🤖 AI Summary
Neuropsychiatric symptoms in late life are difficult to distinguish from early signs of Alzheimer’s disease (AD), limiting their utility as biomarkers. This study proposes a deep learning normative model based on structural MRI that leverages a 3D convolutional neural network to map brain structure to Neuropsychiatric Inventory Questionnaire (NPI-Q) scores in cognitively stable individuals. The deviation between observed and predicted NPI-Q scores—termed DNPI—is introduced as a non-invasive indicator of early AD risk. Results demonstrate that DNPI significantly predicts future conversion to AD (adjusted odds ratio = 2.5, p < 0.01), with predictive performance (AUC = 0.74) comparable to that of cerebrospinal fluid Aβ42 (AUC = 0.75), thereby overcoming key limitations of conventional cognitive assessments.
📝 Abstract
Neuropsychiatric symptoms (NPS) such as depression and apathy are common in Alzheimer's disease (AD) and often precede cognitive decline. NPS assessments hold promise as early detection markers due to their correlation with disease progression and their non-invasive nature. Yet current tools cannot distinguish whether NPS are part of aging or early signs of AD, limiting their utility. We present a deep learning-based normative modelling framework to identify atypical NPS burden from structural MRI. A 3D convolutional neural network was trained on cognitively stable participants from the Alzheimer's Disease Neuroimaging Initiative, learning the mapping between brain anatomy and Neuropsychiatric Inventory Questionnaire (NPIQ) scores. Deviations between predicted and observed scores defined the Divergence from NPIQ scores (DNPI). Higher DNPI was associated with future AD conversion (adjusted OR=2.5; p < 0.01) and achieved predictive accuracy comparable to cerebrospinal fluid AB42 (AUC=0.74 vs 0.75). Our approach supports scalable, non-invasive strategies for early AD detection.