A systematic exploration of parameters affecting evoked intracranial potentials in patients with epilepsy

Kundu, Bornali; Davis, Tyler; Philip, Brian; Smith, Elliot H.; Arain, Amir; Peters, Angela; Newman, Blake J.; Butson, Christopher R.; Rolston, John D.

doi:10.1016/j.brs.2020.06.002

Cited by 39 publications

(51 citation statements)

References 59 publications

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“…Many existing studies have attempted to address this by presuming a specific temporal structure in the evoked response, such as the voltage at a pre-specified timepoint post-stimulation [ 26 ]. However, as illustrated in Fig 2 and S3 Fig and reported by Kundu and colleagues [ 27 ], there is no single canonical CCEP response shape or feature, even when measuring from a single electrode.…”

Section: Discussionmentioning

confidence: 83%

Basis profile curve identification to understand electrical stimulation effects in human brain networks

2021

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Section: Discussionmentioning

confidence: 83%

Basis profile curve identification to understand electrical stimulation effects in human brain networks

2021

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“…The mechanism of RNS remains poorly understood, and undoubtedly a number of factors may influence responder outcome, including location of RNS implant with respect to a patient’s seizure onset zone 37 , programming detection and stimulation parameters 13 , and interactions between RNS therapy and pharmaceutical treatments. 38 Much of the RNS parameter space has not yet been explored, and it is unclear to what extent each factor may contribute to patient outcome.…”

Section: Discussionmentioning

confidence: 99%

“…A purely speculative possibility is that by maintaining a similar level of synchronizability between pre-ictal and ictal periods as shown in our broad band results, RNS responders may be better able to distribute energetic inputs to the broader epileptic network during seizures, when the device is commonly triggered for stimulation. In patients with decreased post-onset synchronizability, RNS may be less able to influence long-range plasticity mechanisms in the broader epileptic network.The mechanism of RNS remains poorly understood, and undoubtedly a number of factors may influence responder outcome, including location of RNS implant with respect to a patient's seizure onset zone37 , programming detection and stimulation parameters13 , and interactions between RNS therapy and pharmaceutical treatments 38. Much of the RNS parameter space has not yet been explored, and it is unclear to what extent each factor may contribute to patient outcome.…”

mentioning

confidence: 99%

Synchronizability predicts effective responsive neurostimulation for epilepsy prior to treatment

Scheid

Bernabei

Khambhati

et al. 2021

Preprint

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Despite the success of responsive neurostimulation (RNS) for epilepsy, clinical outcomes vary significantly and are hard to predict. The ability to forecast clinical response to RNS therapy before device implantation would improve patient selection for RNS surgery and could prevent a costly and ineffective intervention. Determining and validating biomarkers predictive of RNS response is difficult, however, due to the heterogeneity of the RNS patient population and clinical procedures; large, multi-center datasets are needed to quantify patient variability and to account for stereotypy in the treatment paradigm of any one center. Here we use a distributed, cloud-based pipeline to analyze a federated dataset of intracranial EEG recordings, collected prior to RNS surgery, from 30 patients across three major epilepsy centers. Based on recent work modelling the controllability of distributed brain networks, we hypothesize that broader brain network connectivity, beyond the seizure onset zone, can predict RNS response. We demonstrate how intracranial EEG recordings can be leveraged through network analysis to uncover biomarkers that predict response to RNS therapy. Our findings suggest that peri-ictal changes in synchronizability, a global network metric shown to accurately predict outcome from resective epilepsy surgery, can distinguish between good and poor RNS responders under the current RNS therapy guidelines (area under the receiver operating characteristic curve of 0.75). Furthermore, this study also provides a proof-of-concept roadmap for multicenter collaboration where practical considerations impede sharing datasets fully across centers.

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“…Many existing studies have attempted to address this by presuming a specific temporal structure in the evoked response, such as the voltage at a pre-specified timepoint post-stimulation [25]. However, as illustrated in figures 2&S3 and reported by Kundu and colleagues [26], there is no single canonical CCEP response shape or feature, even when measuring from a single electrode.…”

Section: The Convergent Approach To Connectivitymentioning

confidence: 99%

“…Subsequent studies might vary experimental conditions with a task, medication, or property of the stimulation (current magnitude and temporal profile, or timing between pulses), and quantify signal, noise, and residual structure in the responses. For example, one could examine the effect of simply changing the amplitude of stimulation, where it has been demonstrated that different stimulation magnitudes can elicit very different morphologies [26] and examine distribution of BPCs as a function of that.…”

Section: Potential Future Applicationsmentioning

confidence: 99%

Basis profile curve identification to understand electrical stimulation effects in human brain networks

Miller

uumlller

Hermes

2021

Preprint

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Brain networks can be explored by delivering brief pulses of electrical current in one area while measuring voltage responses in other areas. We propose a convergent paradigm to study brain dynamics, focusing on a single brain site to observe the average effect of stimulating each of many other brain sites. Viewed in this manner, visually-apparent motifs in the temporal response shape emerge from adjacent stimulation sites. This work constructs and illustrates a data-driven approach to determine characteristic spatiotemporal structure in these response shapes, summarized by a set of unique “basis profile curves” (BPCs). Each BPC may be mapped back to underlying anatomy in a natural way, quantifying projection strength from each stimulation site using simple metrics. Our technique is demonstrated for an array of implanted brain surface electrodes in a human patient. This framework enables straightforward interpretation of single-pulse brain stimulation data, and can be applied generically to explore the diverse milieu of interactions that comprise the connectome.Author summaryWe present a new machine learning framework to probe how brain regions interact using single-pulse electrical stimulation. Unlike previous studies, this approach does not assume a form for how one brain area will respond to stimulation in another area, but rather discovers the shape of the response in time from the data. We call the set of characteristic discovered response shapes “basis profile curves” (BPCs), and show how these can be mapped back onto the brain quantitatively. An illustrative example is included from one of our human patients to characterize inputs to the parahippocampal gyrus. A code package is downloadable from https://purl.stanford.edu/rc201dv0636 so the reader may explore the technique for own data, or study sample data provided to reproduce the illustrative case presented in the manuscript.

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A systematic exploration of parameters affecting evoked intracranial potentials in patients with epilepsy

Cited by 39 publications

References 59 publications

Basis profile curve identification to understand electrical stimulation effects in human brain networks

Basis profile curve identification to understand electrical stimulation effects in human brain networks

Synchronizability predicts effective responsive neurostimulation for epilepsy prior to treatment

Basis profile curve identification to understand electrical stimulation effects in human brain networks

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