首页|Central South University Reports Findings in Temporal Lobe Epilepsy (Preoperativ e structural-functional coupling at the default mode network predicts surgical o utcomes of temporal lobe epilepsy)
Central South University Reports Findings in Temporal Lobe Epilepsy (Preoperativ e structural-functional coupling at the default mode network predicts surgical o utcomes of temporal lobe epilepsy)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Central Nervous System Diseases and Conditions - Temporal Lobe Epilepsy is the subject of a report. Ac cording to news reporting out of Changsha, People's Republic of China, by NewsRx editors, research stated, "Structural-functional coupling (SFC) has shown great promise in predicting postsurgical seizure recurrence in patients with temporal lobe epilepsy (TLE). In this study, we aimed to clarify the global alterations in SFC in TLE patients and predict their surgical outcomes using SFC features." Financial supporters for this research include National Natural Science Foundati on of China, National Basic Research Program of China. Our news journalists obtained a quote from the research from Central South Unive rsity, "This study analyzed presurgical diffusion and functional magnetic resona nce imaging data from 71 TLE patients and 48 healthy controls (HCs). TLE patient s were categorized into seizure-free (SF) and non-seizure-free (nSF) groups base d on postsurgical recurrence. Individual functional connectivity (FC), structura l connectivity (SC), and SFC were quantified at the regional and modular levels. The data were compared between the TLE and HC groups as well as among the TLE, SF, and nSF groups. The features of SFC, SC, and FC were categorized into three datasets: the modular SFC dataset, regional SFC dataset, and SC/FC dataset. Each dataset was independently integrated into a cross-validated machine learning mo del to classify surgical outcomes. Compared with HCs, the visual and subcortical modules exhibited decoupling in TLE patients (p <.05). Mu ltiple default mode network (DMN)-related SFCs were significantly higher in the nSF group than in the SF group (p <.05). Models trained us ing the modular SFC dataset demonstrated the highest predictive performance. The final prediction model achieved an area under the receiver operating characteri stic curve of .893 with an overall accuracy of .887. Presurgical hyper-SFC in th e DMN was strongly associated with postoperative seizure recurrence."
ChangshaPeople's Republic of ChinaAs iaBrain Diseases and ConditionsBrain ResearchCentral Nervous SystemCentr al Nervous System Diseases and ConditionsCerebral CortexCyborgsEmerging Te chnologiesEpilepsyHealth and MedicineMachine LearningProsencephalonTel encephalonTemporal LobeTemporal Lobe Epilepsy
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