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Abstract #1009

Enhancing brain activity mapping through BOLD-fMRI and MEG data fusion using explainable machine learning.

Jiri Benacek1,2, Krish Singh1, Derek Jones1, David Marshall2, Simon Rushton1, and Marco Palombo1,2
1Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom, 2School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom

Synopsis

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.

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Keywords