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Jiao D, Xu L, Gu Z, Yan H, Shen D, Gu X. Pathogenesis, diagnosis, and treatment of epilepsy: electromagnetic stimulation-mediated neuromodulation therapy and new technologies. Neural Regen Res 2025; 20:917-935. [PMID: 38989927 PMCID: PMC11438347 DOI: 10.4103/nrr.nrr-d-23-01444] [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: 08/28/2023] [Revised: 10/31/2023] [Accepted: 01/18/2024] [Indexed: 07/12/2024] Open
Abstract
Epilepsy is a severe, relapsing, and multifactorial neurological disorder. Studies regarding the accurate diagnosis, prognosis, and in-depth pathogenesis are crucial for the precise and effective treatment of epilepsy. The pathogenesis of epilepsy is complex and involves alterations in variables such as gene expression, protein expression, ion channel activity, energy metabolites, and gut microbiota composition. Satisfactory results are lacking for conventional treatments for epilepsy. Surgical resection of lesions, drug therapy, and non-drug interventions are mainly used in clinical practice to treat pain associated with epilepsy. Non-pharmacological treatments, such as a ketogenic diet, gene therapy for nerve regeneration, and neural regulation, are currently areas of research focus. This review provides a comprehensive overview of the pathogenesis, diagnostic methods, and treatments of epilepsy. It also elaborates on the theoretical basis, treatment modes, and effects of invasive nerve stimulation in neurotherapy, including percutaneous vagus nerve stimulation, deep brain electrical stimulation, repetitive nerve electrical stimulation, in addition to non-invasive transcranial magnetic stimulation and transcranial direct current stimulation. Numerous studies have shown that electromagnetic stimulation-mediated neuromodulation therapy can markedly improve neurological function and reduce the frequency of epileptic seizures. Additionally, many new technologies for the diagnosis and treatment of epilepsy are being explored. However, current research is mainly focused on analyzing patients' clinical manifestations and exploring relevant diagnostic and treatment methods to study the pathogenesis at a molecular level, which has led to a lack of consensus regarding the mechanisms related to the disease.
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Affiliation(s)
- Dian Jiao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Lai Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Zhen Gu
- Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Hua Yan
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Dingding Shen
- Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China
| | - Xiaosong Gu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
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McInnes AN, Olsen ST, Sullivan CR, Cooper DC, Wilson S, Sonmez AI, Albott CS, Olson SC, Peterson CB, Rittberg BR, Herman A, Bajzer M, Nahas Z, Widge AS. Trajectory Modeling and Response Prediction in Transcranial Magnetic Stimulation for Depression. PERSONALIZED MEDICINE IN PSYCHIATRY 2024; 47-48:100135. [PMID: 39257484 PMCID: PMC11382337 DOI: 10.1016/j.pmip.2024.100135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Background Repetitive transcranial magnetic stimulation (rTMS) therapy could be improved by more accurate and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modeling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models are useful in predicting clinically meaningful change in symptom severity, i.e. categorical (non)response as opposed to continuous scores. Methods We compared LCMM and NLME approaches to model the antidepressant response to TMS in a naturalistic sample of 238 patients receiving rTMS for treatment resistant depression, across multiple coils and protocols. We then compared the predictive power of those models. Results LCMM trajectories were influenced largely by baseline symptom severity, but baseline symptoms provided little predictive power for later antidepressant response. Rather, the optimal LCMM model was a nonlinear two-class model that accounted for baseline symptoms. This model accurately predicted patient response at 4 weeks of treatment (AUC = 0.70, 95% CI = [0.52 - 0.87]), but not before. NLME offered slightly improved predictive performance at 4 weeks of treatment (AUC = 0.76, 95% CI = [0.58 - 0.94], but likewise, not before. Conclusions In showing the predictive validity of these approaches to model response trajectories to rTMS, we provided preliminary evidence that trajectory modeling could be used to guide future treatment decisions.
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Affiliation(s)
- Aaron N. McInnes
- Corresponding authors: Aaron N. McInnes PhD and Alik S. Widge MD, PhD, Department of Psychiatry, University of Minnesota, Twin Cities, McGuire Translational Research Facility, 2001 6th St SE, Minneapolis, MN 55455,
| | | | - Christi R.P. Sullivan
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Dawson C. Cooper
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Saydra Wilson
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Ayse Irem Sonmez
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - C. Sophia Albott
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Stephen C. Olson
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Carol B. Peterson
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Barry R. Rittberg
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Alexander Herman
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Matej Bajzer
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Ziad Nahas
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Alik S. Widge
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
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Hadar PN, Zelmann R, Salami P, Cash SS, Paulk AC. The Neurostimulationist will see you now: prescribing direct electrical stimulation therapies for the human brain in epilepsy and beyond. Front Hum Neurosci 2024; 18:1439541. [PMID: 39296917 PMCID: PMC11408201 DOI: 10.3389/fnhum.2024.1439541] [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: 05/28/2024] [Accepted: 08/23/2024] [Indexed: 09/21/2024] Open
Abstract
As the pace of research in implantable neurotechnology increases, it is important to take a step back and see if the promise lives up to our intentions. While direct electrical stimulation applied intracranially has been used for the treatment of various neurological disorders, such as Parkinson's, epilepsy, clinical depression, and Obsessive-compulsive disorder, the effectiveness can be highly variable. One perspective is that the inability to consistently treat these neurological disorders in a standardized way is due to multiple, interlaced factors, including stimulation parameters, location, and differences in underlying network connectivity, leading to a trial-and-error stimulation approach in the clinic. An alternate view, based on a growing knowledge from neural data, is that variability in this input (stimulation) and output (brain response) relationship may be more predictable and amenable to standardization, personalization, and, ultimately, therapeutic implementation. In this review, we assert that the future of human brain neurostimulation, via direct electrical stimulation, rests on deploying standardized, constrained models for easier clinical implementation and informed by intracranial data sets, such that diverse, individualized therapeutic parameters can efficiently produce similar, robust, positive outcomes for many patients closer to a prescriptive model. We address the pathway needed to arrive at this future by addressing three questions, namely: (1) why aren't we already at this prescriptive future?; (2) how do we get there?; (3) how far are we from this Neurostimulationist prescriptive future? We first posit that there are limited and predictable ways, constrained by underlying networks, for direct electrical stimulation to induce changes in the brain based on past literature. We then address how identifying underlying individual structural and functional brain connectivity which shape these standard responses enable targeted and personalized neuromodulation, bolstered through large-scale efforts, including machine learning techniques, to map and reverse engineer these input-output relationships to produce a good outcome and better identify underlying mechanisms. This understanding will not only be a major advance in enabling intelligent and informed design of neuromodulatory therapeutic tools for a wide variety of neurological diseases, but a shift in how we can predictably, and therapeutically, prescribe stimulation treatments the human brain.
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Affiliation(s)
- Peter N Hadar
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Rina Zelmann
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Pariya Salami
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
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Allawala AB, Bijanki KR, Adkinson J, Oswalt D, Tsolaki E, Mathew S, Mathura RK, Bartoli E, Provenza N, Watrous AJ, Xiao J, Pirtle V, Mocchi MM, Rajesh S, Diab N, Cohn JF, Borton DA, Goodman WK, Pouratian N, Sheth SA. Stereo-Electroencephalography-Guided Network Neuromodulation for Psychiatric Disorders: The Neurophysiology Monitoring Unit. Oper Neurosurg (Hagerstown) 2024; 27:329-336. [PMID: 39145663 PMCID: PMC11315541 DOI: 10.1227/ons.0000000000001122] [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: 08/25/2023] [Accepted: 01/19/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Recent advances in stereotactic and functional neurosurgery have brought forth the stereo-electroencephalography approach which allows deeper interrogation and characterization of the contributions of deep structures to neural and affective functioning. We argue that this approach can and should be brought to bear on the notoriously intractable issue of defining the pathophysiology of refractory psychiatric disorders and developing patient-specific optimized stimulation therapies. METHODS We have developed a suite of methods for maximally leveraging the stereo-electroencephalography approach for an innovative application to understand affective disorders, with high translatability across the broader range of refractory neuropsychiatric conditions. RESULTS This article provides a roadmap for determining desired electrode coverage, tracking high-resolution research recordings across a large number of electrodes, synchronizing intracranial signals with ongoing research tasks and other data streams, applying intracranial stimulation during recording, and design choices for patient comfort and safety. CONCLUSION These methods can be implemented across other neuropsychiatric conditions needing intensive electrophysiological characterization to define biomarkers and more effectively guide therapeutic decision-making in cases of severe and treatment-refractory disease.
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Affiliation(s)
- Anusha B. Allawala
- School of Engineering, Brown University, Providence, Rhode Island, USA
- Department of Neurosurgery, University of California San Francisco, San Francisco, California, USA
| | - Kelly R. Bijanki
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - Joshua Adkinson
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - Denise Oswalt
- Department of Neurosurgery, University of Pennsylvania Philadelphia, Pennsylvania, USA
| | - Evangelia Tsolaki
- Department of Neurosurgery, University of California, Los Angeles, California, USA
| | - Sanjay Mathew
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas, USA
| | - Raissa K. Mathura
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - Nicole Provenza
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - Andrew J. Watrous
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - Jiayang Xiao
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - Victoria Pirtle
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - Madaline M. Mocchi
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - Sameer Rajesh
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - Nabeel Diab
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - Jeffrey F. Cohn
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - David A. Borton
- School of Engineering, Brown University, Providence, Rhode Island, USA
| | - Wayne K. Goodman
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas, USA
| | - Nader Pouratian
- Department of Neurosurgery, University of Texas Southwestern, Dallas, Texas, USA
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
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Sretavan K, Braun H, Liu Z, Bullock D, Palnitkar T, Patriat R, Chandrasekaran J, Brenny S, Johnson MD, Widge AS, Harel N, Heilbronner SR. A reproducible pipeline for parcellation of the anterior limb of the internal capsule. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00196-4. [PMID: 39053578 DOI: 10.1016/j.bpsc.2024.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 07/11/2024] [Accepted: 07/11/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND The anterior limb of the internal capsule (ALIC) is a white matter structure connecting the prefrontal cortex (PFC) to the brainstem, thalamus, and subthalamic nucleus. It is a target for deep brain stimulation (DBS) for obsessive-compulsive disorder. There is strong interest in improving DBS targeting by using diffusion tractography to reconstruct and target specific ALIC fiber pathways, but this methodology is susceptible to errors and lacks validation. To address these limitations, we developed a novel diffusion tractography pipeline that generates reliable and biologically validated ALIC white matter reconstructions. METHODS Following algorithm development and refinement, we analyzed 43 control subjects each with 2 sets of 3T MRI data and a subset of 5 controls with 7T data from the Human Connectome Project. We generated 22 segmented ALIC fiber bundles (11 per hemisphere) based on prefrontal PFC regions of interest, and we analyzed the relationships among bundles. RESULTS We successfully reproduced the topographies established by prior anatomical work using images acquired at both 3T and 7T. Quantitative assessment demonstrated significantly smaller intra-subject variability relative to inter-subject variability for both test and retest groups across all but one PFC region. We examined the overlap between fibers from different PFC regions and a response tract for obsessive-compulsive disorder deep brain stimulation, and we reconstructed the PFC hyperdirect pathway using a modified version of our pipeline. DISCUSSION Our dMRI algorithm reliably generates biologically validated ALIC white matter reconstructions, allowing for more precise modelling of fibers for neuromodulation therapies.
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Affiliation(s)
- Karianne Sretavan
- Graduate Program in Neuroscience, University of Minnesota, Minneapolis, Minnesota; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Henry Braun
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Zoe Liu
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota
| | - Daniel Bullock
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota
| | - Tara Palnitkar
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Remi Patriat
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Jayashree Chandrasekaran
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Samuel Brenny
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - Matthew D Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota
| | - Alik S Widge
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Noam Harel
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota; Department of Neurosurgery, University of Minnesota, Minneapolis, Minnesota
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Khazaei S, Parshi S, Alam S, Amin MR, Faghih RT. A multimodal dataset for investigating working memory in presence of music: a pilot study. Front Neurosci 2024; 18:1406814. [PMID: 38962177 PMCID: PMC11220373 DOI: 10.3389/fnins.2024.1406814] [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: 03/25/2024] [Accepted: 05/30/2024] [Indexed: 07/05/2024] Open
Abstract
Introduction Decoding an individual's hidden brain states in responses to musical stimuli under various cognitive loads can unleash the potential of developing a non-invasive closed-loop brain-machine interface (CLBMI). To perform a pilot study and investigate the brain response in the context of CLBMI, we collect multimodal physiological signals and behavioral data within the working memory experiment in the presence of personalized musical stimuli. Methods Participants perform a working memory experiment called the n-back task in the presence of calming music and exciting music. Utilizing the skin conductance signal and behavioral data, we decode the brain's cognitive arousal and performance states, respectively. We determine the association of oxygenated hemoglobin (HbO) data with performance state. Furthermore, we evaluate the total hemoglobin (HbT) signal energy over each music session. Results A relatively low arousal variation was observed with respect to task difficulty, while the arousal baseline changes considerably with respect to the type of music. Overall, the performance index is enhanced within the exciting session. The highest positive correlation between the HbO concentration and performance was observed within the higher cognitive loads (3-back task) for all of the participants. Also, the HbT signal energy peak occurs within the exciting session. Discussion Findings may underline the potential of using music as an intervention to regulate the brain cognitive states. Additionally, the experiment provides a diverse array of data encompassing multiple physiological signals that can be used in the brain state decoder paradigm to shed light on the human-in-the-loop experiments and understand the network-level mechanisms of auditory stimulation.
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Affiliation(s)
- Saman Khazaei
- Department of Biomedical Engineering, New York University, New York, NY, United States
| | - Srinidhi Parshi
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States
| | - Samiul Alam
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States
| | - Md. Rafiul Amin
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States
| | - Rose T. Faghih
- Department of Biomedical Engineering, New York University, New York, NY, United States
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States
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McInnes AN, Olsen ST, Sullivan CR, Cooper DC, Wilson S, Sonmez AI, Albott SC, Olson SC, Peterson CB, Rittberg BR, Herman A, Bajzer M, Nahas Z, Widge AS. Trajectory Modeling and Response Prediction in Transcranial Magnetic Stimulation for Depression. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.30.24308258. [PMID: 38853937 PMCID: PMC11160841 DOI: 10.1101/2024.05.30.24308258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) therapy could be improved by better and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modelling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models can predict clinical outcomes. We compared LCMM and NLME approaches to model the antidepressant response to TMS in a naturalistic sample of 238 patients receiving rTMS for treatment resistant depression (TRD), across multiple coils and protocols. We then compared the predictive power of those models. LCMM trajectories were influenced largely by baseline symptom severity, but baseline symptoms provided little predictive power for later antidepressant response. Rather, the optimal LCMM model was a nonlinear two-class model that accounted for baseline symptoms. This model accurately predicted patient response at 4 weeks of treatment (AUC = 0.70, 95% CI = [0.52-0.87]), but not before. NLME offered slightly improved predictive performance at 4 weeks of treatment (AUC = 0.76, 95% CI = [0.58 - 0.94], but likewise, not before. In showing the predictive validity of these approaches to model response trajectories to rTMS, we provided preliminary evidence that trajectory modeling could be used to guide future treatment decisions.
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Affiliation(s)
- Aaron N. McInnes
- Corresponding authors: Aaron N. McInnes PhD and Alik S. Widge MD, PhD, Department of Psychiatry, University of Minnesota, Twin Cities, McGuire Translational Research Facility, 2001 6th St SE, Minneapolis, MN 55455,
| | | | - Christi R.P. Sullivan
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Dawson C. Cooper
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Saydra Wilson
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Ayse Irem Sonmez
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Sophia C. Albott
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Stephen C. Olson
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Carol B. Peterson
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Barry R. Rittberg
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Alexander Herman
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Matej Bajzer
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Ziad Nahas
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Alik S. Widge
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
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8
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Sellers KK, Cohen JL, Khambhati AN, Fan JM, Lee AM, Chang EF, Krystal AD. Closed-loop neurostimulation for the treatment of psychiatric disorders. Neuropsychopharmacology 2024; 49:163-178. [PMID: 37369777 PMCID: PMC10700557 DOI: 10.1038/s41386-023-01631-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023]
Abstract
Despite increasing prevalence and huge personal and societal burden, psychiatric diseases still lack treatments which can control symptoms for a large fraction of patients. Increasing insight into the neurobiology underlying these diseases has demonstrated wide-ranging aberrant activity and functioning in multiple brain circuits and networks. Together with varied presentation and symptoms, this makes one-size-fits-all treatment a challenge. There has been a resurgence of interest in the use of neurostimulation as a treatment for psychiatric diseases. Initial studies using continuous open-loop stimulation, in which clinicians adjusted stimulation parameters during patient visits, showed promise but also mixed results. Given the periodic nature and fluctuations of symptoms often observed in psychiatric illnesses, the use of device-driven closed-loop stimulation may provide more effective therapy. The use of a biomarker, which is correlated with specific symptoms, to deliver stimulation only during symptomatic periods allows for the personalized therapy needed for such heterogeneous disorders. Here, we provide the reader with background motivating the use of closed-loop neurostimulation for the treatment of psychiatric disorders. We review foundational studies of open- and closed-loop neurostimulation for neuropsychiatric indications, focusing on deep brain stimulation, and discuss key considerations when designing and implementing closed-loop neurostimulation.
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Affiliation(s)
- Kristin K Sellers
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Joshua L Cohen
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Ankit N Khambhati
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Joline M Fan
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, CA, USA
| | - A Moses Lee
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Andrew D Krystal
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA.
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9
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Asir B, Boscutti A, Fenoy AJ, Quevedo J. Deep Brain Stimulation (DBS) in Treatment-Resistant Depression (TRD): Hope and Concern. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1456:161-186. [PMID: 39261429 DOI: 10.1007/978-981-97-4402-2_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
In this chapter, we explore the historical evolution, current applications, and future directions of Deep Brain Stimulation (DBS) for Treatment-Resistant Depression (TRD). We begin by highlighting the early efforts of neurologists and neurosurgeons who laid the foundations for today's DBS techniques, moving from controversial lobotomies to the precision of stereotactic surgery. We focus on the advent of DBS, emphasizing its emergence as a significant breakthrough for movement disorders and its extension to psychiatric conditions, including TRD. We provide an overview of the neural networks implicated in depression, detailing the rationale for the choice of common DBS targets. We also cover the technical aspects of DBS, from electrode placement to programming and parameter selection. We then critically review the evidence from clinical trials and open-label studies, acknowledging the mixed outcomes and the challenges posed by placebo effects and trial design. Safety and ethical considerations are also discussed. Finally, we explore innovative directions for DBS research, including the potential of closed-loop systems, dual stimulation strategies, and noninvasive alternatives like ultrasound neuromodulation. In the last section, we outline recommendations for future DBS studies, including the use of alternative designs for placebo control, the collection of neural and behavioral recordings, and the application of machine-learning approaches.
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Affiliation(s)
- Bashar Asir
- Department of Psychiatry and Behavioral Sciences at McGovern Medical School, UTHealth Houston, Houston, TX, USA.
| | - Andrea Boscutti
- Department of Psychiatry and Behavioral Sciences at McGovern Medical School, UTHealth Houston, Houston, TX, USA
| | - Albert J Fenoy
- Department of Neurosurgery and Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Joao Quevedo
- Department of Psychiatry and Behavioral Sciences at McGovern Medical School, UTHealth Houston, Houston, TX, USA
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10
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McInnes AN, Sullivan CRP, MacDonald AW, Widge AS. Psychometric validation and clinical correlates of an experiential foraging task. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.28.573439. [PMID: 38234810 PMCID: PMC10793407 DOI: 10.1101/2023.12.28.573439] [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: 01/19/2024]
Abstract
Measuring the function of decision-making systems is a central goal of computational psychiatry. Individual measures of decisional function could be used to describe neurocognitive profiles that underpin psychopathology and offer insights into deficits that are shared across traditional diagnostic classes. However, there are few demonstrably reliable and mechanistically relevant metrics of decision making that can accurately capture the complex overlapping domains of cognition whilst also quantifying the heterogeneity of function between individuals. The WebSurf task is a reverse-translational human experiential foraging paradigm which indexes naturalistic and clinically relevant decision-making. To determine its potential clinical utility, we examined the psychometric properties and clinical correlates of behavioural parameters extracted from WebSurf in an initial exploratory experiment and a pre-registered validation experiment. Behaviour was stable over repeated administrations of the task, as were individual differences. The ability to measure decision making consistently supports the potential utility of the task in predicting an individual's propensity for response to psychiatric treatment, in evaluating clinical change during treatment, and in defining neurocognitive profiles that relate to psychopathology. Specific aspects of WebSurf behaviour also correlate with anhedonic and externalising symptoms. Importantly, these behavioural parameters may measure dimensions of psychological variance that are not captured by traditional rating scales. WebSurf and related paradigms might therefore be useful platforms for computational approaches to precision psychiatry.
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Affiliation(s)
- Aaron N. McInnes
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Christi R. P. Sullivan
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | | | - Alik S. Widge
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
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11
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Zelmann R, Paulk AC, Tian F, Balanza Villegas GA, Dezha Peralta J, Crocker B, Cosgrove GR, Richardson RM, Williams ZM, Dougherty DD, Purdon PL, Cash SS. Differential cortical network engagement during states of un/consciousness in humans. Neuron 2023; 111:3479-3495.e6. [PMID: 37659409 PMCID: PMC10843836 DOI: 10.1016/j.neuron.2023.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 06/13/2023] [Accepted: 08/08/2023] [Indexed: 09/04/2023]
Abstract
What happens in the human brain when we are unconscious? Despite substantial work, we are still unsure which brain regions are involved and how they are impacted when consciousness is disrupted. Using intracranial recordings and direct electrical stimulation, we mapped global, network, and regional involvement during wake vs. arousable unconsciousness (sleep) vs. non-arousable unconsciousness (propofol-induced general anesthesia). Information integration and complex processing we`re reduced, while variability increased in any type of unconscious state. These changes were more pronounced during anesthesia than sleep and involved different cortical engagement. During sleep, changes were mostly uniformly distributed across the brain, whereas during anesthesia, the prefrontal cortex was the most disrupted, suggesting that the lack of arousability during anesthesia results not from just altered overall physiology but from a disconnection between the prefrontal and other brain areas. These findings provide direct evidence for different neural dynamics during loss of consciousness compared with loss of arousability.
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Affiliation(s)
- Rina Zelmann
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA.
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA
| | - Fangyun Tian
- Department of Anesthesia, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Britni Crocker
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Harvard-MIT Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick L Purdon
- Department of Anesthesia, Massachusetts General Hospital, Boston, MA, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA
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12
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Rezaei MR, Jeoung H, Gharamani A, Saha U, Bhat V, Popovic MR, Yousefi A, Chen R, Lankarany M. Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input discriminative-generative decoder model. J Neural Eng 2023; 20:056016. [PMID: 37473753 DOI: 10.1088/1741-2552/ace932] [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: 01/19/2023] [Accepted: 07/20/2023] [Indexed: 07/22/2023]
Abstract
Objective. The subthalamic nucleus (STN) of the basal ganglia interacts with the medial prefrontal cortex (mPFC) and shapes a control loop, specifically when the brain receives contradictory information from either different sensory systems or conflicting information from sensory inputs and prior knowledge that developed in the brain. Experimental studies demonstrated that significant increases in theta activities (2-8 Hz) in both the STN and mPFC as well as increased phase synchronization between mPFC and STN are prominent features of conflict processing. While these neural features reflect the importance of STN-mPFC circuitry in conflict processing, a low-dimensional representation of the mPFC-STN interaction referred to as a cognitive state, that links neural activities generated by these sub-regions to behavioral signals (e.g. the response time), remains to be identified.Approach. Here, we propose a new model, namely, the heterogeneous input discriminative-generative decoder (HI-DGD) model, to infer a cognitive state underlying decision-making based on neural activities (STN and mPFC) and behavioral signals (individuals' response time) recorded in ten Parkinson's disease (PD) patients while they performed a Stroop task. PD patients may have conflict processing which is quantitatively (may be qualitative in some) different from healthy populations.Main results. Using extensive synthetic and experimental data, we showed that the HI-DGD model can diffuse information from neural and behavioral data simultaneously and estimate cognitive states underlying conflict and non-conflict trials significantly better than traditional methods. Additionally, the HI-DGD model identified which neural features made significant contributions to conflict and non-conflict choices. Interestingly, the estimated features match well with those reported in experimental studies.Significance. Finally, we highlight the capability of the HI-DGD model in estimating a cognitive state from a single trial of observation, which makes it appropriate to be utilized in closed-loop neuromodulation systems.
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Affiliation(s)
- Mohammad R Rezaei
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
- KITE Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Haseul Jeoung
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Ayda Gharamani
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
- Worcester Polytechnic Institute, MA, United States of America
| | - Utpal Saha
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Venkat Bhat
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University Health Network and University of Toronto, Toronto, ON, Canada
| | - Milos R Popovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- KITE Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Ali Yousefi
- Worcester Polytechnic Institute, MA, United States of America
| | - Robert Chen
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Milad Lankarany
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
- KITE Research Institute, University Health Network (UHN), Toronto, ON, Canada
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13
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Merk T, Köhler R, Peterson V, Lyra L, Vanhoecke J, Chikermane M, Binns T, Li N, Walton A, Bush A, Sisterson N, Busch J, Lofredi R, Habets J, Huebl J, Zhu G, Yin Z, Zhao B, Merkl A, Bajbouj M, Krause P, Faust K, Schneider GH, Horn A, Zhang J, Kühn A, Richardson RM, Neumann WJ. Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants. RESEARCH SQUARE 2023:rs.3.rs-3212709. [PMID: 37790428 PMCID: PMC10543023 DOI: 10.21203/rs.3.rs-3212709/v1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Brain computer interfaces (BCI) provide unprecedented spatiotemporal precision that will enable significant expansion in how numerous brain disorders are treated. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for identifying and advancing novel clinical BCI approaches does not exist. Here, we developed a platform that integrates brain signal decoding with connectomics and demonstrate its utility across 123 hours of invasively recorded brain data from 73 neurosurgical patients treated for movement disorders, depression and epilepsy. First, we introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the US, Europe and China. Next, we reveal network targets for emotion decoding in left prefrontal and cingulate circuits in DBS patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our platform provides rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neuromodulation therapies in response to the individual needs of patients.
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14
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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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15
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van den Boom BJG, Elhazaz-Fernandez A, Rasmussen PA, van Beest EH, Parthasarathy A, Denys D, Willuhn I. Unraveling the mechanisms of deep-brain stimulation of the internal capsule in a mouse model. Nat Commun 2023; 14:5385. [PMID: 37666830 PMCID: PMC10477328 DOI: 10.1038/s41467-023-41026-x] [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: 01/04/2023] [Accepted: 08/17/2023] [Indexed: 09/06/2023] Open
Abstract
Deep-brain stimulation (DBS) is an effective treatment for patients suffering from otherwise therapy-resistant psychiatric disorders, including obsessive-compulsive disorder. Modulation of cortico-striatal circuits has been suggested as a mechanism of action. To gain mechanistic insight, we monitored neuronal activity in cortico-striatal regions in a mouse model for compulsive behavior, while systematically varying clinically-relevant parameters of internal-capsule DBS. DBS showed dose-dependent effects on both brain and behavior: An increasing, yet balanced, number of excited and inhibited neurons was recruited, scattered throughout cortico-striatal regions, while excessive grooming decreased. Such neuronal recruitment did not alter basic brain function such as resting-state activity, and only occurred in awake animals, indicating a dependency on network activity. In addition to these widespread effects, we observed specific involvement of the medial orbitofrontal cortex in therapeutic outcomes, which was corroborated by optogenetic stimulation. Together, our findings provide mechanistic insight into how DBS exerts its therapeutic effects on compulsive behaviors.
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Affiliation(s)
- Bastijn J G van den Boom
- Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands.
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
| | - Alfredo Elhazaz-Fernandez
- Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Peter A Rasmussen
- Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Enny H van Beest
- Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Aishwarya Parthasarathy
- Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Damiaan Denys
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Ingo Willuhn
- Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands.
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
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16
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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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17
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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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18
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Wen X, Yue L, Du Z, Li L, Zhu Y, Yu D, Yuan K. Implications of neuroimaging findings in addiction. PSYCHORADIOLOGY 2023; 3:kkad006. [PMID: 38666116 PMCID: PMC10917371 DOI: 10.1093/psyrad/kkad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 04/14/2023] [Accepted: 04/28/2023] [Indexed: 04/28/2024]
Affiliation(s)
- Xinwen Wen
- School of Life Science and Technology, Xidian University, Xi'an 710126, China
| | - Lirong Yue
- School of Life Science and Technology, Xidian University, Xi'an 710126, China
| | - Zhe Du
- School of Life Science and Technology, Xidian University, Xi'an 710126, China
| | - Linling Li
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen 518060, China
| | - Yuanqiang Zhu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
| | - Kai Yuan
- School of Life Science and Technology, Xidian University, Xi'an 710126, China
- Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xidian University, Xi'an 710126, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an 710126, China
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19
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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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20
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Khodagholy D, Ferrero JJ, Park J, Zhao Z, Gelinas JN. Large-scale, closed-loop interrogation of neural circuits underlying cognition. Trends Neurosci 2022; 45:968-983. [PMID: 36404457 PMCID: PMC10437206 DOI: 10.1016/j.tins.2022.10.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/26/2022] [Accepted: 10/03/2022] [Indexed: 11/05/2022]
Abstract
Cognitive functions are increasingly understood to involve coordinated activity patterns between multiple brain regions, and their disruption by neuropsychiatric disorders is similarly complex. Closed-loop neurostimulation can directly modulate neural signals with temporal and spatial precision. How to leverage such an approach to effectively identify and target distributed neural networks implicated in mediating cognition remains unclear. We review current conceptual and technical advances in this area, proposing that devices that enable large-scale acquisition, integrated processing, and multiregion, arbitrary waveform stimulation will be critical for mechanistically driven manipulation of cognitive processes in physiological and pathological brain networks.
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Affiliation(s)
- Dion Khodagholy
- Department of Electrical Engineering, Columbia University, New York, NY 10027, USA.
| | - Jose J Ferrero
- Institute for Genomic Medicine, Columbia University Irving Medical Center, 701 W 168(th) St., New York, NY 10032, USA
| | - Jaehyo Park
- Department of Electrical Engineering, Columbia University, New York, NY 10027, USA
| | - Zifang Zhao
- Department of Electrical Engineering, Columbia University, New York, NY 10027, USA; Institute for Genomic Medicine, Columbia University Irving Medical Center, 701 W 168(th) St., New York, NY 10032, USA
| | - Jennifer N Gelinas
- Institute for Genomic Medicine, Columbia University Irving Medical Center, 701 W 168(th) St., New York, NY 10032, USA; Department of Neurology, Columbia University Medical Center, New York, NY 10032, USA..
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21
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McNamara CG, Rothwell M, Sharott A. Stable, interactive modulation of neuronal oscillations produced through brain-machine equilibrium. Cell Rep 2022; 41:111616. [PMID: 36351413 PMCID: PMC7614081 DOI: 10.1016/j.celrep.2022.111616] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/28/2022] [Accepted: 10/17/2022] [Indexed: 11/09/2022] Open
Abstract
Closed-loop interaction has the potential to regulate ongoing brain activity by continuously binding an external stimulation to specific dynamics of a neural circuit. Achieving interactive modulation requires a stable brain-machine feedback loop. Here, we demonstrate that it is possible to maintain oscillatory brain activity in a desired state by delivering stimulation accurately aligned with the timing of each cycle. We develop a fast algorithm that responds on a cycle-by-cycle basis to stimulate basal ganglia nuclei at predetermined phases of successive cortical beta cycles in parkinsonian rats. Using this approach, an equilibrium emerges between the modified brain signal and feedback-dependent stimulation pattern, leading to sustained amplification or suppression of the oscillation depending on the phase targeted. Beta amplification slows movement speed by biasing the animal's mode of locomotion. Together, these findings show that highly responsive, phase-dependent stimulation can achieve a stable brain-machine interaction that leads to robust modulation of ongoing behavior.
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Affiliation(s)
- Colin G McNamara
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK.
| | - Max Rothwell
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK
| | - Andrew Sharott
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, UK.
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22
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Xie JX, Cui JJ, Cao Y, Gu YW, Fan JW, Ren L, Liu XF, Zhao SW, Shi WH, Yang Q, Jin YC, Li FZ, Song L, Yin H, Cao F, Li B, Cui LB. Commentary: Targeting the MRI-mapped psychopathology of major psychiatric disorders with neurostimulation. Front Psychiatry 2022; 13:990512. [PMID: 36213932 PMCID: PMC9540217 DOI: 10.3389/fpsyt.2022.990512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 08/22/2022] [Indexed: 11/29/2022] Open
Affiliation(s)
- Jia-Xin Xie
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
| | - Jin-Jin Cui
- The Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yang Cao
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
| | - Yue-Wen Gu
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
| | - Jing-Wen Fan
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
| | - Lei Ren
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
| | - Xiao-Fan Liu
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
| | - Shu-Wan Zhao
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
| | - Wang-Hong Shi
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
| | - Qun Yang
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
| | - Yin-Chuan Jin
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
| | - Feng-Zhan Li
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
| | - Lei Song
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
| | - Hong Yin
- Department of Radiology, Xi'an People's Hospital (Xi'an Fourth Hospital), Xi'an, China
| | - Feng Cao
- The Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Baojuan Li
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Long-Biao Cui
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
- The Second Medical Center, Chinese PLA General Hospital, Beijing, China
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23
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Davila CE, Wang DX, Ritzer M, Moran R, Lega BC. A Control-Theoretical System for Modulating Hippocampal Gamma Oscillations using Stimulation of the Posterior Cingulate Cortex. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2242-2253. [PMID: 35849675 PMCID: PMC9469793 DOI: 10.1109/tnsre.2022.3192170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Closed-loop stimulation for targeted modulation of brain signals has emerged as a promising strategy for episodic memory restoration. In parallel, closed-loop neuromodulation strategies have been applied to treat brain conditions including drug-resistant depression, Parkinson’s Disease, and epilepsy. In this study, we seek to apply control theoretical principles to achieve closed loop modulation of hippocampal oscillatory activity. We focus on hippocampal gamma power, a signal with an established association for episodic memory processing, which may be a promising ‘biomarker’ for the modulation of memory performance. To develop a closed-loop stimulation paradigm that effectively modulates hippocampal gamma power, we use a novel data-set in which open-loop stimulation was applied to the posterior cingulate cortex and hippocampal gamma power was recorded during the encoding of episodic memories. The dataset was used to design and evaluate a linear quadratic integral (LQI) servo-controller in order to determine its viability for in-vivo use. In our simulation framework, we demonstrate that applying an LQI servo controller based on an autoregressive with exogenous input (ARX) plant model achieves effective control of hippocampal gamma power in 15 out of 17 experimental subjects. We demonstrate that we are able to modulate gamma power using stimulation thresholds that are physiologically safe and on time scales that are reasonable for application in a clinical system. We outline further experimentation to test our proposed system and compare our findings to emerging closed-loop neuromodulation strategies.
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24
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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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25
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Ross A, Paulk AC, Cash SS, Widge AS, Basu I. Neural mass model-based study of frontal-temporal theta oscillations in human subjects during the performance of a cognitive control task. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2937-2940. [PMID: 36086466 PMCID: PMC9974231 DOI: 10.1109/embc48229.2022.9871719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Cognitive control, the ability to rapidly shift one's attention and behavioral strategy in response to environmental changes, is often compromised across psychiatric disorders. One of the well-validated behavioral paradigms for tapping into the cognitive control circuits is a cognitive interference task, where subjects must suppress a natural response to follow a less intuitive rule. Slower response times on these tasks indicate difficulty exerting control to overcome response conflict. Conflict evokes robust electrophysiological signatures, such as theta (4-8 Hz) oscillations in the prefrontal cortex (PFC). However, the underlying neural mechanisms of conflict-evoked theta oscillations in the PFC are not clear. The objective of this work is to use a neural mass model (NMM) to find feasible cortical networks generating theta oscillations during conflict processing in human subjects. We used intracranial EEG (iEEG) recorded from dorsolateral PFC (dIPFC) and lateral temporal lobe (LTL) of human subjects with intractable epilepsy undergoing invasive monitoring, while they performed a multi-source interference task (MSIT). We used a dynamic causal modeling (DCM) framework to simulate dIPFC-LTL theta using a Jansen-Rit NMM. We found significant evidence for an LTL input into the dlPFC during the initial 500 ms of conflict processing compared to a bidirectional connection between the dlPFC and LTL. We conclude that a neural mass modeling framework can be used to elucidate candidate mechanisms of neural oscillations underlying conflict resolution in human subjects. Clinical Relevance- This can be used to find feasible target mechanisms for designing therapy in patients with compromised cognitive control. This framework can also be expanded to serve as an in-silico test bed for designing and testing neuromodulatory interventions such as electrical stimulation for improving cognitive control in mood/anxiety disorders.
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Affiliation(s)
| | | | - Sydney S Cash
- Massachusetts General Hospital, Boston, Massachusetts
| | | | - Ishita Basu
- University of Cincinnati, Cincinnati, Ohio 45267
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26
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Fang H, Yang Y. Designing and Validating a Robust Adaptive Neuromodulation Algorithm for Closed-Loop Control of Brain States. J Neural Eng 2022; 19. [PMID: 35576912 DOI: 10.1088/1741-2552/ac7005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/16/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Neuromodulation systems that use closed-loop brain stimulation to control brain states can provide new therapies for brain disorders. To date, closed-loop brain stimulation has largely used linear time-invariant controllers. However, nonlinear time-varying brain network dynamics and external disturbances can appear during real-time stimulation, collectively leading to real-time model uncertainty. Real-time model uncertainty can degrade the performance or even cause instability of time-invariant controllers. Three problems need to be resolved to enable accurate and stable control under model uncertainty. First, an adaptive controller is needed to track the model uncertainty. Second, the adaptive controller additionally needs to be robust to noise and disturbances. Third, theoretical analyses of stability and robustness are needed as prerequisites for stable operation of the controller in practical applications. APPROACH We develop a robust adaptive neuromodulation algorithm that solves the above three problems. First, we develop a state-space brain network model that explicitly includes nonlinear terms of real-time model uncertainty and design an adaptive controller to track and cancel the model uncertainty. Second, to improve the robustness of the adaptive controller, we design two linear filters to increase steady-state control accuracy and reduce sensitivity to high-frequency noise and disturbances. Third, we conduct theoretical analyses to prove the stability of the neuromodulation algorithm and establish a trade-off between stability and robustness, which we further use to optimize the algorithm design. Finally, we validate the algorithm using comprehensive Monte Carlo simulations that span a broad range of model nonlinearity, uncertainty, and complexity. MAIN RESULTS The robust adaptive neuromodulation algorithm accurately tracks various types of target brain state trajectories, enables stable and robust control, and significantly outperforms state-of-the-art neuromodulation algorithms. SIGNIFICANCE Our algorithm has implications for future designs of precise, stable, and robust closed-loop brain stimulation systems to treat brain disorders and facilitate brain functions.
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Affiliation(s)
- Hao Fang
- University of Central Florida, Research 1 Room 334, 313/316, University of Central Florida, 4353 Scorpius St., Orlando, Florida, 32816-2368, UNITED STATES
| | - Yuxiao Yang
- Department of Electrical and Computer Engineering, University of Central Florida, 4353 Scorpius St., Orlando, Florida, 32816-2368, UNITED STATES
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27
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Paulk AC, Zelmann R, Crocker B, Widge AS, Dougherty DD, Eskandar EN, Weisholtz DS, Richardson RM, Cosgrove GR, Williams ZM, Cash SS. Local and distant cortical responses to single pulse intracranial stimulation in the human brain are differentially modulated by specific stimulation parameters. Brain Stimul 2022; 15:491-508. [PMID: 35247646 PMCID: PMC8985164 DOI: 10.1016/j.brs.2022.02.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Electrical neuromodulation via direct electrical stimulation (DES) is an increasingly common therapy for a wide variety of neuropsychiatric diseases. Unfortunately, therapeutic efficacy is inconsistent, likely due to our limited understanding of the relationship between the massive stimulation parameter space and brain tissue responses. OBJECTIVE To better understand how different parameters induce varied neural responses, we systematically examined single pulse-induced cortico-cortico evoked potentials (CCEP) as a function of stimulation amplitude, duration, brain region, and whether grey or white matter was stimulated. METHODS We measured voltage peak amplitudes and area under the curve (AUC) of intracranially recorded stimulation responses as a function of distance from the stimulation site, pulse width, current injected, location relative to grey and white matter, and brain region stimulated (N = 52, n = 719 stimulation sites). RESULTS Increasing stimulation pulse width increased responses near the stimulation location. Increasing stimulation amplitude (current) increased both evoked amplitudes and AUC nonlinearly. Locally (<15 mm), stimulation at the boundary between grey and white matter induced larger responses. In contrast, for distant sites (>15 mm), white matter stimulation consistently produced larger responses than stimulation in or near grey matter. The stimulation location-response curves followed different trends for cingulate, lateral frontal, and lateral temporal cortical stimulation. CONCLUSION These results demonstrate that a stronger local response may require stimulation in the grey-white boundary while stimulation in the white matter could be needed for network activation. Thus, stimulation parameters tailored for a specific anatomical-functional outcome may be key to advancing neuromodulatory therapy.
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Affiliation(s)
- Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
| | - Rina Zelmann
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Britni Crocker
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Harvard-MIT Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Alik S Widge
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, 02129, USA
| | - Darin D Dougherty
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, 02129, USA
| | - Emad N Eskandar
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Daniel S Weisholtz
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, 02114, USA
| | - R Mark Richardson
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, 02114, USA
| | - Ziv M Williams
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
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28
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Bogdan ID, Oterdoom DLM, van Laar T, Huitema RB, Odekerken VJ, Boel JA, de Bie RMA, van Dijk JMC. Serendipitous Stimulation of Nucleus Basalis of Meynert-The Effect of Unintentional, Long-Term High-Frequency Stimulation on Cognition in Parkinson's Disease. J Clin Med 2022; 11:jcm11020337. [PMID: 35054031 PMCID: PMC8779041 DOI: 10.3390/jcm11020337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/05/2022] [Accepted: 01/06/2022] [Indexed: 12/04/2022] Open
Abstract
There is a growing interest in deep brain stimulation (DBS) of the nucleus basalis of Meynert (NBM) as a potential therapeutic modality for Parkinson’s disease dementia (PDD). Low-frequency stimulation has yielded encouraging results in individual patients; however, these are not yet sustained in larger studies. With the aim to expand the understanding of NBM-DBS, we share our experience with serendipitous NBM-DBS in patients treated with DBS of the internal Globus pallidus (GPi) for Parkinson’s disease. Since NBM is anatomically located ventral to GPi, several GPi-treated patients appeared to have the distal contact of DBS-electrode(s) positioned in the NBM. We hypothesized that unintentional high-frequency NBM-DBS over a period of one year would result in the opposite effect of low-frequency NBM-stimulation and cause cognitive decline. We studied a cohort of 33 patients with bilateral high-frequency DBS in the GPi for Parkinson’s disease, of which twelve were unintentionally co-stimulated in NBM. The subgroups of unintentional unilateral (N = 7) and bilateral NBM-DBS (N = 5) were compared to the control group of bilateral GPi-DBS (N = 11). Here, we show that unintentional high-frequency NBM-DBS did not cause a significantly faster decline in cognitive function. Further research is warranted for characterizing the therapeutic role of NBM-DBS.
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Affiliation(s)
- I. Daria Bogdan
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, 9713 Groningen, The Netherlands; (I.D.B.); (J.M.C.v.D.)
| | - D. L. Marinus Oterdoom
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, 9713 Groningen, The Netherlands; (I.D.B.); (J.M.C.v.D.)
- Correspondence:
| | - Teus van Laar
- Department of Neurology, University Medical Center Groningen, University of Groningen, 9713 Groningen, The Netherlands; (T.v.L.); (R.B.H.)
| | - Rients B. Huitema
- Department of Neurology, University Medical Center Groningen, University of Groningen, 9713 Groningen, The Netherlands; (T.v.L.); (R.B.H.)
| | - Vincent J. Odekerken
- Department of Neurology, Amsterdam Neuroscience Institute, Amsterdam University Medical Center, 1105 Amsterdam, The Netherlands; (V.J.O.); (J.A.B.); (R.M.A.d.B.)
| | - Judith A. Boel
- Department of Neurology, Amsterdam Neuroscience Institute, Amsterdam University Medical Center, 1105 Amsterdam, The Netherlands; (V.J.O.); (J.A.B.); (R.M.A.d.B.)
| | - Rob M. A. de Bie
- Department of Neurology, Amsterdam Neuroscience Institute, Amsterdam University Medical Center, 1105 Amsterdam, The Netherlands; (V.J.O.); (J.A.B.); (R.M.A.d.B.)
| | - J. Marc C. van Dijk
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, 9713 Groningen, The Netherlands; (I.D.B.); (J.M.C.v.D.)
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Farkhondeh Tale Navi F, Heysieattalab S, Ramanathan DS, Raoufy MR, Nazari MA. Closed-loop Modulation of the Self-regulating Brain: A Review on Approaches, Emerging Paradigms, and Experimental Designs. Neuroscience 2021; 483:104-126. [PMID: 34902494 DOI: 10.1016/j.neuroscience.2021.12.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/30/2021] [Accepted: 12/06/2021] [Indexed: 11/27/2022]
Abstract
Closed-loop approaches, setups, and experimental designs have been applied within the field of neuroscience to enhance the understanding of basic neurophysiology principles (closed-loop neuroscience; CLNS) and to develop improved procedures for modulating brain circuits and networks for clinical purposes (closed-loop neuromodulation; CLNM). The contents of this review are thus arranged into the following sections. First, we describe basic research findings that have been made using CLNS. Next, we provide an overview of the application, rationale, and therapeutic aspects of CLNM for clinical purposes. Finally, we summarize methodological concerns and critics in clinical practice of neurofeedback and novel applications of closed-loop perspective and techniques to improve and optimize its experiments. Moreover, we outline the theoretical explanations and experimental ideas to test animal models of neurofeedback and discuss technical issues and challenges associated with implementing closed-loop systems. We hope this review is helpful for both basic neuroscientists and clinical/ translationally-oriented scientists interested in applying closed-loop methods to improve mental health and well-being.
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Affiliation(s)
- Farhad Farkhondeh Tale Navi
- Department of Cognitive Neuroscience, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran
| | - Soomaayeh Heysieattalab
- Department of Cognitive Neuroscience, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran
| | | | - Mohammad Reza Raoufy
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Ali Nazari
- Department of Cognitive Neuroscience, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran; Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.
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