Inter-individual differences in associative memory : structural and functional brain correlates and genetic modulators

Our memory for personal experiences (e.g., the first day at school) is termed episodic memory. This form of memory involves the recollection of single information as well as the connection between these pieces of information (e.g., what happened when, and where), referred to as associative memory. Associative memory declines markedly in aging; however, some individuals have proficient associative memory even until late life. These individual differences in associative-memory performance are also observable at younger ages. The underlying sources of these individual differences remain unclear. In this thesis, we aimed to identify the neural underpinnings of individual differences in associative memory, with special regard to brain structure, function, and neurochemistry. In the first part of the thesis, we investigated structural brain correlates of and dopaminergic contributions to associative memory in healthy older adults (studies I and II). In study I, we examined the relationship between regional gray-matter volume and associative memory. Individuals with better associative memory had larger gray-matter volume in dorsolateral and ventrolateral prefrontal cortex, suggesting that organizational and strategic processes distinguish older adults with good from those with poor associative memory. In study II, we examined the influence of dopamine (DA) receptor genes on item and associative memory. Individuals with less beneficial DA genotypes performed worse in the associative-memory task compared with carriers of more beneficial genotypes. Because no such group differences were found with regard to item memory, this suggests that dopaminergic neuromodulation is particularly important for associative memory in older adults. In the second part of the thesis, we examined in a sample of younger adults how different task instructions influence associative encoding, as well as the structural-functional coupling between task-relevant brain regions during associative-memory formation (studies III and IV). In study III, we investigated the effect of encoding instruction on associative memory. Specifically, we examined functional brain correlates of intentional and incidental encoding and demonstrated differential involvement of anterior hippocampus in intentional relative to incidental associative encoding. This suggests that the intent to remember associative information triggers a binding process accomplished by this brain region. Finally, in study IV we explored how gray-matter volume is associated with brain activity during associative-memory formation. We observed a relationship between gray-matter volume in the medial-temporal lobe (MTL) and functional brain activity in the inferior frontal gyrus (IFG). Importantly, this structure-function coupling correlated with performance, such that younger individuals with a stronger MTL-IFG coupling had better associative memory. Collectively, these four studies show that the neural underpinnings of individual differences in associative memory are many-faceted, interacting with each other and vary with regard to age and specific features of the associative task

Disconnected aging: cerebral white matter integrity and age-related differences in cognition.

Cognition arises as a result of coordinated processing among distributed brain regions and disruptions to communication within these neural networks can result in cognitive dysfunction. Cortical disconnection may thus contribute to the declines in some aspects of cognitive functioning observed in healthy aging. Diffusion tensor imaging (DTI) is ideally suited for the study of cortical disconnection as it provides indices of structural integrity within interconnected neural networks. The current review summarizes results of previous DTI aging research with the aim of identifying consistent patterns of age-related differences in white matter integrity, and of relationships between measures of white matter integrity and behavioral performance as a function of adult age. We outline a number of future directions that will broaden our current understanding of these brain-behavior relationships in aging. Specifically, future research should aim to (1) investigate multiple models of age-brain-behavior relationships; (2) determine the tract-specificity versus global effect of aging on white matter integrity; (3) assess the relative contribution of normal variation in white matter integrity versus white matter lesions to age-related differences in cognition; (4) improve the definition of specific aspects of cognitive functioning related to age-related differences in white matter integrity using information processing tasks; and (5) combine multiple imaging modalities (e.g., resting-state and task-related functional magnetic resonance imaging; fMRI) with DTI to clarify the role of cerebral white matter integrity in cognitive aging

Hippocampal subfields and limbic white matter jointly predict learning rate in older adults

First published online: 04 December 2019Age-related memory impairments have been linked to differences in structural brain parameters, including cerebral white matter (WM) microstructure and hippocampal (HC) volume, but their combined influences are rarely investigated. In a population-based sample of 337 older participants aged 61-82 years (Mage = 69.66, SDage = 3.92 years), we modeled the independent and joint effects of limbic WM microstructure and HC subfield volumes on verbal learning. Participants completed a verbal learning task of recall over five repeated trials and underwent magnetic resonance imaging (MRI), including structural and diffusion scans. We segmented three HC subregions on high-resolution MRI data and sampled mean fractional anisotropy (FA) from bilateral limbic WM tracts identified via deterministic fiber tractography. Using structural equation modeling, we evaluated the associations between learning rate and latent factors representing FA sampled from limbic WM tracts, and HC subfield volumes, and their latent interaction. Results showed limbic WM and the interaction of HC and WM-but not HC volume alone-predicted verbal learning rates. Model decomposition revealed HC volume is only positively associated with learning rate in individuals with higher WM anisotropy. We conclude that the structural characteristics of limbic WM regions and HC volume jointly contribute to verbal learning in older adults

Relationship between hippocampal structure and memory function in elderly humans

With progressing age, the ability to recollect personal events declines, whereas familiarity-based memory remains relatively intact. It has been hypothesized that age-related hippocampal atrophy may contribute to this pattern because of its critical role for recollection in younger humans and after acute injury. Here, we show that hippocampal volume loss in healthy older persons correlates with gray matter loss (estimated with voxel-based morphometry) of the entire limbic system and shows no correlation with an electrophysiological (event-related potential [ERP]) index of recollection. Instead, it covaries with more substantial and less specific electrophysiological changes of stimulus processing. Age-related changes in another complementary structural measure, hippocampal diffusion, on the other hand, seemed to be more regionally selective and showed the expected correlation with the ERP index of recollection. Thus, hippocampal atrophy in older persons accompanies limbic atrophy, and its functional impact on memory is more fundamental than merely affecting recollection

Neural correlates of cognitive intervention in persons at risk of developing Alzheimer's disease.

Cognitive training is an emergent approach that has begun to receive increased attention in recent years as a non-pharmacological, cost-effective intervention for Alzheimer's disease (AD). There has been increasing behavioral evidence regarding training-related improvement in cognitive performance in early stages of AD. Although these studies provide important insight about the efficacy of cognitive training, neuroimaging studies are crucial to pinpoint changes in brain structure and function associated with training and to examine their overlap with pathology in AD. In this study, we reviewed the existing neuroimaging studies on cognitive training in persons at risk of developing AD to provide an overview of the overlap between neural networks rehabilitated by the current training methods and those affected in AD. The data suggest a consistent training-related increase in brain activity in medial temporal, prefrontal, and posterior default mode networks, as well as increase in gray matter structure in frontoparietal and entorhinal regions. This pattern differs from the observed pattern in healthy older adults that shows a combination of increased and decreased activity in response to training. Detailed investigation of the data suggests that training in persons at risk of developing AD mainly improves compensatory mechanisms and partly restores the affected functions. While current neuroimaging studies are quite helpful in identifying the mechanisms underlying cognitive training, the data calls for future multi-modal neuroimaging studies with focus on multi-domain cognitive training, network level connectivity, and individual differences in response to training

Structural and functional differences in medial prefrontal cortex underlie distractibility and suppression deficits in ageing.

Older adults experience deficits in working memory (WM) that are acutely exacerbated by the presence of distracting information. Human neurophysiological studies have revealed that these changes are accompanied by a diminished ability to suppress visual cortical activity associated with task-irrelevant information. Although this is often attributed to deficits in top-down control from a prefrontal cortical source, this has not yet been directly demonstrated. Here we evaluate the neural basis of distraction's negative impact on WM and the impairment in neural suppression in older adults by performing structural and functional MRIs while older participants engage in tasks that require remembering relevant visual stimuli in the context of overlapping irrelevant stimuli. Analysis supports both an age-related distraction effect and neural suppression deficit, and extends our understanding by revealing an alteration in functional connectivity between visual cortices and a region in the default network, the medial prefrontal cortex (mPFC). Moreover, within the older population, the magnitude of WM distractibility and neural suppression are both associated with individual differences in cortical volume and activity of the mPFC, as well as its associated white-matter tracts

Limbic Tract Integrity Contributes to Pattern Separation Performance Across the Lifespan.

Accurate memory for discrete events is thought to rely on pattern separation to orthogonalize the representations of similar events. Previously, we reported that a behavioral index of pattern separation was correlated with activity in the hippocampus (dentate gyrus, CA3) and with integrity of the perforant path, which provides input to the hippocampus. If the hippocampus operates as part of a broader neural network, however, pattern separation would likely also relate to integrity of limbic tracts (fornix, cingulum bundle, and uncinate fasciculus) that connect the hippocampus to distributed brain regions. In this study, healthy adults (20-89 years) underwent diffusion tensor imaging and completed the Behavioral Pattern Separation Task-Object Version (BPS-O) and Rey Auditory Verbal Learning Test (RAVLT). After controlling for global effects of brain aging, exploratory skeleton-wise and targeted tractography analyses revealed that fornix integrity (fractional anisotropy, mean diffusivity, and radial diffusivity; but not mode) was significantly related to pattern separation (measured using BPS-O and RAVLT tasks), but not to recognition memory. These data suggest that hippocampal disconnection, via individual- and age-related differences in limbic tract integrity, contributes to pattern separation performance. Extending our earlier work, these results also support the notion that pattern separation relies on broad neural networks interconnecting the hippocampus