Abstract
The MRI environment restricts the types of populations and tasks that can be studied by cognitive neuroscientists (e.g., young infants, face-to-face communication). FNIRS is a neuroimaging modality that records the same physiological signal as fMRI but without the constraints of MRI, and with better spatial localization than EEG. However, research in the fNIRS community largely lacks the analytic sophistication of analogous fMRI work, restricting the application of this imaging technology. The current paper presents a method of multivariate pattern analysis for fNIRS that allows the authors to decode the infant mind (a key fNIRS population). Specifically, multivariate pattern analysis (MVPA) employs a correlation-based decoding method where a group model is constructed for all infants except one; both average patterns (i.e., infant-level) and single trial patterns (i.e., trial-level) of activation are decoded. Between subjects decoding is a particularly difficult task, because each infant has their own somewhat idiosyncratic patterns of neural activation. The fact that our method succeeds at across-subject decoding demonstrates the presence of group-level multi-channel regularities across infants. The code for implementing these analyses has been made readily available online to facilitate the quick adoption of this method to advance the methodological tools available to the fNIRS researcher.
Highlights
The goal of cognitive neuroscience is to use the relationship between activity in the brain and cognitive operations to understand how the mind works
Multivariate methods can be more sensitive: prominent results in the fMRI literature have found that hemodynamic responses encode significant information about participants’ cognitive states without producing a robust univariate contrast (e.g., [1,19]). We demonstrate this same sort of result for the first time using Functional near-infrared spectroscopy (fNIRS) in infants: as is described in more detail below, we show that when the classifier’s task is to distinguish between two conditions which are both audio-visual but which differ in the specific nature of the audio-visual stimuli our multichannel decoding approach is able to succeed whereas purely univariate analysis fails
Since the current multivariate pattern analysis (MVPA) method works on multichannel patterns of arbitrary length, it is possible to perform the infant-level and trial-level analyses on subsets of the fNIRS channels, rather than using all available fNIRS channels as we reported in the previous sub-sections (10 channels)
Summary
The goal of cognitive neuroscience is to use the relationship between activity in the brain and cognitive operations to understand how the mind works. In the last two decades, the use of fMRI has vastly expanded our window on the neural correlates of human cognition. FMRI analyses predominantly facilitated brain mapping: Experiments could tell us where in the brain clusters of voxels show differential BOLD signals to two or more stimulus conditions. With the addition of multivariate analysis techniques (e.g., multivoxel pattern analysis, MVPA), more sophisticated questions can be asked, such as whether the pattern of BOLD can discriminate between two or more stimulus conditions.
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