Individual Differences in Cognitive Function in Older Adults Predicted by Neuronal Selectivity at Corresponding Brain Regions

Abstract

Relating individual differences in cognitive abilities to neural substrates in older adults is of significant scientific and clinical interest, but remains a major challenge. Previous functional magnetic resonance imaging (fMRI) studies of cognitive aging have mainly focused on the amplitude of fMRI response, which does not measure neuronal selectivity and has led to some conflicting findings. Here, using local regional heterogeneity analysis, or Hcorr, a novel fMRI analysis technique developed to probe the sparseness of neuronal activations as an indirect measure of neuronal selectivity, we found that individual differences in two different cognitive functions, episodic memory and letter verbal fluency, are selectively related to Hcorr-estimated neuronal selectivity at their corresponding brain regions (hippocampus and visual-word form area, respectively). This suggests a direct relationship between cognitive function and neuronal selectivity at the corresponding brain regions in healthy older adults, which in turn suggests that age-related neural dedifferentiation might contribute to rather than compensate for cognitive decline in healthy older adults. Additionally, the capability to estimate neuronal selectivity across brain regions with a single data set and link them to cognitive performance suggests that, compared to fMRI-adaptation—the established fMRI technique to assess neuronal selectivity, Hcorr might be a better alternative in studying normal aging and neurodegenerative diseases, both of which are associated with widespread changes across the brain.

Exercise, Fitness and the Aging Brain: A Review of Functional Connectivity in Aging

“…That is, since the overactivation in certain brain regions is often associated with better behavioral performance, older adults displaying such a pattern are effectively compensating for brain aging. Nevertheless, not all age-related increases in brain activation can be interpreted as compensatory, and some are more indicative of neural inefficiency, impending cognitive impairment, and loss of functional independence (Dickerson et al, 2005;Jiang, Petok, Howard, & Howard, 2017;Woodard et al, 2010).…”

Does physical exercise improve perceptual skills and visuospatial attention in older adults? A review

“…According to this theory, increased activation, especially in frontal areas, serves as a compensatory mechanism used by older adults to respond to the demands of certain cognitive tasks. However, it has been suggested [17] that such age-related neural dedifferentiation could contribute to rather than compensate for cognitive age-related declines, as the involvement of additional brain regions of older adults might in part be due to the decrease in neuronal selectivity, which would be a consequence of rather than compensation for cognitive decline.…”

Local response heterogeneity indexes experience-based neural differentiation in reading

“…We introduce an adapted version of the Heterogeneity Correlation (Hcorr) analysis (Jiang et al, 2013, 2017), which was originally developed as a technique for quantifying the relative similarity of BOLD signal across adjacent voxels within the context of a specific task or condition. Although the goal of Hcorr is the same as in the Local-Hreg analysis, the technique is algorithmically different in that it does not rely on a GLM (specifically gPPI) approach, but instead segments the BOLD time series and specifically quantifies the variability of local correlations.…”

“…For example, Zang et al (2004) developed a regional homogeneity (ReHo) analysis which calculates a coherence value for each voxel based on the correlations of its time series with those of its nearest neighbors. More recently, Jiang et al (2013, 2017) developed a similar approach - heterogeneity correlation (Hcorr) - which quantifies local heterogeneity by extracting task-relevant portions of the fMRI time series from adjacent voxels, and then calculates the standard error of the mean of the cross-correlations. The assumption is that well-learned as compared to poorly-learned stimuli recruit a smaller set of sharply tuned neurons, producing relatively more heterogeneous responses across voxels.…”