Evaluating Resective Surgery Targets in Epilepsy Patients: A Comparison of Quantitative EEG Methods.

Müller, Michael; Schindler, Kaspar Anton; Goodfellow, Marc; Pollo, Claudio; Rummel, Christian; Steimer, Andreas (2018). Evaluating Resective Surgery Targets in Epilepsy Patients: A Comparison of Quantitative EEG Methods. Journal of neuroscience methods, 305, pp. 54-66. Elsevier 10.1016/j.jneumeth.2018.04.021

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BACKGROUND Quantitative analysis of intracranial EEG is a promising tool to assist clinicians in the planning of resective brain surgery in patients suffering from pharmacoresistant epilepsies. Quantifying the accuracy of such tools, however, is nontrivial as a ground truth to verify predictions about hypothetical resections is missing. NEW METHOD As one possibility to address this, we use customized hypotheses tests to examine the agreement of the methods on a common set of patients. One method uses machine learning techniques to enable the predictive modeling of EEG time series. The other estimates nonlinear interrelation between EEG channels. Both methods were independently shown to distinguish patients with excellent post-surgical outcome (Engel class I) from those without improvement (Engel class IV) when assessing the electrodes associated with the tissue that was actually resected during brain surgery. Using the AND and OR conjunction of both methods we evaluate the performance gain that can be expected when combining them. RESULTS Both methods' assessments correlate strongly positively with the similarity between a hypothetical resection and the corresponding actual resection in class I patients. Moreover, the Spearman rank correlation between the methods' patient rankings is significantly positive. COMPARISON WITH EXISTING METHOD(S) To our best knowledge, this is the first study comparing surgery target assessments from fundamentally differing techniques. CONCLUSIONS Although conceptually completely independent, there is a relation between the predictions obtained from both methods. Their broad consensus supports their application in clinical practice to provide physicians additional information in the process of presurgical evaluation.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurosurgery
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology

UniBE Contributor:

Müller, Michael; Schindler, Kaspar Anton; Pollo, Claudio; Rummel, Christian and Steimer, Andreas

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0165-0270

Publisher:

Elsevier

Language:

English

Submitter:

Martin Zbinden

Date Deposited:

28 May 2018 17:16

Last Modified:

14 Jun 2018 01:32

Publisher DOI:

10.1016/j.jneumeth.2018.04.021

PubMed ID:

29753683

Uncontrolled Keywords:

Epilepsy Functional network Method validation Predictive modeling Quantitative EEG Resective surgery

BORIS DOI:

10.7892/boris.116539

URI:

https://boris.unibe.ch/id/eprint/116539

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