Memory specificity is linked to repetition effects in event-related potentials across the lifespan

Early visual cortices reveal interrelated item and category representations in aging

Neural dedifferentiation, the finding that neural representations tend to be less distinct in older adults compared with younger adults, has been associated with age-related declines in memory performance. Most studies assessing the relation between memory and neural dedifferentiation have evaluated how age impacts the distinctiveness of neural representations for different visual categories (e.g., scenes and objects). However, how age impacts the quality of neural representations at the level of individual items is still an open question. Here, we present data from an age-comparative fMRI study that aimed to understand how the distinctiveness of neural representations for individual stimuli differs between younger and older adults and relates to memory outcomes. Pattern similarity searchlight analyses yielded indicators of neural dedifferentiation at the level of individual items as well as at the category level in posterior occipital cortices. We asked whether age differences in neural distinctiveness at each representational level were associated with inter- and/or intraindividual variability in memory performance. While age-related dedifferentiation at both the item and category level related to between-person differences in memory, neural distinctiveness at the category level also tracked within-person variability in memory performance. Concurrently, neural distinctiveness at the item level was strongly associated with neural distinctiveness at the category level both within and across participants, elucidating a potential representational mechanism linking item- and category-level distinctiveness. In sum, we provide evidence that age-related neural dedifferentiation co-exists across multiple representational levels and is related to memory performance.Significance Statement Age-related memory decline has been associated with neural dedifferentiation, the finding that older adults have less distinctive neural representations than younger adults. This has been mostly shown for category information, while evidence for age differences in the specificity of item representations is meager. We used pattern similarity searchlight analyses to find indicators of neural dedifferentiation at both levels of representation (category and item) and linked distinctiveness to memory performance. Both item- and category-level dedifferentiation in the calcarine cortex were related to interindividual differences in memory performance, while category-level distinctiveness further tracked intraindividual variability. Crucially, neural distinctiveness was strongly tied between the item and category levels, indicating that intersecting representational properties of posterior occipital cortices reflect both individual exemplars and categories.

Comparing encoding mechanisms in realistic virtual reality and conventional 2D laboratory settings: Event-related potentials in a repetition suppression paradigm

Although the human brain is adapted to function within three-dimensional environments, conventional laboratory research commonly investigates cognitive mechanisms in a reductionist approach using two-dimensional stimuli. However, findings regarding mnemonic processes indicate that realistic experiences in Virtual Reality (VR) are stored in richer and more intertwined engrams than those obtained from the conventional laboratory. Our study aimed to further investigate the generalizability of laboratory findings and to differentiate whether the processes underlying memory formation differ between VR and the conventional laboratory already in early encoding stages. Therefore, we investigated the Repetition Suppression (RS) effect as a correlate of the earliest instance of mnemonic processes under conventional laboratory conditions and in a realistic virtual environment. Analyses of event-related potentials (ERPs) indicate that the ERP deflections at several electrode clusters were lower in VR compared to the PC condition. These results indicate an optimized distribution of cognitive resources in realistic contexts. The typical RS effect was replicated under both conditions at most electrode clusters for a late time window. Additionally, a specific RS effect was found in VR at anterior electrodes for a later time window, indicating more extensive encoding processes in VR compared to the laboratory. Specifically, electrotomographic results (VARETA) indicate multimodal integration involving a broad cortical network and higher cognitive processes during the encoding of realistic objects. Our data suggest that object perception under realistic conditions, in contrast to the conventional laboratory, requires multisensory integration involving an interconnected functional system, facilitating the formation of intertwined memory traces in realistic environments.

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.

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.

Enhancing reproducibility in developmental EEG research: BIDS, cluster-based permutation tests, and effect sizes

Developmental research using electroencephalography (EEG) offers valuable insights in brain processes early in life, but at the same time, applying this sensitive technique to young children who are often non-compliant and have short attention spans comes with practical limitations. It is thus of particular importance to optimally use the limited resources to advance our understanding of development through reproducible and replicable research practices. Here, we describe methodological approaches that help maximize the reproducibility of developmental EEG research. We discuss how to transform EEG data into the standardized Brain Imaging Data Structure (BIDS) which organizes data according to the FAIR data sharing principles. We provide a tutorial on how to use cluster-based permutation testing to analyze developmental EEG data. This versatile test statistic solves the multiple comparison problem omnipresent in EEG analysis and thereby substantially decreases the risk of reporting false discoveries. Finally, we describe how to quantify effect sizes, in particular of cluster-based permutation results. Reporting effect sizes conveys a finding’s impact and robustness which in turn informs future research. To demonstrate these methodological approaches to data organization, analysis and report, we use a publicly accessible infant EEG dataset and provide a complete copy of the analysis code.

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Methods for enhancing reproducibility in developmental EEG research.

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Tutorial for converting EEG data into BIDS to adopt FAIR data sharing principles.

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How to use cluster-based permutation testing to analyze developmental EEG data.

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How to quantify effect sizes, particularly of cluster-based permutation results.

ResMem-Net: memory based deep CNN for image memorability estimation

Image memorability is a very hard problem in image processing due to its subjective nature. But due to the introduction of Deep Learning and the large availability of data and GPUs, great strides have been made in predicting the memorability of an image. In this paper, we propose a novel deep learning architecture called ResMem-Net that is a hybrid of LSTM and CNN that uses information from the hidden layers of the CNN to compute the memorability score of an image. The intermediate layers are important for predicting the output because they contain information about the intrinsic properties of the image. The proposed architecture automatically learns visual emotions and saliency, shown by the heatmaps generated using the GradRAM technique. We have also used the heatmaps and results to analyze and answer one of the most important questions in image memorability: "What makes an image memorable?". The model is trained and evaluated using the publicly available Large-scale Image Memorability dataset (LaMem) from MIT. The results show that the model achieves a rank correlation of 0.679 and a mean squared error of 0.011, which is better than the current state-of-the-art models and is close to human consistency (p = 0.68). The proposed architecture also has a significantly low number of parameters compared to the state-of-the-art architecture, making it memory efficient and suitable for production.

Tracking Age Differences in Neural Distinctiveness across Representational Levels

The distinctiveness of neural information representation is crucial for successful memory performance but declines with advancing age. Computational models implicate age-related neural dedifferentiation on the level of item representations, but previous studies mostly focused on age differences of categorical information representation in higher-order visual regions. In an age-comparative fMRI study, we combined univariate analyses and whole-brain searchlight pattern similarity analyses to elucidate age differences in neural distinctiveness at both category and item levels and their relation to memory. Thirty-five younger (18-27 years old) and 32 older (67-75 years old) women and men incidentally encoded images of faces and houses, followed by an old/new recognition memory task. During encoding, age-related neural dedifferentiation was shown as reduced category-selective processing in ventral visual cortex and impoverished item specificity in occipital regions. Importantly, successful subsequent memory performance built on high item stability, that is, high representational similarity between initial and repeated presentation of an item, which was greater in younger than older adults. Overall, we found that differences in representational distinctiveness coexist across representational levels and contribute to interindividual and intraindividual variability in memory success, with item specificity being the strongest contributor. Our results close an important gap in the literature, showing that older adults' neural representation of item-specific information in addition to categorical information is reduced compared with younger adults.SIGNIFICANCE STATEMENT A long-standing hypothesis links age-related cognitive decline to a loss of neural specificity. While previous evidence supports the notion of age-related neural dedifferentiation of category-level information in ventral visual cortex, whether or not age differences exist at the item level was a matter of debate. Here, we observed age group differences at both levels as well as associations between both categorical distinctiveness and item specificity to memory performance, with item specificity being the strongest contributor. Importantly, age differences in occipital item specificity were largely due to reduced item stability across repetitions in older adults. Our results suggest that age differences in neural representations can be observed across the entire cortical hierarchy and are not limited to category-level information.

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.