Reviewer #1 (Public Review):
In this study, Sims et al. evaluate how system-level brain functional connectivity is associated with cognitive abilities in a sample of older adults aged > 85 years old. Because the study sample of 146 normal older adults has lived into advanced years of age, the novelty here is the opportunity to validate brain-behavioral associations in aging with a reduced concern of the potential influence of undetected incipient neuropsychological pathology. The participants afforded resting-state functional magnetic resonance imaging (rs-fMRI) data as well as behavioral neuropsychological test assessments of various cognitive abilities. Exploratory factor analysis was applied on the behavioral cognitive assessments to arrive at summary measures of participant ability in five cognitive domains including processing speed, executive functioning, episodic memory, working memory, and language. rsfMRI data were submitted to a graph-theoretic approach that derived underlying functional nodes in brain activity, the membership of these nodes in brain network systems, and indices characterizing the organizational properties of these brain networks. The study applies the classification of the various brain networks into a sensory/motor system of networks and an association system of network, with further sub-systems in the latter that includes the frontoparietal network (FPN), the default-mode network (DMN), the cingulo-opercular network (CON), and the dorsal (DA) and ventral (VA) attention networks. Amongst other graph metrics, the study focused on the extent to which networks in these brain systems were segregated (i.e., separable network communities as opposed to a more singular large community network). Evaluation of the brain network segregation indices and cognitive performance metrics showed that in general higher network functional segregation corresponds with higher cognitive performance ability. In particular, this association was seen between the general association system with overall cognition, and the FPN with overall cognition, and processing speed.
The results worthy of highlighting include the documentation of oldest-old individuals with detectable brain neural network segregration at the level of the association system and its FPN sub-system and the association of this brain functional state notably with general cognition and processing speed and less so with the other specific cognitive domains (such as memory). This finding suggests that (a) apparently better cognitive aging might stem from a specific level of neural network functional segregation, and (b) this linkage applies more specifically to the FPN and processing speed. These specific findings inform the broader conceptual perspective of how human brain aging that is normative vs. that which is pathological might be distinguished.
To show the above result, this study defined functional networks that were driven more by the sample data as opposed to a pre-existing generic template. This approach involves a watershed algorithm to obtain functional connectivity boundary maps in which the boundary brain image voxels separate functionally related voxels from unrelated voxels by virtue of their functional covariance as measured in the immediate data. This is also a notable objective and data-driven approach towards defining functional brain regions-of-interest (ROIs), nodes, and networks that are age-appropriate and configured for a given dataset as opposed to using network definitions based on other datasets used as a generic template.
The sample size of 146 for this age group is generally sufficient.
For the analyses considering the significance of the effect of the brain network metrics on the cognitive variables, the usage of heirarchical regression to evaluate whether the additional variables (in the full model) significantly change the model fit relative to the reduced model with covariates-only (data collection site, cortical thickness), while a possible approach, might be problematic, particularly when the full model uses many more regressors than the reduced model. In general, adding more variables to regression models reduces the residual variance. As such, it is possible that adding more regressors in a full model and comparing that to a reduced model with much fewer regressors would yield significant changes in the R^2 fit index, even if the added regressors are not meaningfully modulating the dependent variable. This may not be an issue for the finding on the FPN segregation effect on overall cognition, but it may be important in interpreting the finding on the association system metrics on overall cognition.
Critically, we should note that the correlation effect sizes (justified by the 0.23 value based on the reported power analyses) were all rather small in size. The largest key brain-behavior correlation effect was 0.273 (between DMN segregation and Processing Speed). In the broader perspective, such effects sizes generally suggest that the contribution of this factor is minimal and one should be careful that the results should be understood in this context.
Overall, the findings based on hierarchical regressions that evaluate the network segregation indices in accounting for cognition and the small correlation magnitudes are basically in line with the notion that more segregated neural networks in the oldest-old support better cognitive performance (particularly processing speed). However, the level of positive support for the notion based on these findings is somewhat moderate and requires further study.