# Neural and Behavioral Predictors of Naming Therapy Outcomes in Chronic Post-Stroke Aphasia

> **NIH VA IK1** · VETERANS HEALTH ADMINISTRATION · 2020 · —

## Abstract

More than 2 million people in the U.S. have aphasia, a language disorder most often caused by
stroke that reduces participation in preferred activities, functional independence, and health-related
quality of life. Language therapy for aphasia is efficacious, but outcomes vary across patients,
presenting challenges for treatment-planning and prognostication for recovery. Recent evidence
suggests brain network properties derived from functional connectivity data and quantified via graph
theory may help explain this variability and predict treatment outcomes. However, only a few studies
have used graph theory to investigate aphasia and the relationships between graph metrics, stroke-
related brain damage, and patients’ response to specific types of intervention remain unclear. This
study seeks to address these knowledge gaps by leveraging two potentially informative graph metrics,
modularity and global efficiency, which characterize the brain’s segregation into functionally distinct
subsystems and its capacity to integrate information among separate regions, respectively.
 To advance knowledge of the relationship between brain damage and neural function in
aphasia, this study will determine the association between lesion size and modularity and global
efficiency in Veterans with chronic aphasia (Aim 1). Additionally, to inform predictive models of
recovery, the study will determine if pre-treatment modularity and/or global efficiency are associated
with outcomes from semantic feature analysis (SFA), a popular intervention for naming impairments
(Aim 2a), and whether they provide unique predictive information relative to other neural and
behavioral predictors (e.g., lesion size, pre-treatment aphasia severity, demographics) (Aim 2b).
 This study will include 10 Veterans with chronic aphasia due to left-hemisphere stroke, all of
whom will undergo neuroimaging and treatment in a larger randomized clinical trial of SFA therapy.
Specifically, participants will complete a language evaluation, structural MRI, and resting-state fMRI
(RSfMRI) prior to receiving 60 hours of SFA therapy over 15 days. Treatment outcomes will be
derived from pre- and post-treatment naming assessments of trained items. Lesion volume will be
calculated from lesion maps drawn on participants’ structural scans. Functional connectivity-based
brain graphs (i.e., network representations) consisting of nodes (i.e., 264 brain regions, per a
parcellation scheme from Power et al., 2011) and edges (i.e., pairwise correlations in the BOLD signal
over time between nodes) will be constructed from participants’ RSfMRI scans, and the modularity
and global efficiently of each participant’s graph will subsequently be computed using the Brain
Connectivity Toolbox. Aim 1 will be addressed by correlating lesion volume with modularity and
global efficiency. Aim 2 will be addressed by regressing treatment outcomes on modularity and global
efficiency (Aim 2a), as well as other predictive variables (Aim ...

## Key facts

- **NIH application ID:** 9950515
- **Project number:** 1IK1RX003361-01
- **Recipient organization:** VETERANS HEALTH ADMINISTRATION
- **Principal Investigator:** Jeffrey P Johnson
- **Activity code:** IK1 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2020-06-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9950515

## Citation

> US National Institutes of Health, RePORTER application 9950515, Neural and Behavioral Predictors of Naming Therapy Outcomes in Chronic Post-Stroke Aphasia (1IK1RX003361-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9950515. Licensed CC0.

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