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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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A novel subject-wise dictionary learning approach using multi-subject fMRI spatial and temporal components.

Muhammad Usman Khalid1, Malik Muhammad Nauman2

  • 1College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, 11564, Riyadh, Saudi Arabia.

Scientific Reports
|November 19, 2023
PubMed
Summary
This summary is machine-generated.

New dictionary learning algorithms (swsDL and swbDL) effectively integrate multi-subject fMRI data, improving analysis by consolidating spatiotemporal patterns for better group-level insights.

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Signal Processing

Background:

  • Conventional dictionary learning (DL) adapts to individual fMRI data, neglecting multi-subject spatiotemporal information.
  • Subject-wise data can be decomposed into multi-subject time courses and spatial maps.

Purpose of the Study:

  • Introduce novel dictionary learning algorithms (swsDL and swbDL) to leverage multi-subject fMRI data.
  • Consolidate spatiotemporal diversities across subjects for enhanced analysis.

Main Methods:

  • Developed a novel framework using a mixing model with pre-computed multi-subject base matrices.
  • Employed sparse spatiotemporal blind source separation for base matrix generation.
  • Utilized [Formula: see text]/[Formula: see text]-norm penalization/constraints and alternating minimization for optimization.

Main Results:

  • Proposed swsDL and swbDL algorithms successfully incorporate multi-subject spatiotemporal components.
  • Achieved a [Formula: see text] increase in mean correlation compared to existing methods.
  • Demonstrated a [Formula: see text] reduction in mean computation time with swsDL and swbDL.

Conclusions:

  • swsDL and swbDL offer a unique approach by updating subject-wise atoms/sparse codes with multi-subject components.
  • These algorithms facilitate the extraction of group-level dynamics from fMRI data.
  • The methods show superior performance in correlation and computational efficiency over state-of-the-art algorithms.