Multimodal imaging-based targeting approach for network-level brain stimulation.

Area of Science:

• Neuroscience
• Cognitive Science
• Medical Imaging

Background:

• Transcranial direct current stimulation (tDCS) effects on neural networks are not well understood.
• A multimodal functional magnetic resonance imaging (fMRI) framework is needed to guide tDCS-fMRI studies.

Purpose of the Study:

• To introduce and validate a prospective, empirically informed, multimodal fMRI framework for focal tDCS-fMRI studies.
• To guide target selection and hypothesis-based analysis in future tDCS-fMRI research.

Main Methods:

• Utilized data from 37 healthy individuals undergoing tDCS-fMRI.
• Employed seed-based resting-state (RS) analysis to identify functional networks.
• Quantified overlap between RS networks and task-evoked activity using Dice coefficients.

Main Results:

• Identified highly reliable ventral visual-limbic and language networks with good-to-excellent test-retest reliability (72-77%).
• Overlap between resting-state networks and task activity varied significantly (7.5-55%), depending on target region and task.
• Correspondence was higher for object-location memory tasks compared to associative picture-pseudoword learning tasks.

Conclusions:

• Established a multimodal fMRI framework to bridge a gap in neuromodulation research.
• The framework integrates intrinsic connectivity with task-evoked activity for prospective target identification.
• This approach enables strategic targeting of functional pathways rather than isolated brain regions.