---
title: "Pathological Functional Connectivity and Language Dysfunction in Children with SeLECTS Epilepsy"
id: "biorxiv-20-language-dysfunction-associated-with-pathological-brain-connectivity-in"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-20-language-dysfunction-associated-with-pathological-brain-connectivity-in"
content_type: "clinical_feed_article"
specialty: "Neurology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.07.26.740747v1?rss=1"
published_at: "2026-07-28T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Pathological Functional Connectivity and Language Dysfunction in Children with SeLECTS Epilepsy
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-20-language-dysfunction-associated-with-pathological-brain-connectivity-in
- **Specialty:** [Neurology](https://medichelpline.com/clinical-feed/neurology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.07.26.740747v1?rss=1)
- **Published At:** 2026-07-28T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- Study investigated how distributed brain networks relate to **language impairment** in childhood epilepsy, focusing on self-limited epilepsy with centrotemporal spikes (**SeLECTS**). - High-density EEG was recorded from 27 children with SeLECTS and 29 age-matched controls during a verb-generation task and resting state. - Researchers quantified **functional connectivity** across multiple frequency bands and bilateral frontal, temporal, occipital, and motor regions. - Multivariate pattern analysis identified connectivity patterns that predicted language ability and separated patterns shared across groups from those specific to SeLECTS. - Across groups, **frontal** and **occipitotemporal** connectivity—especially within the **left hemisphere**—predicted language performance. - In SeLECTS, connectivity involving the **motor network** emerged as the dominant disease-specific predictor, linking the epileptogenic network to language dysfunction. - Connectivity between specific region pairs outperformed averaged whole-brain or hemispheric connectivity measures in predicting language ability. - Task-based connectivity during verb generation predicted language ability better than resting-state connectivity. - Regionally specific connectivity explained language performance beyond clinical variables such as epilepsy diagnosis and antiseizure medication use. - Authors conclude that language ability is associated with spatially specific, distributed connectivity patterns, while epilepsy introduces distinct alterations centered on the epileptogenic network, offering a rationale for spatially targeted neuromodulation.
## Clinical Analysis & Structured Key Points
Language Dysfunction Associated with Pathological Brain Connectivity in Children with Epilepsy | bioRxiv Skip to main content New Results Language Dysfunction Associated with Pathological Brain Connectivity in Children with Epilepsy Xiwei She , Olivia Peony , Wendy Qi , Miguel Menchaca , Kerry Nix , Wei Wu , Zihuai He , Fiona Baumer doi: https://doi.org/10.64898/2026.07.26.740747 Xiwei She Stanford University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Olivia Peony Stanford University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wendy Qi Stanford University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Miguel Menchaca Stanford University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kerry Nix Stanford University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wei Wu Stanford University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zihuai He Stanford University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fiona Baumer Stanford University Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: fbaumer{at}stanford.edu Abstract Info/History Metrics Supplementary material Preview PDF Abstract Language impairment is common in childhood epilepsy and may arise in part from disruption of the distributed brain networks that support language. In self-limited epilepsy with centrotemporal spikes (SeLECTS)-the most common focal epilepsy of childhood-hyperconnectivity has been linked to poor language outcomes, but the specific network patterns associated with language dysfunction remain unclear, limiting the ability to target neurostimulation rationally. We recorded high-density EEG from 27 children with SeLECTS and 29 age-matched controls during verb generation and rest, and quantified functional connectivity across multiple frequency bands and bilateral frontal, temporal, occipital, and motor regions. Using multivariate pattern analysis, we identified connectivity patterns that predicted language ability, distinguished patterns shared across groups from those specific to SeLECTS and tested whether spatially specific connectivity provided information beyond whole-brain or hemispheric averages and conventional clinical variables. Frontal and occipitotemporal connectivity, particularly within the left hemisphere, predicted language ability across groups, whereas motor-network connectivity emerged as the dominant SeLECTS-specific predictor, linking the epileptogenic network to language dysfunction. Connectivity between specific regions outperformed averaged connectivity measures and predicted language beyond epilepsy diagnosis and antiseizure medication use. Task-based connectivity also outperformed resting-state connectivity. These findings show that language ability is associated with distributed yet spatially specific patterns of brain connectivity, while epilepsy introduces distinct alterations centered on the epileptogenic network. Identifying these disease-specific network patterns provides mechanistic insight into language dysfunction and a rational basis for spatially targeted neuromodulation. Competing Interest Statement The authors have declared no competing interest. Funder Information Declared National Institute of Neurological Disorders and Stroke, https://ror.org/01s5ya894 , K23NS116110 Stanford Maternal and Child Health Research Institute, https://ror.org/00yt0ea73 , Postdoctoral Support Fellowship Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Back to top Previous Next Posted July 28, 2026. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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Share Language Dysfunction Associated with Pathological Brain Connectivity in Children with Epilepsy Xiwei She , Olivia Peony , Wendy Qi , Miguel Menchaca , Kerry Nix , Wei Wu , Zihuai He , Fiona Baumer bioRxiv 2026.07.26.740747; doi: https://doi.org/10.64898/2026.07.26.740747 Share This Article: Copy Citation Tools Language Dysfunction Associated with Pathological Brain Connectivity in Children with Epilepsy Xiwei She , Olivia Peony , Wendy Qi , Miguel Menchaca , Kerry Nix , Wei Wu , Zihuai He , Fiona Baumer bioRxiv 2026.07.26.740747; doi: https://doi.org/10.64898/2026.07.26.740747 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Areas All Articles Animal Behavior and Cognition (7829) Biochemistry (18304) Bioengineering (14485) Bioinformatics (43307) Biophysics (22071) Cancer Biology (19181) Cell Biology (26301) Clinical Trials (138) Developmental Biology (13697) Ecology (20503) Epidemiology (2067) Evolutionary Biology (24958) Genetics (15909) Genomics (23099) Immunology (18295) Microbiology (41522) Molecular Biology (17617) Neuroscience (91299) Paleontology (683) Pathology (2926) Pharmacology and Toxicology (4973) Physiology (7917) Plant Biology (15590) Scientific Communication and Education (2073) Synthetic Biology (4449) Systems Biology (10042) Zoology (2329)
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