Interpolated functional manifold for functional near-infrared spectroscopy analysis at group level
Author(s)
Ávila-Sansores, Shender-María
Rodríguez-Gómez, Gustavo
Tachtsidis, Ilias
Orihuela-Espina, Felipe
Type
Journal Article
Abstract
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.
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.
Date Issued
2020-11-27
Date Acceptance
2020-11-04
Citation
Neurophotonics, 2020, 7 (4), pp.045009-045009
ISSN
2329-423X
Publisher
SPIE
Start Page
045009
End Page
045009
Journal / Book Title
Neurophotonics
Volume
7
Issue
4
Copyright Statement
© The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.
Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.NPh.7.4.045009]
Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.NPh.7.4.045009]
License URL
Sponsor
Consejo Nacional de Ciencia y Tecnología (CONACYT) de México
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/33269300
PII: 20030RRR
Grant Number
237251
Subjects
connectivity analysis
functional connectivity
functional near-infrared spectroscopy
manifold
topology
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2020-11-27