Large-scale networks in learning analyzed with partial least squares

The development of the ability to express learned behavior during the postnatal period is presumably related to maturational changes in the recruitment of particular neural systems to guide the behavior. By assessing brain functional activity during transitional periods of behavioral development, we may gain valuable insight into when particular neural systems come on-line and impact behavior. These issues were investigated by applying Partial Least Squares (PLS) analysis to metabolic mapping data obtained from developing rats. Preweanling rat pups aged postnatal day 12 (P12) and P17 were trained on two different instrumental reward schedules, injected with fluorodeoxyglucose (FDG), and then shifted to continuous nonreward (extinction). Behavior during extinction varied with training in P17 pups but not P12 pups. In the first application of PLS, an analysis analogous to a traditional univariate means analysis was performed to identify large scale networks, within 39 regions of interest, either commonly activated across groups or which differentiated groups. A second application of PLS was used to identify dominant patterns of covariances between regions (i.e. functional connectivity) that distinguished training and age groups.