# Modeling the neural bases of aphasia in neurosurgical patients: A multivariate, connectivity-based approach

> **NIH NIH F32** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $69,080

## Abstract

PROJECT SUMMARY/ABSTRACT
 Lesion symptom mapping (LSM) is a crucial tool used to make causal inferences about behavior from
neuroimaging data. Recent work has suggested that structural white matter (WM) and functional connectivity
between cortical regions play an important role in supporting healthy language function. However, any causal
role of connectivity in language remains unclear, due to both intrinsic limitations of the cohorts typically studied
with LSM and often discordant findings across patients and healthy controls. To shed light on this problem, the
proposed project will use a multimodal approach to examine connectivity and language in a large and still-
growing dataset of patients undergoing resective neurosurgery. This population (a) regularly experiences
transient, site-specific aphasias in the acute period following surgery, (b) is not subject to the same confounds
of populations typically studied in LSM, and (c) can be studied using electrocorticography (ECoG) prior to
resection, allowing both healthy and aphasic language to be neurally characterized within the same individuals.
The central hypothesis is that the neurosurgical cohort will reveal classical language syndromes to be a
function of disconnection rather than modular damage, with marked deficits in language arising primarily from
lesions to WM bottlenecks supporting functional connectivity within the broader language network. The
rationale is that this unique approach will contribute a new and clarifying perspective on language and the
brain, allowing us to directly examine the extent to which connectivity is necessary for versus simply involved in
healthy language processing. The central hypothesis will be investigated via two specific aims: (1) to use
multivariate LSM (MLSM) to determine the extent to which the structural integrity of white matter (WM) tracts
predicts fluency and comprehension in the acute period following resective neurosurgery over and above what
is predicted by the integrity of classical, cortical language regions alone, and (2) to use network analysis of
ECoG to determine whether the resection of tissue that exhibits strong functional connectivity prior to surgery
predicts poorer fluency and comprehension outcomes post-surgery. In the first aim, MLSM models based on
cortical and WM ROIs will be statistically compared to determine which provide the most accurate predictions
of language outcomes. In the second aim, functional connectivity of ECoG from later-resected tissue will be
analyzed to determine whether pre-surgical measures of connectivity lead to better predictions of language
outcomes. The research proposed here will provide the first multimodal study of language including both
MLSM and ECoG, with a distinct focus on the causal role of connectivity in language. This work is innovative
because it will make use of a rare cohort, sophisticated multivariate and network-based analyses, and an
unusually large dataset to predict language outcome...

## Key facts

- **NIH application ID:** 10630830
- **Project number:** 5F32DC020096-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Deborah Levy
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $69,080
- **Award type:** 5
- **Project period:** 2022-06-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10630830

## Citation

> US National Institutes of Health, RePORTER application 10630830, Modeling the neural bases of aphasia in neurosurgical patients: A multivariate, connectivity-based approach (5F32DC020096-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10630830. Licensed CC0.

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