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Identification of four biotypes in temporal lobe epilepsy via machine learning on brain images

Medicine and Health

Identification of four biotypes in temporal lobe epilepsy via machine learning on brain images

Y. Jiang, W. Li, et al.

This groundbreaking research, conducted by Yuchao Jiang and colleagues, unveiled four distinct subtypes of temporal lobe epilepsy (TLE) by analyzing MRI data. The study's findings highlight diverse patterns of brain atrophy and their impact on disease progression and treatment outcomes, paving the way for personalized medicine approaches in TLE management.

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Playback language: English
Abstract
Using machine learning on MRI data from 296 individuals with temporal lobe epilepsy (TLE) and 91 healthy controls, this study identified four TLE subtypes based on distinct patterns of brain atrophy progression. Two subtypes showed hippocampus-predominant atrophy, one was cortex-predominant, and the fourth exhibited amygdala enlargement without atrophy. These subtypes differed in neuroanatomical signatures, disease progression, epilepsy characteristics, and five-year seizure outcomes after surgery or medication, suggesting a diverse pathobiological basis for TLE with implications for personalized medicine.
Publisher
Nature Communications
Published On
Mar 12, 2024
Authors
Yuchao Jiang, Wei Li, Jinmei Li, Xiuli Li, Heng Zhang, Xiutian Sima, Luying Li, Kang Wang, Qifu Li, Jiajia Fang, Lu Jin, Qiyong Gong, Dezhong Yao, Dong Zhou, Cheng Luo, Dongmei An
Tags
temporal lobe epilepsy
machine learning
brain atrophy
personalized medicine
MRI data
neuroanatomical signatures
seizure outcomes

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