Changes of EEG synchronization during low-frequency electric stimulation of the seizure onset zone

AbstractObjectiveTo investigate the performance of a metric of functional connectivity to classify and grade the excitability of brain regions based on evoked potentials to single pulse electrical stimulation (SPES).MethodsPatients who received 1-Hz frequency stimulation between 2003 and 2014 at Yale at prospectively selected contacts were included. The stimulated contacts were classified as seizure onset zone (SOZ), highly irritative zone (IZp) or control. Response contacts were classified as seizure onset zone (SOZ), active interictal (IZp), quiet or other. The normalized number of responses was defined as the number of contacts with any evoked responses divided by the total number of recorded contacts, and the normalized distance is the ratio of the average distance between the site of stimulation and sites of evoked responses to the average distances between the site of stimulation and all other recording contacts. A new metric we labeled the connectivity index (CI) is defined as the product of the two values.Results57 stimulation-sessions in 22-patients were analyzed. The connectivity index (CI) of the SOZ was higher than control (median CI of 0.74 vs. 0.16, p = 0.0002). The evoked responses after stimulation of SOZ were seen at further distance compared to control (median normalized distance 0.96 vs. 0.62, p = 0.0005). It was 1.8 times more likely to record a response at SOZ than in non-epileptic contacts after stimulation of a control site. Habitual seizures were triggered in 27% of patients and 35 % of SOZ contacts (median stimulation intensity 4 mA) but in none of the control or IZp contacts. Non-SOZ contacts in multifocal or poor surgical outcome cases had a higher CI than non-SOZ contacts in those with localizable onsets (medians CI of 0.5 vs. 0.12, p = 0.04). There was a correlation between the stimulation current intensity and the normalized number of evoked responses (r = + 0.49, p 0.01) but not with distance (r = + 0.1, p 0.64)ConclusionsWe found enhanced connectivity when stimulating the SOZ compared to stimulating control contacts; responses were more distant as well. Habitual auras and seizures provoked by SPES were highly predictive of brain sites involved in seizure generation.

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High-Frequency Activity During Stereotyped Low-Frequency Events Might Help to Identify the Seizure Onset Zone

Epilepsy Currents ◽

10.1177/1535759719842236 ◽

2019 ◽

Vol 19 (3) ◽

pp. 184-186 ◽

Cited By ~ 3

Author(s):

Omar J. Ahmed ◽

Shyam Kumar Sudhakar

Keyword(s):

High Frequency ◽

Low Frequency ◽

Seizure Onset ◽

Seizure Onset Zone ◽

High Frequency Activity

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Seizure Onset Zone Identification Using Phase-Amplitude Coupling and Multiple Machine Learning Approaches for Interictal Electrocorticogram

10.1101/2021.10.27.21265585 ◽

2021 ◽

Author(s):

Yao Miao ◽

Yasushi Iimura ◽

Hidenori Sugano ◽

Kosuke Fukumori ◽

Toshihisa Tanaka

Keyword(s):

Machine Learning ◽

Focal Cortical Dysplasia ◽

Low Frequency ◽

Classification Performance ◽

Learning Approaches ◽

Seizure Onset ◽

Statistical Measures ◽

Seizure Onset Zone ◽

Significant Difference ◽

Phase Amplitude

Automatic seizure onset zone (SOZ) localization using interictal electrocorticogram (ECoG) improves the diagnosis and treatment of patients with medically refractory epilepsy. This study aimed to investigate the characteristics of phase-amplitude coupling (PAC) extracted from interictal ECoG and the feasibility of PAC served as a promising biomarker for SOZ identification. We employed the mean vector length modulation index approach on the 20-s ECoG window to calculate PAC features between low frequency rhythms (0.5–24 Hz) and high frequency oscillations (HFOs) (80–560 Hz). We used statistical measures to test the significant difference in PAC between SOZ and non-seizure onset zone (NSOZ). To overcome the drawback of handcraft feature engineering, we established novel machine learning models to automatically learn the characteristics of PAC features obtained and classify them to identify SOZ. Besides, to conquer the imbalance of datasets, we introduced novel feature-wise/class-wise re-weighting strategies in conjunction with classifiers. In addition, we proposed the time-series nest cross-validation to provide more accurate and unbiased evaluations for this model. Seven patients with focal cortical dysplasia were included in this study. The experiment results not only illustrate that the significant coupling at band pairs of slow waves and HFOs exists in the SOZ when compared with the NSOZ but also indicate the effectiveness of PAC features and the proposed models with better classification performance.

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Effects of early low frequency electric stimulation of quadriceps on function of patients operated with total knee arthroplasty

BIOPHILIA ◽

10.14813/ibra.2015.262 ◽

2015 ◽

Vol 2015 (3) ◽

pp. 262-262

Author(s):

Shaohua QI ◽

Qing XIA ◽

Yunchao SHAO ◽

Xiaofeng WANG ◽

Yuwei LIN ◽

...

Keyword(s):

Total Knee Arthroplasty ◽

Knee Arthroplasty ◽

Electric Stimulation ◽

Low Frequency ◽

Total Knee ◽

Stimulation Of

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Awake perimetry testing for occipital epilepsy surgery

Journal of Neurosurgery ◽

10.3171/2017.6.jns17846 ◽

2018 ◽

Vol 129 (5) ◽

pp. 1195-1199 ◽

Cited By ~ 1

Author(s):

Holger Joswig ◽

John P. Girvin ◽

Warren T. Blume ◽

Jorge G. Burneo ◽

David A. Steven

Keyword(s):

Visual Field ◽

Occipital Lobe ◽

Technical Note ◽

Laser Pointer ◽

Seizure Onset ◽

Resective Surgery ◽

Seizure Onset Zone ◽

Visual Field Impairment ◽

Stimulation Of

In the literature, there are few reports that provide a detailed account on the technique of visual electrocortical stimulation in the setting of resective surgery for occipital epilepsy. In this technical note, the authors describe how a 26-year-old male with long-standing occipital epilepsy underwent resective surgery under awake conditions, using electrocortical stimulation of the occipital lobe, with the aid of a laser pointer and a perimetry chart on a stand within his visual field. The eloquent primary visual cortex was found to overlap with the seizure onset zone that was previously determined with subdural electrodes. A maximum functionally safe resection was performed, rendering the patient seizure free as of his last follow-up at 20 months, with no visual field impairment.

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Low frequency novel interictal EEG biomarker for localizing seizures and predicting outcomes

Brain Communications ◽

10.1093/braincomms/fcab231 ◽

2021 ◽

Author(s):

Brian Nils Lundstrom ◽

Benjamin Brinkmann ◽

Gregory Worrell

Keyword(s):

Positive Predictive Value ◽

Patient Outcomes ◽

Predictive Value ◽

Low Frequency ◽

Seizure Onset ◽

Eeg Activity ◽

Seizure Onset Zone ◽

Interictal Eeg ◽

Eeg Power ◽

Frequency Power

Abstract Localizing hyperexcitable brain tissue to treat focal seizures remains challenging. We want to identify the seizure onset zone from interictal EEG biomarkers. We hypothesize that a combination of interictal EEG biomarkers, including a novel low frequency marker, can predict mesial temporal involvement and can assist in prognosis related to surgical resections. Interictal direct current wide bandwidth invasive EEG recordings from 83 patients implanted with 5,111 electrodes were retrospectively studied. Logistic regression was used to classify electrodes and patient outcomes. A feed-forward neural network was implemented to understand putative mechanisms. Interictal infraslow frequency EEG activity was decreased for seizure onset zone electrodes while faster frequencies such as delta (2–4 Hz) and beta-gamma (20–50 Hz) activity were increased. These spectral changes comprised a novel interictal EEG biomarker that was significantly increased for mesial temporal seizure onset zone electrodes compared to non-seizure onset zone electrodes. Interictal EEG biomarkers correctly classified mesial temporal seizure onset zone electrodes with a specificity of 87% and positive predictive value of 80%. These interictal EEG biomarkers also correctly classified patient outcomes after surgical resection with a specificity of 91% and positive predictive value of 87%. Interictal infraslow EEG activity is decreased near the seizure onset zone while higher frequency power is increased, which may suggest distinct underlying physiologic mechanisms. Narrowband interictal EEG power bands provide information about the seizure onset zone and can help predict mesial temporal involvement in seizure onset. Narrowband interictal EEG power bands may be less useful for predictions related to non-mesial temporal electrodes. Together with interictal epileptiform discharges and high frequency oscillations, these interictal biomarkers may provide prognostic information prior to surgical resection. Computational modeling suggests changes in neural adaptation may be related to the observed low frequency power changes.

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