CNS neuroscience & therapeutics · 2025
Differentiating Temporal Plus "Insula" Epilepsy From Temporal Lobe Epilepsy by Brain Networks Based on Noninvasive Examinations.
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Hu Xu, Li Zilin, Mo Jiajie, Hu Wenhan, Zhang Chao, Wang Xiu, Sang Lin, Zheng Zhong, Zhang Kai
Аннотация
AIMS: To differentiate patients with temporal plus epilepsy (TPE) with insular involvement from those with temporal lobe epilepsy (TLE) using brain network analysis based on noninvasive assessments.
METHOD: TLE and TPE patients were retrospectively selected and matched using propensity score matching (PSM) analysis. Metabolic networks for each patient were constructed utilizing 18F-fluorodeoxyglucose-positron emission tomography, and graphic theory was applied for network analysis. Additionally, brain networks from scalp electroencephalography were assessed through recurrence quantification analysis (RQA). Graphic theoretical and RQA metrics were compared between the cohorts, and an extreme gradient boosting (XGBoost) classifier using graphic theoretical and RQA metrics was developed to differentiate TPE patients from those with TLE.
RESULTS: Twenty-five pairs of TLE and TPE patients were selected through PSM. Among nine graphic theoretical features examined, TLE patients exhibited a significantly higher degree centrality (Dc) in the posterior insula (p = 0.04) and higher nodal clustering coefficients (Ncf) in both the anterior insula (p = 0.03) and posterior insula (p = 0.03), which suggested that TPE patients exhibit disrupted local connectivity and diminished integrative function in the insula, particularly in the posterior region. No significant differences were found among the 13 RQA features. Despite limited differences at the individual feature level, the XGBoost classifier achieved an accuracy of 0.77 and an AUC of 0.83, likely by capturing joint patterns across multimodal connectivity indicators.
CONCLUSION: TLE and TPE patients demonstrate distinct brain network features, particularly Dc and Ncf within the insular region, as revealed by noninvasive assessments, which can be utilized for differentiation through machine learning algorithms.
METHOD: TLE and TPE patients were retrospectively selected and matched using propensity score matching (PSM) analysis. Metabolic networks for each patient were constructed utilizing 18F-fluorodeoxyglucose-positron emission tomography, and graphic theory was applied for network analysis. Additionally, brain networks from scalp electroencephalography were assessed through recurrence quantification analysis (RQA). Graphic theoretical and RQA metrics were compared between the cohorts, and an extreme gradient boosting (XGBoost) classifier using graphic theoretical and RQA metrics was developed to differentiate TPE patients from those with TLE.
RESULTS: Twenty-five pairs of TLE and TPE patients were selected through PSM. Among nine graphic theoretical features examined, TLE patients exhibited a significantly higher degree centrality (Dc) in the posterior insula (p = 0.04) and higher nodal clustering coefficients (Ncf) in both the anterior insula (p = 0.03) and posterior insula (p = 0.03), which suggested that TPE patients exhibit disrupted local connectivity and diminished integrative function in the insula, particularly in the posterior region. No significant differences were found among the 13 RQA features. Despite limited differences at the individual feature level, the XGBoost classifier achieved an accuracy of 0.77 and an AUC of 0.83, likely by capturing joint patterns across multimodal connectivity indicators.
CONCLUSION: TLE and TPE patients demonstrate distinct brain network features, particularly Dc and Ncf within the insular region, as revealed by noninvasive assessments, which can be utilized for differentiation through machine learning algorithms.