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

Key Points

Higher accuracy achieved by integrating resting-state with task-state data, enhancing diagnostic capabilities.
This study noted a significant advancement in interpreting fNIRS data for MCI detection in clinical settings.
Analysis using dimensionality reduction methods improves the interpretability of neurovascular patterns in subjects.
The approach offers promise for non-invasive diagnostics, although further clinical validation is needed.

Abstract

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.

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

Key Points

Abstract

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