Summary: Toward our long-term goal of developing a methodological framework for predicting response to pharmacological treatments for Alzheimer’s disease (AD) and related dementias in clinical settings, we propose a proof of concept study to investigate clinical and neuroanatomical features related to response to treatment with two classes of medications that are currently in wide use: cholinesterase inhibitors (CEI) and serotonin specific reuptake inhibitors (SSRI). These treatments are focused on alleviating symptoms and extending patient autonomy. CEIs have shown efficacy in improving activities of daily living and are more cost- effective than supportive care. SSRIs are a first choice for the pharmacological treatment of neuropsychiatric symptoms (NPS) that affect ~97% of AD patients. However, response to CEI/SSRI varies between patients, with only half responding to these drugs. Moreover, one-third of patients discontinue therapy because of adverse effects. Given the AD pandemic, with 16M patients expected in the US by 2050, the targeted use of CEI/SSRI to patients who will benefit from treatment is a public health priority. Although predictive markers to identify those who will respond to CEI/SSRI have been highly anticipated, there, as yet, no established such markers. Several clinical or genetic markers have been proposed as response predictors, but none have been validated. Studying neuroanatomical features of the brain, which can be detected through MRI (= brain phenotype), has succeeded in the identification of subgroups of AD related to clinical, cognitive, and neuropsychiatric characteristics. However, little is known about whether brain phenotyping predicts response to pharmacological interventions. Available results from pilot studies with small and highly selected samples point to the necessity of studying larger, more inclusive cohorts. Since individualized prediction cannot be made from a single feature, as each feature weakly correlates with outcomes, we hypothesize that patient stratification that combines brain phenotype and clinical characteristics, will be highly accurate at individualized prediction. In this study, we will utilize pre-existing resources to identify brain phenotypes and clinical features that might predict response to CEI/SSRI treatment. First, data extracted from a large pool of electronic medical records acquired through clinical practice will be used to identify brain phenotypes and clinical features related to a response to CEI/SSRI. Second, our automated brain features extraction pipeline, which is robust to the great heterogeneity in diseased brains will be used to quantify brain phenotypes. We propose Aim 1 to assess the feasibility of brain phenotyping and Aim 2 to assess the feasibility of a supervised clustering approach based on brain phenotype and clinical features, in predicting a response to CEI/SSRI. The proposed feasibility testing based on analysis of data from a real world clinical care pa...