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Article ; Online: Electrical Brain Stimulation for Epilepsy and Emerging Applications.

Worrell, Gregory A

Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society

2021 Volume 38, Issue 6, Page(s) 471–477

Abstract: Summary: Electrical brain stimulation is an established therapy for movement disorders, epilepsy, obsessive compulsive disorder, and a potential therapy for many other neurologic and psychiatric disorders. Despite significant progress and FDA approvals, ...

Abstract	Summary: Electrical brain stimulation is an established therapy for movement disorders, epilepsy, obsessive compulsive disorder, and a potential therapy for many other neurologic and psychiatric disorders. Despite significant progress and FDA approvals, there remain significant clinical gaps that can be addressed with next generation systems. Integrating wearable sensors and implantable brain devices with off-the-body computing resources (smart phones and cloud resources) opens a new vista for dense behavioral and physiological signal tracking coupled with adaptive stimulation therapy that should have applications for a range of brain and mind disorders. Here, we briefly review some history and current electrical brain stimulation applications for epilepsy, deep brain stimulation and responsive neurostimulation, and emerging applications for next generation devices and systems.
MeSH term(s)	Brain ; Deep Brain Stimulation ; Epilepsy/therapy ; Humans ; Mental Disorders/therapy ; Stereotaxic Techniques
Language	English
Publishing date	2021-07-14
Publishing country	United States
Document type	Journal Article
ZDB-ID	605640-4
ISSN	1537-1603 ; 0736-0258
ISSN (online)	1537-1603
ISSN	0736-0258
DOI	10.1097/WNP.0000000000000819
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Article: Novel subscalp and intracranial devices to wirelessly record and analyze continuous EEG in unsedated, behaving dogs in their natural environments: A new paradigm in canine epilepsy research.

Löscher, Wolfgang / Worrell, Gregory A

Frontiers in veterinary science

2022 Volume 9, Page(s) 1014269

Abstract: Epilepsy is characterized by unprovoked, recurrent seizures and is a common neurologic disorder in dogs and humans. Roughly 1/3 of canines and humans with epilepsy prove to be drug-resistant and continue to have sporadic seizures despite taking daily ... ...

Abstract	Epilepsy is characterized by unprovoked, recurrent seizures and is a common neurologic disorder in dogs and humans. Roughly 1/3 of canines and humans with epilepsy prove to be drug-resistant and continue to have sporadic seizures despite taking daily anti-seizure medications. The optimization of pharmacologic therapy is often limited by inaccurate seizure diaries and medication side effects. Electroencephalography (EEG) has long been a cornerstone of diagnosis and classification in human epilepsy, but because of several technical challenges has played a smaller clinical role in canine epilepsy. The interictal (between seizures) and ictal (seizure) EEG recorded from the epileptic mammalian brain shows characteristic electrophysiologic biomarkers that are very useful for clinical management. A fundamental engineering gap for both humans and canines with epilepsy has been the challenge of obtaining continuous long-term EEG in the patients' natural environment. We are now on the cusp of a revolution where continuous long-term EEG from behaving canines and humans will be available to guide clinicians in the diagnosis and optimal treatment of their patients. Here we review some of the devices that have recently emerged for obtaining long-term EEG in ambulatory subjects living in their natural environments.
Language	English
Publishing date	2022-10-20
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2834243-4
ISSN	2297-1769
ISSN	2297-1769
DOI	10.3389/fvets.2022.1014269
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Article: It is the Frequency that Matters: Effects of Electromagnetic Fields on the Release and Content of Extracellular Vesicles.

Wang, Yihua / Worrell, Gregory A / Wang, Hai-Long

bioRxiv : the preprint server for biology

2023

Abstract: Extracellular vesicles (EVs) are small membrane-bound structures that originate from various cell types and carry molecular cargo to influence the behavior of recipient cells. The use of EVs as biomarkers and delivery vehicles for diagnosis and treatment ...

Abstract	Extracellular vesicles (EVs) are small membrane-bound structures that originate from various cell types and carry molecular cargo to influence the behavior of recipient cells. The use of EVs as biomarkers and delivery vehicles for diagnosis and treatment in a wide range of human disease is a rapidly growing field of research and clinical practice. Four years ago, we postulated the hypothesis that electromagnetic fields (EMF) will influence the release and content of EVs (1). Since then, we have optimized several technical aspects of our experimental setup. We used a bioreactor system that allows cells to grow in a three-dimensional environment mimicking
Language	English
Publishing date	2023-08-11
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.08.08.552505
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Article: Qualitative Analysis of Decision to Pursue Electrical Brain Stimulation by Patients With Drug-Resistant Epilepsy and Their Caregivers.

Balzekas, Irena / Richardson, Jordan P / Lorence, Isabella / Lundstrom, Brian Nils / Worrell, Gregory A / Sharp, Richard R

Neurology. Clinical practice

2024 Volume 14, Issue 1, Page(s) e200245

Abstract: Background and objectives: To understand why patients with drug-resistant epilepsy (DRE) pursue invasive electrical brain stimulation (EBS).: Methods: We interviewed patients with DRE (n = 20) and their caregivers about their experiences in pursuing ... ...

Abstract	Background and objectives: To understand why patients with drug-resistant epilepsy (DRE) pursue invasive electrical brain stimulation (EBS). Methods: We interviewed patients with DRE (n = 20) and their caregivers about their experiences in pursuing EBS approximately 1 year post device implant. Inductive analysis was applied to identify key motivating factors. Results: The cohort included participants aged from teens to 50s with deep brain stimulation, vagus nerve stimulation, responsive neurostimulation, and chronic subthreshold cortical stimulation. Patients' motivations included (1) improved quality of life (2) intolerability of antiseizure medications, (3) desperation, and (4) patient-family dynamics. Both patients and caregivers described a desire to alleviate burdens of the other. Patient apprehensions about EBS focused on invasiveness and the presence of electrodes in the brain. Previous experiences with invasive monitoring and the ability to see hardware in person during clinical visits influenced patients' comfort in proceeding with EBS. Despite realistic expectations for modest and delayed benefits, patients held out hope for an exceptionally positive outcome. Discussion: Our findings describe the motivations and decision-making process for patients with DRE who pursue invasive EBS. Patients balance feelings of desperation, personal goals, frustration with medication side effects, fears about surgery, and potential pressure from concerned caregivers. These factors together with the sense that patients have exhausted therapeutic alternatives may explain the limited decisional ambivalence observed in this cohort. These themes highlight opportunities for epilepsy care teams to support patient decision-making processes.
Language	English
Publishing date	2024-01-16
Publishing country	United States
Document type	Journal Article
ZDB-ID	2645818-4
ISSN	2163-0933 ; 2163-0402
ISSN (online)	2163-0933
ISSN	2163-0402
DOI	10.1212/CPJ.0000000000200245
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Article ; Online: Letter to the Editor regarding "Management of epilepsy in older adults: A critical review by the ILAE Task Force on Epilepsy in the Elderly".

Kerezoudis, Panagiotis / Worrell, Gregory A / Van Gompel, Jamie J

Epilepsia

2022 Volume 64, Issue 1, Page(s) 247–248

MeSH term(s)	Humans ; Aged ; Epilepsy/drug therapy ; Anticonvulsants/therapeutic use ; Advisory Committees
Chemical Substances	Anticonvulsants
Language	English
Publishing date	2022-11-15
Publishing country	United States
Document type	Review ; Letter ; Comment
ZDB-ID	216382-2
ISSN	1528-1167 ; 0013-9580
ISSN (online)	1528-1167
ISSN	0013-9580
DOI	10.1111/epi.17456
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Zs.A 517: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
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Article: Spatiotemporal Rhythmic Seizure Sources Can be Imaged by means of Biophysically Constrained Deep Neural Networks.

Sun, Rui / Sohrabpour, Abbas / Joseph, Boney / Worrell, Gregory / He, Bin

medRxiv : the preprint server for health sciences

2023

Abstract: Noninvasive dynamic brain imaging of neural oscillations provides valuable insights into both physiological and pathological brain states. Yet, challenges remain due to the ill-posed nature of the problem and high complexity of the solution space, which ... ...

Abstract	Noninvasive dynamic brain imaging of neural oscillations provides valuable insights into both physiological and pathological brain states. Yet, challenges remain due to the ill-posed nature of the problem and high complexity of the solution space, which can be alleviated by advanced computational models. Here, we investigated the capability of a novel deep learning-based source imaging framework (DeepSIF) for imaging ictal activities from high-density electroencephalogram (EEG) recordings in drug-resistant focal epilepsy patients. The neural mass model of ictal oscillations was adopted to generate synthetic training data with spatio-temporal-spectra features similar to ictal dynamics. We rigorously validated the trained DeepSIF model using computer simulations and in a cohort of 33 drug-resistant focal epilepsy patients. The DeepSIF ictal source imaging was compared with interictal source imaging and three conventional imaging methods as benchmark comparisons. Our findings show that the trained DeepSIF model outperforms other methods in estimating the spatial and temporal information of ictal sources. It achieves a high spatial specificity of 96% and a low spatial dispersion of 3.80 ± 5.74 mm when compared to the resection region. The noninvasive source imaging results also demonstrate good coverage of seizure-onset-zone (SOZ), with an average distance of 10.89 ± 10.14 mm (from the SOZ to the reconstruction). These promising results suggest that DeepSIF has significant potential for advancing noninvasive imaging of ictal activities in patients with focal epilepsy. By providing valuable insights into the spatiotemporal dynamics of seizure activity, DeepSIF promises to help guide clinical decisions and improve treatment outcomes for epilepsy patients.
Language	English
Publishing date	2023-12-01
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.11.30.23299218
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Article ; Online: Direct electrical brain stimulation of human memory: lessons learnt and future perspectives.

Kucewicz, Michal T / Worrell, Gregory A / Axmacher, Nikolai

Brain : a journal of neurology

2022 Volume 146, Issue 6, Page(s) 2214–2226

Abstract: Modulation of cognitive functions supporting human declarative memory is one of the grand challenges of neuroscience, and of vast importance for a variety of neuropsychiatric, neurodegenerative and neurodevelopmental diseases. Despite a recent surge of ... ...

Abstract	Modulation of cognitive functions supporting human declarative memory is one of the grand challenges of neuroscience, and of vast importance for a variety of neuropsychiatric, neurodegenerative and neurodevelopmental diseases. Despite a recent surge of successful attempts at improving performance in a range of memory tasks, the optimal approaches and parameters for memory enhancement have yet to be determined. On a more fundamental level, it remains elusive as to how delivering electrical current in a given brain area leads to enhanced memory processing. Starting from the local and distal physiological effects on neural populations, the mechanisms of enhanced memory encoding, maintenance, consolidation or recall in response to direct electrical stimulation are only now being unravelled. With the advent of innovative neurotechnologies for concurrent recording and stimulation intracranially in the human brain, it becomes possible to study both acute and chronic effects of stimulation on memory performance and the underlying neural activities. In this review, we summarize the effects of various invasive stimulation approaches for modulating memory functions. We first outline the challenges that were faced in the initial studies of memory enhancement and the lessons learnt. Electrophysiological biomarkers are then reviewed as more objective measures of the stimulation effects than behavioural outcomes. Finally, we classify the various stimulation approaches into continuous and phasic modulation with an open or closed loop for responsive stimulation based on analysis of the recorded neural activities. Although the potential advantage of closed-loop responsive stimulation over the classic open-loop approaches is inconclusive, we foresee the emerging results from ongoing longitudinal studies and clinical trials will shed light on both the mechanisms and optimal strategies for improving declarative memory. Adaptive stimulation based on the biomarker analysis over extended periods of time is proposed as a future direction for obtaining lasting effects on memory functions. Chronic tracking and modulation of neural activities intracranially through adaptive stimulation opens tantalizing new avenues to continually monitor and treat memory and cognitive deficits in a range of brain disorders. Brain co-processors created with machine-learning tools and wireless bi-directional connectivity to seamlessly integrate implanted devices with smartphones and cloud computing are poised to enable real-time automated analysis of large data volumes and adaptively tune electrical stimulation based on electrophysiological biomarkers of behavioural states. Next-generation implantable devices for high-density recording and stimulation of electrophysiological activities, and technologies for distributed brain-computer interfaces are presented as selected future perspectives for modulating human memory and associated mental processes.
MeSH term(s)	Humans ; Brain/physiology ; Memory/physiology ; Mental Recall/physiology ; Electric Stimulation ; Cognition
Language	English
Publishing date	2022-12-01
Publishing country	England
Document type	Review ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	80072-7
ISSN	1460-2156 ; 0006-8950
ISSN (online)	1460-2156
ISSN	0006-8950
DOI	10.1093/brain/awac435
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Article: Editorial: Seizure Forecasting and Detection: Computational Models, Machine Learning, and Translation Into Devices.

Chiang, Sharon / Baud, Maxime O / Worrell, Gregory A / Rao, Vikram R

Frontiers in neurology

2022 Volume 13, Page(s) 874070

Language	English
Publishing date	2022-03-16
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2564214-5
ISSN	1664-2295
ISSN	1664-2295
DOI	10.3389/fneur.2022.874070
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Article: Thalamic stimulation induced changes in effective connectivity.

Gregg, Nicholas M / Valencia, Gabriela Ojeda / Huang, Harvey / Lundstrom, Brian N / Van Gompel, Jamie J / Miller, Kai J / Worrell, Gregory A / Hermes, Dora

medRxiv : the preprint server for health sciences

2024

Abstract: Deep brain stimulation (DBS) is a viable treatment for a variety of neurological conditions, however, the mechanisms through which DBS modulates large-scale brain networks are unresolved. Clinical effects of DBS are observed over multiple timescales. In ... ...

Abstract	Deep brain stimulation (DBS) is a viable treatment for a variety of neurological conditions, however, the mechanisms through which DBS modulates large-scale brain networks are unresolved. Clinical effects of DBS are observed over multiple timescales. In some conditions, such as Parkinson's disease and essential tremor, clinical improvement is observed within seconds. In many other conditions, such as epilepsy, central pain, dystonia, neuropsychiatric conditions or Tourette syndrome, the DBS related effects are believed to require neuroplasticity or reorganization and often take hours to months to observe. To optimize DBS parameters, it is therefore essential to develop electrophysiological biomarkers that characterize whether DBS settings are successfully engaging and modulating the network involved in the disease of interest. In this study, 10 individuals with drug resistant epilepsy undergoing intracranial stereotactic EEG including a thalamus electrode underwent a trial of repetitive thalamic stimulation. We evaluated thalamocortical effective connectivity using single pulse electrical stimulation, both at baseline and following a 145 Hz stimulation treatment trial. We found that when high frequency stimulation was delivered for >1.5 hours, the evoked potentials measured from remote regions were significantly reduced in amplitude and the degree of modulation was proportional to the strength of baseline connectivity. When stimulation was delivered for shorter time periods, results were more variable. These findings suggest that changes in effective connectivity in the network targeted with DBS accumulate over hours of DBS. Stimulation evoked potentials provide an electrophysiological biomarker that allows for efficient data-driven characterization of neuromodulation effects, which could enable new objective approaches for individualized DBS optimization.
Language	English
Publishing date	2024-03-04
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.03.03.24303480
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Article ; Online: Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics.

Sun, Rui / Sohrabpour, Abbas / Worrell, Gregory A / He, Bin

Proceedings of the National Academy of Sciences of the United States of America

2022 Volume 119, Issue 31, Page(s) e2201128119

Abstract: Many efforts have been made to image the spatiotemporal electrical activity of the brain with the purpose of mapping its function and dysfunction as well as aiding the management of brain disorders. Here, we propose a non-conventional deep learning-based ...

Abstract	Many efforts have been made to image the spatiotemporal electrical activity of the brain with the purpose of mapping its function and dysfunction as well as aiding the management of brain disorders. Here, we propose a non-conventional deep learning-based source imaging framework (DeepSIF) that provides robust and precise spatiotemporal estimates of underlying brain dynamics from noninvasive high-density electroencephalography (EEG) recordings. DeepSIF employs synthetic training data generated by biophysical models capable of modeling mesoscale brain dynamics. The rich characteristics of underlying brain sources are embedded in the realistic training data and implicitly learned by DeepSIF networks, avoiding complications associated with explicitly formulating and tuning priors in an optimization problem, as often is the case in conventional source imaging approaches. The performance of DeepSIF is evaluated by 1) a series of numerical experiments, 2) imaging sensory and cognitive brain responses in a total of 20 healthy subjects from three public datasets, and 3) rigorously validating DeepSIF's capability in identifying epileptogenic regions in a cohort of 20 drug-resistant epilepsy patients by comparing DeepSIF results with invasive measurements and surgical resection outcomes. DeepSIF demonstrates robust and excellent performance, producing results that are concordant with common neuroscience knowledge about sensory and cognitive information processing as well as clinical findings about the location and extent of the epileptogenic tissue and outperforming conventional source imaging methods. The DeepSIF method, as a data-driven imaging framework, enables efficient and effective high-resolution functional imaging of spatiotemporal brain dynamics, suggesting its wide applicability and value to neuroscience research and clinical applications.
MeSH term(s)	Brain/physiology ; Brain Mapping/methods ; Electroencephalography ; Humans ; Magnetic Resonance Imaging/methods ; Neural Networks, Computer
Language	English
Publishing date	2022-07-26
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	209104-5
ISSN	1091-6490 ; 0027-8424
ISSN (online)	1091-6490
ISSN	0027-8424
DOI	10.1073/pnas.2201128119
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