Wilke, Melanie

[["dc.bibliographiccitation.journal","Frontiers in Neuroscience"],["dc.bibliographiccitation.volume","16"],["dc.contributor.affiliation","Steinmann, Iris; 1Department of Cognitive Neurology, University Medical Center Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Williams, Kathleen A.; 1Department of Cognitive Neurology, University Medical Center Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Wilke, Melanie; 1Department of Cognitive Neurology, University Medical Center Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Antal, Andrea; 4Department of Neurology, University Medical Center Göttingen, Göttingen, Germany"],["dc.contributor.author","Steinmann, Iris"],["dc.contributor.author","Williams, Kathleen A."],["dc.contributor.author","Wilke, Melanie"],["dc.contributor.author","Antal, Andrea"],["dc.date.accessioned","2022-07-11T15:00:32Z"],["dc.date.available","2022-07-11T15:00:32Z"],["dc.date.issued","2022-06-27"],["dc.date.updated","2022-07-11T13:34:32Z"],["dc.description.abstract","Non-invasive electrical stimulation methods, such as transcranial alternating current stimulation (tACS), are increasingly used in human neuroscience research and offer potential new avenues to treat neurological and psychiatric disorders. However, their often variable effects have also raised concerns in the scientific and clinical communities. This study aims to investigate the influence of subject-specific factors on the alpha tACS-induced aftereffect on the alpha amplitude (measured with electroencephalography, EEG) as well as on the connectivity strength between nodes of the default mode network (DMN) [measured with functional magnetic resonance imaging (fMRI)]. As subject-specific factors we considered the individual electrical field (EFIELD) strength at target regions in the brain, the frequency mismatch between applied stimulation and individual alpha frequency (IAF) and as a covariate, subject’s changes in mental state, i.e., sleepiness. Eighteen subjects participated in a tACS and a sham session conducted on different days. Each session consisted of three runs (pre/stimulation/). tACS was applied during the second run at each subject’s individual alpha frequency (IAF), applying 1 mA peak-to-peak intensity for 7 min, using an occipital bihemispheric montage. In every run, subjects watched a video designed to increase in-scanner compliance. To investigate the aftereffect of tACS on EEG alpha amplitude and on DMN connectivity strength, EEG data were recorded simultaneously with fMRI data. Self-rated sleepiness was documented using a questionnaire. Conventional statistics (ANOVA) did not show a significant aftereffect of tACS on the alpha amplitude compared to sham stimulation. Including individual EFIELD strengths and self-rated sleepiness scores in a multiple linear regression model, significant tACS-induced aftereffects were observed. However, the subject-wise mismatch between tACS frequency and IAF had no contribution to our model. Neither standard nor extended statistical methods confirmed a tACS-induced aftereffect on DMN functional connectivity. Our results show that it is possible and necessary to disentangle alpha amplitude changes due to intrinsic mechanisms and to external manipulation using tACS on the alpha amplitude that might otherwise be overlooked. Our results suggest that EFIELD is really the most significant factor that explains the alpha amplitude modulation during a tACS session. This knowledge helps to understand the variability of the tACS-induced aftereffects."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2022"],["dc.identifier.doi","10.3389/fnins.2022.870758"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112460"],["dc.language.iso","en"],["dc.relation.eissn","1662-453X"],["dc.rights","CC BY 4.0"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Detection of Transcranial Alternating Current Stimulation Aftereffects Is Improved by Considering the Individual Electric Field Strength and Self-Rated Sleepiness"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Physiology"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Kottlarz, Inga"],["dc.contributor.author","Berg, Sebastian"],["dc.contributor.author","Toscano-Tejeida, Diana"],["dc.contributor.author","Steinmann, Iris"],["dc.contributor.author","Bähr, Mathias"],["dc.contributor.author","Luther, Stefan"],["dc.contributor.author","Wilke, Melanie"],["dc.contributor.author","Parlitz, Ulrich"],["dc.contributor.author","Schlemmer, Alexander"],["dc.date.accessioned","2021-04-14T08:29:50Z"],["dc.date.available","2021-04-14T08:29:50Z"],["dc.date.issued","2021"],["dc.description.abstract","In this study, ordinal pattern analysis and classical frequency-based EEG analysis methods are used to differentiate between EEGs of different age groups as well as individuals. As characteristic features, functional connectivity as well as single-channel measures in both the time and frequency domain are considered. We compare the separation power of each feature set after nonlinear dimensionality reduction using t-distributed stochastic neighbor embedding and demonstrate that ordinal pattern-based measures yield results comparable to frequency-based measures applied to preprocessed data, and outperform them if applied to raw data. Our analysis yields no significant differences in performance between single-channel features and functional connectivity features regarding the question of age group separation."],["dc.identifier.doi","10.3389/fphys.2020.614565"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82999"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1664-042X"],["dc.rights","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]