# Application of graph theory to both resting-state and task-based fMRI data to uncover brain-behavior relationships related to therapy outcomes in aphasia

> **NIH NIH R21** · PENNSYLVANIA STATE UNIVERSITY, THE · 2021 · $15,138

## Abstract

Project Summary
 Although many treatment paradigms exist for aphasia rehabilitation with behaviorally successful
outcomes, there is still much debate regarding the “best” pattern of neural reorganization during recovery. This
lack of consensus may be due, in part, to incomplete exploration of the link between neural reorganization and
behavior. The proposed project will apply graph theoretical measures to both task-based and resting-state
fMRI (rs-fMRI) data to examine changes in functional connectivity related to treatment outcomes in persons
with aphasia. Graph theory and rs-fMRI are currently underutilized in clinical aphasiology. Graph theory is
gaining popularity in other areas of cognitive science as a meaningful technique for the characterization of
brain network dynamics. Resting-state fMRI has been gaining popularity in other areas of clinical research as
an easily acquired and informative measure of general cognitive functioning.
 Twenty participants with aphasia (PWA) will be scanned using fMRI at four time points. Between scan 1
and scan 2, no therapy will be provided, establishing a baseline control for each PWA. Between scan 2 and
scan 3, ten weeks of word finding therapy will be provided. Between scan 3 and scan 4, no therapy will be
provided, allowing for measurement of maintenance effects. The treatment used is abstract word retrieval
training. This treatment was chosen because it has been shown to be not only successful at improving abstract
word retrieval, but also improving retrieval of related concrete words that are not trained (i.e., generalization)
(Kiran, Sandberg, & Abbott, 2009; Sandberg & Kiran, 2014). This makes the treatment more effective and a
good candidate for promoting positive neural changes. Furthermore, abstract and concrete words have been
shown to have differing neural activation patterns. This allows for the systematic examination of direct training
and generalization effects of treatment on brain reorganization. For the graph theory analysis, regions of
interest (ROIs) will be defined using a data-driven spatially-constrained approach developed by Drs. Hillary and
Molenaar. Once correlations matrices are defined for each time point, graph theoretical measures will be
calculated and compared at each time point to systematically examine changes in functional connectivity
related to therapy outcomes.
 The methodological approach in this proposal is innovative in the use of two baseline scans before
treatment and two maintenance scans after treatment and the application of graph theory and rs-fMRI to
examine treatment-related brain reorganization in aphasia. The successful completion of this project is
expected to help inform current theories of optimal reorganization patterns after stroke and provide groundwork
for establishing informative and appropriate methods for measuring brain reorganization in aphasia.

## Key facts

- **NIH application ID:** 10430338
- **Project number:** 3R21DC016708-03S1
- **Recipient organization:** PENNSYLVANIA STATE UNIVERSITY, THE
- **Principal Investigator:** Chaleece Wyatt Sandberg
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $15,138
- **Award type:** 3
- **Project period:** 2018-06-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10430338

## Citation

> US National Institutes of Health, RePORTER application 10430338, Application of graph theory to both resting-state and task-based fMRI data to uncover brain-behavior relationships related to therapy outcomes in aphasia (3R21DC016708-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10430338. Licensed CC0.

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