Association of RDoC dimensions with post mortem brain transcriptional profiles in Alzheimer's disease - Weiqian Jiang; Jonathan Vogelgsang; Shu Dan; Peter Durning; Thomas H. McCoy; Sabina Berretta; Torsten Klengel (Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 2025)
Abstract INTRODUCTION Neuropsychiatric symptoms are common in people with Alzheimer's disease (AD) across all severity stages. Their heterogeneous presentation and variable temporal association with cognitive decline suggest shared and distinct biological mechanisms. We hypothesized that specific patterns of gene expression associate with distinct National Institute of Mental Health Research Domain Criteria (RDoC) domains in AD. METHODS Post‐mortem bulk RNA sequencing of the insula and anterior cingulate cortex from 60 brain donors, representing the spectrum of canonical Alzheimer's disease neuropathology, was combined with natural language processing approaches based on the RDoC Clinical Domains to uncover transcriptomic patterns linked to disease progression. RESULTS Distinct sets of >100 genes (Pfalse discovery rate < 0.05) were specifically associated with at least one clinical domain (cognitive, social, negative, positive, arousal). In addition, dysregulation of immune response pathways was shared across domains and brain regions. DISCUSSION Our findings provide evidence for distinct transcriptional profiles associated with RDoC domains suggesting that each dimension is characterized by sets of genes providing insight into the underlying mechanisms. Highlights Post mortem brain tissue investigations are critically important for Alzheimer's disease (AD) research. Neuropsychiatric symptoms in AD are common and an important aspect of AD. Categorical phenotypes are commonly used, but insufficiently describe the heterogenous presentation of AD. Using natural language processing (NLP) of post mortem brain donor health records provides insight into dimensional phenotypes of AD. We provide evidence for distinct RNA expression profiles associated with NLP‐derived Research Domain Criteria clinical domain scores.