Near infrared spectroscopy of deep brain structures in the neonate

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Functional neuroimaging technologies that are sensitive to haemodynamic changes in the brain including functional near infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI) have advanced rapidly, providing insight into adult brain functional connectivity. However, using fNIRS and fMRI to study newborn brain development has been limited. The proposed set of experiments seeks to advance current fNIRS analytic methods, using fMRI as a validation method, in order to use fNIRS to study functional brain connectivity in newborns. fNIRS is an emerging neonatal brain-imaging technique that measures haemodynamic changes at the cortical surface. fMRI is a preferred method for measuring neonatal brain hemodynamics due to its ability to detect T2* signal throughout the brain. While fNIRS offers a number of advantages for assessing neonatal populations including its high temporal resolution (~100ms), low-cost and portability, its use for the study of subcortical-cortical connectivity has been limited due the technical challenges of obtaining haemodynamic changes below the cortical surface. In order to localize haemodynamic changes in deep brain structures in the neonatal brain, a novel machine-learning algorithm is proposed to expand upon applications of fNIRS to the measurement of subcortical and cortical haemodynamics underlying emerging cognitive and motor systems in the newborn. To achieve the long-term goal of utilizing fNIRS for the study of neonatal subcortical-cortical connectivity, we need to (1) develop a prediction method to extrapolate subcortical haemodynamics; (2) validate the prediction model based on established methods of cortical-subcortical connectivity using fMRI; and (3) apply the prediction method to fNIRS neonatal haemodynamic data. The development of fNIRS systems will offer new insights into how early network connectivity involving specific brain systems contributes to cognitive maturation in particular domains. By providing a better understanding of how individual differences in brain maturation may predict alterations in brain connectivity, this knowledge may lay the foundation for more effective early-intervention teaching methods calibrated to critical periods in the development of specific functional brain systems.