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

Abstract

Abstract 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.