Cortical sites critical to language function act as connectors between language subnetworks

Historically, eloquent functions have been viewed as localized to focal areas of human cerebral cortex, while more recent studies suggest they are encoded by distributed networks. We examined the network properties of cortical sites defined by stimulation to be critical for speech and language, using electrocorticography from sixteen participants during word-reading. We discovered distinct network signatures for sites where stimulation caused speech arrest and language errors. Both demonstrated lower local and global connectivity, whereas sites causing language errors exhibited higher inter-community connectivity, identifying them as connectors between modules in the language network. We used machine learning to classify these site types with reasonably high accuracy, even across participants, suggesting that a site's pattern of connections within the task-activated language network helps determine its importance to function. These findings help to bridge the gap in our understanding of how focal cortical stimulation interacts with complex brain networks to elicit language deficits.

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Publication Details

Journal
Nature Communications
Published
2024-09-16
DOI
https://doi.org/10.1038/s41467-024-51839-z
Citations
7
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
2.40

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article

Cortical sites critical to language function act as connectors between language subnetworks

Zachary Fitzgerald, Matthew C. Tate, Emily M. Mugler, Yujing Wang et al.
7 citations
Nature Communications
Functional Brain Connectivity Studies
2.40
article

Cortical sites critical to language function act as connectors between language subnetworks

Zachary Fitzgerald, Matthew C. Tate, Emily M. Mugler, Yujing Wang, Joshua M. Rosenow, Jessica W. Templer, Jason Hsieh, Nathan E. Crone, Marc W. Slutzky, Robert D. Flint, Richard F. Betzel, Prashanth Prakash
article en
7 citations

Abstract

Historically, eloquent functions have been viewed as localized to focal areas of human cerebral cortex, while more recent studies suggest they are encoded by distributed networks. We examined the network properties of cortical sites defined by stimulation to be critical for speech and language, using electrocorticography from sixteen participants during word-reading. We discovered distinct network signatures for sites where stimulation caused speech arrest and language errors. Both demonstrated lower local and global connectivity, whereas sites causing language errors exhibited higher inter-community connectivity, identifying them as connectors between modules in the language network. We used machine learning to classify these site types with reasonably high accuracy, even across participants, suggesting that a site's pattern of connections within the task-activated language network helps determine its importance to function. These findings help to bridge the gap in our understanding of how focal cortical stimulation interacts with complex brain networks to elicit language deficits.

Nature CommunicationsVol. 15(1)
Northwestern University (US), Cleveland Clinic (US), Johns Hopkins University (US), Johns Hopkins Medicine (US), Northwestern University (PH), Indiana University Bloomington (US), Northwestern Medicine (US)
Northwestern University, Northwestern Memorial Foundation, National Institutes of Health, Robert H. Lurie Comprehensive Cancer Center, National Institute of Neurological Disorders and Stroke
Quality Education
Openalex Percentile: Top 11%
Functional Brain Connectivity Studies
2.40
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