1
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Schlesinger O, Kundu R, Isaev D, Choi JY, Goetz SM, Turner DA, Sapiro G, Peterchev AV, Di Martino JM. Scalp surface estimation and head registration using sparse sampling and 3D statistical models. Comput Biol Med 2024; 178:108689. [PMID: 38875907 PMCID: PMC11265975 DOI: 10.1016/j.compbiomed.2024.108689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/24/2024] [Accepted: 06/01/2024] [Indexed: 06/16/2024]
Abstract
Registering the head and estimating the scalp surface are important for various biomedical procedures, including those using neuronavigation to localize brain stimulation or recording. However, neuronavigation systems rely on manually-identified fiducial head targets and often require a patient-specific MRI for accurate registration, limiting adoption. We propose a practical technique capable of inferring the scalp shape and use it to accurately register the subject's head. Our method does not require anatomical landmark annotation or an individual MRI scan, yet achieves accurate registration of the subject's head and estimation of its surface. The scalp shape is estimated from surface samples easily acquired using existing pointer tools, and registration exploits statistical head model priors. Our method allows for the acquisition of non-trivial shapes from a limited number of data points while leveraging their object class priors, surpassing the accuracy of common reconstruction and registration methods using the same tools. The proposed approach is evaluated in a virtual study with head MRI data from 1152 subjects, achieving an average reconstruction root-mean-square error of 2.95 mm, which outperforms a common neuronavigation technique by 2.70 mm. We also characterize the error under different conditions and provide guidelines for efficient sampling. Furthermore, we demonstrate and validate the proposed method on data from 50 subjects collected with conventional neuronavigation tools and setup, obtaining an average root-mean-square error of 2.89 mm; adding landmark-based registration improves this error to 2.63 mm. The simulation and experimental results support the proposed method's effectiveness with or without landmark annotation, highlighting its broad applicability.
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Affiliation(s)
- Oded Schlesinger
- Department of Electrical and Computer Engineering, Duke University, Durham, 27708, NC, USA.
| | - Raj Kundu
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, 27710, NC, USA; Boston University School of Medicine, Boston, 02118, MA, USA
| | - Dmitry Isaev
- Department of Biomedical Engineering, Duke University, Durham, 27708, NC, USA
| | - Jessica Y Choi
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, 27710, NC, USA
| | - Stefan M Goetz
- Department of Electrical and Computer Engineering, Duke University, Durham, 27708, NC, USA; Department of Psychiatry & Behavioral Sciences, Duke University, Durham, 27710, NC, USA; Department of Neurosurgery, Duke University, Durham, 27710, NC, USA
| | - Dennis A Turner
- Department of Neurosurgery, Duke University, Durham, 27710, NC, USA
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, 27708, NC, USA; Department of Biomedical Engineering, Duke University, Durham, 27708, NC, USA
| | - Angel V Peterchev
- Department of Electrical and Computer Engineering, Duke University, Durham, 27708, NC, USA; Department of Psychiatry & Behavioral Sciences, Duke University, Durham, 27710, NC, USA; Department of Neurosurgery, Duke University, Durham, 27710, NC, USA; Department of Biomedical Engineering, Duke University, Durham, 27708, NC, USA
| | - J Matias Di Martino
- Department of Electrical and Computer Engineering, Duke University, Durham, 27708, NC, USA; Universidad Católica del Uruguay, Montevideo, 11600, Uruguay
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2
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Wang B, Peterchev AV, Gaugain G, Ilmoniemi RJ, Grill WM, Bikson M, Nikolayev D. Quasistatic approximation in neuromodulation. J Neural Eng 2024; 21:041002. [PMID: 38994790 DOI: 10.1088/1741-2552/ad625e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 06/28/2024] [Indexed: 07/13/2024]
Abstract
We define and explain the quasistatic approximation (QSA) as applied to field modeling for electrical and magnetic stimulation. Neuromodulation analysis pipelines include discrete stages, and QSA is applied specifically when calculating the electric and magnetic fields generated in tissues by a given stimulation dose. QSA simplifies the modeling equations to support tractable analysis, enhanced understanding, and computational efficiency. The application of QSA in neuromodulation is based on four underlying assumptions: (A1) no wave propagation or self-induction in tissue, (A2) linear tissue properties, (A3) purely resistive tissue, and (A4) non-dispersive tissue. As a consequence of these assumptions, each tissue is assigned a fixed conductivity, and the simplified equations (e.g. Laplace's equation) are solved for the spatial distribution of the field, which is separated from the field's temporal waveform. Recognizing that electrical tissue properties may be more complex, we explain how QSA can be embedded in parallel or iterative pipelines to model frequency dependence or nonlinearity of conductivity. We survey the history and validity of QSA across specific applications, such as microstimulation, deep brain stimulation, spinal cord stimulation, transcranial electrical stimulation, and transcranial magnetic stimulation. The precise definition and explanation of QSA in neuromodulation are essential for rigor when using QSA models or testing their limits.
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Affiliation(s)
- Boshuo Wang
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, United States of America
| | - Angel V Peterchev
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27710, United States of America
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Neurosurgery, Duke University, Durham, NC 27710, United States of America
| | - Gabriel Gaugain
- Institut d'Électronique et des Technologies du numéRique (IETR UMR 6164), CNRS / University of Rennes, 35000 Rennes, France
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Warren M Grill
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
- Department of Neurosurgery, Duke University, Durham, NC 27710, United States of America
- Department of Neurobiology, Duke University, Durham, NC 27710, United States of America
| | - Marom Bikson
- The City College of New York, New York, NY 11238, United States of America
| | - Denys Nikolayev
- Institut d'Électronique et des Technologies du numéRique (IETR UMR 6164), CNRS / University of Rennes, 35000 Rennes, France
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3
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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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4
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Matsuda RH, Souza VH, Marchetti TC, Soto AM, Kahilakoski OP, Zhdanov A, Malheiro VHE, Laine M, Nyrhinen M, Sinisalo H, Kicic D, Lioumis P, Ilmoniemi RJ, Baffa O. Robotic-electronic platform for autonomous and accurate transcranial magnetic stimulation targeting. Brain Stimul 2024; 17:469-472. [PMID: 38582491 DOI: 10.1016/j.brs.2024.03.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/15/2024] [Accepted: 03/31/2024] [Indexed: 04/08/2024] Open
Affiliation(s)
- Renan H Matsuda
- Department of Physics, Faculty of Philosophy Sciences and Letters of Ribeirão Preto, University of São Paulo, Av. Bandeirantes, 3900, 14040-901, Ribeirão Preto, SP, Brazil; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Rakentajanaukio 2, 02150, Espoo, Finland.
| | - Victor H Souza
- Department of Physics, Faculty of Philosophy Sciences and Letters of Ribeirão Preto, University of São Paulo, Av. Bandeirantes, 3900, 14040-901, Ribeirão Preto, SP, Brazil; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Rakentajanaukio 2, 02150, Espoo, Finland; School of Physiotherapy, Federal University of Juiz de Fora, Juiz de Fora, MG, Brazil
| | - Thais C Marchetti
- Department of Physics, Faculty of Philosophy Sciences and Letters of Ribeirão Preto, University of São Paulo, Av. Bandeirantes, 3900, 14040-901, Ribeirão Preto, SP, Brazil
| | - Ana M Soto
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Rakentajanaukio 2, 02150, Espoo, Finland
| | - Olli-Pekka Kahilakoski
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Rakentajanaukio 2, 02150, Espoo, Finland
| | - Andrey Zhdanov
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Rakentajanaukio 2, 02150, Espoo, Finland
| | - Victor H E Malheiro
- Department of Physics, Faculty of Philosophy Sciences and Letters of Ribeirão Preto, University of São Paulo, Av. Bandeirantes, 3900, 14040-901, Ribeirão Preto, SP, Brazil
| | - Mikael Laine
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Rakentajanaukio 2, 02150, Espoo, Finland
| | - Mikko Nyrhinen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Rakentajanaukio 2, 02150, Espoo, Finland
| | - Heikki Sinisalo
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Rakentajanaukio 2, 02150, Espoo, Finland
| | - Dubravko Kicic
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Rakentajanaukio 2, 02150, Espoo, Finland
| | - Pantelis Lioumis
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Rakentajanaukio 2, 02150, Espoo, Finland
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Rakentajanaukio 2, 02150, Espoo, Finland
| | - Oswaldo Baffa
- Department of Physics, Faculty of Philosophy Sciences and Letters of Ribeirão Preto, University of São Paulo, Av. Bandeirantes, 3900, 14040-901, Ribeirão Preto, SP, Brazil
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5
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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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7
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Hehl M, Van Malderen S, Geraerts M, Meesen RLJ, Rothwell JC, Swinnen SP, Cuypers K. Probing intrahemispheric interactions with a novel dual-site TMS setup. Clin Neurophysiol 2024; 158:180-195. [PMID: 38232610 DOI: 10.1016/j.clinph.2023.12.128] [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: 10/24/2023] [Revised: 12/02/2023] [Accepted: 12/19/2023] [Indexed: 01/19/2024]
Abstract
OBJECTIVE Using dual-site transcranial magnetic stimulation (dsTMS), the effective connectivity between the primary motor cortex (M1) and adjacent brain areas such as the dorsal premotor cortex (PMd) can be investigated. However, stimulating two brain regions in close proximity (e.g., ±2.3 cm for intrahemispheric PMd-M1) is subject to considerable spatial restrictions that potentially can be overcome by combining two standard figure-of-eight coils in a novel dsTMS setup. METHODS After a technical evaluation of its induced electric fields, the dsTMS setup was tested in vivo (n = 23) by applying a short-interval intracortical inhibition (SICI) protocol. Additionally, the intrahemispheric PMd-M1 interaction was probed. E-field modelling was performed using SimNIBS. RESULTS The technical evaluation yielded no major alterations of the induced electric fields due to coil overlap. In vivo, the setup reliably elicited SICI. Investigating intrahemispheric PMd-M1 interactions was feasible (inter-stimulus interval 6 ms), resulting in modulation of M1 output. CONCLUSIONS The presented dsTMS setup provides a novel way to stimulate two adjacent brain regions with fewer technical and spatial limitations than previous attempts. SIGNIFICANCE This dsTMS setup enables more accurate and repeatable targeting of brain regions in close proximity and can facilitate innovation in the field of effective connectivity.
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Affiliation(s)
- Melina Hehl
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, 3001 Heverlee, Belgium; KU Leuven, Leuven Brain Institute (LBI), Leuven, Belgium; Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek, Belgium
| | - Shanti Van Malderen
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, 3001 Heverlee, Belgium; KU Leuven, Leuven Brain Institute (LBI), Leuven, Belgium; Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek, Belgium
| | - Marc Geraerts
- Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek, Belgium
| | - Raf L J Meesen
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, 3001 Heverlee, Belgium; Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek, Belgium
| | - John C Rothwell
- Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, University College London, London, United Kingdom
| | - Stephan P Swinnen
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, 3001 Heverlee, Belgium; KU Leuven, Leuven Brain Institute (LBI), Leuven, Belgium
| | - Koen Cuypers
- Movement Control & Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, 3001 Heverlee, Belgium; KU Leuven, Leuven Brain Institute (LBI), Leuven, Belgium; Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek, Belgium.
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8
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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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9
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Manjunatha KKH, Baron G, Benozzo D, Silvestri E, Corbetta M, Chiuso A, Bertoldo A, Suweis S, Allegra M. Controlling target brain regions by optimal selection of input nodes. PLoS Comput Biol 2024; 20:e1011274. [PMID: 38215166 PMCID: PMC10810536 DOI: 10.1371/journal.pcbi.1011274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 01/25/2024] [Accepted: 12/04/2023] [Indexed: 01/14/2024] Open
Abstract
The network control theory framework holds great potential to inform neurostimulation experiments aimed at inducing desired activity states in the brain. However, the current applicability of the framework is limited by inappropriate modeling of brain dynamics, and an overly ambitious focus on whole-brain activity control. In this work, we leverage recent progress in linear modeling of brain dynamics (effective connectivity) and we exploit the concept of target controllability to focus on the control of a single region or a small subnetwork of nodes. We discuss when control may be possible with a reasonably low energy cost and few stimulation loci, and give general predictions on where to stimulate depending on the subset of regions one wishes to control. Importantly, using the robustly asymmetric effective connectome instead of the symmetric structural connectome (as in previous research), we highlight the fundamentally different roles in- and out-hubs have in the control problem, and the relevance of inhibitory connections. The large degree of inter-individual variation in the effective connectome implies that the control problem is best formulated at the individual level, but we discuss to what extent group results may still prove useful.
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Affiliation(s)
- Karan Kabbur Hanumanthappa Manjunatha
- Physics and Astronomy Department “Galileo Galilei”, University of Padova, Padova, Italy
- Modeling and Engineering Risk and Complexity, Scuola Superiore Meridionale, Napoli, Italy
| | - Giorgia Baron
- Information Engineering Department, University of Padova, Padova, Italy
| | - Danilo Benozzo
- Information Engineering Department, University of Padova, Padova, Italy
| | - Erica Silvestri
- Information Engineering Department, University of Padova, Padova, Italy
| | - Maurizio Corbetta
- Neuroscience Department, University of Padova, Padova, Italy
- Venetian Institute of Molecular Medicine (VIMM), Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Alessandro Chiuso
- Information Engineering Department, University of Padova, Padova, Italy
| | - Alessandra Bertoldo
- Information Engineering Department, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Samir Suweis
- Physics and Astronomy Department “Galileo Galilei”, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Michele Allegra
- Physics and Astronomy Department “Galileo Galilei”, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
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10
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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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11
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Liu F, Zhang Z, Chen Y, Wei L, Xu Y, Li Z, Zhu C. MNI2CPC: A probabilistic cortex-to-scalp mapping for non-invasive brain stimulation targeting. Brain Stimul 2023; 16:1733-1742. [PMID: 38036251 DOI: 10.1016/j.brs.2023.11.011] [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: 09/24/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 12/02/2023] Open
Abstract
BACKGROUND Synthesis of neural imaging information from many studies is valuable for identifying stable cortical targets for non-invasive brain stimulation (NIBS). Typically, these targets are specified in Montreal Neurological Institute (MNI) standard brain space. However, in practical NIBS applications, localizing MNI cortical targets often relies on the International 10-20 system or heuristic scalp approaches, which often lacks precision or applies only to specific targets. OBJECTIVE/HYPOTHESIS We aim to establish a probabilistic mapping from any cortical target in MNI space to continuous proportional coordinate (CPC) standard scalp space (MNI2CPC) and assess the performance of this mapping for NIBS targeting. METHODS The MNI2CPC mapping was calculated based on a large MRI dataset (n = 114). Its targeting error was evaluated via cross-individual validation using a leave-one-out approach, as well as through independent validation across race (n = 27) and across patient (n = 58) cohorts. RESULTS The cross-individual validation demonstrated targeting errors of 4.03 ± 0.69 mm on the scalp and 3.30 ± 0.59 mm in the cortex. For independent cohorts, targeting errors were 4.71 ± 0.81 mm (scalp) and 3.85 ± 0.64 mm (cortex) across race, and 4.66 ± 0.77 mm (scalp) and 3.77 ± 0.61 mm (cortex) across patient. We publish a free online tool to enable querying of the CPC coordinate for any given MNI cortical target. The resulting CPC coordinates enable rapid and accurate manual localization on the scalp in a user-friendly manner. CONCLUSIONS The MNI2CPC mapping developed in this study allows for manual localization of any MNI cortical target, which improves the accessibility and ease of application of NIBS in diverse settings.
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Affiliation(s)
- Farui Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zong Zhang
- Faculty of Psychology, Tianjin Normal University, Tianjin, China
| | - Yuanyuan Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Lijiang Wei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yilong Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zheng Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive, Neuroscience and Learning, Beijing Normal University Zhuhai, Zhuhai, China
| | - Chaozhe Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.
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12
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Hernandez-Pavon JC, San Agustín A, Wang MC, Veniero D, Pons JL. Can we manipulate brain connectivity? A systematic review of cortico-cortical paired associative stimulation effects. Clin Neurophysiol 2023; 154:169-193. [PMID: 37634335 DOI: 10.1016/j.clinph.2023.06.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 05/09/2023] [Accepted: 06/16/2023] [Indexed: 08/29/2023]
Abstract
OBJECTIVE Cortico-cortical paired associative stimulation (ccPAS) is a form of dual-site transcranial magnetic stimulation (TMS) entailing a series of single-TMS pulses paired at specific interstimulus intervals (ISI) delivered to distant cortical areas. The goal of this article is to systematically review its efficacy in inducing plasticity in humans focusing on stimulation parameters and hypotheses of underlying neurophysiology. METHODS A systematic review of the literature from 2009-2023 was undertaken to identify all articles utilizing ccPAS to study brain plasticity and connectivity. Six electronic databases were searched and included. RESULTS 32 studies were identified. The studies targeted connections within the same hemisphere or between hemispheres. 28 ccPAS studies were in healthy participants, 1 study in schizophrenia, and 1 in Alzheimer's disease (AD) patients. 2 additional studies used cortico-cortical repetitive paired associative stimulation (cc-rPAS) in generalized anxiety disorder (GAD) patients. Outcome measures include electromyography (EMG), behavioral measures, electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). ccPAS seems to be able to modulate brain connectivity depending on the ISI. CONCLUSIONS ccPAS can be used to modulate corticospinal excitability, brain activity, and behavior. Although the stimulation parameters used across studies reviewed in this paper are varied, ccPAS is a promising approach for basic research and potential clinical applications. SIGNIFICANCE Recent advances in neuroscience have caused a shift of interest from the study of single areas to a more complex approach focusing on networks of areas that orchestrate brain activity. Consequently, the TMS community is also witnessing a change, with a growing interest in targeting multiple brain areas rather than a single locus, as evidenced by an increasing number of papers using ccPAS. In light of this new enthusiasm for brain connectivity, this review summarizes existing literature and stimulation parameters that have proven effective in changing electrophysiological, behavioral, or neuroimaging-derived measures.
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Affiliation(s)
- Julio C Hernandez-Pavon
- Legs + Walking Lab, Shirley Ryan AbilityLab (Formerly, The Rehabilitation Institute of Chicago), Chicago, IL, USA; Center for Brain Stimulation, Shirley Ryan AbilityLab, Chicago, IL, USA; Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Psychological Sciences, Kansas State University, Manhattan, KS, USA.
| | - Arantzazu San Agustín
- Legs + Walking Lab, Shirley Ryan AbilityLab (Formerly, The Rehabilitation Institute of Chicago), Chicago, IL, USA; Center for Brain Stimulation, Shirley Ryan AbilityLab, Chicago, IL, USA; Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Neural Rehabilitation Group, Cajal Institute, CSIC, Madrid, Spain; PhD Program in Neuroscience, Autonoma de Madrid University-Cajal Institute, Madrid 28029, Spain
| | - Max C Wang
- Department of Physical Therapy and Human Movement Science, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Jose L Pons
- Legs + Walking Lab, Shirley Ryan AbilityLab (Formerly, The Rehabilitation Institute of Chicago), Chicago, IL, USA; Center for Brain Stimulation, Shirley Ryan AbilityLab, Chicago, IL, USA; Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Chicago, IL, USA
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13
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Smith MC, Sievenpiper DF. A new synthesis method for complex electric field patterning using a multichannel dense array system with applications in low-intensity noninvasive neuromodulation. Bioelectromagnetics 2023; 44:156-180. [PMID: 37453053 DOI: 10.1002/bem.22476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 02/01/2023] [Accepted: 04/20/2023] [Indexed: 07/18/2023]
Abstract
Multichannel coil array systems offer precise spatiotemporal electronic steering and patterning of electric and magnetic fields without the physical movement of coils or magnets. This capability could potentially benefit a wide range of biomagnetic applications such as low-intensity noninvasive neuromodulation or magnetic drug delivery. In this regard, the objective of this work is to develop a unique synthesis method, that enabled by a multichannel dense array system, generates complex current pattern distributions not previously reported in the literature. Simulations and experimental results verify that highly curved or irregular (e.g., zig-zag) patterns at singular and multiple sites can be efficiently formed using this method. The synthesis method is composed of three primary components; a pixel cell (basic unit of pattern formation), a template array ("virtual array": code that disseminates the coil current weights to the "physical" dense array), and a hexagonal coordinate system. Low-intensity or low-field magnetic stimulation is identified as a potential application that could benefit from this work in the future and as such is used as an example to frame the research.
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Affiliation(s)
- Matthew C Smith
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California, USA
| | - Daniel F Sievenpiper
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California, USA
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14
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Navarro de Lara LI, Stockmann JP, Meng Q, Keil B, Mareyam A, Uluç I, Daneshzand M, Makarov S, Wald LL, Nummenmaa A. A novel whole-head RF coil design tailored for concurrent multichannel brain stimulation and imaging at 3T. Brain Stimul 2023; 16:1021-1031. [PMID: 37307872 PMCID: PMC10499022 DOI: 10.1016/j.brs.2023.05.025] [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: 12/13/2022] [Revised: 04/27/2023] [Accepted: 05/30/2023] [Indexed: 06/14/2023] Open
Abstract
PURPOSE Multichannel Transcranial Magnetic Stimulation (mTMS) [1] is a novel non-invasive brain stimulation technique allowing multiple sites to be stimulated simultaneously or sequentially under electronic control without movement of the coils. To enable simultaneous mTMS and MR imaging, we have designed and constructed a whole-head 28-channel receive-only RF coil at 3T. METHODS A helmet-shaped structure was designed considering a specific layout for a mTMS system with holes for positioning the TMS units next to the scalp. Diameter of the TMS units defined the diameter of RF loops. The placement of the preamplifiers was designed to minimize possible interactions and to allow straightforward positioning of the mTMS units around the RF coil. Interactions between TMS-MRI were analyzed for the whole-head system extending the results presented in previous publications [2]. Both SNR- and g-factors maps were obtained to compare the imaging performance of the coil with commercial head coils. RESULTS Sensitivity losses for the RF elements containing TMS units show a well-defined spatial pattern. Simulations indicate that the losses are predominantly caused by eddy currents on the coil wire windings. The average SNR performance of the TMSMR 28-channel coil is about 66% and 86% of the SNR of the 32/20-channel head coil respectively. The g-factor values of the TMSMR 28-channel coil are similar to the 32-channel coil and significantly better than the 20-channel coil. CONCLUSION We present the TMSMR 28-channel coil, a head RF coil array to be integrated with a multichannel 3-axisTMS coil system, a novel tool that will enable causal mapping of human brain function.
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Affiliation(s)
- Lucia I Navarro de Lara
- Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA; Harvard Medical School, Boston, MA, 02115, USA.
| | - Jason P Stockmann
- Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA; Harvard Medical School, Boston, MA, 02115, USA
| | - Qinglei Meng
- Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA; Harvard Medical School, Boston, MA, 02115, USA
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection, Department of Life Science Engineering, TH Mittelhessen University of Applied Sciences, Giessen, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Marburg, Phillipps University of Marburg, Marburg, Germany
| | - Azma Mareyam
- Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA
| | - Işıl Uluç
- Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA; Harvard Medical School, Boston, MA, 02115, USA
| | - Mohammad Daneshzand
- Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA; Harvard Medical School, Boston, MA, 02115, USA
| | - Sergey Makarov
- Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA; Harvard Medical School, Boston, MA, 02115, USA; Department of Electrical and Computer Engineering at the Worcester Polytechnic Institute, Worcester, MA, 01609, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA; Harvard Medical School, Boston, MA, 02115, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA; Harvard Medical School, Boston, MA, 02115, USA
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15
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Spampinato DA, Ibanez J, Rocchi L, Rothwell J. Motor potentials evoked by transcranial magnetic stimulation: interpreting a simple measure of a complex system. J Physiol 2023; 601:2827-2851. [PMID: 37254441 PMCID: PMC10952180 DOI: 10.1113/jp281885] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 05/18/2023] [Indexed: 06/01/2023] Open
Abstract
Transcranial magnetic stimulation (TMS) is a non-invasive technique that is increasingly used to study the human brain. One of the principal outcome measures is the motor-evoked potential (MEP) elicited in a muscle following TMS over the primary motor cortex (M1), where it is used to estimate changes in corticospinal excitability. However, multiple elements play a role in MEP generation, so even apparently simple measures such as peak-to-peak amplitude have a complex interpretation. Here, we summarize what is currently known regarding the neural pathways and circuits that contribute to the MEP and discuss the factors that should be considered when interpreting MEP amplitude measured at rest in the context of motor processing and patients with neurological conditions. In the last part of this work, we also discuss how emerging technological approaches can be combined with TMS to improve our understanding of neural substrates that can influence MEPs. Overall, this review aims to highlight the capabilities and limitations of TMS that are important to recognize when attempting to disentangle sources that contribute to the physiological state-related changes in corticomotor excitability.
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Affiliation(s)
- Danny Adrian Spampinato
- Department of Clinical and Movement NeurosciencesUniversity College LondonLondonUK
- Department of Human NeurosciencesSapienza University of RomeRomeItaly
- Department of Clinical and Behavioral NeurologyIRCCS Santa Lucia FoundationRomeItaly
| | - Jaime Ibanez
- Department of Clinical and Movement NeurosciencesUniversity College LondonLondonUK
- BSICoS group, I3A Institute and IIS AragónUniversity of ZaragozaZaragozaSpain
- Department of Bioengineering, Centre for NeurotechnologiesImperial College LondonLondonUK
| | - Lorenzo Rocchi
- Department of Clinical and Movement NeurosciencesUniversity College LondonLondonUK
- Department of Medical Sciences and Public HealthUniversity of CagliariCagliariItaly
| | - John Rothwell
- Department of Clinical and Movement NeurosciencesUniversity College LondonLondonUK
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16
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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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17
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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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18
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Van Malderen S, Hehl M, Verstraelen S, Swinnen SP, Cuypers K. Dual-site TMS as a tool to probe effective interactions within the motor network: a review. Rev Neurosci 2023; 34:129-221. [PMID: 36065080 DOI: 10.1515/revneuro-2022-0020] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/02/2022] [Indexed: 02/07/2023]
Abstract
Dual-site transcranial magnetic stimulation (ds-TMS) is well suited to investigate the causal effect of distant brain regions on the primary motor cortex, both at rest and during motor performance and learning. However, given the broad set of stimulation parameters, clarity about which parameters are most effective for identifying particular interactions is lacking. Here, evidence describing inter- and intra-hemispheric interactions during rest and in the context of motor tasks is reviewed. Our aims are threefold: (1) provide a detailed overview of ds-TMS literature regarding inter- and intra-hemispheric connectivity; (2) describe the applicability and contributions of these interactions to motor control, and; (3) discuss the practical implications and future directions. Of the 3659 studies screened, 109 were included and discussed. Overall, there is remarkable variability in the experimental context for assessing ds-TMS interactions, as well as in the use and reporting of stimulation parameters, hindering a quantitative comparison of results across studies. Further studies examining ds-TMS interactions in a systematic manner, and in which all critical parameters are carefully reported, are needed.
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Affiliation(s)
- Shanti Van Malderen
- Department of Movement Sciences, Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Heverlee 3001, Belgium.,Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek 3590, Belgium
| | - Melina Hehl
- Department of Movement Sciences, Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Heverlee 3001, Belgium.,Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek 3590, Belgium
| | - Stefanie Verstraelen
- Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek 3590, Belgium
| | - Stephan P Swinnen
- Department of Movement Sciences, Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Heverlee 3001, Belgium.,KU Leuven, Leuven Brain Institute (LBI), Leuven, Belgium
| | - Koen Cuypers
- Department of Movement Sciences, Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Heverlee 3001, Belgium.,Neuroplasticity and Movement Control Research Group, Rehabilitation Research Institute (REVAL), Hasselt University, Diepenbeek 3590, Belgium
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19
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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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20
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Scangos KW, State MW, Miller AH, Baker JT, Williams LM. New and emerging approaches to treat psychiatric disorders. Nat Med 2023; 29:317-333. [PMID: 36797480 PMCID: PMC11219030 DOI: 10.1038/s41591-022-02197-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/21/2022] [Indexed: 02/18/2023]
Abstract
Psychiatric disorders are highly prevalent, often devastating diseases that negatively impact the lives of millions of people worldwide. Although their etiological and diagnostic heterogeneity has long challenged drug discovery, an emerging circuit-based understanding of psychiatric illness is offering an important alternative to the current reliance on trial and error, both in the development and in the clinical application of treatments. Here we review new and emerging treatment approaches, with a particular emphasis on the revolutionary potential of brain-circuit-based interventions for precision psychiatry. Limitations of circuit models, challenges of bringing precision therapeutics to market and the crucial advances needed to overcome these obstacles are presented.
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Affiliation(s)
- Katherine W Scangos
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
| | - Matthew W State
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Andrew H Miller
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Justin T Baker
- McLean Hospital Institute for Technology in Psychiatry, Belmont, MA, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Mental Illness Research Education and Clinical Center (MIRECC), VA Palo Alto Health Care System, Palo Alto, CA, USA
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21
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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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22
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Source-based artifact-rejection techniques for TMS-EEG. J Neurosci Methods 2022; 382:109693. [PMID: 36057330 DOI: 10.1016/j.jneumeth.2022.109693] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 08/14/2022] [Accepted: 08/25/2022] [Indexed: 01/05/2023]
Abstract
Neuronal electroencephalography (EEG) signals arise from the cortical postsynaptic currents. Due to the conductive properties of the head, these neuronal sources produce relatively smeared spatial patterns in EEG. We can model these topographies to deduce which signals reflect genuine TMS-evoked cortical activity and which data components are merely noise and artifacts. This review will concentrate on two source-based artifact-rejection techniques developed for TMS-EEG data analysis, signal-space-projection-source-informed reconstruction (SSP-SIR), and the source-estimate-utilizing noise-discarding algorithm (SOUND). The former method was designed for rejecting TMS-evoked muscle artifacts, while the latter was developed to suppress noise signals from EEG and magnetoencephalography (MEG) in general. We shall cover the theoretical background for both methods, but most importantly, we will describe some essential practical perspectives for using these techniques effectively. We demonstrate and explain what approaches produce the most reliable inverse estimates after cleaning the data or how to perform non-biased comparisons between cleaned datasets. All noise-cleaning algorithms compromise the signals of interest to a degree. We elaborate on how the source-based methods allow objective quantification of the overcorrection. Finally, we consider possible future directions. While this article concentrates on TMS-EEG data analysis, many theoretical and practical aspects, presented here, can be readily applied in other EEG/MEG applications. Overall, the source-based cleaning methods provide a valuable set of TMS-EEG preprocessing tools. We can objectively evaluate their performance regarding possible overcorrection. Furthermore, the overcorrection can always be taken into account to compare cleaned datasets reliably. The described methods are based on current electrophysiological and anatomical understanding of the head and the EEG generators; strong assumptions of the statistical properties of the noise and artifact signals, such as independence, are not needed.
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23
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Nieminen JO, Pospelov AS, Koponen LM, Yrjölä P, Shulga A, Khirug S, Rivera C. Transcranial magnetic stimulation set-up for small animals. Front Neurosci 2022; 16:935268. [PMID: 36440290 PMCID: PMC9685557 DOI: 10.3389/fnins.2022.935268] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/27/2022] [Indexed: 12/23/2023] Open
Abstract
Transcranial magnetic stimulation (TMS) is widely applied on humans for research and clinical purposes. TMS studies on small animals, e.g., rodents, can provide valuable knowledge of the underlying neurophysiological mechanisms. Administering TMS on small animals is, however, prone to technical difficulties, mainly due to their small head size. In this study, we aimed to develop an energy-efficient coil and a compatible experimental set-up for administering TMS on rodents. We applied a convex optimization process to develop a minimum-energy coil for TMS on rats. As the coil windings of the optimized coil extend to a wide region, we designed and manufactured a holder on which the rat lies upside down, with its head supported by the coil. We used the set-up to record TMS-electromyography, with electromyography recorded from limb muscles with intramuscular electrodes. The upside-down placement of the rat allowed the operator to easily navigate the TMS without the coil blocking their field of view. With this paradigm, we obtained consistent motor evoked potentials from all tested animals.
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Affiliation(s)
- Jaakko O. Nieminen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, HUS Medical Imaging Centre, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Biomedical Imaging Unit, A.I. Virtanen Institute, University of Eastern Finland, Kuopio, Finland
| | - Alexey S. Pospelov
- Faculty of Biological and Environmental Sciences, Molecular and Integrative Biosciences, University of Helsinki, Helsinki, Finland
- Department of Clinical Neurophysiology, BABA Center, Children’s Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Lari M. Koponen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Pauliina Yrjölä
- BioMag Laboratory, HUS Medical Imaging Centre, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Clinical Neurophysiology, BABA Center, Children’s Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Anastasia Shulga
- BioMag Laboratory, HUS Medical Imaging Centre, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Physical and Rehabilitation Medicine, Helsinki University Hospital, Helsinki, Finland
| | - Stanislav Khirug
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Claudio Rivera
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- INMED (INSERM U1249), Aix-Marseille Université, Marseille, France
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24
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Menardi A, Momi D, Vallesi A, Barabási AL, Towlson EK, Santarnecchi E. Maximizing brain networks engagement via individualized connectome-wide target search. Brain Stimul 2022; 15:1418-1431. [PMID: 36252908 DOI: 10.1016/j.brs.2022.09.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 07/29/2022] [Accepted: 09/23/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND In recent years, the possibility to noninvasively interact with the human brain has led to unprecedented diagnostic and therapeutic opportunities. However, the vast majority of approved interventions and approaches still rely on anatomical landmarks and rarely on the individual structure of networks in the brain, drastically reducing the potential efficacy of neuromodulation. OBJECTIVE Here we implemented a target search algorithm leveraging on mathematical tools from Network Control Theory (NCT) and whole brain connectomics analysis. By means of computational simulations, we aimed to identify the optimal stimulation target(s)- at the individual brain level- capable of reaching maximal engagement of the stimulated networks' nodes. RESULTS At the model level, in silico predictions suggest that stimulation of NCT-derived cerebral sites might induce significantly higher network engagement, compared to traditionally employed neuromodulation sites, demonstrating NCT to be a useful tool in guiding brain stimulation. Indeed, NCT allows us to computationally model different stimulation scenarios tailored on the individual structural connectivity profiles and initial brain states. CONCLUSIONS The use of NCT to computationally predict TMS pulse propagation suggests that individualized targeting is crucial for more successful network engagement. Future studies will be needed to verify such prediction in real stimulation scenarios.
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Affiliation(s)
- Arianna Menardi
- Precision Neuroscience & Neuromodulation Program, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Neuroscience & Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Davide Momi
- Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio", Chieti, Italy; Krembil Centre for Neuroinformatics, Centre for Addiction & Mental Health, Toronto, Canada
| | - Antonino Vallesi
- Department of Neuroscience & Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Albert-László Barabási
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Network and Data Science, Central European University, Budapest, Hungary
| | - Emma K Towlson
- Department of Computer Science, University of Calgary, Calgary, AB, Canada; Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Emiliano Santarnecchi
- Precision Neuroscience & Neuromodulation Program, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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25
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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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26
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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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Identifying novel biomarkers with TMS-EEG - Methodological possibilities and challenges. J Neurosci Methods 2022; 377:109631. [PMID: 35623474 DOI: 10.1016/j.jneumeth.2022.109631] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 05/09/2022] [Accepted: 05/21/2022] [Indexed: 12/17/2022]
Abstract
Biomarkers are essential for understanding the underlying pathologies in brain disorders and for developing effective treatments. Combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) is an emerging neurophysiological tool that can be used for biomarker development. This method can identify biomarkers associated with the function and dynamics of the inhibitory and excitatory neurotransmitter systems and effective connectivity between brain areas. In this review, we outline the current state of the TMS-EEG biomarker field by summarizing the existing protocols and the possibilities and challenges associated with this methodology.
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Bagherzadeh H, Meng Q, Deng ZD, Lu H, Hong E, Yang Y, Choa FS. Angle-tuned coils: attractive building blocks for TMS with improved depth-spread performance. J Neural Eng 2022; 19:10.1088/1741-2552/ac697c. [PMID: 35453132 PMCID: PMC10644970 DOI: 10.1088/1741-2552/ac697c] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 04/21/2022] [Indexed: 11/12/2022]
Abstract
Objective.A novel angle-tuned ring coil is proposed for improving the depth-spread performance of transcranial magnetic stimulation (TMS) coils and serve as the building blocks for high-performance composite coils and multisite TMS systems.Approach.Improving depth-spread performance by reducing field divergence through creating a more elliptical emitted field distribution from the coil. To accomplish that, instead of enriching the Fourier components along the planarized (x-y) directions, which requires different arrays to occupy large brain surface areas, we worked along the radial (z) direction by using tilted coil angles and stacking coil numbers to reduce the divergence of the emitted near field without occupying large head surface areas. The emitted electric field distributions were theoretically simulated in spherical and real human head models to analyze the depth-spread performance of proposed coils and compare with existing figure-8 coils. The results were then experimentally validated with field probes andin-vivoanimal tests.Main results.The proposed 'angle-tuning' concept improves the depth-spread performance of individual coils with a significantly smaller footprint than existing and proposed coils. For composite structures, using the proposed coils as basic building blocks simplifies the design and manufacturing process and helps accomplish a leading depth-spread performance. In addition, the footprint of the proposed system is intrinsically small, making them suitable for multisite stimulations of inter and intra-hemispheric brain regions with an improved spread and less electric field divergence.Significance.Few brain functions are operated by isolated single brain regions but rather by coordinated networks involving multiple brain regions. Simultaneous or sequential multisite stimulations may provide tools for mechanistic studies of brain functions and the treatment of neuropsychiatric disorders. The proposed AT coil goes beyond the traditional depth-spread tradeoff rule of TMS coils, which provides the possibility of building new composite structures and new multisite TMS tools.
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Affiliation(s)
- Hedyeh Bagherzadeh
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD, United States of America
- Co-first Author
| | - Qinglei Meng
- Magnetic Resonance Imaging and Spectroscopy, National Institute on Drug Abuse, Intramural Research Programs, National Institutes of Health, Baltimore, MD, United States of America
- Co-first Author
| | - Zhi-De Deng
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States of America
| | - Hanbing Lu
- Magnetic Resonance Imaging and Spectroscopy, National Institute on Drug Abuse, Intramural Research Programs, National Institutes of Health, Baltimore, MD, United States of America
| | - Elliott Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States of America
| | - Yihong Yang
- Magnetic Resonance Imaging and Spectroscopy, National Institute on Drug Abuse, Intramural Research Programs, National Institutes of Health, Baltimore, MD, United States of America
| | - Fow-Sen Choa
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD, United States of America
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Sorkhabi MM, Denison T. A neurostimulator system for real, sham, and multi-target transcranial magnetic stimulation. J Neural Eng 2022; 19:10.1088/1741-2552/ac60c9. [PMID: 35325879 PMCID: PMC7614713 DOI: 10.1088/1741-2552/ac60c9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 03/24/2022] [Indexed: 11/11/2022]
Abstract
Objective.Transcranial magnetic stimulation (TMS) is a clinically effective therapeutic instrument used to modulate neural activity. Despite three decades of research, two challenging issues remain, the possibility of changing the (a) stimulated spot and (b) stimulation type (real or sham) without physically moving the coil. In this study, a second-generation programmable TMS device with advanced stimulus shaping is introduced that uses a five-level cascaded H-bridge inverter and phase-shifted pulse-width modulation. The principal idea of this research is to obtain real, sham, and multi-locus stimulation using the same TMS system.Approach.We propose a two-channel modulation-based magnetic pulse generator and a novel coil arrangement, consisting of two circular coils with a physical distance of 20 mm between the coils and a control method for modifying the effective stimulus intensity, which leads to the live steerability of the target and type of stimulation.Main results.Based on the measured system performance, the stimulation profile can be steered ±20 mm along a line from the centroid of the coil locations by modifying the modulation index.Significance.The proposed system supports electronic control of the stimulation spot without physical coil movement, resulting in tunable modulation of targets, which is a crucial step towards automated TMS machines.
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Affiliation(s)
- Majid Memarian Sorkhabi
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX1 3TH, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX1 3TH, UK
- Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK
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Tervo AE, Nieminen JO, Lioumis P, Metsomaa J, Souza VH, Sinisalo H, Stenroos M, Sarvas J, Ilmoniemi RJ. Closed-loop optimization of transcranial magnetic stimulation with electroencephalography feedback. Brain Stimul 2022; 15:523-531. [PMID: 35337598 PMCID: PMC8940636 DOI: 10.1016/j.brs.2022.01.016] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 12/17/2021] [Accepted: 01/28/2022] [Indexed: 12/16/2022] Open
Abstract
Background Transcranial magnetic stimulation (TMS) is widely used in brain research and treatment of various brain dysfunctions. However, the optimal way to target stimulation and administer TMS therapies, for example, where and in which electric field direction the stimuli should be given, is yet to be determined. Objective To develop an automated closed-loop system for adjusting TMS parameters (in this work, the stimulus orientation) online based on TMS-evoked brain activity measured with electroencephalography (EEG). Methods We developed an automated closed-loop TMS–EEG set-up. In this set-up, the stimulus parameters are electronically adjusted with multi-locus TMS. As a proof of concept, we developed an algorithm that automatically optimizes the stimulation orientation based on single-trial EEG responses. We applied the algorithm to determine the electric field orientation that maximizes the amplitude of the TMS–EEG responses. The validation of the algorithm was performed with six healthy volunteers, repeating the search twenty times for each subject. Results The validation demonstrated that the closed-loop control worked as desired despite the large variation in the single-trial EEG responses. We were often able to get close to the orientation that maximizes the EEG amplitude with only a few tens of pulses. Conclusion Optimizing stimulation with EEG feedback in a closed-loop manner is feasible and enables effective coupling to brain activity. Closed-loop set-up for guiding TMS with brain activity feedback. Automatic stimulus orientation optimization based on TMS-evoked EEG responses. Adjusting TMS parameters electronically allows fast and effortless procedures. TMS-evoked EEG responses depend on the stimulus orientation.
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Affiliation(s)
- Aino E 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; 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; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki 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 and Helsinki University Hospital, Helsinki, Finland; Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Johanna Metsomaa
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; Department of Neurology & Stroke and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - 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
| | - Heikki Sinisalo
- 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
| | - Matti Stenroos
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Jukka Sarvas
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, 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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Souza VH, Nieminen JO, Tugin S, Koponen LM, Baffa O, Ilmoniemi RJ. TMS with fast and accurate electronic control: Measuring the orientation sensitivity of corticomotor pathways. Brain Stimul 2022; 15:306-315. [PMID: 35038592 DOI: 10.1016/j.brs.2022.01.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 12/30/2021] [Accepted: 01/12/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Transcranial magnetic stimulation (TMS) coils allow only a slow, mechanical adjustment of the stimulating electric field (E-field) orientation in the cerebral tissue. Fast E-field control is needed to synchronize the stimulation with the ongoing brain activity. Also, empirical models that fully describe the relationship between evoked responses and the stimulus orientation and intensity are still missing. OBJECTIVE We aimed to (1) develop a TMS transducer for manipulating the E-field orientation electronically with high accuracy at the neuronally meaningful millisecond-level time scale and (2) devise and validate a physiologically based model describing the orientation selectivity of neuronal excitability. METHODS We designed and manufactured a two-coil TMS transducer. The coil windings were computed with a minimum-energy optimization procedure, and the transducer was controlled with our custom-made electronics. The electronic E-field control was verified with a TMS characterizer. The motor evoked potential amplitude and latency of a hand muscle were mapped in 3° steps of the stimulus orientation in 16 healthy subjects for three stimulation intensities. We fitted a logistic model to the motor response amplitude. RESULTS The two-coil TMS transducer allows one to manipulate the pulse orientation accurately without manual coil movement. The motor response amplitude followed a logistic function of the stimulus orientation; this dependency was strongly affected by the stimulus intensity. CONCLUSION The developed electronic control of the E-field orientation allows exploring new stimulation paradigms and probing neuronal mechanisms. The presented model helps to disentangle the neuronal mechanisms of brain function and guide future non-invasive stimulation protocols.
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Affiliation(s)
- Victor Hugo 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; Department of Physics, School of Philosophy, Science and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil; School of Physiotherapy, Federal University of Juiz de Fora, Juiz de Fora, MG, Brazil.
| | - 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
| | - Sergei Tugin
- 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
| | - Lari M Koponen
- 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; Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC, USA
| | - Oswaldo Baffa
- Department of Physics, School of Philosophy, Science and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - 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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Nieminen JO, Sinisalo H, Souza VH, Malmi M, Yuryev M, Tervo AE, Stenroos M, Milardovich D, Korhonen JT, Koponen LM, Ilmoniemi RJ. Multi-locus transcranial magnetic stimulation system for electronically targeted brain stimulation. Brain Stimul 2022; 15:116-124. [PMID: 34818580 PMCID: PMC8807400 DOI: 10.1016/j.brs.2021.11.014] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 11/17/2021] [Accepted: 11/19/2021] [Indexed: 11/02/2022] Open
Abstract
BACKGROUND Transcranial magnetic stimulation (TMS) allows non-invasive stimulation of the cortex. In multi-locus TMS (mTMS), the stimulating electric field (E-field) is controlled electronically without coil movement by adjusting currents in the coils of a transducer. OBJECTIVE To develop an mTMS system that allows adjusting the location and orientation of the E-field maximum within a cortical region. METHODS We designed and manufactured a planar 5-coil mTMS transducer to allow controlling the maximum of the induced E-field within a cortical region approximately 30 mm in diameter. We developed electronics with a design consisting of independently controlled H-bridge circuits to drive up to six TMS coils. To control the hardware, we programmed software that runs on a field-programmable gate array and a computer. To induce the desired E-field in the cortex, we developed an optimization method to calculate the currents needed in the coils. We characterized the mTMS system and conducted a proof-of-concept motor-mapping experiment on a healthy volunteer. In the motor mapping, we kept the transducer placement fixed while electronically shifting the E-field maximum on the precentral gyrus and measuring electromyography from the contralateral hand. RESULTS The transducer consists of an oval coil, two figure-of-eight coils, and two four-leaf-clover coils stacked on top of each other. The technical characterization indicated that the mTMS system performs as designed. The measured motor evoked potential amplitudes varied consistently as a function of the location of the E-field maximum. CONCLUSION The developed mTMS system enables electronically targeted brain stimulation within a cortical region.
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Affiliation(s)
- 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.
| | - Heikki Sinisalo
- 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
| | - Mikko Malmi
- 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
| | - Mikhail Yuryev
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Aino E 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; AMI Centre, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland
| | - Matti Stenroos
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Diego Milardovich
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; Institute for Microelectronics, Technische Universität Wien, Vienna, Austria
| | - Juuso T Korhonen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Lari M Koponen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, 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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Abstract
For many decades, psychiatric treatment has been primarily guided by two major paradigms of psychopathology: a neurochemical paradigm leading to the development of medications and a psychological paradigm resulting in the development of psychotherapies. A third paradigm positing that psychiatric dysfunction results from abnormal communication within a network of brain regions that regulate mood, thought, and behavior has gained increased attention over the past several years and underlies the development of multiple neuromodulation and neurostimulation therapies. This neural circuit paradigm is not new. In the late 19th and early 20th centuries, it was a common way of understanding psychiatric illness and led to several of our earliest somatic therapies. However, with the rise of effective medications and evidence-based psychotherapies, this paradigm went mostly dormant. Its recent reemergence resulted from a growing recognition that medications and psychotherapy leave many patients inadequately treated, along with technological advances that have revolutionized our ability to understand and modulate the neural circuitry involved in psychiatric disorders. In this overview, the authors review the history and current state of neuromodulation for psychiatric illness and specifically focus on these approaches as a treatment for depression, as this has been the primary indication for these interventions over time.
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Affiliation(s)
- Susan K Conroy
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis (Conroy);National Center for PTSD, Executive Division, White River Junction VA Medical Center, White River Junction, Vt. (Holtzheimer);Departments of Psychiatry and Surgery, Geisel School of Medicine at Dartmouth, Dartmouth Hitchcock Medical Center, Lebanon, N.H. (Holtzheimer)
| | - Paul E Holtzheimer
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis (Conroy);National Center for PTSD, Executive Division, White River Junction VA Medical Center, White River Junction, Vt. (Holtzheimer);Departments of Psychiatry and Surgery, Geisel School of Medicine at Dartmouth, Dartmouth Hitchcock Medical Center, Lebanon, N.H. (Holtzheimer)
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da Silva Machado CB, da Silva LM, Gonçalves AF, Andrade PRD, Mendes CKTT, de Assis TJCF, Godeiro Júnior CDO, Andrade SM. Multisite non-invasive brain stimulation in Parkinson's disease: A scoping review. NeuroRehabilitation 2021; 49:515-531. [PMID: 34776426 PMCID: PMC8764602 DOI: 10.3233/nre-210190] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
BACKGROUND: Parkinson’s disease (PD) is a progressive neurodegenerative disorder, characterized by cardinal motor symptoms in addition to cognitive impairment. New insights concerning multisite non-invasive brain stimulation effects have been gained, which can now be used to develop innovative treatment approaches. OBJECTIVE: Map the researchs involving multisite non-invasive brain stimulation in PD, synthesize the available evidence and discuss future directions. METHODS: The databases PubMed, PsycINFO, CINAHL, LILACS and The Cochrane Library were searched from inception until April 2020, without restrictions on the date of publication or the language in which it was published. The reviewers worked in pairs and sequentially evaluated the titles, abstracts and then the full text of all publications identified as potentially relevant. RESULTS: Twelve articles met the inclusion criteria. The target brain regions included mainly the combination of a motor and a frontal area, such as stimulation of the primary motor córtex associated with the dorsolateral prefrontal cortex. Most of the trials showed that this modality was only more effective for the motor component, or for the cognitive and/or non-motor, separately. CONCLUSIONS: Despite the results being encouraging for the use of the multisite aproach, the indication for PD management should be carried out with caution and deserves scientific deepening.
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Affiliation(s)
| | | | | | | | | | | | - Clécio de Oliveira Godeiro Júnior
- Division of Neurology, CHU of Grenoble, Grenoble Alpes University, La Tronche, Grenoble, France.,Division of Neurology, Hospital Universitario Onofre Lopes, Federal University of Rio Grande do Norte, Natal, Brazil
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Nurmi S, Karttunen J, Souza VH, Ilmoniemi RJ, Nieminen JO. Trade-off between stimulation focality and the number of coils in multi-locus transcranial magnetic stimulation. J Neural Eng 2021; 18. [PMID: 34673563 DOI: 10.1088/1741-2552/ac3207] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 10/21/2021] [Indexed: 11/11/2022]
Abstract
Objective. Coils designed for transcranial magnetic stimulation (TMS) must incorporate trade-offs between the required electrical power or energy, focality and depth penetration of the induced electric field (E-field), coil size, and mechanical properties of the coil, as all of them cannot be optimally met at the same time. In multi-locus TMS (mTMS), a transducer consisting of several coils allows electronically targeted stimulation of the cortex without physically moving a coil. In this study, we aimed to investigate the relationship between the number of coils in an mTMS transducer, the focality of the induced E-field, and the extent of the cortical region within which the location and orientation of the maximum of the induced E-field can be controlled.Approach.We applied convex optimization to design planar and spherically curved mTMS transducers of different E-field focalities and analyzed their properties. We characterized the trade-off between the focality of the induced E-field and the extent of the cortical region that can be stimulated with an mTMS transducer with a given number of coils.Main results.At the expense of the E-field focality, one can, with the same number of coils, design an mTMS transducer that can control the location and orientation of the peak of the induced E-field within a wider cortical region.Significance. With E-fields of moderate focality, the problem of electronically targeted TMS becomes considerably easier compared with highly focal E-fields; this may speed up the development of mTMS and the emergence of new clinical and research applications.
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Affiliation(s)
- Samuel Nurmi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Jere Karttunen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Victor H Souza
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Jaakko O Nieminen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
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Metsomaa J, Belardinelli P, Ermolova M, Ziemann U, Zrenner C. Causal decoding of individual cortical excitability states. Neuroimage 2021; 245:118652. [PMID: 34687858 DOI: 10.1016/j.neuroimage.2021.118652] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 09/30/2021] [Accepted: 10/11/2021] [Indexed: 10/20/2022] Open
Abstract
Brain responsiveness to stimulation fluctuates with rapidly shifting cortical excitability state, as reflected by oscillations in the electroencephalogram (EEG). For example, the amplitude of motor-evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) of motor cortex changes from trial to trial. To date, individual estimation of the cortical processes leading to this excitability fluctuation has not been possible. Here, we propose a data-driven method to derive individually optimized EEG classifiers in healthy humans using a supervised learning approach that relates pre-TMS EEG activity dynamics to MEP amplitude. Our approach enables considering multiple brain regions and frequency bands, without defining them a priori, whose compound phase-pattern information determines the excitability. The individualized classifier leads to an increased classification accuracy of cortical excitability states from 57% to 67% when compared to μ-oscillation phase extracted by standard fixed spatial filters. Results show that, for the used TMS protocol, excitability fluctuates predominantly in the μ-oscillation range, and relevant cortical areas cluster around the stimulated motor cortex, but between subjects there is variability in relevant power spectra, phases, and cortical regions. This novel decoding method allows causal investigation of the cortical excitability state, which is critical also for individualizing therapeutic brain stimulation.
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Affiliation(s)
- J Metsomaa
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen
| | - P Belardinelli
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen; CIMeC, Center for Mind-Brain Sciences, University of Trento, Italy
| | - M Ermolova
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen
| | - U Ziemann
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen.
| | - C Zrenner
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, and Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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Tugin S, Souza VH, Nazarova MA, Novikov PA, Tervo AE, Nieminen JO, Lioumis P, Ziemann U, Nikulin VV, Ilmoniemi RJ. Effect of stimulus orientation and intensity on short-interval intracortical inhibition (SICI) and facilitation (SICF): A multi-channel transcranial magnetic stimulation study. PLoS One 2021; 16:e0257554. [PMID: 34550997 PMCID: PMC8457500 DOI: 10.1371/journal.pone.0257554] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 09/03/2021] [Indexed: 11/18/2022] Open
Abstract
Besides stimulus intensities and interstimulus intervals (ISI), the electric field (E-field) orientation is known to affect both short-interval intracortical inhibition (SICI) and facilitation (SICF) in paired-pulse transcranial magnetic stimulation (TMS). However, it has yet to be established how distinct orientations of the conditioning (CS) and test stimuli (TS) affect the SICI and SICF generation. With the use of a multi-channel TMS transducer that provides electronic control of the stimulus orientation and intensity, we aimed to investigate how changes in the CS and TS orientation affect the strength of SICI and SICF. We hypothesized that the CS orientation would play a major role for SICF than for SICI, whereas the CS intensity would be more critical for SICI than for SICF. In eight healthy subjects, we tested two ISIs (1.5 and 2.7 ms), two CS and TS orientations (anteromedial (AM) and posteromedial (PM)), and four CS intensities (50, 70, 90, and 110% of the resting motor threshold (RMT)). The TS intensity was fixed at 110% RMT. The intensities were adjusted to the corresponding RMT in the AM and PM orientations. SICI and SICF were observed in all tested CS and TS orientations. SICI depended on the CS intensity in a U-shaped manner in any combination of the CS and TS orientations. With 70% and 90% RMT CS intensities, stronger PM-oriented CS induced stronger inhibition than weaker AM-oriented CS. Similar SICF was observed for any CS orientation. Neither SICI nor SICF depended on the TS orientation. We demonstrated that SICI and SICF could be elicited by the CS perpendicular to the TS, which indicates that these stimuli affected either overlapping or strongly connected neuronal populations. We concluded that SICI is primarily sensitive to the CS intensity and that CS intensity adjustment resulted in similar SICF for different CS orientations.
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Affiliation(s)
- Sergei Tugin
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, University of Helsinki and Helsinki University Hospital, HUS Medical Imaging Centre, Helsinki, Finland
- * E-mail:
| | - Victor H. Souza
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, University of Helsinki and Helsinki University Hospital, HUS Medical Imaging Centre, Helsinki, Finland
- School of Physiotherapy, Federal University of Juiz de Fora, Juiz de Fora, MG, Brazil
| | - Maria A. Nazarova
- Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
- Federal State Budgetary Institution “Federal Center of Brain Research and Neurotechnologies” of the Federal Medical Biological Agency, Moscow, Russia
| | - Pavel A. Novikov
- Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - Aino E. Tervo
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, University of Helsinki and Helsinki University Hospital, HUS Medical Imaging Centre, Helsinki, Finland
| | - Jaakko O. Nieminen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, University of Helsinki and Helsinki University Hospital, HUS Medical Imaging Centre, Helsinki, Finland
| | - Pantelis Lioumis
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
- BioMag Laboratory, University of Helsinki and Helsinki University Hospital, HUS Medical Imaging Centre, Helsinki, Finland
| | - Ulf Ziemann
- Department of Neurology and Stroke, Eberhard Karls University, Tübingen, Germany
- Hertie Institute for Clinical Brain Research, Eberhard Karls University, Tübingen, Germany
| | - Vadim V. 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
- BioMag Laboratory, University of Helsinki and Helsinki University Hospital, HUS Medical Imaging Centre, Helsinki, Finland
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Bergmann TO, Varatheeswaran R, Hanlon CA, Madsen KH, Thielscher A, Siebner HR. Concurrent TMS-fMRI for causal network perturbation and proof of target engagement. Neuroimage 2021; 237:118093. [PMID: 33940146 DOI: 10.1016/j.neuroimage.2021.118093] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 04/06/2021] [Accepted: 04/14/2021] [Indexed: 12/12/2022] Open
Abstract
The experimental manipulation of neural activity by neurostimulation techniques overcomes the inherent limitations of correlative recordings, enabling the researcher to investigate causal brain-behavior relationships. But only when stimulation and recordings are combined, the direct impact of the stimulation on neural activity can be evaluated. In humans, this can be achieved non-invasively through the concurrent combination of transcranial magnetic stimulation (TMS) with functional magnetic resonance imaging (fMRI). Concurrent TMS-fMRI allows the assessment of the neurovascular responses evoked by TMS with excellent spatial resolution and full-brain coverage. This enables the functional mapping of both local and remote network effects of TMS in cortical as well as deep subcortical structures, offering unique opportunities for basic research and clinical applications. The purpose of this review is to introduce the reader to this powerful tool. We will introduce the technical challenges and state-of-the art solutions and provide a comprehensive overview of the existing literature and the available experimental approaches. We will highlight the unique insights that can be gained from concurrent TMS-fMRI, including the state-dependent assessment of neural responsiveness and inter-regional effective connectivity, the demonstration of functional target engagement, and the systematic evaluation of stimulation parameters. We will also discuss how concurrent TMS-fMRI during a behavioral task can help to link behavioral TMS effects to changes in neural network activity and to identify peripheral co-stimulation confounds. Finally, we will review the use of concurrent TMS-fMRI for developing TMS treatments of psychiatric and neurological disorders and suggest future improvements for further advancing the application of concurrent TMS-fMRI.
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Affiliation(s)
- Til Ole Bergmann
- Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Langenbeckstr. 1, 55131, Mainz, Germany; Leibniz Institute for Resilience Research, Wallstraße 7-9, 55122, Mainz, Germany.
| | - Rathiga Varatheeswaran
- Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Langenbeckstr. 1, 55131, Mainz, Germany; Leibniz Institute for Resilience Research, Wallstraße 7-9, 55122, Mainz, Germany
| | - Colleen A Hanlon
- Department of Cancer Biology, Wake Forest School of Medicine, 1 Medical Center Blvd., Winston-Salem, NC 27157, USA
| | - Kristoffer H Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Kettegård Allé 30, 2650, Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Axel Thielscher
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Kettegård Allé 30, 2650, Hvidovre, Denmark; Department of Electrical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Hartwig Roman Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Kettegård Allé 30, 2650, Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg, Bispebjerg Bakke 23, 2400 København NV, Denmark; Department of Clinical Medicine, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
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Varone G, Hussain Z, Sheikh Z, Howard A, Boulila W, Mahmud M, Howard N, Morabito FC, Hussain A. Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures. SENSORS 2021; 21:s21020637. [PMID: 33477526 PMCID: PMC7831109 DOI: 10.3390/s21020637] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 01/08/2021] [Accepted: 01/11/2021] [Indexed: 01/24/2023]
Abstract
Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity can be simultaneously recorded using electroencephalography (EEG). However, TMS-evoked EEG potentials (TEPs) do not only reflect transcranial neural stimulation as they can be contaminated by artifacts. Over the last two decades, significant developments in EEG amplifiers, TMS-compatible technology, customized hardware and open source software have enabled researchers to develop approaches which can substantially reduce TMS-induced artifacts. In TMS-EEG experiments, various physiological and external occurrences have been identified and attempts have been made to minimize or remove them using online techniques. Despite these advances, technological issues and methodological constraints prevent straightforward recordings of early TEPs components. To the best of our knowledge, there is no review on both TMS-EEG artifacts and EEG technologies in the literature to-date. Our survey aims to provide an overview of research studies in this field over the last 40 years. We review TMS-EEG artifacts, their sources and their waveforms and present the state-of-the-art in EEG technologies and front-end characteristics. We also propose a synchronization toolbox for TMS-EEG laboratories. We then review subject preparation frameworks and online artifacts reduction maneuvers for improving data acquisition and conclude by outlining open challenges and future research directions in the field.
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Affiliation(s)
- Giuseppe Varone
- Department of Medical and Surgical Sciences, Magna Greacia University of Catanzaro, 88100 Catanzaro, Italy;
| | - Zain Hussain
- College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh EH16 4TJ, UK; (Z.H.); (Z.S.)
- Howard Brain Sciences Foundation, Providence, RI 02906, USA;
| | - Zakariya Sheikh
- College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh EH16 4TJ, UK; (Z.H.); (Z.S.)
| | - Adam Howard
- Howard Brain Sciences Foundation, Providence, RI 02906, USA;
| | - Wadii Boulila
- RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba 2010, Tunisia;
- IS Department, College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia
| | - Mufti Mahmud
- Department of Computer Science and Medical Technology Innovation Facility, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK;
| | - Newton Howard
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK;
| | | | - Amir Hussain
- School of Computing, Edinburgh Napier University, Edinburgh EH11 4BN, UK;
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Rossi S, Antal A, Bestmann S, Bikson M, Brewer C, Brockmöller J, Carpenter LL, Cincotta M, Chen R, Daskalakis JD, Di Lazzaro V, Fox MD, George MS, Gilbert D, Kimiskidis VK, Koch G, Ilmoniemi RJ, Lefaucheur JP, Leocani L, Lisanby SH, Miniussi C, Padberg F, Pascual-Leone A, Paulus W, Peterchev AV, Quartarone A, Rotenberg A, Rothwell J, Rossini PM, Santarnecchi E, Shafi MM, Siebner HR, Ugawa Y, Wassermann EM, Zangen A, Ziemann U, Hallett M. Safety and recommendations for TMS use in healthy subjects and patient populations, with updates on training, ethical and regulatory issues: Expert Guidelines. Clin Neurophysiol 2021; 132:269-306. [PMID: 33243615 PMCID: PMC9094636 DOI: 10.1016/j.clinph.2020.10.003] [Citation(s) in RCA: 524] [Impact Index Per Article: 174.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 10/12/2020] [Accepted: 10/13/2020] [Indexed: 12/11/2022]
Abstract
This article is based on a consensus conference, promoted and supported by the International Federation of Clinical Neurophysiology (IFCN), which took place in Siena (Italy) in October 2018. The meeting intended to update the ten-year-old safety guidelines for the application of transcranial magnetic stimulation (TMS) in research and clinical settings (Rossi et al., 2009). Therefore, only emerging and new issues are covered in detail, leaving still valid the 2009 recommendations regarding the description of conventional or patterned TMS protocols, the screening of subjects/patients, the need of neurophysiological monitoring for new protocols, the utilization of reference thresholds of stimulation, the managing of seizures and the list of minor side effects. New issues discussed in detail from the meeting up to April 2020 are safety issues of recently developed stimulation devices and pulse configurations; duties and responsibility of device makers; novel scenarios of TMS applications such as in the neuroimaging context or imaging-guided and robot-guided TMS; TMS interleaved with transcranial electrical stimulation; safety during paired associative stimulation interventions; and risks of using TMS to induce therapeutic seizures (magnetic seizure therapy). An update on the possible induction of seizures, theoretically the most serious risk of TMS, is provided. It has become apparent that such a risk is low, even in patients taking drugs acting on the central nervous system, at least with the use of traditional stimulation parameters and focal coils for which large data sets are available. Finally, new operational guidelines are provided for safety in planning future trials based on traditional and patterned TMS protocols, as well as a summary of the minimal training requirements for operators, and a note on ethics of neuroenhancement.
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Affiliation(s)
- Simone Rossi
- Department of Scienze Mediche, Chirurgiche e Neuroscienze, Unit of Neurology and Clinical Neurophysiology, Brain Investigation and Neuromodulation Lab (SI-BIN Lab), University of Siena, Italy.
| | - Andrea Antal
- Department of Clinical Neurophysiology, University Medical Center, Georg-August University of Goettingen, Germany; Institue of Medical Psychology, Otto-Guericke University Magdeburg, Germany
| | - Sven Bestmann
- Department of Movement and Clinical Neurosciences, UCL Queen Square Institute of Neurology, London, UK and Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
| | - Marom Bikson
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA
| | - Carmen Brewer
- National Institute on Deafness and Other Communication Disorders, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Jürgen Brockmöller
- Department of Clinical Pharmacology, University Medical Center, Georg-August University of Goettingen, Germany
| | - Linda L Carpenter
- Butler Hospital, Brown University Department of Psychiatry and Human Behavior, Providence, RI, USA
| | - Massimo Cincotta
- Unit of Neurology of Florence - Central Tuscany Local Health Authority, Florence, Italy
| | - Robert Chen
- Krembil Research Institute and Division of Neurology, Department of Medicine, University of Toronto, Canada
| | - Jeff D Daskalakis
- Center for Addiction and Mental Health (CAMH), University of Toronto, Canada
| | - Vincenzo Di Lazzaro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico, Roma, Italy
| | - Michael D Fox
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Mark S George
- Medical University of South Carolina, Charleston, SC, USA
| | - Donald Gilbert
- Division of Neurology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Vasilios K Kimiskidis
- Laboratory of Clinical Neurophysiology, Aristotle University of Thessaloniki, AHEPA University Hospital, Greece
| | | | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering (NBE), Aalto University School of Science, Aalto, Finland
| | - Jean Pascal Lefaucheur
- EA 4391, ENT Team, Faculty of Medicine, Paris Est Creteil University (UPEC), Créteil, France; Clinical Neurophysiology Unit, Henri Mondor Hospital, Assistance Publique Hôpitaux de Paris, (APHP), Créteil, France
| | - Letizia Leocani
- Department of Neurology, Institute of Experimental Neurology (INSPE), IRCCS-San Raffaele Hospital, Vita-Salute San Raffaele University, Milano, Italy
| | - Sarah H Lisanby
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, MD, USA; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Carlo Miniussi
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy
| | - Frank Padberg
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Alvaro Pascual-Leone
- Hinda and Arthur Marcus Institute for Aging Research and Center for Memory Health, Hebrew SeniorLife, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Guttmann Brain Health Institut, Institut Guttmann, Universitat Autonoma Barcelona, Spain
| | - Walter Paulus
- Department of Clinical Neurophysiology, University Medical Center, Georg-August University of Goettingen, Germany
| | - Angel V Peterchev
- Departments of Psychiatry & Behavioral Sciences, Biomedical Engineering, Electrical & Computer Engineering, and Neurosurgery, Duke University, Durham, NC, USA
| | - Angelo Quartarone
- Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Alexander Rotenberg
- Department of Neurology, Division of Epilepsy and Clinical Neurophysiology, Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - John Rothwell
- Department of Movement and Clinical Neurosciences, UCL Queen Square Institute of Neurology, London, UK and Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
| | - Paolo M Rossini
- Department of Neuroscience and Rehabilitation, IRCCS San Raffaele-Pisana, Roma, Italy
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Mouhsin M Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Copenhagen, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg, Copenhagen, Denmark; Institute for Clinical Medicine, Faculty of Medical and Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Yoshikatzu Ugawa
- Department of Human Neurophysiology, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Eric M Wassermann
- National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Abraham Zangen
- Zlotowski Center of Neuroscience, Ben Gurion University, Beer Sheva, Israel
| | - Ulf Ziemann
- Department of Neurology & Stroke, and Hertie-Institute for Clinical Brain Research, University of Tübingen, Germany
| | - Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA.
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Gomez LJ, Dannhauer M, Peterchev AV. Fast computational optimization of TMS coil placement for individualized electric field targeting. Neuroimage 2020; 228:117696. [PMID: 33385544 PMCID: PMC7956218 DOI: 10.1016/j.neuroimage.2020.117696] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 12/04/2020] [Accepted: 12/22/2020] [Indexed: 12/21/2022] Open
Abstract
Background: During transcranial magnetic stimulation (TMS) a coil placed on the scalp is used to non-invasively modulate activity of targeted brain networks via a magnetically induced electric field (E-field). Ideally, the E-field induced during TMS is concentrated on a targeted cortical region of interest (ROI). Determination of the coil position and orientation that best achieve this objective presently requires a large computational effort. Objective: To improve the accuracy of TMS we have developed a fast computational auxiliary dipole method (ADM) for determining the optimum coil position and orientation. The optimum coil placement maximizes the E-field along a predetermined direction or, alternatively, the overall E-field magnitude in the targeted ROI. Furthermore, ADM can assess E-field uncertainty resulting from precision limitations of TMS coil placement protocols. Method: ADM leverages the electromagnetic reciprocity principle to compute rapidly the TMS induced E-field in the ROI by using the E-field generated by a virtual constant current source residing in the ROI. The framework starts by solving for the conduction currents resulting from this ROI current source. Then, it rapidly determines the average E-field induced in the ROI for each coil position by using the conduction currents and a fast-multipole method. To further speed-up the computations, the coil is approximated using auxiliary dipoles enabling it to represent all coil orientations for a given coil position with less than 600 dipoles. Results: Using ADM, the E-fields generated in an MRI-derived head model when the coil is placed at 5900 different scalp positions and 360 coil orientations per position (over 2.1 million unique configurations) can be determined in under 15 min on a standard laptop computer. This enables rapid extraction of the optimum coil position and orientation as well as the E-field variation resulting from coil positioning uncertainty. ADM is implemented in SimNIBS 3.2. Conclusion: ADM enables the rapid determination of coil placement that maximizes E-field delivery to a specific brain target. This method can find the optimum coil placement in under 15 min enabling its routine use for TMS. Furthermore, it enables the fast quantification of uncertainty in the induced E-field due to limited precision of TMS coil placement protocols, enabling minimization and statistical analysis of the E-field dose variability.
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Affiliation(s)
- Luis J Gomez
- Department of Psychiatry and Behavioral Sciences, Duke University, 40 Duke Medicine Circle, Box 3620 DUMC, Durham, NC 27710, USA.
| | - Moritz Dannhauer
- Department of Psychiatry and Behavioral Sciences, Duke University, 40 Duke Medicine Circle, Box 3620 DUMC, Durham, NC 27710, USA.
| | - Angel V Peterchev
- Department of Psychiatry and Behavioral Sciences, Duke University, 40 Duke Medicine Circle, Box 3620 DUMC, Durham, NC 27710, USA; Department of Electrical and Computer Engineering, Duke University, NC 27708, USA; Department of Neurosurgery, Duke University, NC 27710, USA; Department of Biomedical Engineering, Duke University, NC 27708, USA.
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42
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Deng ZD, Luber B, Balderston NL, Velez Afanador M, Noh MM, Thomas J, Altekruse WC, Exley SL, Awasthi S, Lisanby SH. Device-Based Modulation of Neurocircuits as a Therapeutic for Psychiatric Disorders. Annu Rev Pharmacol Toxicol 2020; 60:591-614. [PMID: 31914895 DOI: 10.1146/annurev-pharmtox-010919-023253] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Device-based neuromodulation of brain circuits is emerging as a promising new approach in the study and treatment of psychiatric disorders. This work presents recent advances in the development of tools for identifying neurocircuits as therapeutic targets and in tools for modulating neurocircuits. We review clinical evidence for the therapeutic efficacy of circuit modulation with a range of brain stimulation approaches, including subthreshold, subconvulsive, convulsive, and neurosurgical techniques. We further discuss strategies for enhancing the precision and efficacy of neuromodulatory techniques. Finally, we survey cutting-edge research in therapeutic circuit modulation using novel paradigms and next-generation devices.
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Affiliation(s)
- Zhi-De Deng
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA; .,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina 27710, USA
| | - Bruce Luber
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Nicholas L Balderston
- Section on Neurobiology of Fear and Anxiety, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Melbaliz Velez Afanador
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Michelle M Noh
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Jeena Thomas
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - William C Altekruse
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Shannon L Exley
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Shriya Awasthi
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA;
| | - Sarah H Lisanby
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892, USA; .,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina 27710, USA
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Tervo AE, Metsomaa J, Nieminen JO, Sarvas J, Ilmoniemi RJ. Automated search of stimulation targets with closed-loop transcranial magnetic stimulation. Neuroimage 2020; 220:117082. [PMID: 32593801 DOI: 10.1016/j.neuroimage.2020.117082] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 06/15/2020] [Accepted: 06/22/2020] [Indexed: 12/21/2022] Open
Abstract
Transcranial magnetic stimulation (TMS) protocols often include a manual search of an optimal location and orientation of the coil or peak stimulating electric field to elicit motor responses in a target muscle. This target search is laborious, and the result is user-dependent. Here, we present a closed-loop search method that utilizes automatic electronic adjustment of the stimulation based on the previous responses. The electronic adjustment is achieved by multi-locus TMS, and the adaptive guiding of the stimulation is based on the principles of Bayesian optimization to minimize the number of stimuli (and time) needed in the search. We compared our target-search method with other methods, such as systematic sampling in a predefined cortical grid. Validation experiments on five healthy volunteers and further offline simulations showed that our adaptively guided search method needs only a relatively small number of stimuli to provide outcomes with good accuracy and precision. The automated method enables fast and user-independent optimization of stimulation parameters in research and clinical applications of TMS.
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Affiliation(s)
- Aino E 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
| | - Johanna Metsomaa
- 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; Department of Neurology & Stroke and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - 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.
| | - Jukka Sarvas
- 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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Cuypers K, Marsman A. Transcranial magnetic stimulation and magnetic resonance spectroscopy: Opportunities for a bimodal approach in human neuroscience. Neuroimage 2020; 224:117394. [PMID: 32987106 DOI: 10.1016/j.neuroimage.2020.117394] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 08/18/2020] [Accepted: 09/17/2020] [Indexed: 12/19/2022] Open
Abstract
Over the last decade, there has been an increasing number of studies combining transcranial magnetic stimulation (TMS) and magnetic resonance spectroscopy (MRS). MRS provides a manner to non-invasively investigate molecular concentrations in the living brain and thus identify metabolites involved in physiological and pathological processes. Particularly the MRS-detectable metabolites glutamate, the major excitatory neurotransmitter, and gamma-aminobutyric acid (GABA), the major inhibitory neurotransmitter, are of interest when combining TMS and MRS. TMS is a non-invasive brain stimulation technique that can be applied either as a neuromodulation or neurostimulation tool, specifically targeting glutamatergic and GABAergic mechanisms. The combination of TMS and MRS can be used to evaluate alterations in brain metabolite levels following an interventional TMS protocol such as repetitive TMS (rTMS) or paired associative stimulation (PAS). MRS can also be combined with a variety of non-interventional TMS protocols to identify the interplay between brain metabolite levels and measures of excitability or receptor-mediated inhibition and facilitation. In this review, we provide an overview of studies performed in healthy and patient populations combining MRS and TMS, both as a measurement tool and as an intervention. TMS and MRS may reveal complementary and comprehensive information on glutamatergic and GABAergic neurotransmission. Potentially, connectivity changes and dedicated network interactions can be probed using the combined TMS-MRS approach. Considering the ongoing technical developments in both fields, combined studies hold future promise for investigations of brain network interactions and neurotransmission.
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Affiliation(s)
- Koen Cuypers
- Department of Movement Sciences, Group Biomedical Sciences, Movement Control & Neuroplasticity Research Group, KU Leuven, 3001 Heverlee, Belgium; REVAL Research Institute, Hasselt University, Agoralaan, Building A, 3590 Diepenbeek, Belgium
| | - Anouk Marsman
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Section 714, Kettegård Allé 30, 26500 Hvidovre, Denmark.
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Navarro de Lara LI, Daneshzand M, Mascarenas A, Paulson D, Pratt K, Okada Y, Raij T, Makarov SN, Nummenmaa A. A 3-axis coil design for multichannel TMS arrays. Neuroimage 2020; 224:117355. [PMID: 32916290 PMCID: PMC7837414 DOI: 10.1016/j.neuroimage.2020.117355] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 08/13/2020] [Accepted: 09/04/2020] [Indexed: 01/24/2023] Open
Abstract
Purpose: Multichannel Transcranial Magnetic Stimulation (mTMS) arrays enable
multiple sites to be stimulated simultaneously or sequentially under
electronic control without moving the system’s stimulation coils.
Here, we build and characterize the performance of a novel modular 3-axis
TMS coil that can be utilized as a unit element in large-scale multichannel
TMS arrays. Methods: We determined the basic physical characteristics of the 3-axis TMS
coil x-, y- and
z-elements using a custom 2-channel programmable stimulator
prototype. We mapped the temporal rate-of-change of the induced magnetic
field (dB/dt) on a 2D plane parallel to the coil surface
(including an extended line for full spatial coverage) and compared those
values with predictions from magnetic field simulations. Temperature
measurements were carried out to assess the incorporated air-cooling method.
We measured the mutual and self-inductances of the
x/y/z-elements to assess coupling between them.
Additionally, we measured and calculated the coupling between
z-elements in the array configuration. Finally, we
performed electric field simulations to evaluate the stimulation intensity
and focality of the coil and compared the results to conventional TMS coils
as well as demonstrated suitability of the 3-axis coil for a multichannel
array configuration. Results: The experimentally obtained dB/dt values validated
the computational model of the 3-axis coil and therefore confirmed that both
the coil and stimulator system are operating as intended. The air-cooling
system was effective for brief high-frequency pulse trains and extended
single- and paired-pulse TMS protocols. The electromagnetic simulations
suggested that an array of the 3-axis coils would have comparable
stimulation intensity to conventional TMS coils, therefore enabling clearly
suprathreshold stimulation of the human brain. The recorded coil coupling
between the x/y/z-elements was < 1%
and the maximal coupling between z-elements in the array
configuration was 1.8% and 3.4% for the measured and calculated values,
respectively. Conclusion: We presented a 3-axis coil intended for multichannel TMS arrays. The
electromagnetic measurements and simulations verified that the coil
fabrication met the desired specifications and that the inductive coupling
between the elements was negligible. The air-cooled 3-axis TMS coil appears
suitable to be used as an element in multichannel TMS arrays.
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Affiliation(s)
- Lucia I Navarro de Lara
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA.
| | - Mohammad Daneshzand
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | | | | | - Kevin Pratt
- Tristan Technologies, San Diego, CA 92121, USA
| | | | - Tommi Raij
- Center for Brain Stimulation, Shirley Ryan AbilityLab, Chicago IL 60611 USA; Department of Physical Medicine and Rehabilitation, Department of Neurobiology, Northwestern University, Chicago IL 60611 USA
| | - Sergey N Makarov
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA; Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA 02129, USA.
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Nieminen J, Malmi M, Milardovich D, Sinisalo H, Souza V, Tervo A, Yuryev M, Ilmoniemi R. P78 Multi-locus TMS system for electronically controlled stimulation within a cortical region. Clin Neurophysiol 2020. [DOI: 10.1016/j.clinph.2019.12.189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Tervo A, Nieminen J, Metsomaa J, Lioumis P, Sarvas J, Ilmoniemi R. P145 Automated search of TMS targets based on electromyography and electroencephalography signals. Clin Neurophysiol 2020. [DOI: 10.1016/j.clinph.2019.12.256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Bao SC, Khan A, Song R, Kai-yu Tong R. Rewiring the Lesioned Brain: Electrical Stimulation for Post-Stroke Motor Restoration. J Stroke 2020; 22:47-63. [PMID: 32027791 PMCID: PMC7005350 DOI: 10.5853/jos.2019.03027] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 01/03/2020] [Accepted: 01/06/2020] [Indexed: 02/06/2023] Open
Abstract
Electrical stimulation has been extensively applied in post-stroke motor restoration, but its treatment mechanisms are not fully understood. Stimulation of neuromotor control system at multiple levels manipulates the corresponding neuronal circuits and results in neuroplasticity changes of stroke survivors. This rewires the lesioned brain and advances functional improvement. This review addresses the therapeutic mechanisms of different stimulation modalities, such as noninvasive brain stimulation, peripheral electrical stimulation, and other emerging techniques. The existing applications, the latest progress, and future directions are discussed. The use of electrical stimulation to facilitate post-stroke motor recovery presents great opportunities in terms of targeted intervention and easy applicability. Further technical improvements and clinical studies are required to reveal the neuromodulatory mechanisms and to enhance rehabilitation therapy efficiency in stroke survivors and people with other movement disorders.
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Affiliation(s)
- Shi-chun Bao
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Ahsan Khan
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Rong Song
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Raymond Kai-yu Tong
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China
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Lefaucheur JP, Aleman A, Baeken C, Benninger DH, Brunelin J, Di Lazzaro V, Filipović SR, Grefkes C, Hasan A, Hummel FC, Jääskeläinen SK, Langguth B, Leocani L, Londero A, Nardone R, Nguyen JP, Nyffeler T, Oliveira-Maia AJ, Oliviero A, Padberg F, Palm U, Paulus W, Poulet E, Quartarone A, Rachid F, Rektorová I, Rossi S, Sahlsten H, Schecklmann M, Szekely D, Ziemann U. Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS): An update (2014-2018). Clin Neurophysiol 2020; 131:474-528. [PMID: 31901449 DOI: 10.1016/j.clinph.2019.11.002] [Citation(s) in RCA: 938] [Impact Index Per Article: 234.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 10/21/2019] [Accepted: 11/02/2019] [Indexed: 02/08/2023]
Abstract
A group of European experts reappraised the guidelines on the therapeutic efficacy of repetitive transcranial magnetic stimulation (rTMS) previously published in 2014 [Lefaucheur et al., Clin Neurophysiol 2014;125:2150-206]. These updated recommendations take into account all rTMS publications, including data prior to 2014, as well as currently reviewed literature until the end of 2018. Level A evidence (definite efficacy) was reached for: high-frequency (HF) rTMS of the primary motor cortex (M1) contralateral to the painful side for neuropathic pain; HF-rTMS of the left dorsolateral prefrontal cortex (DLPFC) using a figure-of-8 or a H1-coil for depression; low-frequency (LF) rTMS of contralesional M1 for hand motor recovery in the post-acute stage of stroke. Level B evidence (probable efficacy) was reached for: HF-rTMS of the left M1 or DLPFC for improving quality of life or pain, respectively, in fibromyalgia; HF-rTMS of bilateral M1 regions or the left DLPFC for improving motor impairment or depression, respectively, in Parkinson's disease; HF-rTMS of ipsilesional M1 for promoting motor recovery at the post-acute stage of stroke; intermittent theta burst stimulation targeted to the leg motor cortex for lower limb spasticity in multiple sclerosis; HF-rTMS of the right DLPFC in posttraumatic stress disorder; LF-rTMS of the right inferior frontal gyrus in chronic post-stroke non-fluent aphasia; LF-rTMS of the right DLPFC in depression; and bihemispheric stimulation of the DLPFC combining right-sided LF-rTMS (or continuous theta burst stimulation) and left-sided HF-rTMS (or intermittent theta burst stimulation) in depression. Level A/B evidence is not reached concerning efficacy of rTMS in any other condition. The current recommendations are based on the differences reached in therapeutic efficacy of real vs. sham rTMS protocols, replicated in a sufficient number of independent studies. This does not mean that the benefit produced by rTMS inevitably reaches a level of clinical relevance.
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Affiliation(s)
- Jean-Pascal Lefaucheur
- ENT Team, EA4391, Faculty of Medicine, Paris Est Créteil University, Créteil, France; Clinical Neurophysiology Unit, Department of Physiology, Henri Mondor Hospital, Assistance Publique - Hôpitaux de Paris, Créteil, France.
| | - André Aleman
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Chris Baeken
- Department of Psychiatry and Medical Psychology, Ghent Experimental Psychiatry (GHEP) Lab, Ghent University, Ghent, Belgium; Department of Psychiatry, University Hospital (UZBrussel), Brussels, Belgium; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - David H Benninger
- Neurology Service, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Jérôme Brunelin
- PsyR2 Team, U1028, INSERM and UMR5292, CNRS, Center for Neuroscience Research of Lyon (CRNL), Centre Hospitalier Le Vinatier, Lyon-1 University, Bron, France
| | - Vincenzo Di Lazzaro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Saša R Filipović
- Department of Human Neuroscience, Institute for Medical Research, University of Belgrade, Belgrade, Serbia
| | - Christian Grefkes
- Department of Neurology, Cologne University Hospital, Cologne, Germany; Institute of Neurosciences and Medicine (INM3), Jülich Research Centre, Jülich, Germany
| | - Alkomiet Hasan
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Friedhelm C Hummel
- Defitech Chair in Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland; Defitech Chair in Clinical Neuroengineering, Swiss Federal Institute of Technology (EPFL) Valais and Clinique Romande de Réadaptation, Sion, Switzerland; Clinical Neuroscience, University of Geneva Medical School, Geneva, Switzerland
| | - Satu K Jääskeläinen
- Department of Clinical Neurophysiology, Turku University Hospital and University of Turku, Turku, Finland
| | - Berthold Langguth
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Letizia Leocani
- Department of Neurorehabilitation and Experimental Neurophysiology Unit, Institute of Experimental Neurology (INSPE), IRCCS San Raffaele, University Vita-Salute San Raffaele, Milan, Italy
| | - Alain Londero
- Department of Otorhinolaryngology - Head and Neck Surgery, Université Paris Descartes Sorbonne Paris Cité, Hôpital Européen Georges Pompidou, Paris, France
| | - Raffaele Nardone
- Department of Neurology, Franz Tappeiner Hospital, Merano, Italy; Department of Neurology, Christian Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria; Karl Landsteiner Institut für Neurorehabilitation und Raumfahrtneurologie, Salzburg, Austria
| | - Jean-Paul Nguyen
- Multidisciplinary Pain Center, Clinique Bretéché, ELSAN, Nantes, France; Multidisciplinary Pain, Palliative and Supportive Care Center, UIC22-CAT2-EA3826, University Hospital, CHU Nord-Laënnec, Nantes, France
| | - Thomas Nyffeler
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland; Perception and Eye Movement Laboratory, Department of Neurology, University of Bern, Bern, Switzerland; Neurocenter, Luzerner Kantonsspital, Lucerne, Switzerland
| | - Albino J Oliveira-Maia
- Champalimaud Research & Clinical Centre, Champalimaud Centre for the Unknown, Lisbon, Portugal; Department of Psychiatry and Mental Health, Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal; NOVA Medical School
- Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Antonio Oliviero
- FENNSI Group, Hospital Nacional de Parapléjicos, SESCAM, Toledo, Spain
| | - Frank Padberg
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Ulrich Palm
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany; Medical Park Chiemseeblick, Bernau, Germany
| | - Walter Paulus
- Department of Clinical Neurophysiology, University Medical Center Göttingen, Göttingen, Germany
| | - Emmanuel Poulet
- PsyR2 Team, U1028, INSERM and UMR5292, CNRS, Center for Neuroscience Research of Lyon (CRNL), Centre Hospitalier Le Vinatier, Lyon-1 University, Bron, France; Department of Emergency Psychiatry, Edouard Herriot Hospital, Groupement Hospitalier Centre, Hospices Civils de Lyon, Lyon, France
| | - Angelo Quartarone
- Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | | | - Irena Rektorová
- Applied Neuroscience Research Group, Central European Institute of Technology, CEITEC MU, Masaryk University, Brno, Czech Republic; First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Simone Rossi
- Department of Medicine, Surgery and Neuroscience, Si-BIN Lab Human Physiology Section, Neurology and Clinical Neurophysiology Unit, University of Siena, Siena, Italy
| | - Hanna Sahlsten
- ENT Clinic, Mehiläinen and University of Turku, Turku, Finland
| | - Martin Schecklmann
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - David Szekely
- Department of Psychiatry, Princess Grace Hospital, Monaco
| | - Ulf Ziemann
- Department of Neurology and Stroke, and Hertie Institute for Clinical Brain Research, Eberhard Karls University, Tübingen, Germany
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Nieminen JO, Koponen LM, Mäkelä N, Souza VH, Stenroos M, Ilmoniemi RJ. Short-interval intracortical inhibition in human primary motor cortex: A multi-locus transcranial magnetic stimulation study. Neuroimage 2019; 203:116194. [DOI: 10.1016/j.neuroimage.2019.116194] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 09/03/2019] [Accepted: 09/13/2019] [Indexed: 12/22/2022] Open
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