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Kang JS, Bunker LD, Stockbridge MD, Hillis AE. White Matter Hyperintensities as a Predictor of Aphasia Recovery. Arch Phys Med Rehabil 2024; 105:1089-1098. [PMID: 38281579 DOI: 10.1016/j.apmr.2024.01.008] [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: 02/10/2023] [Revised: 12/18/2023] [Accepted: 01/11/2024] [Indexed: 01/30/2024]
Abstract
OBJECTIVE To examine the relation between baseline white matter hyperintensities (WMH) and change in naming, content production, and efficiency after treatment in subacute aphasia. We hypothesized that more severe baseline WMH would result in less improvement with treatment. DESIGN Retrospective analysis of a cohort from a double-blind randomized controlled trial (RCT). SETTING Outpatient clinical setting or participant home. PARTICIPANTS We retrospectively reviewed imaging and behavioral data for 52 participants with subacute aphasia due to left-hemisphere ischemic stroke enrolled in the RCT. RCT inclusion criteria: English proficiency, normal/corrected-to-normal hearing/vision, and no history of neurologic conditions other than the stroke resulting aphasia. One participant with a chronic right-hemisphere lesion was retained as she presented with no residual deficits on neurologic examination. Individuals with scalp sensitivities or on medications that lower seizure threshold or any N-methyl-D-aspartate (NMDA) antagonists were excluded. Of the 52 participants, for this analysis, 2 were excluded for not having a magnetic resonance imaging, and 7 were excluded for not participating in treatment or pre/post assessment for at least 1 outcome, resulting in final sample of 43 participants (20 women sex, M [SD] age=64.4 [11.9] and M [SD] education=14.9 [3.1] years). INTERVENTIONS Participants received 15 sessions (2-3 times/week) of computerized lexical-semantic (ie, verification) treatment with [sham/active] transcranial direct current stimulation (tDCS). Sessions were approximately 45 minutes each (tDCS for first 20 minutes). MAIN OUTCOME MEASURES Naming accuracy, content units (CUs, a measure of semantically accurate production), and efficiency (ie, syllables/CU) on a picture description task. RESULTS Periventricular WMH severity was independently associated with recovery in picture naming for the active tDCS group. Deep WMH severity was associated with recovery for CU production for the sham tDCS group. CONCLUSION Baseline periventricular and deep WMH, among other factors, may be an important consideration for prognosis and treatment planning, especially when considered in conjunction with tDCS treatment.
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Affiliation(s)
- Joseph S Kang
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Lisa D Bunker
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD; Speech-Language Pathology Program, College of Health Sciences, Midwestern University, Glendale, AZ
| | | | - Argye E Hillis
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD; Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD; Department of Cognitive Science, Johns Hopkins University, Baltimore, MD.
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Gomez-Tames J, Fernández-Corazza M. Perspectives on Optimized Transcranial Electrical Stimulation Based on Spatial Electric Field Modeling in Humans. J Clin Med 2024; 13:3084. [PMID: 38892794 PMCID: PMC11172989 DOI: 10.3390/jcm13113084] [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: 03/08/2024] [Revised: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 06/21/2024] Open
Abstract
Background: Transcranial electrical stimulation (tES) generates an electric field (or current density) in the brain through surface electrodes attached to the scalp. Clinical significance has been demonstrated, although with moderate and heterogeneous results partly due to a lack of control of the delivered electric currents. In the last decade, computational electric field analysis has allowed the estimation and optimization of the electric field using accurate anatomical head models. This review examines recent tES computational studies, providing a comprehensive background on the technical aspects of adopting computational electric field analysis as a standardized procedure in medical applications. Methods: Specific search strategies were designed to retrieve papers from the Web of Science database. The papers were initially screened based on the soundness of the title and abstract and then on their full contents, resulting in a total of 57 studies. Results: Recent trends were identified in individual- and population-level analysis of the electric field, including head models from non-neurotypical individuals. Advanced optimization techniques that allow a high degree of control with the required focality and direction of the electric field were also summarized. There is also growing evidence of a correlation between the computationally estimated electric field and the observed responses in real experiments. Conclusions: Computational pipelines and optimization algorithms have reached a degree of maturity that provides a rationale to improve tES experimental design and a posteriori analysis of the responses for supporting clinical studies.
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Affiliation(s)
- Jose Gomez-Tames
- Department of Medical Engineering, Graduate School of Engineering, Chiba University, Chiba 263-8522, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Mariano Fernández-Corazza
- LEICI Institute of Research in Electronics, Control and Signal Processing, National University of La Plata, La Plata 1900, Argentina
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3
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Fromm AE, Antonenko D. The potential compensatory effect of transcranial electrical stimulation on the adverse impact of white matter damage in the aging brain. Brain Stimul 2024; 17:681-682. [PMID: 38810868 DOI: 10.1016/j.brs.2024.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 04/25/2024] [Accepted: 05/21/2024] [Indexed: 05/31/2024] Open
Affiliation(s)
- Anna E Fromm
- University Medicine Greifswald, Department of Neurology, 17475, Greifswald, Germany
| | - Daria Antonenko
- University Medicine Greifswald, Department of Neurology, 17475, Greifswald, Germany.
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4
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Deng ZD, Argyelan M, Miller J, Jones TR, Upston J, McClintock SM, Abbott CC. On assumptions and key issues in electric field modeling for ECT. Mol Psychiatry 2024:10.1038/s41380-024-02567-9. [PMID: 38671213 DOI: 10.1038/s41380-024-02567-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/11/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024]
Affiliation(s)
- Zhi-De Deng
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Miklos Argyelan
- Department of Psychiatry, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Center for Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
- Zucker School of Medicine at Hofstra/Northwell, Department of Psychiatry, Hempstead, NY, USA
| | - Jeremy Miller
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Thomas R Jones
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Joel Upston
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Shawn M McClintock
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Division of Psychology, Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
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5
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Abbott CC, Miller J, Farrar D, Argyelan M, Lloyd M, Squillaci T, Kimbrell B, Ryman S, Jones TR, Upston J, Quinn DK, Peterchev AV, Erhardt E, Datta A, McClintock SM, Deng ZD. Amplitude-determined seizure-threshold, electric field modeling, and electroconvulsive therapy antidepressant and cognitive outcomes. Neuropsychopharmacology 2024; 49:640-648. [PMID: 38212442 PMCID: PMC10876627 DOI: 10.1038/s41386-023-01780-4] [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: 08/03/2023] [Revised: 11/06/2023] [Accepted: 11/26/2023] [Indexed: 01/13/2024]
Abstract
Electroconvulsive therapy (ECT) pulse amplitude, which dictates the induced electric field (E-field) magnitude in the brain, is presently fixed at 800 or 900 milliamperes (mA) without clinical or scientific rationale. We have previously demonstrated that increased E-field strength improves ECT's antidepressant effect but worsens cognitive outcomes. Amplitude-determined seizure titration may reduce the E-field variability relative to fixed amplitude ECT. In this investigation, we assessed the relationships among amplitude-determined seizure-threshold (STa), E-field magnitude, and clinical outcomes in older adults (age range 50 to 80 years) with depression. Subjects received brain imaging, depression assessment, and neuropsychological assessment pre-, mid-, and post-ECT. STa was determined during the first treatment with a Soterix Medical 4×1 High Definition ECT Multi-channel Stimulation Interface (Investigation Device Exemption: G200123). Subsequent treatments were completed with right unilateral electrode placement (RUL) and 800 mA. We calculated Ebrain defined as the 90th percentile of E-field magnitude in the whole brain for RUL electrode placement. Twenty-nine subjects were included in the final analyses. Ebrain per unit electrode current, Ebrain/I, was associated with STa. STa was associated with antidepressant outcomes at the mid-ECT assessment and bitemporal electrode placement switch. Ebrain/I was associated with changes in category fluency with a large effect size. The relationship between STa and Ebrain/I extends work from preclinical models and provides a validation step for ECT E-field modeling. ECT with individualized amplitude based on E-field modeling or STa has the potential to enhance neuroscience-based ECT parameter selection and improve clinical outcomes.
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Affiliation(s)
| | - Jeremy Miller
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Danielle Farrar
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Miklos Argyelan
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, The Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Megan Lloyd
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Taylor Squillaci
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Brian Kimbrell
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Sephira Ryman
- Mind Research Network, Albuquerque, NM, USA
- Department of Neurology, Albuquerque, NM, USA
| | - Thomas R Jones
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Joel Upston
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Davin K Quinn
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Angel V Peterchev
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Erik Erhardt
- Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM, USA
| | | | - Shawn M McClintock
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Division of Psychology, Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - Zhi-De Deng
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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Albizu A, Indahlastari A, Huang Z, Waner J, Stolte SE, Fang R, Woods AJ. Machine-learning defined precision tDCS for improving cognitive function. Brain Stimul 2023; 16:969-974. [PMID: 37279860 PMCID: PMC11080612 DOI: 10.1016/j.brs.2023.05.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 05/08/2023] [Accepted: 05/22/2023] [Indexed: 06/08/2023] Open
Abstract
BACKGROUND Transcranial direct current stimulation (tDCS) paired with cognitive training (CT) is widely investigated as a therapeutic tool to enhance cognitive function in older adults with and without neurodegenerative disease. Prior research demonstrates that the level of benefit from tDCS paired with CT varies from person to person, likely due to individual differences in neuroanatomical structure. OBJECTIVE The current study aims to develop a method to objectively optimize and personalize current dosage to maximize the functional gains of non-invasive brain stimulation. METHODS A support vector machine (SVM) model was trained to predict treatment response based on computational models of current density in a sample dataset (n = 14). Feature weights of the deployed SVM were used in a weighted Gaussian Mixture Model (GMM) to maximize the likelihood of converting tDCS non-responders to responders by finding the most optimum electrode montage and applied current intensity (optimized models). RESULTS Current distributions optimized by the proposed SVM-GMM model demonstrated 93% voxel-wise coherence within target brain regions between the originally non-responders and responders. The optimized current distribution in original non-responders was 3.38 standard deviations closer to the current dose of responders compared to the pre-optimized models. Optimized models also achieved an average treatment response likelihood and normalized mutual information of 99.993% and 91.21%, respectively. Following tDCS dose optimization, the SVM model successfully predicted all tDCS non-responders with optimized doses as responders. CONCLUSIONS The results of this study serve as a foundation for a custom dose optimization strategy towards precision medicine in tDCS to improve outcomes in cognitive decline remediation for older adults.
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Affiliation(s)
- Alejandro Albizu
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, USA
| | - Aprinda Indahlastari
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, USA
| | - Ziqian Huang
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; Department of Electrical and Computer Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA
| | - Jori Waner
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, USA
| | - Skylar E Stolte
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA
| | - Ruogu Fang
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA; Department of Electrical and Computer Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, USA.
| | - Adam J Woods
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, USA; Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, USA.
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7
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Hausman HK, Alexander GE, Cohen R, Marsiske M, DeKosky ST, Hishaw GA, O'Shea A, Kraft JN, Dai Y, Wu S, Woods AJ. Primary outcome from the augmenting cognitive training in older adults study (ACT): A tDCS and cognitive training randomized clinical trial. Brain Stimul 2023; 16:904-917. [PMID: 37245842 PMCID: PMC10436327 DOI: 10.1016/j.brs.2023.05.021] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 05/08/2023] [Accepted: 05/24/2023] [Indexed: 05/30/2023] Open
Abstract
BACKGROUND There is a need for effective interventions to stave off cognitive decline in older adults. Cognitive training has variably produced gains in untrained tasks and daily functioning. Combining cognitive training with transcranial direct current stimulation (tDCS) may augment cognitive training effects; however, this approach has yet to be tested on a large-scale. OBJECTIVE This paper will present the primary findings of the Augmenting Cognitive Training in Older Adults (ACT) clinical trial. We hypothesize that receiving active stimulation with cognitive training will result in greater improvements on an untrained fluid cognition composite compared to sham following intervention. METHODS 379 older adults were randomized, and 334 were included in intent-to-treat analyses for a 12-week multidomain cognitive training and tDCS intervention. Active or sham tDCS was administered at F3/F4 during cognitive training daily for two weeks then weekly for 10 weeks. To assess the tDCS effect, we fitted regression models for changes in NIH Toolbox Fluid Cognition Composite scores immediately following intervention and one year from baseline controlling for covariates and baseline scores. RESULTS Across the entire sample, there were improvements in NIH Toolbox Fluid Cognition Composite scores immediately post-intervention and one year following baseline; however, there were no significant tDCS group effects at either timepoint. CONCLUSIONS The ACT study models rigorous, safe administration of a combined tDCS and cognitive training intervention in a large sample of older adults. Despite potential evidence of near-transfer effects, we failed to demonstrate an additive benefit of active stimulation. Future analyses will continue to assess the intervention's efficacy by examining additional measures of cognition, functioning, mood, and neural markers.
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Affiliation(s)
- Hanna K Hausman
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Gene E Alexander
- Department of Psychiatry, Neuroscience and Physiological Sciences Graduate Interdisciplinary Programs, and BIO5 Institute, University of Arizona and Arizona Alzheimer's Disease Consortium, Tucson, AZ, USA; Brain Imaging, Behavior and Aging Laboratory, Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Ronald Cohen
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Michael Marsiske
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Steven T DeKosky
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Department of Neurology, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Georg A Hishaw
- Department of Psychiatry, Neuroscience and Physiological Sciences Graduate Interdisciplinary Programs, and BIO5 Institute, University of Arizona and Arizona Alzheimer's Disease Consortium, Tucson, AZ, USA
| | - Andrew O'Shea
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Jessica N Kraft
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Yunfeng Dai
- Department of Biostatistics, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Samuel Wu
- Department of Biostatistics, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Adam J Woods
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
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Nasimova M, Huang Y. Applications of open-source software ROAST in clinical studies: A review. Brain Stimul 2022; 15:1002-1010. [PMID: 35843597 PMCID: PMC9378654 DOI: 10.1016/j.brs.2022.07.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 07/09/2022] [Accepted: 07/10/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Transcranial electrical stimulation (TES) is broadly investigated as a therapeutic technique for a wide range of neurological disorders. The electric fields induced by TES in the brain can be estimated by computational models. A realistic and volumetric approach to simulate TES (ROAST) has been recently released as an open-source software package and has been widely used in TES research and its clinical applications. Rigor and reproducibility of TES studies have recently become a concern, especially in the context of computational modeling. METHODS Here we reviewed 94 clinical TES studies that leveraged ROAST for computational modeling. When reviewing each study, we pay attention to details related to the rigor and reproducibility as defined by the locations of stimulation electrodes and the dose of stimulating current. Specifically, we compared across studies the electrode montages, stimulated brain areas, achieved electric field strength, and the relations between modeled electric field and clinical outcomes. RESULTS We found that over 1800 individual heads have been modeled by ROAST for more than 30 different clinical applications. Similar electric field intensities were found to be reproducible by ROAST across different studies at the same brain area under same or similar stimulation montages. CONCLUSION This article reviews the use cases of ROAST and provides an overview of how ROAST has been leveraged to enhance the rigor and reproducibility of TES research and its applications.
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Affiliation(s)
- Mohigul Nasimova
- Department of Biomedical Engineering, City College of the City University of New York, New York, NY, 10031, USA
| | - Yu Huang
- Department of Biomedical Engineering, City College of the City University of New York, New York, NY, 10031, USA; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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Pilloni G, Charvet LE, Bikson M, Palekar N, Kim MJ. Potential of Transcranial Direct Current Stimulation in Alzheimer's Disease: Optimizing Trials Toward Clinical Use. J Clin Neurol 2022; 18:391-400. [PMID: 35796264 PMCID: PMC9262447 DOI: 10.3988/jcn.2022.18.4.391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/13/2022] [Accepted: 04/13/2022] [Indexed: 11/17/2022] Open
Abstract
Transcranial direct current stimulation (tDCS) is a safe and well-tolerated noninvasive method for stimulating the brain that is rapidly developing into a treatment method for various neurological and psychiatric conditions. In particular, there is growing evidence of a therapeutic role for tDCS in ameliorating or delaying the cognitive decline in Alzheimer's disease (AD). We provide a brief overview of the current development and application status of tDCS as a nonpharmacological therapeutic method for AD and mild cognitive impairment (MCI), summarize the levels of evidence, and identify the improvements needed for clinical applications. We also suggest future directions for large-scale controlled clinical trials of tDCS in AD and MCI, and emphasize the necessity of identifying the mechanistic targets to facilitate clinical applications.
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Affiliation(s)
- Giuseppina Pilloni
- Department of Neurology, New York University Langone Health, New York, NY, USA
| | - Leigh E Charvet
- Department of Neurology, New York University Langone Health, New York, NY, USA
| | - Marom Bikson
- Department of Biomedical Engineering, The City College of New York, City University of New York, NY, USA
| | - Nikhil Palekar
- Department of Psychiatry and Behavioral Health, Stony Brook University Renaissance School of Medicine, Stony Brook, NY, USA
| | - Min-Jeong Kim
- Department of Psychiatry and Behavioral Health, Stony Brook University Renaissance School of Medicine, Stony Brook, NY, USA.
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Kalloch B, Weise K, Lampe L, Bazin PL, Villringer A, Hlawitschka M, Sehm B. The influence of white matter lesions on the electric field in transcranial electric stimulation. Neuroimage Clin 2022; 35:103071. [PMID: 35671557 PMCID: PMC9168230 DOI: 10.1016/j.nicl.2022.103071] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 05/04/2022] [Accepted: 05/30/2022] [Indexed: 11/25/2022]
Abstract
Sensitivity analysis allows the simulation of tDCS with uncertain conductivities. White matter lesions (WML) have no global influence on the electric field in tDCS. In subjects with a high lesion load, a local influence can be observed. In low to medium lesion load subjects, explicit modeling of WML is not required.
Background Transcranial direct current stimulation (tDCS) is a promising tool to enhance therapeutic efforts, for instance, after a stroke. The achieved stimulation effects exhibit high inter-subject variability, primarily driven by perturbations of the induced electric field (EF). Differences are further elevated in the aging brain due to anatomical changes such as atrophy or lesions. Informing tDCS protocols by computer-based, individualized EF simulations is a suggested measure to mitigate this variability. Objective While brain anatomy in general and specifically atrophy as well as stroke lesions are deemed influential on the EF in simulation studies, the influence of the uncertainty in the change of the electrical properties of the white matter due to white matter lesions (WMLs) has not been quantified yet. Methods A group simulation study with 88 subjects assigned into four groups of increasing lesion load was conducted. Due to the lack of information about the electrical conductivity of WMLs, an uncertainty analysis was employed to quantify the variability in the simulation when choosing an arbitrary conductivity value for the lesioned tissue. Results The contribution of WMLs to the EF variance was on average only one tenth to one thousandth of the contribution of the other modeled tissues. While the contribution of the WMLs significantly increased (p≪.01) in subjects exhibiting a high lesion load compared to low lesion load subjects, typically by a factor of 10 and above, the total variance of the EF didnot change with the lesion load. Conclusion Our results suggest that WMLs do not perturb the EF globally and can thus be omitted when modeling subjects with low to medium lesion load. However, for high lesion load subjects, the omission of WMLs may yield less robust local EF estimations in the vicinity of the lesioned tissue. Our results contribute to the efforts of accurate modeling of tDCS for treatment planning.
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Affiliation(s)
- Benjamin Kalloch
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, Leipzig, Germany; Leipzig University of Applied Science, Faculty of Computer Science and Media, Leipzig, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, Methods and Development Group "Brain Networks", Leipzig, Germany; Technische Universität Ilmenau, Instiute of Biomedical Engineering and Informatics, Ilmenau, Germany.
| | - Konstantin Weise
- Max Planck Institute for Human Cognitive and Brain Sciences, Methods and Development Group "Brain Networks", Leipzig, Germany; Technische Universität Ilmenau, Advanced Electromagnetics Group, Ilmenau, Germany
| | - Leonie Lampe
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, Leipzig, Germany
| | - Pierre-Louis Bazin
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, Leipzig, Germany; University of Amsterdam, Faculty of Social and Behavioural Sciences, Amsterdam, The Netherlands
| | - Arno Villringer
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, Leipzig, Germany
| | - Mario Hlawitschka
- Leipzig University of Applied Science, Faculty of Computer Science and Media, Leipzig, Germany
| | - Bernhard Sehm
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, Leipzig, Germany; Department of Neurology, Martin Luther University of Halle-Wittenberg, Germany
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Indahlastari A, Hardcastle C, Albizu A, Alvarez-Alvarado S, Boutzoukas EM, Evangelista ND, Hausman HK, Kraft J, Langer K, Woods AJ. A Systematic Review and Meta-Analysis of Transcranial Direct Current Stimulation to Remediate Age-Related Cognitive Decline in Healthy Older Adults. Neuropsychiatr Dis Treat 2021; 17:971-990. [PMID: 33824591 PMCID: PMC8018377 DOI: 10.2147/ndt.s259499] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 03/11/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Transcranial direct current stimulation (tDCS) has been proposed as a possible method for remediating age-associated cognitive decline in the older adult population. While tDCS has shown potential for improving cognitive functions in healthy older adults, stimulation outcomes on various cognitive domains have been mixed. METHODS A systematic search was performed in four databases: PubMed, EMBASE, Web of Science, and PsychInfo. Search results were then screened for eligibility based on inclusion/exclusion criteria to only include studies where tDCS was applied to improve cognition in healthy older adults 65 years and above. Eligible studies were reviewed and demographic characteristics, tDCS dose parameters, study procedures, and cognitive outcomes were extracted. Reported effect sizes for active compared to sham group in representative cognitive domain were converted to Hedges' g. MAIN RESULTS A total of thirteen studies involving healthy older adults (n=532, mean age=71.2+5.3 years) were included in the meta-analysis. The majority of included studies (94%) targeted the prefrontal cortex with stimulation intensity 1-2 mA using various electrode placements with anodes near the frontal region. Across all studies, we found Hedges' g values ranged from -0.31 to 1.85 as reported group effect sizes of active stimulation compared to sham. CONCLUSION While observed outcomes varied, overall findings indicated promising effects of tDCS to remediate cognitive aging and thus deserves further exploration. Future characterization of inter-individual variability in tDCS dose response and applications in larger cohorts are warranted to further validate benefits of tDCS for cognition in healthy older adults.
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Affiliation(s)
- Aprinda Indahlastari
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.,Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Cheshire Hardcastle
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.,Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Alejandro Albizu
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.,Department of Neuroscience, University of Florida, Gainesville, FL, USA
| | - Stacey Alvarez-Alvarado
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.,Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Emanuel M Boutzoukas
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.,Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Nicole D Evangelista
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.,Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Hanna K Hausman
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.,Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Jessica Kraft
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.,Department of Neuroscience, University of Florida, Gainesville, FL, USA
| | - Kailey Langer
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.,Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Adam J Woods
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.,Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA.,Department of Neuroscience, University of Florida, Gainesville, FL, USA
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