# Identification of Brain Circuit Markers for Psychosis in Alzheimer's Disease by Leveraging Big Data and Machine Learning

> **NIH NIH R21** · STANFORD UNIVERSITY · 2021 · $236,100

## Abstract

Psychotic symptoms are among the most common and persistent neuropsychiatric symptoms in Alzheimer’s
disease, affecting over 50% of patients with Alzheimer’s disease. Yet, the etiology of psychosis in Alzheimer’s
disease is still poorly understood and systematic investigations have been hampered by small samples and
lack of reproducible findings. Critically, robust biomarkers are needed to understand the origins/progression of
psychosis in Alzheimer’s disease, and to identify targets for treatment. Newly available human neurobiological
data offer an unprecedented opportunity for developing robust and predictive biomarkers for psychosis in
Alzheimer’s disease. The overarching goal of our proposal is to identify robust and predictive biomarkers for
psychosis in Alzheimer’s disease using a novel data-driven computational framework. Specifically, we will use
a transformative “Big Data” science approach combining exciting recent advances in deep learning and our
recent work on quantitative dynamic brain circuit analyses with a wealth of newly available large-scale open-
source brain imaging and phenotypic data from multiple consortia, as well as data we have acquired at
Stanford University. To achieve these goals, we propose four aims. In Aim 1, we will develop and validate a
novel data-driven computational framework for identifying neurobiological features that distinguish between
groups, leveraging recent advances in deep learning and brain circuit dynamics. In Aim 2, we will identify
neurobiological features that distinguish idiopathic psychosis (schizophrenia) from neurotypical controls, using
our validated computational framework and “Big” data from schizophrenia. In Aim 3, we will identify
neurobiological features that distinguish Alzheimer’s disease patients with psychosis from Alzheimer’s disease
patients without psychosis, using our validated computational framework and data from Alzheimer’s disease
and schizophrenia. In Aim 4, we will identify neurobiological features that predict onset of psychosis in
Alzheimer’s disease. The proposed studies are highly synergistic with the goals of the PAR-20-159, which
aims to “enhance knowledge of mechanisms associated with neuropsychiatric symptoms in persons with
Alzheimer’s disease”. Through the successful completion of the work described here, the proposed studies will
transform our understanding of brain circuit mechanisms underlying psychosis in Alzheimer’s disease, and
crucially, provide a new computational framework for improved mechanistic understanding of other
neuropsychiatric symptoms in Alzheimer’s disease. Ultimately, these advances will lead to better diagnosis and
more effective treatments for neuropsychiatric symptoms in Alzheimer’s disease and, more broadly, advance
precision medicine.

## Key facts

- **NIH application ID:** 10192576
- **Project number:** 1R21AG072114-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Kaustubh Satyendra Supekar
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $236,100
- **Award type:** 1
- **Project period:** 2021-09-30 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10192576

## Citation

> US National Institutes of Health, RePORTER application 10192576, Identification of Brain Circuit Markers for Psychosis in Alzheimer's Disease by Leveraging Big Data and Machine Learning (1R21AG072114-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10192576. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*