Working memory performance in the elderly relates to theta-alpha oscillations and is predicted by parahippocampal and striatal integrity

Abstract

The ability to maintain information for a short period of time (i.e. working memory, WM) tends to decrease across the life span with large inter-individual variability; the underlying neuronal bases, however, remain unclear. To address this issue, we used a multimodal imaging approach (voxel-based morphometry, diffusion-tensor imaging, electroencephalography) to test the contribution of brain structures and neural oscillations in an elderly population. Thirty-one healthy elderly participants performed a change-detection task with different load conditions. As expected, accuracy decreased with increasing WM load, reflected by power modulations in the theta-alpha band (5-12 Hz). Importantly, these power changes were directly related to the tract strength between parahippocampus and parietal cortex. Furthermore, between-subject variance in gray matter volume of the parahippocampus and dorsal striatum predicted WM accuracy. Together, our findings provide new evidence that WM performance critically depends on parahippocampal and striatal integrity, while theta-alpha oscillations may provide a mechanism to bind the nodes within the WM network.

Figure 1

WM task design. Participants had to retain the colors of two, four or six squares (sample array) and indicate if one of the squares had changed color or not in the test array.

Figure 2

Masks used for DTI based tractography. Mask of the (A) left parietal cortex and (B) left dorsolateral prefrontal cortex (dlPFC). Both masks were based on a meta-analysis of 901 working memory studies (see text) and superimposed on a mean T₁-weighted image derived from all subjects (for display purposes only).

Figure 3

Behavioral results. (A) Reaction time increased and (B) accuracy decreased as a function of load. Average mean values are plotted with standard errors (** p < 0.001; n.s. = not significant, p > 0.017).

Figure 4

Neural oscillations during retention. (A) Theta-alpha power was significantly decreased during the retention period for high vs. low load (averaged over all electrodes). Non-significant samples are displayed opaque. The right plot shows the topographical distribution of the effect on the left, averaged over significant time windows and frequencies (all electrodes significant). (B) Time-frequency plots of the relative change in power from baseline for each load condition during the retention period (averaged over all electrodes).

Figure 5

Relationship between gray matter and accuracy. (A) Within the parahippocampal cortex, gray matter volume correlated with high and (post-hoc) medium load. (B) Within the dorsal striatum (putamen/pallidum), gray matter volume correlated with accuracy under medium, with a trend for correlation with high load (post-hoc). Statistical parametric maps (SPM) in (A,B) (left column) were based on whole brain regression analyses with either high (A) or medium (B) load, and parameter estimates were extracted for planned post-hoc analysis with accuracy for the respective load condition (regression plots).

Figure 6

DTI tractography results. (A) Sample tract between parahippocampal cortex (PHC) and parietal cortex for one single subject (thresholded at 25% connectivity for display purposes). (B) Between-subjects correlations between connectivity strength and the load-specific power change as derived from EEG (see Fig. 4A).