# Statistical ICA Methods for Analysis and Integration of Multi-dimensional Data

> **NIH NIH R01** · EMORY UNIVERSITY · 2022 · $516,357

## Abstract

Project Summary/Abstract
Recent mental health studies have led to an expanded depth of multimodal brain imaging data, clinical
assessments and physiological data. In addition, longitudinal studies have become increasingly important to
capture the trajectory of disease progression, treatment response and relapse. This wealth of datasets
provides an unprecedented opportunity for crosscutting investigations. However, much-needed statistical
methods for exploring discoveries are lacking. In particular, there has been very limited development of
advanced statistical methods for several important objectives: decompose observed brain connectivity
measures to reveal underlying neural circuits which are key biomarkers for mental disorders, effectively extract
low dimensional neural features from imaging to reliably predict clinical outcomes such as treatment response,
and analyze longitudinal multidimensional data including neuroimaging, clinical and behavioral assessments to
study the dynamic interplay between brain and behavior changes due to treatments.
In this competing renewal proposal, we will build upon the theoretical and computational framework
established in our previous award to develop rigorous and computationally efficient statistical methods to
address the aforementioned objectives. Specifically, we plan to develop 1) a sparse and low rank ICA (SLR-
ICA) framework for reliable and parsimonious decomposition of brain connectivity measures to reveal
underlying neural circuits associated with specific clinical symptoms in mental disorders; 2) an ICA-Neural
Network (ICA-NN) predictive model that effectively extracts relevant low dimensional linear and non-linear
neural features to predict clinical outcomes; and (3) longitudinal multidimensional data analysis tools for
investigating heterogeneous changes in neural circuits due to different treatments and disease subtypes, and
disentangle the relationship between changes in neuroimaging phenotypes and clinical symptoms. The
statistical methods will be applied to a major NIH funded longitudinal study of major depressive disorder (MDD)
to help discover neural circuits underlying specific depressive symptoms (e.g. suicidal thoughts) and differential
treatment response, and ultimately help lead to more effective treatment for individual MDD patients based on
his/her own neural circuitry fingerprints and behavior. We plan to replicate the findings using an independent
validation cohort from an R01 study of MDD. User-friendly software will be made available to general research
communities. Our proposed method developments will directly benefit mental health research by providing
innovative statistical tools to effectively extract reliable and highly relevant low dimensional features from
neuroimaging to deepen mechanistic understanding and improve treatment of MDD and other mental
disorders.

## Key facts

- **NIH application ID:** 10475127
- **Project number:** 5R01MH105561-07
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Ying Guo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $516,357
- **Award type:** 5
- **Project period:** 2014-09-25 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10475127

## Citation

> US National Institutes of Health, RePORTER application 10475127, Statistical ICA Methods for Analysis and Integration of Multi-dimensional Data (5R01MH105561-07). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10475127. Licensed CC0.

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