Technical challenges in REM sleep microstructure classification: A study of patients with REM sleep behaviour disorder.

While commonly treated as a uniform state in practice, rapid eye movement sleep contains two distinct microstructures-phasic (presence of rapid eye movement) and tonic (no rapid eye movement). This study aims to identify technical challenges during rapid eye movement sleep microstructure visual classification in patients with rapid eye movement sleep behaviour disorder, and to propose solutions to enhance reliability between scorers. Fifty-seven sleep recordings were randomly allocated into three subsequent batches (n = 10, 13 and 34) for scoring. To reduce single-centre bias, we recruited three raters/scorers, with each trained from a different institution. Two raters independently scored each 30-s rapid eye movement sleep into 10â€…× fSEM3-s phasic/tonic microstructures based on the AASM guidelines. The third rater acted as an "arbitrator" to resolve opposite opinions persisting during the revision between batches. Besides interrater differences in artefact rejection rate, interrater variance frequently occurred due to transitioning between microstructures and moderate-to-severe muscular/electrode artefact interference. To enhance interrater agreement, a rapid eye movement scoring schematic graph was developed, incorporating proxy electrode use, filters and cut-offs for microstructure transitioning. To assess potential effectiveness of the schematic graph proposed, raters were instructed to systematically apply it in scoring for the third batch. Of the 34 recordings, 27 reached a Cohen's kappa score above 0.8 (i.e. almost perfect agreement between raters), significantly improved from the prior batches (p = 0.0003, Kruskal-Wallis test). Our study illustrated potential solutions and guidance for challenges that may be encountered during rapid eye movement sleep microstructure classification.

Spindles are highly heritable as identified by different spindle detectors.

STUDY OBJECTIVES: Sleep spindles, a defining feature of stage N2 sleep, are maximal at central electrodes and are found in the frequency range of the electroencephalogram (EEG) (sigma 11-16 Hz) that is known to be heritable. However, relatively little is known about the heritability of spindles. Two recent studies investigating the heritability of spindles reported moderate heritability, but with conflicting results depending on scalp location and spindle type. The present study aimed to definitively assess the heritability of sleep spindle characteristics. METHODS: We utilized the polysomnography data of 58 monozygotic and 40 dizygotic same-sex twin pairs to identify heritable characteristics of spindles at C3/C4 in stage N2 sleep including density, duration, peak-to-peak amplitude, and oscillation frequency. We implemented and tested a variety of spindle detection algorithms and used two complementary methods of estimating trait heritability. RESULTS: We found robust evidence to support strong heritability of spindles regardless of detector method (h2 > 0.8). However not all spindle characteristics were equally heritable, and each spindle detection method produced a different pattern of results. CONCLUSIONS: The sleep spindle in stage N2 sleep is highly heritable, but the heritability differs for individual spindle characteristics and depends on the spindle detector used for analysis.

Massive online data annotation, crowdsourcing to generate high quality sleep spindle annotations from EEG data.

Spindle event detection is a key component in analyzing human sleep. However, detection of these oscillatory patterns by experts is time consuming and costly. Automated detection algorithms are cost efficient and reproducible but require robust datasets to be trained and validated. Using the MODA (Massive Online Data Annotation) platform, we used crowdsourcing to produce a large open-source dataset of high quality, human-scored sleep spindles (5342 spindles, from 180 subjects). We evaluated the performance of three subtype scorers: "experts, researchers and non-experts", as well as 7 previously published spindle detection algorithms. Our findings show that only two algorithms had performance scores similar to human experts. Furthermore, the human scorers agreed on the average spindle characteristics (density, duration and amplitude), but there were significant age and sex differences (also observed in the set of detected spindles). This study demonstrates how the MODA platform can be used to generate a highly valid open source standardized dataset for researchers to train, validate and compare automated detectors of biological signals such as the EEG.

A sleep spindle detection algorithm that emulates human expert spindle scoring.

BACKGROUND: Sleep spindles are a marker of stage 2 NREM sleep that are linked to learning & memory and are altered by many neurological diseases. Although visual inspection of the EEG is considered the gold standard for spindle detection, it is time-consuming, costly and can introduce inter/ra-scorer bias. NEW METHOD: Our goal was to develop a simple and efficient sleep-spindle detector (algorithm #7, or 'A7') that emulates human scoring. 'A7' runs on a single EEG channel and relies on four parameters: the absolute sigma power, relative sigma power, and correlation/covariance of the sigma band-passed signal to the original EEG signal. To test the performance of the detector, we compared it against a gold standard spindle dataset derived from the consensus of a group of human experts. RESULTS: The by-event performance of the 'A7' spindle detector was 74% precision, 68% recall (sensitivity), and an F1-score of 0.70. This performance was equivalent to an individual human expert (average F1-score = 0.67). COMPARISON WITH EXISTING METHOD(S): The F1-score of 'A7' was 0.17 points higher than other spindle detectors tested. Existing detectors have a tendency to find large numbers of false positives compared to human scorers. On a by-subject basis, the spindle density estimates produced by A7 were well correlated with human experts (r2 = 0.82) compared to the existing detectors (average r2 = 0.27). CONCLUSIONS: The 'A7' detector is a sensitive and precise tool designed to emulate human spindle scoring by minimizing the number of 'hidden spindles' detected. We provide an open-source implementation of this detector for further use and testing.

Auditory repetition suppression alterations in relation to cognitive functioning in fragile X syndrome: a combined EEG and machine learning approach.

BACKGROUND: Fragile X syndrome (FXS) is a neurodevelopmental genetic disorder causing cognitive and behavioural deficits. Repetition suppression (RS), a learning phenomenon in which stimulus repetitions result in diminished brain activity, has been found to be impaired in FXS. Alterations in RS have been associated with behavioural problems in FXS; however, relations between RS and intellectual functioning have not yet been elucidated. METHODS: EEG was recorded in 14 FXS participants and 25 neurotypical controls during an auditory habituation paradigm using repeatedly presented pseudowords. Non-phased locked signal energy was compared across presentations and between groups using linear mixed models (LMMs) in order to investigate RS effects across repetitions and brain areas and a possible relation to non-verbal IQ (NVIQ) in FXS. In addition, we explored group differences according to NVIQ and we probed the feasibility of training a support vector machine to predict cognitive functioning levels across FXS participants based on single-trial RS features. RESULTS: LMM analyses showed that repetition effects differ between groups (FXS vs. controls) as well as with respect to NVIQ in FXS. When exploring group differences in RS patterns, we found that neurotypical controls revealed the expected pattern of RS between the first and second presentations of a pseudoword. More importantly, while FXS participants in the ≤ 42 NVIQ group showed no RS, the > 42 NVIQ group showed a delayed RS response after several presentations. Concordantly, single-trial estimates of repetition effects over the first four repetitions provided the highest decoding accuracies in the classification between the FXS participant groups. CONCLUSION: Electrophysiological measures of repetition effects provide a non-invasive and unbiased measure of brain responses sensitive to cognitive functioning levels, which may be useful for clinical trials in FXS.

Visual short term memory related brain activity predicts mathematical abilities.

OBJECTIVE: Previous research suggests visual short-term memory (VSTM) capacity and mathematical abilities are significantly related. Moreover, both processes activate similar brain regions within the parietal cortex, in particular, the intraparietal sulcus; however, it is still unclear whether the neuronal underpinnings of VSTM directly correlate with mathematical operation and reasoning abilities. The main objective was to investigate the association between parieto-occipital brain activity during the retention period of a VSTM task and performance in mathematics. METHOD: The authors measured mathematical abilities and VSTM capacity as well as brain activity during memory maintenance using magnetoencephalography (MEG) in 19 healthy adult participants. Event-related magnetic fields (ERFs) were computed on the MEG data. Linear regressions were used to estimate the strength of the relation between VSTM related brain activity and mathematical abilities. RESULTS: The amplitude of parieto-occipital cerebral activity during the retention of visual information was related to performance in 2 standardized mathematical tasks: mathematical reasoning and calculation fluency. CONCLUSIONS: The findings show that brain activity during retention period of a VSTM task is associated with mathematical abilities. Contributions of VSTM processes to numerical cognition should be considered in cognitive interventions. (PsycINFO Database Record

Brain signal complexity rises with repetition suppression in visual learning.

Neuronal activity associated with visual processing of an unfamiliar face gradually diminishes when it is viewed repeatedly. This process, known as repetition suppression (RS), is involved in the acquisition of familiarity. Current models suggest that RS results from interactions between visual information processing areas located in the occipito-temporal cortex and higher order areas, such as the dorsolateral prefrontal cortex (DLPFC). Brain signal complexity, which reflects information dynamics of cortical networks, has been shown to increase as unfamiliar faces become familiar. However, the complementarity of RS and increases in brain signal complexity have yet to be demonstrated within the same measurements. We hypothesized that RS and brain signal complexity increase occur simultaneously during learning of unfamiliar faces. Further, we expected alteration of DLPFC function by transcranial direct current stimulation (tDCS) to modulate RS and brain signal complexity over the occipito-temporal cortex. Participants underwent three tDCS conditions in random order: right anodal/left cathodal, right cathodal/left anodal and sham. Following tDCS, participants learned unfamiliar faces, while an electroencephalogram (EEG) was recorded. Results revealed RS over occipito-temporal electrode sites during learning, reflected by a decrease in signal energy, a measure of amplitude. Simultaneously, as signal energy decreased, brain signal complexity, as estimated with multiscale entropy (MSE), increased. In addition, prefrontal tDCS modulated brain signal complexity over the right occipito-temporal cortex during the first presentation of faces. These results suggest that although RS may reflect a brain mechanism essential to learning, complementary processes reflected by increases in brain signal complexity, may be instrumental in the acquisition of novel visual information. Such processes likely involve long-range coordinated activity between prefrontal and lower order visual areas.