# Male/Female differences in psychosis and mood disorders:Dynamic imaging-genomic  models for characterizing and predicting psychosis and mood d

> **NIH NIH R01** · GEORGIA STATE UNIVERSITY · 2020 · $155,500

## 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 in 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 subsumes
existing models while providing important extensions. FSA 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 both linear and nonlinear (shallow and deep)
subspaces. We will apply the developed models to a large (N~80,000) dataset including
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:** 10093861
- **Project number:** 3R01MH118695-03S1
- **Recipient organization:** GEORGIA STATE UNIVERSITY
- **Principal Investigator:** TULAY ADALI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $155,500
- **Award type:** 3
- **Project period:** 2019-05-25 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10093861

## Citation

> US National Institutes of Health, RePORTER application 10093861, Male/Female differences in psychosis and mood disorders:Dynamic imaging-genomic  models for characterizing and predicting psychosis and mood d (3R01MH118695-03S1). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10093861. Licensed CC0.

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