Anatomical Characteristics Predict Response to Transcranial Direct Current Stimulation (tDCS): Development of a Computational Pipeline for Optimizing tDCS Protocols
Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique promisingly used to treat neurological and psychological disorders. Nevertheless, the inter-subject heterogeneity in its after-effects frequently limits its efficacy. This can be attributed to fixed-dose met...
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MDPI AG
2025-06-01
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| author | Giulia Caiani Emma Chiaramello Marta Parazzini Eleonora Arrigoni Leonor J. Romero Lauro Alberto Pisoni Serena Fiocchi |
| author_facet | Giulia Caiani Emma Chiaramello Marta Parazzini Eleonora Arrigoni Leonor J. Romero Lauro Alberto Pisoni Serena Fiocchi |
| author_sort | Giulia Caiani |
| collection | DOAJ |
| description | Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique promisingly used to treat neurological and psychological disorders. Nevertheless, the inter-subject heterogeneity in its after-effects frequently limits its efficacy. This can be attributed to fixed-dose methods, which do not consider inter-subject anatomical variations. This work attempts to overcome this constraint by examining the effects of age and anatomical features, including the volume of cerebrospinal fluid (CSF), the thickness of the skull, and the composition of brain tissue, on electric field distribution and cortical excitability. A computational approach was used to map the electric field distribution over the brain tissues of realistic head models reconstructed from MRI images of twenty-three subjects, including adults and children of both genders. Significant negative correlations (<i>p</i> < 0.05) were found in the data between the maximum electric field strength and anatomical variable parameters. Furthermore, this study showed that the percentage of brain tissue exposed to an electric field amplitude above a pre-defined threshold (i.e., 0.227 V/m) was the main factor influencing the responsiveness to tDCS. In the end, the research suggests multiple regression models as useful tool to predict subjects’ responsiveness and to support a personalized approach that tailors the injected current to the morphology of the patient. |
| format | Article |
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| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-66debdaded8942b2b004951b261d2d9e2025-08-20T02:24:31ZengMDPI AGBioengineering2306-53542025-06-0112665610.3390/bioengineering12060656Anatomical Characteristics Predict Response to Transcranial Direct Current Stimulation (tDCS): Development of a Computational Pipeline for Optimizing tDCS ProtocolsGiulia Caiani0Emma Chiaramello1Marta Parazzini2Eleonora Arrigoni3Leonor J. Romero Lauro4Alberto Pisoni5Serena Fiocchi6Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, 20133 Milan, ItalyInstitute of Electronics, Computer and Telecommunication Engineering (IEIIT), National Research Council (CNR), 20133 Milan, ItalyInstitute of Electronics, Computer and Telecommunication Engineering (IEIIT), National Research Council (CNR), 20133 Milan, ItalyDepartment of Psychology, University of Milano–Bicocca, 20126 Milan, ItalyDepartment of Psychology, University of Milano–Bicocca, 20126 Milan, ItalyDepartment of Psychology, University of Milano–Bicocca, 20126 Milan, ItalyInstitute of Electronics, Computer and Telecommunication Engineering (IEIIT), National Research Council (CNR), 20133 Milan, ItalyTranscranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique promisingly used to treat neurological and psychological disorders. Nevertheless, the inter-subject heterogeneity in its after-effects frequently limits its efficacy. This can be attributed to fixed-dose methods, which do not consider inter-subject anatomical variations. This work attempts to overcome this constraint by examining the effects of age and anatomical features, including the volume of cerebrospinal fluid (CSF), the thickness of the skull, and the composition of brain tissue, on electric field distribution and cortical excitability. A computational approach was used to map the electric field distribution over the brain tissues of realistic head models reconstructed from MRI images of twenty-three subjects, including adults and children of both genders. Significant negative correlations (<i>p</i> < 0.05) were found in the data between the maximum electric field strength and anatomical variable parameters. Furthermore, this study showed that the percentage of brain tissue exposed to an electric field amplitude above a pre-defined threshold (i.e., 0.227 V/m) was the main factor influencing the responsiveness to tDCS. In the end, the research suggests multiple regression models as useful tool to predict subjects’ responsiveness and to support a personalized approach that tailors the injected current to the morphology of the patient.https://www.mdpi.com/2306-5354/12/6/656anodal tDCSclinical outcomecomputational modellingcortical excitabilityneuromodulationpersonalized medicine |
| spellingShingle | Giulia Caiani Emma Chiaramello Marta Parazzini Eleonora Arrigoni Leonor J. Romero Lauro Alberto Pisoni Serena Fiocchi Anatomical Characteristics Predict Response to Transcranial Direct Current Stimulation (tDCS): Development of a Computational Pipeline for Optimizing tDCS Protocols Bioengineering anodal tDCS clinical outcome computational modelling cortical excitability neuromodulation personalized medicine |
| title | Anatomical Characteristics Predict Response to Transcranial Direct Current Stimulation (tDCS): Development of a Computational Pipeline for Optimizing tDCS Protocols |
| title_full | Anatomical Characteristics Predict Response to Transcranial Direct Current Stimulation (tDCS): Development of a Computational Pipeline for Optimizing tDCS Protocols |
| title_fullStr | Anatomical Characteristics Predict Response to Transcranial Direct Current Stimulation (tDCS): Development of a Computational Pipeline for Optimizing tDCS Protocols |
| title_full_unstemmed | Anatomical Characteristics Predict Response to Transcranial Direct Current Stimulation (tDCS): Development of a Computational Pipeline for Optimizing tDCS Protocols |
| title_short | Anatomical Characteristics Predict Response to Transcranial Direct Current Stimulation (tDCS): Development of a Computational Pipeline for Optimizing tDCS Protocols |
| title_sort | anatomical characteristics predict response to transcranial direct current stimulation tdcs development of a computational pipeline for optimizing tdcs protocols |
| topic | anodal tDCS clinical outcome computational modelling cortical excitability neuromodulation personalized medicine |
| url | https://www.mdpi.com/2306-5354/12/6/656 |
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