Using Natural Language Processing to identify psychiatric symptoms related to Alzheimer’s disease: a novel tool to enhance molecular brain research in Alzheimer’s disease

Background Neuropsychiatric symptoms (NPS), such as anxiety, depression, or agitation, are common across different stages of Alzheimer’s disease (AD). However, their molecular course is still poorly understood. While post‐mortem brain research provides a unique source to study brain‐specific molecular changes, clinical characterization of post‐mortem brain donors is challenging and primarily subject to case‐control designs. Here, we present a novel approach to post‐mortem brain donor characterization using natural language processing (NLP). Method We analyzed electronic health records (HER) of 92 brain donors (46 males, 46 females, mean age 81.9 years, B&B 0 ‐ VI) using NLP‐based NIMH Research Domain Criteria (RDoC) quantification. Individual RDoC scores were regressed to their neuropathological findings or RNAseq data from two independent brain regions, BA32 and insula. Result Neuritic plaques (NP) in all four cortices show a high association with RDoC “cognition”, controlled for age and sex (frontal: ß = 0.38, p < 0.001; parietal: ß = 0.35, p < 0.001; temporal: ß = 0.37, p < 0.001; occipital: ß = 0.37, p < 0.001). These findings align with prior clinicopathological correlation studies where cognitive examinations were performed within one year before death (Nelson et al., J Neuropathol Exp Neurol, 2012). At pFDR