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Ding Z, Wang Y, Niu Z, Ouyang G, Li X. The effect of EEG microstate on the characteristics of TMS-EEG. Comput Biol Med 2024; 173:108332. [PMID: 38555703 DOI: 10.1016/j.compbiomed.2024.108332] [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: 01/28/2024] [Revised: 02/26/2024] [Accepted: 03/17/2024] [Indexed: 04/02/2024]
Abstract
OBJECTIVE Differences in neural states at the time of transcranial magnetic stimulation (TMS) can lead to variations in the effectiveness of TMS stimulation. Strategies that aim to lock neural activity states and improve the precision of stimulation timing in TMS optimization should gradually receive attention. One feasible approach is to utilize microstate locking for TMS stimulation, and understanding the impact of microstates at the time of stimulation on TMS response forms the foundation of this approach. APPROACH TMS-EEG data were extracted from 21 healthy subjects through experiments. Based on the different microstates at the time of stimulation, the trials were classified into four datasets. TMS-evoked potential (TEP), topographical distribution, and natural frequency, were computed for each dataset to explore the differences in TMS-EEG characteristics across different microstates. MAIN RESULTS The N100 component of microstate C group (-2.376 μV) was significantly higher (p = 0.003) than of microstate D group (-1.739 μV), and the P180 component of microstate D group (2.482 μV) was significantly higher (p = 0.024) than of microstate B group (1.766 μV) and slightly higher (p = 0.058) than of microstate C group (1.863 μV) by calculating the ROI. The topographical distribution of TEP components during microstate C and microstate D still retained the template characteristics of the microstate at the time of stimulation, and the natural frequencies did not differ among the four classical microstates. SIGNIFICANCE This study showed the potential for future closed-loop TMS based on microstates and would guiding the development of microstate-based closed-loop TMS techniques.
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Affiliation(s)
- Zhaohuan Ding
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 510640, China
| | - Yong Wang
- Zhuhai UM Science & Technology Research Institute, Zhuhai, 519031, China
| | - Zikang Niu
- Aviation Psychology Research Office, Air Force Medical Center, Beijing, 100142, China
| | - Gaoxiang Ouyang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
| | - Xiaoli Li
- Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, 510335, China; School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China.
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2
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Li L, Zhang B, Zhao W, Sheng D, Yin L, Sheng X, Yao D. Multimodal Technologies for Closed-Loop Neural Modulation and Sensing. Adv Healthc Mater 2024:e2303289. [PMID: 38640468 DOI: 10.1002/adhm.202303289] [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: 09/27/2023] [Revised: 03/11/2024] [Indexed: 04/21/2024]
Abstract
Existing methods for studying neural circuits and treating neurological disorders are typically based on physical and chemical cues to manipulate and record neural activities. These approaches often involve predefined, rigid, and unchangeable signal patterns, which cannot be adjusted in real time according to the patient's condition or neural activities. With the continuous development of neural interfaces, conducting in vivo research on adaptive and modifiable treatments for neurological diseases and neural circuits is now possible. In this review, current and potential integration of various modalities to achieve precise, closed-loop modulation, and sensing in neural systems are summarized. Advanced materials, devices, or systems that generate or detect electrical, magnetic, optical, acoustic, or chemical signals are highlighted and utilized to interact with neural cells, tissues, and networks for closed-loop interrogation. Further, the significance of developing closed-loop techniques for diagnostics and treatment of neurological disorders such as epilepsy, depression, rehabilitation of spinal cord injury patients, and exploration of brain neural circuit functionality is elaborated.
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Affiliation(s)
- Lizhu Li
- Sichuan Provincial Key Laboratory for Human Disease Gene Study and the Center for Medical Genetics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bozhen Zhang
- School of Materials Science and Engineering, The Key Laboratory of Advanced Materials of Ministry of Education, State Key Laboratory of New Ceramics and Fine Processing, Laboratory of Flexible Electronics Technology, Tsinghua University, Beijing, 100084, China
| | - Wenxin Zhao
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Institute for Precision Medicine, Laboratory of Flexible Electronics Technology, IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, China
| | - David Sheng
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Institute for Precision Medicine, Laboratory of Flexible Electronics Technology, IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, China
| | - Lan Yin
- School of Materials Science and Engineering, The Key Laboratory of Advanced Materials of Ministry of Education, State Key Laboratory of New Ceramics and Fine Processing, Laboratory of Flexible Electronics Technology, Tsinghua University, Beijing, 100084, China
| | - Xing Sheng
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Institute for Precision Medicine, Laboratory of Flexible Electronics Technology, IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, China
| | - Dezhong Yao
- Sichuan Provincial Key Laboratory for Human Disease Gene Study and the Center for Medical Genetics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
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3
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Gogulski J, Cline CC, Ross JM, Parmigiani S, Keller CJ. Reliability of the TMS-evoked potential in dorsolateral prefrontal cortex. Cereb Cortex 2024; 34:bhae130. [PMID: 38596882 PMCID: PMC11004671 DOI: 10.1093/cercor/bhae130] [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: 11/14/2023] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 04/11/2024] Open
Abstract
We currently lack a reliable method to probe cortical excitability noninvasively from the human dorsolateral prefrontal cortex (dlPFC). We recently found that the strength of early and local dlPFC transcranial magnetic stimulation (TMS)-evoked potentials (EL-TEPs) varied widely across dlPFC subregions. Despite these differences in response amplitude, reliability at each target is unknown. Here we quantified within-session reliability of dlPFC EL-TEPs after TMS to six left dlPFC subregions in 15 healthy subjects. We evaluated reliability (concordance correlation coefficient [CCC]) across targets, time windows, quantification methods, regions of interest, sensor- vs. source-space, and number of trials. On average, the medial target was most reliable (CCC = 0.78) and the most anterior target was least reliable (CCC = 0.24). However, all targets except the most anterior were reliable (CCC > 0.7) using at least one combination of the analytical parameters tested. Longer (20 to 60 ms) and later (30 to 60 ms) windows increased reliability compared to earlier and shorter windows. Reliable EL-TEPs (CCC up to 0.86) were observed using only 25 TMS trials at a medial dlPFC target. Overall, medial dlPFC targeting, wider windows, and peak-to-peak quantification improved reliability. With careful selection of target and analytic parameters, highly reliable EL-TEPs can be extracted from the dlPFC after only a small number of trials.
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Affiliation(s)
- Juha Gogulski
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94305, United States
- Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, United States
- Department of Clinical Neurophysiology, HUS Diagnostic Center, Clinical Neurosciences, Helsinki University Hospital and University of Helsinki, Haartmaninkatu 4, Helsinki FI-00029, Finland
| | - Christopher C Cline
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94305, United States
- Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, United States
| | - Jessica M Ross
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94305, United States
- Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, United States
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), 3801 Miranda Avenue, Palo Alto, CA 94394, United States
| | - Sara Parmigiani
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94305, United States
- Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, United States
| | - Corey J Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA 94305, United States
- Wu Tsai Neurosciences Institute, Stanford University, 290 Jane Stanford Way, Stanford, CA 94305, United States
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), 3801 Miranda Avenue, Palo Alto, CA 94394, United States
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4
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Toth J, Kurtin DL, Brosnan M, Arvaneh M. Opportunities and obstacles in non-invasive brain stimulation. Front Hum Neurosci 2024; 18:1385427. [PMID: 38562225 PMCID: PMC10982339 DOI: 10.3389/fnhum.2024.1385427] [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: 02/12/2024] [Accepted: 03/05/2024] [Indexed: 04/04/2024] Open
Abstract
Non-invasive brain stimulation (NIBS) is a complex and multifaceted approach to modulating brain activity and holds the potential for broad accessibility. This work discusses the mechanisms of the four distinct approaches to modulating brain activity non-invasively: electrical currents, magnetic fields, light, and ultrasound. We examine the dual stochastic and deterministic nature of brain activity and its implications for NIBS, highlighting the challenges posed by inter-individual variability, nebulous dose-response relationships, potential biases and neuroanatomical heterogeneity. Looking forward, we propose five areas of opportunity for future research: closed-loop stimulation, consistent stimulation of the intended target region, reducing bias, multimodal approaches, and strategies to address low sample sizes.
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Affiliation(s)
- Jake Toth
- Automatic Control and Systems Engineering, Neuroscience Institute, Insigneo Institute, University of Sheffield, Sheffield, United Kingdom
| | | | - Méadhbh Brosnan
- School of Psychology, University College Dublin, Dublin, Ireland
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Turner Institute for Brain and Mental Health and School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Mahnaz Arvaneh
- Automatic Control and Systems Engineering, Neuroscience Institute, Insigneo Institute, University of Sheffield, Sheffield, United Kingdom
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5
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Zrenner C, Ziemann U. Closed-Loop Brain Stimulation. Biol Psychiatry 2024; 95:545-552. [PMID: 37743002 PMCID: PMC10881194 DOI: 10.1016/j.biopsych.2023.09.014] [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: 05/16/2023] [Revised: 08/24/2023] [Accepted: 09/18/2023] [Indexed: 09/26/2023]
Abstract
In the same way that beauty lies in the eye of the beholder, what a stimulus does to the brain is determined not simply by the nature of the stimulus but by the nature of the brain that is receiving the stimulus at that instant in time. Over the past decades, therapeutic brain stimulation has typically applied open-loop fixed protocols and has largely ignored this principle. Only recent neurotechnological advancements have enabled us to predict the nature of the brain (i.e., the electrophysiological brain state in the next instance in time) with sufficient temporal precision in the range of milliseconds using feedforward algorithms applied to electroencephalography time-series data. This allows stimulation exclusively whenever the targeted brain area is in a prespecified excitability or connectivity state. Preclinical studies have shown that repetitive stimulation during a particular brain state (e.g., high-excitability state), but not during other states, results in lasting modification (e.g., long-term potentiation) of the stimulated circuits. Here, we survey the evidence that this is also possible at the systems level of the human cortex using electroencephalography-informed transcranial magnetic stimulation. We critically discuss opportunities and difficulties in developing brain state-dependent stimulation for more effective long-term modification of pathological brain networks (e.g., in major depressive disorder) than is achievable with conventional fixed protocols. The same real-time electroencephalography-informed transcranial magnetic stimulation technology will allow closing of the loop by recording the effects of stimulation. This information may enable stimulation protocol adaptation that maximizes treatment response. This way, brain states control brain stimulation, thereby introducing a paradigm shift from open-loop to closed-loop stimulation.
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Affiliation(s)
- Christoph Zrenner
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute for Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany.
| | - Ulf Ziemann
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.
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6
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Trajkovic J, Sack AT, Romei V. EEG-based biomarkers predict individual differences in TMS-induced entrainment of intrinsic brain rhythms. Brain Stimul 2024; 17:224-232. [PMID: 38428585 DOI: 10.1016/j.brs.2024.02.016] [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: 10/26/2023] [Revised: 02/23/2024] [Accepted: 02/24/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Entrainment (increase) and modulation (shift) of intrinsic brain oscillations via rhythmic-TMS (rh-TMS) enables to either increase the amplitude of the individual peak oscillatory frequency, or experimentally slowing/accelerating this intrinsic peak oscillatory frequency by slightly shifting it. Both entrainment, and modulation of brain oscillations can lead to different measurable perceptual and cognitive changes. However, there are noticeable between-participant differences in such experimental entrainment outcomes. OBJECTIVE/HYPOTHESIS The current study aimed at explaining these inter-individual differences in entrainment/frequency shift success. Here we hypothesize that the width and the height of the Arnold tongue, i.e., the frequency offsets that can still lead to oscillatory change, can be individually modelled via resting-state neural markers, and may explain and predict efficacy and limitation of successful rhythmic-TMS (rh-TMS) manipulation. METHODS Spectral decomposition of resting-state data was used to extract the spectral curve of alpha activity, serving as a proxy of an individual Arnold tongue. These parameters were then used as predictors of the rh-TMS outcome, when increasing alpha-amplitude (i.e., applying pulse train tuned to the individual alpha frequency, IAF), or modulating the alpha-frequency (i.e., making alpha faster or slower by stimulating at IAF±1Hz frequencies). RESULTS Our results showed that the height of the at-rest alpha curve predicted how well the entrainment increased the intrinsic oscillatory peak frequency, with a higher at-rest spectral curve negatively predicting amplitude-enhancement during entrainment selectively during IAF-stimulation. In contrast, the wider the resting-state alpha curve, the higher the modulation effects aiming to shift the intrinsic frequency towards faster or slower rhythms. CONCLUSION These results not only offer a theoretical and experimental model for explaining the variance across different rh-TMS studies reporting heterogenous rh-TMS outcomes, but also introduce a potential biomarker and corresponding evaluative tool to develop most optimal and personalized rh-TMS protocols, both in research and clinical applications.
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Affiliation(s)
- Jelena Trajkovic
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6229 ER, the Netherlands; Centro studi e ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, Cesena, 47521, Italy.
| | - Alexander T Sack
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6229 ER, the Netherlands
| | - Vincenzo Romei
- Centro studi e ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, Cesena, 47521, Italy; Facultad de Lenguas y Educación, Universidad Antonio de Nebrija, Madrid, 28015, Spain.
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7
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Abbasi S, Alluri S, Leung V, Asbeck P, Makale MT. Design and Validation of Miniaturized Repetitive Transcranial Magnetic Stimulation (rTMS) Head Coils. SENSORS (BASEL, SWITZERLAND) 2024; 24:1584. [PMID: 38475120 DOI: 10.3390/s24051584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a rapidly developing therapeutic modality for the safe and effective treatment of neuropsychiatric disorders. However, clinical rTMS driving systems and head coils are large, heavy, and expensive, so miniaturized, affordable rTMS devices may facilitate treatment access for patients at home, in underserved areas, in field and mobile hospitals, on ships and submarines, and in space. The central component of a portable rTMS system is a miniaturized, lightweight coil. Such a coil, when mated to lightweight driving circuits, must be able to induce B and E fields of sufficient intensity for medical use. This paper newly identifies and validates salient theoretical considerations specific to the dimensional scaling and miniaturization of coil geometries, particularly figure-8 coils, and delineates novel, key design criteria. In this context, the essential requirement of matching coil inductance with the characteristic resistance of the driver switches is highlighted. Computer simulations predicted E- and B-fields which were validated via benchtop experiments. Using a miniaturized coil with dimensions of 76 mm × 38 mm and weighing only 12.6 g, the peak E-field was 87 V/m at a distance of 1.5 cm. Practical considerations limited the maximum voltage and current to 350 V and 3.1 kA, respectively; nonetheless, this peak E-field value was well within the intensity range, 60-120 V/m, generally held to be therapeutically relevant. The presented parameters and results delineate coil and circuit guidelines for a future miniaturized, power-scalable rTMS system able to generate pulsed E-fields of sufficient amplitude for potential clinical use.
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Affiliation(s)
- Shaghayegh Abbasi
- Electrical Engineering Department, University of Portland, Portland, OR 97203, USA
| | - Sravya Alluri
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
- Calit2 Advanced Circuits Laboratory, University of California San Diego, La Jolla, CA 92093, USA
| | - Vincent Leung
- Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76706, USA
| | - Peter Asbeck
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
- Calit2 Advanced Circuits Laboratory, University of California San Diego, La Jolla, CA 92093, USA
| | - Milan T Makale
- Moores Cancer Center, Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA 92093, USA
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8
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Rösch J, Emanuel Vetter D, Baldassarre A, Souza VH, Lioumis P, Roine T, Jooß A, Baur D, Kozák G, Blair Jovellar D, Vaalto S, Romani GL, Ilmoniemi RJ, Ziemann U. Individualized treatment of motor stroke: A perspective on open-loop, closed-loop and adaptive closed-loop brain state-dependent TMS. Clin Neurophysiol 2024; 158:204-211. [PMID: 37945452 DOI: 10.1016/j.clinph.2023.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/11/2023] [Accepted: 10/18/2023] [Indexed: 11/12/2023]
Affiliation(s)
- Johanna Rösch
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - David Emanuel Vetter
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - Antonello Baldassarre
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Victor H Souza
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Pantelis Lioumis
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Timo Roine
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Andreas Jooß
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - David Baur
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - Gábor Kozák
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - D Blair Jovellar
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - Selja Vaalto
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; HUS Diagnostic Center, Clinical Neurophysiology, Clinical Neurosciences, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Gian Luca Romani
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Ulf Ziemann
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany.
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Sinisalo H, Rissanen I, Kahilakoski OP, Souza VH, Tommila T, Laine M, Nyrhinen M, Ukharova E, Granö I, Soto AM, Matsuda RH, Rantala R, Guidotti R, Kičić D, Lioumis P, Mutanen T, Pizzella V, Marzetti L, Roine T, Stenroos M, Ziemann U, Romani GL, Ilmoniemi RJ. Modulating brain networks in space and time: Multi-locus transcranial magnetic stimulation. Clin Neurophysiol 2024; 158:218-224. [PMID: 38184469 DOI: 10.1016/j.clinph.2023.12.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/17/2023] [Accepted: 12/15/2023] [Indexed: 01/08/2024]
Affiliation(s)
- Heikki Sinisalo
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
| | - Ilkka Rissanen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | | | - Victor H Souza
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University, and Helsinki University Hospital, Helsinki, Finland; Department of Physics, Faculty of Philosophy Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Timo Tommila
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Mikael Laine
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Mikko Nyrhinen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; AMI Centre, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland
| | - Elena Ukharova
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Ida Granö
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Ana M Soto
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Renan H Matsuda
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; Department of Physics, Faculty of Philosophy Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Robin Rantala
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Roberto Guidotti
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Dubravko Kičić
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Pantelis Lioumis
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University, and Helsinki University Hospital, Helsinki, Finland
| | - Tuomas Mutanen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies (ITAB), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies (ITAB), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Timo Roine
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Matti Stenroos
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Ulf Ziemann
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - Gian Luca Romani
- Institute for Advanced Biomedical Technologies (ITAB), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
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10
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Marzetti L, Makkinayeri S, Pieramico G, Guidotti R, D'Andrea A, Roine T, Mutanen TP, Souza VH, Kičić D, Baldassarre A, Ermolova M, Pankka H, Ilmoniemi RJ, Ziemann U, Luca Romani G, Pizzella V. Towards real-time identification of large-scale brain states for improved brain state-dependent stimulation. Clin Neurophysiol 2024; 158:196-203. [PMID: 37827877 DOI: 10.1016/j.clinph.2023.09.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/04/2023] [Accepted: 09/13/2023] [Indexed: 10/14/2023]
Affiliation(s)
- Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy; Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy.
| | - Saeed Makkinayeri
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Giulia Pieramico
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Roberto Guidotti
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Antea D'Andrea
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Timo Roine
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; Turku Brain and Mind Center, University of Turku, Turku, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Tuomas P Mutanen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Victor H Souza
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Dubravko Kičić
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Antonello Baldassarre
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Maria Ermolova
- Hertie-Institute for Clinical Brain Research, Tübingen, Baden-Württemberg, Germany; Department of Neurology & Stroke, University of Tübingen, Tübingen, Baden-Württemberg, Germany
| | - Hanna Pankka
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Ulf Ziemann
- Hertie-Institute for Clinical Brain Research, Tübingen, Baden-Württemberg, Germany; Department of Neurology & Stroke, University of Tübingen, Tübingen, Baden-Württemberg, Germany
| | - Gian Luca Romani
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy; Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy
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11
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Humaidan D, Xu J, Kirchhoff M, Romani GL, Ilmoniemi RJ, Ziemann U. Towards real-time EEG-TMS modulation of brain state in a closed-loop approach. Clin Neurophysiol 2024; 158:212-217. [PMID: 38160069 DOI: 10.1016/j.clinph.2023.12.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/21/2023] [Accepted: 12/15/2023] [Indexed: 01/03/2024]
Affiliation(s)
- Dania Humaidan
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - Jiahua Xu
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - Miriam Kirchhoff
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - Gian Luca Romani
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Ulf Ziemann
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany.
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12
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Fleming JE, Pont Sanchis I, Lemmens O, Denison-Smith A, West TO, Denison T, Cagnan H. From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation. PLoS Comput Biol 2023; 19:e1011674. [PMID: 38091368 PMCID: PMC10718444 DOI: 10.1371/journal.pcbi.1011674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 11/09/2023] [Indexed: 12/18/2023] Open
Abstract
Stimulation optimization has garnered considerable interest in recent years in order to efficiently parametrize neuromodulation-based therapies. To date, efforts focused on automatically identifying settings from parameter spaces that do not change over time. A limitation of these approaches, however, is that they lack consideration for time dependent factors that may influence therapy outcomes. Disease progression and biological rhythmicity are two sources of variation that may influence optimal stimulation settings over time. To account for this, we present a novel time-varying Bayesian optimization (TV-BayesOpt) for tracking the optimum parameter set for neuromodulation therapy. We evaluate the performance of TV-BayesOpt for tracking gradual and periodic slow variations over time. The algorithm was investigated within the context of a computational model of phase-locked deep brain stimulation for treating oscillopathies representative of common movement disorders such as Parkinson's disease and Essential Tremor. When the optimal stimulation settings changed due to gradual and periodic sources, TV-BayesOpt outperformed standard time-invariant techniques and was able to identify the appropriate stimulation setting. Through incorporation of both a gradual "forgetting" and periodic covariance functions, the algorithm maintained robust performance when a priori knowledge differed from observed variations. This algorithm presents a broad framework that can be leveraged for the treatment of a range of neurological and psychiatric conditions and can be used to track variations in optimal stimulation settings such as amplitude, pulse-width, frequency and phase for invasive and non-invasive neuromodulation strategies.
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Affiliation(s)
- John E. Fleming
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford, United Kingdom
| | - Ines Pont Sanchis
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Oxford, United Kingdom
| | - Oscar Lemmens
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Oxford, United Kingdom
| | - Angus Denison-Smith
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Oxford, United Kingdom
| | - Timothy O. West
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford, United Kingdom
- Department of Bioengineering, Imperial College London, White City Campus, London, United Kingdom
| | - Timothy Denison
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford, United Kingdom
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Oxford, United Kingdom
| | - Hayriye Cagnan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford, United Kingdom
- Department of Bioengineering, Imperial College London, White City Campus, London, United Kingdom
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13
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Gutiérrez-Muto AM, Bestmann S, Sánchez de la Torre R, Pons JL, Oliviero A, Tornero J. The complex landscape of TMS devices: A brief overview. PLoS One 2023; 18:e0292733. [PMID: 38015924 PMCID: PMC10684101 DOI: 10.1371/journal.pone.0292733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/27/2023] [Indexed: 11/30/2023] Open
Abstract
The increasing application of TMS in research and therapy has spawned an ever-growing number of commercial and non-commercial TMS devices and technology development. New CE-marked devices appear at a rate of approximately one every two years, with new FDA-approved application of TMS occurring at a similar rate. With the resulting complex landscape of TMS devices and their application, accessible information about the technological characteristics of the TMS devices, such as the type of their circuitry, their pulse characteristics, or permitted protocols would be beneficial. We here present an overview and open access database summarizing key features and applications of available commercial and non-commercial TMS devices (http://www.tmsbase.info). This may guide comparison and decision making about the use of these devices. A bibliometric analysis was performed by identifying commercial and non-commercial TMS devices from which a comprehensive database was created summarizing their publicly available characteristics, both from a technical and clinical point of view. In this document, we introduce both the commercial devices and prototypes found in the literature. The technical specifications that unify these devices are briefly analysed in two separate tables: power electronics, waveform, protocols, and coil types. In the prototype TMS systems, the proposed innovations are focused on improving the treatment regarding the patient: noise cancellation, controllable parameters, and multiple stimulation. This analysis shows that the landscape of TMS is becoming increasingly fragmented, with new devices appearing ever more frequently. The review provided here can support development of benchmarking frameworks and comparison between TMS systems, inform the choice of TMS platforms for specific research and therapeutic applications, and guide future technology development for neuromodulation devices. This standardisation strategy will allow a better end-user choice, with an impact on the TMS manufacturing industry and a homogenisation of patient samples in multi-centre clinical studies. As an open access repository, we envisage the database to grow along with the dynamic development of TMS devices and applications through community-lead curation.
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Affiliation(s)
| | - Sven Bestmann
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | | | - José L. Pons
- Legs and Walking Lab, Shirley Ryan Ability Laboratory (Formerly Rehabilitation Institute of Chicago), Chicago, IL, United States of America
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America
| | - Antonio Oliviero
- Center for Clinical Neuroscience, Hospital Los Madroños, Brunete, Madrid, Spain
- Advanced Neurorehabilitation Unit, Hospital Los Madroños, Brunete, Madrid, Spain
| | - Jesús Tornero
- Center for Clinical Neuroscience, Hospital Los Madroños, Brunete, Madrid, Spain
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14
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Gogulski J, Cline CC, Ross JM, Truong J, Sarkar M, Parmigiani S, Keller CJ. Mapping cortical excitability in the human dorsolateral prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.20.524867. [PMID: 36711689 PMCID: PMC9882363 DOI: 10.1101/2023.01.20.524867] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Objective To characterize early TEPs anatomically and temporally (20-50 ms) close to the TMS pulse (EL-TEPs), as well as associated muscle artifacts (<20 ms), across the dlPFC. We hypothesized that TMS location and angle influence EL-TEPs, and that EL-TEP amplitude is inversely related to muscle artifact. Additionally, we sought to determine an optimal group-level TMS target and angle, while investigating the potential benefits of a personalized approach. Methods In 16 healthy participants, we applied single-pulse TMS to six targets within the dlPFC at two coil angles and measured EEG responses. Results Stimulation location significantly influenced EL-TEPs, with posterior and medial targets yielding larger EL-TEPs. Regions with high EL-TEP amplitude had less muscle artifact, and vice versa. The best group-level target yielded 102% larger EL-TEP responses compared to other dlPFC targets. Optimal dlPFC target differed across subjects, suggesting that a personalized targeting approach might boost the EL-TEP by an additional 36%. Significance Early local TMS-evoked potentials (EL-TEPs) can be probed without significant muscle-related confounds in posterior-medial regions of the dlPFC. The identification of an optimal group-level target and the potential for further refinement through personalized targeting hold significant implications for optimizing depression treatment protocols. Highlights Early local TMS-evoked potentials (EL-TEPs) varied significantly across the dlPFC as a function of TMS target.TMS targets with less muscle artifact had significantly larger EL-TEPs.Selection of a postero-medial target increased EL-TEPs by 102% compared to anterior targets.
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15
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Guidali G, Zazio A, Lucarelli D, Marcantoni E, Stango A, Barchiesi G, Bortoletto M. Effects of transcranial magnetic stimulation (TMS) current direction and pulse waveform on cortico-cortical connectivity: A registered report TMS-EEG study. Eur J Neurosci 2023; 58:3785-3809. [PMID: 37649453 DOI: 10.1111/ejn.16127] [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: 04/30/2023] [Revised: 07/14/2023] [Accepted: 08/04/2023] [Indexed: 09/01/2023]
Abstract
Transcranial magnetic stimulation (TMS)-evoked potentials (TEPs) are a promising proxy for measuring effective connectivity, that is, the directed transmission of physiological signals along cortico-cortical tracts, and for developing connectivity-based biomarkers. A crucial point is how stimulation parameters may affect TEPs, as they may contribute to the general variability of findings across studies. Here, we manipulated two TMS parameters (i.e. current direction and pulse waveform) while measuring (a) an early TEP component reflecting contralateral inhibition of motor areas, namely, M1-P15, as an operative model of interhemispheric cortico-cortical connectivity, and (b) motor-evoked potentials (MEP) for the corticospinal pathway. Our results showed that these two TMS parameters are crucial to evoke the M1-P15, influencing its amplitude, latency, and replicability. Specifically, (a) M1-P15 amplitude was strongly affected by current direction in monophasic stimulation; (b) M1-P15 latency was significantly modulated by current direction for monophasic and biphasic pulses. The replicability of M1-P15 was substantial for the same stimulation condition. At the same time, it was poor when stimulation parameters were changed, suggesting that these factors must be controlled to obtain stable single-subject measures. Finally, MEP latency was modulated by current direction, whereas non-statistically significant changes were evident for amplitude. Overall, our study highlights the importance of TMS parameters for early TEP responses recording and suggests controlling their impact in developing connectivity biomarkers from TEPs. Moreover, these results point out that the excitability of the corticospinal tract, which is commonly used as a reference to set TMS intensity, may not correspond to the excitability of cortico-cortical pathways.
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Affiliation(s)
- Giacomo Guidali
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Agnese Zazio
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Delia Lucarelli
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Eleonora Marcantoni
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Antonietta Stango
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Guido Barchiesi
- Department of Philosophy, University of Milano, Milan, Italy
| | - Marta Bortoletto
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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16
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Kim B, Erickson BA, Fernandez-Nunez G, Rich R, Mentzelopoulos G, Vitale F, Medaglia JD. EEG Phase Can Be Predicted with Similar Accuracy across Cognitive States after Accounting for Power and Signal-to-Noise Ratio. eNeuro 2023; 10:ENEURO.0050-23.2023. [PMID: 37558464 PMCID: PMC10481640 DOI: 10.1523/eneuro.0050-23.2023] [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: 02/13/2023] [Revised: 05/25/2023] [Accepted: 06/15/2023] [Indexed: 08/11/2023] Open
Abstract
EEG phase is increasingly used in cognitive neuroscience, brain-computer interfaces, and closed-loop stimulation devices. However, it is unknown how accurate EEG phase prediction is across cognitive states. We determined the EEG phase prediction accuracy of parieto-occipital alpha waves across rest and task states in 484 participants over 11 public datasets. We were able to track EEG phase accurately across various cognitive conditions and datasets, especially during periods of high instantaneous alpha power and signal-to-noise ratio (SNR). Although resting states generally have higher accuracies than task states, absolute accuracy differences were small, with most of these differences attributable to EEG power and SNR. These results suggest that experiments and technologies using EEG phase should focus more on minimizing external noise and waiting for periods of high power rather than inducing a particular cognitive state.
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Affiliation(s)
- Brian Kim
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
| | - Brian A Erickson
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
| | | | - Ryan Rich
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
| | - Georgios Mentzelopoulos
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania 19104
| | - Flavia Vitale
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania 19104
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, Pennsylvania 19146
| | - John D Medaglia
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Neurology, Drexel University, Philadelphia, Pennsylvania 19104
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17
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Sun W, Wu Q, Gao L, Zheng Z, Xiang H, Yang K, Yu B, Yao J. Advancements in Transcranial Magnetic Stimulation Research and the Path to Precision. Neuropsychiatr Dis Treat 2023; 19:1841-1851. [PMID: 37641588 PMCID: PMC10460597 DOI: 10.2147/ndt.s414782] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 08/02/2023] [Indexed: 08/31/2023] Open
Abstract
Transcranial magnetic stimulation (TMS) has become increasingly popular in clinical practice in recent years, and there have been significant advances in the principles and stimulation modes of TMS. With the development of multi-mode and precise stimulation technology, it is crucial to have a comprehensive understanding of TMS. The neuroregulatory effects of TMS can vary depending on the specific mode of stimulation, highlighting the importance of exploring these effects through multimodal application. Additionally, the use of precise TMS therapy can help enhance our understanding of the neural mechanisms underlying these effects, providing us with a more comprehensive perspective. This article aims to review the mechanism of action, stimulation mode, multimodal application, and precision of TMS.
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Affiliation(s)
- Wei Sun
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Qiao Wu
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Li Gao
- Department of Neurology, The Third People’s Hospital of Chengdu, Chengdu Institute of Neurological Diseases, Chengdu City, Sichuan Province, People’s Republic of China
| | - Zhong Zheng
- Neurobiological Detection Center, West China Hospital Affiliated to Sichuan University, Chengdu City, Sichuan Province, People’s Republic of China
| | - Hu Xiang
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Kun Yang
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Bo Yu
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Jing Yao
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
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18
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Soleimani G, Nitsche MA, Bergmann TO, Towhidkhah F, Violante IR, Lorenz R, Kuplicki R, Tsuchiyagaito A, Mulyana B, Mayeli A, Ghobadi-Azbari P, Mosayebi-Samani M, Zilverstand A, Paulus MP, Bikson M, Ekhtiari H. Closing the loop between brain and electrical stimulation: towards precision neuromodulation treatments. Transl Psychiatry 2023; 13:279. [PMID: 37582922 PMCID: PMC10427701 DOI: 10.1038/s41398-023-02565-5] [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: 07/08/2022] [Revised: 07/06/2023] [Accepted: 07/20/2023] [Indexed: 08/17/2023] Open
Abstract
One of the most critical challenges in using noninvasive brain stimulation (NIBS) techniques for the treatment of psychiatric and neurologic disorders is inter- and intra-individual variability in response to NIBS. Response variations in previous findings suggest that the one-size-fits-all approach does not seem the most appropriate option for enhancing stimulation outcomes. While there is a growing body of evidence for the feasibility and effectiveness of individualized NIBS approaches, the optimal way to achieve this is yet to be determined. Transcranial electrical stimulation (tES) is one of the NIBS techniques showing promising results in modulating treatment outcomes in several psychiatric and neurologic disorders, but it faces the same challenge for individual optimization. With new computational and methodological advances, tES can be integrated with real-time functional magnetic resonance imaging (rtfMRI) to establish closed-loop tES-fMRI for individually optimized neuromodulation. Closed-loop tES-fMRI systems aim to optimize stimulation parameters based on minimizing differences between the model of the current brain state and the desired value to maximize the expected clinical outcome. The methodological space to optimize closed-loop tES fMRI for clinical applications includes (1) stimulation vs. data acquisition timing, (2) fMRI context (task-based or resting-state), (3) inherent brain oscillations, (4) dose-response function, (5) brain target trait and state and (6) optimization algorithm. Closed-loop tES-fMRI technology has several advantages over non-individualized or open-loop systems to reshape the future of neuromodulation with objective optimization in a clinically relevant context such as drug cue reactivity for substance use disorder considering both inter and intra-individual variations. Using multi-level brain and behavior measures as input and desired outcomes to individualize stimulation parameters provides a framework for designing personalized tES protocols in precision psychiatry.
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Affiliation(s)
- Ghazaleh Soleimani
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Michael A Nitsche
- Department of Psychology and Neuroscience, Leibniz Research Center for Working Environment and Human Factors, Dortmund, Germany
- Bielefeld University, University Hospital OWL, Protestant Hospital of Bethel Foundation, University Clinic of Psychiatry and Psychotherapy, and University Clinic of Child and Adolescent Psychiatry and Psychotherapy, Bielefeld, Germany
| | - Til Ole Bergmann
- Neuroimaging Center, Focus Program Translational Neuroscience, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany
- Leibniz Institute for Resilience Research, Mainz, Germany
| | - Farzad Towhidkhah
- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Ines R Violante
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guilford, UK
| | - Romy Lorenz
- Department of Psychology, Stanford University, Stanford, CA, USA
- MRC CBU, University of Cambridge, Cambridge, UK
- Department of Neurophysics, MPI, Leipzig, Germany
| | | | | | - Beni Mulyana
- Laureate Institute for Brain Research, Tulsa, OK, USA
- School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, USA
| | - Ahmad Mayeli
- University of Pittsburgh Medical Center, Pittsburg, PA, USA
| | - Peyman Ghobadi-Azbari
- Department of Biomedical Engineering, Shahed University, Tehran, Iran
- Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohsen Mosayebi-Samani
- Department of Psychology and Neuroscience, Leibniz Research Center for Working Environment and Human Factors, Dortmund, Germany
| | - Anna Zilverstand
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | | | | | - Hamed Ekhtiari
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
- Laureate Institute for Brain Research, Tulsa, OK, USA.
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19
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Eder J, Dom G, Gorwood P, Kärkkäinen H, Decraene A, Kumpf U, Beezhold J, Samochowiec J, Kurimay T, Gaebel W, De Picker L, Falkai P. Improving mental health care in depression: A call for action. Eur Psychiatry 2023; 66:e65. [PMID: 37534402 PMCID: PMC10486253 DOI: 10.1192/j.eurpsy.2023.2434] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/20/2023] [Accepted: 06/29/2023] [Indexed: 08/04/2023] Open
Abstract
Depressive disorders have one of the highest disability-adjusted life years (DALYs) of all medical conditions, which led the European Psychiatric Association to propose a policy paper, pinpointing their unmet health care and research needs. The first part focuses on what can be currently done to improve the care of patients with depression, and then discuss future trends for research and healthcare. Through the narration of clinical cases, the different points are illustrated. The necessary political framework is formulated, to implement such changes to fundamentally improve psychiatric care. The group of European Psychiatrist Association (EPA) experts insist on the need for (1) increased awareness of mental illness in primary care settings, (2) the development of novel (biological) markers, (3) the rapid implementation of machine learning (supporting diagnostics, prognostics, and therapeutics), (4) the generalized use of electronic devices and apps into everyday treatment, (5) the development of the new generation of treatment options, such as plasticity-promoting agents, and (6) the importance of comprehensive recovery approach. At a political level, the group also proposed four priorities, the need to (1) increase the use of open science, (2) implement reasonable data protection laws, (3) establish ethical electronic health records, and (4) enable better healthcare research and saving resources.
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Affiliation(s)
- Julia Eder
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- Graduate Program “POKAL - Predictors and Outcomes in Primary Care Depression Care” (DFG-GrK 2621), Munich, Germany
| | - Geert Dom
- Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp, Antwerp, Belgium
| | - Philip Gorwood
- Université Paris Cité, GHU Paris (Sainte Anne hospital, CMME) & INSERM UMR1266, Paris, France
| | - Hikka Kärkkäinen
- Global Alliance of Mental Illness Advocacy Networks-Europe, Brussels, Belgium
| | - Andre Decraene
- EUFAMI, the European Organisation representing Families of persons affected by severe Mental Ill Health, Leuven, Belgium
| | - Ulrike Kumpf
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
| | - Julian Beezhold
- Norfolk and Suffolk NHS Foundation Trust, Norwich, UK, University of East Anglia, Norwich, UK
| | - Jerzy Samochowiec
- Department of Psychiatry, Pomeranian Medical University, Szczecin, Poland
| | - Tamas Kurimay
- North-Central Buda Center, New Saint John Hospital and Outpatient Clinic, Buda Family Centered Mental Health Centre, Department of Psychiatry and Psychiatric Rehabilitation, Teaching Department of Semmelweis University, Budapest, Hungary
| | - Wolfgang Gaebel
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine-University, Düsseldorf, WHO Collaborating Centre DEU-131, Germany
| | - Livia De Picker
- Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp, Antwerp, Belgium
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- Graduate Program “POKAL - Predictors and Outcomes in Primary Care Depression Care” (DFG-GrK 2621), Munich, Germany
- Max-Planck Institute of Psychiatry, Munich, Germany
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20
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Parmigiani S, Ross JM, Cline CC, Minasi CB, Gogulski J, Keller CJ. Reliability and Validity of Transcranial Magnetic Stimulation-Electroencephalography Biomarkers. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:805-814. [PMID: 36894435 PMCID: PMC10276171 DOI: 10.1016/j.bpsc.2022.12.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 11/15/2022] [Accepted: 12/11/2022] [Indexed: 12/23/2022]
Abstract
Noninvasive brain stimulation and neuroimaging have revolutionized human neuroscience with a multitude of applications, including diagnostic subtyping, treatment optimization, and relapse prediction. It is therefore particularly relevant to identify robust and clinically valuable brain biomarkers linking symptoms to their underlying neural mechanisms. Brain biomarkers must be reproducible (i.e., have internal reliability) across similar experiments within a laboratory and be generalizable (i.e., have external reliability) across experimental setups, laboratories, brain regions, and disease states. However, reliability (internal and external) is not alone sufficient; biomarkers also must have validity. Validity describes closeness to a true measure of the underlying neural signal or disease state. We propose that these metrics, reliability and validity, should be evaluated and optimized before any biomarker is used to inform treatment decisions. Here, we discuss these metrics with respect to causal brain connectivity biomarkers from coupling transcranial magnetic stimulation (TMS) with electroencephalography (EEG). We discuss controversies around TMS-EEG stemming from the multiple large off-target components (noise) and relatively weak genuine brain responses (signal), as is unfortunately often the case in noninvasive human neuroscience. We review the current state of TMS-EEG recordings, which consist of a mix of reliable noise and unreliable signal. We describe methods for evaluating TMS-EEG biomarkers, including how to assess internal and external reliability across facilities, cognitive states, brain networks, and disorders and how to validate these biomarkers using invasive neural recordings or treatment response. We provide recommendations to increase reliability and validity, discuss lessons learned, and suggest future directions for the field.
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Affiliation(s)
- Sara Parmigiani
- Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, California; Wu Tsai Neuroscience Institute, Stanford, California
| | - Jessica M Ross
- Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, California; Wu Tsai Neuroscience Institute, Stanford, California
| | - Christopher C Cline
- Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, California; Wu Tsai Neuroscience Institute, Stanford, California
| | - Christopher B Minasi
- Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, California; Wu Tsai Neuroscience Institute, Stanford, California
| | - Juha Gogulski
- Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, California; Wu Tsai Neuroscience Institute, Stanford, California; Department of Clinical Neurophysiology, HUS Diagnostic Center, Clinical Neurosciences, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Corey J Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, California; Wu Tsai Neuroscience Institute, Stanford, California.
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21
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Wahl T, Riedinger J, Duprez M, Hutt A. Delayed closed-loop neurostimulation for the treatment of pathological brain rhythms in mental disorders: a computational study. Front Neurosci 2023; 17:1183670. [PMID: 37476837 PMCID: PMC10354341 DOI: 10.3389/fnins.2023.1183670] [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: 03/10/2023] [Accepted: 06/13/2023] [Indexed: 07/22/2023] Open
Abstract
Mental disorders are among the top most demanding challenges in world-wide health. A large number of mental disorders exhibit pathological rhythms, which serve as the disorders characteristic biomarkers. These rhythms are the targets for neurostimulation techniques. Open-loop neurostimulation employs stimulation protocols, which are rather independent of the patients health and brain state in the moment of treatment. Most alternative closed-loop stimulation protocols consider real-time brain activity observations but appear as adaptive open-loop protocols, where e.g., pre-defined stimulation sets in if observations fulfil pre-defined criteria. The present theoretical work proposes a fully-adaptive closed-loop neurostimulation setup, that tunes the brain activities power spectral density (PSD) according to a user-defined PSD. The utilized brain model is non-parametric and estimated from the observations via magnitude fitting in a pre-stimulus setup phase. Moreover, the algorithm takes into account possible conduction delays in the feedback connection between observation and stimulation electrode. All involved features are illustrated on pathological α- and γ-rhythms known from psychosis. To this end, we simulate numerically a linear neural population brain model and a non-linear cortico-thalamic feedback loop model recently derived to explain brain activity in psychosis.
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Affiliation(s)
- Thomas Wahl
- ICube, MLMS, MIMESIS Team, Inria Nancy - Grand Est, University of Strasbourg, Strasbourg, France
| | - Joséphine Riedinger
- ICube, MLMS, MIMESIS Team, Inria Nancy - Grand Est, University of Strasbourg, Strasbourg, France
- INSERM U1114, Neuropsychologie Cognitive et Physiopathologie de la Schizophrénie, Strasbourg, France
| | - Michel Duprez
- ICube, MLMS, MIMESIS Team, Inria Nancy - Grand Est, University of Strasbourg, Strasbourg, France
| | - Axel Hutt
- ICube, MLMS, MIMESIS Team, Inria Nancy - Grand Est, University of Strasbourg, Strasbourg, France
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22
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Vucic S, Stanley Chen KH, Kiernan MC, Hallett M, Benninger DH, Di Lazzaro V, Rossini PM, Benussi A, Berardelli A, Currà A, Krieg SM, Lefaucheur JP, Long Lo Y, Macdonell RA, Massimini M, Rosanova M, Picht T, Stinear CM, Paulus W, Ugawa Y, Ziemann U, Chen R. Clinical diagnostic utility of transcranial magnetic stimulation in neurological disorders. Updated report of an IFCN committee. Clin Neurophysiol 2023; 150:131-175. [PMID: 37068329 PMCID: PMC10192339 DOI: 10.1016/j.clinph.2023.03.010] [Citation(s) in RCA: 55] [Impact Index Per Article: 55.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/28/2023] [Accepted: 03/09/2023] [Indexed: 03/31/2023]
Abstract
The review provides a comprehensive update (previous report: Chen R, Cros D, Curra A, Di Lazzaro V, Lefaucheur JP, Magistris MR, et al. The clinical diagnostic utility of transcranial magnetic stimulation: report of an IFCN committee. Clin Neurophysiol 2008;119(3):504-32) on clinical diagnostic utility of transcranial magnetic stimulation (TMS) in neurological diseases. Most TMS measures rely on stimulation of motor cortex and recording of motor evoked potentials. Paired-pulse TMS techniques, incorporating conventional amplitude-based and threshold tracking, have established clinical utility in neurodegenerative, movement, episodic (epilepsy, migraines), chronic pain and functional diseases. Cortical hyperexcitability has emerged as a diagnostic aid in amyotrophic lateral sclerosis. Single-pulse TMS measures are of utility in stroke, and myelopathy even in the absence of radiological changes. Short-latency afferent inhibition, related to central cholinergic transmission, is reduced in Alzheimer's disease. The triple stimulation technique (TST) may enhance diagnostic utility of conventional TMS measures to detect upper motor neuron involvement. The recording of motor evoked potentials can be used to perform functional mapping of the motor cortex or in preoperative assessment of eloquent brain regions before surgical resection of brain tumors. TMS exhibits utility in assessing lumbosacral/cervical nerve root function, especially in demyelinating neuropathies, and may be of utility in localizing the site of facial nerve palsies. TMS measures also have high sensitivity in detecting subclinical corticospinal lesions in multiple sclerosis. Abnormalities in central motor conduction time or TST correlate with motor impairment and disability in MS. Cerebellar stimulation may detect lesions in the cerebellum or cerebello-dentato-thalamo-motor cortical pathways. Combining TMS with electroencephalography, provides a novel method to measure parameters altered in neurological disorders, including cortical excitability, effective connectivity, and response complexity.
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Affiliation(s)
- Steve Vucic
- Brain, Nerve Research Center, The University of Sydney, Sydney, Australia.
| | - Kai-Hsiang Stanley Chen
- Department of Neurology, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan
| | - Matthew C Kiernan
- Brain and Mind Centre, The University of Sydney; and Department of Neurology, Royal Prince Alfred Hospital, Australia
| | - Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, Maryland, United States
| | - David H Benninger
- Department of Neurology, University Hospital of Lausanne (CHUV), Switzerland
| | - Vincenzo Di Lazzaro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, University Campus Bio-Medico of Rome, Rome, Italy
| | - Paolo M Rossini
- Department of Neurosci & Neurorehab IRCCS San Raffaele-Rome, Italy
| | - Alberto Benussi
- Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Alfredo Berardelli
- IRCCS Neuromed, Pozzilli; Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Antonio Currà
- Department of Medico-Surgical Sciences and Biotechnologies, Alfredo Fiorini Hospital, Sapienza University of Rome, Terracina, LT, Italy
| | - Sandro M Krieg
- Department of Neurosurgery, Technical University Munich, School of Medicine, Klinikum rechts der Isar, Munich, Germany
| | - Jean-Pascal Lefaucheur
- Univ Paris Est Creteil, EA4391, ENT, Créteil, France; Clinical Neurophysiology Unit, Henri Mondor Hospital, AP-HP, Créteil, France
| | - Yew Long Lo
- Department of Neurology, National Neuroscience Institute, Singapore General Hospital, Singapore, and Duke-NUS Medical School, Singapore
| | | | - Marcello Massimini
- Dipartimento di Scienze Biomediche e Cliniche, Università degli Studi di Milano, Milan, Italy; Istituto Di Ricovero e Cura a Carattere Scientifico, Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Mario Rosanova
- Department of Biomedical and Clinical Sciences University of Milan, Milan, Italy
| | - Thomas Picht
- Department of Neurosurgery, Charité-Universitätsmedizin Berlin, Cluster of Excellence: "Matters of Activity. Image Space Material," Humboldt University, Berlin Simulation and Training Center (BeST), Charité-Universitätsmedizin Berlin, Germany
| | - Cathy M Stinear
- Department of Medicine Waipapa Taumata Rau, University of Auckland, Auckland, Aotearoa, New Zealand
| | - Walter Paulus
- Department of Neurology, Ludwig-Maximilians-Universität München, München, Germany
| | - Yoshikazu Ugawa
- Department of Human Neurophysiology, School of Medicine, Fukushima Medical University, Japan
| | - Ulf Ziemann
- Department of Neurology and Stroke, Eberhard Karls University of Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany; Hertie Institute for Clinical Brain Research, Eberhard Karls University of Tübingen, Otfried-Müller-Straße 27, 72076 Tübingen, Germany
| | - Robert Chen
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital-UHN, Division of Neurology-University of Toronto, Toronto Canada
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23
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Milardovich D, Souza VH, Zubarev I, Tugin S, Nieminen JO, Bigoni C, Hummel FC, Korhonen JT, Aydogan DB, Lioumis P, Taherinejad N, Grasser T, Ilmoniemi RJ. DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies. Sci Rep 2023; 13:8225. [PMID: 37217502 DOI: 10.1038/s41598-023-34801-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 05/08/2023] [Indexed: 05/24/2023] Open
Abstract
The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the characterization of thousands of MEPs. Given the difficulty of developing reliable and accurate algorithms, currently the assessment of MEPs is performed with visual inspection and manual annotation by a medical expert; making it a time-consuming, inaccurate, and error-prone process. In this study, we developed DELMEP, a deep learning-based algorithm to automate the estimation of MEP latency. Our algorithm resulted in a mean absolute error of about 0.5 ms and an accuracy that was practically independent of the MEP amplitude. The low computational cost of the DELMEP algorithm allows employing it in on-the-fly characterization of MEPs for brain-state-dependent and closed-loop brain stimulation protocols. Moreover, its learning ability makes it a particularly promising option for artificial-intelligence-based personalized clinical applications.
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Affiliation(s)
- Diego Milardovich
- Institute for Microelectronics, Technische Universität Wien, Gußhausstraße 27-29/E360, 1040, Vienna, Austria.
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland.
| | - Victor H Souza
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
- School of Physiotherapy, Federal University of Juiz de Fora, Juiz de Fora, MG, Brazil
| | - Ivan Zubarev
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Sergei Tugin
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
- Department of Neurology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Jaakko O Nieminen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Claudia Bigoni
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202, Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951, Sion, Switzerland
| | - Friedhelm C Hummel
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202, Geneva, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), Ecole Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, 1951, Sion, Switzerland
- Clinical Neuroscience, Geneva University Hospital (HUG), 1205, Geneva, Switzerland
| | - Juuso T Korhonen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Dogu B Aydogan
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Pantelis Lioumis
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Nima Taherinejad
- Institute for Computer Technology, Technische Universität Wien, Vienna, Austria
- Institute of Computer Engineering, Heidelberg University, Heidelberg, Germany
| | - Tibor Grasser
- Institute for Microelectronics, Technische Universität Wien, Gußhausstraße 27-29/E360, 1040, Vienna, Austria
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
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24
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Ding Z, Guan L, He W, Gu H, Wang Y, Li X. Spatial characteristics of closed-loop TMS-EEG with occipital alpha-phase synchronized. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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25
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Hernandez-Pavon JC, Veniero D, Bergmann TO, Belardinelli P, Bortoletto M, Casarotto S, Casula EP, Farzan F, Fecchio M, Julkunen P, Kallioniemi E, Lioumis P, Metsomaa J, Miniussi C, Mutanen TP, Rocchi L, Rogasch NC, Shafi MM, Siebner HR, Thut G, Zrenner C, Ziemann U, Ilmoniemi RJ. TMS combined with EEG: Recommendations and open issues for data collection and analysis. Brain Stimul 2023; 16:567-593. [PMID: 36828303 DOI: 10.1016/j.brs.2023.02.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 02/10/2023] [Accepted: 02/19/2023] [Indexed: 02/25/2023] Open
Abstract
Transcranial magnetic stimulation (TMS) evokes neuronal activity in the targeted cortex and connected brain regions. The evoked brain response can be measured with electroencephalography (EEG). TMS combined with simultaneous EEG (TMS-EEG) is widely used for studying cortical reactivity and connectivity at high spatiotemporal resolution. Methodologically, the combination of TMS with EEG is challenging, and there are many open questions in the field. Different TMS-EEG equipment and approaches for data collection and analysis are used. The lack of standardization may affect reproducibility and limit the comparability of results produced in different research laboratories. In addition, there is controversy about the extent to which auditory and somatosensory inputs contribute to transcranially evoked EEG. This review provides a guide for researchers who wish to use TMS-EEG to study the reactivity of the human cortex. A worldwide panel of experts working on TMS-EEG covered all aspects that should be considered in TMS-EEG experiments, providing methodological recommendations (when possible) for effective TMS-EEG recordings and analysis. The panel identified and discussed the challenges of the technique, particularly regarding recording procedures, artifact correction, analysis, and interpretation of the transcranial evoked potentials (TEPs). Therefore, this work offers an extensive overview of TMS-EEG methodology and thus may promote standardization of experimental and computational procedures across groups.
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Affiliation(s)
- Julio C Hernandez-Pavon
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Legs + Walking Lab, Shirley Ryan AbilityLab, Chicago, IL, USA; Center for Brain Stimulation, Shirley Ryan AbilityLab, Chicago, IL, USA.
| | | | - Til Ole Bergmann
- Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Germany; Leibniz Institute for Resilience Research (LIR), Mainz, Germany
| | - Paolo Belardinelli
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, TN, Italy; Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany
| | - Marta Bortoletto
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Silvia Casarotto
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy; IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Elias P Casula
- Department of Systems Medicine, University of Tor Vergata, Rome, Italy
| | - Faranak Farzan
- Simon Fraser University, School of Mechatronic Systems Engineering, Surrey, British Columbia, Canada
| | - Matteo Fecchio
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Petro Julkunen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland; Department of Clinical Neurophysiology, Kuopio University Hospital, Kuopio, Finland
| | - Elisa Kallioniemi
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Pantelis Lioumis
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Johanna Metsomaa
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Carlo Miniussi
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, TN, Italy
| | - Tuomas P Mutanen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Lorenzo Rocchi
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
| | - Nigel C Rogasch
- University of Adelaide, Adelaide, Australia; South Australian Health and Medical Research Institute, Adelaide, Australia; Monash University, Melbourne, Australia
| | - Mouhsin M Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Gregor Thut
- School of Psychology and Neuroscience, University of Glasgow, United Kingdom
| | - Christoph Zrenner
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Canada; Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Ulf Ziemann
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
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26
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Gebodh N, Miskovic V, Laszlo S, Datta A, Bikson M. A Scalable Framework for Closed-Loop Neuromodulation with Deep Learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.18.524615. [PMID: 36712027 PMCID: PMC9882307 DOI: 10.1101/2023.01.18.524615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Closed-loop neuromodulation measures dynamic neural or physiological activity to optimize interventions for clinical and nonclinical behavioral, cognitive, wellness, attentional, or general task performance enhancement. Conventional closed-loop stimulation approaches can contain biased biomarker detection (decoders and error-based triggering) and stimulation-type application. We present and verify a novel deep learning framework for designing and deploying flexible, data-driven, automated closed-loop neuromodulation that is scalable using diverse datasets, agnostic to stimulation technology (supporting multi-modal stimulation: tACS, tDCS, tFUS, TMS), and without the need for personalized ground-truth performance data. Our approach is based on identified periods of responsiveness - detected states that result in a change in performance when stimulation is applied compared to no stimulation. To demonstrate our framework, we acquire, analyze, and apply a data-driven approach to our open sourced GX dataset, which includes concurrent physiological (ECG, EOG) and neuronal (EEG) measures, paired with continuous vigilance/attention-fatigue tracking, and High-Definition transcranial electrical stimulation (HD-tES). Our framework's decision process for intervention application identified 88.26% of trials as correct applications, showed potential improvement with varying stimulation types, or missed opportunities to stimulate, whereas 11.25% of trials were predicted to stimulate at inopportune times. With emerging datasets and stimulation technologies, our unifying and integrative framework; leveraging deep learning (Convolutional Neural Networks - CNNs); demonstrates the adaptability and feasibility of automated multimodal neuromodulation for both clinical and nonclinical applications.
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Affiliation(s)
- Nigel Gebodh
- The Department of Biomedical Engineering, The City College of New York, The City University of New York, New York USA
| | | | | | | | - Marom Bikson
- The Department of Biomedical Engineering, The City College of New York, The City University of New York, New York USA
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27
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Jannati A, Oberman LM, Rotenberg A, Pascual-Leone A. Assessing the mechanisms of brain plasticity by transcranial magnetic stimulation. Neuropsychopharmacology 2023; 48:191-208. [PMID: 36198876 PMCID: PMC9700722 DOI: 10.1038/s41386-022-01453-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 11/10/2022]
Abstract
Transcranial magnetic stimulation (TMS) is a non-invasive technique for focal brain stimulation based on electromagnetic induction where a fluctuating magnetic field induces a small intracranial electric current in the brain. For more than 35 years, TMS has shown promise in the diagnosis and treatment of neurological and psychiatric disorders in adults. In this review, we provide a brief introduction to the TMS technique with a focus on repetitive TMS (rTMS) protocols, particularly theta-burst stimulation (TBS), and relevant rTMS-derived metrics of brain plasticity. We then discuss the TMS-EEG technique, the use of neuronavigation in TMS, the neural substrate of TBS measures of plasticity, the inter- and intraindividual variability of those measures, effects of age and genetic factors on TBS aftereffects, and then summarize alterations of TMS-TBS measures of plasticity in major neurological and psychiatric disorders including autism spectrum disorder, schizophrenia, depression, traumatic brain injury, Alzheimer's disease, and diabetes. Finally, we discuss the translational studies of TMS-TBS measures of plasticity and their therapeutic implications.
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Affiliation(s)
- Ali Jannati
- Neuromodulation Program, Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
- Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
| | - Lindsay M Oberman
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Alexander Rotenberg
- Neuromodulation Program, Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Alvaro Pascual-Leone
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
- Hinda and Arthur Marcus Institute for Aging Research and Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, Boston, MA, USA.
- Guttmann Brain Health Institute, Institut Guttmann, Barcelona, Spain.
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28
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Nieminen AE, Nieminen JO, Stenroos M, Novikov P, Nazarova M, Vaalto S, Nikulin V, Ilmoniemi RJ. Accuracy and precision of navigated transcranial magnetic stimulation. J Neural Eng 2022; 19. [PMID: 36541458 DOI: 10.1088/1741-2552/aca71a] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 11/29/2022] [Indexed: 12/02/2022]
Abstract
Objective.Transcranial magnetic stimulation (TMS) induces an electric field (E-field) in the cortex. To facilitate stimulation targeting, image-guided neuronavigation systems have been introduced. Such systems track the placement of the coil with respect to the head and visualize the estimated cortical stimulation location on an anatomical brain image in real time. The accuracy and precision of the neuronavigation is affected by multiple factors. Our aim was to analyze how different factors in TMS neuronavigation affect the accuracy and precision of the coil-head coregistration and the estimated E-field.Approach.By performing simulations, we estimated navigation errors due to distortions in magnetic resonance images (MRIs), head-to-MRI registration (landmark- and surface-based registrations), localization and movement of the head tracker, and localization of the coil tracker. We analyzed the effect of these errors on coil and head coregistration and on the induced E-field as determined with simplistic and realistic head models.Main results.Average total coregistration accuracies were in the range of 2.2-3.6 mm and 1°; precision values were about half of the accuracy values. The coregistration errors were mainly due to head-to-MRI registration with average accuracies 1.5-1.9 mm/0.2-0.4° and precisions 0.5-0.8 mm/0.1-0.2° better with surface-based registration. The other major source of error was the movement of the head tracker with average accuracy of 1.5 mm and precision of 1.1 mm. When assessed within an E-field method, the average accuracies of the peak E-field location, orientation, and magnitude ranged between 1.5 and 5.0 mm, 0.9 and 4.8°, and 4.4 and 8.5% across the E-field models studied. The largest errors were obtained with the landmark-based registration. When computing another accuracy measure with the most realistic E-field model as a reference, the accuracies tended to improve from about 10 mm/15°/25% to about 2 mm/2°/5% when increasing realism of the E-field model.Significance.The results of this comprehensive analysis help TMS operators to recognize the main sources of error in TMS navigation and that the coregistration errors and their effect in the E-field estimation depend on the methods applied. To ensure reliable TMS navigation, we recommend surface-based head-to-MRI registration and realistic models for E-field computations.
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Affiliation(s)
- Aino E Nieminen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.,AMI Centre, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland
| | - Jaakko O Nieminen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Matti Stenroos
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Pavel Novikov
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.,Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - Maria Nazarova
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.,Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States of America
| | - Selja Vaalto
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.,HUS Diagnostic Center, Clinical Neurophysiology, Clinical Neurosciences, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Vadim Nikulin
- Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia.,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
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Lioumis P, Rosanova M. The role of neuronavigation in TMS-EEG studies: current applications and future perspectives. J Neurosci Methods 2022; 380:109677. [PMID: 35872153 DOI: 10.1016/j.jneumeth.2022.109677] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 07/12/2022] [Accepted: 07/19/2022] [Indexed: 11/28/2022]
Abstract
Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) allows measuring non-invasively the electrical response of the human cerebral cortex to a direct perturbation. Complementing TMS-EEG with a structural neuronavigation tool (nTMS-EEG) is key for accurately selecting cortical areas, targeting them, and adjusting the stimulation parameters based on some relevant anatomical priors. This step, together with the employment of visualization tools designed to perform a quality check of TMS-evoked potentials (TEPs) in real-time during acquisition, is key for maximizing the impact of the TMS pulse on the cortex and in ensuring highly reproducible measurements within sessions and across subjects. Moreover, storing stimulation parameters in the neuronavigation system can help in reproducing the stimulation parameters within and across experimental sessions and sharing them across research centers. Finally, the systematic employment of neuronavigation in TMS-EEG studies is also key to standardize measurements in clinical populations in search for reliable diagnostic and prognostic TMS-EEG-based biomarkers for neurological and psychiatric disorders.
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Affiliation(s)
- Pantelis Lioumis
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Mario Rosanova
- Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy
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Evidence of Neuroplastic Changes after Transcranial Magnetic, Electric, and Deep Brain Stimulation. Brain Sci 2022; 12:brainsci12070929. [PMID: 35884734 PMCID: PMC9313265 DOI: 10.3390/brainsci12070929] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 07/06/2022] [Accepted: 07/08/2022] [Indexed: 02/04/2023] Open
Abstract
Electric and magnetic stimulation of the human brain can be used to excite or inhibit neurons. Numerous methods have been designed over the years for this purpose with various advantages and disadvantages that are the topic of this review. Deep brain stimulation (DBS) is the most direct and focal application of electric impulses to brain tissue. Electrodes are placed in the brain in order to modulate neural activity and to correct parameters of pathological oscillation in brain circuits such as their amplitude or frequency. Transcranial magnetic stimulation (TMS) is a non-invasive alternative with the stimulator generating a magnetic field in a coil over the scalp that induces an electric field in the brain which, in turn, interacts with ongoing brain activity. Depending upon stimulation parameters, excitation and inhibition can be achieved. Transcranial electric stimulation (tES) applies electric fields to the scalp that spread along the skull in order to reach the brain, thus, limiting current strength to avoid skin sensations and cranial muscle pain. Therefore, tES can only modulate brain activity and is considered subthreshold, i.e., it does not directly elicit neuronal action potentials. In this review, we collect hints for neuroplastic changes such as modulation of behavior, the electric activity of the brain, or the evolution of clinical signs and symptoms in response to stimulation. Possible mechanisms are discussed, and future paradigms are suggested.
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Granö I, Mutanen TP, Tervo A, Nieminen JO, Souza VH, Fecchio M, Rosanova M, Lioumis P, Ilmoniemi RJ. Local brain-state dependency of effective connectivity: a pilot TMS-EEG study. OPEN RESEARCH EUROPE 2022; 2:45. [PMID: 36035767 PMCID: PMC7613446 DOI: 10.12688/openreseurope.14634.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/07/2022] [Indexed: 11/20/2022]
Abstract
Background: Spontaneous cortical oscillations have been shown to modulate cortical responses to transcranial magnetic stimulation (TMS). However, whether these oscillations influence cortical effective connectivity is largely unknown. We conducted a pilot study to set the basis for addressing how spontaneous oscillations affect cortical effective connectivity measured through TMS-evoked potentials (TEPs). Methods: We applied TMS to the left primary motor cortex and right pre-supplementary motor area of three subjects while recording EEG. We classified trials off-line into positive- and negative-phase classes according to the mu and beta rhythms. We calculated differences in the global mean-field amplitude (GMFA) and compared the cortical spreading of the TMS-evoked activity between the two classes. Results: Phase affected the GMFA in four out of 12 datasets (3 subjects × 2 stimulation sites × 2 frequency bands). Two of the observed significant intervals were before 50 ms, two between 50 and 100 ms, and one after 100 ms post-stimulus. Source estimates showed complex spatial differences between the classes in the cortical spreading of the TMS-evoked activity. Conclusions: TMS-evoked effective connectivity seems to depend on the phase of local cortical oscillations at the stimulated site. This work paves the way to design future closed-loop stimulation paradigms.
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Affiliation(s)
- Ida Granö
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Tuomas P. Mutanen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Aino Tervo
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Jaakko O. Nieminen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Victor H. Souza
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- School of Physiotherapy, Federal University of Juiz de Fora, Juiz de Fora, MG, Brazil
| | - Matteo Fecchio
- Department of Biomedical and Clinical Sciences “L. Sacco”, University of Milan, Milan, Italy
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Mario Rosanova
- Department of Biomedical and Clinical Sciences “L. Sacco”, University of Milan, Milan, Italy
| | - Pantelis Lioumis
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Risto J. Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
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Rostami M, Zomorrodi R, Rostami R, Hosseinzadeh GA. Impact of methodological variability on EEG responses evoked by transcranial magnetic stimulation: a meta-analysis. Clin Neurophysiol 2022; 142:154-180. [DOI: 10.1016/j.clinph.2022.07.495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 07/12/2022] [Accepted: 07/15/2022] [Indexed: 12/01/2022]
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Ross JM, Sarkar M, Keller CJ. Experimental suppression of transcranial magnetic stimulation-electroencephalography sensory potentials. Hum Brain Mapp 2022; 43:5141-5153. [PMID: 35770956 PMCID: PMC9812254 DOI: 10.1002/hbm.25990] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 05/13/2022] [Accepted: 06/10/2022] [Indexed: 01/15/2023] Open
Abstract
The sensory experience of transcranial magnetic stimulation (TMS) evokes cortical responses measured in electroencephalography (EEG) that confound interpretation of TMS-evoked potentials (TEPs). Methods for sensory masking have been proposed to minimize sensory contributions to the TEP, but the most effective combination for suprathreshold TMS to dorsolateral prefrontal cortex (dlPFC) is unknown. We applied sensory suppression techniques and quantified electrophysiology and perception from suprathreshold dlPFC TMS to identify the best combination to minimize the sensory TEP. In 21 healthy adults, we applied single pulse TMS at 120% resting motor threshold (rMT) to the left dlPFC and compared EEG vertex N100-P200 and perception. Conditions included three protocols: No masking (no auditory masking, no foam, and jittered interstimulus interval [ISI]), Standard masking (auditory noise, foam, and jittered ISI), and our ATTENUATE protocol (auditory noise, foam, over-the-ear protection, and unjittered ISI). ATTENUATE reduced vertex N100-P200 by 56%, "click" loudness perception by 50%, and scalp sensation by 36%. We show that sensory prediction, induced with predictable ISI, has a suppressive effect on vertex N100-P200, and that combining standard suppression protocols with sensory prediction provides the best N100-P200 suppression. ATTENUATE was more effective than Standard masking, which only reduced vertex N100-P200 by 22%, loudness by 27%, and scalp sensation by 24%. We introduce a sensory suppression protocol superior to Standard masking and demonstrate that using an unjittered ISI can contribute to minimizing sensory confounds. ATTENUATE provides superior sensory suppression to increase TEP signal-to-noise and contributes to a growing understanding of TMS-EEG sensory neuroscience.
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Affiliation(s)
- Jessica M. Ross
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental IllnessResearch, Education, and Clinical Center (MIRECC)Palo AltoCaliforniaUSA,Department of Psychiatry and Behavioral SciencesStanford University Medical CenterStanfordCaliforniaUSA
| | - Manjima Sarkar
- Department of Psychiatry and Behavioral SciencesStanford University Medical CenterStanfordCaliforniaUSA
| | - Corey J. Keller
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental IllnessResearch, Education, and Clinical Center (MIRECC)Palo AltoCaliforniaUSA,Department of Psychiatry and Behavioral SciencesStanford University Medical CenterStanfordCaliforniaUSA
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Using Neural Networks to Uncover the Relationship between Highly Variable Behavior and EEG during a Working Memory Task with Distractors. MATHEMATICS 2022. [DOI: 10.3390/math10111848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Value-driven attention capture (VDAC) occurs when previously rewarded stimuli capture attention and impair goal-directed behavior. In a working memory (WM) task with VDAC-related distractors, we observe behavioral variability both within and across individuals. Individuals differ in their ability to maintain relevant information and ignore distractions. These cognitive components shift over time with changes in motivation and attention, making it difficult to identify underlying neural mechanisms of individual differences. In this study, we develop the first participant-specific feedforward neural network models of reaction time from neural data during a VDAC WM task. We used short epochs of electroencephalography (EEG) data from 16 participants to develop the feedforward neural network (NN) models of RT aimed at understanding both WM and VDAC. Using general linear models (GLM), we identified 20 EEG features to predict RT across participants (r=0.53±0.08). The linear model was compared to the NN model, which improved the predicted trial-by-trial RT for all participants (r=0.87±0.04). We found that right frontal gamma-band activity and fronto-posterior functional connectivity in the alpha, beta, and gamma bands explain individual differences. Our study shows that NN models can link neural activity to highly variable behavior and can identify potential new targets for neuromodulation interventions.
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