Enhanced fNIRS-Based MCI Detection via Resting-State and Task-State Integration With Spatial-Temporal Feature...

Detection of mild cognitive impairment (MCI), a precursor to dementia, is critical for timely intervention. Functional near-infrared spectroscopy (fNIRS) offers non-invasive, cost-effective, and motion-tolerant brain activity monitoring, but existing machine learning approaches for MCI classification using fNIRS face two limitations: 1) underutilization of complementary information between resting-state and task-state data, and 2) high feature dimensionality relative to small sample sizes, limiting model robustness and generalizability. We propose a spatio-temporal feature engineering framework addressing these gaps.

Resting-state fNIRS signals are processed via independent component analysis to derive subject-specific spatial filters, which are then clustered into a universal population-level filter set. This filter set isolates spatial features from task-state signals. Then, temporal feature selection combines variance-based and advanced methods to further reduce dimensionality by identifying discriminative task-evoked time points relevant to MCI detection. The framework integrates fNIRS spatial filtering (resting-state) and temporal selection (task-state) critical for MCI detection.

Validated on 104 participants, this framework achieved a single-run best of 90.91% accuracy for cognitively normal vs. MCI classification, with 91.07% feature dimensionality reduction, suggest the potential for generalizable MCI detection and efficient model retraining for expanding clinical data. Feature analysis reveals (1) universal spatial filters linked to MCI biomarkers and (2) temporal weights highlighting critical decision time points during cognitive tasks.

By resolving the integration gap between resting-state neurovascular patterns with task-evoked hemodynamic dynamics while reducing dimensionality, the framework achieves higher accuracy and interpretability, advancing fNIRS-based MCI detection.