Machine-learning defined precision tDCS for improving cognitive function
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-05-01
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| Series: | Brain Stimulation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1935861X23017874 |
| _version_ | 1827919471891709952 |
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| author | Alejandro Albizu Aprinda Indahlastari Ziqian Huang Jori Waner Skylar E. Stolte Ruogu Fang Adam J. Woods |
| author_facet | Alejandro Albizu Aprinda Indahlastari Ziqian Huang Jori Waner Skylar E. Stolte Ruogu Fang Adam J. Woods |
| author_sort | Alejandro Albizu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T03:59:28Z |
| format | Article |
| id | doaj.art-db9c581c6ec44252ab90ffb6dee8de80 |
| institution | Directory Open Access Journal |
| issn | 1935-861X |
| language | English |
| last_indexed | 2024-03-13T03:59:28Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Brain Stimulation |
| spelling | doaj.art-db9c581c6ec44252ab90ffb6dee8de802023-06-22T05:02:48ZengElsevierBrain Stimulation1935-861X2023-05-01163969974Machine-learning defined precision tDCS for improving cognitive functionAlejandro Albizu0Aprinda Indahlastari1Ziqian Huang2Jori Waner3Skylar E. Stolte4Ruogu Fang5Adam J. Woods6Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA; Department of Neuroscience, College of Medicine, University of Florida, Gainesville, USACenter 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, USACenter 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, USACenter 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, USACenter 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, USACenter 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; Corresponding authors. Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA.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; Corresponding authors. Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, USA.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.http://www.sciencedirect.com/science/article/pii/S1935861X23017874tESAgingMachine-learningMRIFinite element modelPrecision medicine |
| spellingShingle | Alejandro Albizu Aprinda Indahlastari Ziqian Huang Jori Waner Skylar E. Stolte Ruogu Fang Adam J. Woods Machine-learning defined precision tDCS for improving cognitive function Brain Stimulation tES Aging Machine-learning MRI Finite element model Precision medicine |
| title | Machine-learning defined precision tDCS for improving cognitive function |
| title_full | Machine-learning defined precision tDCS for improving cognitive function |
| title_fullStr | Machine-learning defined precision tDCS for improving cognitive function |
| title_full_unstemmed | Machine-learning defined precision tDCS for improving cognitive function |
| title_short | Machine-learning defined precision tDCS for improving cognitive function |
| title_sort | machine learning defined precision tdcs for improving cognitive function |
| topic | tES Aging Machine-learning MRI Finite element model Precision medicine |
| url | http://www.sciencedirect.com/science/article/pii/S1935861X23017874 |
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