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Vissani M, Bush A, Lipski WJ, Fischer P, Neudorfer C, Holt LL, Fiez JA, Turner RS, Richardson RM. Spike-phase coupling of subthalamic neurons to posterior opercular cortex predicts speech sound accuracy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.18.562969. [PMID: 37905141 PMCID: PMC10614892 DOI: 10.1101/2023.10.18.562969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Speech provides a rich context for understanding how cortical interactions with the basal ganglia contribute to unique human behaviors, but opportunities for direct intracranial recordings across cortical-basal ganglia networks are rare. We recorded electrocorticographic signals in the cortex synchronously with single units in the basal ganglia during awake neurosurgeries where subjects spoke syllable repetitions. We discovered that individual STN neurons have transient (200ms) spike-phase coupling (SPC) events with multiple cortical regions. The spike timing of STN neurons was coordinated with the phase of theta-alpha oscillations in the posterior supramarginal and superior temporal gyrus during speech planning and production. Speech sound errors occurred when this STN-cortical interaction was delayed. Our results suggest that the STN supports mechanisms of speech planning and auditory-sensorimotor integration during speech production that are required to achieve high fidelity of the phonological and articulatory representation of the target phoneme. These findings establish a framework for understanding cortical-basal ganglia interaction in other human behaviors, and additionally indicate that firing-rate based models are insufficient for explaining basal ganglia circuit behavior.
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Affiliation(s)
- Matteo Vissani
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Alan Bush
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Witold J. Lipski
- Department of Neurobiology, Systems Neuroscience Center and Center for Neuroscience, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
| | - Petra Fischer
- School of Physiology, Pharmacology & Neuroscience, University of Bristol, University Walk, BS8 1TD Bristol, United Kingdom
| | - Clemens Neudorfer
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Lori L. Holt
- Department of Psychology, The University of Texas at Austin, Austin, TX 78712 USA
| | - Julie A. Fiez
- Department of Psychology, University of Pittsburgh, Pittsburgh 15260, PA, USA
| | - Robert S. Turner
- Department of Neurobiology, Systems Neuroscience Center and Center for Neuroscience, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, USA
| | - R. Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, 02115, USA
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2
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Wu X, Wellington S, Fu Z, Zhang D. Speech decoding from stereo-electroencephalography (sEEG) signals using advanced deep learning methods. J Neural Eng 2024; 21:036055. [PMID: 38885688 DOI: 10.1088/1741-2552/ad593a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 06/17/2024] [Indexed: 06/20/2024]
Abstract
Objective.Brain-computer interfaces (BCIs) are technologies that bypass damaged or disrupted neural pathways and directly decode brain signals to perform intended actions. BCIs for speech have the potential to restore communication by decoding the intended speech directly. Many studies have demonstrated promising results using invasive micro-electrode arrays and electrocorticography. However, the use of stereo-electroencephalography (sEEG) for speech decoding has not been fully recognized.Approach.In this research, recently released sEEG data were used to decode Dutch words spoken by epileptic participants. We decoded speech waveforms from sEEG data using advanced deep-learning methods. Three methods were implemented: a linear regression method, an recurrent neural network (RNN)-based sequence-to-sequence model (RNN), and a transformer model.Main results.Our RNN and transformer models outperformed the linear regression significantly, while no significant difference was found between the two deep-learning methods. Further investigation on individual electrodes showed that the same decoding result can be obtained using only a few of the electrodes.Significance.This study demonstrated that decoding speech from sEEG signals is possible, and the location of the electrodes is critical to the decoding performance.
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Affiliation(s)
- Xiaolong Wu
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Scott Wellington
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Zhichun Fu
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Dingguo Zhang
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
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Bush A, Zou JF, Lipski WJ, Kokkinos V, Richardson RM. Aperiodic components of local field potentials reflect inherent differences between cortical and subcortical activity. Cereb Cortex 2024; 34:bhae186. [PMID: 38725290 PMCID: PMC11082477 DOI: 10.1093/cercor/bhae186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 04/15/2024] [Accepted: 04/18/2024] [Indexed: 05/13/2024] Open
Abstract
Information flow in brain networks is reflected in local field potentials that have both periodic and aperiodic components. The 1/fχ aperiodic component of the power spectra tracks arousal and correlates with other physiological and pathophysiological states. Here we explored the aperiodic activity in the human thalamus and basal ganglia in relation to simultaneously recorded cortical activity. We elaborated on the parameterization of the aperiodic component implemented by specparam (formerly known as FOOOF) to avoid parameter unidentifiability and to obtain independent and more easily interpretable parameters. This allowed us to seamlessly fit spectra with and without an aperiodic knee, a parameter that captures a change in the slope of the aperiodic component. We found that the cortical aperiodic exponent χ, which reflects the decay of the aperiodic component with frequency, is correlated with Parkinson's disease symptom severity. Interestingly, no aperiodic knee was detected from the thalamus, the pallidum, or the subthalamic nucleus, which exhibited an aperiodic exponent significantly lower than in cortex. These differences were replicated in epilepsy patients undergoing intracranial monitoring that included thalamic recordings. The consistently lower aperiodic exponent and lack of an aperiodic knee from all subcortical recordings may reflect cytoarchitectonic and/or functional differences. SIGNIFICANCE STATEMENT The aperiodic component of local field potentials can be modeled to produce useful and reproducible indices of neural activity. Here we refined a widely used phenomenological model for extracting aperiodic parameters (namely the exponent, offset and knee), with which we fit cortical, basal ganglia, and thalamic intracranial local field potentials, recorded from unique cohorts of movement disorders and epilepsy patients. We found that the aperiodic exponent in motor cortex is higher in Parkinson's disease patients with more severe motor symptoms, suggesting that aperiodic features may have potential as electrophysiological biomarkers for movement disorders symptoms. Remarkably, we found conspicuous differences in the aperiodic parameters of basal ganglia and thalamic signals compared to those from neocortex.
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Affiliation(s)
- Alan Bush
- Brain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Department of Neurosurgery, Boston, MA 02115, USA
| | - Jasmine F Zou
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02115, USA
| | - Witold J Lipski
- Department of Neurological Surgery, University of Pittsburgh, School of Medicine, Pittsburgh, PA 15213, USA
| | - Vasileios Kokkinos
- Brain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Department of Neurosurgery, Boston, MA 02115, USA
| | - R Mark Richardson
- Brain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Department of Neurosurgery, Boston, MA 02115, USA
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02115, USA
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4
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Paulk AC, Salami P, Zelmann R, Cash SS. Electrode Development for Epilepsy Diagnosis and Treatment. Neurosurg Clin N Am 2024; 35:135-149. [PMID: 38000837 DOI: 10.1016/j.nec.2023.09.003] [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] [Indexed: 11/26/2023]
Abstract
Recording neural activity has been a critical aspect in the diagnosis and treatment of patients with epilepsy. For those with intractable epilepsy, intracranial neural monitoring has been of substantial importance. Clinically, however, methods for recording neural information have remained essentially unchanged for decades. Over the last decade or so, rapid advances in electrode technology have begun to change this landscape. New systems allow for the observation of neural activity with high spatial resolution and, in some cases, at the level of the activity of individual neurons. These new tools have contributed greatly to our understanding of brain function and dysfunction. Here, the authors review the primary technologies currently in use in humans. The authors discuss other possible systems, some of the challenges which come along with these devices, and how they will become incorporated into the clinical workflow. Ultimately, the expectation is that these new, high-density, high-spatial-resolution recording systems will become a valuable part of the clinical arsenal used in the diagnosis and surgical management of epilepsy.
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Affiliation(s)
- Angelique C Paulk
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA.
| | - Pariya Salami
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA
| | - Rina Zelmann
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA
| | - Sydney S Cash
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA
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Luo S, Angrick M, Coogan C, Candrea DN, Wyse‐Sookoo K, Shah S, Rabbani Q, Milsap GW, Weiss AR, Anderson WS, Tippett DC, Maragakis NJ, Clawson LL, Vansteensel MJ, Wester BA, Tenore FV, Hermansky H, Fifer MS, Ramsey NF, Crone NE. Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2304853. [PMID: 37875404 PMCID: PMC10724434 DOI: 10.1002/advs.202304853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 09/18/2023] [Indexed: 10/26/2023]
Abstract
Brain-computer interfaces (BCIs) can be used to control assistive devices by patients with neurological disorders like amyotrophic lateral sclerosis (ALS) that limit speech and movement. For assistive control, it is desirable for BCI systems to be accurate and reliable, preferably with minimal setup time. In this study, a participant with severe dysarthria due to ALS operates computer applications with six intuitive speech commands via a chronic electrocorticographic (ECoG) implant over the ventral sensorimotor cortex. Speech commands are accurately detected and decoded (median accuracy: 90.59%) throughout a 3-month study period without model retraining or recalibration. Use of the BCI does not require exogenous timing cues, enabling the participant to issue self-paced commands at will. These results demonstrate that a chronically implanted ECoG-based speech BCI can reliably control assistive devices over long time periods with only initial model training and calibration, supporting the feasibility of unassisted home use.
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Affiliation(s)
- Shiyu Luo
- Department of Biomedical EngineeringJohns Hopkins University School of MedicineBaltimoreMD21205USA
| | - Miguel Angrick
- Department of NeurologyJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | - Christopher Coogan
- Department of NeurologyJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | - Daniel N. Candrea
- Department of Biomedical EngineeringJohns Hopkins University School of MedicineBaltimoreMD21205USA
| | - Kimberley Wyse‐Sookoo
- Department of Biomedical EngineeringJohns Hopkins University School of MedicineBaltimoreMD21205USA
| | - Samyak Shah
- Department of NeurologyJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | - Qinwan Rabbani
- Department of Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreMD21218USA
- Center for Language and Speech ProcessingJohns Hopkins UniversityBaltimoreMD21218USA
| | - Griffin W. Milsap
- Research and Exploratory Development DepartmentJohns Hopkins University Applied Physics LaboratoryLaurelMD20723USA
| | - Alexander R. Weiss
- Department of NeurologyJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | - William S. Anderson
- Department of NeurosurgeryJohns Hopkins University School of MedicineBaltimoreMD21205USA
| | - Donna C. Tippett
- Department of NeurologyJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Department of Otolaryngology‐Head and Neck SurgeryJohns Hopkins University School of MedicineBaltimoreMD21205USA
- Department of Physical Medicine and RehabilitationJohns Hopkins University School of MedicineBaltimoreMD21205USA
| | - Nicholas J. Maragakis
- Department of NeurologyJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | - Lora L. Clawson
- Department of NeurologyJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | - Mariska J. Vansteensel
- Department of Neurology and NeurosurgeryUMC Utrecht Brain CenterUtrecht3584The Netherlands
| | - Brock A. Wester
- Research and Exploratory Development DepartmentJohns Hopkins University Applied Physics LaboratoryLaurelMD20723USA
| | - Francesco V. Tenore
- Research and Exploratory Development DepartmentJohns Hopkins University Applied Physics LaboratoryLaurelMD20723USA
| | - Hynek Hermansky
- Department of Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreMD21218USA
- Center for Language and Speech ProcessingJohns Hopkins UniversityBaltimoreMD21218USA
| | - Matthew S. Fifer
- Research and Exploratory Development DepartmentJohns Hopkins University Applied Physics LaboratoryLaurelMD20723USA
| | - Nick F. Ramsey
- Department of Neurology and NeurosurgeryUMC Utrecht Brain CenterUtrecht3584The Netherlands
| | - Nathan E. Crone
- Department of NeurologyJohns Hopkins University School of MedicineBaltimoreMD21287USA
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6
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Peterson V, Vissani M, Luo S, Rabbani Q, Crone NE, Bush A, Mark Richardson R. A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.05.535577. [PMID: 37066306 PMCID: PMC10104030 DOI: 10.1101/2023.04.05.535577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Neurosurgical procedures that enable direct brain recordings in awake patients offer unique opportunities to explore the neurophysiology of human speech. The scarcity of these opportunities and the altruism of participating patients compel us to apply the highest rigor to signal analysis. Intracranial electroencephalography (iEEG) signals recorded during overt speech can contain a speech artifact that tracks the fundamental frequency (F0) of the participant's voice, involving the same high-gamma frequencies that are modulated during speech production and perception. To address this artifact, we developed a spatial-filtering approach to identify and remove acoustic-induced contaminations of the recorded signal. We found that traditional reference schemes jeopardized signal quality, whereas our data-driven method denoised the recordings while preserving underlying neural activity.
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Affiliation(s)
- Victoria Peterson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, United States
- Instituto de Matemática Aplicada del Litoral, IMAL, FIQ-UNL, CONICET, Santa Fe, Argentina
| | - Matteo Vissani
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, United States
| | - Shiyu Luo
- Department of Biomedical Engineering, The Johns Hopkins University School of Medicine
| | - Qinwan Rabbani
- Department of Electrical & Computer Engineering, The Johns Hopkins University
| | - Nathan E. Crone
- Department of Neurology, The Johns Hopkins University School of Medicine
| | - Alan Bush
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, United States
| | - R. Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
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7
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Rezaei MR, Jeoung H, Gharamani A, Saha U, Bhat V, Popovic MR, Yousefi A, Chen R, Lankarany M. Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input discriminative-generative decoder model. J Neural Eng 2023; 20:056016. [PMID: 37473753 DOI: 10.1088/1741-2552/ace932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 07/20/2023] [Indexed: 07/22/2023]
Abstract
Objective. The subthalamic nucleus (STN) of the basal ganglia interacts with the medial prefrontal cortex (mPFC) and shapes a control loop, specifically when the brain receives contradictory information from either different sensory systems or conflicting information from sensory inputs and prior knowledge that developed in the brain. Experimental studies demonstrated that significant increases in theta activities (2-8 Hz) in both the STN and mPFC as well as increased phase synchronization between mPFC and STN are prominent features of conflict processing. While these neural features reflect the importance of STN-mPFC circuitry in conflict processing, a low-dimensional representation of the mPFC-STN interaction referred to as a cognitive state, that links neural activities generated by these sub-regions to behavioral signals (e.g. the response time), remains to be identified.Approach. Here, we propose a new model, namely, the heterogeneous input discriminative-generative decoder (HI-DGD) model, to infer a cognitive state underlying decision-making based on neural activities (STN and mPFC) and behavioral signals (individuals' response time) recorded in ten Parkinson's disease (PD) patients while they performed a Stroop task. PD patients may have conflict processing which is quantitatively (may be qualitative in some) different from healthy populations.Main results. Using extensive synthetic and experimental data, we showed that the HI-DGD model can diffuse information from neural and behavioral data simultaneously and estimate cognitive states underlying conflict and non-conflict trials significantly better than traditional methods. Additionally, the HI-DGD model identified which neural features made significant contributions to conflict and non-conflict choices. Interestingly, the estimated features match well with those reported in experimental studies.Significance. Finally, we highlight the capability of the HI-DGD model in estimating a cognitive state from a single trial of observation, which makes it appropriate to be utilized in closed-loop neuromodulation systems.
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Affiliation(s)
- Mohammad R Rezaei
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
- KITE Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Haseul Jeoung
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Ayda Gharamani
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
- Worcester Polytechnic Institute, MA, United States of America
| | - Utpal Saha
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Venkat Bhat
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University Health Network and University of Toronto, Toronto, ON, Canada
| | - Milos R Popovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- KITE Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Ali Yousefi
- Worcester Polytechnic Institute, MA, United States of America
| | - Robert Chen
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
| | - Milad Lankarany
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Krembil Research Institute, University Health Network (UHN), Toronto, ON, Canada
- KITE Research Institute, University Health Network (UHN), Toronto, ON, Canada
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8
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Wong JK, Mayberg HS, Wang DD, Richardson RM, Halpern CH, Krinke L, Arlotti M, Rossi L, Priori A, Marceglia S, Gilron R, Cavanagh JF, Judy JW, Miocinovic S, Devergnas AD, Sillitoe RV, Cernera S, Oehrn CR, Gunduz A, Goodman WK, Petersen EA, Bronte-Stewart H, Raike RS, Malekmohammadi M, Greene D, Heiden P, Tan H, Volkmann J, Voon V, Li L, Sah P, Coyne T, Silburn PA, Kubu CS, Wexler A, Chandler J, Provenza NR, Heilbronner SR, Luciano MS, Rozell CJ, Fox MD, de Hemptinne C, Henderson JM, Sheth SA, Okun MS. Proceedings of the 10th annual deep brain stimulation think tank: Advances in cutting edge technologies, artificial intelligence, neuromodulation, neuroethics, interventional psychiatry, and women in neuromodulation. Front Hum Neurosci 2023; 16:1084782. [PMID: 36819295 PMCID: PMC9933515 DOI: 10.3389/fnhum.2022.1084782] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/12/2022] [Indexed: 02/05/2023] Open
Abstract
The deep brain stimulation (DBS) Think Tank X was held on August 17-19, 2022 in Orlando FL. The session organizers and moderators were all women with the theme women in neuromodulation. Dr. Helen Mayberg from Mt. Sinai, NY was the keynote speaker. She discussed milestones and her experiences in developing depression 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 the logistical and ethical issues facing the field. The consensus among the DBS Think Tank X speakers was that DBS has continued to expand in scope however several indications have reached the "trough of disillusionment." DBS for depression was considered as "re-emerging" and approaching a slope of enlightenment. DBS for depression will soon re-enter clinical trials. The group estimated that globally more than 244,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. This year's meeting was focused on advances in the following areas: neuromodulation in Europe, Asia, and Australia; cutting-edge technologies, closed loop DBS, DBS tele-health, neuroethics, lesion therapy, interventional psychiatry, and adaptive DBS.
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Affiliation(s)
- Joshua K. Wong
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Helen S. Mayberg
- Department of Neurology, Neurosurgery, Psychiatry, and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Doris D. Wang
- Department of Neurological Surgery, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - R. Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Casey H. Halpern
- Richards Medical Research Laboratories, Department of Neurosurgery, Perelman School of Medicine, Pennsylvania Hospital, University of Pennsylvania, Philadelphia, PA, United States
| | - Lothar Krinke
- Newronika, Goose Creek, SC, United States
- Department of Neuroscience, West Virginia University, Morgantown, WV, United States
| | | | | | | | | | | | - James F. Cavanagh
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States
| | - Jack W. Judy
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States
| | - Svjetlana Miocinovic
- Department of Neurology, School of Medicine, Emory University, Atlanta, GA, United States
| | - Annaelle D. Devergnas
- Department of Neurology, School of Medicine, Emory University, Atlanta, GA, United States
| | - Roy V. Sillitoe
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, United States
| | - Stephanie Cernera
- Department of Neurological Surgery, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Carina R. Oehrn
- Department of Neurological Surgery, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Aysegul Gunduz
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Wayne K. Goodman
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Erika A. Petersen
- Department of Neurosurgery, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Helen Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Robert S. Raike
- Restorative Therapies Group Implantables, Research, and Core Technology, Medtronic Inc., Minneapolis, MN, United States
| | | | - David Greene
- NeuroPace, Inc., Mountain View, CA, United States
| | - Petra Heiden
- Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Huiling Tan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jens Volkmann
- Department of Neurology, University of Würzburg, Würzburg, Germany
| | - Valerie Voon
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Luming Li
- National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Pankaj Sah
- Queensland Brain Institute, University of Queensland, St Lucia, QLD, Australia
| | - Terry Coyne
- Queensland Brain Institute, University of Queensland, St Lucia, QLD, Australia
| | - Peter A. Silburn
- Queensland Brain Institute, University of Queensland, St Lucia, QLD, Australia
| | - Cynthia S. Kubu
- Department of Neurology, Cleveland Clinic, Cleveland, OH, United States
| | - Anna Wexler
- Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, PA, United States
| | - Jennifer Chandler
- Centre for Health Law, Policy, and Ethics, Faculty of Law, University of Ottawa, Ottawa, ON, Canada
| | - Nicole R. Provenza
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Sarah R. Heilbronner
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, United States
| | - Marta San Luciano
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Christopher J. Rozell
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Michael D. Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, Radiology, and Neurosurgery, Brigham and Women’s Hospital, Boston, MA, United States
| | - Coralie de Hemptinne
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Jaimie M. Henderson
- Department of Neurosurgery, Stanford University, Stanford, CA, United States
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Michael S. Okun
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
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9
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Berger JI, Johari K, Kovach CK, Greenlee JD. Speech artifact is also present in spike data. Neuroimage 2022; 263:119642. [PMID: 36150607 DOI: 10.1016/j.neuroimage.2022.119642] [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: 03/07/2022] [Revised: 07/25/2022] [Accepted: 09/20/2022] [Indexed: 11/23/2022] Open
Abstract
Bush et al. (2022) highlight that brain recordings examining speech production can be significantly affected by microphonic artifact, which would change the interpretation of these kinds of data. While these findings are vital in determining whether data are artifactual or physiological in origin, frequencies were only examined up to 250 Hz (i.e., local field potentials), which would imply that spike-related data (single or multi-neuron recordings) are unaffected. We highlight here that this type of contamination may also be present in unit recordings, with the same aim to understand genuine neural mechanisms rather than mis-interpreting artifactual data.
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Affiliation(s)
- Joel I Berger
- Department of Neurosurgery, University of Iowa, Iowa City, 52242 USA
| | - Karim Johari
- Human Neurophysiology & Neuromodulation Lab, Department of Communication Sciences &, Disorders. Louisiana State University, Baton Rouge, 70803 USA
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