Abstract:
: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.
journal_name
Hum Brain Mappjournal_title
Human brain mappingauthors
Steimer A,Müller M,Schindler Kdoi
10.1002/hbm.23537subject
Has Abstractpub_date
2017-05-01 00:00:00pages
2509-2531issue
5eissn
1065-9471issn
1097-0193journal_volume
38pub_type
杂志文章abstract::Despite the clinical significance of event-related potential (ERP) latency abnormalities, little attention has focused on the anatomic substrate of latency variability. Volume conduction models do not identify the anatomy responsible for delayed neural transmission between neural sources. To explore the anatomic subst...
journal_title:Human brain mapping
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journal_title:Human brain mapping
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pub_type: 杂志文章
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journal_title:Human brain mapping
pub_type: 临床试验,杂志文章
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