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Khambhati AN, Chang EF, Baud MO, Rao VR. Hippocampal network activity forecasts epileptic seizures. Nat Med 2024:10.1038/s41591-024-03149-6. [PMID: 38997606 DOI: 10.1038/s41591-024-03149-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 06/24/2024] [Indexed: 07/14/2024]
Abstract
Seizures in people with epilepsy were long thought to occur at random, but recent methods for seizure forecasting enable estimation of the likelihood of seizure occurrence over short horizons. These methods rely on days-long cyclical patterns of brain electrical activity and other physiological variables that determine seizure likelihood and that require measurement through long-term, multimodal recordings. In this retrospective cohort study of 15 adults with bitemporal epilepsy who had a device that provides chronic intracranial recordings, functional connectivity of hippocampal networks fluctuated in multiday cycles with patterns that mirrored cycles of seizure likelihood. A functional connectivity biomarker of seizure likelihood derived from 90-s recordings of background hippocampal activity generalized across individuals and forecasted 24-h seizure likelihood as accurately as cycle-based models requiring months-long baseline recordings. Larger, prospective studies are needed to validate this approach, but our results have the potential to make reliable seizure forecasts accessible to more people with epilepsy.
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Affiliation(s)
- Ankit N Khambhati
- Department of Neurological Surgery and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Edward F Chang
- Department of Neurological Surgery and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Maxime O Baud
- Center for Experimental Neurology, Sleep-Wake Epilepsy Center, NeuroTec, Department of Neurology, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
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2
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Nordli Iii DR, Taha M, Freund B, Nordli DR, Galan F. Minimally Invasive Epilepsy Surgery. Neuropediatrics 2024. [PMID: 38986485 DOI: 10.1055/s-0044-1788061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Surgery remains a critical and often necessary intervention for a subset of patients with epilepsy. The overarching objective of surgical treatment has consistently been to enhance the quality of life for these individuals, either by achieving seizure freedom or by eliminating debilitating seizure types. This review specifically examines minimally invasive surgical approaches for epilepsy. Contemporary advancements have introduced a range of treatments that offer increased safety and efficacy compared to traditional open resective epilepsy surgeries. This manuscript provides a comprehensive review of these techniques and technologies.
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Affiliation(s)
- Douglas R Nordli Iii
- University of Chicago Comer Children's Hospital, Chicago, Illinois, United States
| | - Mohamed Taha
- University of Chicago Comer Children's Hospital, Chicago, Illinois, United States
| | - Brin Freund
- Mayo Clinic in Florida, Jacksonville, Florida, United States
| | - Douglas R Nordli
- University of Chicago Comer Children's Hospital, Chicago, Illinois, United States
| | - Fernando Galan
- Nemours Children's Health System, Jacksonville, Florida, United States
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3
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Samanta D, Aungaroon G, Albert GW, Karakas C, Joshi CN, Singh RK, Oluigbo C, Perry MS, Naik S, Reeders PC, Jain P, Abel TJ, Pati S, Shaikhouni A, Haneef Z. Advancing thalamic neuromodulation in epilepsy: Bridging adult data to pediatric care. Epilepsy Res 2024; 205:107407. [PMID: 38996686 DOI: 10.1016/j.eplepsyres.2024.107407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 06/27/2024] [Accepted: 07/01/2024] [Indexed: 07/14/2024]
Abstract
Thalamic neuromodulation has emerged as a treatment option for drug-resistant epilepsy (DRE) with widespread and/or undefined epileptogenic networks. While deep brain stimulation (DBS) and responsive neurostimulation (RNS) depth electrodes offer means for electrical stimulation of the thalamus in adult patients with DRE, the application of thalamic neuromodulation in pediatric epilepsy remains limited. To address this gap, the Neuromodulation Expert Collaborative was established within the Pediatric Epilepsy Research Consortium (PERC) Epilepsy Surgery Special Interest Group. In this expert review, existing evidence and recommendations for thalamic neuromodulation modalities using DBS and RNS are summarized, with a focus on the anterior (ANT), centromedian(CMN), and pulvinar nuclei of the thalamus. To-date, only DBS of the ANT is FDA approved for treatment of DRE in adult patients based on the results of the pivotal SANTE (Stimulation of the Anterior Nucleus of Thalamus for Epilepsy) study. Evidence for other thalamic neurmodulation indications and targets is less abundant. Despite the lack of evidence, positive responses to thalamic stimulation in adults with DRE have led to its off-label use in pediatric patients. Although caution is warranted due to differences between pediatric and adult epilepsy, the efficacy and safety of pediatric neuromodulation appear comparable to that in adults. Indeed, CMN stimulation is increasingly accepted for generalized and diffuse onset epilepsies, with recent completion of one randomized trial. There is also growing interest in using pulvinar stimulation for temporal plus and posterior quadrant epilepsies with one ongoing clinical trial in Europe. The future of thalamic neuromodulation holds promise for revolutionizing the treatment landscape of childhood epilepsy. Ongoing research, technological advancements, and collaborative efforts are poised to refine and improve thalamic neuromodulation strategies, ultimately enhancing the quality of life for children with DRE.
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Affiliation(s)
- Debopam Samanta
- Division of Child Neurology, Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
| | - Gewalin Aungaroon
- Comprehensive Epilepsy Center, Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Gregory W Albert
- Department of Neurosurgery, University of Arkansas for Medical Sciences, USA
| | - Cemal Karakas
- Division of Pediatric Neurology, Department of Neurology, Norton Children's Hospital, University of Louisville, Louisville, KY 40202, USA
| | - Charuta N Joshi
- Division of Pediatric Neurology, Childrens Medical Center Dallas, UTSW, USA
| | - Rani K Singh
- Department of Pediatrics, Atrium Health-Levine Children's; Wake Forest University School of Medicine, USA
| | - Chima Oluigbo
- Department of Neurosurgery, Children's National Hospital, Washington, DC, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health, Cook Children's Medical Center, Ft Worth, TX, USA
| | - Sunil Naik
- Department of Pediatrics and Neurology, Penn State Health Milton S. Hershey Medical Center, Hershey, PA 17033, USA
| | - Puck C Reeders
- Brain Institute, Nicklaus Children's Hospital, Miami, FL, USA
| | - Puneet Jain
- Epilepsy Program, Division of Neurology, Department of Pediatrics, Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | - Taylor J Abel
- Department of Neurological Surgery, University of Pittsburgh School of Medicine and Department of Bioengineering, University of Pittsburgh
| | - Sandipan Pati
- The University of Texas Health Science Center at Houston, USA
| | - Ammar Shaikhouni
- Department of Pediatric Neurosurgery, Nationwide Children's Hospital, The Ohio State University, Columbus, OH, USA
| | - Zulfi Haneef
- Neurology Care Line, VA Medical Center, Houston, TX 77030, USA; Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA
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4
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Schulze-Bonhage A, Nitsche MA, Rotter S, Focke NK, Rao VR. Neurostimulation targeting the epileptic focus: Current understanding and perspectives for treatment. Seizure 2024; 117:183-192. [PMID: 38452614 DOI: 10.1016/j.seizure.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 02/29/2024] [Accepted: 03/02/2024] [Indexed: 03/09/2024] Open
Abstract
For the one third of people with epilepsy whose seizures are not controlled with medications, targeting the seizure focus with neurostimulation can be an effective therapeutic strategy. In this focused review, we summarize a discussion of targeted neurostimulation modalities during a workshop held in Frankfurt, Germany in September 2023. Topics covered include: available devices for seizure focus stimulation; alternating current (AC) and direct current (DC) stimulation to reduce focal cortical excitability; modeling approaches to simulate DC stimulation; reconciling the efficacy of focal stimulation with the network theory of epilepsy; and the emerging concept of 'neurostimulation zones,' which are defined as cortical regions where focal stimulation is most effective for reducing seizures and which may or may not directly involve the seizure onset zone. By combining experimental data, modeling results, and clinical outcome analysis, rational selection of target regions and stimulation parameters is increasingly feasible, paving the way for a broader use of neurostimulation for epilepsy in the future.
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Affiliation(s)
- Andreas Schulze-Bonhage
- Epilepsy Center, University Medical Center, University of Freiburg, Germany; European Reference Network EpiCare, Belgium; NeuroModul Basic, University of Freiburg, Freiburg, Germany.
| | - Michael A Nitsche
- Dept. Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany; Bielefeld University, University Hospital OWL, Protestant Hospital of Bethel Foundation, University Clinic of Psychiatry and Psychotherapy, Germany; German Center for Mental Health (DZPG), Germany
| | - Stefan Rotter
- Bernstein Center Freiburg & Faculty of Biology, University of Freiburg, Germany
| | - Niels K Focke
- Epilepsy Center, Clinic for Neurology, University Medical Center Göttingen, Germany
| | - Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, USA
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5
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Terman SW, Kirkpatrick L, Akiyama LF, Baajour W, Atilgan D, Dorotan MKC, Choi HW, French JA. Current state of the epilepsy drug and device pipeline. Epilepsia 2024; 65:833-845. [PMID: 38345387 PMCID: PMC11018510 DOI: 10.1111/epi.17884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/14/2023] [Accepted: 01/05/2024] [Indexed: 02/18/2024]
Abstract
The field of epilepsy has undergone substantial advances as we develop novel drugs and devices. Yet considerable challenges remain in developing broadly effective, well-tolerated treatments, but also precision treatments for rare epilepsies and seizure-monitoring devices. We summarize major recent and ongoing innovations in diagnostic and therapeutic products presented at the seventeenth Epilepsy Therapies & Diagnostics Development (ETDD) conference, which occurred May 31 to June 2, 2023, in Aventura, Florida. Therapeutics under development are targeting genetics, ion channels and other neurotransmitters, and many other potentially first-in-class interventions such as stem cells, glycogen metabolism, cholesterol, the gut microbiome, and novel modalities for delivering electrical neuromodulation.
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Affiliation(s)
- Samuel W Terman
- University of Michigan Department of Neurology, Ann Arbor, MI 48109, USA
| | - Laura Kirkpatrick
- University of Pittsburgh Department of Neurology, Pittsburgh, PA 15213, USA
- University of Pittsburgh Department of Pediatrics, Pittsburgh, PA 15213, USA
| | - Lisa F Akiyama
- University of Washington Department of Neurology, Seattle, WA 98105, USA
| | - Wadih Baajour
- University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX 77030, USA
| | - Deniz Atilgan
- University of Texas Health Science Center at Houston, Department of Neurology, Houston, TX 77030, USA
| | | | - Hyoung Won Choi
- Emory University Department of Pediatrics, Division of Neurology, Atlanta, GA 30322
| | - Jacqueline A French
- NYU Grossman School of Medicine and NYU Langone Health, New York, NY 10016, USA
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6
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Kobayashi K, Taylor KN, Shahabi H, Krishnan B, Joshi A, Mackow MJ, Feldman L, Zamzam O, Medani T, Bulacio J, Alexopoulos AV, Najm I, Bingaman W, Leahy RM, Nair DR. Effective connectivity relates seizure outcome to electrode placement in responsive neurostimulation. Brain Commun 2024; 6:fcae035. [PMID: 38390255 PMCID: PMC10882982 DOI: 10.1093/braincomms/fcae035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 09/06/2023] [Accepted: 02/19/2024] [Indexed: 02/24/2024] Open
Abstract
Responsive neurostimulation is a closed-loop neuromodulation therapy for drug resistant focal epilepsy. Responsive neurostimulation electrodes are placed near ictal onset zones so as to enable detection of epileptiform activity and deliver electrical stimulation. There is no standard approach for determining the optimal placement of responsive neurostimulation electrodes. Clinicians make this determination based on presurgical tests, such as MRI, EEG, magnetoencephalography, ictal single-photon emission computed tomography and intracranial EEG. Currently functional connectivity measures are not being used in determining the placement of responsive neurostimulation electrodes. Cortico-cortical evoked potentials are a measure of effective functional connectivity. Cortico-cortical evoked potentials are generated by direct single-pulse electrical stimulation and can be used to investigate cortico-cortical connections in vivo. We hypothesized that the presence of high amplitude cortico-cortical evoked potentials, recorded during intracranial EEG monitoring, near the eventual responsive neurostimulation contact sites is predictive of better outcomes from its therapy. We retrospectively reviewed 12 patients in whom cortico-cortical evoked potentials were obtained during stereoelectroencephalography evaluation and subsequently underwent responsive neurostimulation therapy. We studied the relationship between cortico-cortical evoked potentials, the eventual responsive neurostimulation electrode locations and seizure reduction. Directional connectivity indicated by cortico-cortical evoked potentials can categorize stereoelectroencephalography electrodes as either receiver nodes/in-degree (an area of greater inward connectivity) or projection nodes/out-degree (greater outward connectivity). The follow-up period for seizure reduction ranged from 1.3-4.8 years (median 2.7) after responsive neurostimulation therapy started. Stereoelectroencephalography electrodes closest to the eventual responsive neurostimulation contact site tended to show larger in-degree cortico-cortical evoked potentials, especially for the early latency cortico-cortical evoked potentials period (10-60 ms period) in six out of 12 patients. Stereoelectroencephalography electrodes closest to the responsive neurostimulation contacts (≤5 mm) also had greater significant out-degree in the early cortico-cortical evoked potentials latency period than those further away (≥10 mm) (P < 0.05). Additionally, significant correlation was noted between in-degree cortico-cortical evoked potentials and greater seizure reduction with responsive neurostimulation therapy at its most effective period (P < 0.05). These findings suggest that functional connectivity determined by cortico-cortical evoked potentials may provide additional information that could help guide the optimal placement of responsive neurostimulation electrodes.
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Affiliation(s)
- Katsuya Kobayashi
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Kenneth N Taylor
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Hossein Shahabi
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Balu Krishnan
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Anand Joshi
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Michael J Mackow
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Lauren Feldman
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Omar Zamzam
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Takfarinas Medani
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Juan Bulacio
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | | | - Imad Najm
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - William Bingaman
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Richard M Leahy
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90007, USA
| | - Dileep R Nair
- Charles Shor Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
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7
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Anderson DN, Charlebois CM, Smith EH, Davis TS, Peters AY, Newman BJ, Arain AM, Wilcox KS, Butson CR, Rolston JD. Closed-loop stimulation in periods with less epileptiform activity drives improved epilepsy outcomes. Brain 2024; 147:521-531. [PMID: 37796038 PMCID: PMC10834245 DOI: 10.1093/brain/awad343] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 07/17/2023] [Accepted: 08/28/2023] [Indexed: 10/06/2023] Open
Abstract
In patients with drug-resistant epilepsy, electrical stimulation of the brain in response to epileptiform activity can make seizures less frequent and debilitating. This therapy, known as closed-loop responsive neurostimulation (RNS), aims to directly halt seizure activity via targeted stimulation of a burgeoning seizure. Rather than immediately stopping seizures as they start, many RNS implants produce slower, long-lasting changes in brain dynamics that better predict clinical outcomes. Here we hypothesize that stimulation during brain states with less epileptiform activity drives long-term changes that restore healthy brain networks. To test this, we quantified stimulation episodes during low- and high-risk brain states-that is, stimulation during periods with a lower or higher risk of generating epileptiform activity-in a cohort of 40 patients treated with RNS. More frequent stimulation in tonic low-risk states and out of rhythmic high-risk states predicted seizure reduction. Additionally, stimulation events were more likely to be phase-locked to prolonged episodes of abnormal activity for intermediate and poor responders when compared to super-responders, consistent with the hypothesis that improved outcomes are driven by stimulation during low-risk states. These results support the hypothesis that stimulation during low-risk periods might underlie the mechanisms of RNS, suggesting a relationship between temporal patterns of neuromodulation and plasticity that facilitates long-term seizure reduction.
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Affiliation(s)
- Daria Nesterovich Anderson
- Department of Neurosurgery, University of Utah, Salt Lake City, UT 84132, USA
- Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112, USA
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Darlington, NSW 2008, Australia
| | - Chantel M Charlebois
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Elliot H Smith
- Department of Neurosurgery, University of Utah, Salt Lake City, UT 84132, USA
| | - Tyler S Davis
- Department of Neurosurgery, University of Utah, Salt Lake City, UT 84132, USA
| | - Angela Y Peters
- Department of Neurology, University of Utah, Salt Lake City, UT 84132, USA
| | - Blake J Newman
- Department of Neurology, University of Utah, Salt Lake City, UT 84132, USA
| | - Amir M Arain
- Department of Neurology, University of Utah, Salt Lake City, UT 84132, USA
| | - Karen S Wilcox
- Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112, USA
| | - Christopher R Butson
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL 32608, USA
- Department of Neurology, University of Florida, Gainesville, FL 32611, USA
- Department of Neurosurgery, University of Florida, Gainesville, FL 32608, USA
- Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA
| | - John D Rolston
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Department of Neurosurgery, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
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8
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Sellers KK, Cohen JL, Khambhati AN, Fan JM, Lee AM, Chang EF, Krystal AD. Closed-loop neurostimulation for the treatment of psychiatric disorders. Neuropsychopharmacology 2024; 49:163-178. [PMID: 37369777 PMCID: PMC10700557 DOI: 10.1038/s41386-023-01631-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023]
Abstract
Despite increasing prevalence and huge personal and societal burden, psychiatric diseases still lack treatments which can control symptoms for a large fraction of patients. Increasing insight into the neurobiology underlying these diseases has demonstrated wide-ranging aberrant activity and functioning in multiple brain circuits and networks. Together with varied presentation and symptoms, this makes one-size-fits-all treatment a challenge. There has been a resurgence of interest in the use of neurostimulation as a treatment for psychiatric diseases. Initial studies using continuous open-loop stimulation, in which clinicians adjusted stimulation parameters during patient visits, showed promise but also mixed results. Given the periodic nature and fluctuations of symptoms often observed in psychiatric illnesses, the use of device-driven closed-loop stimulation may provide more effective therapy. The use of a biomarker, which is correlated with specific symptoms, to deliver stimulation only during symptomatic periods allows for the personalized therapy needed for such heterogeneous disorders. Here, we provide the reader with background motivating the use of closed-loop neurostimulation for the treatment of psychiatric disorders. We review foundational studies of open- and closed-loop neurostimulation for neuropsychiatric indications, focusing on deep brain stimulation, and discuss key considerations when designing and implementing closed-loop neurostimulation.
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Affiliation(s)
- Kristin K Sellers
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Joshua L Cohen
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Ankit N Khambhati
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Joline M Fan
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, CA, USA
| | - A Moses Lee
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Andrew D Krystal
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA.
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9
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Khambhati AN. Utility of Chronic Intracranial Electroencephalography in Responsive Neurostimulation Therapy. Neurosurg Clin N Am 2024; 35:125-133. [PMID: 38000836 DOI: 10.1016/j.nec.2023.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2023]
Abstract
Responsive neurostimulation (RNS) therapy is an effective treatment for reducing seizures in some patients with focal epilepsy. Utilizing a chronically implanted device, RNS involves monitoring brain activity signals for user-defined patterns of seizure activity and delivering electrical stimulation in response. Devices store chronic data including counts of detected activity patterns and brief recordings of intracranial electroencephalography signals. Data platforms for reviewing stored chronic data retrospectively may be used to evaluate therapy performance and to fine-tune detection and stimulation settings. New frontiers in RNS research can leverage raw chronic data to reverse engineer neurostimulation mechanisms and improve therapy effectiveness.
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Affiliation(s)
- Ankit N Khambhati
- Department of Neurosurgery, Weill Institute for Neurosciences, University of California, San Francisco, Joan and Sanford I. Weill Neurosciences Building, 1651 4th Street, 671C, San Francisco, CA 94158, USA.
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10
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Frauscher B, Bartolomei F, Baud MO, Smith RJ, Worrell G, Lundstrom BN. Stimulation to probe, excite, and inhibit the epileptic brain. Epilepsia 2023; 64 Suppl 3:S49-S61. [PMID: 37194746 PMCID: PMC10654261 DOI: 10.1111/epi.17640] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 05/18/2023]
Abstract
Direct cortical stimulation has been applied in epilepsy for nearly a century and has experienced a renaissance, given unprecedented opportunities to probe, excite, and inhibit the human brain. Evidence suggests stimulation can increase diagnostic and therapeutic utility in patients with drug-resistant epilepsies. However, choosing appropriate stimulation parameters is not a trivial issue, and is further complicated by epilepsy being characterized by complex brain state dynamics. In this article derived from discussions at the ICTALS 2022 Conference (International Conference on Technology and Analysis for Seizures), we succinctly review the literature on cortical stimulation applied acutely and chronically to the epileptic brain for localization, monitoring, and therapeutic purposes. In particular, we discuss how stimulation is used to probe brain excitability, discuss evidence on the usefulness of stimulation to trigger and stop seizures, review therapeutic applications of stimulation, and finally discuss how stimulation parameters are impacted by brain dynamics. Although research has advanced considerably over the past decade, there are still significant hurdles to optimizing use of this technique. For example, it remains unclear to what extent short timescale diagnostic biomarkers can predict long-term outcomes and to what extent these biomarkers add information to already existing biomarkers from passive electroencephalographic recordings. Further questions include the extent to which closed loop stimulation offers advantages over open loop stimulation, what the optimal closed loop timescales may be, and whether biomarker-informed stimulation can lead to seizure freedom. The ultimate goal of bioelectronic medicine remains not just to stop seizures but rather to cure epilepsy and its comorbidities.
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Affiliation(s)
- Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada
| | - Fabrice Bartolomei
- Institut de Neurosciences des Systèmes, Aix Marseille University, Marseille, France. AP-HM, Service de Neurophysiologie Clinique, Hôpital de la Timone, Marseille, France
| | - Maxime O. Baud
- Sleep-Wake-Epilepsy Center, NeuroTec and Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern
| | - Rachel J. Smith
- University of Alabama at Birmingham, Electrical and Computer Engineering Department, Birmingham, Alabama, US. University of Alabama at Birmingham, Neuroengineering Program, Birmingham, Alabama, US
| | - Greg Worrell
- Department of Neurology, Mayo Clinic, Rochester, US
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11
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Rao VR, Rolston JD. Unearthing the mechanisms of responsive neurostimulation for epilepsy. COMMUNICATIONS MEDICINE 2023; 3:166. [PMID: 37974025 PMCID: PMC10654422 DOI: 10.1038/s43856-023-00401-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 11/03/2023] [Indexed: 11/19/2023] Open
Abstract
Responsive neurostimulation (RNS) is an effective therapy for people with drug-resistant focal epilepsy. In clinical trials, RNS therapy results in a meaningful reduction in median seizure frequency, but the response is highly variable across individuals, with many receiving minimal or no benefit. Understanding why this variability occurs will help improve use of RNS therapy. Here we advocate for a reexamination of the assumptions made about how RNS reduces seizures. This is now possible due to large patient cohorts having used this device, some long-term. Two foundational assumptions have been that the device's intracranial leads should target the seizure focus/foci directly, and that stimulation should be triggered only in response to detected epileptiform activity. Recent studies have called into question both hypotheses. Here, we discuss these exciting new studies and suggest future approaches to patient selection, lead placement, and device programming that could improve clinical outcomes.
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Affiliation(s)
- Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
| | - John D Rolston
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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12
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Wang ET, Chiang S, Haneef Z, Rao VR, Moss R, Vannucci M. BAYESIAN NON-HOMOGENEOUS HIDDEN MARKOV MODEL WITH VARIABLE SELECTION FOR INVESTIGATING DRIVERS OF SEIZURE RISK CYCLING. Ann Appl Stat 2023; 17:333-356. [PMID: 38486612 PMCID: PMC10939012 DOI: 10.1214/22-aoas1630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
A major issue in the clinical management of epilepsy is the unpredictability of seizures. Yet, traditional approaches to seizure forecasting and risk assessment in epilepsy rely heavily on raw seizure frequencies, which are a stochastic measurement of seizure risk. We consider a Bayesian non-homogeneous hidden Markov model for unsupervised clustering of zero-inflated seizure count data. The proposed model allows for a probabilistic estimate of the sequence of seizure risk states at the individual level. It also offers significant improvement over prior approaches by incorporating a variable selection prior for the identification of clinical covariates that drive seizure risk changes and accommodating highly granular data. For inference, we implement an efficient sampler that employs stochastic search and data augmentation techniques. We evaluate model performance on simulated seizure count data. We then demonstrate the clinical utility of the proposed model by analyzing daily seizure count data from 133 patients with Dravet syndrome collected through the Seizure Tracker™ system, a patient-reported electronic seizure diary. We report on the dynamics of seizure risk cycling, including validation of several known pharmacologic relationships. We also uncover novel findings characterizing the presence and volatility of risk states in Dravet syndrome, which may directly inform counseling to reduce the unpredictability of seizures for patients with this devastating cause of epilepsy.
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13
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Charlebois CM, Anderson DN, Johnson KA, Philip BJ, Davis TS, Newman BJ, Peters AY, Arain AM, Dorval AD, Rolston JD, Butson CR. Patient-specific structural connectivity informs outcomes of responsive neurostimulation for temporal lobe epilepsy. Epilepsia 2022; 63:2037-2055. [PMID: 35560062 DOI: 10.1111/epi.17298] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/09/2022] [Accepted: 05/10/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Responsive neurostimulation is an effective therapy for patients with refractory mesial temporal lobe epilepsy. However, clinical outcomes are variable, few patients become seizure-free, and the optimal stimulation location is currently undefined. The aim of this study was to quantify responsive neurostimulation in the mesial temporal lobe, identify stimulation-dependent networks associated with seizure reduction, and determine if stimulation location or stimulation-dependent networks inform outcomes. METHODS We modeled patient-specific volumes of tissue activated and created probabilistic stimulation maps of local regions of stimulation across a retrospective cohort of 22 patients with mesial temporal lobe epilepsy. We then mapped the network stimulation effects by seeding tractography from the volume of tissue activated with both patient-specific and normative diffusion-weighted imaging. We identified networks associated with seizure reduction across patients using the patient-specific tractography maps and then predicted seizure reduction across the cohort. RESULTS Patient-specific stimulation-dependent connectivity was correlated with responsive neurostimulation effectiveness after cross-validation (p = .03); however, normative connectivity derived from healthy subjects was not (p = .44). Increased connectivity from the volume of tissue activated to the medial prefrontal cortex, cingulate cortex, and precuneus was associated with greater seizure reduction. SIGNIFICANCE Overall, our results suggest that the therapeutic effect of responsive neurostimulation may be mediated by specific networks connected to the volume of tissue activated. In addition, patient-specific tractography was required to identify structural networks correlated with outcomes. It is therefore likely that altered connectivity in patients with epilepsy may be associated with the therapeutic effect and that utilizing patient-specific imaging could be important for future studies. The structural networks identified here may be utilized to target stimulation in the mesial temporal lobe and to improve seizure reduction for patients treated with responsive neurostimulation.
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Affiliation(s)
- Chantel M Charlebois
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
- Scientific Computing & Imaging Institute, University of Utah, Salt Lake City, Utah, USA
| | - Daria Nesterovich Anderson
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
- Department of Pharmacology & Toxicology, University of Utah, Salt Lake City, Utah, USA
| | - Kara A Johnson
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida, USA
- Department of Neurology, University of Florida, Gainesville, Florida, USA
| | - Brian J Philip
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Tyler S Davis
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Blake J Newman
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Angela Y Peters
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Amir M Arain
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Alan D Dorval
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
| | - John D Rolston
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
- Scientific Computing & Imaging Institute, University of Utah, Salt Lake City, Utah, USA
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Christopher R Butson
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida, USA
- Department of Neurology, University of Florida, Gainesville, Florida, USA
- Department of Neurosurgery, University of Florida, Gainesville, Florida, USA
- Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA
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14
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Wheless JW, Friedman D, Krauss GL, Rao VR, Sperling MR, Carrazana E, Rabinowicz AL. Future Opportunities for Research in Rescue Treatments. Epilepsia 2022; 63 Suppl 1:S55-S68. [PMID: 35822912 PMCID: PMC9541657 DOI: 10.1111/epi.17363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 06/16/2022] [Accepted: 07/11/2022] [Indexed: 11/30/2022]
Abstract
Clinical studies of rescue medications for seizure clusters are limited and are designed to satisfy regulatory requirements, which may not fully consider the needs of the diverse patient population that experiences seizure clusters or utilize rescue medication. The purpose of this narrative review is to examine the factors that contribute to, or may influence the quality of, seizure cluster research with a goal of improving clinical practice. We address five areas of unmet needs and provide advice for how they could enhance future trials of seizure cluster treatments. The topics addressed in this article are: (1) unaddressed end points to pursue in future studies, (2) roles for devices to enhance rescue medication clinical development programs, (3) tools to study seizure cluster prediction and prevention, (4) the value of other designs for seizure cluster studies, and (5) unique challenges of future trial paradigms for seizure clusters. By focusing on novel end points and technologies with value to patients, caregivers, and clinicians, data obtained from future studies can benefit the diverse patient population that experiences seizure clusters, providing more effective, appropriate care as well as alleviating demands on health care resources.
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Affiliation(s)
- James W Wheless
- Le Bonheur Children's Hospital, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Daniel Friedman
- New York University Grossman School of Medicine, New York, New York, USA
| | - Gregory L Krauss
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Vikram R Rao
- University of California, San Francisco, California, USA
| | | | - Enrique Carrazana
- Neurelis, San Diego, California, USA.,John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA
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15
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Arcot Desai S, Tcheng T, Morrell M. Non-linear Embedding Methods for Identifying Similar Brain Activity in 1 Million iEEG Records Captured From 256 RNS System Patients. Front Big Data 2022; 5:840508. [PMID: 35668816 PMCID: PMC9163709 DOI: 10.3389/fdata.2022.840508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 04/20/2022] [Indexed: 12/05/2022] Open
Abstract
Finding electrophysiological features that are similar across patients with epilepsy may facilitate identifying treatment options for one patient that worked in patients with similar brain activity patterns. Three non-linear iEEG (intracranial electroencephalogram) embedding methods of finding similar cross-patient iEEG records in a large iEEG dataset were developed and compared. About 1 million iEEG records from 256 patients with drug-resistant focal onset seizures who were treated in prospective trials of the RNS System were used for analyses. Data from 200, 25, and 31 patients were randomly selected to be in the train, validation, and test datasets. In method 1, ResNet50 convolutional neural network (CNN) model pre-trained on the ImageNet dataset was used for extracting feature maps from spectrogram images (ImageNet-ResNet) of iEEG records. In method 2, ResNet50 custom trained on an iEEG classification task using ~138,000 manually labeled iEEG records was used as the feature extractor (ESC-ResNet). Feature maps were passed through dimensionality reduction and k nearest neighbors were found in the reduced feature space. In method 3, a 256 dimensional iEEG embedding space was learned via contrastive learning by training a ResNet50 model with triplet training sets generated using within-patient iEEG clustering (CL-ResNet). All three methods had comparable performance when identifying iEEG records from the search dataset similar to test iEEG records of baseline (non-seizure) and interictal spiking activity. Epileptic interictal spikes are represented by vertical (broadband) edges in spectrogram images, and hence even generic features extracted using models trained on everyday images appear to be sufficient to represent iEEG records with similar levels of interictal spiking activity in close proximity. In the case of electrographic seizures, however, the ESC-ResNet model, identified cross-patient iEEG records with electrographic seizure morphology features that were most similar to the test iEEG records. For nuanced electrographic seizure iEEG representation learning, domain specific model training with manually generated labels had the advantage. Finally, representative iEEG records were selected from every patient using an unsupervised clustering method which effectively reduced the number of iEEG records in the search dataset from ~750,000 to 2,148, thus substantially reducing the time required for finding similar cross-patient iEEG records.
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Affiliation(s)
| | | | - Martha Morrell
- NeuroPace, Inc., Mountain View, CA, United States
- Stanford University, Stanford, CA, United States
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16
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Fang H, Yang Y. Designing and Validating a Robust Adaptive Neuromodulation Algorithm for Closed-Loop Control of Brain States. J Neural Eng 2022; 19. [PMID: 35576912 DOI: 10.1088/1741-2552/ac7005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/16/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Neuromodulation systems that use closed-loop brain stimulation to control brain states can provide new therapies for brain disorders. To date, closed-loop brain stimulation has largely used linear time-invariant controllers. However, nonlinear time-varying brain network dynamics and external disturbances can appear during real-time stimulation, collectively leading to real-time model uncertainty. Real-time model uncertainty can degrade the performance or even cause instability of time-invariant controllers. Three problems need to be resolved to enable accurate and stable control under model uncertainty. First, an adaptive controller is needed to track the model uncertainty. Second, the adaptive controller additionally needs to be robust to noise and disturbances. Third, theoretical analyses of stability and robustness are needed as prerequisites for stable operation of the controller in practical applications. APPROACH We develop a robust adaptive neuromodulation algorithm that solves the above three problems. First, we develop a state-space brain network model that explicitly includes nonlinear terms of real-time model uncertainty and design an adaptive controller to track and cancel the model uncertainty. Second, to improve the robustness of the adaptive controller, we design two linear filters to increase steady-state control accuracy and reduce sensitivity to high-frequency noise and disturbances. Third, we conduct theoretical analyses to prove the stability of the neuromodulation algorithm and establish a trade-off between stability and robustness, which we further use to optimize the algorithm design. Finally, we validate the algorithm using comprehensive Monte Carlo simulations that span a broad range of model nonlinearity, uncertainty, and complexity. MAIN RESULTS The robust adaptive neuromodulation algorithm accurately tracks various types of target brain state trajectories, enables stable and robust control, and significantly outperforms state-of-the-art neuromodulation algorithms. SIGNIFICANCE Our algorithm has implications for future designs of precise, stable, and robust closed-loop brain stimulation systems to treat brain disorders and facilitate brain functions.
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Affiliation(s)
- Hao Fang
- University of Central Florida, Research 1 Room 334, 313/316, University of Central Florida, 4353 Scorpius St., Orlando, Florida, 32816-2368, UNITED STATES
| | - Yuxiao Yang
- Department of Electrical and Computer Engineering, University of Central Florida, 4353 Scorpius St., Orlando, Florida, 32816-2368, UNITED STATES
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17
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Scheid BH, Bernabei JM, Khambhati AN, Mouchtaris S, Jeschke J, Bassett DS, Becker D, Davis KA, Lucas T, Doyle W, Chang EF, Friedman D, Rao VR, Litt B. Intracranial electroencephalographic biomarker predicts effective responsive neurostimulation for epilepsy prior to treatment. Epilepsia 2022; 63:652-662. [PMID: 34997577 PMCID: PMC9887634 DOI: 10.1111/epi.17163] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 11/22/2021] [Accepted: 12/27/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Despite the overall success of responsive neurostimulation (RNS) therapy for drug-resistant focal epilepsy, clinical outcomes in individuals vary significantly and are hard to predict. Biomarkers that indicate the clinical efficacy of RNS-ideally before device implantation-are critically needed, but challenges include the intrinsic heterogeneity of the RNS patient population and variability in clinical management across epilepsy centers. The aim of this study is to use a multicenter dataset to evaluate a candidate biomarker from intracranial electroencephalographic (iEEG) recordings that predicts clinical outcome with subsequent RNS therapy. METHODS We assembled a federated dataset of iEEG recordings, collected prior to RNS implantation, from a retrospective cohort of 30 patients across three major epilepsy centers. Using ictal iEEG recordings, each center independently calculated network synchronizability, a candidate biomarker indicating the susceptibility of epileptic brain networks to RNS therapy. RESULTS Ictal measures of synchronizability in the high-γ band (95-105 Hz) significantly distinguish between good and poor RNS responders after at least 3 years of therapy under the current RNS therapy guidelines (area under the curve = .83). Additionally, ictal high-γ synchronizability is inversely associated with the degree of therapeutic response. SIGNIFICANCE This study provides a proof-of-concept roadmap for collaborative biomarker evaluation in federated data, where practical considerations impede full data sharing across centers. Our results suggest that network synchronizability can help predict therapeutic response to RNS therapy. With further validation, this biomarker could facilitate patient selection and help avert a costly, invasive intervention in patients who are unlikely to benefit.
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Affiliation(s)
- Brittany H. Scheid
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John M. Bernabei
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ankit N. Khambhati
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA,Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Sofia Mouchtaris
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jay Jeschke
- Comprehensive Epilepsy Center, NYU Langone Health, New York, New York, USA
| | - Dani S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Santa Fe Institute, Santa Fe, New Mexico, USA
| | - Danielle Becker
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kathryn A. Davis
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Timothy Lucas
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Werner Doyle
- Department of Neurosurgery, NYU Langone, New York, New York, USA
| | - Edward F. Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA,Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Daniel Friedman
- Comprehensive Epilepsy Center, NYU Langone Health, New York, New York, USA,Department of Neurology, NYU Langone, New York, New York, USA
| | - Vikram R. Rao
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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18
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Papasavvas C, Taylor PN, Wang Y. Long-term changes in functional connectivity improve prediction of responses to intracranial stimulation of the human brain. J Neural Eng 2022; 19. [PMID: 35168208 DOI: 10.1088/1741-2552/ac5568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 02/15/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Targeted electrical stimulation of the brain perturbs neural networks and modulates their rhythmic activity both at the site of stimulation and at remote brain regions. Understanding, or even predicting, this neuromodulatory effect is crucial for any therapeutic use of brain stimulation. The objective of this study was to investigate if brain network properties prior to stimulation sessions hold associative and predictive value in understanding the neuromodulatory effect of electrical stimulation in a clinical context. APPROACH We analysed the stimulation responses in 131 stimulation sessions across 66 patients with focal epilepsy recorded through intracranial electroencephalogram (iEEG). We considered functional and structural connectivity features as predictors of the response at every iEEG contact. Taking advantage of multiple recordings over days, we also investigated how slow changes in interictal functional connectivity (FC) ahead of the stimulation, representing the long-term variability of FC, relate to stimulation responses. MAIN RESULTS The long-term variability of FC exhibits strong association with the stimulation-induced increases in delta and theta band power. Furthermore, we show through cross-validation that long-term variability of FC improves prediction of responses above the performance of spatial predictors alone. SIGNIFICANCE This study highlights the importance of the slow dynamics of functional connectivity in the prediction of brain stimulation responses. Furthermore, these findings can enhance the patient-specific design of effective neuromodulatory protocols for therapeutic interventions.
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Affiliation(s)
- Christoforos Papasavvas
- School of Computing, Newcastle University, Science Square, Newcastle upon Tyne, NE1 7RU, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Peter Neal Taylor
- School of Computing, Newcastle University, Science Square, Newcastle upon Tyne, NE1 7RU, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Yujiang Wang
- School of Computing, Newcastle University, Science Square, Newcastle upon Tyne, NE1 7RU, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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19
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Fan JM, Lee AT, Kudo K, Ranasinghe KG, Morise H, Findlay AM, Kirsch HE, Chang EF, Nagarajan SS, Rao VR. Network connectivity predicts effectiveness of responsive neurostimulation in focal epilepsy. Brain Commun 2022; 4:fcac104. [PMID: 35611310 PMCID: PMC9123848 DOI: 10.1093/braincomms/fcac104] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/23/2022] [Accepted: 04/21/2022] [Indexed: 11/13/2022] Open
Abstract
Responsive neurostimulation is a promising treatment for drug-resistant focal epilepsy; however, clinical outcomes are highly variable across individuals. The therapeutic mechanism of responsive neurostimulation 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 responsive neurostimulation therapy. Resting-state magnetoencephalography was obtained in 31 participants with subsequent responsive neurostimulation device implantation between 15 August 2014 and 1 October 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-responsive neurostimulation baseline. Area under the receiver operating characteristic curve 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 responsive neurostimulation 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 with responders (alpha, pfdr < 0.001; beta, pfdr < 0.001). The classification of responsive neurostimulation 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 area under the receiver operating characteristic curve 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 with 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 responsive neurostimulation effectiveness and to identify patients most likely to benefit from responsive neurostimulation 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 responsive neurostimulation involves network-level effects in the brain.
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Affiliation(s)
- Joline M Fan
- Department of Neurology and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Anthony T Lee
- Department of Neurosurgery, University of California San Francisco, San Francisco, CA, USA
| | - Kiwamu Kudo
- Medical Imaging Center, Ricoh Company, Ltd., Kanazawa, Japan.,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Kamalini G Ranasinghe
- Department of Neurology and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Hirofumi Morise
- Medical Imaging Center, Ricoh Company, Ltd., Kanazawa, Japan.,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Anne M Findlay
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Heidi E Kirsch
- Department of Neurology and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Edward F Chang
- Department of Neurosurgery, University of California San Francisco, San Francisco, CA, USA
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
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20
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Rao VR. Chronic electroencephalography in epilepsy with a responsive neurostimulation device: current status and future prospects. Expert Rev Med Devices 2021; 18:1093-1105. [PMID: 34696676 DOI: 10.1080/17434440.2021.1994388] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Implanted neurostimulation devices are gaining traction as therapeutic options for people with certain forms of drug-resistant focal epilepsy. Some of these devices enable chronic electroencephalography (cEEG), which offers views of the dynamics of brain activity in epilepsy over unprecedented time horizons. AREAS COVERED This review focuses on clinical insights and basic neuroscience discoveries enabled by analyses of cEEG from an exemplar device, the NeuroPace RNS® System. Applications of RNS cEEG covered here include counting and lateralizing seizures, quantifying medication response, characterizing spells, forecasting seizures, and exploring mechanisms of cognition. Limitations of the RNS System are discussed in the context of next-generation devices in development. EXPERT OPINION The wide temporal lens of cEEG helps capture the dynamism of epilepsy, revealing phenomena that cannot be appreciated with short duration recordings. The RNS System is a vanguard device whose diagnostic utility rivals its therapeutic benefits, but emerging minimally invasive devices, including those with subscalp recording electrodes, promise to be more applicable within a broad population of people with epilepsy. Epileptology is on the precipice of a paradigm shift in which cEEG is a standard part of diagnostic evaluations and clinical management is predicated on quantitative observations integrated over long timescales.
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Affiliation(s)
- Vikram R Rao
- Associate Professor of Clinical Neurology, Chief, Epilepsy Division, Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
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