Stratification of responses to tDCS intervention in a healthy pediatric population based on resting-state EEG profiles
Abstract Transcranial Direct Current Stimulation (tDCS) is a non-invasive neuromodulation technique with a wide variety of clinical and research applications. As increasingly acknowledged, its effectiveness is subject dependent, which may lead to time consuming and cost ineffective treatment develop...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-34724-5 |
| _version_ | 1797818057118187520 |
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| author | Paulina Clara Dagnino Claire Braboszcz Eleni Kroupi Maike Splittgerber Hannah Brauer Astrid Dempfle Carolin Breitling-Ziegler Alexander Prehn-Kristensen Kerstin Krauel Michael Siniatchkin Vera Moliadze Aureli Soria-Frisch |
| author_facet | Paulina Clara Dagnino Claire Braboszcz Eleni Kroupi Maike Splittgerber Hannah Brauer Astrid Dempfle Carolin Breitling-Ziegler Alexander Prehn-Kristensen Kerstin Krauel Michael Siniatchkin Vera Moliadze Aureli Soria-Frisch |
| author_sort | Paulina Clara Dagnino |
| collection | DOAJ |
| description | Abstract Transcranial Direct Current Stimulation (tDCS) is a non-invasive neuromodulation technique with a wide variety of clinical and research applications. As increasingly acknowledged, its effectiveness is subject dependent, which may lead to time consuming and cost ineffective treatment development phases. We propose the combination of electroencephalography (EEG) and unsupervised learning for the stratification and prediction of individual responses to tDCS. A randomized, sham-controlled, double-blind crossover study design was conducted within a clinical trial for the development of pediatric treatments based on tDCS. The tDCS stimulation (sham and active) was applied either in the left dorsolateral prefrontal cortex or in the right inferior frontal gyrus. Following the stimulation session, participants performed 3 cognitive tasks to assess the response to the intervention: the Flanker Task, N-Back Task and Continuous Performance Test (CPT). We used data from 56 healthy children and adolescents to implement an unsupervised clustering approach that stratify participants based on their resting-state EEG spectral features before the tDCS intervention. We then applied a correlational analysis to characterize the clusters of EEG profiles in terms of participant’s difference in the behavioral outcome (accuracy and response time) of the cognitive tasks when performed after a tDCS-sham or a tDCS-active session. Better behavioral performance following the active tDCS session compared to the sham tDCS session is considered a positive intervention response, whilst the reverse is considered a negative one. Optimal results in terms of validity measures was obtained for 4 clusters. These results show that specific EEG-based digital phenotypes can be associated to particular responses. While one cluster presents neurotypical EEG activity, the remaining clusters present non-typical EEG characteristics, which seem to be associated with a positive response. Findings suggest that unsupervised machine learning can be successfully used to stratify and eventually predict responses of individuals to a tDCS treatment. |
| first_indexed | 2024-03-13T09:02:32Z |
| format | Article |
| id | doaj.art-4bfe3ff085df44d89e3754527a682735 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T09:02:32Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-4bfe3ff085df44d89e3754527a6827352023-05-28T11:13:24ZengNature PortfolioScientific Reports2045-23222023-05-0113111510.1038/s41598-023-34724-5Stratification of responses to tDCS intervention in a healthy pediatric population based on resting-state EEG profilesPaulina Clara Dagnino0Claire Braboszcz1Eleni Kroupi2Maike Splittgerber3Hannah Brauer4Astrid Dempfle5Carolin Breitling-Ziegler6Alexander Prehn-Kristensen7Kerstin Krauel8Michael Siniatchkin9Vera Moliadze10Aureli Soria-Frisch11Neuroscience BU, Starlab Barcelona SLNeuroscience BU, Starlab Barcelona SLNeuroscience BU, Starlab Barcelona SLInstitute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel UniversityDepartment of Child and Adolescent Psychiatry, Center for Integrative Psychiatry Kiel, University Medical Center Schleswig-HolsteinInstitute of Medical Informatics and Statistics, University Hospital Schleswig Holstein, Kiel UniversityDepartment of Child and Adolescent Psychiatry and Psychotherapy, University of MagdeburgDepartment of Child and Adolescent Psychiatry, Center for Integrative Psychiatry Kiel, University Medical Center Schleswig-HolsteinDepartment of Child and Adolescent Psychiatry and Psychotherapy, University of MagdeburgClinic for Child and Adolescent Psychiatry and Psychotherapy, Protestant Hospital Bethel, University of BielefeldInstitute of Medical Psychology and Medical Sociology, University Medical Center Schleswig-Holstein, Kiel UniversityNeuroscience BU, Starlab Barcelona SLAbstract Transcranial Direct Current Stimulation (tDCS) is a non-invasive neuromodulation technique with a wide variety of clinical and research applications. As increasingly acknowledged, its effectiveness is subject dependent, which may lead to time consuming and cost ineffective treatment development phases. We propose the combination of electroencephalography (EEG) and unsupervised learning for the stratification and prediction of individual responses to tDCS. A randomized, sham-controlled, double-blind crossover study design was conducted within a clinical trial for the development of pediatric treatments based on tDCS. The tDCS stimulation (sham and active) was applied either in the left dorsolateral prefrontal cortex or in the right inferior frontal gyrus. Following the stimulation session, participants performed 3 cognitive tasks to assess the response to the intervention: the Flanker Task, N-Back Task and Continuous Performance Test (CPT). We used data from 56 healthy children and adolescents to implement an unsupervised clustering approach that stratify participants based on their resting-state EEG spectral features before the tDCS intervention. We then applied a correlational analysis to characterize the clusters of EEG profiles in terms of participant’s difference in the behavioral outcome (accuracy and response time) of the cognitive tasks when performed after a tDCS-sham or a tDCS-active session. Better behavioral performance following the active tDCS session compared to the sham tDCS session is considered a positive intervention response, whilst the reverse is considered a negative one. Optimal results in terms of validity measures was obtained for 4 clusters. These results show that specific EEG-based digital phenotypes can be associated to particular responses. While one cluster presents neurotypical EEG activity, the remaining clusters present non-typical EEG characteristics, which seem to be associated with a positive response. Findings suggest that unsupervised machine learning can be successfully used to stratify and eventually predict responses of individuals to a tDCS treatment.https://doi.org/10.1038/s41598-023-34724-5 |
| spellingShingle | Paulina Clara Dagnino Claire Braboszcz Eleni Kroupi Maike Splittgerber Hannah Brauer Astrid Dempfle Carolin Breitling-Ziegler Alexander Prehn-Kristensen Kerstin Krauel Michael Siniatchkin Vera Moliadze Aureli Soria-Frisch Stratification of responses to tDCS intervention in a healthy pediatric population based on resting-state EEG profiles Scientific Reports |
| title | Stratification of responses to tDCS intervention in a healthy pediatric population based on resting-state EEG profiles |
| title_full | Stratification of responses to tDCS intervention in a healthy pediatric population based on resting-state EEG profiles |
| title_fullStr | Stratification of responses to tDCS intervention in a healthy pediatric population based on resting-state EEG profiles |
| title_full_unstemmed | Stratification of responses to tDCS intervention in a healthy pediatric population based on resting-state EEG profiles |
| title_short | Stratification of responses to tDCS intervention in a healthy pediatric population based on resting-state EEG profiles |
| title_sort | stratification of responses to tdcs intervention in a healthy pediatric population based on resting state eeg profiles |
| url | https://doi.org/10.1038/s41598-023-34724-5 |
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