Multimodal Imaging-Based Targeting Approach for Network-Level Brain Stimulation

BackgroundNeural network effects of transcranial direct current stimulation (tDCS) are poorly understood. Here, we introduce an empirically informed, multimodal functional magnetic resonance imaging (fMRI) based framework suited for guiding stimulation target selection and hypothesis-based data analysis in focal tDCS-fMRI studies. MethodsWe illustrate our approach using data of 37 healthy individuals (19 females; mean {+/-} SD age = 25.8 {+/-} 5.9) recruited from a multicenter tDCS-fMRI study. Participants completed resting-state (RS- and task-fMRI (object-location memory, OLM, or associative picture-pseudoword learning, APPL, experiments) with placebo tDCS. Seed-based RS data analysis identified the functional networks originating from project-specific target regions for focal tDCS (right occipito-temporal cortex, rOTC; left ventral IFG, lvIFG) and also established their test-retest reliability using intraclass correlation coefficients (ICC). Dice coefficients analyzed the overlap between the seeded RS- and task-evoked networks. This aimed to identify task-active regions potentially affected by downstream neural network effects from the target regions. ResultsSeed-based analyses identified two highly reliable ventral visual-limbic (rOTC) and language-related networks (lvIFG), with >72-77% of voxels showing good-to-excellent TRR (ICC [≥] 0.75). Only a subset of voxels identified by the RS analyses overlapped with activity elicited by the experimental paradigms (ranging from 7.5-55%), with larger correspondence for the OLM task (Dice OLM: 0.249-0.349; APPL 0.065-0.106). Therefore, the degree of potential tDCS network effects varied substantially depending on the target region, the extent of its functional network and task-specific activity patterns. Degree of correspondence was further mediated by the selected contrasts-of-interest in the task-based analyses, with more conservative control conditions resulting in reduced overlap. ConclusionWe established a principled, multimodal fMRI framework bridging a critical gap in neuromodulation research. By integrating reliable intrinsic connectivity maps with task-evoked activity patterns, we provide a method to prospectively identify network-level targets for focal brain stimulation and to generate hypotheses for data analyses in tDCS-fMRI studies. This approach paves the way for investigating modulation of specific functional networks, shifting the rationale from stimulating an isolated brain region to strategically targeting key nodes within a predefined functional pathway.