Methodology for tDCS integration with fMRI.

Area of Science:

• Neuroscience
• Neuroimaging
• Neuromodulation

Background:

• Individual variability in transcranial direct current stimulation (tDCS) response necessitates understanding predetermining factors and tracking outcomes.
• Neural system structure and function are key sources of tDCS response variance.
• Functional magnetic resonance imaging (fMRI) is used in 118 studies (pre-October 2018) to investigate tDCS mechanisms, predictions, and localization.

Purpose of the Study:

• To systematically review the methodological parameter space of integrating tDCS with fMRI.
• To explore how fMRI can measure baseline brain activation, inform stimulation montage design, and serve as an outcome measure for tDCS.
• To discuss methodological confounds and outline how computational models can enhance individualized tDCS-fMRI analysis.

Main Methods:

• Systematic review of tDCS-fMRI studies, examining parameters like fMRI timing, study design, control conditions, stimulation dose, and imaging sequences.
• Analysis of a representative study using task and resting-state fMRI before and after tDCS in a crossover design.
• Integration of computational models with imaging data to understand variability sources.

Main Results:

• The existing tDCS-fMRI literature exhibits limited replication and few comparable study designs.
• Methodological confounds in tDCS-fMRI integration are discussed using a representative study.
• Demonstration of integrating modeling and imaging for individualized tDCS analysis.

Conclusions:

• tDCS-fMRI integration requires careful consideration of methodological parameters and potential artifacts.
• Combining computational modeling with fMRI data is crucial for understanding tDCS response variability.
• Rational study designs for tDCS-fMRI are essential for addressing functional mechanisms and enhancing behavioral interventions.