Fig 1.
Functional near-infrared spectroscopy (fNIRS) records cortical hemodynamic responses in populations that cannot comfortably be inside the MR scanner such as young infants.
Pairs of detectors and emitters form an fNIRS channel (from Gervain et al., 2011 with permission) which covers a localizable region of the cortex.
Fig 2.
Illustration of the multivariate methods applied to fNIRS in this paper.
Fig 3.
Depiction of the two datasets and the decoding results (infant-level and trial-level) for each.
Error bars depict the bootstrapped confidence intervals of the mean across infants.
Fig 4.
Decoding accuracy of infant-level activation patterns by subset size for Datasets #1 (purple boxes) and #2 (blue boxes).
Far right, decoding using three most informative channels (most informative channels determined using subset size 2, Fig 3). Note: For the subset size of 10 channels, there is only one subset and so there is no range to estimate.
Fig 5.
Accuracy for each of the 10 NIRS channels for Dataset #1 (left) and Dataset #2 (right) in different subset sizes (from 2 to 10 channels with each line labeled at the right with the subset size).
Fig 6.
Comparison of the relative informativeness across channels from multivariate analysis (from dark to light, least to most informative respectively) and channels which exhibit a significant difference between the same two conditions in a univariate analysis.
Across both datasets, only a single channel that exhibits a significant univariate response is one of the most informative channels in the multivariate analyses. In Dataset #2, not a single channel was significant for our univariate analysis but we achieve significant infant-level decoding in the multivariate analysis.