Additionally our data raise the possibility that extreme gro
Additionally, our data raise the possibility that extreme group differences in aerobic fitness (e.g., 70th percentile versus 30th percentile VO2max) may be needed to demonstrate hippocampal volume effects (Chaddock et al., 2010a), whereas CBF differences may be a more sensitive marker to understand how small relative differences in aerobic fitness influence Talabostat mesylate health, particularly in terms of microcirculation, during development. We did not observe a significant relationship between aerobic fitness and hippocampal volume in the present study, which included a relatively lower fit sample of children (average VO2max percentile=33.5%). However, our previous work demonstrates that higher fit children (>70th percentile VO2max) show larger hippocampal volumes compared to their lower fit peers (<30th percentile VO2max) (Shvartz and Reibold, 1990). Thus, different child fitness spectrums in each study may lead to different results and outcomes. Whereas the present study provides a first step in understanding the predictive power of aerobic fitness in hippocampal perfusion during child development, questions still remain regarding the associations among individual differences in aerobic fitness, hippocampal structure and function, and performance on specific memory tasks (e.g., relational, spatial) which require additional research.
Despite our result that aerobic fitness is related to increased hippocampal blood flow in children, we acknowledge limitations of the study, including the choice of an ATT of 700ms used in post-processing CBF quantification. This value is used in the FSL software based on experience with adult ASL studies (Chappell et al., 2009), although it may not be appropriate for the study of a pediatric population. However, choice of ATT simply provides a scaling factor in CBF calculations and would not affect the relationships presented here. Future work may look to incorporate a multi-delay labeling scheme to simultaneously estimate ATT and CBF, thus providing a more quantitatively accurate measure. We also note that the parameters of the ASL sequence in this study used a short post-label delay for the tagging scheme employed. Specifically, we used a pCASL acquisition as it is recognized to offer the highest signal to noise ratio of all ASL labeling schemes, but with a post-label delay of 700ms. Subsequent to the present study, the consensus recommendation in the field of ASL acquisition was a longer post-labeling delay for routine pCASL perfusion studies of 1500ms for children (Alsop et al., 2015). A likely consequence of a shorter post-label delay is that some labeled blood may remain in the arterial vasculature, thereby not being delivered to the tissue at the time of imaging. At the voxel level, the image intensity may not be a pure measure of perfusion, but also include a contribution from arterial blood volume (Chappell et al., 2010). Thus, it is possible that the region of interest measurements are a mixture of CBF and arterial blood volume, with the amount of arterial blood in the signal dependent on the ATT and post-label delay. In addition, as we did not account for partial volume effects in the hippocampus, this may have contributed to the fitness associations we report. A goal for future research will be to use partial volume correction methods on ASL data derived from the hippocampus to determine whether the effects change after accounting for this factor.
This research has important implications, as physical activity is decreasing in and out of the school environment (Troiano et al., 2008), and children are becoming increasingly unfit (Centers for Disease Control and Prevention, 2009). We provide additional evidence to suggest that the developing brain may be plastic and sensitive to individual differences in aerobic fitness levels. Specifically, aerobic fitness may influence how the brain regulates its metabolic demands via blood flow in a region of the brain important for learning and memory.