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Predictability of uncontrollable multifocal seizures - towards new treatment options.

Lehnertz K, Dickten H, Porz S, Helmstaedter C, Elger CE - Sci Rep (2016)

Bottom Line: Drug-resistant, multifocal, non-resectable epilepsies are among the most difficult epileptic disorders to manage.Our findings clearly indicate that epileptic networks, spanning lobes and hemispheres, underlie generation of seizures.Our proof-of-concept study is an important milestone towards new therapeutic strategies based on seizure-prediction techniques for clinical practice.

View Article: PubMed Central - PubMed

Affiliation: Department of Epileptology, University of Bonn, Sigmund-Freud-Str. 25, 53105 Bonn, Germany.

ABSTRACT
Drug-resistant, multifocal, non-resectable epilepsies are among the most difficult epileptic disorders to manage. An approach to control previously uncontrollable seizures in epilepsy patients would consist of identifying seizure precursors in critical brain areas combined with delivering a counteracting influence to prevent seizure generation. Predictability of seizures with acceptable levels of sensitivity and specificity, even in an ambulatory setting, has been repeatedly shown, however, in patients with a single seizure focus only. We did a study to assess feasibility of state-of-the-art, electroencephalogram-based seizure-prediction techniques in patients with uncontrollable multifocal seizures. We obtained significant predictive information about upcoming seizures in more than two thirds of patients. Unexpectedly, the emergence of seizure precursors was confined to non-affected brain areas. Our findings clearly indicate that epileptic networks, spanning lobes and hemispheres, underlie generation of seizures. Our proof-of-concept study is an important milestone towards new therapeutic strategies based on seizure-prediction techniques for clinical practice.

No MeSH data available.


Related in: MedlinePlus

Identifying critical brain areas I.(A) The level of synchrony R25 between iEEG data from all pairs of sampled brain areas is estimated in a sliding-window fashion, resulting in sequence of symmetric synchrony matrices (B). (C) Temporal sequence of the level of synchrony between a pair of brain areas (marked with X in (B)). Seizures are marked as red vertical lines, and their assumed pre-ictal periods are indicated as yellow-shaded areas. (D) Corresponding frequency distributions (top) and cumulative distribution functions (bottom) of the level of synchrony for inter-ictal (black, solid line) and pre-ictal data (yellow, dashed line). Separability of inter-ictal and pre-ictal distributions is indicated by the maximum distance between cumulative distribution functions and is used for an automated pre-selection of pairs of brain areas.
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f1: Identifying critical brain areas I.(A) The level of synchrony R25 between iEEG data from all pairs of sampled brain areas is estimated in a sliding-window fashion, resulting in sequence of symmetric synchrony matrices (B). (C) Temporal sequence of the level of synchrony between a pair of brain areas (marked with X in (B)). Seizures are marked as red vertical lines, and their assumed pre-ictal periods are indicated as yellow-shaded areas. (D) Corresponding frequency distributions (top) and cumulative distribution functions (bottom) of the level of synchrony for inter-ictal (black, solid line) and pre-ictal data (yellow, dashed line). Separability of inter-ictal and pre-ictal distributions is indicated by the maximum distance between cumulative distribution functions and is used for an automated pre-selection of pairs of brain areas.

Mentions: A large number of analysis techniques and prediction algorithms as well as statistical approaches to evaluate prediction performance have been proposed to identify a transitional pre-seizure state and the brain region(s) from which seizure precursors emerge1214. However, the rather restricted knowledge about seizure precursor dynamics in patients with chronic intractable epilepsy renders an a priori choice of methods and algorithms quite difficult. We therefore designed a retrospective feasibility study to provide proof-of-concept data for seizure predictability in patients with uncontrollable multifocal, drug-resistant epilepsy. We probed for the existence of seizure precursors in continuous, multi-contact, multi-day, intracranial electroencephalographic (iEEG) recordings capturing more than 200 seizures from two patient groups (see Methods and Materials). Group 1 comprised patients for which multiple, non-resectable seizure onset zones (SOZs) had been identified using established presurgical evaluation techniques18. Patients from group 2 achieved complete seizure control after resection of a single SOZ, and these patients were included for the purpose of comparison. We employed a widely used seizure-prediction technique, for which a prediction performance clearly exceeding the chance level (as evidenced by robust statistical validation) has repeatedly been shown192021222324. In addition, we made use of several sophisticated statistical techniques that helped us to avoid drawing incorrect conclusions about seizure precursors and about critical brain areas, which may arise due to various influencing factors and due to issues of multiple comparisons. Our analysis strategy is summarized in Figs 1, 2, 3.


Predictability of uncontrollable multifocal seizures - towards new treatment options.

Lehnertz K, Dickten H, Porz S, Helmstaedter C, Elger CE - Sci Rep (2016)

Identifying critical brain areas I.(A) The level of synchrony R25 between iEEG data from all pairs of sampled brain areas is estimated in a sliding-window fashion, resulting in sequence of symmetric synchrony matrices (B). (C) Temporal sequence of the level of synchrony between a pair of brain areas (marked with X in (B)). Seizures are marked as red vertical lines, and their assumed pre-ictal periods are indicated as yellow-shaded areas. (D) Corresponding frequency distributions (top) and cumulative distribution functions (bottom) of the level of synchrony for inter-ictal (black, solid line) and pre-ictal data (yellow, dashed line). Separability of inter-ictal and pre-ictal distributions is indicated by the maximum distance between cumulative distribution functions and is used for an automated pre-selection of pairs of brain areas.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835791&req=5

f1: Identifying critical brain areas I.(A) The level of synchrony R25 between iEEG data from all pairs of sampled brain areas is estimated in a sliding-window fashion, resulting in sequence of symmetric synchrony matrices (B). (C) Temporal sequence of the level of synchrony between a pair of brain areas (marked with X in (B)). Seizures are marked as red vertical lines, and their assumed pre-ictal periods are indicated as yellow-shaded areas. (D) Corresponding frequency distributions (top) and cumulative distribution functions (bottom) of the level of synchrony for inter-ictal (black, solid line) and pre-ictal data (yellow, dashed line). Separability of inter-ictal and pre-ictal distributions is indicated by the maximum distance between cumulative distribution functions and is used for an automated pre-selection of pairs of brain areas.
Mentions: A large number of analysis techniques and prediction algorithms as well as statistical approaches to evaluate prediction performance have been proposed to identify a transitional pre-seizure state and the brain region(s) from which seizure precursors emerge1214. However, the rather restricted knowledge about seizure precursor dynamics in patients with chronic intractable epilepsy renders an a priori choice of methods and algorithms quite difficult. We therefore designed a retrospective feasibility study to provide proof-of-concept data for seizure predictability in patients with uncontrollable multifocal, drug-resistant epilepsy. We probed for the existence of seizure precursors in continuous, multi-contact, multi-day, intracranial electroencephalographic (iEEG) recordings capturing more than 200 seizures from two patient groups (see Methods and Materials). Group 1 comprised patients for which multiple, non-resectable seizure onset zones (SOZs) had been identified using established presurgical evaluation techniques18. Patients from group 2 achieved complete seizure control after resection of a single SOZ, and these patients were included for the purpose of comparison. We employed a widely used seizure-prediction technique, for which a prediction performance clearly exceeding the chance level (as evidenced by robust statistical validation) has repeatedly been shown192021222324. In addition, we made use of several sophisticated statistical techniques that helped us to avoid drawing incorrect conclusions about seizure precursors and about critical brain areas, which may arise due to various influencing factors and due to issues of multiple comparisons. Our analysis strategy is summarized in Figs 1, 2, 3.

Bottom Line: Drug-resistant, multifocal, non-resectable epilepsies are among the most difficult epileptic disorders to manage.Our findings clearly indicate that epileptic networks, spanning lobes and hemispheres, underlie generation of seizures.Our proof-of-concept study is an important milestone towards new therapeutic strategies based on seizure-prediction techniques for clinical practice.

View Article: PubMed Central - PubMed

Affiliation: Department of Epileptology, University of Bonn, Sigmund-Freud-Str. 25, 53105 Bonn, Germany.

ABSTRACT
Drug-resistant, multifocal, non-resectable epilepsies are among the most difficult epileptic disorders to manage. An approach to control previously uncontrollable seizures in epilepsy patients would consist of identifying seizure precursors in critical brain areas combined with delivering a counteracting influence to prevent seizure generation. Predictability of seizures with acceptable levels of sensitivity and specificity, even in an ambulatory setting, has been repeatedly shown, however, in patients with a single seizure focus only. We did a study to assess feasibility of state-of-the-art, electroencephalogram-based seizure-prediction techniques in patients with uncontrollable multifocal seizures. We obtained significant predictive information about upcoming seizures in more than two thirds of patients. Unexpectedly, the emergence of seizure precursors was confined to non-affected brain areas. Our findings clearly indicate that epileptic networks, spanning lobes and hemispheres, underlie generation of seizures. Our proof-of-concept study is an important milestone towards new therapeutic strategies based on seizure-prediction techniques for clinical practice.

No MeSH data available.


Related in: MedlinePlus