1
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Balderston NL, Duprat RJ, Long H, Scully M, Deluisi JA, Figueroa-Gonzalez A, Teferi M, Sheline YI, Oathes DJ. Neuromodulatory transcranial magnetic stimulation (TMS) changes functional connectivity proportional to the electric-field induced by the TMS pulse. Clin Neurophysiol 2024; 165:16-25. [PMID: 38945031 DOI: 10.1016/j.clinph.2024.06.007] [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: 03/27/2023] [Revised: 04/15/2024] [Accepted: 06/09/2024] [Indexed: 07/02/2024]
Abstract
OBJECTIVE Transcranial magnetic stimulation (TMS) can efficiently and robustly modulate synaptic plasticity, but little is known about how TMS affects functional connectivity (rs-fMRI). Accordingly, this project characterized TMS-induced rsFC changes in depressed patients who received 3 days of left prefrontal intermittent theta burst stimulation (iTBS). METHODS rs-fMRI was collected from 16 subjects before and after iTBS. Correlation matrices were constructed from the cleaned rs-fMRI data. Electric-field models were conducted and used to predict pre-post changes in rs-fMRI. Site by orientation heatmaps were created for vectors centered on the stimulation site and a control site (contralateral motor cortex). RESULTS For the stimulation site, there was a clear relationship between both site and coil orientation, and connectivity changes. As distance from the stimulation site increased, prediction accuracy decreased. Similarly, as eccentricity from the optimal orientation increased, prediction accuracy decreased. The systematic effects described above were not apparent in the heatmap centered on the control site. CONCLUSIONS These results suggest that rs-fMRI following iTBS changes systematically as a function of the distribution of electrical energy delivered from the TMS pulse, as represented by the e-field model. SIGNIFICANCE This finding lays the groundwork for future studies to individualize TMS targeting based on how predicted rs-fMRI changes might impact psychiatric symptoms.
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Affiliation(s)
- Nicholas L Balderston
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA.
| | - Romain J Duprat
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA
| | - Hannah Long
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA
| | - Morgan Scully
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA
| | - Joseph A Deluisi
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA
| | - Almaris Figueroa-Gonzalez
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA
| | - Marta Teferi
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry University of Pennsylvania, Philadelphia, PA, USA
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2
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Cash RFH, Zalesky A. Personalized and Circuit-Based Transcranial Magnetic Stimulation: Evidence, Controversies, and Opportunities. Biol Psychiatry 2024; 95:510-522. [PMID: 38040047 DOI: 10.1016/j.biopsych.2023.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/13/2023] [Accepted: 11/18/2023] [Indexed: 12/03/2023]
Abstract
The development of neuroimaging methodologies to map brain connectivity has transformed our understanding of psychiatric disorders, the distributed effects of brain stimulation, and how transcranial magnetic stimulation can be best employed to target and ameliorate psychiatric symptoms. In parallel, neuroimaging research has revealed that higher-order brain regions such as the prefrontal cortex, which represent the most common therapeutic brain stimulation targets for psychiatric disorders, show some of the highest levels of interindividual variation in brain connectivity. These findings provide the rationale for personalized target site selection based on person-specific brain network architecture. Recent advances have made it possible to determine reproducible personalized targets with millimeter precision in clinically tractable acquisition times. These advances enable the potential advantages of spatially personalized transcranial magnetic stimulation targeting to be evaluated and translated to basic and clinical applications. In this review, we outline the motivation for target site personalization, preliminary support (mostly in depression), convergent evidence from other brain stimulation modalities, and generalizability beyond depression and the prefrontal cortex. We end by detailing methodological recommendations, controversies, and notable alternatives. Overall, while this research area appears highly promising, the value of personalized targeting remains unclear, and dedicated large prospective randomized clinical trials using validated methodology are critical.
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Affiliation(s)
- Robin F H Cash
- Melbourne Neuropsychiatry Centre and Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria, Australia.
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria, Australia
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3
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Roalf DR, Figee M, Oathes DJ. Elevating the field for applying neuroimaging to individual patients in psychiatry. Transl Psychiatry 2024; 14:87. [PMID: 38341414 PMCID: PMC10858949 DOI: 10.1038/s41398-024-02781-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 12/06/2023] [Accepted: 01/15/2024] [Indexed: 02/12/2024] Open
Abstract
Although neuroimaging has been widely applied in psychiatry, much of the exuberance in decades past has been tempered by failed replications and a lack of definitive evidence to support the utility of imaging to inform clinical decisions. There are multiple promising ways forward to demonstrate the relevance of neuroimaging for psychiatry at the individual patient level. Ultra-high field magnetic resonance imaging is developing as a sensitive measure of neurometabolic processes of particular relevance that holds promise as a new way to characterize patient abnormalities as well as variability in response to treatment. Neuroimaging may also be particularly suited to the science of brain stimulation interventions in psychiatry given that imaging can both inform brain targeting as well as measure changes in brain circuit communication as a function of how effectively interventions improve symptoms. We argue that a greater focus on individual patient imaging data will pave the way to stronger relevance to clinical care in psychiatry. We also stress the importance of using imaging in symptom-relevant experimental manipulations and how relevance will be best demonstrated by pairing imaging with differential treatment prediction and outcome measurement. The priorities for using brain imaging to inform psychiatry may be shifting, which compels the field to solidify clinical relevance for individual patients over exploratory associations and biomarkers that ultimately fail to replicate.
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Affiliation(s)
- David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Martijn Figee
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Desmond J Oathes
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Center for Brain Imaging and Stimulation, University of Pennsylvania, Philadelphia, PA, USA.
- Center for Neuromodulation in Depression and Stress, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Brain Science Translation, Innovation, and Modulation Center, University of Pennsylvania, Philadelphia, PA, USA.
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4
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van Rooij SJH, Arulpragasam AR, McDonald WM, Philip NS. Accelerated TMS - moving quickly into the future of depression treatment. Neuropsychopharmacology 2024; 49:128-137. [PMID: 37217771 PMCID: PMC10700378 DOI: 10.1038/s41386-023-01599-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/20/2023] [Accepted: 04/22/2023] [Indexed: 05/24/2023]
Abstract
Accelerated TMS is an emerging application of Transcranial Magnetic Stimulation (TMS) aimed to reduce treatment length and improve response time. Extant literature generally shows similar efficacy and safety profiles compared to the FDA-cleared protocols for TMS to treat major depressive disorder (MDD), yet accelerated TMS research remains at a very early stage in development. The few applied protocols have not been standardized and vary significantly across a set of core elements. In this review, we consider nine elements that include treatment parameters (i.e., frequency and inter-stimulation interval), cumulative exposure (i.e., number of treatment days, sessions per day, and pulses per session), individualized parameters (i.e., treatment target and dose), and brain state (i.e., context and concurrent treatments). Precisely which of these elements is critical and what parameters are most optimal for the treatment of MDD remains unclear. Other important considerations for accelerated TMS include durability of effect, safety profiles as doses increase over time, the possibility and advantage of individualized functional neuronavigation, use of biological readouts, and accessibility for patients most in need of the treatment. Overall, accelerated TMS appears to hold promise to reduce treatment time and achieve rapid reduction in depressive symptoms, but at this time significant work remains to be done. Rigorous clinical trials combining clinical outcomes and neuroscientific measures such as electroencephalogram, magnetic resonance imaging and e-field modeling are needed to define the future of accelerated TMS for MDD.
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Affiliation(s)
- Sanne J H van Rooij
- Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences, Atlanta, GA, USA
| | - Amanda R Arulpragasam
- Alpert Medical School of Brown University, Department of Psychiatry and Human Behavior, Providence, RI, USA
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence Healthcare System, Providence, RI, USA
| | - William M McDonald
- Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences, Atlanta, GA, USA
| | - Noah S Philip
- Alpert Medical School of Brown University, Department of Psychiatry and Human Behavior, Providence, RI, USA.
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence Healthcare System, Providence, RI, USA.
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5
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Daneshzand M, Lankinen K, Ahveninen J, Wang QM, Green JR, Kimberley TJ, Li S. Personalized 7T fMRI-Guided navigation TMS targeting: Preliminary data of speech-motor cortex in speech perception. Brain Stimul 2023; 16:1455-1457. [PMID: 37774914 PMCID: PMC10810261 DOI: 10.1016/j.brs.2023.09.020] [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: 03/16/2023] [Revised: 09/12/2023] [Accepted: 09/18/2023] [Indexed: 10/01/2023] Open
Affiliation(s)
- Mohammad Daneshzand
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Qing Mei Wang
- Stroke Biological Recovery Laboratory, Spaulding Rehabilitation Hospital, The Teaching Affiliate of Harvard Medical School, Charlestown, MA, USA
| | - Jordan R Green
- Department of Communication Sciences and Disorders, MGH Institute of Health Professions Boston, MA, USA
| | - Teresa J Kimberley
- Department of Physical Therapy, School of Health and Rehabilitation Sciences, MGH Institute of Health Professions, Boston, MA, USA
| | - Shasha Li
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
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6
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Chai Y, Sheline YI, Oathes DJ, Balderston NL, Rao H, Yu M. Functional connectomics in depression: insights into therapies. Trends Cogn Sci 2023; 27:814-832. [PMID: 37286432 PMCID: PMC10476530 DOI: 10.1016/j.tics.2023.05.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 06/09/2023]
Abstract
Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.
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Affiliation(s)
- Ya Chai
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Brain Science, Translation, Innovation and Modulation Center (brainSTIM), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Nicholas L Balderston
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hengyi Rao
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA.
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7
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Boscutti A, Murphy N, Cho R, Selvaraj S. Noninvasive Brain Stimulation Techniques for Treatment-Resistant Depression: Transcranial Magnetic Stimulation and Transcranial Direct Current Stimulation. Psychiatr Clin North Am 2023; 46:307-329. [PMID: 37149347 DOI: 10.1016/j.psc.2023.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Transcranial magnetic stimulation is a safe, effective, and well-tolerated intervention for depression; it is currently approved for treatment-resistant depression. This article summarizes the mechanism of action, evidence of clinical efficacy, and the clinical aspects of this intervention, including patient evaluation, stimulation parameters selection, and safety considerations. Transcranial direct current stimulation is another neuromodulation treatment for depression; although promising, the technique is not currently approved for clinical use in the United States. The final section outlines the open challenges and future directions of the field.
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Affiliation(s)
- Andrea Boscutti
- Louis. A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Nicholas Murphy
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA; The Menninger Clinic, Houston, TX, USA
| | - Raymond Cho
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA; The Menninger Clinic, Houston, TX, USA
| | - Sudhakar Selvaraj
- Louis. A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA.
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8
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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: 0] [Impact Index Per Article: 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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Cao Z, Xiao X, Zhao Y, Jiang Y, Xie C, Paillère-Martinot ML, Artiges E, Li Z, Daskalakis ZJ, Yang Y, Zhu C. Targeting the pathological network: Feasibility of network-based optimization of transcranial magnetic stimulation coil placement for treatment of psychiatric disorders. Front Neurosci 2023; 16:1079078. [PMID: 36685239 PMCID: PMC9846047 DOI: 10.3389/fnins.2022.1079078] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 12/05/2022] [Indexed: 01/06/2023] Open
Abstract
It has been recognized that the efficacy of TMS-based modulation may depend on the network profile of the stimulated regions throughout the brain. However, what profile of this stimulation network optimally benefits treatment outcomes is yet to be addressed. The answer to the question is crucial for informing network-based optimization of stimulation parameters, such as coil placement, in TMS treatments. In this study, we aimed to investigate the feasibility of taking a disease-specific network as the target of stimulation network for guiding individualized coil placement in TMS treatments. We present here a novel network-based model for TMS targeting of the pathological network. First, combining E-field modeling and resting-state functional connectivity, stimulation networks were modeled from locations and orientations of the TMS coil. Second, the spatial anti-correlation between the stimulation network and the pathological network of a given disease was hypothesized to predict the treatment outcome. The proposed model was validated to predict treatment efficacy from the position and orientation of TMS coils in two depression cohorts and one schizophrenia cohort with auditory verbal hallucinations. We further demonstrate the utility of the proposed model in guiding individualized TMS treatment for psychiatric disorders. In this proof-of-concept study, we demonstrated the feasibility of the novel network-based targeting strategy that uses the whole-brain, system-level abnormity of a specific psychiatric disease as a target. Results based on empirical data suggest that the strategy may potentially be utilized to identify individualized coil parameters for maximal therapeutic effects.
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Affiliation(s)
- Zhengcao Cao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xiang Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China,Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
| | - Yang Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yihan Jiang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Cong Xie
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Marie-Laure Paillère-Martinot
- Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, APHP.Sorbonne Université, Paris, France,INSERM U A10 Developmental Trajectories and Psychiatry, Ecole Normale Supérieure Paris-Saclay, CNRS, Center Borelli, University of Paris-Saclay, Gif-sur-Yvette, France
| | - Eric Artiges
- INSERM U A10 Developmental Trajectories and Psychiatry, Ecole Normale Supérieure Paris-Saclay, CNRS, Center Borelli, University of Paris-Saclay, Gif-sur-Yvette, France,Department of Psychiatry, Etablissement Public de Santé (EPS) Barthélemy Durand, tampes, France
| | - Zheng Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Center for Cognition and Neuroergonomics, Beijing Normal University at Zhuhai, Zhuhai, China,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zafiris J. Daskalakis
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States,*Correspondence: Yihong Yang,
| | - Chaozhe Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China,Chaozhe Zhu,
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10
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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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