Older adults are impaired at implicit associative learning (IAL), or the learning of relationships between stimuli in the environment without conscious awareness. These age effects have been attributed to differential engagement of the basal ganglia (e.g. caudate, globus pallidus) and hippocampus throughout learning. However, no studies have examined gray matter diffusion relations with IAL, which can reveal microstructural properties that vary with age and contribute to learning. In this study, young (18-29 years) and older (65-87 years) adults completed the Triplet Learning Task, in which participants implicitly learn that the location of cues predict the target location on some trials (high frequency triplets). Diffusion imaging was also acquired and multicompartment diffusion metrics were calculated using neurite orientation dispersion and density imaging (NODDI). As expected, results revealed age deficits in IAL (smaller differences in performance to high versus low frequency triplets in the late learning stage) and age-related differences in basal ganglia and hippocampus free, hindered, and restricted diffusion. Significant correlations were seen between restricted caudate diffusion and early IAL and between hindered globus pallidus diffusion and late IAL, which were not moderated by age group. These findings indicate that individual differences in basal ganglia, but not hippocampal, gray matter microstructure contribute to learning, independent of age, further supporting basal ganglia involvement in IAL.

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Probabilistic reinforcement learning declines in healthy cognitive aging. While some findings suggest impairments are especially conspicuous in learning from rewards, resembling deficits in Parkinson’s disease, others also show impairments in learning from punishments. To reconcile these findings, we tested 252 adults from three age groups on a probabilistic reinforcement learning task, analyzed trial-by-trial performance with a Q-reinforcement learning model, and correlated both fitted model parameters and behavior to polymorphisms in dopamine-related genes. Analyses revealed that learning from both positive and negative feedback declines with age, but through different mechanisms: When learning from negative feedback, older adults were slower due to noisy decision-making; when learning from positive feedback, they tended to settle for a non-optimal solution due to an imbalance in learning from positive and negative prediction errors. The imbalance was associated with polymorphisms in the DARPP-32 gene and appeared to arise from mechanisms different from those previously attributed to Parkinson’s disease. Moreover, this imbalance predicted previous findings on aging using the Probabilistic Selection Task, which were misattributed to Parkinsonian mechanisms.

Relating individual differences in cognitive abilities to neural substrates in older adults is of significant scientific and clinical interest, but remains a major challenge. Previous functional magnetic resonance imaging (fMRI) studies of cognitive aging have mainly focused on the amplitude of fMRI response, which does not measure neuronal selectivity and has led to some conflicting findings. Here, using local regional heterogeneity analysis, or Hcorr, a novel fMRI analysis technique developed to probe the sparseness of neuronal activations as an indirect measure of neuronal selectivity, we found that individual differences in two different cognitive functions, episodic memory and letter verbal fluency, are selectively related to Hcorr-estimated neuronal selectivity at their corresponding brain regions (hippocampus and visual-word form area, respectively). This suggests a direct relationship between cognitive function and neuronal selectivity at the corresponding brain regions in healthy older adults, which in turn suggests that age-related neural dedifferentiation might contribute to rather than compensate for cognitive decline in healthy older adults. Additionally, the capability to estimate neuronal selectivity across brain regions with a single data set and link them to cognitive performance suggests that, compared to fMRI-adaptation—the established fMRI technique to assess neuronal selectivity, Hcorr might be a better alternative in studying normal aging and neurodegenerative diseases, both of which are associated with widespread changes across the brain.

Fast, inexpensive, and noninvasive identification of Alzheimer's disease (AD) before clinical symptoms emerge would augment our ability to intervene early in the disease. Individuals with fully penetrant genetic mutations causing autosomal dominant Alzheimer's disease (ADAD) are essentially certain to develop the disease, providing a unique opportunity to examine biomarkers during the preclinical stage. Using a generalization task that has previously shown to be sensitive to medial temporal lobe pathology, we compared preclinical individuals carrying ADAD mutations to noncarrying kin to determine whether generalization (the ability to transfer previous learning to novel but familiar recombinations) is vulnerable early, before overt cognitive decline. As predicted, results revealed that preclinical ADAD mutation carriers made significantly more errors during generalization than noncarrying kin, despite no differences between groups during learning or retention. This impairment correlated with the left hippocampal volume, particularly in mutation carriers. Such identification of generalization deficits in early ADAD may provide an easily implementable and potentially linguistically and culturally neutral way to identify and track cognition in ADAD.

Probabilistic category learning involves complex interactions between the hippocampus and striatum that may depend on whether acquisition occurs via feedback or observation. Little is known about how healthy aging affects these processes. We tested whether age-related behavioral differences in probabilistic category learning from feedback or observation depend on a genetic factor known to influence individual differences in hippocampal function, the KIBRA gene (single nucleotide polymorphism rs17070145). Results showed comparable age-related performance impairments in observational as well as feedback-based learning. Moreover, genetic analyses indicated an age-related interactive effect of KIBRA on learning: among older adults, the beneficial T-allele was positively associated with learning from feedback, but negatively with learning from observation. In younger adults, no effects of KIBRA were found. Our results add behavioral genetic evidence to emerging data showing age-related differences in how neural resources relate to memory functions, namely that hippocampal and striatal contributions to probabilistic category learning may vary with age. Our findings highlight the effects genetic factors can have on differential age-related decline of different memory functions.

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