The Aging Navigational System

Abstract

The discovery of neuronal systems dedicated to computing spatial information, composed of functionally distinct cell types such as place and grid cells, combined with an extensive body of human-based behavioral and neuroimaging research has provided us with a detailed understanding of the brain's navigation circuit. In this review, we discuss emerging evidence from rodents, non-human primates, and humans that demonstrates how cognitive aging affects the navigational computations supported by these systems. Critically, we show 1) that navigational deficits cannot solely be explained by general deficits in learning and memory, 2) that there is no uniform decline across different navigational computations, and 3) that navigational deficits might be sensitive markers for impending pathological decline. Following an introduction to the mechanisms underlying spatial navigation and how they relate to general processes of learning and memory, the review discusses how aging affects the perception and integration of spatial information, the creation and storage of memory traces for spatial information, and the use of spatial information during navigational behavior. The closing section highlights the clinical potential of behavioral and neural markers of spatial navigation, with a particular emphasis on neurodegenerative disorders.

Keywords: Alzheimer’s disease; aging; cognitive map; dementia; entorhinal cortex; grid cells; hippocampus; memory; place cells; spatial navigation.

Figure 1. Navigation within different spatial reference frames and spatial scales

(A) An example of an Allocentric Reference Frame. Spatial information, such as the position of a landmark, is encoded with respect to other objects in the environment, i.e., the edge of the soccer field. The solid vertical arrow represents the allocentric reference direction that is fixed with respect to the dominant geometric boundary, and hatched lines represent the allocentric direction to other features in the space (e.g., the soccer goal). (B) An example of an Egocentric Reference Frame. The solid vertical arrow represents the egocentric reference direction, which is aligned to the orientation of the observer, and hatched lines denote the egocentric self-to-object distance and direction. (C) An example of a typical Vista Space for humans (top) and rats (bottom). Vista space refers to the space that is visible from a single location with little or no movement (Wolbers and Wiener, 2014). Both the person standing in the corner of the open soccer stadium and the rat performing the Morris Water Maze task have nearly full visual access to their surroundings. (D) An example of a typical Environmental Space for humans (top) and rats (bottom). Environmental space refers to large-scale spaces that require substantial movement or exploration to be sampled and comprehended. For humans city environments such as the example shown (top), or complex mazes with multiple hallways qualify as environmental spaces. In animal studies of navigation, multicompartment environments, such as the Hairpin Maze shown (bottom), would qualify as an environmental space. (E) The visual field of view is shown for an observer within an example environmental space. The pink shaded region represents the visual horizon for the observer. For any location within an environmental space the visual horizon is limited, requiring the navigator to move through multiple connected (vista) spaces and to integrate information over extended space and time to create a complete representation of that space. Furthermore, target locations may lie outside the sensory horizon, such as the bus stop in in the example shown, requiring the planning of more complex routes with multiple decision points.

Figure 2. Correspondence of age-related navigational deficits in rodents and humans: Cognitive mapping

(A) Aged rats periodically generate new and distinct cognitive maps after repeated exposure to a familiar environment. Place field distributions are shown for one young and one old rat recorded over two consecutive episodes of running on a figure-8 maze (Barnes et al., 1997). Individual place cells are denoted with colored points. Place-field maps of young animals are highly correlated between consecutive exposures while aged animals occasionally showed uncorrelated firing, indicative of remapping. When the proportion of rats that remapped between morning and afternoon sessions was tracked over 31 days (Schimanski et al., 2013), maps were stable between sessions until day 14, when both age groups began to show periodic remapping episodes, with aged animals remapping more frequently. Aged rats also show reduced spatial navigation accuracy in the Morris Water Maze task (Lindner, 1997) (bottom right, shows performance for rats from age 1.3 to 26.3 months old). (B) In a virtual Morris Water Maze task, there was a non-linear relationship between age and total distance traveled in the virtual environment (Moffat and Resnick, 2002) in humans. As with a number of rodent aging studies (e.g., A, bottom right), older adults took a longer time and traversed a greater linear distance in locating the hidden platform compared to younger adults (bottom). In a neuroimaging study (top), older adults showed reduced activation in the hippocampus and parahippocampal gyrus and in the retrosplenial cortex compared to younger participants (Moffat et al., 2006).

Figure 3. Neurophysiological changes with age

Graphical overview of the major age-related neurophysiological changes discussed in the text. Examples from young animals are indicated in dark gray and those from age animals are indicated with light gray. NMDA, N-methyl-D-aspartate; mGluR-5, metabotropic glutamate receptor 5; GABA(B), gamma-amino butyric acid receptor B-type; PSD, postsynaptic density; LTP, long- term potentiation; LTD, long- term depression.

Figure 4. Correspondence of age-related navigational deficits in rodents and humans: Strategy preferences

(A) In the T-maze task, an egocentric strategy was coded when a rat ‘turned right’ following 180° rotation of the start location and an allocentric strategy was coded when an animal moved to the same learned goal location relative to the external cues. Older rats overwhelmingly revealed an egocentric strategy on the probe trials (Barnes et al., 1980). (B) In the virtual Y-maze task, an egocentric strategy was coded when a human participant ‘turned right’ following displacement to a new starting location and an allocentric strategy was coded when the participant moved to the same learned goal location in absolute space (Rodgers et al., 2012). As with the rodent study (A), older adults spontaneously chose an egocentric strategy over an allocentric strategy compared to younger adults. (C) Similar results are observed in an environmental space task (top), in which older adults were impaired at switching from a learned route to a more optimal allocentric strategy, leading to increased route lengths and a reduced use of shortcuts (Harris and Wolbers, 2014). Critically, in a task that distinguishes between allocentric and two egocentric (beacon/associative cue, not shown) strategies (bottom), older adults remain biased toward using an egocentric navigation strategy (Wiener et al., 2013). These results suggest that egocentric strategy preferences are difficult to overcome for older adults, even when they are maladaptive and lead to suboptimal task performance.

Figure 5. Alzheimer’s disease-related grid cell dysfunction in mice and humans

(A) Firing rate map (left panels) and spatial autocorrelograms (right panels) are shown for an example grid cell recorded from a control mouse and a grid cell from an age-matched mouse expressing a human tau mutation (EC-Tau; Fu et al., 2017). (B) The EC-Tau mice who formed mature tangles also had impaired grid cell function at 30+ months of age. (C) Top-down (top panel) and first-person (bottom panel) view of a virtual reality environment used to test memory performance and grid cell function in young controls and young human adults at risk of developing AD (i.e., those carrying the APOE- ε4 allele; Kunz et al., 2015). (D) fMRI was used to assess a correlate of grid cell activity in the entorhinal cortex, and the pattern of activity suggested disrupted grid cell-like representations in the at risk population.

Figure 6. Mechanisms underlying navigational deficits in old age

The age-related neurophysiological changes described in Figure 3 affect neural computations in multiple (sub-)cortical structures, thus leading to changes in general learning and memory processes (upper left box) as well as changes in processes that are more specific to spatial cognition (upper right box). Given that aberrant retrosplenial processing and altered hippocampal dynamics (which include aberrant pattern separation/completion, delayed spatial firing, etc.) are thought to also play a more general role in episodic memory, these processing deficits are likely to contribute to navigational deficits at both levels. Potential changes in grid, border or head direction cell coding are highlighted to indicate that direct evidence is missing at present. Collectively, both general learning and memory deficits as well as spatially specific changes can give rise to multiple functional deficits (middle boxes). These functional deficits will in turn affect various navigational processes (lower box), thereby causing the everyday navigational difficulties (e.g., impaired cognitive mapping) often seen in older adults.