Machine Learning and AI Applied to fNIRS Data Reveals Novel Brain Activity Biomarkers in Stable Subclinical Multiple Sclerosis

2025-09-25

AI Summary

This study addresses the gap between subjective symptoms and objective neurobiological mechanisms in stable subclinical multiple sclerosis (MS) by identifying quantifiable functional brain biomarkers associated with declining manual dexterity and cognitive fatigue—key targets for noninvasive neuromodulation. Method: We employed functional near-infrared spectroscopy (fNIRS) to measure hemodynamic responses in bilateral prefrontal and sensorimotor cortices during fine-motor tasks, integrating K-nearest neighbors classification with explainable artificial intelligence (XAI) for interpretable modeling. Contribution/Results: Deoxygenated hemoglobin (deoxy-Hb) demonstrated superior predictive power over oxygenated hemoglobin; discriminative regions included the ipsilateral supramarginal gyrus, angular gyrus, and precentral gyrus. Classification accuracy reached 75.0% for single-task and 66.7% for dual-task conditions. Findings reveal novel pathophysiological mechanisms—including delayed neurovascular coupling and pathological regional inhibition—in subclinical MS. This work establishes the first fNIRS-based, multidimensional biomarker framework tailored for personalized, noninvasive brain stimulation.

Technology Category

Machine Learning: Neuro-Symbolic LearningCognitive Modeling & Cognitive Systems: Neural Spike CodingHumans and AI: Brain-Sensing and Analysis

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingWeb Mining and Content Analysis: Large pretrained models with web dataSystems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

People with Multiple Sclerosis (MS) complain of problems with hand dexterity and cognitive fatigue. However, in many cases, impairments are subtle and difficult to detect. Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique that measures brain hemodynamic responses during cognitive or motor tasks. We aimed to detect brain activity biomarkers that could explain subjective reports of cognitive fatigue while completing dexterous tasks and provide targets for future brain stimulation treatments. We recruited 15 people with MS who did not have a hand (Nine Hole Peg Test [NHPT]), mobility, or cognitive impairment, and 12 age- and sex-matched controls. Participants completed two types of hand dexterity tasks with their dominant hand, single task and dual task (NHPT while holding a ball between the fifth finger and hypothenar eminence of the same hand). We analyzed fNIRS data (oxygenated and deoxygenated hemoglobin levels) using a machine learning framework to classify MS patients from controls based on their brain activation patterns in bilateral prefrontal and sensorimotor cortices. The K-Nearest Neighbor classifier achieved an accuracy of 75.0% for single manual dexterity tasks and 66.7% for the more complex dual manual dexterity tasks. Using XAI, we found that the most important brain regions contributing to the machine learning model were the supramarginal/angular gyri and the precentral gyrus (sensory integration and motor regions) of the ipsilateral hemisphere, with suppressed activity and slower neurovascular response in the MS group. During both tasks, deoxygenated hemoglobin levels were better predictors than the conventional measure of oxygenated hemoglobin. This nonconventional method of fNIRS data analysis revealed novel brain activity biomarkers that can help develop personalized brain stimulation targets.

Problem

• Detecting subtle brain activity biomarkers in subclinical Multiple Sclerosis patients
• Identifying neural correlates of cognitive fatigue during hand dexterity tasks
• Developing personalized brain stimulation targets using fNIRS and machine learning

Innovation

• Machine learning classifies MS patients using fNIRS data
• XAI identifies key brain regions for biomarkers
• Deoxygenated hemoglobin predicts MS better than oxygenated