PROJECT SUMMARY 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, and others. NPS in AD respond poorly
to conventional treatments and can lead to severe functional impairment, increased caregiver burden, and
institutionalization. There is profound disease-related degeneration in neurocircuitry in AD that may be a
mechanism for the clinical course and treatment resistance of NPS in AD. The overarching goal of our
funded parent grant is to identify relationships between the neurocircuitry underlying NPS and AD
neurocircuit degeneration that ultimately may drive worse outcomes in AD with NPS. To probe these
relationships, we are conducting secondary analyses of NIMH Research Domain Criteria (RDoC)-related
measures from Human Connectome Project (HCP) Young Adult and Aging datasets, and the Connectomes
Related to Human Disease (CRHD) on Treatment Resistant Depression, Anxious Misery, Alzheimer’s Disease,
and Brain Aging and Dementia. We apply computational methods for big data analysis that inherently embody
the principles of RDoC, which treats NPS and AD as extremes from normal values of brain – behavior
mappings. We maximize scientific rigor via large sample size of these six combined datasets (N~3,500), and
by adopting ReproNim practices designed for reproducible neuroimaging research. In this Administrative
Supplement request, we propose to create a cloud-based big neuroimaging data resource for
harmonized research on NPS and AD that will allow investigators to leverage cloud-based resources
for their research. Our data resource is comprised of: 1) four CRHD datasets used in the parent study,
including the fully pre-processed imaging data harmonized with HCP YA and Aging and our novel image-
derived phenotypes that will be open access on Amazon Web Service (AWS), and 2) a containerized software
tool that leverages AWS cloud computing for complete start-to-finish processing of new datasets so
researchers can harmonize their datasets with HCP/CRHD, compute ours or their own novel imaging features,
and apply our normative models to their patient data. This Admin Supp will allow us to preserve the overall
impact of our study, increase the overall impact of our parent award, exert a sustained high impact influence on
AD research, and increase the benefits of our outcomes to the research community and NIH-funded research.
