# Combining Computational Methods, RDoC, and Big Neuroimaging Data to Understand Mechanisms of Neuropsychiatric Symptoms in Alzheimer's Disease

> **NIH NIH RF1** · MCLEAN HOSPITAL · 2022 · $2,432,945

## Abstract

More than 6 million Americans suffer from Alzheimer’s disease (AD), the most common age-associated,
neurodegenerative dementia. 80% of AD patients also exhibit neuropsychiatric symptoms (NPS), including
depression, anxiety, agitation, aggression, apathy and others. NPS in AD respond poorly to conventional
treatments and can lead to severe functional impairment and consequent increased caregiver burden. While
NPS occur during “normal” aging, there is profound disease-related degeneration in neurocircuitry in AD that
may be a mechanism for the differences in clinical course of NPS and lack of response to conventional NPS
treatments in AD patients. Our proposed study aims to identify relationships between the neurocircuitry
underlying NPS and AD neurocircuit degeneration that ultimately may drive worse outcomes in AD
with NPS. Neurodegeneration in AD first targets hippocampus and temporal regions, then spreads along other
nodes of the Default Mode Network (DMN), a key brain circuit implicated in cognition and emotions. Functional
magnetic resonance imaging (fMRI) studies have shown that impairments in access and engagement of the
DMN with the central executive network (CEN; cognitive processing) and salience network (SN; salience
mapping) underly psychopathology. We apply this “Triple Network” model which links neurocircuitry of NPS to
AD neurodegeneration to investigate the mechanisms of NPS in AD. In addition, we apply NIMH’s Research
Domain Criteria (RDoC) framework, which casts brain disorders as extremes from the normal range of
behavior. We propose secondary analyses of RDoC-related measures from Human Connectome Project
(HCP) Young Adult and Aging Lifespan datasets, and the HCP Disordered Emotional States, Anxiety and
Depression, Alzheimer’s Disease, and Brain Aging and Dementia datasets, using computational methods for
big data analysis that inherently embody the principles of RDoC, treating NPS and AD as having extremes
from normal values of brain – behavior mappings. First, we identify brain circuits for RDoC negative and
positive valence and cognitive system constructs, then map these circuits to behavior and self-report
measures. We then construct normative models of brain – behavior mappings in healthy individuals, then apply
those models to disentangle the complex interactions between NPS and AD. Our overall hypothesis is that
deviations from normative values of RDoC-related Triple Network brain – behavior mappings will
elucidate mechanisms of NPS in AD. We maximize scientific rigor via a very large sample size for our study,
and by adopting ReproNim practices designed for replicable and generalizable neuroimaging research.

## Key facts

- **NIH application ID:** 10500666
- **Project number:** 1RF1AG078304-01
- **Recipient organization:** MCLEAN HOSPITAL
- **Principal Investigator:** DAVID G HARPER
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $2,432,945
- **Award type:** 1
- **Project period:** 2022-09-30 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10500666

## Citation

> US National Institutes of Health, RePORTER application 10500666, Combining Computational Methods, RDoC, and Big Neuroimaging Data to Understand Mechanisms of Neuropsychiatric Symptoms in Alzheimer's Disease (1RF1AG078304-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10500666. Licensed CC0.

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