Abstract
.Significance: Solutions for group-level analysis of connectivity from fNIRS observations exist, but groupwise explorative analysis with classical solutions is often cumbersome. Manifold-based 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 and reduced 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 for [A.U.] and [A.U.] for . Agreement with classical group analysis was for . Agreement with PPI analysis was for and for . IFM successfully decoded group differences [ANOVA: : ; ; : ; ].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.
Highlights
Functional near-infrared spectroscopy uses infrared light to probe indirect markers of brain hemodynamics.[1]
interpolated functional manifold (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 Functional near-infrared spectroscopy (fNIRS) datasets
Activity analysis can be achieved for instance by projecting to the manifold a synthetic point encoding the convolution of a given stimulus train with some hypothesized hemodynamic response function
Summary
Functional near-infrared spectroscopy (fNIRS) uses infrared light to probe indirect markers of brain hemodynamics.[1] The continuous wave submodality continuously irradiates the scalp with near-infrared light often at several wavelengths. Attenuated backscattered light is detected with photodiodes. Light absorption changes are related to differential changes in oxyhemoglobin (ΔcHbO2), deoxyhemoglobin (ΔcHbR), and total hemoglobin (ΔcHbT) that might be associated with neural activity. In some applications of fNIRS neuroimaging, the inference of brain activity at the group level is an important aspect of supporting or refuting the neuroscience hypothesis. Classical statistics has made an excellent work in allowing analysis of the cortical activity records.[2] Random effects or second-level models are traditional avenues for group-level
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
