Tracking Age Differences in Neural Distinctiveness across Representational Levels

Age differences in functional connectivity track dedifferentiation of category representations

With advancing age, the distinctiveness of neural representations of information declines. While the finding of this so-called 'age-related neural dedifferentiation' in category-selective neural regions is well-described, the contribution of age-related changes in network organization to dedifferentiation is unknown. Here, we asked whether age differences in a) whole-brain network segregation (i.e., network dedifferentiation) and b) functional connectivity to category-selective neural regions are related to regional dedifferentiation of categorical representations. Younger and older adults viewed blocks of face and house stimuli in the fMRI scanner. We found an age-related decline in neural distinctiveness for faces in the fusiform gyrus (FG) and for houses in the parahippocampal gyrus (PHG). Functional connectivity analyses revealed age-related dedifferentiation of global network structure as well as age differences in connectivity between the FG and early visual cortices. Interindividual correlations demonstrated that regional distinctiveness was related to network segregation. Together, our findings suggest that dedifferentiation of categorical representations may be linked to age-related reorganization of functional networks.

Age differences in neural distinctiveness during memory encoding, retrieval, and reinstatement

Robust evidence points to mnemonic deficits in older adults related to dedifferentiated, i.e. less distinct, neural responses during memory encoding. However, less is known about retrieval-related dedifferentiation and its role in age-related memory decline. In this study, younger and older adults were scanned both while incidentally learning face and house stimuli and while completing a surprise recognition memory test. Using pattern similarity searchlight analyses, we looked for indicators of neural dedifferentiation during encoding, retrieval, and encoding-retrieval reinstatement. Our findings revealed age-related reductions in neural distinctiveness during all memory phases in visual processing regions. Interindividual differences in retrieval- and reinstatement-related distinctiveness were strongly associated with distinctiveness during memory encoding. Both item- and category-level distinctiveness predicted trial-wise mnemonic outcomes. We further demonstrated that the degree of neural distinctiveness during encoding tracked interindividual variability in memory performance better than both retrieval- and reinstatement-related distinctiveness. All in all, we contribute to meager existing evidence for age-related neural dedifferentiation during memory retrieval. We show that neural distinctiveness during retrieval is likely tied to recapitulation of encoding-related perceptual and mnemonic processes.

The effects of age on category learning and prototype- and exemplar-based generalization

The need to learn new concepts and categories persists through the lifespan, yet little is known about how aging affects the concept learning and generalization. Here, we trained young and older adults to classify typical and boundary category members, and then tested category generalization to new stimuli. During training, older adults had increased difficulty compared to young adults learning category labels for boundary items, but not typical items. At test, categorization performance that included new items at all levels of typicality was comparable across age groups, but formal categorization models indicated that older adults relied to a greater degree on generalized (prototype) category representations than young adults. These findings align with the proposal that older adults are able to form category representations based on central tendency even when they have difficulty learning and remembering individual category members. More broadly, the results contribute to our understanding of multiple categorization strategies and the limited strategy flexibility in older adults. They also highlight how reliance on preserved cognitive functions may sometimes help older adults maintain performance. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Behavioral Studies Using Large-Scale Brain Networks – Methods and Validations

Mapping human behaviors to brain activity has become a key focus in modern cognitive neuroscience. As methods such as functional MRI (fMRI) advance cognitive scientists show an increasing interest in investigating neural activity in terms of functional connectivity and brain networks, rather than activation in a single brain region. Due to the noisy nature of neural activity, determining how behaviors are associated with specific neural signals is not well-established. Previous research has suggested graph theory techniques as a solution. Graph theory provides an opportunity to interpret human behaviors in terms of the topological organization of brain network architecture. Graph theory-based approaches, however, only scratch the surface of what neural connections relate to human behavior. Recently, the development of data-driven methods, e.g., machine learning and deep learning approaches, provide a new perspective to study the relationship between brain networks and human behaviors across the whole brain, expanding upon past literatures. In this review, we sought to revisit these data-driven approaches to facilitate our understanding of neural mechanisms and build models of human behaviors. We start with the popular graph theory approach and then discuss other data-driven approaches such as connectome-based predictive modeling, multivariate pattern analysis, network dynamic modeling, and deep learning techniques that quantify meaningful networks and connectivity related to cognition and behaviors. Importantly, for each topic, we discuss the pros and cons of the methods in addition to providing examples using our own data for each technique to describe how these methods can be applied to real-world neuroimaging data.

Shared vivid remembering: age-related differences in across-participants similarity of neural representations during encoding and retrieval

Recent advances in multivariate neuroimaging analyses have made possible the examination of the similarity of the neural patterns of activations measured across participants, but it has not been investigated yet whether such measure is age-sensitive. Here, in the scanner, young and older participants viewed scene pictures associated with labels. At test, participants were presented with the labels and were asked to recollect the associated picture. We used Pattern Similarity Analyses by which we compared patterns of neural activation during the encoding or the remembering of each picture of one participant with the averaged pattern of activation across the remaining participants. Results revealed that across-participants neural similarity was higher in young than in older adults in distributed occipital, temporal and parietal areas during encoding and retrieval. These findings demonstrate that an age-related reduction in specificity of neural activation is also evident when the similarity of neural representations is examined across participants.

Contributions of representational distinctiveness and stability to memory performance and age differences

Long-standing theories of cognitive aging suggest that memory decline is associated with age-related differences in the way information is neurally represented. Multivariate pattern similarity analyses enabled researchers to take a representational perspective on brain and cognition, and allowed them to study the properties of neural representations that support successful episodic memory. Two representational properties have been identified as crucial for memory performance, namely the distinctiveness and the stability of neural representations. Here, we review studies that used multivariate analysis tools for different neuroimaging techniques to clarify how these representational properties relate to memory performance across adulthood. While most evidence on age differences in neural representations involved stimulus category information , recent studies demonstrated that particularly item-level stability and specificity of activity patterns are linked to memory success and decline during aging. Overall, multivariate methods offer a versatile tool for our understanding of age differences in the neural representations underlying memory.

Age-related neural dedifferentiation for individual stimuli: an across-participant pattern similarity analysis

Age-related neural dedifferentiation - reductions in the regional specificity and precision of neural representations - is proposed to compromise the ability of older adults to form sufficiently distinct neural representations to support episodic memory encoding. The computational model that spurred investigations of age-related neural dedifferentiation initially characterized this phenomenon as a reduction in the specificity of neural patterns for individual items or stimuli. Most investigations have focused on reductions in neural differentiation for patterns of neural activity associated with category-level information, such as reduced neural selectivity between categories of visual stimuli (e.g., scenes, objects, and faces). Here, I report a novel across-participant pattern similarity analysis method to measure neural distinctiveness for individual stimuli that were presented to participants on a single occasion. Measures of item-level pattern similarity during encoding showed a graded positive subsequent memory effect in younger, with no significant subsequent memory effect in older adults. These results suggest that age-related reductions in the distinctiveness of neural patterns for individual stimuli during age differences in memory encoding. Moreover, a measure of category-level similarity demonstrated a significant subsequent memory effect associated with item recognition (regardless of an object source memory detail), whereas the effect in older was associated with source memory. These results converge with predictions of computational models of dedifferentiation showing age-related reductions in the distinctiveness of neural patterns across multiple levels of representation.

Spectral Pattern Similarity Analysis: Tutorial and Application in Developmental Cognitive Neuroscience

The human brain encodes information in neural activation patterns. While standard approaches to analyzing neural data focus on brain (de-)activation (e.g., regarding the location, timing, or magnitude of neural responses), multivariate neural pattern similarity analyses target the informational content represented by neural activity. In adults, a number of representational properties have been identified that are linked to cognitive performance, in particular the stability, distinctiveness, and specificity of neural patterns. However, although growing cognitive abilities across childhood suggest advancements in representational quality, developmental studies still rarely utilize information-based pattern similarity approaches, especially in electroencephalography (EEG) research. Here, we provide a comprehensive methodological introduction and step-by-step tutorial for pattern similarity analysis of spectral (frequency-resolved) EEG data including a publicly available pipeline and sample dataset with data from children and adults. We discuss computation of single-subject pattern similarities and their statistical comparison at the within-person to the between-group level as well as the illustration and interpretation of the results. This tutorial targets both novice and more experienced EEG researchers and aims to facilitate the usage of spectral pattern similarity analyses, making these methodologies more readily accessible for (developmental) cognitive neuroscientists.

Age-related declines in neural distinctiveness correlate across brain areas and result from both decreased reliability and increased confusability

According to the neural dedifferentiation hypothesis, age-related reductions in the distinctiveness of neural representations contribute to sensory, cognitive, and motor declines associated with aging: neural activity associated with different stimulus categories becomes more confusable with age and behavioral performance suffers as a result. Initial studies investigated age-related dedifferentiation in the visual cortex, but subsequent research has revealed declines in other brain regions, suggesting that dedifferentiation may be a general feature of the aging brain. In the present study, we used functional magnetic resonance imaging to investigate age-related dedifferentiation in the visual, auditory, and motor cortices. Participants were 58 young adults and 79 older adults. The similarity of activation patterns across different blocks of the same category was calculated (within-category correlation, a measure of reliability) as was the similarity of activation patterns elicited by different categories (between-category correlations, a measure of confusability). Neural distinctiveness was defined as the difference between the mean within- and between-category similarity. We found age-related reductions in neural distinctiveness in the visual, auditory, and motor cortices, which were driven by both decreases in within-category similarity and increases in between-category similarity. There were significant positive cross-region correlations between neural distinctiveness in different regions. These correlations were driven by within-category similarities, a finding that indicates that declines in the reliability of neural activity appear to occur in tandem across the brain. These findings suggest that the changes in neural distinctiveness that occur in healthy aging result from changes in both the reliability and confusability of patterns of neural activity.

Effects of age differences in memory formation on neural mechanisms of consolidation and retrieval

Episodic memory decline is a hallmark of cognitive aging and a multifaceted phenomenon. We review studies that target age differences across different memory processing stages, i.e., from encoding to retrieval. The available evidence suggests that age differences during memory formation may affect the quality of memory representations in an age-graded manner with downstream consequences for later processing stages. We argue that low memory quality in combination with age-related neural decline of key regions of the episodic memory network puts older adults in a double jeopardy situation that finally results in broader memory impairments in older compared to younger adults.