamic changes across time windows.
  • Classified SZs and HCs based on connectivity variance and Markov chain transitions.

Main Results:

  • SSP-dsFNC demonstrated greater dynamic range compared to magnitude-only methods.
  • Significant differences were observed between SZs and HCs using SSP-dsFNC.
  • The method effectively utilized complete brain information from complex-valued fMRI data.

Conclusions:

  • SSP-dsFNC offers enhanced sensitivity to functional alterations in schizophrenia.
  • This approach identified additional, meaningful brain connections compared to existing methods.
  • SSP-dsFNC achieved a 14.6% higher classification accuracy for SZs vs. HCs.
  • The findings suggest SSP-dsFNC's potential as an imaging biomarker for psychotic disorders.