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Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches.

Evrim Acar1, Marie Roald1,2, Khondoker M Hossain3

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|May 13, 2022
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Summary
This summary is machine-generated.

This study introduces a tensor factorization method (PARAFAC2) to analyze dynamic brain activity in fMRI data, revealing evolving spatial networks and temporal patterns. The method effectively captures brain dynamics, outperforming traditional approaches in certain scenarios.

Keywords:
PARAFAC2evolving networksindependent component analysis (ICA)independent vector analysis (IVA)spatial dynamicstensor factorizationstime-evolving data

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Area of Science:

  • Neuroimaging
  • Dynamic Systems Analysis
  • Computational Neuroscience

Background:

  • Analyzing time-evolving data is critical for understanding dynamic systems like the brain.
  • Traditional fMRI analysis often assumes static spatial regions, limiting the capture of temporal dynamics.
  • Fractional amplitude of low-frequency fluctuations (fALFF) summarizes fMRI data variability.

Purpose of the Study:

  • To develop and evaluate a tensor factorization approach (PARAFAC2) for analyzing time-evolving fMRI data.
  • To reveal spatial dynamics, including evolving spatial networks and temporal patterns, in fMRI data.
  • To compare the performance of PARAFAC2 with matrix factorization methods like joint ICA and IVA.

Main Methods:

  • Arranged time-evolving fMRI data as a subjects x voxels x time windows tensor.
  • Applied the PARAFAC2 tensor factorization model for joint analysis across time windows.
  • Compared PARAFAC2 with joint Independent Component Analysis (ICA) and Independent Vector Analysis (IVA) using numerical experiments and real fMRI data.

Main Results:

  • PARAFAC2 provides a compact representation, revealing temporal patterns and evolving spatial networks.
  • Joint ICA effectively reveals evolving networks but not temporal patterns.
  • IVA's accuracy depends on data characteristics; it excels when subject-mode patterns vary across time windows.
  • Real fMRI data analysis identified a significant group difference component in schizophrenia patients, showing spatial maps and temporal changes in motor regions.

Conclusions:

  • The PARAFAC2 model offers a powerful tool for uncovering spatial dynamics and temporal patterns in fMRI data.
  • PARAFAC2 demonstrates advantages over joint ICA and IVA in specific aspects of dynamic network analysis.
  • The findings highlight the utility of tensor factorization for understanding brain function during tasks and in clinical populations.