Intracranial electroencephalographic biomarker predicts effective responsive neurostimulation for epilepsy prior to treatment

Scheid, Brittany H; Bernabei, John M.; Khambhati, Ankit N.; Mouchtaris, Sofia; Jeschke, Jay; Bassett, Dani S.; Becker, Danielle A.; Davis, Kathryn A.; Lucas, Timothy H.; Doyle, Werner; Chang, Edward F.; Friedman, Daniel; Rao, Vikram R.; Litt, Brian

doi:10.1111/epi.17163

Cited by 36 publications

(25 citation statements)

References 43 publications

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“…Furthermore, Scheid et al . 26 used pre-RNS functional network data derived from 30 patients undergoing intracranial EEG. They tested the hypothesis that wide-scale networks (i.e.…”

Section: Towards Personalized Network-guided Neurostimulationmentioning

confidence: 99%

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Towards network-guided neuromodulation for epilepsy

Piper

Richardson

Worrell

et al. 2022

Brain

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Epilepsy is well-recognized as a disorder of brain networks. There is a growing body of research to identify critical nodes within dynamic epileptic networks with the aim to target therapies that halt the onset and propagation of seizures. In parallel, intracranial neuromodulation, including deep brain stimulation and responsive neurostimulation, are well-established and expanding as therapies to reduce seizures in adults with focal epilepsy; and there is emerging evidence for their efficacy in children and generalized seizure disorders. The convergence of these advancing fields is driving an era of ‘network-guided neuromodulation’ for epilepsy. In this review we distil the current literature on network mechanisms underlying neurostimulation for epilepsy. We discuss the modulation of key propagation points in the epileptogenic network, focusing primarily on thalamic nuclei targeted in current clinical practice. These include (a) the anterior nucleus of thalamus, now a clinically approved and targeted site for open loop stimulation, and increasingly targeted for responsive neurostimulation; and (b) the centromedian nucleus of the thalamus, a target for both deep brain stimulation and responsive neurostimulation in generalized-onset epilepsies. We discuss briefly the networks associated with other emerging neuromodulation targets, such as the pulvinar of the thalamus, piriform cortex, septal area, subthalamic nucleus, cerebellum and others. We report synergistic findings garnered from multiple modalities of investigation that revealed structural and functional networks associated with these propagation points – including scalp and invasive electroencephalography, diffusion and functional magnetic resonance imaging. We also report on intracranial recordings from implanted devices which provide us data on the dynamic networks we are aiming to modulate. Finally, we review the continuing evolution of network-guided neurostimulation for epilepsy to accelerate progress towards two major goals: (1) to use pre-surgical network analyses to determine patient candidacy for neurostimulation for epilepsy by providing network biomarkers that predict efficacy; and (2) to deliver precise, personalized and effective antiepileptic stimulation to prevent and arrest seizures, limit seizure propagation through mapping and modulation of each patients’ individual epileptogenic networks.

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“…Furthermore, Scheid et al . 26 used pre-RNS functional network data derived from 30 patients undergoing intracranial EEG. They tested the hypothesis that wide-scale networks (i.e.…”

Section: Towards Personalized Network-guided Neurostimulationmentioning

confidence: 99%

“… 18–21 Our understanding of how neurostimulation works on a network level has been made possible by studying and combining multiple complimentary methods such as diffusion and functional MRI (fMRI), 22–25 scalp EEG and intracranial EEG. 26 …”

Section: Introductionmentioning

confidence: 99%

Towards network-guided neuromodulation for epilepsy

Piper

Richardson

Worrell

et al. 2022

Brain

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“…These findings are consistent with iEEG data suggesting that decreased synchronizability at seizure onset is correlated with poor RNS outcome. 11 In contrast, increased local FC within the resection site 31 , 35 or decreased network synchronizability 36 have been shown to predict favorable surgical resection outcomes, suggesting that brain network characteristics which portend favorable response to neurostimulation versus resection may be as distinct as the therapies themselves.…”

Section: Discussionmentioning

confidence: 99%

“…As such, each patient undergoes the same diagnostic testing, in which MEG sensor data are uniform or ‘templated’ across all patients, and the heterogenous SOZs are accounted for by the normalization process. By contrast, FC biomarkers based on iEEG 11 are challenged by the disparate and ‘tailored’ electrode locations across patients, oversampling of the SOZ, and the long recording durations often needed to capture seizures.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, electrographic biomarkers of clinical response have been identified in RNS system electrocorticograms 8–10 and in pre-implant intracranial electroencephalography (iEEG). 11 These biomarkers derive from invasive recordings and spatially restricted sampling of the epileptogenic network, limiting their clinical utility. An ideal biomarker would be measurable non-invasively, before device implantation, and would not depend on the specific brain regions sampled by intracranial electrodes.…”

Section: Introductionmentioning

confidence: 99%

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Network connectivity predicts effectiveness of responsive neurostimulation in focal epilepsy

Fan

Lee

Kudo

et al. 2022

Brain Communications

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Responsive neurostimulation (RNS) is a promising treatment for drug-resistant focal epilepsy; however, clinical outcomes are highly variable across individuals. The therapeutic mechanism of RNS likely involves modulatory effects on brain networks; however, with no known biomarkers that predict clinical response, patient selection remains empiric. This study aimed to determine whether functional brain connectivity measured non-invasively prior to device implantation predicts clinical response to RNS therapy. Resting-state magnetoencephalography was obtained in 31 participants with subsequent RNS device implantation between August 15, 2014 and October 1, 2020. Functional connectivity was computed across multiple spatial scales (global, hemispheric, and lobar) using pre-implantation magnetoencephalography and normalized to maps of healthy controls. Normalized functional connectivity was investigated as a predictor of clinical response, defined as percent change in self-reported seizure frequency in the most recent year of clinic visits relative to pre-RNS baseline. Area under the receiver operating characteristic curve (AUC) quantified the performance of functional connectivity in predicting responders (≥50% reduction in seizure frequency) and non-responders (<50%). Leave-one-out cross validation was furthermore performed to characterize model performance. The relationship between seizure frequency reduction and frequency-specific functional connectivity was further assessed as a continuous measure. Across participants, stimulation was enabled for a median duration of 52.2 (interquartile range, 27.0-62.3) months. Demographics, seizure characteristics, and RNS lead configurations were matched across 22 responders and 9 non-responders. Global functional connectivity in the alpha and beta bands were lower in non-responders as compared to responders (alpha, pfdr < 0.001; beta, pfdr < 0.001). The classification of RNS outcome was improved by combining feature inputs; the best model incorporated four-features (i.e. mean and dispersion of alpha and beta bands) and yielded an AUC of 0.970 (0.919-1.00). The leave-one-out cross validation analysis of this four-feature model yielded a sensitivity of 86.3%, specificity of 77.8%, positive predictive value of 90.5%, and negative predictive value of 70%. Global functional connectivity in alpha band correlated with seizure frequency reduction (alpha, p = 0.010). Global functional connectivity predicted responder status more strongly, as compared to hemispheric predictors. Lobar functional connectivity was not a predictor. These findings suggest that non-invasive functional connectivity may be a candidate personalized biomarker that has the potential to predict RNS effectiveness and to identify patients most likely to benefit from RNS therapy. Follow-up large-cohort, prospective studies are required to validate this biomarker. These findings furthermore support an emerging view that the therapeutic mechanism of RNS involves network-level effects in the brain.

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Dominant, Lesional Temporal Lobe Epilepsy

Herlopian

2024

Epilepsy Surgery: A Practical Case-Based Approach

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Intracranial electroencephalographic biomarker predicts effective responsive neurostimulation for epilepsy prior to treatment

Cited by 36 publications

References 43 publications

Towards network-guided neuromodulation for epilepsy

Towards network-guided neuromodulation for epilepsy

Network connectivity predicts effectiveness of responsive neurostimulation in focal epilepsy

Dominant, Lesional Temporal Lobe Epilepsy

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