Interpolated functional manifold for functional near-infrared spectroscopy analysis at group level

Significance: Solutions for group-level analysis of connectivity from fNIRS observations exist, but groupwise explorative analysis with classical solutions is often cumbersome. Manifoldbased solutions excel at data exploration, but there are infinite surfaces crossing the observations cloud of points. Aim: We aim to provide a systematic choice of surface for a manifold-based analysis of connectivity at group level with small surface interpolation error. Approach: This research introduces interpolated functional manifold (IFM). IFM builds a manifold from reconstructed changes in concentrations of oxygenated ΔcHbO2 and reduced ΔcHbR hemoglobin species by means of radial basis functions (RBF). We evaluate the root mean square error (RMSE) associated to four families of RBF. We validated our model against psychophysiological interactions (PPI) analysis using the Jaccard index (JI). We demonstrate the usability in an experimental dataset of surgical neuroergonomics. Results: Lowest interpolation RMSE was 1.26e − 4 1.32e − 8 for ΔcHbO2 [A.U.] and 4.30e − 7 2.50e − 13 [A.U.] for ΔcHbR. Agreement with classical group analysis was JI ¼ 0.89 0.01 for ΔcHbO2. Agreement with PPI analysis was JI ¼ 0.83 0.07 for ΔcHbO2 and JI ¼ 0.77 0.06 for ΔcHbR. IFM successfully decoded group differences [ANOVA: ΔcHbO2: Fð2;117Þ ¼ 3.07; p < 0.05; ΔcHbR: Fð2;117Þ ¼ 3.35; p < 0.05]. Conclusions: IFM provides a pragmatic solution to the problem of choosing the manifold associated to a cloud of points, facilitating the use of manifold-based solutions for the group analysis of fNIRS datasets.