An integrated framework for targeting functional networks via transcranial magnetic stimulation

Area of Science:

• Neuroscience
• Biomedical Engineering

Background:

• Transcranial magnetic stimulation (TMS) is a key tool for manipulating brain activity.
• Current TMS targeting relies on simplified brain models, limiting accuracy.
• Anatomofunctional relationships are often assumed to be one-to-one, which is unrealistic.

Purpose of the Study:

• To present an integrated framework for predicting functional networks targeted by TMS.
• To combine anatomically realistic head models with resting-state functional MRI (fMRI) for precise targeting.
• To address limitations in current TMS targeting strategies.

Main Methods:

• Developed an integrated framework using finite element models of the human head.
• Incorporated resting-state fMRI data from the Human Connectome Project.
• Applied the framework to predict TMS effects on dorsolateral prefrontal cortex (DLPFC) networks.

Main Results:

• Identified three distinct DLPFC stimulation zones with varying network effects (default, frontoparietal).
• Demonstrated differential sensitivity to coil orientation across stimulation zones.
• Revealed substantial variability in network profiles for previously published DLPFC targets for depression treatment.

Conclusions:

• The proposed framework enhances the accuracy of TMS targeting by using realistic brain models.
• Variability in network targeting highlights a critical technical issue in current TMS research and application.
• This approach offers a more precise method for optimizing TMS protocols for clinical use.