Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS

• Published in: PloS one Vol. 12; no. 4; p. e0172500
• Main Authors: Emberson, Lauren L., Zinszer, Benjamin D., Raizada, Rajeev D. S., Aslin, Richard N.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20.04.2017; Public Library of Science (PLoS)
• Subjects: Activation; Artificial intelligence; Biology and Life Sciences; Brain - physiology; Cognitive ability; Correlation analysis; Data analysis; Decoding; EEG; Functional magnetic resonance imaging; Hemodynamics; Humans; Infant; Infants; Localization; Magnetic fields; Magnetic resonance imaging; Medical imaging; Medicine and Health Sciences; Methods; Multivariate Analysis; Neuroimaging; Neurology; Neurosciences; Pattern analysis; People and Places; Physical Sciences; Research and Analysis Methods; Scanners; Spatial discrimination; Spectroscopy, Near-Infrared - methods; Spectrum analysis; Studies; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0172500

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.