Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence
Motivation: Both magnetoencephalography (MEG) and functional MRI (fMRI) map brain activity, but with complementary spatial and temporal resolutions.
Goal(s): Exploiting MEG and fMRI complementarity to enhance MEG spatial resolution via transfer learning from fMRI and explainable machine learning (xML).
Approach: Sixteen participants underwent fMRI and MEG scans while watching the same movie clip in both. Tree-based ensemble learning models were trained to predict MEG from fMRI, enabling voxel-level super-resolution.
Results: The trained model achieved high R² (0.93) on a test subset and outperformed naïve interpolation. Upsampled MEG maps showed greater specificity than interpolated images, suggesting promising applications for functional super-resolution.
Impact: Our proof-of-concept approach demonstrates a strong predictive relationship between fMRI and MEG data. Using explainable machine learning, we generated high-resolution MEG maps from fMRI inputs, and provided data-driven insights into their relationship, opening new avenues for investigating neurovascular coupling.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords