Abstract
Multi-center fMRI studies help accumulate significant number of subjects to increase the statistical power of data analyses. However, the seemingly ambitious gain is hindered by the fact that differences between centers have significant effects on the imaging results. We present a novel machine learning (ML) based technique, which uses non-linear regression with multi-voxel based anatomically informed contextual information, to help normalize multi-center fMRI data to a chosen reference center. Accuracy graphs were obtained by thresholding the estimated maps at high p-values of \(p < 0.001\) after kernel density estimation. Results indicate significant reduction in spurious activations and more importantly, enhancement of the genuine activation clusters. Group level ROI based analysis reveals changes in activation pattern of clusters that are consistent with their role in cognitive function. Furthermore, as the mapping functions exhibit the tendency to induce sensitivity to the regions associated with the task they can help identify small but significant activations which could otherwise be lost due to population based inferences across centers.
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Acknowledgements
Financial assistance from DST (grant no. SB/FTP/ETA-353/2013) New Delhi, India to DRB. The data used in this study was acquired through and provided by the Biomedical Informatics Research Network under the following support: U24-RR021992, Function BIRN and U24 GM104203, Bio-Informatics Research Network Coordinating Center (BIRN-CC).
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Thomas, A.J., Bathula, D.R. (2022). 3D Multi-voxel Pattern Based Machine Learning for Multi-center fMRI Data Normalization. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_45
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