Abstract
INTRODUCTION: Digital speech biomarkers (DSBs) support the detection and monitoring of Alzheimer's disease (AD) in Latinos. However, they have not been benchmarked against standard cognitive and neuroimaging measures, missing a critical validation milestone. METHODS: Thirty-three AD patients and 33 healthy controls completed verbal fluency tasks, episodic memory and executive tests, and magnetic resonance imaging (MRI) (volume) and functional MRI (fMRI) (connectivity) scans. Between-group machine learning classification was compared among fluency-derived DSBs, episodic and executive test scores, MRI, and fMRI measures. RESULTS: The fluency classifier's performance (area under the curve [AUC] = 0.84) was comparable (p > 0.14) to the episodic (AUC = 0.90), executive (AUC = 0.79), and structural (AUC = 0.90) classifiers and superior to the functional classifier (AUC = 0.65, p = 0.002). Top discriminating features were word length and frequency, both associated with right (pre)frontal volume upon adjusting for sociodemographic factors. DISCUSSION: DSBs appear non-inferior to standard cognitive and imaging measures, supporting scalable AD assessments in Latinos. HIGHLIGHTS: We examined digital speech biomarkers (DSBs) for detecting AD in Latinos. DSBs were benchmarked against cognitive and neuroimaging features. DSB-based classifiers matched or outperformed cognitive and brain classifiers. Top DSBs included word length, phonological neighborhood, and frequency. Word length and frequency correlated with right (pre)frontal brain volume.