Age-related differences in prefrontal glutamate are associated with increased working memory decay that gives the appearance of learning deficits

Abstract

The ability to use past experience to effectively guide decision making declines in older adulthood. Such declines have been theorized to emerge from either impairments of striatal reinforcement learning systems (RL) or impairments of recurrent networks in prefrontal and parietal cortex that support working memory (WM). Distinguishing between these hypotheses has been challenging because either RL or WM could be used to facilitate successful decision making in typical laboratory tasks. Here we investigated the neurocomputational correlates of age-related decision making deficits using an RL-WMtask to disentangle these mechanisms, a computational model to quantify them, and magnetic resonance spectroscopy to link them to their molecular bases. Our results reveal that task performance is worse in older age, in a manner best explained by working memory deficits, as might be expected if cortical recurrent networks were unable to sustain persistent activity across multiple trials. Consistent with this, we show that older adults had lower levels of prefrontal glutamate, the excitatory neurotransmitter thought to support persistent activity, compared to younger adults. Individuals with the lowest prefrontal glutamate levels displayed the greatest impairments in working memory after controlling for other anatomical and metabolic factors. Together, our results suggest that lower levels of prefrontal glutamate may contribute to failures of working memory systems and impaired decision making in older adulthood.

Differential Aging Effects on Implicit and Explicit Sensorimotor Learning

“…The most striking finding in both the meta-analysis and Experiment 2 is the age-related enhancement of implicit recalibration. The absence of a deficit on this process jives with other reports of preserved implicit learning in older adults seen in other domains including reinforcement learning (Rmus et al, 2023), sequence learning (Rieckmann & Bäckman, 2009), and implicit priming (Mitchell et al, 1990). The preservation of function has been hypothesized to reflect slower age-related decline of function in subcortical areas such as the cerebellum relative to areas such as prefrontal cortex that are associated with explicit learning and cognitive control (Hedden & Gabrieli, 2005).…”

Artificial neural networks for model identification and parameter estimation in computational cognitive models

“…Furthermore, by estimating model parameters that quantify underlying cognitive processes, researchers have been able to characterize important individual differences (e.g. developmental: [7][8][9][10]; clinical: [11][12][13][14][15]) as well as condition effects [16,17].…”

Artificial neural networks for model identification and parameter estimation in computational cognitive models

“…Furthermore, by estimating model parameters that quantify underlying cognitive processes, researchers have been able to characterize important individual differences (e.g. developmental: Eppinger et al, 2013 ; Hauser et al, 2015 ; Nussenbaum et al, 2022 ; Rmus et al, 2023 ; clinical: C. Chen et al, 2015 ; Collins et al, 2014 ; Gillan et al, 2016 ; Peterson et al, 2009 ; Zou et al, 2022 ) as well as condition effects ( Sheynin et al, 2015 ; Weber et al, 2022 ).…”