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Herzfeld DJ. Linking abnormal neural activity patterns to motor deficits. eLife 2024; 13:e100833. [PMID: 39045856 PMCID: PMC11268882 DOI: 10.7554/elife.100833] [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: 07/25/2024] Open
Abstract
Abnormal activity in the cerebellar nuclei can be used to predict motor symptoms and induce them experimentally, pointing to potential therapeutic strategies.
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Affiliation(s)
- David J Herzfeld
- Department of Neurobiology, Duke University School of MedicineDurhamUnited States
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Li Y, Nie Y, Quan Z, Zhang H, Song R, Feng H, Cheng X, Liu W, Geng X, Sun X, Fu Y, Wang S. Brain-machine interactive neuromodulation research tool with edge AI computing. Heliyon 2024; 10:e32609. [PMID: 38975192 PMCID: PMC11225749 DOI: 10.1016/j.heliyon.2024.e32609] [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: 06/03/2024] [Accepted: 06/06/2024] [Indexed: 07/09/2024] Open
Abstract
Closed-loop neuromodulation with intelligence methods has shown great potentials in providing novel neuro-technology for treating neurological and psychiatric diseases. Development of brain-machine interactive neuromodulation strategies could lead to breakthroughs in precision and personalized electronic medicine. The neuromodulation research tool integrating artificial intelligent computing and performing neural sensing and stimulation in real-time could accelerate the development of closed-loop neuromodulation strategies and translational research into clinical application. In this study, we developed a brain-machine interactive neuromodulation research tool (BMINT), which has capabilities of neurophysiological signals sensing, computing with mainstream machine learning algorithms and delivering electrical stimulation pulse by pulse in real-time. The BMINT research tool achieved system time delay under 3 ms, and computing capabilities in feasible computation cost, efficient deployment of machine learning algorithms and acceleration process. Intelligent computing framework embedded in the BMINT enable real-time closed-loop neuromodulation developed with mainstream AI ecosystem resources. The BMINT could provide timely contribution to accelerate the translational research of intelligent neuromodulation by integrating neural sensing, edge AI computing and stimulation with AI ecosystems.
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Affiliation(s)
- Yan Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yingnan Nie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Zhaoyu Quan
- Engineering Research Center of AI & Robotics, Ministry of Education, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Han Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Rui Song
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Hao Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Xi Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Wei Liu
- Engineering Research Center of AI & Robotics, Ministry of Education, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Xinyi Geng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Xinwei Sun
- School of Data Science, Fudan University, Shanghai, China
| | - Yanwei Fu
- School of Data Science, Fudan University, Shanghai, China
| | - Shouyan Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
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Tian Y, Saradhi S, Bello E, Johnson MD, D’Eleuterio G, Popovic MR, Lankarany M. Model-based closed-loop control of thalamic deep brain stimulation. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1356653. [PMID: 38650608 PMCID: PMC11033853 DOI: 10.3389/fnetp.2024.1356653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/18/2024] [Indexed: 04/25/2024]
Abstract
Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson's disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists' expertise and patients' experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients' symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity. Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By using a proportional-integral-derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim-DBS so that the power of EMG reaches a desired control target. Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.
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Affiliation(s)
- Yupeng Tian
- Krembil Brain Institute—University Health Network, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada
| | - Srikar Saradhi
- Krembil Brain Institute—University Health Network, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Edward Bello
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Matthew D. Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | | | - Milos R. Popovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application, University Health Network and University of Toronto, Toronto, ON, Canada
| | - Milad Lankarany
- Krembil Brain Institute—University Health Network, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application, University Health Network and University of Toronto, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
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Carè M, Chiappalone M, Cota VR. Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function. Front Neurosci 2024; 18:1363128. [PMID: 38516316 PMCID: PMC10954825 DOI: 10.3389/fnins.2024.1363128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 02/22/2024] [Indexed: 03/23/2024] Open
Abstract
Despite considerable advancement of first choice treatment (pharmacological, physical therapy, etc.) over many decades, neurological disorders still represent a major portion of the worldwide disease burden. Particularly concerning, the trend is that this scenario will worsen given an ever expanding and aging population. The many different methods of brain stimulation (electrical, magnetic, etc.) are, on the other hand, one of the most promising alternatives to mitigate the suffering of patients and families when conventional treatment fall short of delivering efficacious treatment. With applications in virtually all neurological conditions, neurostimulation has seen considerable success in providing relief of symptoms. On the other hand, a large variability of therapeutic outcomes has also been observed, particularly in the usage of non-invasive brain stimulation (NIBS) modalities. Borrowing inspiration and concepts from its pharmacological counterpart and empowered by unprecedented neurotechnological advancement, the neurostimulation field has seen in recent years a widespread of methods aimed at the personalization of its parameters, based on biomarkers of the individuals being treated. The rationale is that, by taking into account important factors influencing the outcome, personalized stimulation can yield a much-improved therapy. Here, we review the literature to delineate the state-of-the-art of personalized stimulation, while also considering the important aspects of the type of informing parameter (anatomy, function, hybrid), invasiveness, and level of development (pre-clinical experimentation versus clinical trials). Moreover, by reviewing relevant literature on closed loop neuroengineering solutions in general and on activity dependent stimulation method in particular, we put forward the idea that improved personalization may be achieved when the method is able to track in real time brain dynamics and adjust its stimulation parameters accordingly. We conclude that such approaches have great potential of promoting the recovery of lost functions and enhance the quality of life for patients.
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Affiliation(s)
- Marta Carè
- IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Michela Chiappalone
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, Genova, Italy
- Rehab Technologies Lab, Istituto Italiano di Tecnologia, Genova, Italy
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Torrente A, Maccora S, Prinzi F, Alonge P, Pilati L, Lupica A, Di Stefano V, Camarda C, Vitabile S, Brighina F. The Clinical Relevance of Artificial Intelligence in Migraine. Brain Sci 2024; 14:85. [PMID: 38248300 PMCID: PMC10813497 DOI: 10.3390/brainsci14010085] [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: 12/22/2023] [Revised: 01/09/2024] [Accepted: 01/11/2024] [Indexed: 01/23/2024] Open
Abstract
Migraine is a burdensome neurological disorder that still lacks clear and easily accessible diagnostic biomarkers. Furthermore, a straightforward pathway is hard to find for migraineurs' management, so the search for response predictors has become urgent. Nowadays, artificial intelligence (AI) has pervaded almost every aspect of our lives, and medicine has not been missed. Its applications are nearly limitless, and the ability to use machine learning approaches has given researchers a chance to give huge amounts of data new insights. When it comes to migraine, AI may play a fundamental role, helping clinicians and patients in many ways. For example, AI-based models can increase diagnostic accuracy, especially for non-headache specialists, and may help in correctly classifying the different groups of patients. Moreover, AI models analysing brain imaging studies reveal promising results in identifying disease biomarkers. Regarding migraine management, AI applications showed value in identifying outcome measures, the best treatment choices, and therapy response prediction. In the present review, the authors introduce the various and most recent clinical applications of AI regarding migraine.
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Affiliation(s)
- Angelo Torrente
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Simona Maccora
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
- Neurology Unit, ARNAS Civico di Cristina and Benfratelli Hospitals, 90127 Palermo, Italy
| | - Francesco Prinzi
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
- Department of Computer Science and Technology, University of Cambridge, Cambridge CB2 1TN, UK
| | - Paolo Alonge
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Laura Pilati
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
- Neurology and Stroke Unit, P.O. “S. Antonio Abate”, 91016 Trapani, Italy
| | - Antonino Lupica
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Vincenzo Di Stefano
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Cecilia Camarda
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
| | - Filippo Brighina
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), University of Palermo, 90127 Palermo, Italy; (A.T.); (S.M.); (F.P.); (P.A.); (L.P.); (A.L.); (V.D.S.); (C.C.); (S.V.)
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Piazza MG, Varga G, Welch W, Abel TJ. The Utility of Responsive Neurostimulation for the Treatment of Pediatric Drug-Resistant Epilepsy. Brain Sci 2023; 13:1455. [PMID: 37891823 PMCID: PMC10605851 DOI: 10.3390/brainsci13101455] [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: 09/11/2023] [Revised: 10/07/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023] Open
Abstract
Drug-resistant epilepsy (DRE) has a strongly negative impact on quality of life, as well as the development of pediatric patients. Surgical treatments have evolved over time, including more invasive craniotomies for resection or disconnection. More recently, neuromodulation techniques have been employed as a less invasive option for patients. Responsive neurostimulation (RNS) is the first closed-loop technology that allows for both treatment and device data collection, which allows for an internal assessment of the efficacy of treatment. This novel technology has been approved in adults and has been used off label in pediatrics. This review seeks to describe this technology, its history, and future directions.
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Affiliation(s)
- Martin G. Piazza
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15260, USA; (M.G.P.); (G.V.)
| | - Gregory Varga
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15260, USA; (M.G.P.); (G.V.)
| | - William Welch
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15260, USA;
| | - Taylor J. Abel
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15260, USA; (M.G.P.); (G.V.)
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Ghosh S, Sinha JK, Ghosh S, Sharma H, Bhaskar R, Narayanan KB. A Comprehensive Review of Emerging Trends and Innovative Therapies in Epilepsy Management. Brain Sci 2023; 13:1305. [PMID: 37759906 PMCID: PMC10527076 DOI: 10.3390/brainsci13091305] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 09/09/2023] [Accepted: 09/10/2023] [Indexed: 09/29/2023] Open
Abstract
Epilepsy is a complex neurological disorder affecting millions worldwide, with a substantial number of patients facing drug-resistant epilepsy. This comprehensive review explores innovative therapies for epilepsy management, focusing on their principles, clinical evidence, and potential applications. Traditional antiseizure medications (ASMs) form the cornerstone of epilepsy treatment, but their limitations necessitate alternative approaches. The review delves into cutting-edge therapies such as responsive neurostimulation (RNS), vagus nerve stimulation (VNS), and deep brain stimulation (DBS), highlighting their mechanisms of action and promising clinical outcomes. Additionally, the potential of gene therapies and optogenetics in epilepsy research is discussed, revealing groundbreaking findings that shed light on seizure mechanisms. Insights into cannabidiol (CBD) and the ketogenic diet as adjunctive therapies further broaden the spectrum of epilepsy management. Challenges in achieving seizure control with traditional therapies, including treatment resistance and individual variability, are addressed. The importance of staying updated with emerging trends in epilepsy management is emphasized, along with the hope for improved therapeutic options. Future research directions, such as combining therapies, AI applications, and non-invasive optogenetics, hold promise for personalized and effective epilepsy treatment. As the field advances, collaboration among researchers of natural and synthetic biochemistry, clinicians from different streams and various forms of medicine, and patients will drive progress toward better seizure control and a higher quality of life for individuals living with epilepsy.
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Affiliation(s)
- Shampa Ghosh
- GloNeuro, Sector 107, Vishwakarma Road, Noida 201301, India
- ICMR—National Institute of Nutrition, Tarnaka, Hyderabad 500007, India
| | | | - Soumya Ghosh
- GloNeuro, Sector 107, Vishwakarma Road, Noida 201301, India
| | | | - Rakesh Bhaskar
- School of Chemical Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Republic of Korea
| | - Kannan Badri Narayanan
- School of Chemical Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Republic of Korea
- Research Institute of Cell Culture, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Republic of Korea
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