Slowing down: age-related neurobiological predictors of processing speed

Abstract

Processing speed, or the rate at which tasks can be performed, is a robust predictor of age-related cognitive decline and an indicator of independence among older adults. This review examines evidence for neurobiological predictors of age-related changes in processing speed, which is guided in part by our source based morphometry findings that unique patterns of frontal and cerebellar gray matter predict age-related variation in processing speed. These results, together with the extant literature on morphological predictors of age-related changes in processing speed, suggest that specific neural systems undergo declines and as a result slow processing speed. Future studies of processing speed - dependent neural systems will be important for identifying the etiologies for processing speed change and the development of interventions that mitigate gradual age-related declines in cognitive functioning and enhance healthy cognitive aging.

Keywords: aging; cerebellum; prefrontal; processing speed; source based morphometry.

Figure 1

Voxel-based gray matter variation (modulated) predicts Connections Simple processing speed performance (A). These results are not significant at the p < 0.001 (uncorrected) level after controlling for age, thereby demonstrating the dependence of the results on age. Posterior cingulate, cuneus, cerebellar, and medial temporal regions exhibited weak associations with processing speed (p < 0.05, uncorrected) after controlling for age, suggesting that age may exaggerate normal variation in brain morphology and processing speed. SBM (please see Figure 3 for an SBM summary) demonstrates unique patterns of frontal and cerebellar gray matter variance (B,C) that each accounted for different distributions of the processing speed results [(D): p < 0.001 after controlling for variance representing either the frontal or the cerebellar independent components]. Importantly, by controlling for either gray matter component we can see that there are at least two sources that contribute to the association between gray matter and processing speed in this sample. In a separate analysis, these component specific effects were not significant using a p < 0.001 threshold when age was covaried. There were weak p < 0.05 associations with processing speed in cingulate and cerebellar regions after controlling for either component and age, again suggesting the influence of additive developmental factors (Deary et al., 2010). Results displayed on the MNI template. PS, processing speed; SBM, source based morphometry.

Figure 2

Voxel-based analysis of fractional anisotropy data demonstrates a right frontal association with Connections Simple processing speed (p < 0.01, family-wise discovery rate corrected) in 36 of the 42 subjects whose data were included in the Figure 1 analyses. The graph shows the association between frontal fractional anisotropy and the frontal gray matter component from Figure 1, the presence of white matter hyperintensities among people with low fractional anisotropy and low frontal gray matter (circle color: no hyperintensities – blue; some hyperintensities – orange; pronounced hyperintensities – green), and that PS is related to each of these variables (larger circle size reflects faster processing speed). (The TSPOON approach designed to control for smoothing kernel effects, Lee et al., , was used to process the 2 mm × 2 mm in plane resolution images that were collected on a Philips 3T with 32 directions using an 8-channel phased array head coil. The B0 and FA data were co-registered to each subject's T1 image and then normalized into study-specific space using the DARTEL parameters obtained for the T1 images.)

Figure 3

Source based morphometry or ICA of gray matter probability images across 42 subjects. Each subject's T1-weighted image is segmented to generate a gray matter probability image that is normalized to a common coordinate space and smoothed. ICA is performed across the sample of gray matter images. The degree to which each independent component (IC) or unique pattern of variance can be compared to the other ICs with a variety of metrics, including an estimate of similarity space. The brain regions that contribute most to each component can be identified by displaying each component with scaled intensity values (Z score = 1–3 above). Each IC also has an inverse component or regions that are negatively correlated with regions in the IC. An example is presented for IC7 where white matter hyperintensity related segmentation error (yellow arrow) is identified by ICA and is inversely correlated with decreased frontal gray matter (e.g., anterior cingulate, anterior insula, and superior frontal sulcus regions represented by hot signal intensities above). ICs 4 and 7 are discussed below and were uniquely related to processing speed. This is important because these results suggest there are independent age-based sources that affect gray matter variation in cerebellar (IC4) and frontal (IC7) regions that are associated with processing speed.

Figure 4

Summary of potential factors affecting frontal and cerebellar morphology and processing speed. On the left side of the figure, aging affects brain morphology through the damaging effects of oxidative stress and inflammation (small vessel disease is in red because of consistent evidence linking cerebral small vessel disease to structural declines and slowed processing speed). These detrimental effects are likely buffered by neuroprotective factors such as (1) leptin and growth factor levels that limit the impact of oxidative stress, (2) norepinephrine or blueberry limits on inflammatory responses in animal models (Heneka et al., ; Willis et al., 2010), (3) neurotrophic factors (Kim et al., in press; Raz et al., 2009), and (4) positive lifestyle behaviors such as aerobic exercise (Rosano et al., ; Voss et al., 2010). The gray arrow indicates that oxidative stress and inflammation likely affects cerebellar morphology, in comparison to the black arrows for which there is empirical evidence of significant associations. The right side of the figure is designed to emphasize that early development has a significant influence on the degree to which older adults will demonstrate atypical morphology and impaired processing speed (Deary et al., 2006, 2010), but could also influence the expression of neuroprotective factors and the development of lifestyle patterns of behavior and modulate risk for age-related neural declines. While this summary figure is general in nature, it is designed to emphasize the multifactorial and interactive effects of aging neural systems on processing speed.