Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation With a Case Study on...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Medical Imaging

Background:

• Standardized network thresholding methods are crucial for interpreting functional brain connectivity.
• Non-standardized methods complicate the analysis of functional near-infrared spectroscopy-based functional connectivity (fNIRS-FC).

Purpose of the Study:

• To propose and validate a novel method for analyzing fNIRS-FC.
• To improve the interpretation of brain network data in neurological conditions.

Main Methods:

• Wavelet analysis was employed for motion correction in fNIRS data.
• Orthogonal minimal spanning trees (OMSTs) were utilized to derive brain connectivity networks.
• The proposed method was applied to an Alzheimer's disease (AD) dataset and compared against existing thresholding techniques.

Main Results:

• The proposed method demonstrated superior performance in filtering cost-effective networks compared to benchmarks.
• The technique effectively differentiated between patients with mild Alzheimer's disease and healthy controls.
• Cost-efficiency, assortativity, and laterality were identified as effective features for AD diagnosis.

Conclusions:

• The developed method is a feasible and effective technique for analyzing fNIRS-FC.
• The proposed approach offers improved diagnostic capabilities for Alzheimer's disease.
• The method provides valuable insights into brain health and network interpretation.