A virtual water maze revisited: Two-year changes in navigation performance and their neural correlates in healthy adults

Abstract

Age-related declines in spatial navigation are associated with deficits in procedural and episodic memory and deterioration of their neural substrates. For the lack of longitudinal evidence, the pace and magnitude of these declines and their neural mediators remain unclear. Here we examined virtual navigation in healthy adults (N=213, age 18-77 years) tested twice, two years apart, with complementary indices of navigation performance (path length and complexity) measured over six learning trials at each occasion. Slopes of skill acquisition curves and longitudinal change therein were estimated in structural equation modeling, together with change in regional brain volumes and iron content (R2* relaxometry). Although performance on the first trial did not differ between occasions separated by two years, the slope of path length improvement over trials was shallower and end-of-session performance worse at follow-up. Advanced age, higher pulse pressure, smaller cerebellar and caudate volumes, and greater caudate iron content were associated with longer search paths, i.e. poorer navigation performance. In contrast, path complexity diminished faster over trials at follow-up, albeit less so in older adults. Improvement in path complexity after two years was predicted by lower baseline hippocampal iron content and larger parahippocampal volume. Thus, navigation path length behaves as an index of perceptual-motor skill that is vulnerable to age-related decline, whereas path complexity may reflect cognitive mapping in episodic memory that improves with repeated testing, although not enough to overcome age-related deficits.

Keywords: Aging; Brain; Caudate; Hippocampus; Iron; Longitudinal.

Copyright © 2016 Elsevier Inc. All rights reserved.

Figures

Figure 1

An over-head diagram of the virtual Morris water maze. The location of the platform is illustrated by a square. The platform was stationary and hidden for all learning trials. The task was adopted from Moffat and Resnick (2002).

Figure 2

Mean acquisition curves across six repeated trials within a session at baseline and follow-up after approximately 2 years. Figures A1 (path length) and B1 (path complexity) display the original data as sample average performance and standard error of the means for each trial, and non-linear regression lines were fit to the acquisition curves—greater distance and fractal dimension indicate worse navigation skill. The lack of complete overlap between acquisition curves (solid lines) fit to the original data at each occasion is in accord with the significant latent change scores estimated from fitted non-linear growth curves. Figures A2 and B2 display the latent means at each occasion and bars represent bias-corrected bootstrapped 95% confidence intervals of the three components to the acquisition curves derived from the multilevel latent change score models; for all components, greater, more positive values correspond to worse performance. d is a standardized effect size of longitudinal latent change that can be compared between measures; * indicates significant change p < 0.01, Bonferroni correction α′ = 0.03. Intercept corresponds to performance on trial 1 as defined by the latent growth curves. Acquisition curves were defined as unspecified non-linear latent growth curves with loadings constrained to be equal between baseline and follow-up. End-of-session performance was a latent composite of performance on trials 4–6 (see Materials and Methods section for more detail). As seen in panel A, path length decreased across repeated trials within a session to indicate learning, but the acquisition was slower after the longitudinal delay and end-of-session performance was worse. In panel B, decreasing path complexity across trials supports learning, and steeper acquisition curves and better end-of-session performance at follow-up suggest greater repeated-testing gains after exposure to the task 2 years prior.

Figure 3

Example navigation paths from task trial 5 at baseline and follow-up. In each example, path length was greater at follow-up as compared to baseline, but path complexity was either similar or improved.

Figure 4

Individual longitudinal change trajectories between baseline and follow-up for path length (distance traveled) and complexity (fractal dimension, FD) in each of the three acquisition curve components: intercept (corresponding to task trial 1), acquisition curve (non-linear change across repeated trials within a session), and end-of-session (E-o-S) performance (composite of trials 4–6). Values are latent estimates derived from the multi-level latent change score models. For all components, greater, more positive values correspond to worse performance. Individuals significantly varied (p

Figure 5

Small caudate volume and greater iron content predicted longer paths at baseline and greater longitudinal decline in distance traveled. Values are latent estimates derived from the final multi-level latent change score models that included baseline and longitudinal change in caudate volume and iron, as well as baseline age, pulse pressure, and control variables. A regression line was fit to the scatter plots, including prediction intervals (long dash line) and 95% confidence intervals (short dash line). Greater, more positive values correspond to larger volumes, greater increase in iron content (change in R2*), and longer paths. Individuals with smaller caudate volume at baseline traveled longer paths on trial 1 (i.e., intercept; p = 0.001) and at end-of-session (E-o-S; p = 0.03). Individuals who demonstrated greater increase in iron content across 2 years showed a trend for greater longitudinal increase in end-of-session performance path length (i.e., greater decline in navigation skill; p = 0.06).

Figure 6

Greater hippocampal iron content and smaller parahippocampal gyrus volume predicted more complex paths (fractal dimension) at baseline and lesser longitudinal improvement. Values are latent estimates derived from the final multi-level latent change score models that included baseline and longitudinal change in hippocampal volume and iron content, or parahippocampal gyrus volume, as well as baseline age, pulse pressure, and control variables. A regression line was fit to the scatter plots, including prediction intervals (long dash line) and 95% confidence intervals (short dash line). Greater, more positive values correspond to larger volumes, greater iron content (R2*), and more complex paths. Individuals with smaller parahippocampal gyrus volume (p = 0.01) and greater hippocampal iron (p = 0.04) at baseline showed slower (less negative) acquisition curves at follow-up. Greater hippocampal iron at baseline also predicted lesser longitudinal improvement in end-of-session performance (E-o-S; p = 0.01).