Neuropsychiatric Deviations From Normative Profiles: An MRI-Derived Marker for Early Alzheimer's Disease Detection
This work addresses early detection of Alzheimer's disease for patients and clinicians by providing a non-invasive, scalable method, though it is incremental as it builds on existing normative modelling and biomarker comparisons.
The researchers tackled the problem of distinguishing neuropsychiatric symptoms as early signs of Alzheimer's disease from normal aging by developing a deep learning-based normative model using structural MRI, which predicted future AD conversion with an adjusted odds ratio of 2.5 and achieved an AUC of 0.74, comparable to cerebrospinal fluid biomarkers.
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.