# Data-driven approaches to identify biomarkers from multimodal imaging big data

> **NIH NIH R01** · GEORGIA STATE UNIVERSITY · 2020 · $395,331

## Abstract

1. PROJECT SUMMARY/ABSTRACT
 The study of translational biomarkers in brain disorders is a very challenging and fruitful approach, which
will empower a better understanding of healthy and diseased brains. This project will promote the translation of
advanced engineering solutions and mathematic tools to novel neuroimaging applications in psychiatric
disorders including major depression disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ), allowing
sophisticated and powerful analyses on highly complex datasets. To date, the unifying syndrome classification
(ICD-9/10;DSM-IV/5) for these mental disorders obscures our knowledge of underlying pathophysiology and
cannot guide optimal treatments. For example, there is no biomarker that is able to precisely predict response
of MDD to some treatments. One reason for this is that most neuroimaging prediction studies to date have used
a single imaging measure or reported simple correlation relationships, without considering multimodal cross-
information, nonlinear relationships, or multi-site cross-validation. Hence, developing novel data mining
techniques such as deep learning, fusion with reference, and sparse regression can complement and exploit the
richness of neuroimaging data, providing promising avenues to identify objective biomarkers and going beyond
a descriptive use of brain imaging as traditionally used in studies of brain disease to individualized prediction.
We will facilitate the translational biomarker identification by developing 3 novel data-driven methods: 1) A
supervised fusion model that can provide insight on how cognitive impairment may affect covarying brain function
and structure in mental disorder, by using different clinical measures as a reference to guide multimodal MRI
fusion; 2) A cutting-edge prediction framework with aggregated feature selection techniques that is able to
estimate clinical outcome more precisely, e.g., remission/relapse status of individual MDD patient after
electroconvulsive treatment(ECT) using baseline brain imaging and demographic measures of 3) We will draw
on advances and ideas from deep learning combined with layer-wise relevance propagation (LRP) or attention
modules, to classify multiple groups of psychiatric disorders by incorporating dynamic functional measures. The
proposed (Deep/Recurrent/Convolutional Neural Network, DNN/RNN/CNN) models will have enhanced
interpretability that is able to trace back and discover the most predictive functional networks from input. All
above proposed methods will be applied to big data containing both multimodal imaging and behavioral
information (n~5000) pooled from existing studies, and our developed open-source toolboxes will be shared
publicly. This pioneering study may provide an urgently-needed paradigm shift in the treatment and diagnosis of
psychiatric disorders, thereby guiding personalized clinical care. Accomplishment of this project has great
potential to discover neuroimaging biomarkers that ...

## Key facts

- **NIH application ID:** 9999673
- **Project number:** 5R01MH117107-02
- **Recipient organization:** GEORGIA STATE UNIVERSITY
- **Principal Investigator:** Jing Sui
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $395,331
- **Award type:** 5
- **Project period:** 2019-08-20 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9999673

## Citation

> US National Institutes of Health, RePORTER application 9999673, Data-driven approaches to identify biomarkers from multimodal imaging big data (5R01MH117107-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9999673. Licensed CC0.

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