A functional neuroimaging biomarker of mild cognitive impairment using TD-fNIRS

This is the authors' abstract. We don't add key points for this paper.

Diagnostic assessments of mild cognitive impairment (MCI) are lengthy and burdensome, highlighting the need for new tools to detect MCI. Time-domain functional near-infrared spectroscopy (TD-fNIRS) can measure brain function in clinical settings and may address this need. In this study (NCT05996575), MCI patients ( n = 50) and age-matched healthy controls (HC; n = 51) underwent TD-fNIRS recordings during cognitive tasks (Verbal Fluency, N-Back). Machine learning models were trained to distinguish MCI from HC using neural activity, cognitive task behavior, and self-reported impairment as input features. Significant group-level differences (MCI vs HC) were demonstrated in self-report, N-Back and Verbal Fluency behavior, and task-related brain activation. Classifier performance was similar when using self-report (AUC = 0.76) and self-report plus behavior (AUC = 0.79) as input features, but was strongest when neural metrics were included (AUC = 0.92). This study demonstrates the potential of TD-fNIRS to assess MCI with short brain scans in clinical settings. Clinical trial registration: NCT05996575.

TD-fNIRS combined with machine learning provides a highly accurate, short, and non-invasive biomarker for detecting mild cognitive impairment in clinical settings.