Mapping fNIRS to fMRI with Neural Data Augmentation and Machine Learning Models
This work addresses the high cost and accessibility limitations of fMRI by providing a potential surrogate marker using fNIRS, which could benefit research on populations like infants, though it is incremental in applying existing methods to a new data mapping task.
The study tackled the problem of predicting fMRI markers from fNIRS data using machine learning and data augmentation, achieving successful prediction of response inhibition and prediction error signals from 48-channel fNIRS activation in the prefrontal cortex with 50 human participants.
Advances in neuroimaging techniques have provided us novel insights into understanding how the human mind works. Functional magnetic resonance imaging (fMRI) is the most popular and widely used neuroimaging technique, and there is growing interest in fMRI-based markers of individual differences. However, its utility is often limited due to its high cost and difficulty acquiring from specific populations, including children and infants. Surrogate markers, or neural correlates of fMRI markers, would have important practical implications, but we have few stand-alone predictors for the fMRI markers. Here, using machine learning (ML) models and data augmentation, we predicted well-validated fMRI markers of human cognition from multivariate patterns of functional near-infrared spectroscopy (fNIRS), a portable and relatively inexpensive optical neuroimaging technique. We recruited 50 human participants who performed two cognitive tasks (stop signal task and probabilistic reversal learning task), while neural activation was measured with either fNIRS or fMRI at each of the total two visits. Using ML models and data augmentation, we could predict the well-established fMRI markers of response inhibition or prediction error signals from 48-channel fNIRS activation in the prefrontal cortex. These results suggest that fNIRS might offer a surrogate marker of fMRI activation, which would broaden our understanding of various populations, including infants.