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Asaad WF, Sheth SA. What's the n? On sample size vs. subject number for brain-behavior neurophysiology and neuromodulation. Neuron 2024; 112:2086-2090. [PMID: 38781973 DOI: 10.1016/j.neuron.2024.04.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/01/2024] [Accepted: 04/26/2024] [Indexed: 05/25/2024]
Abstract
Neurophysiology and neuromodulation strive to understand the neural basis of behavior through a one-to-one correspondence between a particular brain and its behavioral output. Within this framework, studies with few subjects but sufficient sample sizes can be both rigorous and impactful.
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Affiliation(s)
- Wael F Asaad
- Department of Neuroscience, Brown University, Providence, RI, USA; Department of Neurosurgery, Brown University Alpert Medical School, Providence, RI, USA; Carney Institute for Brain Science, Brown University, Providence, RI, USA; Norman Prince Neurosciences Institute, Rhode Island Hospital, Providence, RI, USA.
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA; Jan and Dan Duncan Neurological Research Institute, Baylor College of Medicine, Houston, TX, USA
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2
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Mishra A, Begley SL, Shah HA, Santhumayor BA, Ramdhani RA, Fenoy AJ, Schulder M. Why are clinical trials of deep brain stimulation terminated? An analysis of clinicaltrials.gov. World Neurosurg X 2024; 23:100378. [PMID: 38595675 PMCID: PMC11002890 DOI: 10.1016/j.wnsx.2024.100378] [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: 12/31/2023] [Revised: 03/30/2024] [Accepted: 04/02/2024] [Indexed: 04/11/2024] Open
Abstract
Background Although deep brain stimulation (DBS) has established uses for patients with movement disorders and epilepsy, it is under consideration for a wide range of neurologic and neuropsychiatric conditions. Objective To review successful and unsuccessful DBS clinical trials and identify factors associated with early trial termination. Methods The ClinicalTrials.gov database was screened for all studies related to DBS. Information regarding condition of interest, study aim, trial design, trial success, and, if applicable, reason for failure was collected. Trials were compared and logistic regression was utilized to identify independent factors associated with trial termination. Results Of 325 identified trials, 79.7% were successful and 20.3% unsuccessful. Patient recruitment, sponsor decision, and device issues were the most cited reasons for termination. 242 trials (74.5%) were interventional with 78.1% successful. There was a statistically significant difference between successful and unsuccessful trials in number of funding sources (p = 0.0375). NIH funding was associated with successful trials while utilization of other funding sources (academic institutions and community organizations) was associated with unsuccessful trials. 83 trials (25.5%) were observational with 84.0% successful; there were no statistically significant differences between successful and unsuccessful observational trials. Conclusion One in five clinical trials for DBS were found to be unsuccessful, most commonly due to patient recruitment difficulties. The source of funding was the only factor associated with trial success. As DBS research continues to grow, understanding the current state of clinical trials will help design successful future studies, thereby minimizing futile expenditures of time, cost, and patient engagement.
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Affiliation(s)
- Akash Mishra
- Department of Neurological Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, USA
| | - Sabrina L. Begley
- Department of Neurological Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, USA
| | - Harshal A. Shah
- Department of Neurological Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, USA
| | - Brandon A. Santhumayor
- Department of Neurological Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, USA
| | - Ritesh A. Ramdhani
- Department of Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, USA
| | - Albert J. Fenoy
- Department of Neurological Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, USA
| | - Michael Schulder
- Department of Neurological Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, USA
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3
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Xiao J, Adkinson JA, Allawala AB, Banks G, Bartoli E, Fan X, Mocchi M, Pascuzzi B, Pulapaka S, Franch MC, Mathew SJ, Mathura RK, Myers J, Pirtle V, Provenza NR, Shofty B, Watrous AJ, Pitkow X, Goodman WK, Pouratian N, Sheth S, Bijanki KR, Hayden BY. Insula uses overlapping codes for emotion in self and others. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.04.596966. [PMID: 38895233 PMCID: PMC11185604 DOI: 10.1101/2024.06.04.596966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
In daily life, we must recognize others' emotions so we can respond appropriately. This ability may rely, at least in part, on neural responses similar to those associated with our own emotions. We hypothesized that the insula, a cortical region near the junction of the temporal, parietal, and frontal lobes, may play a key role in this process. We recorded local field potential (LFP) activity in human neurosurgical patients performing two tasks, one focused on identifying their own emotional response and one on identifying facial emotional responses in others. We found matching patterns of gamma- and high-gamma band activity for the two tasks in the insula. Three other regions (MTL, ACC, and OFC) clearly encoded both self- and other-emotions, but used orthogonal activity patterns to do so. These results support the hypothesis that the insula plays a particularly important role in mediating between experienced vs. observed emotions.
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Affiliation(s)
- Jiayang Xiao
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Joshua A. Adkinson
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | | | - Garrett Banks
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Xiaoxu Fan
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Madaline Mocchi
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Bailey Pascuzzi
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Suhruthaa Pulapaka
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Melissa C. Franch
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Sanjay J. Mathew
- Department of Psychiatry, Baylor College of Medicine, Houston, TX, 77030
| | - Raissa K. Mathura
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - John Myers
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Victoria Pirtle
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Nicole R Provenza
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Ben Shofty
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Andrew J. Watrous
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Xaq Pitkow
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Wayne K. Goodman
- Department of Psychiatry, Baylor College of Medicine, Houston, TX, 77030
| | - Nader Pouratian
- Department of Neurosurgery, University of Texas Southwestern, Dallas, TX, 75390
| | - Sameer Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Kelly R. Bijanki
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
| | - Benjamin Y. Hayden
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030
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4
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Remore LG, Tolossa M, Wei W, Karnib M, Tsolaki E, Rifi Z, Bari AA. Deep Brain Stimulation of the Medial Forebrain Bundle for Treatment-Resistant Depression: A Systematic Review Focused on the Long-Term Antidepressive Effect. Neuromodulation 2024; 27:690-700. [PMID: 37115122 DOI: 10.1016/j.neurom.2023.03.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 03/11/2023] [Accepted: 03/20/2023] [Indexed: 04/29/2023]
Abstract
OBJECTIVE Major depression affects millions of people worldwide and has important social and economic consequences. Since up to 30% of patients do not respond to several lines of antidepressive drugs, deep brain stimulation (DBS) has been evaluated for the management of treatment-resistant depression (TRD). The superolateral branch of the medial forebrain bundle (slMFB) appears as a "hypothesis-driven target" because of its role in the reward-seeking system, which is dysfunctional in depression. Although initial results of slMFB-DBS from open-label studies were promising and characterized by a rapid clinical response, long-term outcomes of neurostimulation for TRD deserve particular attention. Therefore, we performed a systematic review focused on the long-term outcome of slMFB-DBS. MATERIALS AND METHODS A literature search using Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria was conducted to identify all studies reporting changes in depression scores after one-year follow-up and beyond. Patient, disease, surgical, and outcome data were extracted for statistical analysis. The Montgomery-Åsberg Depression Rating Scale (ΔMADRS) was used as the clinical outcome, defined as percentage reduction from baseline to follow-up evaluation. Responders' and remitters' rates were also calculated. RESULTS From 56 studies screened for review, six studies comprising 34 patients met the inclusion criteria and were analyzed. After one year of active stimulation, ΔMADRS was 60.7% ± 4%; responders' and remitters' rates were 83.8% and 61.5%, respectively. At the last follow-up, four to five years after the implantation, ΔMADRS reached 74.7% ± 4.6%. The most common side effects were stimulation related and reversible with parameter adjustments. CONCLUSIONS slMFB-DBS appears to have a strong antidepressive effect that increases over the years. Nevertheless, to date, the overall number of patients receiving implantations is limited, and the slMFB-DBS surgical technique seems to have an important impact on the clinical outcome. Further multicentric studies in a larger population are needed to confirm slMFB-DBS clinical outcomes.
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Affiliation(s)
- Luigi Gianmaria Remore
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA; University of Milan "La Statale," Milan, Italy.
| | - Meskerem Tolossa
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA
| | - Wexin Wei
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA
| | | | - Evangelia Tsolaki
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA
| | - Ziad Rifi
- University of California Los Angeles, Los Angeles, CA, USA
| | - Ausaf Ahmad Bari
- Department of Neurosurgery, University of California Los Angeles, Los Angeles, CA, USA; David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
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5
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De Ridder D, Siddiqi MA, Dauwels J, Serdijn WA, Strydis C. NeuroDots: From Single-Target to Brain-Network Modulation: Why and What Is Needed? Neuromodulation 2024; 27:711-729. [PMID: 38639704 DOI: 10.1016/j.neurom.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/05/2023] [Accepted: 01/10/2024] [Indexed: 04/20/2024]
Abstract
OBJECTIVES Current techniques in brain stimulation are still largely based on a phrenologic approach that a single brain target can treat a brain disorder. Nevertheless, meta-analyses of brain implants indicate an overall success rate of 50% improvement in 50% of patients, irrespective of the brain-related disorder. Thus, there is still a large margin for improvement. The goal of this manuscript is to 1) develop a general theoretical framework of brain functioning that is amenable to surgical neuromodulation, and 2) describe the engineering requirements of the next generation of implantable brain stimulators that follow from this theoretic model. MATERIALS AND METHODS A neuroscience and engineering literature review was performed to develop a universal theoretical model of brain functioning and dysfunctioning amenable to surgical neuromodulation. RESULTS Even though a single target can modulate an entire network, research in network science reveals that many brain disorders are the consequence of maladaptive interactions among multiple networks rather than a single network. Consequently, targeting the main connector hubs of those multiple interacting networks involved in a brain disorder is theoretically more beneficial. We, thus, envision next-generation network implants that will rely on distributed, multisite neuromodulation targeting correlated and anticorrelated interacting brain networks, juxtaposing alternative implant configurations, and finally providing solid recommendations for the realization of such implants. In doing so, this study pinpoints the potential shortcomings of other similar efforts in the field, which somehow fall short of the requirements. CONCLUSION The concept of network stimulation holds great promise as a universal approach for treating neurologic and psychiatric disorders.
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Affiliation(s)
- Dirk De Ridder
- Section of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
| | - Muhammad Ali Siddiqi
- Department of Electrical Engineering, Lahore University of Management Sciences, Lahore, Pakistan; Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Quantum and Computer Engineering Department, Delft University of Technology, Delft, The Netherlands
| | - Justin Dauwels
- Microelectronics Department, Delft University of Technology, Delft, The Netherlands
| | - Wouter A Serdijn
- Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Section Bioelectronics, Delft University of Technology, Delft, The Netherlands
| | - Christos Strydis
- Neuroscience Department, Erasmus Medical Center, Rotterdam, The Netherlands; Quantum and Computer Engineering Department, Delft University of Technology, Delft, The Netherlands
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Liu HL, Sun J, Meng SF, Sun N. Physiotherapy for patients with depression: Recent research progress. World J Psychiatry 2024; 14:635-643. [PMID: 38808078 PMCID: PMC11129148 DOI: 10.5498/wjp.v14.i5.635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 03/15/2024] [Accepted: 04/18/2024] [Indexed: 05/16/2024] Open
Abstract
Depression, a common mental illness, seriously affects the health of individuals and has deleterious effects on society. The prevention and treatment of depression has drawn the attention of many researchers and has become an important social issue. The treatment strategies for depression include drugs, psychotherapy, and physiotherapy. Drug therapy is ineffective in some patients and psychotherapy has treatment limitations. As a reliable adjuvant therapy, physiotherapy compensates for the shortcomings of drug and psychotherapy and effectively reduces the disease recurrence rate. Physiotherapy is more scientific and rigorous, its methods are diverse, and to a certain extent, provides more choices for the treatment of depression. Physiotherapy can relieve symptoms in many ways, such as by improving the levels of neurobiochemical molecules, inhibiting the inflammatory response, regulating the neuroendocrine system, and increasing neuroplasticity. Physiotherapy has biological effects similar to those of antidepressants and may produce a superimposed impact when combined with other treatments. This article summarizes the findings on the use of physiotherapy to treat patients with depression over the past five years. It also discusses several methods of physiotherapy for treating depression from the aspects of clinical effect, mechanism of action, and disadvantages, thereby serving as a reference for the in-depth development of physiotherapy research.
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Affiliation(s)
- Hui-Ling Liu
- Department of Mental Health, First Clinical Medical College of Shanxi Medical University, Taiyuan 030000, Shanxi Province, China
- Department of Rehabilitation, First Hospital of Shanxi Medical University, Taiyuan 030000, Shanxi Province, China
| | - Jing Sun
- Department of Rehabilitation, First Hospital of Shanxi Medical University, Taiyuan 030000, Shanxi Province, China
| | - Shi-Feng Meng
- Department of Rehabilitation, First Hospital of Shanxi Medical University, Taiyuan 030000, Shanxi Province, China
| | - Ning Sun
- Department of Mental Health, First Hospital of Shanxi Medical University, Taiyuan 030000, Shanxi Province, China
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7
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Di Passa AM, Prokop-Millar S, Yaya H, Dabir M, McIntyre-Wood C, Fein A, MacKillop E, MacKillop J, Duarte D. Clinical efficacy of deep transcranial magnetic stimulation (dTMS) in psychiatric and cognitive disorders: A systematic review. J Psychiatr Res 2024; 175:287-315. [PMID: 38759496 DOI: 10.1016/j.jpsychires.2024.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/23/2024] [Accepted: 05/02/2024] [Indexed: 05/19/2024]
Abstract
Deep transcranial magnetic stimulation (dTMS) has gained attention as an enhanced form of traditional TMS, targeting broader and deeper regions of the brain. However, a fulsome synthesis of dTMS efficacy across psychiatric and cognitive disorders using sham-controlled trials is lacking. We systematically reviewed 28 clinical trials comparing active dTMS to a sham/controlled condition to characterize dTMS efficacy across diverse psychiatric and cognitive disorders. A comprehensive search of APA PsycINFO, Cochrane, Embase, Medline, and PubMed databases was conducted. Predominant evidence supports dTMS efficacy in patients with obsessive-compulsive disorder (OCD; n = 2), substance use disorders (SUDs; n = 8), and in those experiencing depressive episodes with major depressive disorder (MDD) or bipolar disorder (BD; n = 6). However, the clinical efficacy of dTMS in psychiatric disorders characterized by hyperactivity or hyperarousal (i.e., attention-deficit/hyperactivity disorder, posttraumatic stress disorder, and schizophrenia) was heterogeneous. Common side effects included headaches and pain/discomfort, with rare but serious adverse events such as seizures and suicidal ideation/attempts. Risk of bias ratings indicated a collectively low risk according to the Grading of Recommendations, Assessment, Development, and Evaluations checklist (Meader et al., 2014). Literature suggests promise for dTMS as a beneficial alternative or add-on treatment for patients who do not respond well to traditional treatment, particularly for depressive episodes, OCD, and SUDs. Mixed evidence and limited clinical trials for other psychiatric and cognitive disorders suggest more extensive research is warranted. Future research should examine the durability of dTMS interventions and identify moderators of clinical efficacy.
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Affiliation(s)
- Anne-Marie Di Passa
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Shelby Prokop-Millar
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Horodjei Yaya
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Melissa Dabir
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Carly McIntyre-Wood
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada; Michael G DeGroote Centre for Medicinal Cannabis Research, McMaster University, Hamilton, ON, Canada
| | - Allan Fein
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada; Michael G DeGroote Centre for Medicinal Cannabis Research, McMaster University, Hamilton, ON, Canada
| | - Emily MacKillop
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - James MacKillop
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada; Michael G DeGroote Centre for Medicinal Cannabis Research, McMaster University, Hamilton, ON, Canada
| | - Dante Duarte
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada; Seniors Mental Health Program, Department of Psychiatry and Neurosciences, McMaster University, Hamilton, ON, Canada.
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8
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Schmid W, Danstrom IA, Crespo Echevarria M, Adkinson J, Mattar L, Banks GP, Sheth SA, Watrous AJ, Heilbronner SR, Bijanki KR, Alabastri A, Bartoli E. A biophysically constrained brain connectivity model based on stimulation-evoked potentials. J Neurosci Methods 2024; 405:110106. [PMID: 38453060 PMCID: PMC11233030 DOI: 10.1016/j.jneumeth.2024.110106] [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: 11/03/2023] [Revised: 01/24/2024] [Accepted: 03/04/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. NEW METHOD Using intracranial electrophysiology data recorded from a single patient undergoing stereo-electroencephalography (sEEG) evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D electrical conductivity to infer neural pathways from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. RESULTS The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlated with additional PEP features and displayed stable, weak correlations with tractography measures. COMPARISON WITH EXISTING METHOD Existing methods for estimating neural signal pathways are imaging-based and thus rely on anatomical inferences. CONCLUSIONS These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.
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Affiliation(s)
- William Schmid
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA
| | - Isabel A Danstrom
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Maria Crespo Echevarria
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Joshua Adkinson
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Layth Mattar
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Garrett P Banks
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Andrew J Watrous
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Sarah R Heilbronner
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Kelly R Bijanki
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Alessandro Alabastri
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA.
| | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA.
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9
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Sadeghzadeh S, Swaminathan A, Bhanot P, Steeman S, Xu A, Shah V, Purger DA, Buch VP. Emerging Outlook on Personalized Neuromodulation for Depression: Insights From Tractography-Based Targeting. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00109-5. [PMID: 38679323 DOI: 10.1016/j.bpsc.2024.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/07/2024] [Accepted: 04/11/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND Deep brain stimulation has shown promise in treating individual patients with treatment-resistant depression, but larger-scale trials have been less successful. Here, we created what is, to our knowledge, the largest meta-analysis with individual patient data to date to explore whether the use of tractography enhances the efficacy of deep brain stimulation for treatment-resistant depression. METHODS We systematically reviewed 1823 articles, selecting 32 that contributed data from 366 patients. We stratified the individual patient data based on stimulation target and use of tractography. Using 2-way type III analysis of variance, Welch's 2-sample t tests, and mixed-effects linear regression models, we evaluated changes in depression severity 1 year (9-15 months) postoperatively and at last follow-up (4 weeks to 8 years) as assessed by depression scales. RESULTS Tractography was used for medial forebrain bundle (MFB) (n = 17 tractography/32 total), subcallosal cingulate (SCC) (n = 39 tractography/241 total), and ventral capsule/ventral striatum (n = 3 tractography/41 total) targets; it was not used for bed nucleus of stria terminalis (n = 11), lateral habenula (n = 10), and inferior thalamic peduncle (n = 1). Across all patients, tractography significantly improved mean depression scores at 1 year (p < .001) and last follow-up (p = .009). Within the target cohorts, tractography improved depression scores at 1 year for both MFB and SCC, though significance was met only at the α = 0.1 level (SCC: β = 15.8%, p = .09; MFB: β = 52.4%, p = .10). Within the tractography cohort, patients with MFB tractography showed greater improvement than patients with SCC tractography (72.42 ± 7.17% vs. 54.78 ± 4.08%) at 1 year (p = .044). CONCLUSIONS Our findings underscore the promise of tractography in deep brain stimulation for treatment-resistant depression as a method for personalization of therapy, supporting its inclusion in future trials.
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Affiliation(s)
- Sina Sadeghzadeh
- Department of Neurosurgery, Stanford University, Stanford, California.
| | | | - Priya Bhanot
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Samantha Steeman
- Department of Neurosurgery, Stanford University, Stanford, California
| | - Audrey Xu
- Department of Neurosurgery, Stanford University, Stanford, California
| | - Vaibhavi Shah
- Department of Neurosurgery, Stanford University, Stanford, California
| | - David A Purger
- Department of Neurosurgery, Stanford University, Stanford, California
| | - Vivek P Buch
- Department of Neurosurgery, Stanford University, Stanford, California.
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10
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Chen D, Zhao Z, Zhang S, Chen S, Wu X, Shi J, Liu N, Pan C, Tang Y, Meng C, Zhao X, Tao B, Liu W, Chen D, Ding H, Zhang P, Tang Z. Evolving Therapeutic Landscape of Intracerebral Hemorrhage: Emerging Cutting-Edge Advancements in Surgical Robots, Regenerative Medicine, and Neurorehabilitation Techniques. Transl Stroke Res 2024:10.1007/s12975-024-01244-x. [PMID: 38558011 DOI: 10.1007/s12975-024-01244-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/06/2024] [Accepted: 03/19/2024] [Indexed: 04/04/2024]
Abstract
Intracerebral hemorrhage (ICH) is the most serious form of stroke and has limited available therapeutic options. As knowledge on ICH rapidly develops, cutting-edge techniques in the fields of surgical robots, regenerative medicine, and neurorehabilitation may revolutionize ICH treatment. However, these new advances still must be translated into clinical practice. In this review, we examined several emerging therapeutic strategies and their major challenges in managing ICH, with a particular focus on innovative therapies involving robot-assisted minimally invasive surgery, stem cell transplantation, in situ neuronal reprogramming, and brain-computer interfaces. Despite the limited expansion of the drug armamentarium for ICH over the past few decades, the judicious selection of more efficacious therapeutic modalities and the exploration of multimodal combination therapies represent opportunities to improve patient prognoses after ICH.
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Affiliation(s)
- Danyang Chen
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhixian Zhao
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shenglun Zhang
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shiling Chen
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xuan Wu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jian Shi
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Na Liu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Chao Pan
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yingxin Tang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Cai Meng
- School of Astronautics, Beihang University, Beijing, China
| | - Xingwei Zhao
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Bo Tao
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenjie Liu
- Beijing WanTeFu Medical Instrument Co., Ltd., Beijing, China
| | - Diansheng Chen
- Institute of Robotics, School of Mechanical Engineering and Automation, Beihang University, Beijing, China
| | - Han Ding
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ping Zhang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Zhouping Tang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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11
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Johnson KA, Okun MS, Scangos KW, Mayberg HS, de Hemptinne C. Deep brain stimulation for refractory major depressive disorder: a comprehensive review. Mol Psychiatry 2024; 29:1075-1087. [PMID: 38287101 DOI: 10.1038/s41380-023-02394-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 12/18/2023] [Accepted: 12/21/2023] [Indexed: 01/31/2024]
Abstract
Deep brain stimulation (DBS) has emerged as a promising treatment for select patients with refractory major depressive disorder (MDD). The clinical effectiveness of DBS for MDD has been demonstrated in meta-analyses, open-label studies, and a few controlled studies. However, randomized controlled trials have yielded mixed outcomes, highlighting challenges that must be addressed prior to widespread adoption of DBS for MDD. These challenges include tracking MDD symptoms objectively to evaluate the clinical effectiveness of DBS with sensitivity and specificity, identifying the patient population that is most likely to benefit from DBS, selecting the optimal patient-specific surgical target and stimulation parameters, and understanding the mechanisms underpinning the therapeutic benefits of DBS in the context of MDD pathophysiology. In this review, we provide an overview of the latest clinical evidence of MDD DBS effectiveness and the recent technological advancements that could transform our understanding of MDD pathophysiology, improve the clinical outcomes for MDD DBS, and establish a path forward to develop more effective neuromodulation therapies to alleviate depressive symptoms.
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Affiliation(s)
- Kara A Johnson
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
- Department of Neurology, University of Florida, Gainesville, FL, USA
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
- Department of Neurology, University of Florida, Gainesville, FL, USA
| | - Katherine W Scangos
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Helen S Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Coralie de Hemptinne
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA.
- Department of Neurology, University of Florida, Gainesville, FL, USA.
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12
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Unadkat P, Quevedo J, Soares J, Fenoy A. Opportunities and challenges for the use of deep brain stimulation in the treatment of refractory major depression. DISCOVER MENTAL HEALTH 2024; 4:9. [PMID: 38483709 PMCID: PMC10940557 DOI: 10.1007/s44192-024-00062-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 03/08/2024] [Indexed: 03/17/2024]
Abstract
Major Depressive Disorder continues to remain one of the most prevalent psychiatric diseases globally. Despite multiple trials of conventional therapies, a subset of patients fail to have adequate benefit to treatment. Deep brain stimulation (DBS) is a promising treatment in this difficult to treat population and has shown strong antidepressant effects across multiple cohorts. Nearly two decades of work have provided insights into the potential for chronic focal stimulation in precise brain targets to modulate pathological brain circuits that are implicated in the pathogenesis of depression. In this paper we review the rationale that prompted the selection of various brain targets for DBS, their subsequent clinical outcomes and common adverse events reported. We additionally discuss some of the pitfalls and challenges that have prevented more widespread adoption of this technology as well as future directions that have shown promise in improving therapeutic efficacy of DBS in the treatment of depression.
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Affiliation(s)
- Prashin Unadkat
- Elmezzi Graduate School of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Department of Neurosurgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, Hempstead, NY, USA
| | - Joao Quevedo
- Center of Excellence On Mood Disorders, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, (UT Health), Houston, TX, USA
| | - Jair Soares
- Center of Excellence On Mood Disorders, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, (UT Health), Houston, TX, USA
| | - Albert Fenoy
- Elmezzi Graduate School of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA.
- Department of Neurosurgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, Hempstead, NY, USA.
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA.
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, Hempstead, NY, USA.
- Department of Neurosurgery, Donald and Barbara Zucker School of Medicine, Feinstein Institutes for Medical Research, Northwell Health, 805 Northern Boulevard, Suite 100, Great Neck, NY, 11021, USA.
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13
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Iravani B, Kaboodvand N, Stieger JR, Liang EY, Lusk Z, Fransson P, Deutsch GK, Gotlib IH, Parvizi J. Intracranial Recordings of the Human Orbitofrontal Cortical Activity during Self-Referential Episodic and Valenced Self-Judgments. J Neurosci 2024; 44:e1634232024. [PMID: 38316564 PMCID: PMC10941238 DOI: 10.1523/jneurosci.1634-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 12/23/2023] [Accepted: 01/17/2024] [Indexed: 02/07/2024] Open
Abstract
We recorded directly from the orbital (oPFC) and ventromedial (vmPFC) subregions of the orbitofrontal cortex (OFC) in 22 (9 female, 13 male) epilepsy patients undergoing intracranial electroencephalography (iEEG) monitoring during an experimental task in which the participants judged the accuracy of self-referential autobiographical statements as well as valenced self-judgments (SJs). We found significantly increased high-frequency activity (HFA) in ∼13% of oPFC sites (10/18 subjects) and 16% of vmPFC sites (4/12 subjects) during both of these self-referential thought processes, with the HFA power being modulated by the content of self-referential stimuli. The location of these activated sites corresponded with the location of fMRI-identified limbic network. Furthermore, the onset of HFA in the vmPFC was significantly earlier than that in the oPFC in all patients with simultaneous recordings in both regions. In 11 patients with available depression scores from comprehensive neuropsychological assessments, we documented diminished HFA in the OFC during positive SJ trials among individuals with higher depression scores; responses during negative SJ trials were not related to the patients' depression scores. Our findings provide new temporal and anatomical information about the mode of engagement in two important subregions of the OFC during autobiographical memory and SJ conditions. Our findings from the OFC support the hypothesis that diminished brain activity during positive self-evaluations, rather than heightened activity during negative self-evaluations, plays a key role in the pathophysiology of depression.
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Affiliation(s)
- Behzad Iravani
- Laboratory of Behavioral and Cognitive Neuroscience, Stanford University, Palo Alto, California 94305
- Departments of Neurology and Neurological Sciences, Stanford University, Palo Alto, California 94305
- Department of Clinical Neuroscience, Karolinska Institute, 17177 Stockholm, Sweden
| | - Neda Kaboodvand
- Laboratory of Behavioral and Cognitive Neuroscience, Stanford University, Palo Alto, California 94305
- Department of Clinical Neuroscience, Karolinska Institute, 17177 Stockholm, Sweden
| | - James R Stieger
- Laboratory of Behavioral and Cognitive Neuroscience, Stanford University, Palo Alto, California 94305
- Departments of Neurology and Neurological Sciences, Stanford University, Palo Alto, California 94305
| | - Eugene Y Liang
- Laboratory of Behavioral and Cognitive Neuroscience, Stanford University, Palo Alto, California 94305
- Departments of Neurology and Neurological Sciences, Stanford University, Palo Alto, California 94305
| | - Zoe Lusk
- Laboratory of Behavioral and Cognitive Neuroscience, Stanford University, Palo Alto, California 94305
- Departments of Neurology and Neurological Sciences, Stanford University, Palo Alto, California 94305
| | - Peter Fransson
- Department of Clinical Neuroscience, Karolinska Institute, 17177 Stockholm, Sweden
| | - Gayle K Deutsch
- Departments of Neurology and Neurological Sciences, Stanford University, Palo Alto, California 94305
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Palo Alto, California 94305
| | - Josef Parvizi
- Laboratory of Behavioral and Cognitive Neuroscience, Stanford University, Palo Alto, California 94305
- Departments of Neurology and Neurological Sciences, Stanford University, Palo Alto, California 94305
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14
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Johnson KA, Dosenbach NUF, Gordon EM, Welle CG, Wilkins KB, Bronte-Stewart HM, Voon V, Morishita T, Sakai Y, Merner AR, Lázaro-Muñoz G, Williamson T, Horn A, Gilron R, O'Keeffe J, Gittis AH, Neumann WJ, Little S, Provenza NR, Sheth SA, Fasano A, Holt-Becker AB, Raike RS, Moore L, Pathak YJ, Greene D, Marceglia S, Krinke L, Tan H, Bergman H, Pötter-Nerger M, Sun B, Cabrera LY, McIntyre CC, Harel N, Mayberg HS, Krystal AD, Pouratian N, Starr PA, Foote KD, Okun MS, Wong JK. Proceedings of the 11th Annual Deep Brain Stimulation Think Tank: pushing the forefront of neuromodulation with functional network mapping, biomarkers for adaptive DBS, bioethical dilemmas, AI-guided neuromodulation, and translational advancements. Front Hum Neurosci 2024; 18:1320806. [PMID: 38450221 PMCID: PMC10915873 DOI: 10.3389/fnhum.2024.1320806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 02/05/2024] [Indexed: 03/08/2024] Open
Abstract
The Deep Brain Stimulation (DBS) Think Tank XI was held on August 9-11, 2023 in Gainesville, Florida with the theme of "Pushing the Forefront of Neuromodulation". The keynote speaker was Dr. Nico Dosenbach from Washington University in St. Louis, Missouri. He presented his research recently published in Nature inn a collaboration with Dr. Evan Gordon to identify and characterize the somato-cognitive action network (SCAN), which has redefined the motor homunculus and has led to new hypotheses about the integrative networks underpinning therapeutic DBS. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers, and researchers (from industry and academia) can freely discuss current and emerging DBS technologies, as well as logistical and ethical issues facing the field. The group estimated that globally more than 263,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. This year's meeting was focused on advances in the following areas: cutting-edge translational neuromodulation, cutting-edge physiology, advances in neuromodulation from Europe and Asia, neuroethical dilemmas, artificial intelligence and computational modeling, time scales in DBS for mood disorders, and advances in future neuromodulation devices.
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Affiliation(s)
- Kara A. Johnson
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
- Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Nico U. F. Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Evan M. Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Cristin G. Welle
- Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, CO, United States
- Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO, United States
| | - Kevin B. Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Helen M. Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Valerie Voon
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Takashi Morishita
- Department of Neurosurgery, Fukuoka University Faculty of Medicine, Fukuoka, Japan
| | - Yuki Sakai
- ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Amanda R. Merner
- Center for Bioethics, Harvard Medical School, Boston, MA, United States
| | - Gabriel Lázaro-Muñoz
- Center for Bioethics, Harvard Medical School, Boston, MA, United States
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
| | - Theresa Williamson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, United States
| | - Andreas Horn
- Department of Neurology, Center for Brain Circuit Therapeutics, Harvard Medical School, Brigham & Women's Hospital, Boston, MA, United States
- MGH Neurosurgery and Center for Neurotechnology and Neurorecovery (CNTR) at MGH Neurology Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Berlin, Germany
| | | | | | - Aryn H. Gittis
- Biological Sciences and Center for Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Berlin, Germany
| | - Simon Little
- Department of Neurological Surgery, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Nicole R. Provenza
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Alfonso Fasano
- Edmond J. Safra Program in Parkinson's Disease, Division of Neurology, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network (UHN), University of Toronto, Toronto, ON, Canada
- Krembil Brain Institute, Toronto, ON, Canada
| | - Abbey B. Holt-Becker
- Restorative Therapies Group Implantables, Research, and Core Technology, Medtronic Inc., Minneapolis, MN, United States
| | - Robert S. Raike
- Restorative Therapies Group Implantables, Research, and Core Technology, Medtronic Inc., Minneapolis, MN, United States
| | - Lisa Moore
- Boston Scientific Neuromodulation Corporation, Valencia, CA, United States
| | | | - David Greene
- NeuroPace, Inc., Mountain View, CA, United States
| | - Sara Marceglia
- Department of Engineering and Architecture, University of Trieste, Trieste, Italy
| | - Lothar Krinke
- Newronika SPA, Milan, Italy
- Department of Neuroscience, West Virginia University, Morgantown, WV, United States
| | - Huiling Tan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Hagai Bergman
- Edmond and Lily Safar Center (ELSC) for Brain Research and Department of Medical Neurobiology (Physiology), Institute of Medical Research Israel-Canada, Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Neurosurgery, Hadassah Medical Center, Jerusalem, Israel
| | - Monika Pötter-Nerger
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Bomin Sun
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Laura Y. Cabrera
- Neuroethics, Department of Engineering Science and Mechanics, Philosophy, and Bioethics, and the Rock Ethics Institute, Pennsylvania State University, State College, PA, United States
| | - Cameron C. McIntyre
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
- Department of Neurosurgery, Duke University, Durham, NC, United States
| | - Noam Harel
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| | - Helen S. Mayberg
- Department of Neurology, Neurosurgery, Psychiatry, and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Andrew D. Krystal
- Departments of Psychiatry and Behavioral Science and Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Nader Pouratian
- Department of Neurological Surgery, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Philip A. Starr
- Department of Neurological Surgery, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Kelly D. Foote
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
- Department of Neurosurgery, University of Florida, Gainesville, FL, United States
| | - Michael S. Okun
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
- Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Joshua K. Wong
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
- Department of Neurology, University of Florida, Gainesville, FL, United States
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15
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Zhao H, Zhang S, Wang Y, Zhang C, Gong Z, Zhang M, Dai W, Ran Y, Shi W, Dang Y, Liu A, Zhang Z, Yeh CH, Dong Z, Yu S. A pilot study on a patient with refractory headache: Personalized deep brain stimulation through stereoelectroencephalography. iScience 2024; 27:108847. [PMID: 38313047 PMCID: PMC10837616 DOI: 10.1016/j.isci.2024.108847] [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: 07/21/2023] [Revised: 10/23/2023] [Accepted: 01/03/2024] [Indexed: 02/06/2024] Open
Abstract
The integration of stereoelectroencephalography with therapeutic deep brain stimulation (DBS) holds immense promise as a viable approach for precise treatment of refractory disorders, yet it has not been explored in the domain of headache or pain management. Here, we implanted 14 electrodes in a patient with refractory migraine and integrated clinical assessment and electrophysiological data to investigate personalized targets for refractory headache treatment. Using statistical analyses and cross-validated machine-learning models, we identified high-frequency oscillations in the right nucleus accumbens as a critical headache-related biomarker. Through a systematic bipolar stimulation approach and blinded sham-controlled survey, combined with real-time electrophysiological data, we successfully identified the left dorsal anterior cingulate cortex as the optimal target for the best potential treatment. In this pilot study, the concept of the herein-proposed data-driven approach to optimizing precise and personalized treatment strategies for DBS may create a new frontier in the field of refractory headache and even pain disorders.
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Affiliation(s)
- Hulin Zhao
- Department of Neurosurgery, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Shuhua Zhang
- Department of Neurology, Chinese PLA General Hospital, Beijing 100853, China
- International Headache Centre, Chinese PLA General Hospital, Beijing 100853, China
| | - Yining Wang
- School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Chuting Zhang
- School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Zihua Gong
- Department of Neurology, Chinese PLA General Hospital, Beijing 100853, China
- International Headache Centre, Chinese PLA General Hospital, Beijing 100853, China
| | - Mingjie Zhang
- Department of Neurology, Chinese PLA General Hospital, Beijing 100853, China
- International Headache Centre, Chinese PLA General Hospital, Beijing 100853, China
| | - Wei Dai
- Department of Neurology, Chinese PLA General Hospital, Beijing 100853, China
- International Headache Centre, Chinese PLA General Hospital, Beijing 100853, China
| | - Ye Ran
- Department of Neurology, Chinese PLA General Hospital, Beijing 100853, China
- International Headache Centre, Chinese PLA General Hospital, Beijing 100853, China
| | - Wenbin Shi
- School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, Beijing 100081, China
| | - Yuanyuan Dang
- Department of Neurosurgery, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Aijun Liu
- Department of Neurosurgery, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Zhengbo Zhang
- Center for Artificial Intelligence in Medicine, Chinese PLA General Hospital, Beijing 100853, China
| | - Chien-Hung Yeh
- School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, Beijing 100081, China
| | - Zhao Dong
- Department of Neurology, Chinese PLA General Hospital, Beijing 100853, China
- International Headache Centre, Chinese PLA General Hospital, Beijing 100853, China
| | - Shengyuan Yu
- Department of Neurology, Chinese PLA General Hospital, Beijing 100853, China
- International Headache Centre, Chinese PLA General Hospital, Beijing 100853, China
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16
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Hurley ME, Sonig A, Herrington J, Storch EA, Lázaro-Muñoz G, Blumenthal-Barby J, Kostick-Quenet K. Ethical considerations for integrating multimodal computer perception and neurotechnology. Front Hum Neurosci 2024; 18:1332451. [PMID: 38435745 PMCID: PMC10904467 DOI: 10.3389/fnhum.2024.1332451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/30/2024] [Indexed: 03/05/2024] Open
Abstract
Background Artificial intelligence (AI)-based computer perception technologies (e.g., digital phenotyping and affective computing) promise to transform clinical approaches to personalized care in psychiatry and beyond by offering more objective measures of emotional states and behavior, enabling precision treatment, diagnosis, and symptom monitoring. At the same time, passive and continuous nature by which they often collect data from patients in non-clinical settings raises ethical issues related to privacy and self-determination. Little is known about how such concerns may be exacerbated by the integration of neural data, as parallel advances in computer perception, AI, and neurotechnology enable new insights into subjective states. Here, we present findings from a multi-site NCATS-funded study of ethical considerations for translating computer perception into clinical care and contextualize them within the neuroethics and neurorights literatures. Methods We conducted qualitative interviews with patients (n = 20), caregivers (n = 20), clinicians (n = 12), developers (n = 12), and clinician developers (n = 2) regarding their perspective toward using PC in clinical care. Transcripts were analyzed in MAXQDA using Thematic Content Analysis. Results Stakeholder groups voiced concerns related to (1) perceived invasiveness of passive and continuous data collection in private settings; (2) data protection and security and the potential for negative downstream/future impacts on patients of unintended disclosure; and (3) ethical issues related to patients' limited versus hyper awareness of passive and continuous data collection and monitoring. Clinicians and developers highlighted that these concerns may be exacerbated by the integration of neural data with other computer perception data. Discussion Our findings suggest that the integration of neurotechnologies with existing computer perception technologies raises novel concerns around dignity-related and other harms (e.g., stigma, discrimination) that stem from data security threats and the growing potential for reidentification of sensitive data. Further, our findings suggest that patients' awareness and preoccupation with feeling monitored via computer sensors ranges from hypo- to hyper-awareness, with either extreme accompanied by ethical concerns (consent vs. anxiety and preoccupation). These results highlight the need for systematic research into how best to implement these technologies into clinical care in ways that reduce disruption, maximize patient benefits, and mitigate long-term risks associated with the passive collection of sensitive emotional, behavioral and neural data.
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Affiliation(s)
- Meghan E. Hurley
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Anika Sonig
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - John Herrington
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Eric A. Storch
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Gabriel Lázaro-Muñoz
- Center for Bioethics, Harvard Medical School, Boston, MA, United States
- Department of Psychiatry and Behavioral Sciences, Massachusetts General Hospital, Boston, MA, United States
| | | | - Kristin Kostick-Quenet
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
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17
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Roalf DR, Figee M, Oathes DJ. Elevating the field for applying neuroimaging to individual patients in psychiatry. Transl Psychiatry 2024; 14:87. [PMID: 38341414 PMCID: PMC10858949 DOI: 10.1038/s41398-024-02781-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 12/06/2023] [Accepted: 01/15/2024] [Indexed: 02/12/2024] Open
Abstract
Although neuroimaging has been widely applied in psychiatry, much of the exuberance in decades past has been tempered by failed replications and a lack of definitive evidence to support the utility of imaging to inform clinical decisions. There are multiple promising ways forward to demonstrate the relevance of neuroimaging for psychiatry at the individual patient level. Ultra-high field magnetic resonance imaging is developing as a sensitive measure of neurometabolic processes of particular relevance that holds promise as a new way to characterize patient abnormalities as well as variability in response to treatment. Neuroimaging may also be particularly suited to the science of brain stimulation interventions in psychiatry given that imaging can both inform brain targeting as well as measure changes in brain circuit communication as a function of how effectively interventions improve symptoms. We argue that a greater focus on individual patient imaging data will pave the way to stronger relevance to clinical care in psychiatry. We also stress the importance of using imaging in symptom-relevant experimental manipulations and how relevance will be best demonstrated by pairing imaging with differential treatment prediction and outcome measurement. The priorities for using brain imaging to inform psychiatry may be shifting, which compels the field to solidify clinical relevance for individual patients over exploratory associations and biomarkers that ultimately fail to replicate.
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Affiliation(s)
- David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Martijn Figee
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Desmond J Oathes
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Center for Brain Imaging and Stimulation, University of Pennsylvania, Philadelphia, PA, USA.
- Center for Neuromodulation in Depression and Stress, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Brain Science Translation, Innovation, and Modulation Center, University of Pennsylvania, Philadelphia, PA, USA.
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18
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Wang M, G'Sell M, Castellano JF, Richardson RM, Ghuman A. A week in the life of the human brain: stable states punctuated by chaotic transitions. RESEARCH SQUARE 2024:rs.3.rs-2752903. [PMID: 37034705 PMCID: PMC10081438 DOI: 10.21203/rs.3.rs-2752903/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Many important neurocognitive states, such as performing natural activities and fluctuations of arousal, shift over minutes-to-hours in the real-world. We harnessed 3-12 days of continuous multi-electrode intracranial recordings in twenty humans during natural behavior (socializing, using digital devices, sleeping, etc.) to study real-world neurodynamics. Applying deep learning with dynamical systems approaches revealed that brain networks formed consistent stable states that predicted behavior and physiology. Changes in behavior were associated with bursts of rapid neural fluctuations where brain networks chaotically explored many configurations before settling into new states. These trajectories traversed an hourglass-shaped structure anchored around a set of networks that slowly tracked levels of outward awareness related to wake-sleep stages, and a central attractor corresponding to default mode network activation. These findings indicate ways our brains use rapid, chaotic transitions that coalesce into neurocognitive states slowly fluctuating around a stabilizing central equilibrium to balance flexibility and stability during real-world behavior.
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19
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Allawala A, Bijanki KR, Oswalt D, Mathura RK, Adkinson J, Pirtle V, Shofty B, Robinson M, Harrison MT, Mathew SJ, Goodman WK, Pouratian N, Sheth SA, Borton DA. Prefrontal network engagement by deep brain stimulation in limbic hubs. Front Hum Neurosci 2024; 17:1291315. [PMID: 38283094 PMCID: PMC10813208 DOI: 10.3389/fnhum.2023.1291315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 12/26/2023] [Indexed: 01/30/2024] Open
Abstract
Prefrontal circuits in the human brain play an important role in cognitive and affective processing. Neuromodulation therapies delivered to certain key hubs within these circuits are being used with increasing frequency to treat a host of neuropsychiatric disorders. However, the detailed neurophysiological effects of stimulation to these hubs are largely unknown. Here, we performed intracranial recordings across prefrontal networks while delivering electrical stimulation to two well-established white matter hubs involved in cognitive regulation and depression: the subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VC/VS). We demonstrate a shared frontotemporal circuit consisting of the ventromedial prefrontal cortex, amygdala, and lateral orbitofrontal cortex where gamma oscillations are differentially modulated by stimulation target. Additionally, we found participant-specific responses to stimulation in the dorsal anterior cingulate cortex and demonstrate the capacity for further tuning of neural activity using current-steered stimulation. Our findings indicate a potential neurophysiological mechanism for the dissociable therapeutic effects seen across the SCC and VC/VS targets for psychiatric neuromodulation and our results lay the groundwork for personalized, network-guided neurostimulation therapy.
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Affiliation(s)
- Anusha Allawala
- School of Engineering, Brown University, Providence, RI, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Kelly R. Bijanki
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Denise Oswalt
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, United States
| | - Raissa K. Mathura
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Joshua Adkinson
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Victoria Pirtle
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Ben Shofty
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, United States
| | - Meghan Robinson
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Matthew T. Harrison
- Division of Applied Mathematics, Brown University, Providence, RI, United States
| | - Sanjay J. Mathew
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Wayne K. Goodman
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Nader Pouratian
- Department of Neurological Surgery, UT Southwestern Medical Center, Dallas, TX, United States
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - David A. Borton
- School of Engineering, Brown University, Providence, RI, United States
- Department of Veterans Affairs, Center for Neurorestoration and Neurotechnology, Providence, RI, United States
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20
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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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21
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Scott PD, Bajaj A, McMullen DP. Navigating the FDA regulatory landscape. Neuropsychopharmacology 2024; 49:18-22. [PMID: 37853093 PMCID: PMC10700528 DOI: 10.1038/s41386-023-01723-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/27/2023] [Accepted: 08/27/2023] [Indexed: 10/20/2023]
Abstract
Recent research and technological developments have led to an expanding number of novel and rapidly acting therapeutics being developed across a variety of neuropsychiatric disorders. Novel medical devices range from implantable and non-invasive brain stimulating and recording technologies to digital therapeutics. This perspective provides an overview of FDA regulatory oversight for medical devices, including a discussion of regulatory pathways and the review of neuromodulation devices for psychiatric disorders. We highlight the importance of early engagement with FDA and special programs that may be useful to device developers participating in interactions with the FDA that are solution focused. We explore current novel and rapid treatments for psychiatric disorders and those on the horizon. Lastly, we provide considerations for developers in navigating the regulatory landscape for neuromodulation devices intended for psychiatric disorders, including approaches to incorporating patient perspectives.
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Affiliation(s)
- Pamela D Scott
- Food and Drug Administration, Center for Devices and Radiological Health, Office of Product Evaluation and Quality, Office of Neurological and Physical Medicine Devices, 10903 New Hampshire Ave., Silver Spring, MD, 20993, USA.
| | - Anita Bajaj
- Food and Drug Administration, Center for Devices and Radiological Health, Office of Product Evaluation and Quality, Office of Neurological and Physical Medicine Devices, 10903 New Hampshire Ave., Silver Spring, MD, 20993, USA
| | - David P McMullen
- Food and Drug Administration, Center for Devices and Radiological Health, Office of Product Evaluation and Quality, Office of Neurological and Physical Medicine Devices, 10903 New Hampshire Ave., Silver Spring, MD, 20993, USA
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22
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Caruso JP, Adenwalla A, Venishetty N, Tamimi MA, Bagley CA, Aoun SG. 3D-Printed Spine Models for Planning Staged Minimally Invasive Transverse Process Resections for Bertolotti Syndrome: Technical Note. J Orthop Case Rep 2024; 14:88-91. [PMID: 38292111 PMCID: PMC10823808 DOI: 10.13107/jocr.2024.v14.i01.4152] [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: 10/20/2023] [Revised: 11/18/2023] [Indexed: 02/01/2024] Open
Abstract
Background Three-dimensional (3D) printing has enabled numerous advances in spine surgery execution and education. However, few examples exist to outline how this technology can aid the performance of complex spine surgery using minimally invasive surgery (MIS) techniques. Therefore, we present a case that illustrates the benefits of 3D-printed spine model production before and after correction of a congenital lumbosacral anomaly using an MIS approach. Case Report A 40-year-old woman with Bertolotti syndrome underwent a staged bilateral L6 MIS transverse process resection for the treatment of severe and progressive axial back pain which had repeatedly failed conservative management. 3D-printed spine models were used for pre- and post-operative surgical planning and patient counseling. Conclusion 3D-printed spine models can aid in the planning of complex spine cases suited for an MIS approach.
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Affiliation(s)
- James P Caruso
- Department of Neurosurgery, The University of Texas Southwestern, Dallas, Texas
| | - Ammar Adenwalla
- Department of Neurosurgery, The University of Texas Southwestern, Dallas, Texas
| | - Nikit Venishetty
- Paul L. Foster School of Medicine, Texas Tech Health Sciences Center El Paso, TX
| | - Mazin Al Tamimi
- Department of Neurosurgery, The University of Texas Southwestern, Dallas, Texas
| | - Carlos A Bagley
- Department of Neurosurgery, The University of Texas Southwestern, Dallas, Texas
| | - Salah G Aoun
- Department of Neurosurgery, The University of Texas Southwestern, Dallas, Texas
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23
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Widge AS. Closing the loop in psychiatric deep brain stimulation: physiology, psychometrics, and plasticity. Neuropsychopharmacology 2024; 49:138-149. [PMID: 37415081 PMCID: PMC10700701 DOI: 10.1038/s41386-023-01643-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 05/28/2023] [Accepted: 06/20/2023] [Indexed: 07/08/2023]
Abstract
Deep brain stimulation (DBS) is an invasive approach to precise modulation of psychiatrically relevant circuits. Although it has impressive results in open-label psychiatric trials, DBS has also struggled to scale to and pass through multi-center randomized trials. This contrasts with Parkinson disease, where DBS is an established therapy treating thousands of patients annually. The core difference between these clinical applications is the difficulty of proving target engagement, and of leveraging the wide range of possible settings (parameters) that can be programmed in a given patient's DBS. In Parkinson's, patients' symptoms change rapidly and visibly when the stimulator is tuned to the correct parameters. In psychiatry, those same changes take days to weeks, limiting a clinician's ability to explore parameter space and identify patient-specific optimal settings. I review new approaches to psychiatric target engagement, with an emphasis on major depressive disorder (MDD). Specifically, I argue that better engagement may come by focusing on the root causes of psychiatric illness: dysfunction in specific, measurable cognitive functions and in the connectivity and synchrony of distributed brain circuits. I overview recent progress in both those domains, and how it may relate to other technologies discussed in companion articles in this issue.
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Affiliation(s)
- Alik S Widge
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
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24
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Schmid W, Danstrom IA, Echevarria MC, Adkinson J, Mattar L, Banks GP, Sheth SA, Watrous AJ, Heilbronner SR, Bijanki KR, Alabastri A, Bartoli E. A biophysically constrained brain connectivity model based on stimulation-evoked potentials. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.03.565525. [PMID: 37986830 PMCID: PMC10659345 DOI: 10.1101/2023.11.03.565525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Background Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. New Method Using intracranial electrophysiology data recorded from a single patient undergoing sEEG evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D conductivity propagation from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. Results The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlates with additional PEP features and displayed stable, weak correlations with tractography measures. Comparison with existing methods Existing methods for estimating conductivity propagation are imaging-based and thus rely on anatomical inferences. Conclusions These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.
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Affiliation(s)
- William Schmid
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston 77005, Texas, USA
| | - Isabel A. Danstrom
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Maria Crespo Echevarria
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Joshua Adkinson
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Layth Mattar
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Garrett P. Banks
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Andrew J. Watrous
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Sarah R. Heilbronner
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Kelly R. Bijanki
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Alessandro Alabastri
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston 77005, Texas, USA
| | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
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25
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Noecker AM, Mlakar J, Bijanki KR, Griswold MA, Pouratian N, Sheth SA, McIntyre CC. Stereo-EEG-guided network modulation for psychiatric disorders: Interactive holographic planning. Brain Stimul 2023; 16:1799-1805. [PMID: 38135359 PMCID: PMC10784872 DOI: 10.1016/j.brs.2023.11.003] [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: 05/31/2023] [Revised: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 12/24/2023] Open
Abstract
BACKGROUND Connectomic modeling studies are expanding understanding of the brain networks that are modulated by deep brain stimulation (DBS) therapies. However, explicit integration of these modeling results into prospective neurosurgical planning is only beginning to evolve. One challenge of employing connectomic models in patient-specific surgical planning is the inherent 3D nature of the results, which can make clinically useful data integration and visualization difficult. METHODS We developed a holographic stereotactic neurosurgery research tool (HoloSNS) that integrates patient-specific brain models into a group-based visualization environment for interactive surgical planning using connectomic hypotheses. HoloSNS currently runs on the HoloLens 2 platform and it enables remote networking between headsets. This allowed us to perform surgical planning group meetings with study co-investigators distributed across the country. RESULTS We used HoloSNS to plan stereo-EEG and DBS electrode placements for each patient participating in a clinical trial (NCT03437928) that is targeting both the subcallosal cingulate and ventral capsule for the treatment of depression. Each patient model consisted of multiple components of scientific data and anatomical reconstructions of the head and brain (both patient-specific and atlas-based), which far exceed the data integration capabilities of traditional neurosurgical planning workstations. This allowed us to prospectively discuss and evaluate the positioning of the electrodes based on novel connectomic hypotheses. CONCLUSIONS The 3D nature of the surgical procedure, brain imaging data, and connectomic modeling results all highlighted the utility of employing holographic visualization to support the design of unique clinical experiments to explore brain network modulation with DBS.
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Affiliation(s)
- Angela M Noecker
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Jeffrey Mlakar
- Interactive Commons, Case Western Reserve University, Cleveland, OH, USA
| | - Kelly R Bijanki
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Mark A Griswold
- Interactive Commons, Case Western Reserve University, Cleveland, OH, USA; Department of Radiology, Case Western Reserve University, Cleveland, OH, USA
| | - Nader Pouratian
- Department of Neurosurgery, University of Texas Southwestern, Dallas, TX, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Neurosurgery, Duke University, Durham, NC, USA.
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26
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Sanchez SM, Tsuchiyagaito A, Kuplicki R, Park H, Postolski I, Rohan M, Paulus MP, Guinjoan SM. Repetitive Negative Thinking-Specific and -Nonspecific White Matter Tracts Engaged by Historical Psychosurgical Targets for Depression. Biol Psychiatry 2023; 94:661-671. [PMID: 36965550 PMCID: PMC10517085 DOI: 10.1016/j.biopsych.2023.03.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/06/2023] [Accepted: 03/09/2023] [Indexed: 03/27/2023]
Abstract
BACKGROUND Repetitive negative thinking (RNT) is a frequent symptom of major depressive disorder (MDD) that is associated with poor outcomes and treatment resistance. While most studies on RNT have focused on structural and functional characteristics of gray matter, this study aimed to examine the association between white matter (WM) tracts and interindividual variability in RNT. METHODS A probabilistic tractography approach was used to characterize differences in the size and anatomical trajectory of WM fibers traversing psychosurgery targets historically useful in the treatment of MDD (anterior capsulotomy, anterior cingulotomy, and subcaudate tractotomy) in patients with MDD and low (n = 53) or high (n = 52) RNT, and healthy control subjects (n = 54). MDD samples were propensity matched on depression and anxiety severity and demographics. RESULTS WM tracts traversing left hemisphere targets and reaching the ventral anterior body of the corpus callosum (thus extending to contralateral regions) were larger in the high-RNT MDD group compared with low-RNT (effect size D = 0.27, p = .042) and healthy control (D = 0.23, p = .02) groups. MDD was associated with greater size of tracts that converge onto the right medial orbitofrontal cortex regardless of RNT intensity. Other RNT-nonspecific findings in MDD involved tracts reaching the left primary motor and right primary somatosensory cortices. CONCLUSIONS This study provides the first evidence to our knowledge that WM connectivity patterns, which could become targets of intervention, differ between high- and low-RNT participants with MDD. These WM differences extend to circuits that are not specific to RNT, possibly subserving reward mechanisms and psychomotor activity.
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Affiliation(s)
| | - Aki Tsuchiyagaito
- Laureate Institute for Brain Research, Tulsa, Oklahoma; Research Center for Child Mental Development, Chiba University, Chiba, Japan
| | | | - Heekyeong Park
- Laureate Institute for Brain Research, Tulsa, Oklahoma; Department of Psychology, University of North Texas, Dallas, Texas
| | - Ivan Postolski
- Institute for Research in Computational Sciences, National Scientific and Technical Research Council-University of Buenos Aires, Buenos Aires, Argentina
| | - Michael Rohan
- Laureate Institute for Brain Research, Tulsa, Oklahoma
| | - Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma; Oxley College of Health Sciences, University of Tulsa, Tulsa, Oklahoma
| | - Salvador M Guinjoan
- Laureate Institute for Brain Research, Tulsa, Oklahoma; Department of Psychiatry, Oklahoma University Health Sciences Center, Tulsa, Oklahoma.
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27
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Alagapan S, Choi KS, Heisig S, Riva-Posse P, Crowell A, Tiruvadi V, Obatusin M, Veerakumar A, Waters AC, Gross RE, Quinn S, Denison L, O'Shaughnessy M, Connor M, Canal G, Cha J, Hershenberg R, Nauvel T, Isbaine F, Afzal MF, Figee M, Kopell BH, Butera R, Mayberg HS, Rozell CJ. Cingulate dynamics track depression recovery with deep brain stimulation. Nature 2023; 622:130-138. [PMID: 37730990 PMCID: PMC10550829 DOI: 10.1038/s41586-023-06541-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 08/09/2023] [Indexed: 09/22/2023]
Abstract
Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) can provide long-term symptom relief for treatment-resistant depression (TRD)1. However, achieving stable recovery is unpredictable2, typically requiring trial-and-error stimulation adjustments due to individual recovery trajectories and subjective symptom reporting3. We currently lack objective brain-based biomarkers to guide clinical decisions by distinguishing natural transient mood fluctuations from situations requiring intervention. To address this gap, we used a new device enabling electrophysiology recording to deliver SCC DBS to ten TRD participants (ClinicalTrials.gov identifier NCT01984710). At the study endpoint of 24 weeks, 90% of participants demonstrated robust clinical response, and 70% achieved remission. Using SCC local field potentials available from six participants, we deployed an explainable artificial intelligence approach to identify SCC local field potential changes indicating the patient's current clinical state. This biomarker is distinct from transient stimulation effects, sensitive to therapeutic adjustments and accurate at capturing individual recovery states. Variable recovery trajectories are predicted by the degree of preoperative damage to the structural integrity and functional connectivity within the targeted white matter treatment network, and are matched by objective facial expression changes detected using data-driven video analysis. Our results demonstrate the utility of objective biomarkers in the management of personalized SCC DBS and provide new insight into the relationship between multifaceted (functional, anatomical and behavioural) features of TRD pathology, motivating further research into causes of variability in depression treatment.
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Affiliation(s)
- Sankaraleengam Alagapan
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stephen Heisig
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Patricio Riva-Posse
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Andrea Crowell
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Vineet Tiruvadi
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Emory University School of Medicine, Atlanta, GA, USA
| | - Mosadoluwa Obatusin
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ashan Veerakumar
- Department of Psychiatry, Schulich School of Medicine and Dentistry at Western University, London, Ontario, Canada
| | - Allison C Waters
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert E Gross
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Sinead Quinn
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Lydia Denison
- Emory University School of Medicine, Atlanta, GA, USA
| | - Matthew O'Shaughnessy
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Marissa Connor
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Gregory Canal
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Jungho Cha
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rachel Hershenberg
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Tanya Nauvel
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Faical Isbaine
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Muhammad Furqan Afzal
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Martijn Figee
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brian H Kopell
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert Butera
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Helen S Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Christopher J Rozell
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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Venkatesh P, Wolfe C, Lega B. Neuromodulation of the anterior thalamus: Current approaches and opportunities for the future. CURRENT RESEARCH IN NEUROBIOLOGY 2023; 5:100109. [PMID: 38020810 PMCID: PMC10663132 DOI: 10.1016/j.crneur.2023.100109] [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: 05/14/2023] [Revised: 08/28/2023] [Accepted: 08/31/2023] [Indexed: 12/01/2023] Open
Abstract
The role of thalamocortical circuits in memory has driven a recent burst of scholarship, especially in animal models. Investigating this circuitry in humans is more challenging. And yet, the development of new recording and stimulation technologies deployed for clinical indications has created novel opportunities for data collection to elucidate the cognitive roles of thalamic structures. These technologies include stereoelectroencephalography (SEEG), deep brain stimulation (DBS), and responsive neurostimulation (RNS), all of which have been applied to memory-related thalamic regions, specifically for seizure localization and treatment. This review seeks to summarize the existing applications of neuromodulation of the anterior thalamic nuclei (ANT) and highlight several devices and their capabilities that can allow cognitive researchers to design experiments to assay its functionality. Our goal is to introduce to investigators, who may not be familiar with these clinical devices, the capabilities, and limitations of these tools for understanding the neurophysiology of the ANT as it pertains to memory and other behaviors. We also briefly cover the targeting of other thalamic regions including the centromedian (CM) nucleus, dorsomedial (DM) nucleus, and pulvinar, with associated potential avenues of experimentation.
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Affiliation(s)
- Pooja Venkatesh
- Department of Neurosurgery, University of Texas Southwestern, Dallas, TX, 75390, USA
| | - Cody Wolfe
- Department of Neurosurgery, University of Texas Southwestern, Dallas, TX, 75390, USA
| | - Bradley Lega
- Department of Neurosurgery, University of Texas Southwestern, Dallas, TX, 75390, USA
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Xiao J, Provenza NR, Asfouri J, Myers J, Mathura RK, Metzger B, Adkinson JA, Allawala AB, Pirtle V, Oswalt D, Shofty B, Robinson ME, Mathew SJ, Goodman WK, Pouratian N, Schrater PR, Patel AB, Tolias AS, Bijanki KR, Pitkow X, Sheth SA. Decoding Depression Severity From Intracranial Neural Activity. Biol Psychiatry 2023; 94:445-453. [PMID: 36736418 PMCID: PMC10394110 DOI: 10.1016/j.biopsych.2023.01.020] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 01/09/2023] [Accepted: 01/25/2023] [Indexed: 02/05/2023]
Abstract
BACKGROUND Disorders of mood and cognition are prevalent, disabling, and notoriously difficult to treat. Fueling this challenge in treatment is a significant gap in our understanding of their neurophysiological basis. METHODS We recorded high-density neural activity from intracranial electrodes implanted in depression-relevant prefrontal cortical regions in 3 human subjects with severe depression. Neural recordings were labeled with depression severity scores across a wide dynamic range using an adaptive assessment that allowed sampling with a temporal frequency greater than that possible with typical rating scales. We modeled these data using regularized regression techniques with region selection to decode depression severity from the prefrontal recordings. RESULTS Across prefrontal regions, we found that reduced depression severity is associated with decreased low-frequency neural activity and increased high-frequency activity. When constraining our model to decode using a single region, spectral changes in the anterior cingulate cortex best predicted depression severity in all 3 subjects. Relaxing this constraint revealed unique, individual-specific sets of spatiospectral features predictive of symptom severity, reflecting the heterogeneous nature of depression. CONCLUSIONS The ability to decode depression severity from neural activity increases our fundamental understanding of how depression manifests in the human brain and provides a target neural signature for personalized neuromodulation therapies.
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Affiliation(s)
- Jiayang Xiao
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas; Department of Neuroscience, Baylor College of Medicine, Houston, Texas
| | - Nicole R Provenza
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Joseph Asfouri
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas
| | - John Myers
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Raissa K Mathura
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Brian Metzger
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Joshua A Adkinson
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | | | - Victoria Pirtle
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Denise Oswalt
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Ben Shofty
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Meghan E Robinson
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Sanjay J Mathew
- Department of Psychiatry, Baylor College of Medicine, Houston, Texas
| | - Wayne K Goodman
- Department of Psychiatry, Baylor College of Medicine, Houston, Texas
| | - Nader Pouratian
- Department of Neurological Surgery, UT Southwestern Medical Center, Dallas, Texas
| | - Paul R Schrater
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota; Department of Psychology, University of Minnesota, Minneapolis, Minnesota
| | - Ankit B Patel
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas; Department of Electrical and Computer Engineering, Rice University, Houston, Texas; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas; Department of Electrical and Computer Engineering, Rice University, Houston, Texas; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas
| | - Kelly R Bijanki
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
| | - Xaq Pitkow
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas; Department of Electrical and Computer Engineering, Rice University, Houston, Texas; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas.
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30
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Fan X, Mocchi M, Pascuzzi B, Xiao J, Metzger BA, Mathura RK, Hacker C, Adkinson JA, Bartoli E, Elhassa S, Watrous AJ, Zhang Y, Goodman W, Pouratian N, Bijanki KR. Brain mechanisms underlying the emotion processing bias in treatment-resistant depression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.26.554837. [PMID: 37693557 PMCID: PMC10491112 DOI: 10.1101/2023.08.26.554837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Depression is associated with a cognitive bias towards negative information and away from positive information. This biased emotion processing may underlie core depression symptoms, including persistent feelings of sadness or low mood and a reduced capacity to experience pleasure. The neural mechanisms responsible for this biased emotion processing remain unknown. Here, we had a unique opportunity to record stereotactic electroencephalography (sEEG) signals in the amygdala and prefrontal cortex (PFC) from 5 treatment-resistant depression (TRD) patients and 12 epilepsy patients (as control) while they participated in an affective bias task in which happy and sad faces were rated. First, compared with the control group, patients with TRD showed increased amygdala responses to sad faces in the early stage (around 300 ms) and decreased amygdala responses to happy faces in the late stage (around 600 ms) following the onset of faces. Further, during the late stage of happy face processing, alpha-band activity in PFC as well as alpha-phase locking between the amygdala and PFC were significantly greater in TRD patients compared to the controls. Second, after deep brain stimulation (DBS) delivered to bilateral subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VC/VS), atypical amygdala and PFC processing of happy faces in TRD patients remitted toward the normative pattern. The increased amygdala activation during the early stage of sad face processing suggests an overactive bottom-up processing system in TRD. Meanwhile, the reduced amygdala response during the late stage of happy face processing could be attributed to inhibition by PFC through alpha-band oscillation, which can be released by DBS in SCC and VC/VS.
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31
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Cao D, Liu Q, Zhang J, Li J, Jiang T. State-specific modulation of mood using intracranial electrical stimulation of the orbitofrontal cortex. Brain Stimul 2023; 16:1112-1122. [PMID: 37467951 DOI: 10.1016/j.brs.2023.07.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/09/2023] [Accepted: 07/13/2023] [Indexed: 07/21/2023] Open
Abstract
BACKGROUND Orbitofrontal cortex (OFC) is a promising target for intracranial electrical stimulation (iES) aimed at improving mood states. However, knowledge gaps remain regarding the underlying neural mechanisms of iES effects, such as the effect of the OFC target in comparison with other emotional network targets, the impact of brain state at the time of stimulation, and the neural response induced by iES at both local and network scales. OBJECTIVE Our study aims to address the neural mechanisms underlying the effects of iES in improving mood states. METHODS We conducted a study in 24 epilepsy patients who received iES through implanted electrodes in the emotional network and compared the effects of iES on multiple targets in the emotional network. RESULTS We found that only iES applied to the orbitofrontal cortex (OFC) led to mood improvement and changes in neural activity. We also observed that iES to the OFC suppressed the delta-theta power when the brain was in a low mood state. Moreover, the iES to the OFC decreased delta-theta power and increased gamma power at local regions within the emotional network, and enhanced the information flow through the frequency domain among regions within the emotional network. CONCLUSIONS These findings provide insight into the neural correlates of iES-induced mood improvement and support the potential of iES as a therapeutic intervention for mood disorders.
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Affiliation(s)
- Dan Cao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Qihong Liu
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiaqi Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jin Li
- School of Psychology, Capital Normal University, Beijing, 100048, China.
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Research Center for Augmented Intelligence, Zhejiang Lab, 311100, Hangzhou, China.
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32
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Aibar-Durán JÁ, Corripio Collado I, Roldán Bejarano A, Sánchez Nevado R, Aracil Bolanos I, García-Cornet J, Alonso-Solís A, Grasa Bello EM, de Quintana Schmidt C, Muñoz Hernández F, Molet Teixidó J, Rodríguez RR. Long-term outcomes of deep brain stimulation for treatment-resistant schizophrenia: Exploring potential targets. J Psychiatr Res 2023; 163:296-304. [PMID: 37245316 DOI: 10.1016/j.jpsychires.2023.05.056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/03/2023] [Accepted: 05/15/2023] [Indexed: 05/30/2023]
Abstract
BACKGROUND Schizophrenia is a complex and disabling disorder. Around 30% of patients have treatment-resistant schizophrenia (TRS). OBJECTIVE This study summarizes the outcomes after three years follow-up of the first series of patients with TRS treated with deep brain stimulation (DBS) and discuss surgical, clinical and imaging analysis. METHODS Eight patients with TRS treated with DBS in the nucleus accumbens (NAcc) or the subgenual cingulate gyrus (SCG) were included. Symptoms were rated with the PANSS scale and normalized using the illness density index (IDI). A reduction in IDI-PANSS of ≥25% compared to baseline was the criterion of good response. The volume of activated tissue was calculated to perform a connectomic analysis for each patient. An estimation of the tracts and cortical areas modulated was generated. RESULTS Five women and three men were analyzed. After 3 years' follow-up, positive symptoms improved in 50% of the SCG group and 75% of the NAcc group (p = 0.06), and general symptoms improved in 25% and 50% respectively (p = 0.06). The SCG group showed activation of the cingulate bundle and modulation of orbitofrontal and frontomesial regions; in contrast, the NAcc group showed activation of the ventral tegmental area projections pathway and modulation of regions associated with the "default mode network" (precuneus) and Brodmann areas 19 and 20. CONCLUSIONS These results showed a trend toward improvement for positive and general symptoms in patients with TRS treated with DBS. The connectomic analysis will help us understand the interaction of this treatment with the disease to pursue future trial designs.
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Affiliation(s)
- Juan Ángel Aibar-Durán
- Department of Neurosurgery, Hospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Iluminada Corripio Collado
- Department of Psychiatry, Hospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
| | - Alexandra Roldán Bejarano
- Department of Psychiatry, Hospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain.
| | - Raquel Sánchez Nevado
- Department of Neurosurgery, Hospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Ignacio Aracil Bolanos
- Deparment of Neurology, Hospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Julia García-Cornet
- Ingeniering imaging and Signaling, Hospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Anna Alonso-Solís
- Department of Psychiatry, Hospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
| | - Eva Ma Grasa Bello
- Department of Psychiatry, Hospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
| | - Cristian de Quintana Schmidt
- Department of Neurosurgery, Hospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Fernando Muñoz Hernández
- Department of Neurosurgery, Hospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Joan Molet Teixidó
- Department of Neurosurgery, Hospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
| | - Rodrigo Rodríguez Rodríguez
- Department of Neurosurgery, Hospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau (IIB-Sant Pau), Universitat Autònoma de Barcelona (UAB), Barcelona, Spain
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Qiu L, Halpern CH, Barbosa DAN. Are we getting closer to offering deep brain stimulation for treatment-resistant depression in clinical practice? Mol Psychiatry 2023; 28:2627-2629. [PMID: 37106119 DOI: 10.1038/s41380-023-02078-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Affiliation(s)
- Liming Qiu
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Casey H Halpern
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Surgery, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - Daniel A N Barbosa
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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34
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Wang DX, Ng N, Seger SE, Ekstrom AD, Kriegel JL, Lega BC. Machine learning classifiers for electrode selection in the design of closed-loop neuromodulation devices for episodic memory improvement. Cereb Cortex 2023; 33:8150-8163. [PMID: 36997155 PMCID: PMC10321120 DOI: 10.1093/cercor/bhad105] [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: 08/01/2022] [Revised: 03/04/2023] [Accepted: 03/05/2023] [Indexed: 04/01/2023] Open
Abstract
Successful neuromodulation approaches to alter episodic memory require closed-loop stimulation predicated on the effective classification of brain states. The practical implementation of such strategies requires prior decisions regarding electrode implantation locations. Using a data-driven approach, we employ support vector machine (SVM) classifiers to identify high-yield brain targets on a large data set of 75 human intracranial electroencephalogram subjects performing the free recall (FR) task. Further, we address whether the conserved brain regions provide effective classification in an alternate (associative) memory paradigm along with FR, as well as testing unsupervised classification methods that may be a useful adjunct to clinical device implementation. Finally, we use random forest models to classify functional brain states, differentiating encoding versus retrieval versus non-memory behavior such as rest and mathematical processing. We then test how regions that exhibit good classification for the likelihood of recall success in the SVM models overlap with regions that differentiate functional brain states in the random forest models. Finally, we lay out how these data may be used in the design of neuromodulation devices.
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Affiliation(s)
- David X Wang
- Department of Neurosurgery, The University of Texas – Southwestern Medical Center, Dallas, Texas 75390, United States
| | - Nicole Ng
- Department of Neurosurgery, The University of Texas – Southwestern Medical Center, Dallas, Texas 75390, United States
| | - Sarah E Seger
- Department of Neuroscience, University of Arizona, Tucson, Arizona 85721, United States
| | - Arne D Ekstrom
- Department of Neuroscience, University of Arizona, Tucson, Arizona 85721, United States
- Department of Psychology, University of Arizona, Tucson, Arizona 85721, United States
| | - Jennifer L Kriegel
- Department of Neurosurgery, The University of Texas – Southwestern Medical Center, Dallas, Texas 75390, United States
| | - Bradley C Lega
- Department of Neurosurgery, The University of Texas – Southwestern Medical Center, Dallas, Texas 75390, United States
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35
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Motzkin JC, Kanungo I, D’Esposito M, Shirvalkar P. Network targets for therapeutic brain stimulation: towards personalized therapy for pain. FRONTIERS IN PAIN RESEARCH 2023; 4:1156108. [PMID: 37363755 PMCID: PMC10286871 DOI: 10.3389/fpain.2023.1156108] [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: 02/01/2023] [Accepted: 05/19/2023] [Indexed: 06/28/2023] Open
Abstract
Precision neuromodulation of central brain circuits is a promising emerging therapeutic modality for a variety of neuropsychiatric disorders. Reliably identifying in whom, where, and in what context to provide brain stimulation for optimal pain relief are fundamental challenges limiting the widespread implementation of central neuromodulation treatments for chronic pain. Current approaches to brain stimulation target empirically derived regions of interest to the disorder or targets with strong connections to these regions. However, complex, multidimensional experiences like chronic pain are more closely linked to patterns of coordinated activity across distributed large-scale functional networks. Recent advances in precision network neuroscience indicate that these networks are highly variable in their neuroanatomical organization across individuals. Here we review accumulating evidence that variable central representations of pain will likely pose a major barrier to implementation of population-derived analgesic brain stimulation targets. We propose network-level estimates as a more valid, robust, and reliable way to stratify personalized candidate regions. Finally, we review key background, methods, and implications for developing network topology-informed brain stimulation targets for chronic pain.
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Affiliation(s)
- Julian C. Motzkin
- Departments of Neurology and Anesthesia and Perioperative Care (Pain Management), University of California, San Francisco, San Francisco, CA, United States
| | - Ishan Kanungo
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Mark D’Esposito
- Department of Psychology, University of California, Berkeley, Berkeley, CA, United States
| | - Prasad Shirvalkar
- Departments of Neurology and Anesthesia and Perioperative Care (Pain Management), University of California, San Francisco, San Francisco, CA, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
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36
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Manssuer L, Ding Q, Zhang Y, Gong H, Liu W, Yang R, Zhang C, Zhao Y, Pan Y, Zhan S, Li D, Sun B, Voon V. Risk and aversion coding in human habenula high gamma activity. Brain 2023; 146:2642-2653. [PMID: 36445730 PMCID: PMC10232252 DOI: 10.1093/brain/awac456] [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] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 11/14/2022] [Accepted: 11/19/2022] [Indexed: 11/22/2023] Open
Abstract
Neurons in the primate lateral habenula fire in response to punishments and are inhibited by rewards. Through its modulation of midbrain monoaminergic activity, the habenula is believed to play an important role in adaptive behavioural responses to punishment and underlie depressive symptoms and their alleviation with ketamine. However, its role in value-based decision-making in humans is poorly understood due to limitations with non-invasive imaging methods which measure metabolic, not neural, activity with poor temporal resolution. Here, we overcome these limitations to more closely bridge the gap between species by recording local field potentials directly from the habenula in 12 human patients receiving deep brain stimulation treatment for bipolar disorder (n = 4), chronic pain (n = 3), depression (n = 3) and schizophrenia (n = 2). This allowed us to record neural activity during value-based decision-making tasks involving monetary rewards and losses. High-frequency gamma (60-240 Hz) activity, a proxy for population-level spiking involved in cognitive computations, increased during the receipt of loss and decreased during receipt of reward. Furthermore, habenula high gamma also encoded risk during decision-making, being larger in amplitude for high compared to low risk. For both risk and aversion, differences between conditions peaked approximately between 400 and 750 ms after stimulus onset. The findings not only demonstrate homologies with the primate habenula but also extend its role to human decision-making, showing its temporal dynamics and suggesting revisions to current models. The findings suggest that habenula high gamma could be used to optimize real-time closed-loop deep brain stimulation treatment for mood disturbances and impulsivity in psychiatric disorders.
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Affiliation(s)
- Luis Manssuer
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Department of Psychiatry, Addenbrookes Hospital, University of Cambridge, Cambridge CB2 0QQ, UK
- Neural and Intelligence Engineering Centre, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Qiong Ding
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Department of Psychiatry, Addenbrookes Hospital, University of Cambridge, Cambridge CB2 0QQ, UK
- Neural and Intelligence Engineering Centre, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Yingying Zhang
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Neural and Intelligence Engineering Centre, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Hengfeng Gong
- Shanghai Pudong New Area Mental Health Centre, Tongji University School of Medicine, Shanghai 200124, China
| | - Wei Liu
- Neural and Intelligence Engineering Centre, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Ruoqi Yang
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Chencheng Zhang
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yijie Zhao
- Neural and Intelligence Engineering Centre, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Yixin Pan
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shikun Zhan
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Dianyou Li
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Bomin Sun
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Valerie Voon
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Department of Psychiatry, Addenbrookes Hospital, University of Cambridge, Cambridge CB2 0QQ, UK
- Neural and Intelligence Engineering Centre, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
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37
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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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Petersen MV, McIntyre CC. Comparison of Anatomical Pathway Models with Tractography Estimates of the Pallidothalamic, Cerebellothalamic, and Corticospinal Tracts. Brain Connect 2023; 13:237-246. [PMID: 36772800 PMCID: PMC10178936 DOI: 10.1089/brain.2022.0068] [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] [Indexed: 02/12/2023] Open
Abstract
Introduction: Models of structural connectivity in the human brain are typically simulated using tractographic approaches. However, the nonlinear fitting of anatomical pathway atlases to de novo subject brains represents a simpler alternative that is hypothesized to provide more anatomically realistic results. Therefore, the goal of this study was to perform a side-by-side comparison of the streamline estimates generated by either pathway atlas fits or tractographic reconstructions in the same subjects. Methods: Our analyses focused on reconstruction of the corticospinal tract (CST), cerebellothalamic (CBT), and pallidothalamic (PT) pathways using example datasets from the Human Connectome Project (HCP). We used MRtrix3 to explore whole brain, as well as manual seed-to-target, tractography approaches. In parallel, we performed nonlinear fits of an axonal pathway atlas to each HCP dataset using Advanced Normalization Tools (ANTs). Results: The different methods produced notably different estimates for each pathway in each subject. The fitted atlas pathways were highly stereotyped and exhibited low variability in their streamline trajectories. Manual tractography resulted in pathway estimates that generally corresponded with the fitted atlas pathways, but with a higher degree of variability in the individual streamlines. Pathway reconstructions derived from whole-brain tractography exhibited the highest degree of variability and struggled to create anatomically realistic representations for either the CBT or PT pathways. Conclusion: The speed, simplicity, reproducibility, and realism of anatomical pathway model fits makes them an appealing option for some forms of structural connectivity modeling in the human brain. Impact statement Axonal pathway modeling is an important component of deep brain stimulation (DBS) research studies that seek to identify the brain connections that are directly activated by stimulation. The corticospinal tract, cerebellothalamic (CBT), and pallidothalamic (PT) pathways are specifically relevant to the study of subthalamic DBS for the treatment of Parkinson's disease. Our results suggest that anatomical pathway model fits of the CBT and PT pathways to de novo subject brains represent a more anatomically realistic option than tractographic approaches when studying subthalamic DBS.
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Affiliation(s)
- Mikkel V. Petersen
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Cameron C. McIntyre
- Department of Biomedical Engineering and Duke University, Durham, North Carolina, USA
- Department of Neurosurgery, Duke University, Durham, North Carolina, USA
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Hitti FL, Widge AS, Riva-Posse P, Malone DA, Okun MS, Shanechi MM, Foote KD, Lisanby SH, Ankudowich E, Chivukula S, Chang EF, Gunduz A, Hamani C, Feinsinger A, Kubu CS, Chiong W, Chandler JA, Carbunaru R, Cheeran B, Raike RS, Davis RA, Halpern CH, Vanegas-Arroyave N, Markovic D, Bick SK, McIntyre CC, Richardson RM, Dougherty DD, Kopell BH, Sweet JA, Goodman WK, Sheth SA, Pouratian N. Future directions in psychiatric neurosurgery: Proceedings of the 2022 American Society for Stereotactic and Functional Neurosurgery meeting on surgical neuromodulation for psychiatric disorders. Brain Stimul 2023; 16:867-878. [PMID: 37217075 PMCID: PMC11189296 DOI: 10.1016/j.brs.2023.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/10/2023] [Accepted: 05/14/2023] [Indexed: 05/24/2023] Open
Abstract
OBJECTIVE Despite advances in the treatment of psychiatric diseases, currently available therapies do not provide sufficient and durable relief for as many as 30-40% of patients. Neuromodulation, including deep brain stimulation (DBS), has emerged as a potential therapy for persistent disabling disease, however it has not yet gained widespread adoption. In 2016, the American Society for Stereotactic and Functional Neurosurgery (ASSFN) convened a meeting with leaders in the field to discuss a roadmap for the path forward. A follow-up meeting in 2022 aimed to review the current state of the field and to identify critical barriers and milestones for progress. DESIGN The ASSFN convened a meeting on June 3, 2022 in Atlanta, Georgia and included leaders from the fields of neurology, neurosurgery, and psychiatry along with colleagues from industry, government, ethics, and law. The goal was to review the current state of the field, assess for advances or setbacks in the interim six years, and suggest a future path forward. The participants focused on five areas of interest: interdisciplinary engagement, regulatory pathways and trial design, disease biomarkers, ethics of psychiatric surgery, and resource allocation/prioritization. The proceedings are summarized here. CONCLUSION The field of surgical psychiatry has made significant progress since our last expert meeting. Although weakness and threats to the development of novel surgical therapies exist, the identified strengths and opportunities promise to move the field through methodically rigorous and biologically-based approaches. The experts agree that ethics, law, patient engagement, and multidisciplinary teams will be critical to any potential growth in this area.
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Affiliation(s)
- Frederick L Hitti
- Department of Neurosurgery, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Alik S Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Patricio Riva-Posse
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Donald A Malone
- Department of Psychiatry, Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA
| | - Michael S Okun
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Gainesville, FL, USA
| | - Maryam M Shanechi
- Departments of Electrical and Computer Engineering and Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Kelly D Foote
- Department of Neurosurgery, Norman Fixel Institute for Neurological Diseases, Gainesville, FL, USA
| | - Sarah H Lisanby
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Elizabeth Ankudowich
- Division of Translational Research, National Institute of Mental Health, Bethesda, MD, USA
| | - Srinivas Chivukula
- Department of Neurosurgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Aysegul Gunduz
- Department of Biomedical Engineering and Fixel Institute for Neurological Disorders, University of Florida, Gainesville, FL, USA
| | - Clement Hamani
- Sunnybrook Research Institute, Hurvitz Brain Sciences Centre, Harquail Centre for Neuromodulation, Division of Neurosurgery, University of Toronto, Toronto, Canada
| | - Ashley Feinsinger
- Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Cynthia S Kubu
- Department of Neurology, Cleveland Clinic and Case Western Reserve University, School of Medicine, Cleveland, OH, USA
| | - Winston Chiong
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Jennifer A Chandler
- Faculty of Law, University of Ottawa, Ottawa, ON, USA; Affiliate Investigator, Bruyère Research Institute, Ottawa, ON, USA
| | | | | | - Robert S Raike
- Global Research Organization, Medtronic Inc. Neuromodulation, Minneapolis, MN, USA
| | - Rachel A Davis
- Departments of Psychiatry and Neurosurgery, University of Colorado Anschutz, Aurora, CO, USA
| | - Casey H Halpern
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; The Cpl Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | | | - Dejan Markovic
- Department of Electrical Engineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Sarah K Bick
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Cameron C McIntyre
- Departments of Biomedical Engineering and Neurosurgery, Duke University, Durham, NC, USA
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Brian H Kopell
- Department of Neurosurgery, Center for Neuromodulation, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jennifer A Sweet
- Department of Neurosurgery, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Wayne K Goodman
- Department of Psychiatry and Behavior Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Nader Pouratian
- Department of Neurosurgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Widge AS. Closed-Loop Deep Brain Stimulation for Psychiatric Disorders. Harv Rev Psychiatry 2023; 31:162-171. [PMID: 37171475 PMCID: PMC10188203 DOI: 10.1097/hrp.0000000000000367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
ABSTRACT Deep brain stimulation (DBS) is a well-established approach to treating medication-refractory neurological disorders and holds promise for treating psychiatric disorders. Despite strong open-label results in extremely refractory patients, DBS has struggled to meet endpoints in randomized controlled trials. A major challenge is stimulation "dosing"-DBS systems have many adjustable parameters, and clinicians receive little feedback on whether they have chosen the correct parameters for an individual patient. Multiple groups have proposed closed loop technologies as a solution. These systems sense electrical activity, identify markers of an (un)desired state, then automatically deliver or adjust stimulation to alter that electrical state. Closed loop DBS has been successfully deployed in movement disorders and epilepsy. The availability of that technology, as well as advances in opportunities for invasive research with neurosurgical patients, has yielded multiple pilot demonstrations in psychiatric illness. Those demonstrations split into two schools of thought, one rooted in well-established diagnoses and symptom scales, the other in the more experimental Research Domain Criteria (RDoC) framework. Both are promising, and both are limited by the boundaries of current stimulation technology. They are in turn driving advances in implantable recording hardware, signal processing, and stimulation paradigms. The combination of these advances is likely to change both our understanding of psychiatric neurobiology and our treatment toolbox, though the timeframe may be limited by the realities of implantable device development.
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Affiliation(s)
- Alik S Widge
- From the Department of Psychiatry & Behavioral Sciences and Medical Discovery Team on Addictions, University of Minnesota
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Kelly DF, Heinzerling K, Sharma A, Gowrinathan S, Sergi K, Mallari RJ. Psychedelic-Assisted Therapy and Psychedelic Science: A Review and Perspective on Opportunities in Neurosurgery and Neuro-Oncology. Neurosurgery 2023; 92:680-694. [PMID: 36512813 PMCID: PMC9988324 DOI: 10.1227/neu.0000000000002275] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/23/2022] [Indexed: 12/14/2022] Open
Abstract
After a decades-long pause, psychedelics are again being intensely investigated for treating a wide range of neuropsychiatric ailments including depression, anxiety, addiction, post-traumatic stress disorder, anorexia, and chronic pain syndromes. The classic serotonergic psychedelics psilocybin and lysergic acid diethylamide and nonclassic psychedelics 3,4-methylenedioxymethamphetamine and ketamine are increasingly appreciated as neuroplastogens given their potential to fundamentally alter mood and behavior well beyond the time window of measurable exposure. Imaging studies with psychedelics are also helping advance our understanding of neural networks and connectomics. This resurgence in psychedelic science and psychedelic-assisted therapy has potential significance for the fields of neurosurgery and neuro-oncology and their diverse and challenging patients, many of whom continue to have mental health issues and poor quality of life despite receiving state-of-the-art care. In this study, we review recent and ongoing clinical trials, the set and setting model of psychedelic-assisted therapy, potential risks and adverse events, proposed mechanisms of action, and provide a perspective on how the safe and evidence-based use of psychedelics could potentially benefit many patients, including those with brain tumors, pain syndromes, ruminative disorders, stroke, SAH, TBI, and movement disorders. By leveraging psychedelics' neuroplastic potential to rehabilitate the mind and brain, novel treatments may be possible for many of these patient populations, in some instances working synergistically with current treatments and in some using subpsychedelic doses that do not require mind-altering effects for efficacy. This review aims to encourage broader multidisciplinary collaboration across the neurosciences to explore and help realize the transdiagnostic healing potential of psychedelics.
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Affiliation(s)
- Daniel F. Kelly
- Treatment & Research in Psychedelics Program, Pacific Neuroscience Institute, Santa Monica, California, USA
- Saint John's Cancer Institute, Providence Saint John's Health Center, Santa Monica, California, USA
| | - Keith Heinzerling
- Treatment & Research in Psychedelics Program, Pacific Neuroscience Institute, Santa Monica, California, USA
- Saint John's Cancer Institute, Providence Saint John's Health Center, Santa Monica, California, USA
| | - Akanksha Sharma
- Treatment & Research in Psychedelics Program, Pacific Neuroscience Institute, Santa Monica, California, USA
- Saint John's Cancer Institute, Providence Saint John's Health Center, Santa Monica, California, USA
| | - Shanthi Gowrinathan
- Treatment & Research in Psychedelics Program, Pacific Neuroscience Institute, Santa Monica, California, USA
- Saint John's Cancer Institute, Providence Saint John's Health Center, Santa Monica, California, USA
| | - Karina Sergi
- Treatment & Research in Psychedelics Program, Pacific Neuroscience Institute, Santa Monica, California, USA
| | - Regin Jay Mallari
- Treatment & Research in Psychedelics Program, Pacific Neuroscience Institute, Santa Monica, California, USA
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Abed T, Ganser K, Eckert F, Stransky N, Huber SM. Ion channels as molecular targets of glioblastoma electrotherapy. Front Cell Neurosci 2023; 17:1133984. [PMID: 37006466 PMCID: PMC10064067 DOI: 10.3389/fncel.2023.1133984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 02/10/2023] [Indexed: 03/19/2023] Open
Abstract
Therapies with weak, non-ionizing electromagnetic fields comprise FDA-approved treatments such as Tumor Treating Fields (TTFields) that are used for adjuvant therapy of glioblastoma. In vitro data and animal models suggest a variety of biological TTFields effects. In particular, effects ranging from direct tumoricidal, radio- or chemotherapy-sensitizing, metastatic spread-inhibiting, up to immunostimulation have been described. Diverse underlying molecular mechanisms, such as dielectrophoresis of cellular compounds during cytokinesis, disturbing the formation of the spindle apparatus during mitosis, and perforating the plasma membrane have been proposed. Little attention, however, has been paid to molecular structures that are predestinated to percept electromagnetic fields-the voltage sensors of voltage-gated ion channels. The present review article briefly summarizes the mode of action of voltage sensing by ion channels. Moreover, it introduces into the perception of ultra-weak electric fields by specific organs of fishes with voltage-gated ion channels as key functional units therein. Finally, this article provides an overview of the published data on modulation of ion channel function by diverse external electromagnetic field protocols. Combined, these data strongly point to a function of voltage-gated ion channels as transducers between electricity and biology and, hence, to voltage-gated ion channels as primary targets of electrotherapy.
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Affiliation(s)
- Tayeb Abed
- Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
| | - Katrin Ganser
- Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
| | - Franziska Eckert
- Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
- Department of Radiation Oncology, Medical University Vienna, Vienna, Austria
| | - Nicolai Stransky
- Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
- Department of Pharmacology, Toxicology and Clinical Pharmacy, Institute of Pharmacy, University of Tübingen, Tübingen, Germany
| | - Stephan M. Huber
- Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
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A wearable platform for closed-loop stimulation and recording of single-neuron and local field potential activity in freely moving humans. Nat Neurosci 2023; 26:517-527. [PMID: 36804647 PMCID: PMC9991917 DOI: 10.1038/s41593-023-01260-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 01/17/2023] [Indexed: 02/22/2023]
Abstract
Advances in technologies that can record and stimulate deep brain activity in humans have led to impactful discoveries within the field of neuroscience and contributed to the development of novel therapies for neurological and psychiatric disorders. Further progress, however, has been hindered by device limitations in that recording of single-neuron activity during freely moving behaviors in humans has not been possible. Additionally, implantable neurostimulation devices, currently approved for human use, have limited stimulation programmability and restricted full-duplex bidirectional capability. In this study, we developed a wearable bidirectional closed-loop neuromodulation system (Neuro-stack) and used it to record single-neuron and local field potential activity during stationary and ambulatory behavior in humans. Together with a highly flexible and customizable stimulation capability, the Neuro-stack provides an opportunity to investigate the neurophysiological basis of disease, develop improved responsive neuromodulation therapies, explore brain function during naturalistic behaviors in humans and, consequently, bridge decades of neuroscientific findings across species.
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Scangos KW, State MW, Miller AH, Baker JT, Williams LM. New and emerging approaches to treat psychiatric disorders. Nat Med 2023; 29:317-333. [PMID: 36797480 PMCID: PMC11219030 DOI: 10.1038/s41591-022-02197-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/21/2022] [Indexed: 02/18/2023]
Abstract
Psychiatric disorders are highly prevalent, often devastating diseases that negatively impact the lives of millions of people worldwide. Although their etiological and diagnostic heterogeneity has long challenged drug discovery, an emerging circuit-based understanding of psychiatric illness is offering an important alternative to the current reliance on trial and error, both in the development and in the clinical application of treatments. Here we review new and emerging treatment approaches, with a particular emphasis on the revolutionary potential of brain-circuit-based interventions for precision psychiatry. Limitations of circuit models, challenges of bringing precision therapeutics to market and the crucial advances needed to overcome these obstacles are presented.
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Affiliation(s)
- Katherine W Scangos
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
| | - Matthew W State
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Andrew H Miller
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Justin T Baker
- McLean Hospital Institute for Technology in Psychiatry, Belmont, MA, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Mental Illness Research Education and Clinical Center (MIRECC), VA Palo Alto Health Care System, Palo Alto, CA, USA
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Fang H, Yang Y. Predictive neuromodulation of cingulo-frontal neural dynamics in major depressive disorder using a brain-computer interface system: A simulation study. Front Comput Neurosci 2023; 17:1119685. [PMID: 36950505 PMCID: PMC10025398 DOI: 10.3389/fncom.2023.1119685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 02/15/2023] [Indexed: 03/08/2023] Open
Abstract
Introduction Deep brain stimulation (DBS) is a promising therapy for treatment-resistant major depressive disorder (MDD). MDD involves the dysfunction of a brain network that can exhibit complex nonlinear neural dynamics in multiple frequency bands. However, current open-loop and responsive DBS methods cannot track the complex multiband neural dynamics in MDD, leading to imprecise regulation of symptoms, variable treatment effects among patients, and high battery power consumption. Methods Here, we develop a closed-loop brain-computer interface (BCI) system of predictive neuromodulation for treating MDD. We first use a biophysically plausible ventral anterior cingulate cortex (vACC)-dorsolateral prefrontal cortex (dlPFC) neural mass model of MDD to simulate nonlinear and multiband neural dynamics in response to DBS. We then use offline system identification to build a dynamic model that predicts the DBS effect on neural activity. We next use the offline identified model to design an online BCI system of predictive neuromodulation. The online BCI system consists of a dynamic brain state estimator and a model predictive controller. The brain state estimator estimates the MDD brain state from the history of neural activity and previously delivered DBS patterns. The predictive controller takes the estimated MDD brain state as the feedback signal and optimally adjusts DBS to regulate the MDD neural dynamics to therapeutic targets. We use the vACC-dlPFC neural mass model as a simulation testbed to test the BCI system and compare it with state-of-the-art open-loop and responsive DBS treatments of MDD. Results We demonstrate that our dynamic model accurately predicts nonlinear and multiband neural activity. Consequently, the predictive neuromodulation system accurately regulates the neural dynamics in MDD, resulting in significantly smaller control errors and lower DBS battery power consumption than open-loop and responsive DBS. Discussion Our results have implications for developing future precisely-tailored clinical closed-loop DBS treatments for MDD.
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Affiliation(s)
- Hao Fang
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, United States
| | - Yuxiao Yang
- Ministry of Education (MOE) Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University, Hangzhou, Zhejiang, China
- State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- *Correspondence: Yuxiao Yang
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Lai Y, Dai L, Wang T, Zhang Y, Zhao Y, Wang F, Liu Q, Zhan S, Li D, Jin H, Fang Y, Voon V, Sun B. Structural and functional correlates of the response to deep brain stimulation at ventral capsule/ventral striatum region for treatment-resistant depression. J Neurol Neurosurg Psychiatry 2022; 94:379-388. [PMID: 36585242 PMCID: PMC10176394 DOI: 10.1136/jnnp-2022-329702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 12/11/2022] [Indexed: 12/31/2022]
Abstract
BACKGROUND Though deep brain stimulation (DBS) shows increasing potential in treatment-resistant depression (TRD), the underlying neural mechanisms remain unclear. Here, we investigated functional and structural connectivities related to and predictive of clinical effectiveness of DBS at ventral capsule/ventral striatum region for TRD. METHODS Stimulation effects of 71 stimulation settings in 10 TRD patients were assessed. The electric fields were estimated and combined with normative functional and structural connectomes to identify connections as well as fibre tracts beneficial for outcome. We calculated stimulation-dependent optimal connectivity and constructed models to predict outcome. Leave-one-out cross-validation was used to validate the prediction value. RESULTS Successful prediction of antidepressant effectiveness in out-of-sample patients was achieved by the optimal connectivity profiles constructed with both the functional connectivity (R=0.49 at p<10-4; deviated by 14.4±10.9% from actual, p<0.001) and structural connectivity (R=0.51 at p<10-5; deviated by 15.2±11.5% from actual, p<10-5). Frontothalamic pathways and cortical projections were delineated for optimal clinical outcome. Similarity estimates between optimal connectivity profile from one modality (functional/structural) and individual brain connectivity in the other modality (structural/functional) significantly cross-predicted the outcome of DBS. The optimal structural and functional connectivity mainly converged at the ventral and dorsal lateral prefrontal cortex and orbitofrontal cortex. CONCLUSIONS Connectivity profiles and fibre tracts following frontothalamic streamlines appear to predict outcome of DBS for TRD. The findings shed light on the neural pathways in depression and may be used to guide both presurgical planning and postsurgical programming after further validation.
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Affiliation(s)
- Yijie Lai
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lulin Dai
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Wang
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingying Zhang
- Department of Neurosurgery, Clinical Neuroscience Center Comprehensive Epilepsy Unit, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yijie Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Fengting Wang
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qimin Liu
- Department of Psychology and Human Development, Vanderbilt University, Nashville, Tennessee, USA
| | - Shikun Zhan
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dianyou Li
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haiyan Jin
- Department of Psychiatry, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yiru Fang
- Clinical Research Center and Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Valerie Voon
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China .,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.,Department of Psychiatry, Cambridge University, Cambridge, UK
| | - Bomin Sun
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Drew L. Wiring up the brain to beat depression. Nature 2022; 608:S46-S47. [DOI: 10.1038/d41586-022-02209-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Malekmohammadi M, Mustakos R, Sheth S, Pouratian N, McIntyre CC, Bijanki KR, Tsolaki E, Chiu K, Robinson ME, Adkinson JA, Oswalt D, Carcieri S. Automated optimization of deep brain stimulation parameters for modulating neuroimaging-based targets. J Neural Eng 2022; 19:10.1088/1741-2552/ac7e6c. [PMID: 35790135 PMCID: PMC11090244 DOI: 10.1088/1741-2552/ac7e6c] [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: 02/25/2022] [Accepted: 07/05/2022] [Indexed: 11/12/2022]
Abstract
Objective.Therapeutic efficacy of deep brain stimulation (DBS) in both established and emerging indications, is highly dependent on accurate lead placement and optimized clinical programming. The latter relies on clinicians' experience to search among available sets of stimulation parameters and can be limited by the time constraints of clinical practice. Recent innovations in device technology have expanded the number of possible electrode configurations and parameter sets available to clinicians, amplifying the challenge of time constraints. We hypothesize that patient specific neuroimaging data can effectively assist the clinical programming using automated algorithms.Approach.This paper introduces the DBS Illumina 3D algorithm as a tool which uses patient-specific imaging to find stimulation settings that optimizes activating a target area while minimizing the stimulation of areas outside the target that could result in unknown or undesired side effects. This approach utilizes preoperative neuroimaging data paired with the postoperative reconstruction of the lead trajectory to search the available stimulation space and identify optimized stimulation parameters. We describe the application of this algorithm in three patients with treatment-resistant depression who underwent bilateral implantation of DBS in subcallosal cingulate cortex and ventral capsule/ventral striatum using tractography optimized targeting with an imaging defined target previously described.Main results.Compared to the stimulation settings selected by the clinicians (informed by anatomy), stimulation settings produced by the algorithm that achieved similar or greater target coverage, produced a significantly smaller stimulation area that spilled outside the target (P= 0.002).Significance. The DBS Illumina 3D algorithm is seamlessly integrated with the clinician programmer software and effectively and rapidly assists clinicians with the analysis of image based anatomy, and provides a starting point to search the highly complex stimulation parameter space and arrive at the stimulation settings that optimize activating a target area.
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Affiliation(s)
- Mahsa Malekmohammadi
- Boston Scientific Neuromodulation, 25155 Rye Canyon Loop, Valencia, CA 91355, USA
| | - Richard Mustakos
- Boston Scientific Neuromodulation, 25155 Rye Canyon Loop, Valencia, CA 91355, USA
| | - Sameer Sheth
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Nader Pouratian
- Department of Neurological Surgery, University of Texas Southwestern Medical Center, 8353 Harry Hines Blvd MC8855, Dallas, TX 75239, USA
| | - Cameron C. McIntyre
- Departments of Biomedical Engineering and Neurosurgery, Duke University, 100 Science Drive, Durham, NC 27708, USA
| | - Kelly R. Bijanki
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Evangelia Tsolaki
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, 300 Stein Plaza Suite 562, Los Angeles, CA 90095, USA
| | - Kevin Chiu
- Brainlab, Inc., 5 Westbrook Corporate Center, Suite 1000, Westchester IL 60154, USA
| | - Meghan E. Robinson
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Joshua A. Adkinson
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Denise Oswalt
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Stephen Carcieri
- Boston Scientific Neuromodulation, 25155 Rye Canyon Loop, Valencia, CA 91355, USA
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Figee M, Riva-Posse P, Choi KS, Bederson L, Mayberg HS, Kopell BH. Deep Brain Stimulation for Depression. Neurotherapeutics 2022; 19:1229-1245. [PMID: 35817944 PMCID: PMC9587188 DOI: 10.1007/s13311-022-01270-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2022] [Indexed: 11/29/2022] Open
Abstract
Deep brain stimulation has been extensively studied as a therapeutic option for treatment-resistant depression (TRD). DBS across different targets is associated with on average 60% response rates in previously refractory chronically depressed patients. However, response rates vary greatly between patients and between studies and often require extensive trial-and-error optimizations of stimulation parameters. Emerging evidence from tractography imaging suggests that targeting combinations of white matter tracts, rather than specific grey matter regions, is necessary for meaningful antidepressant response to DBS. In this article, we review efficacy of various DBS targets for TRD, which networks are involved in their therapeutic effects, and how we can use this information to improve targeting and programing of DBS for individual patients. We will also highlight how to integrate these DBS network findings into developing adaptive stimulation and optimal trial designs.
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Affiliation(s)
- Martijn Figee
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Patricio Riva-Posse
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Georgia, GA, USA
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lucia Bederson
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Helen S Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brian H Kopell
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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