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Mizutani-Tiebel Y, Tik M, Chang KY, Padberg F, Soldini A, Wilkinson Z, Voon CC, Bulubas L, Windischberger C, Keeser D. Concurrent TMS-fMRI: Technical Challenges, Developments, and Overview of Previous Studies. Front Psychiatry 2022; 13:825205. [PMID: 35530029 PMCID: PMC9069063 DOI: 10.3389/fpsyt.2022.825205] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/09/2022] [Indexed: 11/13/2022] Open
Abstract
Transcranial magnetic stimulation (TMS) is a promising treatment modality for psychiatric and neurological disorders. Repetitive TMS (rTMS) is widely used for the treatment of psychiatric and neurological diseases, such as depression, motor stroke, and neuropathic pain. However, the underlying mechanisms of rTMS-mediated neuronal modulation are not fully understood. In this respect, concurrent or simultaneous TMS-fMRI, in which TMS is applied during functional magnetic resonance imaging (fMRI), is a viable tool to gain insights, as it enables an investigation of the immediate effects of TMS. Concurrent application of TMS during neuroimaging usually causes severe artifacts due to magnetic field inhomogeneities induced by TMS. However, by carefully interleaving the TMS pulses with MR signal acquisition in the way that these are far enough apart, we can avoid any image distortions. While the very first feasibility studies date back to the 1990s, recent developments in coil hardware and acquisition techniques have boosted the number of TMS-fMRI applications. As such, a concurrent application requires expertise in both TMS and MRI mechanisms and sequencing, and the hurdle of initial technical set up and maintenance remains high. This review gives a comprehensive overview of concurrent TMS-fMRI techniques by collecting (1) basic information, (2) technical challenges and developments, (3) an overview of findings reported so far using concurrent TMS-fMRI, and (4) current limitations and our suggestions for improvement. By sharing this review, we hope to attract the interest of researchers from various backgrounds and create an educational knowledge base.
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Affiliation(s)
- Yuki Mizutani-Tiebel
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany.,Neuroimaging Core Unit Munich - NICUM, University Hospital LMU, Munich, Germany
| | - Martin Tik
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Kai-Yen Chang
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany.,Neuroimaging Core Unit Munich - NICUM, University Hospital LMU, Munich, Germany
| | - Frank Padberg
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany.,Neuroimaging Core Unit Munich - NICUM, University Hospital LMU, Munich, Germany
| | - Aldo Soldini
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany.,Neuroimaging Core Unit Munich - NICUM, University Hospital LMU, Munich, Germany.,International Max Planck Research School for Translational Psychiatry, Munich, Germany
| | - Zane Wilkinson
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany.,Neuroimaging Core Unit Munich - NICUM, University Hospital LMU, Munich, Germany
| | - Cui Ci Voon
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany.,Neuroimaging Core Unit Munich - NICUM, University Hospital LMU, Munich, Germany
| | - Lucia Bulubas
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany.,Neuroimaging Core Unit Munich - NICUM, University Hospital LMU, Munich, Germany.,International Max Planck Research School for Translational Psychiatry, Munich, Germany
| | - Christian Windischberger
- High Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Daniel Keeser
- Department of Psychiatry and Psychotherapy, University Hospital LMU, Munich, Germany.,Neuroimaging Core Unit Munich - NICUM, University Hospital LMU, Munich, Germany.,Department of Radiology, University Hospital LMU, Munich, Germany
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Rafiei F, Safrin M, Wokke ME, Lau H, Rahnev D. Transcranial magnetic stimulation alters multivoxel patterns in the absence of overall activity changes. Hum Brain Mapp 2021; 42:3804-3820. [PMID: 33991165 PMCID: PMC8288086 DOI: 10.1002/hbm.25466] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 04/07/2021] [Accepted: 04/26/2021] [Indexed: 01/18/2023] Open
Abstract
Transcranial magnetic stimulation (TMS) has become one of the major tools for establishing the causal role of specific brain regions in perceptual, motor, and cognitive processes. Nevertheless, a persistent limitation of the technique is the lack of clarity regarding its precise effects on neural activity. Here, we examined the effects of TMS intensity and frequency on concurrently recorded blood‐oxygen‐level‐dependent (BOLD) signals at the site of stimulation. In two experiments, we delivered TMS to the dorsolateral prefrontal cortex in human subjects of both sexes. In Experiment 1, we delivered a series of pulses at high (100% of motor threshold) or low (50% of motor threshold) intensity, whereas, in Experiment 2, we always used high intensity but delivered stimulation at four different frequencies (5, 8.33, 12.5, and 25 Hz). We found that the TMS intensity and frequency could be reliably decoded using multivariate analysis techniques even though TMS had no effect on the overall BOLD activity at the site of stimulation in either experiment. These results provide important insight into the mechanisms through which TMS influences neural activity.
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Affiliation(s)
- Farshad Rafiei
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Martin Safrin
- School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Martijn E Wokke
- Programs in Psychology and Biology, The Graduate Center of the City University of New York, New York, New York, USA
| | - Hakwan Lau
- Department of Psychology, University of California Los Angeles, Los Angeles, California, USA.,The Brain Research Institute, University of California, Los Angeles, Los Angeles, California, USA
| | - Dobromir Rahnev
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, USA
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Bergmann TO, Varatheeswaran R, Hanlon CA, Madsen KH, Thielscher A, Siebner HR. Concurrent TMS-fMRI for causal network perturbation and proof of target engagement. Neuroimage 2021; 237:118093. [PMID: 33940146 DOI: 10.1016/j.neuroimage.2021.118093] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 04/06/2021] [Accepted: 04/14/2021] [Indexed: 12/12/2022] Open
Abstract
The experimental manipulation of neural activity by neurostimulation techniques overcomes the inherent limitations of correlative recordings, enabling the researcher to investigate causal brain-behavior relationships. But only when stimulation and recordings are combined, the direct impact of the stimulation on neural activity can be evaluated. In humans, this can be achieved non-invasively through the concurrent combination of transcranial magnetic stimulation (TMS) with functional magnetic resonance imaging (fMRI). Concurrent TMS-fMRI allows the assessment of the neurovascular responses evoked by TMS with excellent spatial resolution and full-brain coverage. This enables the functional mapping of both local and remote network effects of TMS in cortical as well as deep subcortical structures, offering unique opportunities for basic research and clinical applications. The purpose of this review is to introduce the reader to this powerful tool. We will introduce the technical challenges and state-of-the art solutions and provide a comprehensive overview of the existing literature and the available experimental approaches. We will highlight the unique insights that can be gained from concurrent TMS-fMRI, including the state-dependent assessment of neural responsiveness and inter-regional effective connectivity, the demonstration of functional target engagement, and the systematic evaluation of stimulation parameters. We will also discuss how concurrent TMS-fMRI during a behavioral task can help to link behavioral TMS effects to changes in neural network activity and to identify peripheral co-stimulation confounds. Finally, we will review the use of concurrent TMS-fMRI for developing TMS treatments of psychiatric and neurological disorders and suggest future improvements for further advancing the application of concurrent TMS-fMRI.
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Affiliation(s)
- Til Ole Bergmann
- Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Langenbeckstr. 1, 55131, Mainz, Germany; Leibniz Institute for Resilience Research, Wallstraße 7-9, 55122, Mainz, Germany.
| | - Rathiga Varatheeswaran
- Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Langenbeckstr. 1, 55131, Mainz, Germany; Leibniz Institute for Resilience Research, Wallstraße 7-9, 55122, Mainz, Germany
| | - Colleen A Hanlon
- Department of Cancer Biology, Wake Forest School of Medicine, 1 Medical Center Blvd., Winston-Salem, NC 27157, USA
| | - Kristoffer H Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Kettegård Allé 30, 2650, Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Axel Thielscher
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Kettegård Allé 30, 2650, Hvidovre, Denmark; Department of Electrical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Hartwig Roman Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Kettegård Allé 30, 2650, Hvidovre, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg, Bispebjerg Bakke 23, 2400 København NV, Denmark; Department of Clinical Medicine, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
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Esmaeilpour Z, Shereen AD, Ghobadi‐Azbari P, Datta A, Woods AJ, Ironside M, O'Shea J, Kirk U, Bikson M, Ekhtiari H. Methodology for tDCS integration with fMRI. Hum Brain Mapp 2020; 41:1950-1967. [PMID: 31872943 PMCID: PMC7267907 DOI: 10.1002/hbm.24908] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 11/09/2019] [Accepted: 12/10/2019] [Indexed: 12/28/2022] Open
Abstract
Understanding and reducing variability of response to transcranial direct current stimulation (tDCS) requires measuring what factors predetermine sensitivity to tDCS and tracking individual response to tDCS. Human trials, animal models, and computational models suggest structural traits and functional states of neural systems are the major sources of this variance. There are 118 published tDCS studies (up to October 1, 2018) that used fMRI as a proxy measure of neural activation to answer mechanistic, predictive, and localization questions about how brain activity is modulated by tDCS. FMRI can potentially contribute as: a measure of cognitive state-level variance in baseline brain activation before tDCS; inform the design of stimulation montages that aim to target functional networks during specific tasks; and act as an outcome measure of functional response to tDCS. In this systematic review, we explore methodological parameter space of tDCS integration with fMRI spanning: (a) fMRI timing relative to tDCS (pre, post, concurrent); (b) study design (parallel, crossover); (c) control condition (sham, active control); (d) number of tDCS sessions; (e) number of follow up scans; (f) stimulation dose and combination with task; (g) functional imaging sequence (BOLD, ASL, resting); and (h) additional behavioral (cognitive, clinical) or quantitative (neurophysiological, biomarker) measurements. Existing tDCS-fMRI literature shows little replication across these permutations; few studies used comparable study designs. Here, we use a representative sample study with both task and resting state fMRI before and after tDCS in a crossover design to discuss methodological confounds. We further outline how computational models of current flow should be combined with imaging data to understand sources of variability. Through the representative sample study, we demonstrate how modeling and imaging methodology can be integrated for individualized analysis. Finally, we discuss the importance of conducting tDCS-fMRI with stimulation equipment certified as safe to use inside the MR scanner, and of correcting for image artifacts caused by tDCS. tDCS-fMRI can address important questions on the functional mechanisms of tDCS action (e.g., target engagement) and has the potential to support enhancement of behavioral interventions, provided studies are designed rationally.
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Affiliation(s)
- Zeinab Esmaeilpour
- Neural Engineering Laboratory, Department of Biomedical EngineeringThe City College of the City University of New York, City College Center for Discovery and InnovationNew YorkNew York
| | - A. Duke Shereen
- Advanced Science Research Center, The Graduate CenterCity University of New YorkNew YorkNew York
| | | | | | - Adam J. Woods
- Center for Cognitive Aging and Memory, McKnight Brain Institute, Department of Clinical and Health PsychologyUniversity of FloridaGainesvilleFlorida
| | - Maria Ironside
- Center for Depression, Anxiety and Stress Research, McLean HospitalBelmontMassachusetts
- Department of PsychiatryHarvard Medical SchoolBostonMassachusetts
| | - Jacinta O'Shea
- Nuffield Department of Clinical Neuroscience, Medical Science DivisionUniversity of OxfordOxfordEnglandUK
| | - Ulrich Kirk
- Department of PsychologyUniversity of Southern DenmarkOdenseDenmark
| | - Marom Bikson
- Neural Engineering Laboratory, Department of Biomedical EngineeringThe City College of the City University of New York, City College Center for Discovery and InnovationNew YorkNew York
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EPI distortion correction for concurrent human brain stimulation and imaging at 3T. J Neurosci Methods 2019; 327:108400. [PMID: 31434000 DOI: 10.1016/j.jneumeth.2019.108400] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 08/15/2019] [Accepted: 08/17/2019] [Indexed: 01/21/2023]
Abstract
BACKGROUND Transcranial magnetic stimulation (TMS) can be paired with functional magnetic resonance imaging (fMRI) in concurrent TMS-fMRI experiments. These multimodal experiments enable causal probing of network architecture in the human brain which can complement alternative network mapping approaches. Critically, merely introducing the TMS coil into the scanner environment can sometimes produce substantial magnetic field inhomogeneities and spatial distortions which limit the utility of concurrent TMS-fMRI. METHOD AND RESULTS We assessed the efficacy of point spread function corrected echo planar imaging (PSF-EPI) in correcting for the field inhomogeneities associated with a TMS coil at 3 T. In phantom and brain scans, we quantitatively compared the coil-induced distortion artifacts measured in EPI scans with and without PSF correction. We found that the application of PSF corrections to the EPI data significantly improved signal-to-noise and reduced distortions. In phantom scans with the PSF-EPI sequence, we also characterized the temporal profile of dynamic artifacts associated with TMS delivery and found that image quality remained high as long as the TMS pulse preceded the RF excitation pulses by at least 50 ms. Lastly, we validated the PSF-EPI sequence in human brain scans involving TMS and motor behavior as well as resting state fMRI scans. CONCLUSIONS Our collective results demonstrate the potential benefits of PSF-EPI for concurrent TMS-fMRI when coil-related artifacts are a concern. The ability to collect high quality resting state fMRI data in the same session as the concurrent TMS-fMRI experiment offers a unique opportunity to interrogate network architecture in the human brain.
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