Predictive modeling of EEG time series for evaluating surgery targets in epilepsy patients.

Steimer, Andreas; Müller, Michael; Schindler, Kaspar Anton (2017). Predictive modeling of EEG time series for evaluating surgery targets in epilepsy patients. Human brain mapping, 38(5), pp. 2509-2531. Wiley-Blackwell 10.1002/hbm.23537

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During the last 20 years, predictive modeling in epilepsy research has largely been concerned with the prediction of seizure events, whereas the inference of effective brain targets for resective surgery has received surprisingly little attention. In this exploratory pilot study, we describe a distributional clustering framework for the modeling of multivariate time series and use it to predict the effects of brain surgery in epilepsy patients. By analyzing the intracranial EEG, we demonstrate how patients who became seizure free after surgery are clearly distinguished from those who did not. More specifically, for 5 out of 7 patients who obtained seizure freedom (= Engel class I) our method predicts the specific collection of brain areas that got actually resected during surgery to yield a markedly lower posterior probability for the seizure related clusters, when compared to the resection of random or empty collections. Conversely, for 4 out of 5 Engel class III/IV patients who still suffer from postsurgical seizures, performance of the actually resected collection is not significantly better than performances displayed by random or empty collections. As the number of possible collections ranges into billions and more, this is a substantial contribution to a problem that today is still solved by visual EEG inspection. Apart from epilepsy research, our clustering methodology is also of general interest for the analysis of multivariate time series and as a generative model for temporally evolving functional networks in the neurosciences and beyond. Hum Brain Mapp 38:2509-2531, 2017. © 2017 Wiley Periodicals, Inc.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DCR Unit Sahli Building > Forschungsgruppe Neurologie

UniBE Contributor:

Steimer, Andreas; Müller, Michael and Schindler, Kaspar Anton

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1065-9471

Publisher:

Wiley-Blackwell

Language:

English

Submitter:

Stefanie Hetzenecker

Date Deposited:

27 Jul 2017 08:03

Last Modified:

05 Jun 2018 12:33

Publisher DOI:

10.1002/hbm.23537

PubMed ID:

28205340

Uncontrolled Keywords:

Bayesian inference; Chow-Liu tree; Hidden Markov Model; distributional clustering; epilepsy; graphical models; predictive modeling; quantitative EEG; rate distortion theory; resective surgery

BORIS DOI:

10.7892/boris.99476

URI:

https://boris.unibe.ch/id/eprint/99476

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