The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024
Neuroanatomical Predictors of Transcranial Direct Current Stimulation (tDCS)-Induced Modifications in Neurocognitive Task Performance in Typically Developing Individuals.
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Gurr Caroline, Splittgerber Maike, Puonti Oula, Siemann Julia, Luckhardt Christina, Pereira Helena C, Amaral Joana, Crisóstomo Joana, Sayal Alexandre, Ribeiro Mário, Sousa Daniela, Dempfle Astrid, Krauel Kerstin, Borzikowsky Christoph, Brauer Hannah, Prehn-Kristensen Alexander, Breitling-Ziegler Carolin, Castelo-Branco Miguel, Salvador Ricardo, Damiani Giada, Ruffini Giulio, Siniatchkin Michael, Thielscher Axel, Freitag Christine M, Moliadze Vera, Ecker Christine
Аннотация
Transcranial direct current stimulation (tDCS) is a noninvasive neuromodulation technique gaining more attention in neurodevelopmental disorders (NDDs). Due to the phenotypic heterogeneity of NDDs, tDCS is unlikely to be equally effective in all individuals. The present study aimed to establish neuroanatomical markers in typically developing (TD) individuals that may be used for the prediction of individual responses to tDCS. Fifty-seven male and female children received 2 mA anodal and sham tDCS, targeting the left dorsolateral prefrontal cortex (DLPFCleft), right inferior frontal gyrus, and bilateral temporoparietal junction. Response to tDCS was assessed based on task performance differences between anodal and sham tDCS in different neurocognitive tasks (N-back, flanker, Mooney faces detection, attentional emotional recognition task). Measures of cortical thickness (CT) and surface area (SA) were derived from 3 Tesla structural MRI scans. Associations between neuroanatomy and task performance were assessed using general linear models (GLM). Machine learning (ML) algorithms were employed to predict responses to tDCS. Vertex-wise estimates of SA were more closely linked to differences in task performance than measures of CT. Across ML algorithms, highest accuracies were observed for the prediction of N-back task performance differences following stimulation of the DLPFCleft, where 65% of behavioral variance was explained by variability in SA. Lower accuracies were observed for all other tasks and stimulated regions. This suggests that it may be possible to predict individual responses to tDCS for some behavioral measures and target regions. In the future, these models might be extended to predict treatment outcome in individuals with NDDs.