1
|
Zheng B, Chen J, Cao M, Zhang Y, Chen S, Yu H, Liang K. The effect of intermittent theta burst stimulation for cognitive dysfunction: a meta-analysis. Brain Inj 2024; 38:675-686. [PMID: 38651344 DOI: 10.1080/02699052.2024.2344087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 04/12/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Growing evidence suggests that cognitive dysfunction significantly impacts patients' quality of life. Intermittent theta burst stimulation (iTBS) has emerged as a potential intervention for cognitive dysfunction. However, consensus on the iTBS protocol for cognitive impairment is lacking. METHODS We conducted searches in the Cochrane Central Register of Controlled Trials, EMBASE, PubMed, Chinese National Knowledge Infrastructure, Wanfang Database and the Chongqing VIP Chinese Science and Technology Periodical Database from their inception to January 2024. Random-effects meta-analyzes were used to calculate standardized mean differences and 95% confidence intervals. The quality of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach. RESULTS Twelve studies involving 506 participants were included in the meta-analysis. The analysis showed a trend toward improvement of total cognitive function, activities of daily living and P300 latency compared to sham stimulation in patients with cognitive dysfunction. Subgroup analysis demonstrated that these effects were restricted to patients with post-stroke cognitive impairment but not Alzheimer's disease or Parkinson's disease. Furthermore, subthreshold stimulation also exhibited a significant improvement. CONCLUSIONS The results suggest that iTBS may improve cognitive function in patients with cognitive dysfunction, although the quality of evidence remains low. Further studies with better methodological quality should explore the effects of iTBS on cognitive function.
Collapse
Affiliation(s)
- Beisi Zheng
- The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Jianer Chen
- The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
- The Third Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
- Department of Center for Rehabilitation Assessment and Therapy, Zhejiang Rehabilitation Medical Center, Hangzhou, Zhejiang, China
| | - Manting Cao
- The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Yujia Zhang
- The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Shishi Chen
- The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Hong Yu
- Department of Center for Rehabilitation Assessment and Therapy, Zhejiang Rehabilitation Medical Center, Hangzhou, Zhejiang, China
| | - Kang Liang
- The Third Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
- Neurorehabilitation Department, Zhejiang Rehabilitation Medical Center, Hangzhou, Zhejiang, China
| |
Collapse
|
2
|
Murphy DLK, Koponen LM, Wood E, Li Y, Bukhari-Parlakturk N, Goetz SM, Peterchev AV. Reduced Auditory Perception and Brain Response with Quiet TMS Coil. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.24.600400. [PMID: 39005397 PMCID: PMC11244855 DOI: 10.1101/2024.06.24.600400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
BACKGROUND Electromagnetic forces in transcranial magnetic stimulation (TMS) coils generate a loud clicking sound that produces confounding auditory activation and is potentially hazardous to hearing. To reduce this noise while maintaining stimulation efficiency similar to conventional TMS coils, we previously developed a quiet TMS double containment coil (qTMS-DCC). OBJECTIVE To compare the stimulation strength, perceived loudness, and EEG response between qTMS-DCC and a commercial TMS coil. METHODS Nine healthy volunteers participated in a within-subject study design. The resting motor thresholds (RMTs) for qTMS-DCC and MagVenture Cool-B65 were measured. Psychoacoustic titration matched the Cool-B65 loudness to qTMS-DCC pulsed at 80, 100, and 120% RMT. Event-related potentials (ERPs) were recorded for both coils. The psychoacoustic titration and ERPs were acquired with the coils both on and 6 cm off the scalp, the latter isolating the effects of airborne auditory stimulation from body sound and electromagnetic stimulation. The ERP comparisons focused on a centro-frontal region that encompassed peak responses in the global signal. RESULTS RMT did not differ significantly between the coils, with or without the EEG cap on the head. qTMS-DCC was perceived to be substantially quieter than Cool-B65. For example, qTMS-DCC at 100% coil-specific RMT sounded like Cool-B65 at 34% RMT. The general ERP waveform and topography were similar between the two coils, as were early-latency components, indicating comparable electromagnetic brain stimulation in the on-scalp condition. qTMS-DCC had a significantly smaller P180 component in both on-scalp and off-scalp conditions, supporting reduced auditory activation. CONCLUSIONS The stimulation efficiency of qTMS-DCC matched Cool-B65, while having substantially lower perceived loudness and auditory-evoked potentials. Highlights qTMS coil is subjectively and objectively quieter than conventional Cool-B65 coilqTMS coil at 100% motor threshold was as loud as Cool-B65 at 34% motor thresholdAttenuated coil noise reduced auditory N100 and P180 evoked response componentsqTMS coil enables reduction of auditory activation without masking.
Collapse
|
3
|
Xia AWL, Jin M, Qin PPI, Kan RLD, Zhang BBB, Giron CG, Lin TTZ, Li ASM, Kranz GS. Instantaneous effects of prefrontal transcranial magnetic stimulation on brain oxygenation: A systematic review. Neuroimage 2024; 293:120618. [PMID: 38636640 DOI: 10.1016/j.neuroimage.2024.120618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 04/20/2024] Open
Abstract
This systematic review investigates how prefrontal transcranial magnetic stimulation (TMS) immediately influences neuronal excitability based on oxygenation changes measured by functional magnetic resonance imaging (fMRI) or functional near-infrared spectroscopy (fNIRS). A thorough understanding of TMS-induced excitability changes may enable clinicians to adjust TMS parameters and optimize treatment plans proactively. Five databases were searched for human studies evaluating brain excitability using concurrent TMS/fMRI or TMS/fNIRS. Thirty-seven studies (13 concurrent TMS/fNIRS studies, 24 concurrent TMS/fMRI studies) were included in a qualitative synthesis. Despite methodological inconsistencies, a distinct pattern of activated nodes in the frontoparietal central executive network, the cingulo-opercular salience network, and the default-mode network emerged. The activated nodes included the prefrontal cortex (particularly dorsolateral prefrontal cortex), insula cortex, striatal regions (especially caudate, putamen), anterior cingulate cortex, and thalamus. High-frequency repetitive TMS most consistently induced expected facilitatory effects in these brain regions. However, varied stimulation parameters (e.g., intensity, coil orientation, target sites) and the inter- and intra-individual variability of brain state contribute to the observed heterogeneity of target excitability and co-activated regions. Given the considerable methodological and individual variability across the limited evidence, conclusions should be drawn with caution.
Collapse
Affiliation(s)
- Adam W L Xia
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Minxia Jin
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
| | - Penny P I Qin
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Rebecca L D Kan
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Bella B B Zhang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Cristian G Giron
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Tim T Z Lin
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Ami S M Li
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Georg S Kranz
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China; Mental Health Research Center (MHRC), The Hong Kong Polytechnic University, Hong Kong, China; Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
| |
Collapse
|
4
|
Wang JB, Hassan U, Bruss JE, Oya H, Uitermarkt BD, Trapp NT, Gander PE, Howard MA, Keller CJ, Boes AD. Effects of transcranial magnetic stimulation on the human brain recorded with intracranial electrocorticography. Mol Psychiatry 2024; 29:1228-1240. [PMID: 38317012 DOI: 10.1038/s41380-024-02405-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 12/19/2023] [Accepted: 01/02/2024] [Indexed: 02/07/2024]
Abstract
Transcranial magnetic stimulation (TMS) is increasingly used as a noninvasive technique for neuromodulation in research and clinical applications, yet its mechanisms are not well understood. Here, we present the neurophysiological effects of TMS using intracranial electrocorticography (iEEG) in neurosurgical patients. We first evaluated safety in a gel-based phantom. We then performed TMS-iEEG in 22 neurosurgical participants with no adverse events. We next evaluated intracranial responses to single pulses of TMS to the dorsolateral prefrontal cortex (dlPFC) (N = 10, 1414 electrodes). We demonstrate that TMS is capable of inducing evoked potentials both locally within the dlPFC and in downstream regions functionally connected to the dlPFC, including the anterior cingulate and insular cortex. These downstream effects were not observed when stimulating other distant brain regions. Intracranial dlPFC electrical stimulation had similar timing and downstream effects as TMS. These findings support the safety and promise of TMS-iEEG in humans to examine local and network-level effects of TMS with higher spatiotemporal resolution than currently available methods.
Collapse
Affiliation(s)
- Jeffrey B Wang
- Biophysics Graduate Program, Stanford University Medical Center, Stanford, CA, 94305, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, CA, 94305, USA
| | - Umair Hassan
- Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, CA, 94305, USA
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94305, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Joel E Bruss
- Department of Neurology, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 52242, USA
- Department of Pediatrics, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 52242, USA
| | - Hiroyuki Oya
- Department of Neurosurgery, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 52242, USA
| | - Brandt D Uitermarkt
- Department of Pediatrics, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 52242, USA
| | - Nicholas T Trapp
- Department of Psychiatry, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 52242, USA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, 52242, USA
| | - Phillip E Gander
- Department of Neurosurgery, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 52242, USA
- Department of Radiology, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 52242, USA
| | - Matthew A Howard
- Department of Neurosurgery, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 52242, USA
| | - Corey J Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, CA, 94305, USA
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94305, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Aaron D Boes
- Department of Neurology, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 52242, USA.
- Department of Pediatrics, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 52242, USA.
- Department of Psychiatry, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 52242, USA.
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, 52242, USA.
| |
Collapse
|
5
|
Park TY, Franke L, Pieper S, Haehn D, Ning L. A review of algorithms and software for real-time electric field modeling techniques for transcranial magnetic stimulation. Biomed Eng Lett 2024; 14:393-405. [PMID: 38645587 PMCID: PMC11026361 DOI: 10.1007/s13534-024-00373-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 02/27/2024] [Accepted: 03/04/2024] [Indexed: 04/23/2024] Open
Abstract
Transcranial magnetic stimulation (TMS) is a device-based neuromodulation technique increasingly used to treat brain diseases. Electric field (E-field) modeling is an important technique in several TMS clinical applications, including the precision stimulation of brain targets with accurate stimulation density for the treatment of mental disorders and the localization of brain function areas for neurosurgical planning. Classical methods for E-field modeling usually take a long computation time. Fast algorithms are usually developed with significantly lower spatial resolutions that reduce the prediction accuracy and limit their usage in real-time or near real-time TMS applications. This review paper discusses several modern algorithms for real-time or near real-time TMS E-field modeling and their advantages and limitations. The reviewed methods include techniques such as basis representation techniques and deep neural-network-based methods. This paper also provides a review of software tools that can integrate E-field modeling with navigated TMS, including a recent software for real-time navigated E-field mapping based on deep neural-network models.
Collapse
Affiliation(s)
- Tae Young Park
- Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, 02792 Republic of Korea
- Division of Biomedical Science and Technology, KIST School, Korea University of Science and Technology, Seoul, 02792 Republic of Korea
- Brigham and Women’s Hospital, Boston, MA 02115 USA
| | - Loraine Franke
- University of Massachusetts Boston, Boston, MA 02125 USA
| | | | - Daniel Haehn
- University of Massachusetts Boston, Boston, MA 02125 USA
| | - Lipeng Ning
- Brigham and Women’s Hospital, Boston, MA 02115 USA
- Harvard Medical School, Boston, MA 02115 USA
| |
Collapse
|
6
|
Dannhauer M, Gomez LJ, Robins PL, Wang D, Hasan NI, Thielscher A, Siebner HR, Fan Y, Deng ZD. Electric Field Modeling in Personalizing Transcranial Magnetic Stimulation Interventions. Biol Psychiatry 2024; 95:494-501. [PMID: 38061463 PMCID: PMC10922371 DOI: 10.1016/j.biopsych.2023.11.022] [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: 08/14/2023] [Revised: 11/21/2023] [Accepted: 11/25/2023] [Indexed: 01/21/2024]
Abstract
The modeling of transcranial magnetic stimulation (TMS)-induced electric fields (E-fields) is a versatile technique for evaluating and refining brain targeting and dosing strategies, while also providing insights into dose-response relationships in the brain. This review outlines the methodologies employed to derive E-field estimations, covering TMS physics, modeling assumptions, and aspects of subject-specific head tissue and coil modeling. We also summarize various numerical methods for solving the E-field and their suitability for various applications. Modeling methodologies have been optimized to efficiently execute numerous TMS simulations across diverse scalp coil configurations, facilitating the identification of optimal setups or rapid cortical E-field visualization for specific brain targets. These brain targets are extrapolated from neurophysiological measurements and neuroimaging, enabling precise and individualized E-field dosing in experimental and clinical applications. This necessitates the quantification of E-field estimates using metrics that enable the comparison of brain target engagement, functional localization, and TMS intensity adjustments across subjects. The integration of E-field modeling with empirical data has the potential to uncover pivotal insights into the aspects of E-fields responsible for stimulating and modulating brain function and states, enhancing behavioral task performance, and impacting the clinical outcomes of personalized TMS interventions.
Collapse
Affiliation(s)
- Moritz Dannhauer
- Computational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, Maryland
| | - Luis J Gomez
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - Pei L Robins
- Computational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, Maryland
| | - Dezhi Wang
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - Nahian I Hasan
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana
| | - 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, Kongens Lyngby, Denmark
| | - 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
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Zhi-De Deng
- Computational Neurostimulation Research Program, Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, Maryland.
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Gao C, Wu X, Cheng X, Madsen KH, Chu C, Yang Z, Fan L. Individualized brain mapping for navigated neuromodulation. Chin Med J (Engl) 2024; 137:508-523. [PMID: 38269482 PMCID: PMC10932519 DOI: 10.1097/cm9.0000000000002979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Indexed: 01/26/2024] Open
Abstract
ABSTRACT The brain is a complex organ that requires precise mapping to understand its structure and function. Brain atlases provide a powerful tool for studying brain circuits, discovering biological markers for early diagnosis, and developing personalized treatments for neuropsychiatric disorders. Neuromodulation techniques, such as transcranial magnetic stimulation and deep brain stimulation, have revolutionized clinical therapies for neuropsychiatric disorders. However, the lack of fine-scale brain atlases limits the precision and effectiveness of these techniques. Advances in neuroimaging and machine learning techniques have led to the emergence of stereotactic-assisted neurosurgery and navigation systems. Still, the individual variability among patients and the diversity of brain diseases make it necessary to develop personalized solutions. The article provides an overview of recent advances in individualized brain mapping and navigated neuromodulation and discusses the methodological profiles, advantages, disadvantages, and future trends of these techniques. The article concludes by posing open questions about the future development of individualized brain mapping and navigated neuromodulation.
Collapse
Affiliation(s)
- Chaohong Gao
- Sino–Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Xia Wu
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Xinle Cheng
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Kristoffer Hougaard Madsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby 2800, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre 2650, Denmark
| | - Congying Chu
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Zhengyi Yang
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Lingzhong Fan
- Sino–Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao, Shandong 266000, China
| |
Collapse
|
9
|
Baldi S, Schuhmann T, Goossens L, Schruers KRJ. Individualized, connectome-based, non-invasive stimulation of OCD deep-brain targets: A proof-of-concept. Neuroimage 2024; 288:120527. [PMID: 38286272 DOI: 10.1016/j.neuroimage.2024.120527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 12/09/2023] [Accepted: 01/26/2024] [Indexed: 01/31/2024] Open
Abstract
Treatment-resistant obsessive-compulsive disorder (OCD) generally improves with deep-brain stimulation (DBS), thought to modulate neural activity at both the implantation site and in connected brain regions. However, its invasive nature, side-effects, and lack of customization, make non-invasive treatments preferable. Harnessing the established remote effects of cortical transcranial magnetic stimulation (TMS), connectivity-based approaches have emerged for depression that aim at influencing distant regions connected to the stimulation site. We here investigated whether effective OCD DBS targets (here subthalamic nucleus [STN] and nucleus accumbens [NAc]) could be modulated non-invasively with TMS. In a proof-of-concept study with nine healthy individuals, we used 7T magnetic resonance imaging (MRI) and probabilistic tractography to reconstruct the fiber tracts traversing manually segmented STN/NAc. Two TMS targets were individually selected based on the strength of their structural connectivity to either the STN, or both the STN and NAc. In a sham-controlled, within-subject cross-over design, TMS was administered over the personalized targets, located around the precentral and middle frontal gyrus. Resting-state functional 3T MRI was acquired before, and at 5 and 25 min after stimulation to investigate TMS-induced changes in the functional connectivity of the STN and NAc with other regions of the brain. Static and dynamic seed-to-voxel correlation analyses were conducted. TMS over both targets was able to modulate the functional connectivity of the STN and NAc, engaging both overlapping and distinct regions, and unfolding following different temporal dynamics. Given the relevance of the engaged connected regions to OCD pathology, we argue that a personalized, connectivity-based procedure is worth investigating as potential treatment for refractory OCD.
Collapse
Affiliation(s)
- Samantha Baldi
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands.
| | - Teresa Schuhmann
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; Maastricht Brain Imaging Centre, Maastricht, the Netherlands
| | - Liesbet Goossens
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Koen R J Schruers
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| |
Collapse
|
10
|
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.
Collapse
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.
| |
Collapse
|
11
|
Sinisalo H, Rissanen I, Kahilakoski OP, Souza VH, Tommila T, Laine M, Nyrhinen M, Ukharova E, Granö I, Soto AM, Matsuda RH, Rantala R, Guidotti R, Kičić D, Lioumis P, Mutanen T, Pizzella V, Marzetti L, Roine T, Stenroos M, Ziemann U, Romani GL, Ilmoniemi RJ. Modulating brain networks in space and time: Multi-locus transcranial magnetic stimulation. Clin Neurophysiol 2024; 158:218-224. [PMID: 38184469 DOI: 10.1016/j.clinph.2023.12.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/17/2023] [Accepted: 12/15/2023] [Indexed: 01/08/2024]
Affiliation(s)
- Heikki Sinisalo
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
| | - Ilkka Rissanen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | | | - Victor H Souza
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University, and Helsinki University Hospital, Helsinki, Finland; Department of Physics, Faculty of Philosophy Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Timo Tommila
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Mikael Laine
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Mikko Nyrhinen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; AMI Centre, Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland
| | - Elena Ukharova
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Ida Granö
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Ana M Soto
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Renan H Matsuda
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; Department of Physics, Faculty of Philosophy Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Robin Rantala
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Roberto Guidotti
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Dubravko Kičić
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Pantelis Lioumis
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University, and Helsinki University Hospital, Helsinki, Finland
| | - Tuomas Mutanen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies (ITAB), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies (ITAB), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Timo Roine
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Matti Stenroos
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Ulf Ziemann
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - Gian Luca Romani
- Institute for Advanced Biomedical Technologies (ITAB), University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| |
Collapse
|
12
|
Sun W, Wu Q, Gao L, Zheng Z, Xiang H, Yang K, Yu B, Yao J. Advancements in Transcranial Magnetic Stimulation Research and the Path to Precision. Neuropsychiatr Dis Treat 2023; 19:1841-1851. [PMID: 37641588 PMCID: PMC10460597 DOI: 10.2147/ndt.s414782] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 08/02/2023] [Indexed: 08/31/2023] Open
Abstract
Transcranial magnetic stimulation (TMS) has become increasingly popular in clinical practice in recent years, and there have been significant advances in the principles and stimulation modes of TMS. With the development of multi-mode and precise stimulation technology, it is crucial to have a comprehensive understanding of TMS. The neuroregulatory effects of TMS can vary depending on the specific mode of stimulation, highlighting the importance of exploring these effects through multimodal application. Additionally, the use of precise TMS therapy can help enhance our understanding of the neural mechanisms underlying these effects, providing us with a more comprehensive perspective. This article aims to review the mechanism of action, stimulation mode, multimodal application, and precision of TMS.
Collapse
Affiliation(s)
- Wei Sun
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Qiao Wu
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Li Gao
- Department of Neurology, The Third People’s Hospital of Chengdu, Chengdu Institute of Neurological Diseases, Chengdu City, Sichuan Province, People’s Republic of China
| | - Zhong Zheng
- Neurobiological Detection Center, West China Hospital Affiliated to Sichuan University, Chengdu City, Sichuan Province, People’s Republic of China
| | - Hu Xiang
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Kun Yang
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Bo Yu
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Jing Yao
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| |
Collapse
|
13
|
Kong Q, Sacca V, Zhu M, Ursitti AK, Kong J. Anatomical and Functional Connectivity of Critical Deep Brain Structures and Their Potential Clinical Application in Brain Stimulation. J Clin Med 2023; 12:4426. [PMID: 37445460 DOI: 10.3390/jcm12134426] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 06/22/2023] [Accepted: 06/23/2023] [Indexed: 07/15/2023] Open
Abstract
Subcortical structures, such as the hippocampus, amygdala, and nucleus accumbens (NAcc), play crucial roles in human cognitive, memory, and emotional processing, chronic pain pathophysiology, and are implicated in various psychiatric and neurological diseases. Interventions modulating the activities of these deep brain structures hold promise for improving clinical outcomes. Recently, non-invasive brain stimulation (NIBS) has been applied to modulate brain activity and has demonstrated its potential for treating psychiatric and neurological disorders. However, modulating the above deep brain structures using NIBS may be challenging due to the nature of these stimulations. This study attempts to identify brain surface regions as source targets for NIBS to reach these deep brain structures by integrating functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). We used resting-state functional connectivity (rsFC) and probabilistic tractography (PTG) analysis to identify brain surface stimulation targets that are functionally and structurally connected to the hippocampus, amygdala, and NAcc in 119 healthy participants. Our results showed that the medial prefrontal cortex (mPFC) is functionally and anatomically connected to all three subcortical regions, while the precuneus is connected to the hippocampus and amygdala. The mPFC and precuneus, two key hubs of the default mode network (DMN), as well as other cortical areas distributed at the prefrontal cortex and the parietal, temporal, and occipital lobes, were identified as potential locations for NIBS to modulate the function of these deep structures. The findings may provide new insights into the NIBS target selections for treating psychiatric and neurological disorders and chronic pain.
Collapse
Affiliation(s)
- Qiao Kong
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Building 120, 2nd Ave., Charlestown, MA 02129, USA
| | - Valeria Sacca
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Building 120, 2nd Ave., Charlestown, MA 02129, USA
| | - Meixuan Zhu
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Building 120, 2nd Ave., Charlestown, MA 02129, USA
| | - Amy Katherine Ursitti
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Building 120, 2nd Ave., Charlestown, MA 02129, USA
| | - Jian Kong
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Building 120, 2nd Ave., Charlestown, MA 02129, USA
| |
Collapse
|
14
|
Kahnt T. Computationally Informed Interventions for Targeting Compulsive Behaviors. Biol Psychiatry 2023; 93:729-738. [PMID: 36464521 PMCID: PMC9989040 DOI: 10.1016/j.biopsych.2022.08.028] [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: 05/12/2022] [Revised: 08/04/2022] [Accepted: 08/30/2022] [Indexed: 11/02/2022]
Abstract
Compulsive behaviors are central to addiction and obsessive-compulsive disorder and can be understood as a failure of adaptive decision making. Particularly, they can be conceptualized as an imbalance in behavioral control, such that behavior is guided predominantly by learned rather than inferred outcome expectations. Inference is a computational process required for adaptive behavior, and recent work across species has identified the neural circuitry that supports inference-based decision making. This includes the orbitofrontal cortex, which has long been implicated in disorders of compulsive behavior. Inspired by evidence that modulating orbitofrontal cortex activity can alter inference-based behaviors, here we discuss noninvasive approaches to target these circuits in humans. Specifically, we discuss the potential of network-targeted transcranial magnetic stimulation and real-time neurofeedback to modulate the neural underpinnings of inference. Both interventions leverage recent advances in our understanding of the neurocomputational mechanisms of inference-based behavior and may be used to complement current treatment approaches for behavioral disorders.
Collapse
Affiliation(s)
- Thorsten Kahnt
- National Institute on Drug Abuse Intramural Research Program, Baltimore, Maryland.
| |
Collapse
|
15
|
Caparelli EDC, Abulseoud OA, Gu H, Zhai T, Schleyer B, Yang Y. Low frequency repetitive transcranial magnetic stimulation to the right dorsolateral prefrontal cortex engages thalamus, striatum, and the default mode network. Front Neurosci 2022; 16:997259. [PMID: 36248660 PMCID: PMC9565480 DOI: 10.3389/fnins.2022.997259] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/16/2022] [Indexed: 11/25/2022] Open
Abstract
The positive treatment outcomes of low frequency (LF) repetitive transcranial magnetic stimulation (rTMS) when applied over the right dorsolateral prefrontal cortex (DLPFC) in treatment-refractory depression has been verified. However, the mechanism of action behind these results have not been well-explored. In this work we used simultaneous functional magnetic resonance imaging (fMRI) during TMS to explore the effect of LF rTMS on brain activity when applied to the right [RDLPFC1 (MNI: 50, 30, 36)] and left DLPFC sites [LDLPFC1 (MNI: -50, 30, 36), LDLPFC2 (MNI: -41, 16, 54)]. Seventeen healthy adult volunteers participated in this study. To identify brain areas affected by rTMS, an independent component analysis and a general linear model were used. Our results showed an important laterality effect when contrasting rTMS over the left and right sites. Specifically, LF rTMS increased brain activity at the striatum, thalamus, and areas of the default mode network when applied to the right, but not to the contralateral left DLPFC. In contrast, no site differences were observed when evaluating the effect of LF rTMS over the two left sites. These findings demonstrate that LF rTMS to the right DLPFC was able to stimulate the cortico-striato-thalamo-cortical pathway, which is dysregulated in patients with major depressive disorder; therefore, possibly providing some neurobiological justification for the successful outcomes found thus far for LF rTMS in the treatment of depression.
Collapse
Affiliation(s)
- Elisabeth de Castro Caparelli
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
- *Correspondence: Elisabeth de Castro Caparelli,
| | - Osama A. Abulseoud
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
- Department of Psychiatry and Psychology, Mayo Clinic, Phoenix, AZ, United States
| | - Hong Gu
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
| | - Tianye Zhai
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
| | - Brooke Schleyer
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
- Department of Psychology, College of Liberal Arts, Temple University, Philadelphia, PA, United States
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
| |
Collapse
|
16
|
Joutsa J, Corp DT, Fox MD. Lesion network mapping for symptom localization: recent developments and future directions. Curr Opin Neurol 2022; 35:453-459. [PMID: 35788098 PMCID: PMC9724189 DOI: 10.1097/wco.0000000000001085] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Focal lesions causing specific neurological or psychiatric symptoms can occur in multiple different brain locations, complicating symptom localization. Here, we review lesion network mapping, a technique used to aid localization by mapping lesion-induced symptoms to brain circuits rather than individual brain regions. We highlight recent examples of how this technique is being used to investigate clinical entities and identify therapeutic targets. RECENT FINDINGS To date, lesion network mapping has successfully been applied to more than 40 different symptoms or symptom complexes. In each case, lesion locations were combined with an atlas of human brain connections (the human connectome) to map heterogeneous lesion locations causing the same symptom to a common brain circuit. This approach has lent insight into symptoms that have been difficult to localize using other techniques, such as hallucinations, tics, blindsight, and pathological laughter and crying. Further, lesion network mapping has recently been applied to lesions that improve symptoms, such as tremor and addiction, which may translate into new therapeutic targets. SUMMARY Lesion network mapping can be used to map lesion-induced symptoms to brain circuits rather than single brain regions. Recent findings have provided insight into long-standing clinical mysteries and identified testable treatment targets for circuit-based and symptom-based neuromodulation.
Collapse
Affiliation(s)
- Juho Joutsa
- Turku Brain and Mind Center, Clinical Neurosciences, University of Turku
- Turku PET Centre, Neurocenter, Turku University Hospital, Turku, Finland
| | - Daniel T Corp
- Faculty of Health, Deakin University, Geelong, Australia
- Center for Brain Circuit Therapeutics, Department of Neurology, Department of Psychiatry, Department of Neurosurgery, and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Department of Psychiatry, Department of Neurosurgery, and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
17
|
Fang F, Godlewska B, Cho RY, Savitz SI, Selvaraj S, Zhang Y. Personalizing repetitive transcranial magnetic stimulation for precision depression treatment based on functional brain network controllability and optimal control analysis. Neuroimage 2022; 260:119465. [PMID: 35835338 DOI: 10.1016/j.neuroimage.2022.119465] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 06/05/2022] [Accepted: 07/11/2022] [Indexed: 11/16/2022] Open
Abstract
Brain neuromodulation effectively treats neurological diseases and psychiatric disorders such as Depression. However, due to patient heterogeneity, neuromodulation treatment outcomes are often highly variable, requiring patient-specific stimulation protocols throughout the recovery stages to optimize treatment outcomes. Therefore, it is critical to personalize neuromodulation protocol to optimize the patient-specific stimulation targets and parameters by accommodating inherent interpatient variability and intersession alteration during treatments. The study aims to develop a personalized repetitive transcranial magnetic stimulation (rTMS) protocol and evaluate its feasibility in optimizing the treatment efficiency using an existing dataset from an antidepressant experimental imaging study in depression. The personalization of the rTMS treatment protocol was achieved by personalizing both stimulation targets and parameters via a novel approach integrating the functional brain network controllability analysis and optimal control analysis. First, the functional brain network controllability analysis was performed to identify the optimal rTMS stimulation target from the effective connectivity network constructed from patient-specific resting-state functional magnetic resonance imaging data. The optimal control algorithm was then applied to optimize the rTMS stimulation parameters based on the optimized target. The performance of the proposed personalized rTMS technique was evaluated using datasets collected from a longitudinal antidepressant experimental imaging study in depression (n = 20). Simulation models demonstrated that the proposed personalized rTMS protocol outperformed the standard rTMS treatment by efficiently steering a depressive resting brain state to a healthy resting brain state, indicated by the significantly less control energy needed and higher model fitting accuracy achieved. The node with the maximum average controllability of each patient was designated as the optimal target region for the personalized rTMS protocol. Our results also demonstrated the theoretical feasibility of achieving comparable neuromodulation efficacy by stimulating a single node compared to stimulating multiple driver nodes. The findings support the feasibility of developing personalized neuromodulation protocols to more efficiently treat depression and other neurological diseases.
Collapse
Affiliation(s)
- Feng Fang
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
| | - Beata Godlewska
- Department of Psychiatry, Medical Sciences Division, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Raymond Y Cho
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, and Menninger Clinic, Houston, TX, United States
| | - Sean I Savitz
- Department of Neurology, The McGovern Medical School of UT Health Houston, Houston, TX, United States
| | - Sudhakar Selvaraj
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, The McGovern Medical School of UT Health Houston, Houston, TX, United States
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA.
| |
Collapse
|