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Sleep disturbances are common in Alzheimer’s disease and other neurodegenerative disorders, and together represent a potential therapeutic target for disease modification. A major barrier for studying sleep in patients with dementia is the requirement for overnight polysomnography (PSG) to achieve formal sleep staging. This is not only costly, but also spending a night in a hospital setting is not always advisable in this patient group. As an alternative to PSG, portable electroencephalography (EEG) headbands (HB) have been developed, which reduce cost, increase patient comfort, and allow sleep recordings in a person’s home environment. However, naïve applications of current automated sleep staging systems tend to perform inadequately with HB data, due to their relatively lower quality. Here we present a deep learning (DL) model for automated sleep staging of HB EEG data to overcome these critical limitations. The solution includes a simple band-pass filtering, a data augmentation step, and a model using convolutional (CNN) and long short-term memory (LSTM) layers. With this model, we have achieved 74% (±10%) validation accuracy on low-quality two-channel EEG headband data and 77% (±10%) on gold-standard PSG. Our results suggest that DL approaches achieve robust sleep staging of both portable and in-hospital EEG recordings, and may allow for more widespread use of ambulatory sleep assessments across clinical conditions, including neurodegenerative disorders.

We examined how subjective assessments of recollection guide decision making. Subjective recollection was dissociated from accuracy during a forced-choice recognition task. Distracters were either similar to targets (match condition) or to other studied, but untested items (nonmatch condition). We assessed 223 participants (112 males) across three experiments (137 White, 37 Asian-American, 7 African-American, 4 American-Indian, 32 mixed race, 6 undisclosed). In Experiment 1, 6-to 10-year-olds and adults (N = 119) were less accurate (d = 0.70), but more likely to claim subjective recollection and make memory selections in anticipation of a reward in the nonmatch condition (ds = 0.64-0.70). This pattern was eliminated in 6-to 7-year-olds when we limited the number of selections (Experiment 2, N = 52), but was replicated when we required the selections to be counted (Experiment 3, N = 52), underscoring the effects of decision complexity on children's selfreflections.

Functional divisions of labor in support of memory have been reported along the anterior–posterior axis of the hippocampus. However, little is known about how the developing hippocampus represents associative memories along this axis. The present research employed representational similarity analysis to ask whether developmental differences exist in the extent to which the anterior versus the posterior hippocampus represent features of the context and associative memories. Functional magnetic resonance imaging data were collected during the retrieval phase of an associative recognition task from 8‐year‐olds, 10‐year‐olds, and adults (N = 58). Participants were asked to retrieve pairs of items, which were presented either in the same location as during encoding or in a flipped location. In the anterior hippocampus and only for adults, pattern similarity between the two studied pair conditions was greater than pattern similarity between studied pairs presented in the same location and novel pairs. In contrast, this difference was not significant in the posterior hippocampus. Older, but not younger, children showed a similar, albeit attenuated, similarity pattern to that of adults, but measures of patterns similarity predicted associative recognition across ages. In addition, exploratory analyses showed that similarity patterns in the adult posterior, but not anterior, hippocampus tracked the order of the runs. Overall, the results suggest functional and developmental dissociations in processing different contextual features, with the anterior hippocampus responding to salient and rapid‐changing features and the posterior hippocampus responding to slower‐changing features of the context.

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