# Dynamic imaging-genomic models for characterizing and predicting psychosis and mood disorders

> **NIH NIH R01** · GEORGIA STATE UNIVERSITY · 2022 · $705,294

## Abstract

Project Summary/Abstract
 Disorders of mood and psychosis such as schizophrenia, bipolar disorder, and unipolar depression are 
incredibly complex, influenced by both genetic and environmental factors, and the clinical characterizations are
primarily based on symptoms rather than biological information. Current diagnostic approaches are based on
symptoms, which overlap extensively in some cases, and there is growing consensus that we should approach
mental illness as a continuum, rather than as a categorical entity. Since both genetic and environmental factors
play a large role in mental illness, the combination of brain imaging and genomic data are poised to play an
important role is clarifying our understanding of mental illness. However, both imaging and genomic data are
high dimensional and include complex relationships that are poorly understood. To characterize the available
information, we are in need of approaches that can deal with high-dimensional data exhibiting interactions at
multiple levels (i.e., data fusion), while providing interpretable solutions (i.e., a focus on brain and genomic 
networks). An additional challenge exists because the available data has mixed temporal dimensionality, e.g., single
nucleotide polymorphisms (SNPs) do not change over time, brain structure changes slowly over time, while fMRI
changes rapidly over time. To address these challenges, we introduce a new unified framework called flexible
subspace analysis (FSA) that can automatically identify subspaces (groupings of unimodal or multimodal 
components) in joint multimodal data. Our approach leverages the interpretability of source separation approaches
and can include additional flexibility by allowing for a combination of shallow and ‘deep’ subspaces, thus 
leveraging the power of deep learning. We will apply the developed models to a large (N>60,000) dataset of 
individuals along the mood and psychosis spectrum to evaluate the important question of disease categorization. We
will compute fully cross-validated genomic-neuro-behavioral profiles of individuals including a comparison of the
predictive accuracy of 1) standard categories from the diagnostic and statistical manual of mental disorders
(DSM), 2) data-driven subgroups, and 3) dimensional relationships. We will also evaluate the single subject
predictive power of these profiles in independent data to maximize generalization. All methods and results will
be shared with the community. The combination of advanced algorithmic approach plus the large N data 
promises to advance our understanding of the nosology of mood and psychosis disorders in addition to providing new
tools that can be widely applied to other studies of complex disease.

## Key facts

- **NIH application ID:** 10359205
- **Project number:** 5R01MH118695-05
- **Recipient organization:** GEORGIA STATE UNIVERSITY
- **Principal Investigator:** TULAY ADALI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $705,294
- **Award type:** 5
- **Project period:** 2019-05-25 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10359205

## Citation

> US National Institutes of Health, RePORTER application 10359205, Dynamic imaging-genomic models for characterizing and predicting psychosis and mood disorders (5R01MH118695-05). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/10359205. Licensed CC0.

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