Explore the vast range of titles available.

Why do we remember some events but forget others? Previous studies attempting to decode successful vs. unsuccessful brain states to investigate this question have met with limited success, potentially due, in part, to assessing episodic memory as a unidimensional process, despite evidence that multiple domains contribute to episodic encoding. Using a machine learning algorithm known as “transfer learning”, we leveraged visual perception, sustained attention, and selective attention brain states to better predict episodic memory performance from trial-to-trial encoding electroencephalography (EEG) activity. We found that this multidimensional treatment of memory decoding improved prediction performance compared to traditional, unidimensional, methods, with each cognitive domain explaining unique variance in decoding of successful encoding-related neural activity. Importantly, this approach could be applied to cognitive domains outside of memory. Overall, this study provides critical insight into the underlying reasons why some events are remembered while others are not. Mirjalili and Duarte use EEG and machine learning to simultaneously investigate how perception and attention contribute to episodic memory encoding. The study provides insights into episodic memory’s multidimensionality and what shapes encoding success.

There is a strong relationship between sleep and memory for the details of past events. In old age, both episodic memory performance and related neural activity decline. These changes occur in parallel to age-related decreases in sleep quality. Thus, poor sleep quality may be an explanatory factor for poor memory in older adulthood. Furthermore, Black adults tend to sleep more poorly than White adults, and this could be explained by differences in health and psychosocial factors (e.g., socioeconomic status, race-related stress). However, there have been no studies investigating the effect of race on sleep quality, episodic memory, and memory-related neural function. In the current pilot study, we recruited a diverse sample of older and younger adults and measured their habitual sleep using a wrist-worn accelerometer for 1 week. We recorded their electroencephalography (EEG) as they performed an episodic memory task to assess the impact of habitual sleep on memory-related neural oscillations. We found that more variable sleep quality was associated with worse memory performance, particularly for older adults. Additionally, Black participants demonstrated greater intraindividual sleep variance than White participants, and greater sleep variance was strongly linked to reduced memory-related neural activity in Black participants. Taken together, maintaining good sleep quality is especially important for memory performance in older adulthood, and greater sleep variation, that is evident in Black adults, may hamper memory-related neural function.

Behavioral evidence suggests that young and older adults show a benefit in source memory accuracy when processing materials in reference to the self. In the young, activity within the medial prefrontal cortex supports this source memory benefit at study. In this investigation, we examined whether the same neural regions support this memory benefit in both age groups. Using fMRI, we scanned participants while they studied and retrieved pictures of objects paired with one of three scenes (source) under self-reference and other-reference conditions. At the time of study, half of the items were presented once and half twice, allowing us to match behavioral performance between the groups. Both groups showed equivalent source accuracy benefits for objects encoded self-referentially. Activity in the left dorsal medial prefrontal cortex supported subsequent source memory in both age groups for the self-referenced relative to the other-referenced items. At the time of test, source accuracy for both the self- and other-referenced items was supported by a network of regions including the precuneus in both age groups. At both study and test, little in the way of age differences emerged, suggesting that when they are matched on behavioral performance, young and older adults engage similar regions in support of source memory when processing materials in reference to the self; however, when we did not match performance, age differences in functional recruitment were prevalent. These results suggest that by capitalizing on preserved processes (self-referential encoding), older adults can show improvement in memory for source details that they would typically not remember well, relative to the young.

•Compared results for every step of classification of an episodic memory EEG dataset•Performance improves with extracting greater number of feature types.•Combination of filter and wrapper feature selection outperformed other methods•LASSO outperformed other classifiers, while naive Bayes was the fastest classifier•We provide recommendations for achieving highest classification performance Previous studies have attempted to separate single trial neural responses for events a person is likely to remember from those they are likely to forget using machine learning classification methods. Successful single trial classification holds potential for translation into the clinical realm for real-time detection of memory and other cognitive states to provide real-time interventions (i.e., brain-computer interfaces). However, most of these studies—and classification analyses in general— do not make clear if the chosen methodology is optimally suited for the classification of memory-related brain states. To address this problem, we systematically compared different methods for every step of classification (i.e., feature extraction, feature selection, classifier selection) to investigate which methods work best for decoding episodic memory brain states—the first analysis of its kind. Using an adult lifespan sample EEG dataset collected during performance of an episodic context encoding and retrieval task, we found that no specific feature type (including Common Spatial Pattern (CSP)-based features, mean, variance, correlation, features based on AR model, entropy, phase, and phase synchronization) outperformed others consistently in distinguishing different memory classes. However, extracting all of these feature types consistently outperformed extracting only one type of feature. Additionally, the combination of filtering and sequential forward selection was the optimal method to select the effective features compared to filtering alone or performing no feature selection at all. Moreover, although all classifiers performed at a fairly similar level, LASSO was consistently the highest performing classifier compared to other commonly used options (i.e., naïve Bayes, SVM, and logistic regression) while naïve Bayes was the fastest classifier. Lastly, for multiclass classification (i.e., levels of context memory confidence and context feature perception), generalizing the binary classification using the binary decision tree performed better than the voting or one versus rest method. These methods were shown to outperform alternative approaches for three orthogonal datasets (i.e., EEG working memory, EEG motor imagery, and MEG working memory), supporting their generalizability. Our results provide an optimized methodological process for classifying single-trial neural data and provide important insight and recommendations for a cognitive neuroscientist's ability to make informed choices at all stages of the classification process for predicting memory and other cognitive states.

Emotional arousal is known to enhance episodic memory in young adults. However, compared to valence, little is known about how healthy aging impacts arousal-enhanced memory effects. Furthermore, while emotion regulation is believed to improve with age, it is unclear how individual differences in emotion regulation influence arousal-enhanced memory. In this large-scale online study, we investigated the impact of age and individual differences in emotion regulation on arousal-enhanced memory. During encoding, participants made arousal ratings about negative, neutral, and positive images, and we compared their subsequent memory of high and low-arousal images. We found the impact of emotional arousal on memory was reduced with age, especially for older adults who habitually suppress their emotions. Our findings show that arousal-related memory benefits are reduced with advancing age, and that individual differences in habitual usage of emotion regulation impact these age-related alterations.

Research on memory in autism spectrum disorder (ASD) finds increased difficulty encoding contextual associations in episodic memory and suggests executive dysfunction (e.g., selective attention, cognitive flexibility) and deficient metacognitive monitoring as potential contributing factors. Findings from our lab suggest that age-related impairments in selective attention contribute to those in context memory accuracy and older adults tended to show dependence in context memory accuracy between relevant and irrelevant context details (i.e., hyper-binding). Using an aging framework, we tested the effects of selective attention on context memory in a sample of 23 adults with ASD and 23 typically developed adults. Participants studied grayscale objects flanked by two types of contexts (color, scene) on opposing sides and were told to attend to only one object-context relationship, ignoring the other context. At test, participants made object and context recognition decisions and judgment of confidence decisions allowing for an evaluation of context memory performance, hyper-binding, and metacognitive performance for context judgments in a single task. Results showed that adults with ASD performed similarly to typically developed adults on all measures. These findings suggest that context memory performance is not always disrupted in adults with ASD, even when demands on selective attention are high. We discuss the need for continued research to evaluate episodic memory in a wider variety of adults with ASD.

Variation in human brains creates difficulty in implementing electroencephalography into universal brain–machine interfaces. Conventional electroencephalography systems typically suffer from motion artefacts, extensive preparation time and bulky equipment, while existing electroencephalography classification methods require training on a per-subject or per-session basis. Here, we introduce a fully portable, wireless, flexible scalp electronic system, incorporating a set of dry electrodes and a flexible membrane circuit. Time-domain analysis using convolutional neural networks allows for accurate, real-time classification of steady-state visually evoked potentials in the occipital lobe. Compared to commercial systems, the flexible electronics show the improved performance in detection of evoked potentials due to significant reduction of noise and electromagnetic interference. The two-channel scalp electronic system achieves a high information transfer rate (122.1 ± 3.53 bits per minute) with six human subjects, allowing for wireless, real-time, universal electroencephalography classification for an electric wheelchair, a motorized vehicle and a keyboard-less presentation. Brain–machine interfaces using steady-state visually evoked potentials (SSVEPs) show promise in therapeutic applications. With a combination of innovations in flexible and soft electronics and in deep learning approaches to classify potentials from two channels and from any subject, a compact, wireless and universal SSVEP interface is designed. Subjects can operate a wheelchair in real time with eye movements while wearing the new brain–machine interface.

Previous behavioral evidence suggests that instructed strategy use benefits associative memory formation in paired associate tasks. Two such effective encoding strategies--visual imagery and sentence generation--facilitate memory through the production of different types of mediators (e.g., mental images and sentences). Neuroimaging evidence suggests that regions of the brain support memory reflecting the mental operations engaged at the time of study. That work, however, has not taken into account self-reported encoding task success (i.e., whether participants successfully generated a mediator). It is unknown, therefore, whether task-selective memory effects specific to each strategy might be found when encoding strategies are successfully implemented. In this experiment, participants studied pairs of abstract nouns under either visual imagery or sentence generation encoding instructions. At the time of study, participants reported their success at generating a mediator. Outside of the scanner, participants further reported the quality of the generated mediator (e.g., images, sentences) for each word pair. We observed task-selective memory effects for visual imagery in the left middle occipital gyrus, the left precuneus, and the lingual gyrus. No such task-selective effects were observed for sentence generation. Intriguingly, activity at the time of study in the left precuneus was modulated by the self-reported quality (vividness) of the generated mental images with greater activity for trials given higher ratings of quality. These data suggest that regions of the brain support memory in accord with the encoding operations engaged at the time of study.

The current \"one size fits all\" approach to our cognitive aging population is not adequate to close the gap between cognitive health span and lifespan. In this review article, we present a novel model for understanding, preventing, and treating age-related cognitive impairment (ARCI) based on concepts borrowed from precision medicine. We will discuss how multiple risk factors can be classified into because of their interrelatedness in real life, the that increase sensitivity to, or ameliorate, risk for ARCI, and the or common mechanisms mediating brain aging. Rather than providing a definitive model of risk for ARCI and cognitive decline, the Precision Aging model is meant as a starting point to guide future research. To that end, after briefly discussing key risk categories, genetic risks, and brain drivers, we conclude with a discussion of steps that must be taken to move the field forward.

The Wiley Handbook on the Cognitive Neuroscience of Memory presents a comprehensive overview of the latest, cutting-edge neuroscience research being done relating to the study of human memory and cognition.   * Features the analysis of original data using cutting edge methods in cognitive neuroscience research * Presents a conceptually accessible discussion of human memory research * Includes contributions from authors that represent a \"who's who\" of human memory neuroscientists from the U.S. and abroad * Supplemented with a variety of excellent and accessible diagrams to enhance comprehension