# Beyond lesion-language mapping in aphasia: A novel imaging-based prediction model

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

## Abstract

Response to post-stroke aphasia language rehabilitation is variable, mainly because there are few
predictors that can help identify individualized treatment options. Imaging techniques, such as Voxel-based
Lesion Symptom Mapping (VLSM) have been useful in linking specific brain areas to language behavior;
however, further development is required to optimize use of structural and functional information in guiding
individualized treatment for our Veterans with aphasia. This CDA1 addresses this gap through development of a
novel technique that improves prediction of language behavior using anatomical measure of gliosis+ as well as
physiological measures such as Cerebral Blood Flow (CBF) and Glucose Extraction Fraction (GEF).
 In the first aim, we test the sensitivity of our novel anatomical measure, gliosis+, in relating to
confrontation naming of nouns and verbs, thereby advancing current VLSM techniques. Our approach to testing
the working hypothesis will be to recruit patients with aphasia on whom we will test confrontation naming of
nouns and verbs and acquire high-resolution structural MRI scans. The study of nouns and verbs has multiple
avenues of clinical significance, including (1) anomia is the most pervasive deficit in aphasia, (2) noun and verb
naming can be differentially impaired in aphasia, and (3) nouns and verbs are often targets in impairment-based
treatment. We plan to determine the structure-behavior association with three different methodologies (binary
lesion maps, continuous T1w signal maps, gliosis + maps), and statistically compare the amount of behavioral
variance that each VLSM technique accounts for. After completing Aim 1, it is our expectation that we will have
identified which VLSM methodology accounts for the most behavioral variance.
 In the second aim, we develop a novel methodology called Voxel-based Lesion and Physiology Symptom
Mapping (VLPSM) by combining gliosis+ VLSM with regional CBF to improve the predictive capability of
structure-behavior association maps, and expand them to structure-function-behavior association maps. We will
then compare VLPSM to VLSM, and determine which methodology accounts for more variance in language
behavior, with the expectation that combining structural and physiological information will account for more
language behavior variance across subjects than when considering structural information alone. After
completing Aim 2, it is our expectation that we will have a robust and novel method of mapping language
behavior to brain areas utilizing information from both structure and physiology.
 In the third aim, we will develop regional GEF measures on a Magnetic Resonance Imaging (MRI) system,
which can be used as a better marker of neuronal health in VLPSM. Our approach will be to develop the theory
of the pulse sequence, ensure proper MRI signal formation via Bloch equation modeling, simulate different
conditions of the MRI signal formation, and implement the pulse sequence on a 3T Si...

## Key facts

- **NIH application ID:** 9824467
- **Project number:** 5IK1RX002629-02
- **Recipient organization:** VETERANS HEALTH ADMINISTRATION
- **Principal Investigator:** Lisa C. Krishnamurthy
- **Activity code:** IK1 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2018-10-01 → 2021-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9824467

## Citation

> US National Institutes of Health, RePORTER application 9824467, Beyond lesion-language mapping in aphasia: A novel imaging-based prediction model (5IK1RX002629-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9824467. Licensed CC0.

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