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Traditional Bivariate Functional Connectivity (BFC) computes correlations between two target brain regions (ROIs), which has the potential weakness of ignoring associations with other ROIs. Multivariate functional connectivity (MFC) solves for weights between one target ROI and the rest of the brain; thereby changing the question of correlation to a question of prediction, which is potentially more powerful. The fundamental difference lies essentially in multiplying the seed region time-course by the transpose of the data matrix (BFC) vs. multiplying it by the pseudoinverse of the data matrix (MFC). Human resting-state DOT data was analyzed for homotopic contralateral FC using both approaches.
Wiete Fehner,Morgan Fogarty,Mark A. Anastasio, andJoseph P. Culver
"Evaluation of multivariate approaches to functional connectivity mapping with fNIRS", Proc. SPIE PC12365, Neural Imaging and Sensing 2023, PC123650C (17 March 2023); https://doi.org/10.1117/12.2649434
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Wiete Fehner, Morgan Fogarty, Mark A. Anastasio, Joseph P. Culver, "Evaluation of multivariate approaches to functional connectivity mapping with fNIRS," Proc. SPIE PC12365, Neural Imaging and Sensing 2023, PC123650C (17 March 2023); https://doi.org/10.1117/12.2649434