Sunday, May 18, 2025
5/18/2025
Identifying the multi-modal factors that determine individual response to tDCS - PROJECT SUMMARY Transcranial direct current stimulation (tDCS) has clear translational potential for a wide range of clinical conditions, from post-stroke recovery to psychiatric and neurodevelopmental conditions. TDCS is a portable and relatively inexpensive form of neuromodulation, with an outstanding safety profile. However, the mechanisms underlying the effects of tDCS on brain and behavior have yet to be clarified, and relatively few studies acquire neuroimaging data during and/or after tDCS administration. We have acquired a substantial database (>225 sessions in >105 unique participants) of concurrent tDCS-functional MRI (fMRI) data targeting different cerebellar subregions, with cognitive and behavioral baseline measures, post-tDCS resting state and task fMRI, task performance data, and tDCS symptom ratings. The cerebellum interconnects with all major cortical networks, including the prefrontal cortex, and we and others have shown that targeted cerebellar neuromodulation can alter performance on a broad range of tasks (language, working memory, face recognition) and in different clinical populations (aphasia, autism, schizophrenia). That said, it is clear that there are individual differences in response to tDCS – while some participants have a robust improvement in performance, others show decreased performance or no measurable behavioral change. Multiple factors could drive these individual differences, including baseline task performance, resting-state brain connectivity, idiosyncratic task activation patterns, and anatomical targeting of the neuromodulation, to name a few. However, we do not know which (if any) of these factors are the most robust predictors of individual response. Our goal is to apply state-of-art machine learning algorithms to this unique, multimodal dataset to determine which factor(s) explain an individual’s response to tDCS. First, we will use individual participant’s structural MRI scans to model the peak electric field on a participant-by-participant basis. We will use these models and during-tDCS functional MRI data to determine the individual anatomical targeting of tDCS. Next, we will calculate the behavioral and neural effects of tDCS based on in-scanner task performance and activation patterns. We will extract metrics of baseline and post-tDCS resting-state connectivity in each participant. Then, we will use the curated dataset (electric field maps, cognitive performance, age, sex, resting state data, task activation, etc.) to determine which variables best predict the direction and size of the post-tDCS behavioral shift. While it has been hypothesized that anatomical targeting is a critical factor in individual response, it is also possible that baseline neural activation or even baseline task performance are the most predictive features. These are critical outstanding questions that, if answered, will accelerate the field toward more effective, better designed clinical trials. From a basic science perspective, this work will inform our understanding of how tDCS impacts both brain and behavior. Our goal is to conduct work that will transform the therapeutic efficacy of tDCS for neurological, psychiatric and developmental conditions.
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