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Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning
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TLDR
In this article, a machine learning-based method for determining the laterality of temporal lobe epilepsy (TLE), using features extracted from resting-state functional connectivity of the brain, was presented.Abstract:
Lateralization of temporal lobe epilepsy (TLE) is critical for successful outcome of surgery to relieve seizures. TLE affects brain regions beyond the temporal lobes and has been associated with aberrant brain networks, based on evidence from functional magnetic resonance imaging. We present here a machine learning-based method for determining the laterality of TLE, using features extracted from resting-state functional connectivity of the brain. A comprehensive feature space was constructed to include network properties within local brain regions, between brain regions, and across the whole network. Feature selection was performed based on random forest and a support vector machine was employed to train a linear model to predict the laterality of TLE on unseen patients. A leave-one-patient-out cross validation was carried out on 12 patients and a prediction accuracy of 83% was achieved. The importance of selected features was analyzed to demonstrate the contribution of resting-state connectivity attributes at voxel, region, and network levels to TLE lateralization.read more
Citations
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Journal ArticleDOI
Brain imaging in the assessment for epilepsy surgery
John S. Duncan,John S. Duncan,Gavin P. Winston,Gavin P. Winston,Matthias J. Koepp,Matthias J. Koepp,Sebastien Ourselin,Sebastien Ourselin,Sebastien Ourselin +8 more
TL;DR: Progress in semi-automated methods to register imaging data into a common space is enabling the creation of multimodal three-dimensional patient-specific datasets, which show promise for the demonstration of the complex relations between normal and abnormal structural and functional data.
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A review of epileptic seizure detection using machine learning classifiers.
TL;DR: An overview of the wide varieties of techniques based on the taxonomy of statistical features and machine learning classifiers—‘black-box’ and ‘non-black- box’ will give a detailed understanding about seizure detection and classification, and research directions in the future.
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Regional and global connectivity disturbances in focal epilepsy, related neurocognitive sequelae, and potential mechanistic underpinnings
TL;DR: Overall, studying the connectome in focal epilepsy is a critical endeavor that may lead to improved strategies for epileptogenic‐zone localization, surgical outcome prediction, and a better understanding of the neuropsychological implications of recurrent seizures.
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Structural brain changes in medically refractory focal epilepsy resemble premature brain aging.
TL;DR: Individuals with medically refractory focal epilepsy had a difference between predicted brain age and chronological age that was on average 4.5 years older than healthy controls and no significant differences were observed in newly diagnosed focal epilepsy.
Journal ArticleDOI
Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes.
Thibault Verhoeven,Ana Coito,Gijs Plomp,Aljoscha Thomschewski,Francesca Pittau,Eugen Trinka,Roland Wiest,Karl Lothard Schaller,Christoph M. Michel,Margitta Seeck,Joni Dambre,Serge Vulliemoz,Pieter van Mierlo,Pieter van Mierlo +13 more
TL;DR: This is the first study to automatically diagnose and lateralise temporal lobe epilepsy based on EEG, and demonstrates the potential of directed functional connectivity estimated from EEG periods without visible pathological activity for helping in the diagnosis and lateralization of TLE.
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