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A Synchronous EEG-fNIRS BCI: A Proof-of-Concept for Multimodal Avalanche Analysis of Motor Cognition in Older Adults

Eva Guttmann-Flury, Yun-Hsuan Chen, Qiaoyuan Xiang, Hao Zhang, Mohamad Sawan

Abstract

This proof-of-concept study introduces a novel multimodal framework combining synchronized EEG-fNIRS modalities with neuronal avalanche analysis to identify early network dysfunction in Alzheimer's disease. The approach leverages complementary neural signals to examine motor network dynamics during execution and imagery tasks within an interactive task environment. Preliminary analysis of a small pilot cohort (N=4 subjects, including one with Mild Cognitive Impairment) validated the technical feasibility of the multimodal framework and revealed observable condition-dependent patterns in network organization. Two primary observations emerged: a reduced neural contrast between motor execution and imagery states, and increased trial-to-trial variability in network organization in the MCI participant. These initial results successfully validate the technical pipeline and provide hypothesis-generating observations for future statistically powered studies. The convergence of findings across modalities suggests that multimodal assessment of network flexibility may help detect functional changes in early Alzheimer's continuum, supporting the future development of this BCI-inspired framework into an engaging diagnostic tool.

A Synchronous EEG-fNIRS BCI: A Proof-of-Concept for Multimodal Avalanche Analysis of Motor Cognition in Older Adults

Abstract

This proof-of-concept study introduces a novel multimodal framework combining synchronized EEG-fNIRS modalities with neuronal avalanche analysis to identify early network dysfunction in Alzheimer's disease. The approach leverages complementary neural signals to examine motor network dynamics during execution and imagery tasks within an interactive task environment. Preliminary analysis of a small pilot cohort (N=4 subjects, including one with Mild Cognitive Impairment) validated the technical feasibility of the multimodal framework and revealed observable condition-dependent patterns in network organization. Two primary observations emerged: a reduced neural contrast between motor execution and imagery states, and increased trial-to-trial variability in network organization in the MCI participant. These initial results successfully validate the technical pipeline and provide hypothesis-generating observations for future statistically powered studies. The convergence of findings across modalities suggests that multimodal assessment of network flexibility may help detect functional changes in early Alzheimer's continuum, supporting the future development of this BCI-inspired framework into an engaging diagnostic tool.
Paper Structure (17 sections, 2 equations, 3 figures)

This paper contains 17 sections, 2 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Methods
  3. A Priori Sample Size Estimation
  4. Participants and Experimental Paradigm
  5. Pilot Cohort
  6. Motor Task Design
  7. Multimodal Data Acquisition and Synchronization
  8. Signal Pre-Processing Pipeline
  9. Neuronal Avalanches
  10. Theoretical Framework
  11. Avalanche Detection
  12. Quantifying Propagation Dynamics
  13. Avalanche Transition Matrix Construction
  14. Results
  15. Multimodal Convergence in Network Dynamics
  16. ...and 2 more sections

Figures (3)

  • Figure 1: Analytical pipeline for multimodal neuronal avalanche analysis: from synchronized EEG-fNIRS acquisition to network construction in Alzheimer's disease.
  • Figure 2: fNIRS and EEG condition contrasts (Motor Imagery – Execution). Contrast values for HbO (top) and HbR (middle), and ATM-derived source-localized EEG connectivity maps (bottom) are shown for a representative healthy control (left) and mild cognitive impaired participant (right)
  • Figure 3: Single-trial network connectivity across motor conditions. (A) Healthy control demonstrates consistent Phase Lag Index connectivity patterns across individual trials of both execution and imagery tasks, indicating robust neural dynamics. (B) Mild cognitive impairment participant exhibits substantial trial-to-trial variability and condition-dependent network reorganization, particularly during motor imagery, suggesting unstable neural computation in early cognitive impairment.