Touchscreen-Based Cognitive Training Alters Functional Connectivity Patterns in Aged But Not Young Male Rats.

Age-related cognitive decline is related to cellular and systems-level disruptions across multiple brain regions. Because age-related cellular changes within different structures do not show the same patterns of dysfunction, interventions aimed at optimizing function of large-scale brain networks may show greater efficacy at improving cognitive outcomes in older adults than traditional pharmacotherapies. The current study aimed to leverage a preclinical rat model of aging to determine whether cognitive training in young and aged male rats with a computerized paired-associates learning (PAL) task resulted in changes in global resting-state functional connectivity. Moreover, seed-based functional connectivity was used to examine resting state connectivity of cortical areas involved in object-location associative memory and vulnerable in old age, namely the medial temporal lobe (MTL; hippocampal cortex and perirhinal cortex), retrosplenial cortex (RSC), and frontal cortical areas (prelimbic and infralimbic cortices). There was an age-related increase in global functional connectivity between baseline and post-training resting state scans in aged, cognitively trained rats. This change in connectivity following cognitive training was not observed in young animals, or rats that traversed a track for a reward between scan sessions. Relatedly, an increase in connectivity between perirhinal and prelimbic cortices, as well as reduced reciprocal connectivity within the RSC, was found in aged rats that underwent cognitive training, but not the other groups. Subnetwork activation was associated with task performance across age groups. Greater global functional connectivity and connectivity between task-relevant brain regions may elucidate compensatory mechanisms that can be engaged by cognitive training.

Age-Related Changes in Risky Decision Making and Associated Neural Circuitry in a Rat Model.

Altered decision making at advanced ages can have a significant impact on an individual's quality of life and the ability to maintain personal independence. Relative to young adults, older adults make less impulsive and less risky choices; although these changes in decision making could be considered beneficial, they can also lead to choices with potentially negative consequences (e.g., avoidance of medical procedures). Rodent models of decision making have been invaluable for dissecting cognitive and neurobiological mechanisms that contribute to age-related changes in decision making, but they have predominantly used costs related to timing or probability of reward delivery and have not considered other equally important costs, such as the risk of adverse consequences. The current study therefore used a rat model of decision making involving risk of explicit punishment to examine age-related changes in this form of choice behavior in male rats, and to identify potential cognitive and neurobiological mechanisms that contribute to these changes. Relative to young rats, aged rats displayed greater risk aversion, which was not attributable to reduced motivation for food, changes in shock sensitivity, or impaired cognitive flexibility. Functional MRI analyses revealed that, overall, functional connectivity was greater in aged rats compared with young rats, particularly among brain regions implicated in risky decision making such as basolateral amygdala, orbitofrontal cortex, and ventral tegmental area. Collectively, these findings are consistent with greater risk aversion found in older humans, and reveal age-related changes in brain connectivity.

Age-related impairments on the touchscreen paired associates learning (PAL) task in male rats.

Discovery research in rodent models of cognitive aging is instrumental for identifying mechanisms of behavioral decline in old age that can be therapeutically targeted. Clinically relevant behavioral paradigms, however, have not been widely employed in aged rats. The current study aimed to bridge this translational gap by testing cognition in a cross-species touchscreen-based platform known as paired-associates learning (PAL) and then utilizing a trial-by-trial behavioral analysis approach. This study found age-related deficits in PAL task acquisition in male rats. Furthermore, trial-by-trial analyses and testing rats on a novel interference version of PAL suggested that age-related impairments were not due to differences in vulnerability to an irrelevant distractor, motivation, or to forgetting. Rather, impairment appeared to arise from vulnerability to accumulating, proactive interference, with aged animals performing worse than younger rats in later trial blocks within a single testing session. The detailed behavioral analysis employed in this study provides new insights into the etiology of age-associated cognitive deficits.

An Alternative to Dye-Based Approaches to Remove Background Autofluorescence From Primate Brain Tissue.

Brain tissue contains autofluorescing elements that potentially impede accurate identification of neurons when visualized with fluorescent microscopy. Age-related accumulation of molecules with autofluorescent properties, such as lipofuscin, can possess spectral profiles that invade the typical emission range of fluorophores commonly utilized in fluorescent microscopy. The traditional method for accounting for this native fluorescence is to apply lipophilic dyes that are able to sequester these unwanted signals. While effective, such dyes can present a range of problems including the obstruction of fluorescent probe emissions. The present study utilizes aged primate midbrain tissue stained for tyrosine hydroxylase and calbindin to investigate an image processing approach for removing autofluorescence utilizing spectral imaging and linear unmixing. This technique is then compared against the traditional, dye-based autofluorescence sequestration method using Sudan Black B (SBB). Spectral imaging and linear unmixing yielded significantly higher cell numbers than SBB treatment. This finding suggests that computational approaches for removing autofluorescence in neural tissue are both viable and preferential to dye-based approaches for estimation of cell body numbers.