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Liufu M, Leveroni ZM, Shridhar S, Zhou N, Yu JY. Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specific neuromodulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.24.609522. [PMID: 39253473 PMCID: PMC11383035 DOI: 10.1101/2024.08.24.609522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Closed-loop, phase-specific neurostimulation is a powerful method to modulate ongoing brain activity for clinical and research applications. Phase-specific stimulation relies on estimating the phase of an ongoing oscillation in real time and issuing a control command at a target phase. Phase detection algorithms based on Fast Fourier transform (FFT) are widely used due to their computational efficiency and robustness. However, it is unclear how algorithm performance depends on the spectral properties of the input signal and how algorithm parameters can be optimized. We used offline simulation to evaluate the performance of three algorithms (endpoint-corrected Hilbert Transform, Hilbert Transform and phase mapping) on three rhythmic biological signals with distinct spectral properties (rodent hippocampal theta potential, human EEG alpha and human essential tremor). First, we found that algorithm performance was more strongly influenced by signal amplitude and frequency variation compared with signal to noise ratio. Second, our simulations showed that the size of the data window for phase estimation was critical for the performance of FFT-based algorithms, where the optimal data window corresponds to the period of the oscillation. We validated this prediction with real time phase detection of hippocampal theta oscillations in freely behaving rats performing spatial navigation. Our findings define the relationship between signal properties and algorithm performance and provide a convenient method for optimizing FFT-based phase detection algorithms.
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Guidetti M, Bocci T, De Pedro Del Álamo M, Deuschl G, Fasano A, Fernandez RM, Gasca-Salas C, Hamani C, Krauss JK, Kühn AA, Limousin P, Little S, Lozano AM, Maiorana NV, Marceglia S, Okun MS, Oliveri S, Ostrem JL, Scelzo E, Schnitzler A, Starr PA, Temel Y, Timmermann L, Tinkhauser G, Visser-Vandewalle V, Volkmann J, Priori A. Adaptive Deep Brain Stimulation in Parkinson's Disease: A Delphi Consensus Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.26.24312580. [PMID: 39252901 PMCID: PMC11383503 DOI: 10.1101/2024.08.26.24312580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Importance If history teaches, as cardiac pacing moved from fixed-rate to on-demand delivery in in 80s of the last century, there are high probabilities that closed-loop and adaptive approaches will become, in the next decade, the natural evolution of conventional Deep Brain Stimulation (cDBS). However, while devices for aDBS are already available for clinical use, few data on their clinical application and technological limitations are available so far. In such scenario, gathering the opinion and expertise of leading investigators worldwide would boost and guide practice and research, thus grounding the clinical development of aDBS. Observations We identified clinical and academically experienced DBS clinicians (n=21) to discuss the challenges related to aDBS. A 5-point Likert scale questionnaire along with a Delphi method was employed. 42 questions were submitted to the panel, half of them being related to technical aspects while the other half to clinical aspects of aDBS. Experts agreed that aDBS will become clinical practice in 10 years. In the present scenario, although the panel agreed that aDBS applications require skilled clinicians and that algorithms need to be further optimized to manage complex PD symptoms, consensus was reached on aDBS safety and its ability to provide a faster and more stable treatment response than cDBS, also for tremor-dominant Parkinson's disease patients and for those with motor fluctuations and dyskinesias. Conclusions and Relevance Despite the need of further research, the panel concluded that aDBS is safe, promises to be maximally effective in PD patients with motor fluctuation and dyskinesias and therefore will enter into the clinical practice in the next years, with further research focused on algorithms and markers for complex symptoms.
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Tolmacheva A, Agranovich O, Blagovechtchenski E. The importance of brain mapping for rehabilitation in birth nonprogressive neuromuscular diseases. FRONTIERS IN NEUROIMAGING 2024; 3:1359491. [PMID: 39077762 PMCID: PMC11284525 DOI: 10.3389/fnimg.2024.1359491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 05/30/2024] [Indexed: 07/31/2024]
Abstract
While motor mapping has been extensively studied in acquired motor conditions, a lack has been observed in terms of research on neurological disorders present since birth, with damage to the spinal cord and peripheral nerves (hence, defined in this study as nonprogressive neuromuscular diseases). Despite an injury at the level below the brain, the subsequent changes in the motor system involve cortical reorganization. In the scientific community, the need for a comprehensive approach targeting the brain is increasingly recognized for greater motor recovery in these patients. Transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) are the most utilized techniques for motor mapping. The knowledge obtained through motor mapping may be used to develop effective individual neuromodulation therapy that helps in functional motor recovery. This brief review compares the results of the brain mapping of a few existing studies in individuals with nonprogressive motor disorders of nonbrain origin present at birth to the brain mapping of individuals with similar acquired motor conditions. The review reveals some particular features in terms of central adaptation in individuals with birth conditions compared to their acquired counterparts, such as the nonsomatotopic presentation of involved muscles in the sensorimotor cortex and nonadjacent cortical areas. This topic is undoubtedly intriguing, justifying further research in the field. This review also discusses the benefits these patients can obtain from neuromodulation therapy addressed to the central nervous system and the importance of individual neurophysiological assessment in designing rehabilitation therapy for children with birth motor disorders.
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Affiliation(s)
- Aleksandra Tolmacheva
- Center for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia
| | - Olga Agranovich
- G.I. Turner Scientific Research Institute for Children's Orthopaedics, Ministry of Health of Russia, Saint Petersburg, Russia
| | - Evgeny Blagovechtchenski
- Center for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia
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Sakai JT, Tanabe J, Battula S, Zipperly M, Mikulich-Gilbertson SK, Kern DS, Thompson JA, Raymond K, Gerecht PD, Foster K, Abosch A. Deep brain stimulation for the treatment of substance use disorders: a promising approach requiring caution. Front Psychiatry 2024; 15:1435109. [PMID: 39071229 PMCID: PMC11272460 DOI: 10.3389/fpsyt.2024.1435109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Accepted: 06/25/2024] [Indexed: 07/30/2024] Open
Abstract
Substance use disorders are prevalent, causing extensive morbidity and mortality worldwide. Evidence-based treatments are of low to moderate effect size. Growth in the neurobiological understanding of addiction (e.g., craving) along with technological advancements in neuromodulation have enabled an evaluation of neurosurgical treatments for substance use disorders. Deep brain stimulation (DBS) involves surgical implantation of leads into brain targets and subcutaneous tunneling to connect the leads to a programmable implanted pulse generator (IPG) under the skin of the chest. DBS allows direct testing of neurobiologically-guided hypotheses regarding the etiology of substance use disorders in service of developing more effective treatments. Early studies, although with multiple limitations, have been promising. Still the authors express caution regarding implementation of DBS studies in this population and emphasize the importance of safeguards to ensure patient safety and meaningful study results. In this perspectives article, we review lessons learned through the years of planning an ongoing trial of DBS for methamphetamine use disorder.
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Affiliation(s)
- Joseph T. Sakai
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, United States
| | - Jody Tanabe
- Department of Radiology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Sharonya Battula
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, United States
| | - Morgan Zipperly
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, United States
| | | | - Drew S. Kern
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO, United States
- Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO, United States
| | - John A. Thompson
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, United States
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO, United States
- Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO, United States
| | - Kristen Raymond
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, United States
| | - Pamela David Gerecht
- Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO, United States
| | - Katrina Foster
- National Institute on Drug Abuse, Bethesda, MD, United States
| | - Aviva Abosch
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE, United States
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Widge AS. Closing the loop in psychiatric deep brain stimulation: physiology, psychometrics, and plasticity. Neuropsychopharmacology 2024; 49:138-149. [PMID: 37415081 PMCID: PMC10700701 DOI: 10.1038/s41386-023-01643-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 05/28/2023] [Accepted: 06/20/2023] [Indexed: 07/08/2023]
Abstract
Deep brain stimulation (DBS) is an invasive approach to precise modulation of psychiatrically relevant circuits. Although it has impressive results in open-label psychiatric trials, DBS has also struggled to scale to and pass through multi-center randomized trials. This contrasts with Parkinson disease, where DBS is an established therapy treating thousands of patients annually. The core difference between these clinical applications is the difficulty of proving target engagement, and of leveraging the wide range of possible settings (parameters) that can be programmed in a given patient's DBS. In Parkinson's, patients' symptoms change rapidly and visibly when the stimulator is tuned to the correct parameters. In psychiatry, those same changes take days to weeks, limiting a clinician's ability to explore parameter space and identify patient-specific optimal settings. I review new approaches to psychiatric target engagement, with an emphasis on major depressive disorder (MDD). Specifically, I argue that better engagement may come by focusing on the root causes of psychiatric illness: dysfunction in specific, measurable cognitive functions and in the connectivity and synchrony of distributed brain circuits. I overview recent progress in both those domains, and how it may relate to other technologies discussed in companion articles in this issue.
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Affiliation(s)
- Alik S Widge
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
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Schutter DJ, Smits F, Klaus J. Mind matters: A narrative review on affective state-dependency in non-invasive brain stimulation. Int J Clin Health Psychol 2023; 23:100378. [PMID: 36866122 PMCID: PMC9971283 DOI: 10.1016/j.ijchp.2023.100378] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 02/03/2023] [Indexed: 02/18/2023] Open
Abstract
Variability in findings related to non-invasive brain stimulation (NIBS) have increasingly been described as a result of differences in neurophysiological state. Additionally, there is some evidence suggesting that individual differences in psychological states may correlate with the magnitude and directionality of effects of NIBS on the neural and behavioural level. In this narrative review, it is proposed that the assessment of baseline affective states can quantify non-reductive properties which are not readily accessible to neuroscientific methods. Particularly, affective-related states are theorized to correlate with physiological, behavioural and phenomenological effects of NIBS. While further systematic research is needed, baseline psychological states are suggested to provide a complementary cost-effective source of information for understanding variability in NIBS outcomes. Implementing measures of psychological state may potentially contribute to increasing the sensitivity and specificity of results in experimental and clinical NIBS studies.
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Affiliation(s)
- Dennis J.L.G. Schutter
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
| | - Fenne Smits
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
- Brain Research & Innovation Centre, Ministry of Defence, Utrecht, the Netherlands
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, The Netherlands
| | - Jana Klaus
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
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Nagrale SS, Yousefi A, Netoff TI, Widge AS. In silicodevelopment and validation of Bayesian methods for optimizing deep brain stimulation to enhance cognitive control. J Neural Eng 2023; 20:036015. [PMID: 37105164 PMCID: PMC10193041 DOI: 10.1088/1741-2552/acd0d5] [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: 08/30/2022] [Revised: 03/18/2023] [Accepted: 04/27/2023] [Indexed: 04/29/2023]
Abstract
Objective.deep brain stimulation (DBS) of the ventral internal capsule/striatum (VCVS) is a potentially effective treatment for several mental health disorders when conventional therapeutics fail. Its effectiveness, however, depends on correct programming to engage VCVS sub-circuits. VCVS programming is currently an iterative, time-consuming process, with weeks between setting changes and reliance on noisy, subjective self-reports. An objective measure of circuit engagement might allow individual settings to be tested in seconds to minutes, reducing the time to response and increasing patient and clinician confidence in the chosen settings. Here, we present an approach to measuring and optimizing that circuit engagement.Approach.we leverage prior results showing that effective VCVS DBS engages cognitive control circuitry and improves performance on the multi-source interference task, that this engagement depends primarily on which contact(s) are activated, and that circuit engagement can be tracked through a state space modeling framework. We develop a simulation framework based on those empirical results, then combine this framework with an adaptive optimizer to simulate a principled exploration of electrode contacts and identify the contacts that maximally improve cognitive control. We explore multiple optimization options (algorithms, number of inputs, speed of stimulation parameter changes) and compare them on problems of varying difficulty.Main results.we show that an upper confidence bound algorithm outperforms other optimizers, with roughly 80% probability of convergence to a global optimum when used in a majority-vote ensemble.Significance.we show that the optimization can converge even with lag between stimulation and effect, and that a complete optimization can be done in a clinically feasible timespan (a few hours). Further, the approach requires no specialized recording or imaging hardware, and thus could be a scalable path to expand the use of DBS in psychiatric and other non-motor applications.
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Affiliation(s)
- Sumedh S Nagrale
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States of America
| | - Ali Yousefi
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, United States of America
| | - Theoden I Netoff
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
| | - Alik S Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States of America
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8
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Hitti FL, Widge AS, Riva-Posse P, Malone DA, Okun MS, Shanechi MM, Foote KD, Lisanby SH, Ankudowich E, Chivukula S, Chang EF, Gunduz A, Hamani C, Feinsinger A, Kubu CS, Chiong W, Chandler JA, Carbunaru R, Cheeran B, Raike RS, Davis RA, Halpern CH, Vanegas-Arroyave N, Markovic D, Bick SK, McIntyre CC, Richardson RM, Dougherty DD, Kopell BH, Sweet JA, Goodman WK, Sheth SA, Pouratian N. Future directions in psychiatric neurosurgery: Proceedings of the 2022 American Society for Stereotactic and Functional Neurosurgery meeting on surgical neuromodulation for psychiatric disorders. Brain Stimul 2023; 16:867-878. [PMID: 37217075 PMCID: PMC11189296 DOI: 10.1016/j.brs.2023.05.011] [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: 04/05/2023] [Revised: 05/10/2023] [Accepted: 05/14/2023] [Indexed: 05/24/2023] Open
Abstract
OBJECTIVE Despite advances in the treatment of psychiatric diseases, currently available therapies do not provide sufficient and durable relief for as many as 30-40% of patients. Neuromodulation, including deep brain stimulation (DBS), has emerged as a potential therapy for persistent disabling disease, however it has not yet gained widespread adoption. In 2016, the American Society for Stereotactic and Functional Neurosurgery (ASSFN) convened a meeting with leaders in the field to discuss a roadmap for the path forward. A follow-up meeting in 2022 aimed to review the current state of the field and to identify critical barriers and milestones for progress. DESIGN The ASSFN convened a meeting on June 3, 2022 in Atlanta, Georgia and included leaders from the fields of neurology, neurosurgery, and psychiatry along with colleagues from industry, government, ethics, and law. The goal was to review the current state of the field, assess for advances or setbacks in the interim six years, and suggest a future path forward. The participants focused on five areas of interest: interdisciplinary engagement, regulatory pathways and trial design, disease biomarkers, ethics of psychiatric surgery, and resource allocation/prioritization. The proceedings are summarized here. CONCLUSION The field of surgical psychiatry has made significant progress since our last expert meeting. Although weakness and threats to the development of novel surgical therapies exist, the identified strengths and opportunities promise to move the field through methodically rigorous and biologically-based approaches. The experts agree that ethics, law, patient engagement, and multidisciplinary teams will be critical to any potential growth in this area.
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Affiliation(s)
- Frederick L Hitti
- Department of Neurosurgery, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Alik S Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Patricio Riva-Posse
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Donald A Malone
- Department of Psychiatry, Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA
| | - Michael S Okun
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Gainesville, FL, USA
| | - Maryam M Shanechi
- Departments of Electrical and Computer Engineering and Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Kelly D Foote
- Department of Neurosurgery, Norman Fixel Institute for Neurological Diseases, Gainesville, FL, USA
| | - Sarah H Lisanby
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Elizabeth Ankudowich
- Division of Translational Research, National Institute of Mental Health, Bethesda, MD, USA
| | - Srinivas Chivukula
- Department of Neurosurgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Aysegul Gunduz
- Department of Biomedical Engineering and Fixel Institute for Neurological Disorders, University of Florida, Gainesville, FL, USA
| | - Clement Hamani
- Sunnybrook Research Institute, Hurvitz Brain Sciences Centre, Harquail Centre for Neuromodulation, Division of Neurosurgery, University of Toronto, Toronto, Canada
| | - Ashley Feinsinger
- Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Cynthia S Kubu
- Department of Neurology, Cleveland Clinic and Case Western Reserve University, School of Medicine, Cleveland, OH, USA
| | - Winston Chiong
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Jennifer A Chandler
- Faculty of Law, University of Ottawa, Ottawa, ON, USA; Affiliate Investigator, Bruyère Research Institute, Ottawa, ON, USA
| | | | | | - Robert S Raike
- Global Research Organization, Medtronic Inc. Neuromodulation, Minneapolis, MN, USA
| | - Rachel A Davis
- Departments of Psychiatry and Neurosurgery, University of Colorado Anschutz, Aurora, CO, USA
| | - Casey H Halpern
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; The Cpl Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | | | - Dejan Markovic
- Department of Electrical Engineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Sarah K Bick
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Cameron C McIntyre
- Departments of Biomedical Engineering and Neurosurgery, Duke University, Durham, NC, USA
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Brian H Kopell
- Department of Neurosurgery, Center for Neuromodulation, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jennifer A Sweet
- Department of Neurosurgery, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Wayne K Goodman
- Department of Psychiatry and Behavior Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Nader Pouratian
- Department of Neurosurgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Widge AS. Closed-Loop Deep Brain Stimulation for Psychiatric Disorders. Harv Rev Psychiatry 2023; 31:162-171. [PMID: 37171475 PMCID: PMC10188203 DOI: 10.1097/hrp.0000000000000367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
ABSTRACT Deep brain stimulation (DBS) is a well-established approach to treating medication-refractory neurological disorders and holds promise for treating psychiatric disorders. Despite strong open-label results in extremely refractory patients, DBS has struggled to meet endpoints in randomized controlled trials. A major challenge is stimulation "dosing"-DBS systems have many adjustable parameters, and clinicians receive little feedback on whether they have chosen the correct parameters for an individual patient. Multiple groups have proposed closed loop technologies as a solution. These systems sense electrical activity, identify markers of an (un)desired state, then automatically deliver or adjust stimulation to alter that electrical state. Closed loop DBS has been successfully deployed in movement disorders and epilepsy. The availability of that technology, as well as advances in opportunities for invasive research with neurosurgical patients, has yielded multiple pilot demonstrations in psychiatric illness. Those demonstrations split into two schools of thought, one rooted in well-established diagnoses and symptom scales, the other in the more experimental Research Domain Criteria (RDoC) framework. Both are promising, and both are limited by the boundaries of current stimulation technology. They are in turn driving advances in implantable recording hardware, signal processing, and stimulation paradigms. The combination of these advances is likely to change both our understanding of psychiatric neurobiology and our treatment toolbox, though the timeframe may be limited by the realities of implantable device development.
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Affiliation(s)
- Alik S Widge
- From the Department of Psychiatry & Behavioral Sciences and Medical Discovery Team on Addictions, University of Minnesota
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Resonance Scanning as an Efficiency Enhancer for EEG-Guided Adaptive Neurostimulation. Life (Basel) 2023; 13:life13030620. [PMID: 36983776 PMCID: PMC10056921 DOI: 10.3390/life13030620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 02/16/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023] Open
Abstract
Electroencephalogram (EEG)-guided adaptive neurostimulation is an innovative kind of non-invasive closed-loop brain stimulation technique that uses audio–visual stimulation on-line modulated by rhythmical EEG components of the individual. However, the opportunity to enhance its effectiveness is a challenging task and needs further investigation. The present study aims to experimentally test whether it is possible to increase the efficiency of EEG-guided adaptive neurostimulation by pre- strengthening the modulating factor (subject’s EEG) through the procedure of resonance scanning, i.e., LED photostimulation with the frequency gradually increasing in the range of main EEG rhythms (4–20 Hz). Thirty-six university students in a state of exam stress were randomly assigned to two matched groups. One group was presented with the EEG-guided adaptive neurostimulation alone, whereas another matched group was presented with the combination of resonance scanning and EEG-guided adaptive neurostimulation. The changes in psychophysiological indicators after stimulation relative to the initial level were used. Although both types of stimulation led to an increase in the power of EEG rhythms, accompanied by a decrease in the number of errors in the word recognition test and a decrease in the degree of emotional maladjustment, these changes reached the level of significance only in experiments with preliminary resonance scanning. Resonance scanning increases the brain’s responsiveness to subsequent EEG-guided adaptive neurostimulation, acting as a tool to enhance its efficiency. The results obtained clearly indicate that the combination of resonance scanning and EEG-guided adaptive neurostimulation is an effective way to reach the signs of cognitive improvement in stressed individuals.
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Shin U, Ding C, Woods V, Widge AS, Shoaran M. A 16-Channel Low-Power Neural Connectivity Extraction and Phase-Locked Deep Brain Stimulation SoC. IEEE SOLID-STATE CIRCUITS LETTERS 2023; 6:21-24. [PMID: 36909935 PMCID: PMC9997065 DOI: 10.1109/lssc.2023.3238797] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Growing evidence suggests that phase-locked deep brain stimulation (DBS) can effectively regulate abnormal brain connectivity in neurological and psychiatric disorders. This letter therefore presents a low-power SoC with both neural connectivity extraction and phase-locked DBS capabilities. A 16-channel low-noise analog front-end (AFE) records local field potentials (LFPs) from multiple brain regions with precise gain matching. A novel low-complexity phase estimator and neural connectivity processor subsequently enable energy-efficient, yet accurate measurement of the instantaneous phase and cross-regional synchrony measures. Through flexible combination of neural biomarkers such as phase synchrony and spectral energy, a four-channel charge-balanced neurostimulator is triggered to treat various pathological brain conditions. Fabricated in 65-nm CMOS, the SoC occupies a silicon area of 2.24 mm2 and consumes 60 μW, achieving over 60% power saving in neural connectivity extraction compared to the state-of-the-art. Extensive in-vivo measurements demonstrate multi-channel LFP recording, real-time extraction of phase and neural connectivity measures, and phase-locked stimulation in rats.
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Affiliation(s)
- Uisub Shin
- Institute of Electrical and Micro Engineering, EPFL, 1202 Geneva, Switzerland, and the School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853 USA
| | - Cong Ding
- Institute of Electrical and Micro Engineering and Neuro-X Institute, EPFL, 1202 Geneva, Switzerland
| | - Virginia Woods
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455 USA
| | - Alik S Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455 USA
| | - Mahsa Shoaran
- Institute of Electrical and Micro Engineering and Neuro-X Institute, EPFL, 1202 Geneva, Switzerland
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Gebodh N, Miskovic V, Laszlo S, Datta A, Bikson M. A Scalable Framework for Closed-Loop Neuromodulation with Deep Learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.18.524615. [PMID: 36712027 PMCID: PMC9882307 DOI: 10.1101/2023.01.18.524615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Closed-loop neuromodulation measures dynamic neural or physiological activity to optimize interventions for clinical and nonclinical behavioral, cognitive, wellness, attentional, or general task performance enhancement. Conventional closed-loop stimulation approaches can contain biased biomarker detection (decoders and error-based triggering) and stimulation-type application. We present and verify a novel deep learning framework for designing and deploying flexible, data-driven, automated closed-loop neuromodulation that is scalable using diverse datasets, agnostic to stimulation technology (supporting multi-modal stimulation: tACS, tDCS, tFUS, TMS), and without the need for personalized ground-truth performance data. Our approach is based on identified periods of responsiveness - detected states that result in a change in performance when stimulation is applied compared to no stimulation. To demonstrate our framework, we acquire, analyze, and apply a data-driven approach to our open sourced GX dataset, which includes concurrent physiological (ECG, EOG) and neuronal (EEG) measures, paired with continuous vigilance/attention-fatigue tracking, and High-Definition transcranial electrical stimulation (HD-tES). Our framework's decision process for intervention application identified 88.26% of trials as correct applications, showed potential improvement with varying stimulation types, or missed opportunities to stimulate, whereas 11.25% of trials were predicted to stimulate at inopportune times. With emerging datasets and stimulation technologies, our unifying and integrative framework; leveraging deep learning (Convolutional Neural Networks - CNNs); demonstrates the adaptability and feasibility of automated multimodal neuromodulation for both clinical and nonclinical applications.
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Affiliation(s)
- Nigel Gebodh
- The Department of Biomedical Engineering, The City College of New York, The City University of New York, New York USA
| | | | | | | | - Marom Bikson
- The Department of Biomedical Engineering, The City College of New York, The City University of New York, New York USA
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Widge AS. Deep Brain Stimulation for Treatment-Resistant Mental Illness. Psychiatr Ann 2022. [DOI: 10.3928/00485713-20220621-02] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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