1
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Sengupta A, Banerjee S, Ganesh S, Grover S, Sridharan D. The right posterior parietal cortex mediates spatial reorienting of attentional choice bias. Nat Commun 2024; 15:6938. [PMID: 39138185 PMCID: PMC11322534 DOI: 10.1038/s41467-024-51283-z] [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: 09/20/2022] [Accepted: 08/02/2024] [Indexed: 08/15/2024] Open
Abstract
Attention facilitates behavior by enhancing perceptual sensitivity (sensory processing) and choice bias (decisional weighting) for attended information. Whether distinct neural substrates mediate these distinct components of attention remains unknown. We investigate the causal role of key nodes of the right posterior parietal cortex (rPPC) in the forebrain attention network in sensitivity versus bias control. Two groups of participants performed a cued attention task while we applied either inhibitory, repetitive transcranial magnetic stimulation (n = 28) or 40 Hz transcranial alternating current stimulation (n = 26) to the dorsal rPPC. We show that rPPC stimulation - with either modality - impairs task performance by selectively altering attentional modulation of bias but not sensitivity. Specifically, participants' bias toward the uncued, but not the cued, location reduced significantly following rPPC stimulation - an effect that was consistent across both neurostimulation cohorts. In sum, the dorsal rPPC causally mediates the reorienting of choice bias, one particular component of visual spatial attention.
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Affiliation(s)
- Ankita Sengupta
- Centre for Neuroscience, Indian Institute of Science, Bangalore, 560012, India
| | - Sanjna Banerjee
- Centre for Neuroscience, Indian Institute of Science, Bangalore, 560012, India
- Foundation of Art and Health India, Bangalore, 560066, India
| | - Suhas Ganesh
- Centre for Neuroscience, Indian Institute of Science, Bangalore, 560012, India
- Verily Life Sciences, San Francisco, CA, 94080, USA
| | - Shrey Grover
- Centre for Neuroscience, Indian Institute of Science, Bangalore, 560012, India
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, 02215, USA
| | - Devarajan Sridharan
- Centre for Neuroscience, Indian Institute of Science, Bangalore, 560012, India.
- Department of Computer Science and Automation, Indian Institute of Science, Bangalore, 560012, India.
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Sundaram P, Dong C, Makaroff S, Okada Y. How Conductivity Boundaries Influence the Electric Field Induced by Transcranial Magnetic Stimulation in in vitro Experiments. Brain Stimul 2024:S1935-861X(24)00140-2. [PMID: 39142380 DOI: 10.1016/j.brs.2024.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 08/10/2024] [Accepted: 08/10/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND Although transcranial magnetic stimulation (TMS) has become a valuable method for non-invasive brain stimulation, the cellular basis of TMS activation of neurons is still not fully understood. In vitro preparations have been used to understand the biophysical mechanisms of TMS, but in many cases these studies have encountered substantial difficulties in activating neurons. OBJECTIVE/HYPOTHESIS The hypothesis of this work is that conductivity boundaries can have large effects on the electric field in commonly used in vitro preparations. Our goal was to analyze the resulting difficulties in in vitro TMS using a simulation study, using a charge-based boundary element model. METHODS We decomposed the total electric field into the sum of the primary electric field, which only depends on coil geometry and current, and the secondary electric field arising from conductivity boundaries, which strongly depends on tissue and chamber geometry. We investigated the effect of the conductivity boundaries on the electric field strength for a variety of in vitro experimental settings to determine the sources of difficulty. RESULTS We showed that conductivity boundaries can have large effects on the electric field in in vitro preparations. Depending on the geometry of the air-saline and the saline-tissue interfaces, the secondary electric field can significantly enhance, or attenuate the primary electric field, resulting in a much stronger or weaker total electric field inside the tissue; we showed this using a realistic preparation. Submerged chambers are generally much more efficient than interface chambers since the secondary field due to the thin film of saline covering the tissue in the interface chamber opposes the primary field and significantly reduces the total field in the tissue placed in the interface chamber. The relative dimensions of the chamber and the TMS coil critically determine the total field; the popular setup with a large coil and a small chamber is particularly sub-optimal because the secondary field due to the air-chamber boundary opposes the primary field, thereby attenuating the total field. The form factor (length vs width) of the tissue in the direction of the induced field can be important since a relatively narrow tissue enhances the total field at the saline-tissue boundary. CONCLUSIONS Overall, we found that the total electric field in the tissue is higher in submerged chambers, is higher if the chamber size is larger than the coil and if the shorter tissue dimension is in the direction of the electric field. Decomposing the total field into the primary and secondary fields is useful for designing in vitro experiments and interpreting the results.
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Affiliation(s)
- Padmavathi Sundaram
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
| | - Chunling Dong
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Sergey Makaroff
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA; Worcester Polytechnic Institute, Worcester, MA
| | - Yoshio Okada
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA; Boston Children's Hospital, Harvard Medical School, Boston, MA
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Cerins A, Thomas EHX, Barbour T, Taylor JJ, Siddiqi SH, Trapp N, McGirr A, Caulfield KA, Brown JC, Chen L. A New Angle on Transcranial Magnetic Stimulation Coil Orientation: A Targeted Narrative Review. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:744-753. [PMID: 38729243 DOI: 10.1016/j.bpsc.2024.04.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/19/2024] [Accepted: 04/26/2024] [Indexed: 05/12/2024]
Abstract
Transcranial magnetic stimulation (TMS) is used to treat several neuropsychiatric disorders including depression, where it is effective in approximately one half of patients for whom pharmacological approaches have failed. Treatment response is related to stimulation parameters such as the stimulation frequency, pattern, intensity, location, total number of pulses and sessions applied, and target brain network engagement. One critical but underexplored component of the stimulation procedure is the orientation or yaw angle of the commonly used figure-of-eight TMS coil, which is known to impact neuronal response to TMS. However, coil orientation has remained largely unchanged since TMS was first used to treat depression and continues to be based on motor cortex anatomy, which may not be optimal for the dorsolateral prefrontal cortex treatment site. In this targeted narrative review, we evaluate experimental, clinical, and computational evidence indicating that optimizing coil orientation may improve TMS treatment outcomes. The properties of the electric field induced by TMS, the changes to this field caused by the differing conductivities of head tissues, and the interaction between coil orientation and the underlying cortical anatomy are summarized. We describe evidence that the magnitude and site of cortical activation, surrogate markers of TMS dosing and brain network targeting considered central in clinical response to TMS, are influenced by coil orientation. We suggest that coil orientation should be considered when applying therapeutic TMS and propose several approaches to optimizing this potentially important treatment parameter.
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Affiliation(s)
- Andris Cerins
- Department of Psychiatry, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia.
| | - Elizabeth H X Thomas
- Department of Psychiatry, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
| | - Tracy Barbour
- Massachusetts General Hospital, Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Joseph J Taylor
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Shan H Siddiqi
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Nicholas Trapp
- University of Iowa, Department of Psychiatry, Carver College of Medicine, Iowa City, Iowa; Iowa Neuroscience Institute, Iowa City, Iowa
| | - Alexander McGirr
- Department of Psychiatry, University of Calgary, Alberta, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Kevin A Caulfield
- Brain Stimulation Division, Department of Psychiatry, Medical University of South Carolina, Charleston, South Carolina
| | - Joshua C Brown
- Brain Stimulation Mechanisms Laboratory, Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Leo Chen
- Department of Psychiatry, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia; Alfred Mental and Addiction Health, Alfred Health, Melbourne, Victoria, Australia
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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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Hess S, Yeshua M, Eisen A, Burnishev Y, Sultan E, Pell G, Hanlon CA, Weizman A, Zangen A, Roth Y, Moses E, Weiss D. Efficacy of rotational field TMS in major depressive disorder - A pilot study. Brain Stimul 2024; 17:907-910. [PMID: 39069126 DOI: 10.1016/j.brs.2024.07.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Accepted: 07/24/2024] [Indexed: 07/30/2024] Open
Affiliation(s)
- Shmuel Hess
- Lev Hasharon Mental Health Center, Tsur Moshe, Israel; Faculty of Medicine and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Maor Yeshua
- Department of Psychology, Ben Gurion University in the Negev, Beer Sheva, Israel; Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel
| | - Ami Eisen
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel
| | - Yuri Burnishev
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel
| | - Elsa Sultan
- Geha Mental Health Center, Petah Tikva, Israel
| | - Gaby Pell
- Department of Life Sciences, Ben Gurion University in the Negev, Beer Sheva, Israel; BrainsWay Ltd, Jerusalem, Israel
| | - Colleen A Hanlon
- BrainsWay Ltd, Jerusalem, Israel; Wake Forest University School of Medicine, Winston Salem, USA
| | - Abraham Weizman
- Geha Mental Health Center, Petah Tikva, Israel; Faculty of Medicine and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Abraham Zangen
- Department of Life Sciences, Ben Gurion University in the Negev, Beer Sheva, Israel
| | - Yiftach Roth
- Department of Life Sciences, Ben Gurion University in the Negev, Beer Sheva, Israel; BrainsWay Ltd, Jerusalem, Israel
| | - Elisha Moses
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel.
| | - Dror Weiss
- Geha Mental Health Center, Petah Tikva, Israel; Faculty of Medicine and Health Sciences, Tel Aviv University, Tel Aviv, Israel
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6
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Xu X, Deng B, Wang J, Yi G. Individual Prediction of Electric Field Induced by Deep-Brain Magnetic Stimulation With CNN-Transformer. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2143-2152. [PMID: 38829755 DOI: 10.1109/tnsre.2024.3408902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Deep-brain Magnetic Stimulation (DMS) can improve the symptoms caused by Alzheimer's disease by inducing rhythmic electric field in the deep brain, and the induced electric field is rhythm-dependent. However, calculating the induced electric field requires building a voxel model of the brain for the stimulated object, which usually takes several hours. In order to obtain the rhythm-dependent electric field induced by DMS in real time, we adopt a CNN-Transformer model to predict it. A data set with a sample size of 7350 is established for the training and testing of the model. 10-fold cross validation is used to determine the optimal hyperparameters for training CNN-Transformer. The combination of 5-layer CNN and 6-layer Transformer is verified as the optimal combination of CNN-Transformer model. The experimental results show that the CNN-Transformer model can complete the prediction in 0.731s (CPU) or 0.042s (GPU), and the overall performance metrics of prediction can reach: MAE =0.0269, RMSE =0.0420, MAPE =4.61% and R2=0.9627. The prediction performance of the CNN-Transformer model for the hippocampal electric field is better than that of the brain grey matter electric field, and the stimulation rhythm has less influence on the model performance than the coil configuration. Taking the same dataset to train and test the separate CNN model and Transformer model, it is found that CNN-Transformer has better prediction performance than the separate CNN model and Transformer model in the task of predicting electric field induced by DMS.
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Galanis C, Neuhaus L, Hananeia N, Turi Z, Jedlicka P, Vlachos A. Axon morphology and intrinsic cellular properties determine repetitive transcranial magnetic stimulation threshold for plasticity. Front Cell Neurosci 2024; 18:1374555. [PMID: 38638302 PMCID: PMC11025360 DOI: 10.3389/fncel.2024.1374555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/13/2024] [Indexed: 04/20/2024] Open
Abstract
Introduction Repetitive transcranial magnetic stimulation (rTMS) is a widely used therapeutic tool in neurology and psychiatry, but its cellular and molecular mechanisms are not fully understood. Standardizing stimulus parameters, specifically electric field strength, is crucial in experimental and clinical settings. It enables meaningful comparisons across studies and facilitates the translation of findings into clinical practice. However, the impact of biophysical properties inherent to the stimulated neurons and networks on the outcome of rTMS protocols remains not well understood. Consequently, achieving standardization of biological effects across different brain regions and subjects poses a significant challenge. Methods This study compared the effects of 10 Hz repetitive magnetic stimulation (rMS) in entorhino-hippocampal tissue cultures from mice and rats, providing insights into the impact of the same stimulation protocol on similar neuronal networks under standardized conditions. Results We observed the previously described plastic changes in excitatory and inhibitory synaptic strength of CA1 pyramidal neurons in both mouse and rat tissue cultures, but a higher stimulation intensity was required for the induction of rMS-induced synaptic plasticity in rat tissue cultures. Through systematic comparison of neuronal structural and functional properties and computational modeling, we found that morphological parameters of CA1 pyramidal neurons alone are insufficient to explain the observed differences between the groups. Although morphologies of mouse and rat CA1 neurons showed no significant differences, simulations confirmed that axon morphologies significantly influence individual cell activation thresholds. Notably, differences in intrinsic cellular properties were sufficient to account for the 10% higher intensity required for the induction of synaptic plasticity in the rat tissue cultures. Conclusion These findings demonstrate the critical importance of axon morphology and intrinsic cellular properties in predicting the plasticity effects of rTMS, carrying valuable implications for the development of computer models aimed at predicting and standardizing the biological effects of rTMS.
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Affiliation(s)
- Christos Galanis
- Department of Neuroanatomy, Institute of Anatomy and Cell Biology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Lena Neuhaus
- Department of Neuroanatomy, Institute of Anatomy and Cell Biology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Nicholas Hananeia
- 3R-Zentrum Gießen, Justus-Liebig-Universitat Giessen, Giessen, Germany
| | - Zsolt Turi
- Department of Neuroanatomy, Institute of Anatomy and Cell Biology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Peter Jedlicka
- 3R-Zentrum Gießen, Justus-Liebig-Universitat Giessen, Giessen, Germany
| | - Andreas Vlachos
- Department of Neuroanatomy, Institute of Anatomy and Cell Biology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Center BrainLinks-BrainTools, University of Freiburg, Freiburg, Germany
- Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany
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8
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Du X, Choa FS, Chiappelli J, Bruce H, Kvarta M, Summerfelt A, Ma Y, Regenold WT, Walton K, Wittenberg GF, Hare S, Gao S, van der Vaart A, Zhao Z, Chen S, Kochunov P, Hong LE. Combining neuroimaging and brain stimulation to test alternative causal pathways for nicotine addiction in schizophrenia. Brain Stimul 2024; 17:324-332. [PMID: 38453003 DOI: 10.1016/j.brs.2024.02.020] [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: 10/20/2023] [Revised: 02/23/2024] [Accepted: 02/28/2024] [Indexed: 03/09/2024] Open
Abstract
The smoking rate is high in patients with schizophrenia. Brain stimulation targeting conventional brain circuits associated with nicotine addiction has also yielded mixed results. We aimed to identify alternative circuitries associated with nicotine addiction in both the general population and schizophrenia, and then test whether modulation of such circuitries may alter nicotine addiction behaviors in schizophrenia. In Study I of 40 schizophrenia smokers and 51 non-psychiatric smokers, cross-sectional neuroimaging analysis identified resting state functional connectivity (rsFC) between the dorsomedial prefrontal cortex (dmPFC) and multiple extended amygdala regions to be most robustly associated with nicotine addiction severity in healthy controls and schizophrenia patients (p = 0.006 to 0.07). In Study II with another 30 patient smokers, a proof-of-concept, patient- and rater-blind, randomized, sham-controlled rTMS design was used to test whether targeting the newly identified dmPFC location may causally enhance the rsFC and reduce nicotine addiction in schizophrenia. Although significant interactions were not observed, exploratory analyses showed that this dmPFC-extended amygdala rsFC was enhanced by 4-week active 10Hz rTMS (p = 0.05) compared to baseline; the severity of nicotine addiction showed trends of reduction after 3 and 4 weeks (p ≤ 0.05) of active rTMS compared to sham; Increased rsFC by active rTMS predicted reduction of cigarettes/day (R = -0.56, p = 0.025 uncorrected) and morning smoking severity (R = -0.59, p = 0.016 uncorrected). These results suggest that the dmPFC-extended amygdala circuit may be linked to nicotine addiction in schizophrenia and healthy individuals, and future efforts targeting its underlying pathophysiological mechanisms may yield more effective treatment for nicotine addiction.
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Affiliation(s)
- Xiaoming Du
- Louis A. Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.
| | - Fow-Sen Choa
- Department of Electrical Engineering and Computer Science, University of Maryland Baltimore County, Baltimore, MD, USA
| | - Joshua Chiappelli
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Heather Bruce
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Mark Kvarta
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ann Summerfelt
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yizhou Ma
- Louis A. Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - William T Regenold
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, Division of Intramural Research Program, National Institute of Mental Health, National Institutes of Health, NIH Clinical Center, Bethesda, MD, USA
| | - Kevin Walton
- Clinical Research Grants Branch, Division of Therapeutics and Medical Consequences, National Institute on Drug Abuse, National Institutes of Health, Bethesda, MD, USA
| | - George F Wittenberg
- Human Engineering Research Laboratories, VA RR&D Center of Excellence, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA; Rehabilitation Neural Engineering Laboratories, University of Pittsburgh, Pittsburgh, PA, USA; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Stephanie Hare
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Si Gao
- Louis A. Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Andrew van der Vaart
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Zhiwei Zhao
- Department of Mathematics, University of Maryland, College Park, USA
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Peter Kochunov
- Louis A. Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - L Elliot Hong
- Louis A. Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
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Abbasi S, Alluri S, Leung V, Asbeck P, Makale MT. Design and Validation of Miniaturized Repetitive Transcranial Magnetic Stimulation (rTMS) Head Coils. SENSORS (BASEL, SWITZERLAND) 2024; 24:1584. [PMID: 38475120 DOI: 10.3390/s24051584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a rapidly developing therapeutic modality for the safe and effective treatment of neuropsychiatric disorders. However, clinical rTMS driving systems and head coils are large, heavy, and expensive, so miniaturized, affordable rTMS devices may facilitate treatment access for patients at home, in underserved areas, in field and mobile hospitals, on ships and submarines, and in space. The central component of a portable rTMS system is a miniaturized, lightweight coil. Such a coil, when mated to lightweight driving circuits, must be able to induce B and E fields of sufficient intensity for medical use. This paper newly identifies and validates salient theoretical considerations specific to the dimensional scaling and miniaturization of coil geometries, particularly figure-8 coils, and delineates novel, key design criteria. In this context, the essential requirement of matching coil inductance with the characteristic resistance of the driver switches is highlighted. Computer simulations predicted E- and B-fields which were validated via benchtop experiments. Using a miniaturized coil with dimensions of 76 mm × 38 mm and weighing only 12.6 g, the peak E-field was 87 V/m at a distance of 1.5 cm. Practical considerations limited the maximum voltage and current to 350 V and 3.1 kA, respectively; nonetheless, this peak E-field value was well within the intensity range, 60-120 V/m, generally held to be therapeutically relevant. The presented parameters and results delineate coil and circuit guidelines for a future miniaturized, power-scalable rTMS system able to generate pulsed E-fields of sufficient amplitude for potential clinical use.
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Affiliation(s)
- Shaghayegh Abbasi
- Electrical Engineering Department, University of Portland, Portland, OR 97203, USA
| | - Sravya Alluri
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
- Calit2 Advanced Circuits Laboratory, University of California San Diego, La Jolla, CA 92093, USA
| | - Vincent Leung
- Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76706, USA
| | - Peter Asbeck
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
- Calit2 Advanced Circuits Laboratory, University of California San Diego, La Jolla, CA 92093, USA
| | - Milan T Makale
- Moores Cancer Center, Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA 92093, USA
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Kim HJ, Phan TT, Lee K, Kim JS, Lee SY, Lee JM, Do J, Lee D, Kim SP, Lee KP, Park J, Lee CJ, Park JM. Long-lasting forms of plasticity through patterned ultrasound-induced brainwave entrainment. SCIENCE ADVANCES 2024; 10:eadk3198. [PMID: 38394205 PMCID: PMC10889366 DOI: 10.1126/sciadv.adk3198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 01/22/2024] [Indexed: 02/25/2024]
Abstract
Achieving long-lasting neuronal modulation with low-intensity, low-frequency ultrasound is challenging. Here, we devised theta burst ultrasound stimulation (TBUS) with gamma bursts for brain entrainment and modulation of neuronal plasticity in the mouse motor cortex. We demonstrate that two types of TBUS, intermittent and continuous TBUS, induce bidirectional long-term potentiation or depression-like plasticity, respectively, as evidenced by changes in motor-evoked potentials. These effects depended on molecular pathways associated with long-term plasticity, including N-methyl-d-aspartate receptor and brain-derived neurotrophic factor/tropomyosin receptor kinase B activation, as well as de novo protein synthesis. Notably, bestrophin-1 and transient receptor potential ankyrin 1 play important roles in these enduring effects. Moreover, pretraining TBUS enhances the acquisition of previously unidentified motor skills. Our study unveils a promising protocol for ultrasound neuromodulation, enabling noninvasive and sustained modulation of brain function.
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Affiliation(s)
- Ho-Jeong Kim
- Center for Cognition and Sociality, Institute for Basic Science, Daejeon, Republic of Korea
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
| | - Tien Thuy Phan
- Center for Cognition and Sociality, Institute for Basic Science, Daejeon, Republic of Korea
- University of Science and Technology (UST), Daejeon, Republic of Korea
| | - Keunhyung Lee
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Jeong Sook Kim
- Department of Physiology, College of Veterinary Medicine, Chungnam National University, Daejeon, Republic of Korea
| | - Sang-Yeong Lee
- Center for Cognition and Sociality, Institute for Basic Science, Daejeon, Republic of Korea
- University of Science and Technology (UST), Daejeon, Republic of Korea
| | - Jung Moo Lee
- Center for Cognition and Sociality, Institute for Basic Science, Daejeon, Republic of Korea
| | - Jongrok Do
- Center for Cognition and Sociality, Institute for Basic Science, Daejeon, Republic of Korea
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Doyun Lee
- Center for Cognition and Sociality, Institute for Basic Science, Daejeon, Republic of Korea
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
| | - Kyu Pil Lee
- Department of Physiology, College of Veterinary Medicine, Chungnam National University, Daejeon, Republic of Korea
| | - Jinhyoung Park
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - C. Justin Lee
- Center for Cognition and Sociality, Institute for Basic Science, Daejeon, Republic of Korea
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
- University of Science and Technology (UST), Daejeon, Republic of Korea
| | - Joo Min Park
- Center for Cognition and Sociality, Institute for Basic Science, Daejeon, Republic of Korea
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
- University of Science and Technology (UST), Daejeon, Republic of Korea
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11
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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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12
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Berger T, Xu T, Opitz A. Systematic cross-species comparison of prefrontal cortex functional networks targeted via Transcranial Magnetic Stimulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.20.572653. [PMID: 38187657 PMCID: PMC10769354 DOI: 10.1101/2023.12.20.572653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation method that safely modulates neural activity in vivo. Its precision in targeting specific brain networks makes TMS invaluable in diverse clinical applications. For example, TMS is used to treat depression by targeting prefrontal brain networks and their connection to other brain regions. However, despite its widespread use, the underlying neural mechanisms of TMS are not completely understood. Non-human primates (NHPs) offer an ideal model to study TMS mechanisms through invasive electrophysiological recordings. As such, bridging the gap between NHP experiments and human applications is imperative to ensure translational relevance. Here, we systematically compare the TMS-targeted functional networks in the prefrontal cortex in humans and NHPs. To conduct this comparison, we combine TMS electric field modeling in humans and macaques with resting-state functional magnetic resonance imaging (fMRI) data to compare the functional networks targeted via TMS across species. We identified distinct stimulation zones in macaque and human models, each exhibiting variations in the impacted networks (macaque: Frontoparietal Network, Somatomotor Network; human: Frontoparietal Network, Default Network). We identified differences in brain gyrification and functional organization across species as the underlying cause of found network differences. The TMS-network profiles we identified will allow researchers to establish consistency in network activation across species, aiding in the translational efforts to develop improved TMS functional network targeting approaches.
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13
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Perera ND, Alekseichuk I, Shirinpour S, Wischnewski M, Linn G, Masiello K, Butler B, Russ BE, Schroeder CE, Falchier A, Opitz A. Dissociation of Centrally and Peripherally Induced Transcranial Magnetic Stimulation Effects in Nonhuman Primates. J Neurosci 2023; 43:8649-8662. [PMID: 37852789 PMCID: PMC10727178 DOI: 10.1523/jneurosci.1016-23.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 10/02/2023] [Accepted: 10/10/2023] [Indexed: 10/20/2023] Open
Abstract
Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation method that is rapidly growing in popularity for studying causal brain-behavior relationships. However, its dose-dependent centrally induced neural mechanisms and peripherally induced sensory costimulation effects remain debated. Understanding how TMS stimulation parameters affect brain responses is vital for the rational design of TMS protocols. Studying these mechanisms in humans is challenging because of the limited spatiotemporal resolution of available noninvasive neuroimaging methods. Here, we leverage invasive recordings of local field potentials in a male and a female nonhuman primate (rhesus macaque) to study TMS mesoscale responses. We demonstrate that early TMS-evoked potentials show a sigmoidal dose-response curve with stimulation intensity. We further show that stimulation responses are spatially specific. We use several control conditions to dissociate centrally induced neural responses from auditory and somatosensory coactivation. These results provide crucial evidence regarding TMS neural effects at the brain circuit level. Our findings are highly relevant for interpreting human TMS studies and biomarker developments for TMS target engagement in clinical applications.SIGNIFICANCE STATEMENT Transcranial magnetic stimulation (TMS) is a widely used noninvasive brain stimulation method to stimulate the human brain. To advance its utility for clinical applications, a clear understanding of its underlying physiological mechanisms is crucial. Here, we perform invasive electrophysiological recordings in the nonhuman primate brain during TMS, achieving a spatiotemporal precision not available in human EEG experiments. We find that evoked potentials are dose dependent and spatially specific, and can be separated from peripheral stimulation effects. This means that TMS-evoked responses can indicate a direct physiological stimulation response. Our work has important implications for the interpretation of human TMS-EEG recordings and biomarker development.
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Affiliation(s)
- Nipun D Perera
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455
| | - Ivan Alekseichuk
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455
| | - Sina Shirinpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455
| | - Miles Wischnewski
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455
| | - Gary Linn
- Translational Neuroscience Lab Division, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962
- Department of Psychiatry, NYU Grossman School of Medicine, New York, New York 10016
| | - Kurt Masiello
- Translational Neuroscience Lab Division, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962
| | - Brent Butler
- Translational Neuroscience Lab Division, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962
| | - Brian E Russ
- Translational Neuroscience Lab Division, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962
| | - Charles E Schroeder
- Translational Neuroscience Lab Division, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, New York 10032
- Department of Neurosurgery, The Neurological Institute of New York, Columbia University Irving Medical Center, New York, New York 10032
| | - Arnaud Falchier
- Translational Neuroscience Lab Division, Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962
- Department of Psychiatry, NYU Grossman School of Medicine, New York, New York 10016
| | - Alexander Opitz
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455
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14
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Evans C, Johnstone A, Zich C, Lee JSA, Ward NS, Bestmann S. The impact of brain lesions on tDCS-induced electric fields. Sci Rep 2023; 13:19430. [PMID: 37940660 PMCID: PMC10632455 DOI: 10.1038/s41598-023-45905-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 10/25/2023] [Indexed: 11/10/2023] Open
Abstract
Transcranial direct current stimulation (tDCS) can enhance motor and language rehabilitation after stroke. Though brain lesions distort tDCS-induced electric field (E-field), systematic accounts remain limited. Using electric field modelling, we investigated the effect of 630 synthetic lesions on E-field magnitude in the region of interest (ROI). Models were conducted for two tDCS montages targeting either primary motor cortex (M1) or Broca's area (BA44). Absolute E-field magnitude in the ROI differed by up to 42% compared to the non-lesioned brain depending on lesion size, lesion-ROI distance, and lesion conductivity value. Lesion location determined the sign of this difference: lesions in-line with the predominant direction of current increased E-field magnitude in the ROI, whereas lesions located in the opposite direction decreased E-field magnitude. We further explored how individualised tDCS can control lesion-induced effects on E-field. Lesions affected the individualised electrode configuration needed to maximise E-field magnitude in the ROI, but this effect was negligible when prioritising the maximisation of radial inward current. Lesions distorting tDCS-induced E-field, is likely to exacerbate inter-individual variability in E-field magnitude. Individualising electrode configuration and stimulator output can minimise lesion-induced variability but requires improved estimates of lesion conductivity. Individualised tDCS is critical to overcome E-field variability in lesioned brains.
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Affiliation(s)
- Carys Evans
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Ainslie Johnstone
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Catharina Zich
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
- Nuffield Department of Clinical Neurosciences, FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Jenny S A Lee
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Nick S Ward
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
- The National Hospital for Neurology and Neurosurgery, London, UK
- UCLP Centre for Neurorehabilitation, London, UK
| | - Sven Bestmann
- Department for Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
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15
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Van Hoornweder S, Nuyts M, Frieske J, Verstraelen S, Meesen RLJ, Caulfield KA. Outcome measures for electric field modeling in tES and TMS: A systematic review and large-scale modeling study. Neuroimage 2023; 281:120379. [PMID: 37716590 PMCID: PMC11008458 DOI: 10.1016/j.neuroimage.2023.120379] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/18/2023] [Accepted: 09/13/2023] [Indexed: 09/18/2023] Open
Abstract
BACKGROUND Electric field (E-field) modeling is a potent tool to estimate the amount of transcranial magnetic and electrical stimulation (TMS and tES, respectively) that reaches the cortex and to address the variable behavioral effects observed in the field. However, outcome measures used to quantify E-fields vary considerably and a thorough comparison is missing. OBJECTIVES This two-part study aimed to examine the different outcome measures used to report on tES and TMS induced E-fields, including volume- and surface-level gray matter, region of interest (ROI), whole brain, geometrical, structural, and percentile-based approaches. The study aimed to guide future research in informed selection of appropriate outcome measures. METHODS Three electronic databases were searched for tES and/or TMS studies quantifying E-fields. The identified outcome measures were compared across volume- and surface-level E-field data in ten tES and TMS modalities targeting two common targets in 100 healthy individuals. RESULTS In the systematic review, we extracted 308 outcome measures from 202 studies that adopted either a gray matter volume-level (n = 197) or surface-level (n = 111) approach. Volume-level results focused on E-field magnitude, while surface-level data encompassed E-field magnitude (n = 64) and normal/tangential E-field components (n = 47). E-fields were extracted in ROIs, such as brain structures and shapes (spheres, hexahedra and cylinders), or the whole brain. Percentiles or mean values were mostly used to quantify E-fields. Our modeling study, which involved 1,000 E-field models and > 1,000,000 extracted E-field values, revealed that different outcome measures yielded distinct E-field values, analyzed different brain regions, and did not always exhibit strong correlations in the same within-subject E-field model. CONCLUSIONS Outcome measure selection significantly impacts the locations and intensities of extracted E-field data in both tES and TMS E-field models. The suitability of different outcome measures depends on the target region, TMS/tES modality, individual anatomy, the analyzed E-field component and the research question. To enhance the quality, rigor, and reproducibility in the E-field modeling domain, we suggest standard reporting practices across studies and provide four recommendations.
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Affiliation(s)
- Sybren Van Hoornweder
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, Diepenbeek, Belgium.
| | - Marten Nuyts
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, Diepenbeek, Belgium
| | - Joana Frieske
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, Diepenbeek, Belgium; Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Stefanie Verstraelen
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, Diepenbeek, Belgium
| | - Raf L J Meesen
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, University of Hasselt, Diepenbeek, Belgium; Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Kevin A Caulfield
- Brain Stimulation Laboratory, Department of Psychiatry, Medical University of South Carolina, Charleston, SC, United States.
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16
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Aberra AS, Wang R, Grill WM, Peterchev AV. Multi-scale model of axonal and dendritic polarization by transcranial direct current stimulation in realistic head geometry. Brain Stimul 2023; 16:1776-1791. [PMID: 38056825 PMCID: PMC10842743 DOI: 10.1016/j.brs.2023.11.018] [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: 07/30/2023] [Revised: 11/06/2023] [Accepted: 11/29/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation modality that can alter cortical excitability. However, it remains unclear how the subcellular elements of different neuron types are polarized by specific electric field (E-field) distributions. OBJECTIVE To quantify neuronal polarization generated by tDCS in a multi-scale computational model. METHODS We embedded layer-specific, morphologically-realistic cortical neuron models in a finite element model of the E-field in a human head and simulated steady-state polarization generated by conventional primary-motor-cortex-supraorbital (M1-SO) and 4 × 1 high-definition (HD) tDCS. We quantified somatic, axonal, and dendritic polarization of excitatory pyramidal cells in layers 2/3, 5, and 6, as well as inhibitory interneurons in layers 1 and 4 of the hand knob. RESULTS Axonal and dendritic terminals were polarized more than the soma in all neurons, with peak axonal and dendritic polarization of 0.92 mV and 0.21 mV, respectively, compared to peak somatic polarization of 0.07 mV for 1.8 mA M1-SO stimulation. Both montages generated regions of depolarization and hyperpolarization beneath the M1 anode; M1-SO produced slightly stronger, more diffuse polarization peaking in the central sulcus, while 4 × 1 HD produced higher peak polarization in the gyral crown. The E-field component normal to the cortical surface correlated strongly with pyramidal neuron somatic polarization (R2>0.9), but exhibited weaker correlations with peak pyramidal axonal and dendritic polarization (R2:0.5-0.9) and peak polarization in all subcellular regions of interneurons (R2:0.3-0.6). Simulating polarization by uniform local E-field extracted at the soma approximated the spatial distribution of tDCS polarization but produced large errors in some regions (median absolute percent error: 7.9 %). CONCLUSIONS Polarization of pre- and postsynaptic compartments of excitatory and inhibitory cortical neurons may play a significant role in tDCS neuromodulation. These effects cannot be predicted from the E-field distribution alone but rather require calculation of the neuronal response.
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Affiliation(s)
- Aman S Aberra
- Dept. of Biomedical Engineering, Pratt School of Engineering, Duke University, NC, USA.
| | - Ruochen Wang
- Dept. of Biomedical Engineering, Pratt School of Engineering, Duke University, NC, USA; Dept. of Psychiatry and Behavioral Sciences, School of Medicine, Duke University, NC, USA.
| | - Warren M Grill
- Dept. of Biomedical Engineering, Pratt School of Engineering, Duke University, NC, USA; Dept. of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, NC, USA; Dept. of Neurobiology, School of Medicine, Duke University, NC, USA; Dept. of Neurosurgery, School of Medicine, Duke University, NC, USA.
| | - Angel V Peterchev
- Dept. of Biomedical Engineering, Pratt School of Engineering, Duke University, NC, USA; Dept. of Psychiatry and Behavioral Sciences, School of Medicine, Duke University, NC, USA; Dept. of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, NC, USA; Dept. of Neurosurgery, School of Medicine, Duke University, NC, USA.
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17
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Galanis C, Neuhaus L, Hananeia N, Turi Z, Jedlicka P, Vlachos A. Axon morphology and intrinsic cellular properties determine repetitive transcranial magnetic stimulation threshold for plasticity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.25.559399. [PMID: 37808716 PMCID: PMC10557586 DOI: 10.1101/2023.09.25.559399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a widely used therapeutic tool in neurology and psychiatry, but its cellular and molecular mechanisms are not fully understood. Standardizing stimulus parameters, specifically electric field strength and direction, is crucial in experimental and clinical settings. It enables meaningful comparisons across studies and facilitating the translation of findings into clinical practice. However, the impact of biophysical properties inherent to the stimulated neurons and networks on the outcome of rTMS protocols remains not well understood. Consequently, achieving standardization of biological effects across different brain regions and subjects poses a significant challenge. This study compared the effects of 10 Hz repetitive magnetic stimulation (rMS) in entorhino-hippocampal tissue cultures from mice and rats, providing insights into the impact of the same stimulation protocol on similar neuronal networks under standardized conditions. We observed the previously described plastic changes in excitatory and inhibitory synaptic strength of CA1 pyramidal neurons in both mouse and rat tissue cultures, but a higher stimulation intensity was required for the induction of rMS-induced synaptic plasticity in rat tissue cultures. Through systematic comparison of neuronal structural and functional properties and computational modeling, we found that morphological parameters of CA1 pyramidal neurons alone are insufficient to explain the observed differences between the groups. However, axon morphologies of individual cells played a significant role in determining activation thresholds. Notably, differences in intrinsic cellular properties were sufficient to account for the 10 % higher intensity required for the induction of synaptic plasticity in the rat tissue cultures. These findings demonstrate the critical importance of axon morphology and intrinsic cellular properties in predicting the plasticity effects of rTMS, carrying valuable implications for the development of computer models aimed at predicting and standardizing the biological effects of rTMS.
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18
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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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19
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Mantell KE, Perera ND, Shirinpour S, Puonti O, Xu T, Zimmermann J, Falchier A, Heilbronner SR, Thielscher A, Opitz A. Anatomical details affect electric field predictions for non-invasive brain stimulation in non-human primates. Neuroimage 2023; 279:120343. [PMID: 37619797 PMCID: PMC10961993 DOI: 10.1016/j.neuroimage.2023.120343] [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: 12/06/2022] [Revised: 08/09/2023] [Accepted: 08/21/2023] [Indexed: 08/26/2023] Open
Abstract
Non-human primates (NHPs) have become key for translational research in noninvasive brain stimulation (NIBS). However, in order to create comparable stimulation conditions for humans it is vital to study the accuracy of current modeling practices across species. Numerical models to simulate electric fields are an important tool for experimental planning in NHPs and translation to human studies. It is thus essential whether and to what extent the anatomical details of NHP models agree with current modeling practices when calculating NIBS electric fields. Here, we create highly accurate head models of two non-human primates (NHP) MR data. We evaluate how muscle tissue and head field of view (depending on MRI parameters) affect simulation results in transcranial electric and magnetic stimulation (TES and TMS). Our findings indicate that the inclusion of anisotropic muscle can affect TES electric field strength up to 22% while TMS is largely unaffected. Additionally, comparing a full head model to a cropped head model illustrates the impact of head field of view on electric fields for both TES and TMS. We find opposing effects between TES and TMS with an increase up to 24.8% for TES and a decrease up to 24.6% for TMS for the cropped head model compared to the full head model. Our results provide important insights into the level of anatomical detail needed for NHP head models and can inform future translational efforts for NIBS studies.
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Affiliation(s)
- Kathleen E Mantell
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, USA
| | - Nipun D Perera
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, USA
| | - Sina Shirinpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, USA
| | - Oula Puonti
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, USA
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, USA
| | - Arnaud Falchier
- Translational Neuroscience Lab Division, Center for Biomedical Imaging and Neuromodulation, 9 The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | | | - Axel Thielscher
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark; Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Alexander Opitz
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, USA.
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20
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Pérez-Benítez JA, Martínez-Ortiz P, Aguila-Muñoz J. A Review of Formulations, Boundary Value Problems and Solutions for Numerical Computation of Transcranial Magnetic Stimulation Fields. Brain Sci 2023; 13:1142. [PMID: 37626498 PMCID: PMC10452852 DOI: 10.3390/brainsci13081142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/22/2023] [Accepted: 07/24/2023] [Indexed: 08/27/2023] Open
Abstract
Since the inception of the transcranial magnetic stimulation (TMS) technique, it has become imperative to numerically compute the distribution of the electric field induced in the brain. Various models of the coil-brain system have been proposed for this purpose. These models yield a set of formulations and boundary conditions that can be employed to calculate the induced electric field. However, the literature on TMS simulation presents several of these formulations, leading to potential confusion regarding the interpretation and contribution of each source of electric field. The present study undertakes an extensive compilation of widely utilized formulations, boundary value problems and numerical solutions employed in TMS fields simulations, analyzing the advantages and disadvantages associated with each used formulation and numerical method. Additionally, it explores the implementation strategies employed for their numerical computation. Furthermore, this work provides numerical expressions that can be utilized for the numerical computation of TMS fields using the finite difference and finite element methods. Notably, some of these expressions are deduced within the present study. Finally, an overview of some of the most significant results obtained from numerical computation of TMS fields is presented. The aim of this work is to serve as a guide for future research endeavors concerning the numerical simulation of TMS.
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Affiliation(s)
- J. A. Pérez-Benítez
- Laboratorio de Bio-Electromagnetismo, ESIME-SEPI, Edif. Z-4, Instituto Politécnico Nacional, Mexico City 07738, CDMX, Mexico;
| | - P. Martínez-Ortiz
- Laboratorio de Bio-Electromagnetismo, ESIME-SEPI, Edif. Z-4, Instituto Politécnico Nacional, Mexico City 07738, CDMX, Mexico;
| | - J. Aguila-Muñoz
- CONAHCYT—Centro de Nanociencias y Nanotecnología, Universidad Nacional Autónoma de México, km 107 Carretera Tijuana-Ensenada, Apartado Postal 14, Ensenada 22800, BC, Mexico
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21
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Aberra AS, Lopez A, Grill WM, Peterchev AV. Rapid estimation of cortical neuron activation thresholds by transcranial magnetic stimulation using convolutional neural networks. Neuroimage 2023; 275:120184. [PMID: 37230204 PMCID: PMC10281353 DOI: 10.1016/j.neuroimage.2023.120184] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 04/13/2023] [Accepted: 05/22/2023] [Indexed: 05/27/2023] Open
Abstract
BACKGROUND Transcranial magnetic stimulation (TMS) can modulate neural activity by evoking action potentials in cortical neurons. TMS neural activation can be predicted by coupling subject-specific head models of the TMS-induced electric field (E-field) to populations of biophysically realistic neuron models; however, the significant computational cost associated with these models limits their utility and eventual translation to clinically relevant applications. OBJECTIVE To develop computationally efficient estimators of the activation thresholds of multi-compartmental cortical neuron models in response to TMS-induced E-field distributions. METHODS Multi-scale models combining anatomically accurate finite element method (FEM) simulations of the TMS E-field with layer-specific representations of cortical neurons were used to generate a large dataset of activation thresholds. 3D convolutional neural networks (CNNs) were trained on these data to predict thresholds of model neurons given their local E-field distribution. The CNN estimator was compared to an approach using the uniform E-field approximation to estimate thresholds in the non-uniform TMS-induced E-field. RESULTS The 3D CNNs estimated thresholds with mean absolute percent error (MAPE) on the test dataset below 2.5% and strong correlation between the CNN predicted and actual thresholds for all cell types (R2 > 0.96). The CNNs estimated thresholds with a 2-4 orders of magnitude reduction in the computational cost of the multi-compartmental neuron models. The CNNs were also trained to predict the median threshold of populations of neurons, speeding up computation further. CONCLUSION 3D CNNs can estimate rapidly and accurately the TMS activation thresholds of biophysically realistic neuron models using sparse samples of the local E-field, enabling simulating responses of large neuron populations or parameter space exploration on a personal computer.
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Affiliation(s)
- Aman S Aberra
- Department of Biomedical Engineering, School of Engineering, Duke University, NC, USA
| | - Adrian Lopez
- Department of Electrical and Computer Engineering, School of Engineering, Duke University, NC, USA; Department of Mathematics, College of Arts and Sciences, Duke University, NC, USA
| | - Warren M Grill
- Department of Biomedical Engineering, School of Engineering, Duke University, NC, USA; Department of Electrical and Computer Engineering, School of Engineering, Duke University, NC, USA; Department of Neurobiology, School of Medicine, Duke University, NC, USA; Department of Neurosurgery, School of Medicine, Duke University, NC, USA
| | - Angel V Peterchev
- Department of Biomedical Engineering, School of Engineering, Duke University, NC, USA; Department of Electrical and Computer Engineering, School of Engineering, Duke University, NC, USA; Department of Neurosurgery, School of Medicine, Duke University, NC, USA; Department of Psychiatry and Behavioral Sciences, School of Medicine, Duke University, NC, USA.
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22
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Syrov N, Mustafina A, Berkmush-Antipova A, Yakovlev L, Demchinsky A, Petrova D, Kaplan A. Single-Subject TMS Pulse Visualization on MRI-Based Brain Model: A precise method for mapping TMS pulses on cortical surface. MethodsX 2023; 10:102213. [PMID: 37292240 PMCID: PMC10244693 DOI: 10.1016/j.mex.2023.102213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/09/2023] [Indexed: 06/10/2023] Open
Abstract
Highly accurate visualization of the points of transcranial magnetic stimulation (TMS) application on the brain cortical surface could provide anatomy-specific analysis of TMS effects. TMS is widely used to activate cortical areas with high spatial resolution, and neuronavigation enables site-specific TMS of particular gyrus sites. Precise control of TMS application points is crucial in determining the stimulation effects. Here, we propose a method that gives an opportunity to visualize and analyze the stimulated cortical sites by processing multi-parameter data.•This method uses MRI data to create a participant's brain model for visualization. The MRI data is segmented to obtain a raw 3D model, which is further optimized in 3D modeling software.•A Python script running in Blender uses the TMS coil's orientation data and participant's brain 3D model to define and mark the cortical sites affected by the particular TMS pulse.•The Python script can be easily customized to visualize TMS points task-specifically.
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Affiliation(s)
- Nikolay Syrov
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Alfiia Mustafina
- Department of Invertebrate Zoology, Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia
| | - Artemiy Berkmush-Antipova
- Baltic Center Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Lev Yakovlev
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
- Baltic Center Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | | | - Daria Petrova
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Alexander Kaplan
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
- Baltic Center Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
- Department of Human and Animal Physiology, Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia
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23
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Choung JS, Bhattacharjee S, Son JP, Kim JM, Cho DS, Cho CS, Kim M. Development and application of rTMS device to murine model. Sci Rep 2023; 13:5490. [PMID: 37016000 PMCID: PMC10073209 DOI: 10.1038/s41598-023-32646-w] [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: 12/09/2022] [Accepted: 03/30/2023] [Indexed: 04/06/2023] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is attracting attention as a new treatment technique for brain lesions, and many animal studies showing its effects have been reported. However, the findings of animal application researches cannot directly represent the effects of rTMS in human, mainly due to size difference and mechanistic characteristics of rTMS. Therefore, the authors purposed to develop a mouse rTMS to simulate clinical application and to confirm. Firstly, a virtual head model was created according to magnetic resonance images of murine head. Then, simulations of rTMS stimulation with different coils were performed on the murine head phantom, and an rTMS device for mice was fabricated based on the optimal voltage conditions. Lastly, strengths of magnetic fields generated by the two rTMS devices, for human (conventional clinical use) and mouse (newly fabricated), were measured in air and on mouse head and compared. Resultantly, the magnetic field intensity generated by coil of mouse was lower than human's (p < 0.01), and no differences were found between the predicted simulation values and the measured intensity in vivo (p > 0.05). Further in vivo researches using miniaturized rTMS devices for murine head should be followed to be more meaningful for human.
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Affiliation(s)
- Jin Seung Choung
- Department of Rehabilitation Medicine, CHA Bundang Medical Center, CHA University School of Medicine, 59 Yatap-ro, Bundang-gu, Seongnam, Gyeonggi-do, 13496, Republic of Korea
- Department of Biomedical Science, CHA University, Seongnam, Republic of Korea
| | - Sohom Bhattacharjee
- School of Electronic and Information Engineering, Korea Aerospace University, 76, Hanggongdaehak-ro, Goyang-si, Gyeonggi-do, 10540, Republic of Korea
| | - Jeong Pyo Son
- Advanced Radiation Technology Institute (ARTI), Korea Atomic Energy Research Institute (KAERI), Jeongeup, Republic of Korea
| | - Jong Moon Kim
- Department of Rehabilitation Medicine, CHA Bundang Medical Center, CHA University School of Medicine, 59 Yatap-ro, Bundang-gu, Seongnam, Gyeonggi-do, 13496, Republic of Korea
- Rehabilitation and Regeneration Research Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Dong Sik Cho
- R&D Center, Remed Co., Ltd., Seongnam, Republic of Korea
| | - Choon Sik Cho
- School of Electronic and Information Engineering, Korea Aerospace University, 76, Hanggongdaehak-ro, Goyang-si, Gyeonggi-do, 10540, Republic of Korea.
| | - MinYoung Kim
- Department of Rehabilitation Medicine, CHA Bundang Medical Center, CHA University School of Medicine, 59 Yatap-ro, Bundang-gu, Seongnam, Gyeonggi-do, 13496, Republic of Korea.
- Department of Biomedical Science, CHA University, Seongnam, Republic of Korea.
- Rehabilitation and Regeneration Research Center, CHA University School of Medicine, Seongnam, Republic of Korea.
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24
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Numssen O, van der Burght CL, Hartwigsen G. Revisiting the focality of non-invasive brain stimulation - implications for studies of human cognition. Neurosci Biobehav Rev 2023; 149:105154. [PMID: 37011776 DOI: 10.1016/j.neubiorev.2023.105154] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/06/2023] [Accepted: 03/31/2023] [Indexed: 04/04/2023]
Abstract
Non-invasive brain stimulation techniques are popular tools to investigate brain function in health and disease. Although transcranial magnetic stimulation (TMS) is widely used in cognitive neuroscience research to probe causal structure-function relationships, studies often yield inconclusive results. To improve the effectiveness of TMS studies, we argue that the cognitive neuroscience community needs to revise the stimulation focality principle - the spatial resolution with which TMS can differentially stimulate cortical regions. In the motor domain, TMS can differentiate between cortical muscle representations of adjacent fingers. However, this high degree of spatial specificity cannot be obtained in all cortical regions due to the influences of cortical folding patterns on the TMS-induced electric field. The region-dependent focality of TMS should be assessed a priori to estimate the experimental feasibility. Post-hoc simulations allow modeling of the relationship between cortical stimulation exposure and behavioral modulation by integrating data across stimulation sites or subjects.
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Affiliation(s)
- Ole Numssen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Methods and Development Group Brain Networks, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | | | - Gesa Hartwigsen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Wilhelm Wundt Institute for Psychology, Leipzig University, Germany
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25
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Gogulski J, Ross JM, Talbot A, Cline CC, Donati FL, Munot S, Kim N, Gibbs C, Bastin N, Yang J, Minasi C, Sarkar M, Truong J, Keller CJ. Personalized Repetitive Transcranial Magnetic Stimulation for Depression. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:351-360. [PMID: 36792455 DOI: 10.1016/j.bpsc.2022.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 10/20/2022] [Accepted: 10/23/2022] [Indexed: 11/06/2022]
Abstract
Personalized treatments are gaining momentum across all fields of medicine. Precision medicine can be applied to neuromodulatory techniques, in which focused brain stimulation treatments such as repetitive transcranial magnetic stimulation (rTMS) modulate brain circuits and alleviate clinical symptoms. rTMS is well tolerated and clinically effective for treatment-resistant depression and other neuropsychiatric disorders. Despite its wide stimulation parameter space (location, angle, pattern, frequency, and intensity can be adjusted), rTMS is currently applied in a one-size-fits-all manner, potentially contributing to its suboptimal clinical response (∼50%). In this review, we examine components of rTMS that can be optimized to account for interindividual variability in neural function and anatomy. We discuss current treatment options for treatment-resistant depression, the neural mechanisms thought to underlie treatment, targeting strategies, stimulation parameter selection, and adaptive closed-loop treatment. We conclude that a better understanding of the wide and modifiable parameter space of rTMS will greatly improve the clinical outcome.
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Affiliation(s)
- Juha Gogulski
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; HUS Diagnostic Center, Clinical Neurophysiology, Clinical Neurosciences, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Jessica M Ross
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Austin Talbot
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Christopher C Cline
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Francesco L Donati
- Department of Health Sciences, San Paolo Hospital, University of Milan, Milan, Italy
| | - Saachi Munot
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Naryeong Kim
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Ciara Gibbs
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Nikita Bastin
- Department of Radiology and Orthopedics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jessica Yang
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Christopher Minasi
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Manjima Sarkar
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Jade Truong
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California
| | - Corey J Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, California.
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26
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Carlson HL, Giuffre A, Ciechanski P, Kirton A. Electric field simulations of transcranial direct current stimulation in children with perinatal stroke. Front Hum Neurosci 2023; 17:1075741. [PMID: 36816507 PMCID: PMC9932338 DOI: 10.3389/fnhum.2023.1075741] [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: 10/20/2022] [Accepted: 01/16/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction Perinatal stroke (PS) is a focal vascular brain injury and the leading cause of hemiparetic cerebral palsy. Motor impairments last a lifetime but treatments are limited. Transcranial direct-current stimulation (tDCS) may enhance motor learning in adults but tDCS effects on motor learning are less studied in children. Imaging-based simulations of tDCS-induced electric fields (EF) suggest differences in the developing brain compared to adults but have not been applied to common pediatric disease states. We created estimates of tDCS-induced EF strength using five tDCS montages targeting the motor system in children with PS [arterial ischemic stroke (AIS) or periventricular infarction (PVI)] and typically developing controls (TDC) aged 6-19 years to explore associates between simulation values and underlying anatomy. Methods Simulations were performed using SimNIBS https://simnibs.github.io/simnibs/build/html/index.html using T1, T2, and diffusion-weighted images. After tissue segmentation and tetrahedral mesh generation, tDCS-induced EF was estimated based on the finite element model (FEM). Five 1mA tDCS montages targeting motor function in the paretic (non-dominant) hand were simulated. Estimates of peak EF strength, EF angle, field focality, and mean EF in motor cortex (M1) were extracted for each montage and compared between groups. Results Simulations for eighty-three children were successfully completed (21 AIS, 30 PVI, 32 TDC). Conventional tDCS montages utilizing anodes over lesioned cortex had higher peak EF strength values for the AIS group compared to TDC. These montages showed lower mean EF strength within target M1 regions suggesting that peaks were not necessarily localized to motor network-related targets. EF angle was lower for TDC compared to PS groups for a subset of montages. Montages using anodes over lesioned cortex were more sensitive to variations in underlying anatomy (lesion and tissue volumes) than those using cathodes over non-lesioned cortex. Discussion Individualized patient-centered tDCS EF simulations are prudent for clinical trial planning and may provide insight into the efficacy of tDCS interventions in children with PS.
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Affiliation(s)
- Helen L. Carlson
- Calgary Pediatric Stroke Program, Alberta Children’s Hospital, Calgary, AB, Canada,Alberta Children’s Hospital Research Institute (ACHRI), Calgary, AB, Canada,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Pediatrics, University of Calgary, Calgary, AB, Canada,*Correspondence: Helen L. Carlson,
| | - Adrianna Giuffre
- Calgary Pediatric Stroke Program, Alberta Children’s Hospital, Calgary, AB, Canada,Alberta Children’s Hospital Research Institute (ACHRI), Calgary, AB, Canada,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Pediatrics, University of Calgary, Calgary, AB, Canada
| | - Patrick Ciechanski
- Calgary Pediatric Stroke Program, Alberta Children’s Hospital, Calgary, AB, Canada,Alberta Children’s Hospital Research Institute (ACHRI), Calgary, AB, Canada,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Pediatrics, University of Calgary, Calgary, AB, Canada
| | - Adam Kirton
- Calgary Pediatric Stroke Program, Alberta Children’s Hospital, Calgary, AB, Canada,Alberta Children’s Hospital Research Institute (ACHRI), Calgary, AB, Canada,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Pediatrics, University of Calgary, Calgary, AB, Canada,Department of Clinical Neuroscience and Radiology, University of Calgary, Calgary, AB, Canada
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27
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Weise K, Numssen O, Kalloch B, Zier AL, Thielscher A, Haueisen J, Hartwigsen G, Knösche TR. Precise motor mapping with transcranial magnetic stimulation. Nat Protoc 2023; 18:293-318. [PMID: 36460808 DOI: 10.1038/s41596-022-00776-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 08/17/2022] [Indexed: 12/03/2022]
Abstract
We describe a routine to precisely localize cortical muscle representations within the primary motor cortex with transcranial magnetic stimulation (TMS) based on the functional relation between induced electric fields at the cortical level and peripheral muscle activation (motor-evoked potentials; MEPs). Besides providing insights into structure-function relationships, this routine lays the foundation for TMS dosing metrics based on subject-specific cortical electric field thresholds. MEPs for different coil positions and orientations are combined with electric field modeling, exploiting the causal nature of neuronal activation to pinpoint the cortical origin of the MEPs. This involves constructing an individual head model using magnetic resonance imaging, recording MEPs via electromyography during TMS and computing the induced electric fields with numerical modeling. The cortical muscle representations are determined by relating the TMS-induced electric fields to the MEP amplitudes. Subsequently, the coil position to optimally stimulate the origin of the identified cortical MEP can be determined by numerical modeling. The protocol requires 2 h of manual preparation, 10 h for the automated head model construction, one TMS session lasting 2 h, 12 h of computational postprocessing and an optional second TMS session lasting 30 min. A basic level of computer science expertise and standard TMS neuronavigation equipment suffices to perform the protocol.
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Affiliation(s)
- Konstantin Weise
- Methods and Development Group 'Brain Networks', Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. .,Technische Universität Ilmenau, Advanced Electromagnetics Group, Ilmenau, Germany.
| | - Ole Numssen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Benjamin Kalloch
- Methods and Development Group 'Brain Networks', Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Technische Universität Ilmenau, Institute of Biomedical Engineering and Informatics, Ilmenau, Germany
| | - Anna Leah Zier
- Institute of Medical Psychology, Medical Faculty, Goethe-University, Frankfurt/Main, Germany
| | - Axel Thielscher
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark.,Technical University of Denmark, Center for Magnetic Resonance, Department of Health Technology, Kongens Lyngby, Denmark
| | - Jens Haueisen
- Technische Universität Ilmenau, Institute of Biomedical Engineering and Informatics, Ilmenau, Germany
| | - Gesa Hartwigsen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Thomas R Knösche
- Methods and Development Group 'Brain Networks', Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Technische Universität Ilmenau, Institute of Biomedical Engineering and Informatics, Ilmenau, Germany
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28
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Segar DJ, Bernstock JD, Arnaout O, Bi WL, Friedman GK, Langer R, Traverso G, Rampersad SM. Modeling of intracranial tumor treating fields for the treatment of complex high-grade gliomas. Sci Rep 2023; 13:1636. [PMID: 36717682 PMCID: PMC9886948 DOI: 10.1038/s41598-023-28769-9] [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: 09/21/2022] [Accepted: 01/24/2023] [Indexed: 02/01/2023] Open
Abstract
Increasing the intensity of tumor treating fields (TTF) within a tumor bed improves clinical efficacy, but reaching sufficiently high field intensities to achieve growth arrest remains challenging due in part to the insulating nature of the cranium. Using MRI-derived finite element models (FEMs) and simulations, we optimized an exhaustive set of intracranial electrode locations to obtain maximum TTF intensities in three clinically challenging high-grade glioma (HGG) cases (i.e., thalamic, left temporal, brainstem). Electric field strengths were converted into therapeutic enhancement ratios (TER) to evaluate the predicted impact of stimulation on tumor growth. Concurrently, conventional transcranial configurations were simulated/optimized for comparison. Optimized intracranial TTF were able to achieve field strengths that have previously been shown capable of inducing complete growth arrest, in 98-100% of the tumor volumes using only 0.54-0.64 A current. The reconceptualization of TTF as a targeted, intracranial therapy has the potential to provide a meaningful survival benefit to patients with HGG and other brain tumors, including those in surgically challenging, deep, or anatomically eloquent locations which may preclude surgical resection. Accordingly, such an approach may ultimately represent a paradigm shift in the use of TTFs for the treatment of brain cancer.
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Affiliation(s)
- David J Segar
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. .,David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Omar Arnaout
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Wenya Linda Bi
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Gregory K Friedman
- Department of Pediatrics, University of Alabama at Birmingham, Birmingham, USA
| | - Robert Langer
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Giovanni Traverso
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.,Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sumientra M Rampersad
- Department of Physics, University of Massachusetts, Boston, MA, USA.,Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
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29
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Revisiting the Rotational Field TMS Method for Neurostimulation. J Clin Med 2023; 12:jcm12030983. [PMID: 36769630 PMCID: PMC9917411 DOI: 10.3390/jcm12030983] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/23/2023] [Accepted: 01/25/2023] [Indexed: 01/31/2023] Open
Abstract
Transcranial magnetic stimulation (TMS) is a non-invasive technique that has shown high efficacy in the treatment of major depressive disorder (MDD) and is increasingly utilized for various neuropsychiatric disorders. However, conventional TMS is limited to activating only a small fraction of neurons that have components parallel to the induced electric field. This likely contributes to the significant variability observed in clinical outcomes. A novel method termed rotational field TMS (rfTMS or TMS 360°) enables the activation of a greater number of neurons by reducing the sensitivity to orientation. Recruitment of a larger number of neurons offers the potential to enhance efficacy and reduce variability in the treatment of clinical indications for which neuronal recruitment and organization may play a significant role, such as MDD and stroke. The potential of the method remains to be validated in clinical trials. Here, we revisit and describe in detail the rfTMS method, its principles, mode of operation, effects on the brain, and potential benefits for clinical TMS.
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30
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Gaugain G, Quéguiner L, Bikson M, Sauleau R, Zhadobov M, Modolo J, Nikolayev D. Quasi-static approximation error of electric field analysis for transcranial current stimulation. J Neural Eng 2023; 20. [PMID: 36621858 DOI: 10.1088/1741-2552/acb14d] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 01/06/2023] [Indexed: 01/10/2023]
Abstract
Objective.Numerical modeling of electric fields induced by transcranial alternating current stimulation (tACS) is currently a part of the standard procedure to predict and understand neural response. Quasi-static approximation (QSA) for electric field calculations is generally applied to reduce the computational cost. Here, we aimed to analyze and quantify the validity of the approximation over a broad frequency range.Approach.We performed electromagnetic modeling studies using an anatomical head model and considered approximations assuming either a purely ohmic medium (i.e. static formulation) or a lossy dielectric medium (QS formulation). The results were compared with the solution of Maxwell's equations in the cases of harmonic and pulsed signals. Finally, we analyzed the effect of electrode positioning on these errors.Main results.Our findings demonstrate that the QSA is valid and produces a relative error below 1% up to 1.43 MHz. The largest error is introduced in the static case, where the error is over 1% across the entire considered spectrum and as high as 20% in the brain at 10 Hz. We also highlight the special importance of considering the capacitive effect of tissues for pulsed waveforms, which prevents signal distortion induced by the purely ohmic approximation. At the neuron level, the results point a difference of sense electric field as high as 22% at focusing point, impacting pyramidal cells firing times.Significance.QSA remains valid in the frequency range currently used for tACS. However, neglecting permittivity (static formulation) introduces significant error for both harmonic and non-harmonic signals. It points out that reliable low frequency dielectric data are needed for accurate transcranial current stimulation numerical modeling.
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Affiliation(s)
- Gabriel Gaugain
- Univ Rennes, CNRS, IETR (Institut d'électronique et des technologies du numérique) - UMR 6164, 35000 Rennes, France
| | - Lorette Quéguiner
- Univ Rennes, CNRS, IETR (Institut d'électronique et des technologies du numérique) - UMR 6164, 35000 Rennes, France
| | - Marom Bikson
- Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, United States of America
| | - Ronan Sauleau
- Univ Rennes, CNRS, IETR (Institut d'électronique et des technologies du numérique) - UMR 6164, 35000 Rennes, France
| | - Maxim Zhadobov
- Univ Rennes, CNRS, IETR (Institut d'électronique et des technologies du numérique) - UMR 6164, 35000 Rennes, France
| | - Julien Modolo
- Univ Rennes, INSERM, LTSI (Laboratoire traitement du signal et de l'image) - U1099, 35000 Rennes, France
| | - Denys Nikolayev
- Univ Rennes, CNRS, IETR (Institut d'électronique et des technologies du numérique) - UMR 6164, 35000 Rennes, France
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Hikita K, Gomez-Tames J, Hirata A. Mapping Brain Motor Functions Using Transcranial Magnetic Stimulation with a Volume Conductor Model and Electrophysiological Experiments. Brain Sci 2023; 13:brainsci13010116. [PMID: 36672097 PMCID: PMC9856731 DOI: 10.3390/brainsci13010116] [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: 12/03/2022] [Revised: 12/26/2022] [Accepted: 01/05/2023] [Indexed: 01/11/2023] Open
Abstract
Transcranial magnetic stimulation (TMS) activates brain cells in a noninvasive manner and can be used for mapping brain motor functions. However, the complexity of the brain anatomy prevents the determination of the exact location of the stimulated sites, resulting in the limitation of the spatial resolution of multiple targets. The aim of this study is to map two neighboring muscles in cortical motor areas accurately and quickly. Multiple stimuli were applied to the subject using a TMS stimulator to measure the motor-evoked potentials (MEPs) in the corresponding muscles. For each stimulation condition (coil location and angle), the induced electric field (EF) in the brain was computed using a volume conductor model for an individualized head model of the subject constructed from magnetic resonance images. A post-processing method was implemented to determine a TMS hotspot using EF corresponding to multiple stimuli, considering the amplitude of the measured MEPs. The dependence of the computationally estimated hotspot distribution on two target muscles was evaluated (n = 11). The center of gravity of the first dorsal interosseous cortical representation was lateral to the abductor digiti minimi by a minimum of 2 mm. The localizations were consistent with the putative sites obtained from previous EF-based studies and fMRI studies. The simultaneous cortical mapping of two finger muscles was achieved with only several stimuli, which is one or two orders of magnitude smaller than that in previous studies. Our proposal would be useful in the preoperative mapping of motor or speech areas to plan brain surgery interventions.
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Affiliation(s)
- Keigo Hikita
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Aichi, Japan
| | - Jose Gomez-Tames
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Chiba, Japan
| | - Akimasa Hirata
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Aichi, Japan
- Correspondence:
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32
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Seghier ML. Multiple functions of the angular gyrus at high temporal resolution. Brain Struct Funct 2023; 228:7-46. [PMID: 35674917 DOI: 10.1007/s00429-022-02512-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 05/22/2022] [Indexed: 02/07/2023]
Abstract
Here, the functions of the angular gyrus (AG) are evaluated in the light of current evidence from transcranial magnetic/electric stimulation (TMS/TES) and EEG/MEG studies. 65 TMS/TES and 52 EEG/MEG studies were examined in this review. TMS/TES literature points to a causal role in semantic processing, word and number processing, attention and visual search, self-guided movement, memory, and self-processing. EEG/MEG studies reported AG effects at latencies varying between 32 and 800 ms in a wide range of domains, with a high probability to detect an effect at 300-350 ms post-stimulus onset. A three-phase unifying model revolving around the process of sensemaking is then suggested: (1) early AG involvement in defining the current context, within the first 200 ms, with a bias toward the right hemisphere; (2) attention re-orientation and retrieval of relevant information within 200-500 ms; and (3) cross-modal integration at late latencies with a bias toward the left hemisphere. This sensemaking process can favour accuracy (e.g. for word and number processing) or plausibility (e.g. for comprehension and social cognition). Such functions of the AG depend on the status of other connected regions. The much-debated semantic role is also discussed as follows: (1) there is a strong TMS/TES evidence for a causal semantic role, (2) current EEG/MEG evidence is however weak, but (3) the existing arguments against a semantic role for the AG are not strong. Some outstanding questions for future research are proposed. This review recognizes that cracking the role(s) of the AG in cognition is possible only when its exact contributions within the default mode network are teased apart.
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Affiliation(s)
- Mohamed L Seghier
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE. .,Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, UAE.
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Targeting neural correlates of placebo effects. COGNITIVE, AFFECTIVE, & BEHAVIORAL NEUROSCIENCE 2022; 23:217-236. [PMID: 36517733 DOI: 10.3758/s13415-022-01039-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/26/2022] [Indexed: 12/15/2022]
Abstract
Harnessing the placebo effects would prompt critical ramifications for research and clinical practice. Noninvasive brain stimulation (NIBS) techniques, such as transcranial magnetic stimulation and multifocal transcranial electric stimulation, could manipulate the placebo response by modulating the activity and excitability of its neural correlates. To identify potential stimulation targets, we conducted a meta-analysis to investigate placebo-associated regions in healthy volunteers, including studies with emotional components and painful stimuli. Using biophysical modeling, we identified NIBS solutions to manipulate placebo effects by targeting either a single key region or multiple connected areas. Moving to a network-oriented approach, we then ran a quantitative network mapping analysis on the functional connectivity profile of clusters emerging from the meta-analysis. As a result, we suggest a multielectrode optimized montage engaging the connectivity patterns of placebo-associated functional brain networks. These NIBS solutions hope to provide a starting point to actively control, modulate or enhance placebo effects in future clinical studies and cognitive enhancement studies.
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Ye H, Hendee J, Ruan J, Zhirova A, Ye J, Dima M. Neuron matters: neuromodulation with electromagnetic stimulation must consider neurons as dynamic identities. J Neuroeng Rehabil 2022; 19:116. [PMID: 36329492 PMCID: PMC9632094 DOI: 10.1186/s12984-022-01094-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 10/15/2022] [Indexed: 11/06/2022] Open
Abstract
Neuromodulation with electromagnetic stimulation is widely used for the control of abnormal neural activity, and has been proven to be a valuable alternative to pharmacological tools for the treatment of many neurological diseases. Tremendous efforts have been focused on the design of the stimulation apparatus (i.e., electrodes and magnetic coils) that delivers the electric current to the neural tissue, and the optimization of the stimulation parameters. Less attention has been given to the complicated, dynamic properties of the neurons, and their context-dependent impact on the stimulation effects. This review focuses on the neuronal factors that influence the outcomes of electromagnetic stimulation in neuromodulation. Evidence from multiple levels (tissue, cellular, and single ion channel) are reviewed. Properties of the neural elements and their dynamic changes play a significant role in the outcome of electromagnetic stimulation. This angle of understanding yields a comprehensive perspective of neural activity during electrical neuromodulation, and provides insights in the design and development of novel stimulation technology.
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Affiliation(s)
- Hui Ye
- grid.164971.c0000 0001 1089 6558Department of Biology, Quinlan Life Sciences Education and Research Center, Loyola University Chicago, 1032 W. Sheridan Rd., Chicago, IL 60660 USA
| | - Jenna Hendee
- grid.164971.c0000 0001 1089 6558Department of Biology, Quinlan Life Sciences Education and Research Center, Loyola University Chicago, 1032 W. Sheridan Rd., Chicago, IL 60660 USA
| | - Joyce Ruan
- grid.164971.c0000 0001 1089 6558Department of Biology, Quinlan Life Sciences Education and Research Center, Loyola University Chicago, 1032 W. Sheridan Rd., Chicago, IL 60660 USA
| | - Alena Zhirova
- grid.164971.c0000 0001 1089 6558Department of Biology, Quinlan Life Sciences Education and Research Center, Loyola University Chicago, 1032 W. Sheridan Rd., Chicago, IL 60660 USA
| | - Jayden Ye
- grid.164971.c0000 0001 1089 6558Department of Biology, Quinlan Life Sciences Education and Research Center, Loyola University Chicago, 1032 W. Sheridan Rd., Chicago, IL 60660 USA
| | - Maria Dima
- grid.164971.c0000 0001 1089 6558Department of Biology, Quinlan Life Sciences Education and Research Center, Loyola University Chicago, 1032 W. Sheridan Rd., Chicago, IL 60660 USA
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35
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Maran M, Numssen O, Hartwigsen G, Zaccarella E. Online neurostimulation of Broca's area does not interfere with syntactic predictions: A combined TMS-EEG approach to basic linguistic combination. Front Psychol 2022; 13:968836. [PMID: 36619118 PMCID: PMC9815778 DOI: 10.3389/fpsyg.2022.968836] [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: 06/14/2022] [Accepted: 09/13/2022] [Indexed: 01/11/2023] Open
Abstract
Categorical predictions have been proposed as the key mechanism supporting the fast pace of syntactic composition in language. Accordingly, grammar-based expectations are formed-e.g., the determiner "a" triggers the prediction for a noun-and facilitate the analysis of incoming syntactic information, which is then checked against a single or few other word categories. Previous functional neuroimaging studies point towards Broca's area in the left inferior frontal gyrus (IFG) as one fundamental cortical region involved in categorical prediction during incremental language processing. Causal evidence for this hypothesis is however still missing. In this study, we combined Electroencephalography (EEG) and Transcranial Magnetic Stimulation (TMS) to test whether Broca's area is functionally relevant in predictive mechanisms for language. We transiently perturbed Broca's area during the first word in a two-word construction, while simultaneously measuring the Event-Related Potential (ERP) correlates of syntactic composition. We reasoned that if Broca's area is involved in predictive mechanisms for syntax, disruptive TMS during the first word would mitigate the difference in the ERP responses for predicted and unpredicted categories in basic two-word constructions. Contrary to this hypothesis, perturbation of Broca's area at the predictive stage did not affect the ERP correlates of basic composition. The correlation strength between the electrical field induced by TMS and the ERP responses further confirmed this pattern. We discuss the present results considering an alternative account of the role of Broca's area in syntactic composition, namely the bottom-up integration of words into constituents, and of compensatory mechanisms within the language predictive network.
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Affiliation(s)
- Matteo Maran
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany,International Max Planck Research School on Neuroscience of Communication: Function, Structure, and Plasticity, Leipzig, Germany,*Correspondence: Matteo Maran,
| | - Ole Numssen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Gesa Hartwigsen
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Emiliano Zaccarella
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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Siebner HR, Funke K, Aberra AS, Antal A, Bestmann S, Chen R, Classen J, Davare M, Di Lazzaro V, Fox PT, Hallett M, Karabanov AN, Kesselheim J, Beck MM, Koch G, Liebetanz D, Meunier S, Miniussi C, Paulus W, Peterchev AV, Popa T, Ridding MC, Thielscher A, Ziemann U, Rothwell JC, Ugawa Y. Transcranial magnetic stimulation of the brain: What is stimulated? - A consensus and critical position paper. Clin Neurophysiol 2022; 140:59-97. [PMID: 35738037 PMCID: PMC9753778 DOI: 10.1016/j.clinph.2022.04.022] [Citation(s) in RCA: 131] [Impact Index Per Article: 65.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 03/14/2022] [Accepted: 04/15/2022] [Indexed: 12/11/2022]
Abstract
Transcranial (electro)magnetic stimulation (TMS) is currently the method of choice to non-invasively induce neural activity in the human brain. A single transcranial stimulus induces a time-varying electric field in the brain that may evoke action potentials in cortical neurons. The spatial relationship between the locally induced electric field and the stimulated neurons determines axonal depolarization. The induced electric field is influenced by the conductive properties of the tissue compartments and is strongest in the superficial parts of the targeted cortical gyri and underlying white matter. TMS likely targets axons of both excitatory and inhibitory neurons. The propensity of individual axons to fire an action potential in response to TMS depends on their geometry, myelination and spatial relation to the imposed electric field and the physiological state of the neuron. The latter is determined by its transsynaptic dendritic and somatic inputs, intrinsic membrane potential and firing rate. Modeling work suggests that the primary target of TMS is axonal terminals in the crown top and lip regions of cortical gyri. The induced electric field may additionally excite bends of myelinated axons in the juxtacortical white matter below the gyral crown. Neuronal excitation spreads ortho- and antidromically along the stimulated axons and causes secondary excitation of connected neuronal populations within local intracortical microcircuits in the target area. Axonal and transsynaptic spread of excitation also occurs along cortico-cortical and cortico-subcortical connections, impacting on neuronal activity in the targeted network. Both local and remote neural excitation depend critically on the functional state of the stimulated target area and network. TMS also causes substantial direct co-stimulation of the peripheral nervous system. Peripheral co-excitation propagates centrally in auditory and somatosensory networks, but also produces brain responses in other networks subserving multisensory integration, orienting or arousal. The complexity of the response to TMS warrants cautious interpretation of its physiological and behavioural consequences, and a deeper understanding of the mechanistic underpinnings of TMS will be critical for advancing it as a scientific and therapeutic tool.
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Affiliation(s)
- Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg, Copenhagen, Denmark; Institute for Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
| | - Klaus Funke
- Department of Neurophysiology, Medical Faculty, Ruhr-University Bochum, Bochum, Germany
| | - Aman S Aberra
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Andrea Antal
- Department of Clinical Neurophysiology, University Medical Center, Georg-August-University, Göttingen, Germany
| | - 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
| | - Robert Chen
- Krembil Brain Institute, University Health Network and Division of Neurology, University of Toronto, Toronto, Ontario, Canada
| | - Joseph Classen
- Department of Neurology, University of Leipzig, Leipzig, Germany
| | - Marco Davare
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Vincenzo Di Lazzaro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, via Álvaro del Portillo 21, 00128 Rome, Italy
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Anke N Karabanov
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Nutrition and Exercise, University of Copenhagen, Copenhagen, Denmark
| | - Janine Kesselheim
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Mikkel M Beck
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Giacomo Koch
- Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy; Non-invasive Brain Stimulation Unit, Laboratorio di NeurologiaClinica e Comportamentale, Fondazione Santa Lucia IRCCS, Rome, Italy
| | - David Liebetanz
- Department of Clinical Neurophysiology, University Medical Center, Georg-August-University, Göttingen, Germany
| | - Sabine Meunier
- Sorbonne Université, Faculté de Médecine, INSERM U 1127, CNRS 4 UMR 7225, Institut du Cerveau, F-75013, Paris, France
| | - Carlo Miniussi
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Italy; Cognitive Neuroscience Section, IRCCS Centro San Giovanni di DioFatebenefratelli, Brescia, Italy
| | - Walter Paulus
- Department of Clinical Neurophysiology, University Medical Center, Georg-August-University, Göttingen, Germany
| | - Angel V Peterchev
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Psychiatry & Behavioral Sciences, School of Medicine, Duke University, Durham, NC, USA; Department of Electrical & Computer Engineering, Duke University, Durham, NC, USA; Department of Neurosurgery, School of Medicine, Duke University, Durham, NC, USA
| | - Traian Popa
- Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland; Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
| | - Michael C Ridding
- University of South Australia, IIMPACT in Health, Adelaide, Australia
| | - Axel Thielscher
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Ulf Ziemann
- Department of Neurology & Stroke, University Tübingen, Tübingen, Germany; Hertie Institute for Clinical Brain Research, University Tübingen, Tübingen, Germany
| | - John C Rothwell
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yoshikazu Ugawa
- Department of Neurology, Fukushima Medical University, Fukushima, Japan; Fukushima Global Medical Science Centre, Advanced Clinical Research Centre, Fukushima Medical University, Fukushima, Japan
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37
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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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38
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Cellular mechanisms underlying state-dependent neural inhibition with magnetic stimulation. Sci Rep 2022; 12:12131. [PMID: 35840656 PMCID: PMC9287388 DOI: 10.1038/s41598-022-16494-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 07/11/2022] [Indexed: 12/29/2022] Open
Abstract
Novel stimulation protocols for neuromodulation with magnetic fields are explored in clinical and laboratory settings. Recent evidence suggests that the activation state of the nervous system plays a significant role in the outcome of magnetic stimulation, but the underlying cellular and molecular mechanisms of state-dependency have not been completely investigated. We recently reported that high frequency magnetic stimulation could inhibit neural activity when the neuron was in a low active state. In this paper, we investigate state-dependent neural modulation by applying a magnetic field to single neurons, using the novel micro-coil technology. High frequency magnetic stimulation suppressed single neuron activity in a state-dependent manner. It inhibited neurons in slow-firing states, but spared neurons from fast-firing states, when the same magnetic stimuli were applied. Using a multi-compartment NEURON model, we found that dynamics of voltage-dependent sodium and potassium channels were significantly altered by the magnetic stimulation in the slow-firing neurons, but not in the fast-firing neurons. Variability in neural activity should be monitored and explored to optimize the outcome of magnetic stimulation in basic laboratory research and clinical practice. If selective stimulation can be programmed to match the appropriate neural state, prosthetic implants and brain-machine interfaces can be designed based on these concepts to achieve optimal results.
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39
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Passera B, Chauvin A, Raffin E, Bougerol T, David O, Harquel S. Exploring the spatial resolution of TMS-EEG coupling on the sensorimotor region. Neuroimage 2022; 259:119419. [PMID: 35777633 DOI: 10.1016/j.neuroimage.2022.119419] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 04/12/2022] [Accepted: 06/27/2022] [Indexed: 02/06/2023] Open
Abstract
The use of TMS-EEG coupling as a neuroimaging tool for the functional exploration of the human brain recently gained strong interest. If this tool directly inherits the fine temporal resolution from EEG, its spatial counterpart remains unknown. In this study, we explored the spatial resolution of TMS-EEG coupling by evaluating the minimal distance between two stimulated cortical sites that would significantly evoke different response dynamics. TMS evoked responses were mapped on the sensorimotor region in twenty participants. The stimulation grid was composed of nine targets separated between 10 and 15 mm on average. The dynamical signatures of TMS evoked activity were extracted and compared between sites using both local and remote linear regression scores and spatial generalized mixed models. We found a significant effect of the distance between stimulated sites on their dynamical signatures, neighboring sites showing differentiable response dynamics. Besides, common dynamical signatures were also found between sites up to 25-30 mm from each other. This overlap in dynamical properties decreased with distance and was stronger between sites within the same Brodmann area. Our results suggest that the spatial resolution of TMS-EEG coupling might be at least as high as 10 mm. Furthermore, our results reveal an anisotropic spatial resolution that was higher across than within the same Brodmann areas, in accordance with the TMS induced E-field modeling. Common cytoarchitectonic leading to shared dynamical properties within the same Brodmann area could also explain this anisotropy. Overall, these findings suggest that TMS-EEG benefits from the spatial resolution of TMS, which makes it an accurate technique for meso-scale brain mapping.
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Affiliation(s)
- Brice Passera
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France; Univ. Grenoble Alpes, CNRS, UMR5105, Laboratoire Psychologie et NeuroCognition, LPNC, F-38000 Grenoble, France; Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Alan Chauvin
- Univ. Grenoble Alpes, CNRS, UMR5105, Laboratoire Psychologie et NeuroCognition, LPNC, F-38000 Grenoble, France
| | - Estelle Raffin
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
| | - Thierry Bougerol
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France; Centre Hospitalier Univ. Grenoble Alpes, Service de Psychiatrie, F-38000 Grenoble, France
| | - Olivier David
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France; Aix Marseille Univ, Inserm, U1106, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Sylvain Harquel
- Univ. Grenoble Alpes, CNRS, UMR5105, Laboratoire Psychologie et NeuroCognition, LPNC, F-38000 Grenoble, France; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland.
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40
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Klooster DCW, Ferguson MA, Boon PAJM, Baeken C. Personalizing Repetitive Transcranial Magnetic Stimulation Parameters for Depression Treatment Using Multimodal Neuroimaging. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:536-545. [PMID: 34800726 DOI: 10.1016/j.bpsc.2021.11.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/24/2021] [Accepted: 11/09/2021] [Indexed: 06/13/2023]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a tool that can be used to administer treatment for neuropsychiatric disorders such as major depressive disorder, although the clinical efficacy is still rather modest. Overly general stimulation protocols that consider neither patient-specific depression symptomology nor individualized brain characteristics, such as anatomy or structural and functional connections, may be the cause of the high inter- and intraindividual variability in rTMS clinical responses. Multimodal neuroimaging can provide the necessary insights into individual brain characteristics and can therefore be used to personalize rTMS parameters. Optimal coil positioning should include a three-step process: 1) identify the optimal (indirect) target area based on the exact symptom pattern of the patient; 2) derive the cortical (direct) target location based on functional and/or structural connectomes derived from functional and diffusion magnetic resonance imaging data; and 3) determine the ideal coil position by computational modeling, such that the electric field distribution overlaps with the cortical target. These TMS-induced electric field simulations, derived from anatomical and diffusion magnetic resonance imaging data, can be further applied to compute optimal stimulation intensities. In addition to magnetic resonance imaging, electroencephalography can provide complementary information regarding the ongoing brain oscillations. This information can be used to determine the optimal timing and frequency of the stimuli. The heightened benefits of these personalized stimulation approaches are logically reasoned, but speculative. Randomized clinical trials will be required to compare clinical responses from standard rTMS protocols to personalized protocols. Ultimately, an optimized clinical response may result from precision protocols derived from combinations of personalized stimulation parameters.
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Affiliation(s)
- Deborah C W Klooster
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; 4Brain, Department of Head and Skin, Ghent University, Ghent, Belgium; Ghent Experimental Psychiatry Laboratory, Department of Head and Skin, Ghent University, Ghent, Belgium.
| | - Michael A Ferguson
- Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Paul A J M Boon
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; 4Brain, Department of Head and Skin, Ghent University, Ghent, Belgium; Department of Neurology, Ghent University Hospital, Ghent, Belgium
| | - Chris Baeken
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Ghent Experimental Psychiatry Laboratory, Department of Head and Skin, Ghent University, Ghent, Belgium; Department of Psychiatry, University Hospital Brussels, Jette, Belgium
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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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Weise K, Wartman WA, Knösche TR, Nummenmaa AR, Makarov SN. The effect of meninges on the electric fields in TES and TMS. Numerical modeling with adaptive mesh refinement. Brain Stimul 2022; 15:654-663. [PMID: 35447379 DOI: 10.1016/j.brs.2022.04.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/07/2022] [Accepted: 04/08/2022] [Indexed: 11/02/2022] Open
Abstract
BACKGROUND When modeling transcranial electrical stimulation (TES) and transcranial magnetic stimulation (TMS) in the brain, the meninges - dura, arachnoid, and pia mater - are often neglected due to high computational costs. OBJECTIVE We investigate the impact of the meningeal layers on the cortical electric field in TES and TMS while considering the headreco segmentation as the base model. METHOD We use T1/T2 MRI data from 16 subjects and apply the boundary element fast multipole method with adaptive mesh refinement, which enables us to accurately solve this problem and establish method convergence at reasonable computational cost. We compare electric fields in the presence and absence of various meninges for two brain areas (M1HAND and DLPFC) and for several distinct TES and TMS setups. RESULTS Maximum electric fields in the cortex for focal TES consistently increase by approximately 30% on average when the meninges are present in the CSF volume. Their effect on the maximum field can be emulated by reducing the CSF conductivity from 1.65 S/m to approximately 0.85 S/m. In stark contrast to that, the TMS electric fields in the cortex are only weakly affected by the meningeal layers and slightly (∼6%) decrease on average when the meninges are included. CONCLUSION Our results quantify the influence of the meninges on the cortical TES and TMS electric fields. Both focal TES and TMS results are very consistent. The focal TES results are also in a good agreement with a prior relevant study. The solver and the mesh generator for the meningeal layers (compatible with SimNIBS) are available online.
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Affiliation(s)
- Konstantin Weise
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstr. 1a, 04103, Leipzig, Germany; Technische Universität Ilmenau, Advanced Electromagnetics Group, Helmholtzplatz 2, 98693, Ilmenau, Germany.
| | - William A Wartman
- Electrical & Computer Engineering Dept., Worcester Polytechnic Institute, Worcester, MA, 01609, USA.
| | - Thomas R Knösche
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstr. 1a, 04103, Leipzig, Germany; Technische Universität Ilmenau, Institute of Biomedical Engineering and Informatics, Gustav-Kirchhoff Str. 2, 98693, Ilmenau, Germany.
| | - Aapo R Nummenmaa
- Electrical & Computer Engineering Dept., Worcester Polytechnic Institute, Worcester, MA, 01609, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| | - Sergey N Makarov
- Electrical & Computer Engineering Dept., Worcester Polytechnic Institute, Worcester, MA, 01609, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02115, USA.
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Oliver LD, Hawco C, Viviano JD, Voineskos AN. From the Group to the Individual in Schizophrenia Spectrum Disorders: Biomarkers of Social Cognitive Impairments and Therapeutic Translation. Biol Psychiatry 2022; 91:699-708. [PMID: 34799097 DOI: 10.1016/j.biopsych.2021.09.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/11/2021] [Accepted: 09/11/2021] [Indexed: 12/23/2022]
Abstract
People with schizophrenia spectrum disorders (SSDs) often experience persistent social cognitive impairments, associated with poor functional outcome. There are currently no approved treatment options for these debilitating symptoms, highlighting the need for novel therapeutic strategies. Work to date has elucidated differential social processes and underlying neural circuitry affected in SSDs, which may be amenable to modulation using neurostimulation. Further, advances in functional connectivity mapping and electric field modeling may be used to identify individualized treatment targets to maximize the impact of brain stimulation on social cognitive networks. Here, we review literature supporting a roadmap for translating functional connectivity biomarker discovery to individualized treatment development for social cognitive impairments in SSDs. First, we outline the relevance of social cognitive impairments in SSDs. We review machine learning approaches for dimensional brain-behavior biomarker discovery, emphasizing the importance of individual differences. We synthesize research showing that brain stimulation techniques, such as repetitive transcranial magnetic stimulation, can be used to target relevant networks. Further, functional connectivity-based individualized targeting may enhance treatment response. We then outline recent approaches to account for neuroanatomical variability and optimize coil positioning to individually maximize target engagement. Overall, the synthesized literature provides support for the utility and feasibility of this translational approach to precision treatment. The proposed roadmap to translate biomarkers of social cognitive impairments to individualized treatment is currently under evaluation in precision-guided trials. Such a translational approach may also be applicable across conditions and generalizable for the development of individualized neurostimulation targeting other behavioral deficits.
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Affiliation(s)
- Lindsay D Oliver
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Joseph D Viviano
- Mila-Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
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Lu S, Jiang H, Li C, Hong B, Zhang P, Liu W. Genetic Algorithm for TMS Coil Position Optimization in Stroke Treatment. Front Public Health 2022; 9:794167. [PMID: 35360667 PMCID: PMC8962518 DOI: 10.3389/fpubh.2021.794167] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/30/2021] [Indexed: 11/13/2022] Open
Abstract
Transcranial magnetic stimulation (TMS), a non-invasive technique to stimulate human brain, has been widely used in stroke treatment for its capability of regulating synaptic plasticity and promoting cortical functional reconstruction. As shown in previous studies, the high electric field (E-field) intensity around the lesion helps in the recovery of brain function, thus the spatial location and angle of coil truly matter for the significant correlation with therapeutic effect of TMS. But, the error caused by coil placement in current clinical setting is still non-negligible and a more precise coil positioning method needs to be proposed. In this study, two kinds of real brain stroke models of ischemic stroke and hemorrhagic stroke were established by inserting relative lesions into three human head models. A coil position optimization algorithm, based on the genetic algorithm (GA), was developed to search the spatial location and rotation angle of the coil in four 4 × 4 cm search domains around the lesion. It maximized the average intensity of the E-field in the voxel of interest (VOI). In this way, maximum 17.48% higher E-field intensity than that of clinical TMS stimulation was obtained. Besides, our method also shows the potential to avoid unnecessary exposure to the non-target regions. The proposed algorithm was verified to provide an optimal position after nine iterations and displayed good robustness for coil location optimization between different stroke models. To conclude, the optimized spatial location and rotation angle of the coil for TMS stroke treatment could be obtained through our algorithm, reducing the intensity and duration of human electromagnetic exposure and presenting a significant therapeutic potential of TMS for stroke.
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Affiliation(s)
- Shujie Lu
- Center for Medical Metrology, National Institute of Metrology, Beijing, China
| | - Haoyu Jiang
- China Academy of Telecommunications Technology, Beijing, China
| | - Chengwei Li
- Center for Medical Metrology, National Institute of Metrology, Beijing, China
| | - Baoyu Hong
- Center for Medical Metrology, National Institute of Metrology, Beijing, China
| | - Pu Zhang
- Center for Medical Metrology, National Institute of Metrology, Beijing, China
- *Correspondence: Pu Zhang
| | - Wenli Liu
- Center for Medical Metrology, National Institute of Metrology, Beijing, China
- Wenli Liu
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Gomez-Feria J, Fernandez-Corazza M, Martin-Rodriguez JF, Mir P. TMS intensity and focality correlation with coil orientation at three non-motor regions. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac4ef9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 01/26/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Objective. The aim of this study is to define the best coil orientations for transcranial magnetic stimulation (TMS) for three clinically relevant brain areas: pre-supplementary motor area (pre-SMA), inferior frontal gyrus (IFG), and posterior parietal cortex (PPC), by means of simulations in 12 realistic head models of the electric field (E-field). Methods. We computed the E-field generated by TMS in our three volumes of interest (VOI) that were delineated based on published atlases. We then analysed the maximum intensity and spatial focality for the normal and absolute components of the E-field considering different percentile thresholds. Lastly, we correlated these results with the different anatomical properties of our VOIs. Results. Overall, the spatial focality of the E-field for the three VOIs varied depending on the orientation of the coil. Further analysis showed that differences in individual brain anatomy were related to the amount of focality achieved. In general, a larger percentage of sulcus resulted in better spatial focality. Additionally, a higher normal E-field intensity was achieved when the coil axis was placed perpendicular to the predominant orientations of the gyri of each VOI. A positive correlation between spatial focality and E-field intensity was found for PPC and IFG but not for pre-SMA. Conclusions. For a rough approximation, better coil orientations can be based on the individual’s specific brain morphology at the VOI. Moreover, TMS computational models should be employed to obtain better coil orientations in non-motor regions of interest. Significance. Finding better coil orientations in non-motor regions is a challenge in TMS and seeks to reduce interindividual variability. Our individualized TMS simulation pipeline leads to fewer inter-individual variability in the focality, likely enhancing the efficacy of the stimulation and reducing the risk of stimulating adjacent, non-targeted areas.
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Jannati A, Ryan MA, Kaye HL, Tsuboyama M, Rotenberg A. Biomarkers Obtained by Transcranial Magnetic Stimulation in Neurodevelopmental Disorders. J Clin Neurophysiol 2022; 39:135-148. [PMID: 34366399 PMCID: PMC8810902 DOI: 10.1097/wnp.0000000000000784] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
SUMMARY Transcranial magnetic stimulation (TMS) is a method for focal brain stimulation that is based on the principle of electromagnetic induction where small intracranial electric currents are generated by a powerful fluctuating magnetic field. Over the past three decades, TMS has shown promise in the diagnosis, monitoring, and treatment of neurological and psychiatric disorders in adults. However, the use of TMS in children has been more limited. We provide a brief introduction to the TMS technique; common TMS protocols including single-pulse TMS, paired-pulse TMS, paired associative stimulation, and repetitive TMS; and relevant TMS-derived neurophysiological measurements including resting and active motor threshold, cortical silent period, paired-pulse TMS measures of intracortical inhibition and facilitation, and plasticity metrics after repetitive TMS. We then discuss the biomarker applications of TMS in a few representative neurodevelopmental disorders including autism spectrum disorder, fragile X syndrome, attention-deficit hyperactivity disorder, Tourette syndrome, and developmental stuttering.
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Affiliation(s)
- Ali Jannati
- Neuromodulation Program and Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Mary A. Ryan
- Neuromodulation Program and Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Harper Lee Kaye
- Behavioral Neuroscience Program, Division of Medical Sciences, Boston University School of Medicine, Boston, USA
| | - Melissa Tsuboyama
- Neuromodulation Program and Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexander Rotenberg
- Neuromodulation Program and Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
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Key factors in the cortical response to transcranial electrical Stimulations—A multi-scale modeling study. Comput Biol Med 2022; 144:105328. [DOI: 10.1016/j.compbiomed.2022.105328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 01/26/2022] [Accepted: 02/14/2022] [Indexed: 11/24/2022]
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Harita S, Momi D, Mazza F, Griffiths JD. Mapping Inter-individual Functional Connectivity Variability in TMS Targets for Major Depressive Disorder. Front Psychiatry 2022; 13:902089. [PMID: 35815008 PMCID: PMC9260048 DOI: 10.3389/fpsyt.2022.902089] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 05/23/2022] [Indexed: 11/17/2022] Open
Abstract
Transcranial magnetic stimulation (TMS) is an emerging alternative to existing treatments for major depressive disorder (MDD). The effects of TMS on both brain physiology and therapeutic outcomes are known to be highly variable from subject to subject, however. Proposed reasons for this variability include individual differences in neurophysiology, in cortical geometry, and in brain connectivity. Standard approaches to TMS target site definition tend to focus on coordinates or landmarks within the individual brain regions implicated in MDD, such as the dorsolateral prefrontal cortex (dlPFC) and orbitofrontal cortex (OFC). Additionally considering the network connectivity of these sites (i.e., the wider set of brain regions that may be mono- or poly-synaptically activated by TMS stimulation) has the potential to improve subject-specificity of TMS targeting and, in turn, improve treatment outcomes. In this study, we looked at the functional connectivity (FC) of dlPFC and OFC TMS targets, based on induced electrical field (E-field) maps, estimated using the SimNIBS library. We hypothesized that individual differences in spontaneous functional brain dynamics would contribute more to downstream network engagement than individual differences in cortical geometry (i.e., E-field variability). We generated individualized E-field maps on the cortical surface for 121 subjects (67 female) from the Human Connectome Project database using tetrahedral head models generated from T1- and T2-weighted MR images. F3 and Fp1 electrode positions were used to target the left dlPFC and left OFC, respectively. We analyzed inter-subject variability in the shape and location of these TMS target E-field patterns, their FC, and the major functional networks to which they belong. Our results revealed the key differences in TMS target FC between the dlPFC and OFC, and also how this connectivity varies across subjects. Three major functional networks were targeted across the dlPFC and OFC: the ventral attention, fronto-parietal and default-mode networks in the dlPFC, and the fronto-parietal and default mode networks in the OFC. Inter-subject variability in cortical geometry and in FC was high. Our analyses showed that the use of normative neuroimaging reference data (group-average or representative FC and subject E-field) allows prediction of which networks are targeted, but fails to accurately quantify the relative loading of TMS targeting on each of the principal networks. Our results characterize the FC patterns of canonical therapeutic TMS targets, and the key dimensions of their variability across subjects. The high inter-individual variability in cortical geometry and FC, leading to high variability in distributions of targeted brain networks, may account for the high levels of variability in physiological and therapeutic TMS outcomes. These insights should, we hope, prove useful as part of the broader effort by the psychiatry, neurology, and neuroimaging communities to help improve and refine TMS therapy, through a better understanding of the technology and its neurophysiological effects.
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Affiliation(s)
- Shreyas Harita
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada.,Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Davide Momi
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Frank Mazza
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada.,Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - John D Griffiths
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada.,Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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Levi D, Vignati S, Guida E, Oliva A, Cecconi P, Sironi A, Corso A, Broggi G. Tailored repetitive transcranial magnetic stimulation for depression and addictions. PROGRESS IN BRAIN RESEARCH 2022; 270:105-121. [DOI: 10.1016/bs.pbr.2022.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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50
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Multichannel anodal tDCS over the left dorsolateral prefrontal cortex in a paediatric population. Sci Rep 2021; 11:21512. [PMID: 34728684 PMCID: PMC8563927 DOI: 10.1038/s41598-021-00933-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 10/18/2021] [Indexed: 02/06/2023] Open
Abstract
Methodological studies investigating transcranial direct current stimulation (tDCS) over the left dorsolateral prefrontal cortex (lDLPFC) in paediatric populations are limited. Therefore, we investigated in a paediatric population whether stimulation success of multichannel tDCS over the lDLPFC depends on concurrent task performance and individual head anatomy. In a randomised, sham-controlled, double-blind crossover study 22 healthy participants (10–17 years) received 2 mA multichannel anodal tDCS (atDCS) over the lDLPFC with and without a 2-back working memory (WM) task. After stimulation, the 2-back task and a Flanker task were performed. Resting state and task-related EEG were recorded. In 16 participants we calculated the individual electric field (E-field) distribution. Performance and neurophysiological activity in the 2-back task were not affected by atDCS. atDCS reduced reaction times in the Flanker task, independent of whether atDCS had been combined with the 2-back task. Flanker task related beta oscillation increased following stimulation without 2-back task performance. atDCS effects were not correlated with the E-field. We found no effect of multichannel atDCS over the lDLPFC on WM in children/adolescents but a transfer effect on interference control. While this effect on behaviour was independent of concurrent task performance, neurophysiological activity might be more sensitive to cognitive activation during stimulation. However, our results are limited by the small sample size, the lack of an active control group and variations in WM performance.
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