# Interpretable Network-Level Biomarker Discovery for Alzheimer’s Stage Assessment Using Resting-State fNIRS Complexity Graphs

## TL;DR

This study introduces a new method using resting-state fNIRS to detect Alzheimer’s-related changes in brain networks, especially during the MCI stage.

## Contribution

A novel graph-based framework using complexity-fluctuation graphs for interpretable and reproducible Alzheimer’s biomarker discovery in resting-state fNIRS.

## Key findings

- Complexity-fluctuation graphs outperform traditional amplitude-based methods in resting-state fNIRS analysis.
- Prefrontal network changes are most consistent and detectable during the MCI stage of Alzheimer’s.
- Attention-based network patterns align with statistically significant biomarkers for MCI.

## Abstract

What are the main findings?
Complexity–fluctuation graphs outperform amplitude-based functional connectivity in resting fNIRS.Reproducible prefrontal network alterations are most prominent at the MCI stage.

Complexity–fluctuation graphs outperform amplitude-based functional connectivity in resting fNIRS.

Reproducible prefrontal network alterations are most prominent at the MCI stage.

What are the implications of the main findings?
Graph-based resting-state fNIRS enables interpretable, task-free Alzheimer’s assessment.MCI-specific network disruptions highlight targets for early detection and monitoring.

Graph-based resting-state fNIRS enables interpretable, task-free Alzheimer’s assessment.

MCI-specific network disruptions highlight targets for early detection and monitoring.

Background/Objectives: This study introduces a reproducible and interpretable graph-based framework for resting-state functional near-infrared spectroscopy (fNIRS) that enables network-level biomarker discovery for Alzheimer’s disease (AD). Although resting-state fNIRS is well suited for task-free assessment, most existing approaches rely on static channel-wise features or conventional functional connectivity, limiting insight into coordinated network dynamics and reproducibility. Methods: Resting-state prefrontal fNIRS signals were represented as subject-level graphs in which edges captured coordinated fluctuations of nonlinear signal complexity across channels, computed using sliding-window analysis. Graph neural networks (GNNs) were employed as analytical tools to identify disease-stage-related network patterns. Interpretability was assessed using edge-level importance measures, and reproducibility was evaluated through fold-wise stability analysis and consensus network construction. Results: The proposed complexity–fluctuation-based graph representation consistently outperformed conventional amplitude-based functional connectivity. Statistically supported prefrontal network biomarkers distinguishing mild cognitive impairment (MCI) from healthy aging were identified, with statistically significant group differences (p = 0.001). In contrast, network patterns associated with Alzheimer’s disease were more heterogeneous and less consistently expressed. Consensus analysis revealed a subset of prefrontal connections repeatedly selected across cross-validation folds, and attention-based network patterns showed strong spatial correspondence with statistically derived biomarkers. Conclusions: This study establishes a reproducible and interpretable framework for resting-state fNIRS analysis that emphasizes coordinated complexity dynamics rather than classification accuracy. The results indicate that network-level alterations are most consistently expressed at the MCI stage, highlighting its role as a critical transitional state and supporting the potential of the proposed approach for longitudinal monitoring and clinically applicable fNIRS-based assessment of neurodegenerative disease.