Caudate nucleus-dependent navigation strategies are associated with increased risk-taking and set-shifting behavior

Getting LOST: A conceptual framework for supporting and enhancing spatial navigation in aging

Spatial navigation is more difficult and effortful for older than younger individuals, a shift which occurs for a variety of neurological, physical, and cognitive reasons associated with aging. Despite a large body of evidence documenting age-related deficits in spatial navigation, comparatively less research addresses how to facilitate more effective navigation behavior for older adults. Since navigation challenges arise for a variety of reasons in old age, a one-size-fits-all solution is unlikely to work. Here, we introduce a framework for the variety of spatial navigation challenges faced in aging, which we call LOST-Location, Orientation, Spatial mapping, and Transit. The LOST framework builds on evidence from the cognitive neuroscience of spatial navigation, which reveals distinct components underpinning human wayfinding. We evaluate research on navigational aids-devices and depictions-which help people find their way around; and we reflect on how navigation aids solve (or fail to solve) specific wayfinding difficulties faced by older adults. In summary, we emphasize a bespoke approach to improving spatial navigation in aging, which focuses on tailoring navigation solutions to specific navigation challenges. Our hope is that by providing precise support to older navigators, navigation opportunities can facilitate independence and exploration, while minimizing the danger of becoming lost. We conclude by delineating critical knowledge gaps in how to improve older adults' spatial navigation capacities that the novel LOST framework could guide to address. This article is categorized under: Psychology > Development and Aging Neuroscience > Cognition Neuroscience > Behavior.

Toward a statistical validation of brain signatures as robust measures of behavioral substrates

The "brain signature of cognition" concept has garnered interest as a data-driven, exploratory approach to better understand key brain regions involved in specific cognitive functions, with the potential to maximally characterize brain substrates of behavioral outcomes. Previously we presented a method for computing signatures of episodic memory. However, to be a robust brain measure, the signature approach requires a rigorous validation of model performance across a variety of cohorts. Here we report validation results and provide an example of extending it to a second behavioral domain. In each of two discovery data cohorts, we derived regional brain gray matter thickness associations for two domains: neuropsychological and everyday cognition memory. We computed regional association to outcome in 40 randomly selected discovery subsets of size 400 in each cohort. We generated spatial overlap frequency maps and defined high-frequency regions as "consensus" signature masks. Using separate validation datasets, we evaluated replicability of cohort-based consensus model fits and explanatory power by comparing signature model fits with each other and with competing theory-based models. Spatial replications produced convergent consensus signature regions. Consensus signature model fits were highly correlated in 50 random subsets of each validation cohort, indicating high replicability. In comparisons over each full cohort, signature models outperformed other models. In this validation study, we produced signature models that replicated model fits to outcome and outperformed other commonly used measures. Signatures in two memory domains suggested strongly shared brain substrates. Robust brain signatures may therefore be achievable, yielding reliable and useful measures for modeling substrates of behavioral domains.

Landmark-dependent Navigation Strategy Declines across the Human Life-Span: Evidence from Over 37,000 Participants

Humans show a remarkable capacity to navigate various environments using different navigation strategies, and we know that strategy changes across the life span. However, this observation has been based on studies of small sample sizes. To this end, we used a mobile app-based video game (Sea Hero Quest) to test virtual navigation strategies and memory performance within a distinct radial arm maze level in over 37,000 participants. Players were presented with six pathways (three open and three closed) and were required to navigate to the three open pathways to collect a target. Next, all six pathways were made available and the player was required to visit the pathways that were previously unavailable. Both reference memory and working memory errors were calculated. Crucially, at the end of the level, the player was asked a multiple-choice question about how they found the targets (i.e., a counting-dependent strategy vs. a landmark-dependent strategy). As predicted from previous laboratory studies, we found the use of landmarks declined linearly with age. Those using landmark-based strategies also performed better on reference memory than those using a counting-based strategy. These results extend previous observations in the laboratory showing a decreased use of landmark-dependent strategies with age.

Brain networks under uncertainty: A coordinate-based meta-analysis of brain imaging studies

In recent years, uncertainty has been extensively studied as a core factor in anxiety models. However, it remains unclear whether there is a stable brain circuitry to cope with uncertainty. Addressing this yet open question, we first distinguish uncertainty into three different states: risky, ambiguity, and threat anticipation. Then, we performed three meta-analyses of fMRI studies to identify those regions that are commonly activated by the three domains using activation likelihood estimation (ALE). The overlapping analyses of the three ALE maps revealed major conjunctions of the risk decision making, ambiguity decision making, and the threat anticipation in specifically the right insula. Contrast analysis further confirmed this finding. In addition, different uncertainty states also have different brain networks involved. Specifically, a large number of brain regions in the frontal-parietal cortex were recruited under ambiguity state, while subcortical gray matter regions were recruited under risk decision making, and the bilateral insula were closely associated with threat anticipation. Additionally, we assessed the co-activation pattern of identified regions using meta-analytic connectivity modeling (MACM) to investigate the potential network underlying the relationship of three domains. The MACM analysis further confirmed that different uncertain states have specific brain network basis. We concluded that the right insula serves as a convergent brain region for brain regions recruited for different uncertain states, and its co-activation pattern also corresponds to the brain network of the three uncertain states. This study is a preliminary attempt to further uncover the brain circuitry of anxiety models with uncertainty at their core.

Application of Real and Virtual Radial Arm Maze Task in Human

Virtual Reality (VR) emerges as a promising technology capable of creating different scenarios in which the body, environment, and brain are closely related, proving enhancements in the diagnosis and treatment of several spatial memory deficits. In recent years, human spatial navigation has increasingly been studied in interactive virtual environments. However, navigational tasks are still not completely adapted in immersive 3D VR systems. We stipulate that an immersive Radial Arm Maze (RAM) is an excellent instrument, allowing the participants to be physically active within the maze exactly as in the walking RAM version in reality modality. RAM is a behavioral ecological task that allows the analyses of different facets of spatial memory, distinguishing declarative components from procedural ones. In addition to describing the characteristics of RAM, we will also analyze studies in which RAM has been used in virtual modality to provide suggestions into RAM building in immersive modality.

The BDNF val66met polymorphism is associated with decreased use of landmarks and decreased fMRI activity in the hippocampus during virtual navigation

People can navigate in a new environment using multiple strategies dependent on different memory systems. A series of studies have dissociated between hippocampus-dependent 'spatial' navigation and habit-based 'response' learning mediated by the caudate nucleus. The val66met polymorphism of the brain-derived neurotrophic factor (BDNF) gene leads to decreased secretion of BDNF in the brain, including the hippocampus. Here, we aim to investigate the role of the BDNF val66met polymorphism on virtual navigation behaviour and brain activity in healthy older adults. A total of 139 healthy older adult participants (mean age = 65.8 ± 4.4 years) were tested in this study. Blood samples were collected, and BDNF val66met genotyping was performed. Participants were divided into two genotype groups: val homozygotes and met carriers. Participants were tested on virtual dual-solution navigation tasks in which they could use either a hippocampus-dependent spatial strategy or a caudate nucleus-dependent response strategy to solve the task. A subset of the participants (n = 66) were then scanned in a 3T functional magnetic resonance imaging (fMRI) scanner while engaging in another dual-solution navigation task. BDNF val/val individuals and met carriers did not differ in learning performance. However, the two BDNF groups differed in learning strategy. BDNF val/val individuals relied more on landmarks to remember target locations (i.e., increased use of flexible spatial learning), while met carriers relied more on sequences and patterns to remember target locations (i.e., increased use of inflexible response learning). Additionally, BDNF val/val individuals had more fMRI activity in the hippocampus compared with BDNF met carriers during performance on the navigation task. This is the first study to show in older adults that BDNF met carriers use alternate learning strategies from val/val individuals and to identify differential brain activation of this behavioural difference between the two groups.