# Learning high-dimensional functional connectomes of heterogeneous populations

> **NIH NIH R21** · CORNELL UNIVERSITY · 2020 · $428,417

## Abstract

PROJECT SUMMARY
Network analysis of brain connectivity, or Connectomics, has emerged as an important
interdisciplinary field, making strides in advancing both fundamental scientific knowledge on the
structure of the brain, as well as providing insights into the pathology of neurological disorders.
Advances in neuroimaging technologies have enabled acquisition of high-resolution datasets on
brain activities in normal and diseased populations, while advanced machine learning methods
hold promise to obtain data-driven insights into the functional architecture of the brain.
Functional connectivity analysis is typically carried out in two steps. First, one estimates a
network among different brain regions by studying strengths of associations among the time
course of neurophysiological signals for different subjects (patients or healthy controls). Next,
one compares the networks between different groups of subjects and seeks network features
prevalent in specific groups of interest using statistical methods. Two emerging challenges in
this field are the presence of heterogeneity amongst subjects in large study cohorts, and
developing predictive models to construct robust and interpretable results.
The central goal of this proposal is to address these challenges by developing machine learning
methods equipped with uncertainty quantification measures, suitable for high-dimensional
network data for heterogeneous populations. Upon completion, these methods are expected
to provide automated, robust and more accurate discovery of connectivity patterns that is
prevalent in heterogeneous populations of patients. We aim to accomplish this goal by pursuing
two specific aims: (1) develop estimation and inference methods for frequency domain
measures of high-dimensional functional connectivity networks, (2) develop a framework of
mixed effects model of high-dimensional functional connectivity networks that accounts for
heterogeneity among subjects and enables discoveries more likely to generalize in large
cohorts. For each aim, novel machine learning methods for integrative analysis of structural and
functional connectivity will be developed using a mathematical model of network diffusion. We
will also calibrate and validate our proposed methods on data from Human Connectome Project,
and on multiple sclerosis (MS) patients. The proposed approach is innovative since it integrates
machinery across diverse disciplines, including statistics, machine learning and network analysis
to address important challenges learning large functional connectivity graphs. The proposed
research is significant in that it is expected to have both scientific and translational impact.

## Key facts

- **NIH application ID:** 10110789
- **Project number:** 1R21NS120227-01
- **Recipient organization:** CORNELL UNIVERSITY
- **Principal Investigator:** Sumanta Basu
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $428,417
- **Award type:** 1
- **Project period:** 2020-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10110789

## Citation

> US National Institutes of Health, RePORTER application 10110789, Learning high-dimensional functional connectomes of heterogeneous populations (1R21NS120227-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10110789. Licensed CC0.

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