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Alfihed S, Majrashi M, Ansary M, Alshamrani N, Albrahim SH, Alsolami A, Alamari HA, Zaman A, Almutairi D, Kurdi A, Alzaydi MM, Tabbakh T, Al-Otaibi F. Non-Invasive Brain Sensing Technologies for Modulation of Neurological Disorders. BIOSENSORS 2024; 14:335. [PMID: 39056611 PMCID: PMC11274405 DOI: 10.3390/bios14070335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 07/01/2024] [Accepted: 07/06/2024] [Indexed: 07/28/2024]
Abstract
The non-invasive brain sensing modulation technology field is experiencing rapid development, with new techniques constantly emerging. This study delves into the field of non-invasive brain neuromodulation, a safer and potentially effective approach for treating a spectrum of neurological and psychiatric disorders. Unlike traditional deep brain stimulation (DBS) surgery, non-invasive techniques employ ultrasound, electrical currents, and electromagnetic field stimulation to stimulate the brain from outside the skull, thereby eliminating surgery risks and enhancing patient comfort. This study explores the mechanisms of various modalities, including transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS), highlighting their potential to address chronic pain, anxiety, Parkinson's disease, and depression. We also probe into the concept of closed-loop neuromodulation, which personalizes stimulation based on real-time brain activity. While we acknowledge the limitations of current technologies, our study concludes by proposing future research avenues to advance this rapidly evolving field with its immense potential to revolutionize neurological and psychiatric care and lay the foundation for the continuing advancement of innovative non-invasive brain sensing technologies.
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Affiliation(s)
- Salman Alfihed
- Microelectronics and Semiconductor Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia; (S.A.)
| | - Majed Majrashi
- Bioengineering Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
| | - Muhammad Ansary
- Neuroscience Center Research Unit, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia
| | - Naif Alshamrani
- Microelectronics and Semiconductor Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia; (S.A.)
| | - Shahad H. Albrahim
- Bioengineering Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
| | - Abdulrahman Alsolami
- Microelectronics and Semiconductor Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia; (S.A.)
| | - Hala A. Alamari
- Bioengineering Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
| | - Adnan Zaman
- Microelectronics and Semiconductor Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia; (S.A.)
| | - Dhaifallah Almutairi
- Microelectronics and Semiconductor Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia; (S.A.)
| | - Abdulaziz Kurdi
- Advanced Materials Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia;
| | - Mai M. Alzaydi
- Bioengineering Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia
| | - Thamer Tabbakh
- Microelectronics and Semiconductor Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh 11442, Saudi Arabia; (S.A.)
| | - Faisal Al-Otaibi
- Neuroscience Center Research Unit, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia
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De Ridder D, Siddiqi MA, Dauwels J, Serdijn WA, Strydis C. NeuroDots: From Single-Target to Brain-Network Modulation: Why and What Is Needed? Neuromodulation 2024; 27:711-729. [PMID: 38639704 DOI: 10.1016/j.neurom.2024.01.003] [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: 08/07/2023] [Revised: 11/05/2023] [Accepted: 01/10/2024] [Indexed: 04/20/2024]
Abstract
OBJECTIVES Current techniques in brain stimulation are still largely based on a phrenologic approach that a single brain target can treat a brain disorder. Nevertheless, meta-analyses of brain implants indicate an overall success rate of 50% improvement in 50% of patients, irrespective of the brain-related disorder. Thus, there is still a large margin for improvement. The goal of this manuscript is to 1) develop a general theoretical framework of brain functioning that is amenable to surgical neuromodulation, and 2) describe the engineering requirements of the next generation of implantable brain stimulators that follow from this theoretic model. MATERIALS AND METHODS A neuroscience and engineering literature review was performed to develop a universal theoretical model of brain functioning and dysfunctioning amenable to surgical neuromodulation. RESULTS Even though a single target can modulate an entire network, research in network science reveals that many brain disorders are the consequence of maladaptive interactions among multiple networks rather than a single network. Consequently, targeting the main connector hubs of those multiple interacting networks involved in a brain disorder is theoretically more beneficial. We, thus, envision next-generation network implants that will rely on distributed, multisite neuromodulation targeting correlated and anticorrelated interacting brain networks, juxtaposing alternative implant configurations, and finally providing solid recommendations for the realization of such implants. In doing so, this study pinpoints the potential shortcomings of other similar efforts in the field, which somehow fall short of the requirements. CONCLUSION The concept of network stimulation holds great promise as a universal approach for treating neurologic and psychiatric disorders.
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Affiliation(s)
- Dirk De Ridder
- Section of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
| | - Muhammad Ali Siddiqi
- Department of Electrical Engineering, Lahore University of Management Sciences, Lahore, Pakistan; Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Quantum and Computer Engineering Department, Delft University of Technology, Delft, The Netherlands
| | - Justin Dauwels
- Microelectronics Department, Delft University of Technology, Delft, The Netherlands
| | - Wouter A Serdijn
- Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Section Bioelectronics, Delft University of Technology, Delft, The Netherlands
| | - Christos Strydis
- Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Quantum and Computer Engineering Department, Delft University of Technology, Delft, The Netherlands
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Saway BF, Palmer C, Hughes C, Triano M, Suresh RE, Gilmore J, George M, Kautz SA, Rowland NC. The evolution of neuromodulation for chronic stroke: From neuroplasticity mechanisms to brain-computer interfaces. Neurotherapeutics 2024; 21:e00337. [PMID: 38377638 PMCID: PMC11103214 DOI: 10.1016/j.neurot.2024.e00337] [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: 10/16/2023] [Revised: 02/05/2024] [Accepted: 02/13/2024] [Indexed: 02/22/2024] Open
Abstract
Stroke is one of the most common and debilitating neurological conditions worldwide. Those who survive experience motor, sensory, speech, vision, and/or cognitive deficits that severely limit remaining quality of life. While rehabilitation programs can help improve patients' symptoms, recovery is often limited, and patients frequently continue to experience impairments in functional status. In this review, invasive neuromodulation techniques to augment the effects of conventional rehabilitation methods are described, including vagus nerve stimulation (VNS), deep brain stimulation (DBS) and brain-computer interfaces (BCIs). In addition, the evidence base for each of these techniques, pivotal trials, and future directions are explored. Finally, emerging technologies such as functional near-infrared spectroscopy (fNIRS) and the shift to artificial intelligence-enabled implants and wearables are examined. While the field of implantable devices for chronic stroke recovery is still in a nascent stage, the data reviewed are suggestive of immense potential for reducing the impact and impairment from this globally prevalent disorder.
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Affiliation(s)
- Brian F Saway
- Department of Neurosurgery, Medical University of South Carolina, SC 29425, USA.
| | - Charles Palmer
- Department of Psychiatry, Medical University of South Carolina, SC 29425, USA
| | - Christopher Hughes
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Matthew Triano
- Department of Neurosurgery, Medical University of South Carolina, SC 29425, USA
| | - Rishishankar E Suresh
- College of Medicine, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Jordon Gilmore
- Department of Bioengineering, Clemson University, Clemson, SC 29634, USA
| | - Mark George
- Department of Psychiatry, Medical University of South Carolina, SC 29425, USA; Ralph H Johnson VA Health Care System, Charleston, SC 29425, USA
| | - Steven A Kautz
- Department of Health Science and Research, Medical University of South Carolina, SC 29425, USA; Ralph H Johnson VA Health Care System, Charleston, SC 29425, USA
| | - Nathan C Rowland
- Department of Neurosurgery, Medical University of South Carolina, SC 29425, USA; MUSC Institute for Neuroscience Discovery (MIND), Medical University of South Carolina, SC 29425, USA
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Fassi L, Hochman S, Daskalakis ZJ, Blumberger DM, Cohen Kadosh R. The importance of individual beliefs in assessing treatment efficacy. eLife 2024; 12:RP88889. [PMID: 38547008 PMCID: PMC10977967 DOI: 10.7554/elife.88889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024] Open
Abstract
In recent years, there has been debate about the effectiveness of treatments from different fields, such as neurostimulation, neurofeedback, brain training, and pharmacotherapy. This debate has been fuelled by contradictory and nuanced experimental findings. Notably, the effectiveness of a given treatment is commonly evaluated by comparing the effect of the active treatment versus the placebo on human health and/or behaviour. However, this approach neglects the individual's subjective experience of the type of treatment she or he received in establishing treatment efficacy. Here, we show that individual differences in subjective treatment - the thought of receiving the active or placebo condition during an experiment - can explain variability in outcomes better than the actual treatment. We analysed four independent datasets (N = 387 participants), including clinical patients and healthy adults from different age groups who were exposed to different neurostimulation treatments (transcranial magnetic stimulation: Studies 1 and 2; transcranial direct current stimulation: Studies 3 and 4). Our findings show that the inclusion of subjective treatment can provide a better model fit either alone or in interaction with objective treatment (defined as the condition to which participants are assigned in the experiment). These results demonstrate the significant contribution of subjective experience in explaining the variability of clinical, cognitive, and behavioural outcomes. We advocate for existing and future studies in clinical and non-clinical research to start accounting for participants' subjective beliefs and their interplay with objective treatment when assessing the efficacy of treatments. This approach will be crucial in providing a more accurate estimation of the treatment effect and its source, allowing the development of effective and reproducible interventions.
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Affiliation(s)
- Luisa Fassi
- MRC Cognition and Brain Sciences Unit, University of CambridgeCambridgeUnited Kingdom
- Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
- Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
| | - Shachar Hochman
- School of Psychology, University of SurreySurreyUnited Kingdom
| | - Zafiris J Daskalakis
- Department of Psychiatry, University of California, San DiegoSan DiegoUnited States
| | - Daniel M Blumberger
- Temerty Centre for Therapeutic Brain Intervention at the Centre for Addiction and Mental Health and Department of Psychiatry, Temerty Faculty of Medicine, University of TorontoTorontoCanada
| | - Roi Cohen Kadosh
- Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
- School of Psychology, University of SurreySurreyUnited Kingdom
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Zhang S, Zhang C, Fan M, Chen T, Yan H, Shi N, Chen Y. Neuromodulation and Functional Gastrointestinal Disease. Neuromodulation 2024; 27:243-255. [PMID: 37690016 DOI: 10.1016/j.neurom.2023.08.001] [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: 05/06/2023] [Revised: 07/10/2023] [Accepted: 08/03/2023] [Indexed: 09/11/2023]
Abstract
INTRODUCTION Functional gastrointestinal disorders (FGIDs) are common, and they severely impair an individual's quality of life. The mechanism of pathogenesis and the effective treatments for FGIDs remain elusive. Neuromodulation-a relatively new treatment-has exhibited a good therapeutic effect on FGIDs, although there are different methods for different symptoms of FGIDs. MATERIALS AND METHODS We used PubMed to review the history of neuromodulation for the treatment of FGIDs and to review several recently proposed neuromodulation approaches with improved effects on FGIDs. CONCLUSION Electroacupuncture, transcutaneous electroacupuncture, transcutaneous auricular vagal nerve stimulation, sacral nerve stimulation (SNS) (which relies on vagal nerve stimulation), and gastric electrical stimulation (which works through the modulation of slow waves generated by the interstitial cells of Cajal), in addition to the noninvasive neurostimulation alternative approach method of SNS-tibial nerve stimulation and transcutaneous electrical stimulation (which is still in its infancy), are some of the proposed neuromodulation approaches with improved effects on FGIDs. This review has discussed some critical issues related to the selection of stimulation parameters and the underlying mechanism and attempts to outline future research directions backed by the existing literature.
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Affiliation(s)
- Shuhui Zhang
- Department of Gastroenterology, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Can Zhang
- Department of Gastroenterology, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Mingwei Fan
- Department of Gastroenterology, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Tan Chen
- Department of Gastroenterology, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Hui Yan
- Department of Gastroenterology, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Ning Shi
- Department of Gastroenterology, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Yan Chen
- Department of Gastroenterology, Binzhou Medical University Hospital, Binzhou, Shandong, China.
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Thanh Nhu N, Chen DYT, Kang JH. Identification of Resting-State Network Functional Connectivity and Brain Structural Signatures in Fibromyalgia Using a Machine Learning Approach. Biomedicines 2022; 10:biomedicines10123002. [PMID: 36551758 PMCID: PMC9775534 DOI: 10.3390/biomedicines10123002] [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: 10/12/2022] [Revised: 11/12/2022] [Accepted: 11/19/2022] [Indexed: 11/23/2022] Open
Abstract
Abnormal resting-state functional connectivity (rs-FC) and brain structure have emerged as pathological hallmarks of fibromyalgia (FM). This study investigated and compared the accuracy of network rs-FC and brain structural features in identifying FM with a machine learning (ML) approach. Twenty-six FM patients and thirty healthy controls were recruited. Clinical presentation was measured by questionnaires. After MRI acquisitions, network rs-FC z-score and network-based gray matter volume matrices were exacted and preprocessed. The performance of feature selection and classification methods was measured. Correlation analyses between predictive features in final models and clinical data were performed. The combination of the recursive feature elimination (RFE) selection method and support vector machine (rs-FC data) or logistic regression (structural data), after permutation importance feature selection, showed high performance in distinguishing FM patients from pain-free controls, in which the rs-FC ML model outperformed the structural ML model (accuracy: 0.91 vs. 0.86, AUC: 0.93 vs. 0.88). The combined rs-FC and structural ML model showed the best performance (accuracy: 0.95, AUC: 0.95). Additionally, several rs-FC features in the final ML model correlated with FM's clinical data. In conclusion, ML models based on rs-FC and brain structural MRI features could effectively differentiate FM patients from pain-free subjects.
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Affiliation(s)
- Nguyen Thanh Nhu
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- Faculty of Medicine, Can Tho University of Medicine and Pharmacy, Can Tho 94117, Vietnam
| | - David Yen-Ting Chen
- Department of Medical Imaging, Taipei Medical University-Shuang-Ho Hospital, New Taipei City 235, Taiwan
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Jiunn-Horng Kang
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- Department of Physical Medicine and Rehabilitation, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei 110, Taiwan
- Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan
- Correspondence: ; Tel.: +886-2-27372181 (ext. 1236)
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Adhia DB, Mani R, Reynolds JN, Hall M, Vanneste S, De Ridder D. High-Definition Transcranial Infraslow Pink-Noise Stimulation Can Influence Functional and Effective Cortical Connectivity in Individuals With Chronic Low Back Pain: A Pilot Randomized Placebo-Controlled Study. Neuromodulation 2022:S1094-7159(22)01225-9. [DOI: 10.1016/j.neurom.2022.08.450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 08/02/2022] [Accepted: 08/15/2022] [Indexed: 11/06/2022]
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De Ridder D, Vanneste S, Smith M, Adhia D. Pain and the Triple Network Model. Front Neurol 2022; 13:757241. [PMID: 35321511 PMCID: PMC8934778 DOI: 10.3389/fneur.2022.757241] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 01/28/2022] [Indexed: 12/15/2022] Open
Abstract
Acute pain is a physiological response that causes an unpleasant sensory and emotional experience in the presence of actual or potential tissue injury. Anatomically and symptomatically, chronic pathological pain can be divided into three distinct but interconnected pathways, a lateral “painfulness” pathway, a medial “suffering” pathway and a descending pain inhibitory circuit. Pain (fullness) can exist without suffering and suffering can exist without pain (fullness). The triple network model is offering a generic unifying framework that may be used to understand a variety of neuropsychiatric illnesses. It claims that brain disorders are caused by aberrant interactions within and between three cardinal brain networks: the self-representational default mode network, the behavioral relevance encoding salience network and the goal oriented central executive network. A painful stimulus usually leads to a negative cognitive, emotional, and autonomic response, phenomenologically expressed as pain related suffering, processed by the medial pathway. This anatomically overlaps with the salience network, which encodes behavioral relevance of the painful stimuli and the central sympathetic control network. When pain lasts longer than the healing time and becomes chronic, the pain- associated somatosensory cortex activity may become functionally connected to the self-representational default mode network, i.e., it becomes an intrinsic part of the self-percept. This is most likely an evolutionary adaptation to save energy, by separating pain from sympathetic energy-consuming action. By interacting with the frontoparietal central executive network, this can eventually lead to functional impairment. In conclusion, the three well-known pain pathways can be combined into the triple network model explaining the whole range of pain related co-morbidities. This paves the path for the creation of new customized and personalized treatment methods.
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Affiliation(s)
- Dirk De Ridder
- Section of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
- *Correspondence: Dirk De Ridder
| | - Sven Vanneste
- School of Psychology, Global Brain Health Institute, Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Mark Smith
- Neurofeedbackservices of New York, New York, NY, United States
| | - Divya Adhia
- Section of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
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Rapid development of an integrated remote programming platform for neuromodulation systems through the biodesign process. Sci Rep 2022; 12:2269. [PMID: 35145143 PMCID: PMC8831590 DOI: 10.1038/s41598-022-06098-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 01/24/2022] [Indexed: 12/12/2022] Open
Abstract
Treating chronic symptoms for pain and movement disorders with neuromodulation therapies involves fine-tuning of programming parameters over several visits to achieve and maintain symptom relief. This, together with challenges in access to trained specialists, has led to a growing need for an integrated wireless remote care platform for neuromodulation devices. In March of 2021, we launched the first neuromodulation device with an integrated remote programming platform. Here, we summarize the biodesign steps taken to identify the unmet patient need, invent, implement, and test the new technology, and finally gain market approval for the remote care platform. Specifically, we illustrate how agile development aligned with the evolving regulatory requirements can enable patient-centric digital health technology in neuromodulation, such as the remote care platform. The three steps of the biodesign process applied for remote care platform development are: (1) Identify, (2) Invent, and (3) Implement. First, we identified the unmet patient needs through market research and voice-of-customer (VOC) process. Next, during the concept generation phase of the invention step, we integrated the results from the VOC into defining requirements for prototype development. Subsequently, in the concept screening phase, ten subjects with PD participated in a clinical pilot study aimed at characterizing the safety of the remote care prototype. Lastly, during the implementation step, lessons learned from the pilot experience were integrated into final product development as new features. Following final product development, we completed usability testing to validate the full remote care system and collected preliminary data from the limited market release experience. The VOC data, during prototype development, helped us identify thresholds for video quality and needs priorities for clinicians and patients. During the pilot study, one subject reported anticipated remote-care-related adverse events that were resolved without sequelae. For usability analysis following final product development, the failure rates for task completion for both user groups were about 1%. Lastly, during the initial 4 weeks of the limited market release experience, a total of 858 remote care sessions were conducted with a 93% success rate. Overall, we developed a remote care platform by adopting a user-centric approach. Although the system intended to address pre-COVID19 challenges associated with disease management, the unforeseen overlap of the study with the pandemic elevated the importance of such a system and an innovative development process enabled us to advance a patient-centric platform to gain regulatory approval and successfully launch the remote care platform to market.
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Ezeokafor I, Upadhya A, Shetty S. Neurosensory Prosthetics: An Integral Neuromodulation Part of Bioelectronic Device. Front Neurosci 2021; 15:671767. [PMID: 34867141 PMCID: PMC8637173 DOI: 10.3389/fnins.2021.671767] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 10/07/2021] [Indexed: 12/28/2022] Open
Abstract
Bioelectronic medicines (BEMs) constitute a branch of bioelectronic devices (BEDs), which are a class of therapeutics that combine neuroscience with molecular biology, immunology, and engineering technologies. Thus, BEMs are the culmination of thought processes of scientists of varied fields and herald a new era in the treatment of chronic diseases. BEMs work on the principle of neuromodulation of nerve stimulation. Examples of BEMs based on neuromodulation are those that modify neural circuits through deep brain stimulation, vagal nerve stimulation, spinal nerve stimulation, and retinal and auditory implants. BEDs may also serve as diagnostic tools by mimicking human sensory systems. Two examples of in vitro BEDs used as diagnostic agents in biomedical applications based on in vivo neurosensory circuits are the bioelectronic nose and bioelectronic tongue. The review discusses the ever-growing application of BEDs to a wide variety of health conditions and practices to improve the quality of life.
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Affiliation(s)
| | - Archana Upadhya
- Shobhaben Pratapbhai Patel School of Pharmacy & Technology Management, Shri Vile Parle Kelavani Mandal (SVKM) Narsee Monjee Institute of Management Studies (NMiMS) (SVKM’S NMiMS), Mumbai, India
| | - Saritha Shetty
- Shobhaben Pratapbhai Patel School of Pharmacy & Technology Management, Shri Vile Parle Kelavani Mandal (SVKM) Narsee Monjee Institute of Management Studies (NMiMS) (SVKM’S NMiMS), Mumbai, India
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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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