EEG alpha and theta time-frequency structure during a written mathematical task.

Since the first electroencephalogram (EEG) was obtained, there have been many possibilities to use it as a tool to access brain cognitive dynamics. Mathematical (Math) problem solving is one of the most important cortical processes, but it is still far from being well understood. EEG is an inexpensive and simple indirect measure of brain operation, but only recently has low-cost equipment (mobile EEG) allowed sophisticated analyses in non-clinical settings. The main purpose of this work is to study EEG activation during a Math task in a realistic environment, using mobile EEG. A matching pursuit (MP)-based signal analysis technique was employed, since MP properties render it a priori suitable to study induced EEG activity over long time sequences, when it is not tightly locked to a given stimulus. The study sample comprised sixty healthy volunteers. Unlike the majority of previous studies, subjects were studied in a sitting position with their eyes open. They completed a written Math task outside the EEG lab, wearing a mobile EEG device (EPOC+). Theta [4 Hz-7.5 Hz], alpha (7.5 Hz-13 Hz] and 0.5 Hz micro-bands in the [0.5 Hz-20 Hz] range were studied with a low-density stochastic MP dictionary. Over 1-min windows, ongoing EEG alpha and theta activity was decomposed into numerous MP atoms with median duration around 3 s, similar to the duration of induced, time-locked activity obtained with event-related (des)synchronization (ERS/ERD) studies. Relative to Rest, there was lower right-side and posterior MP alpha atom/min during Math, whereas MP theta atom/min was significantly higher on anteriorly located electrodes, especially on the left side. MP alpha findings were particularly significant on a narrow range around 10 Hz-10.5 Hz, consistent with FFT alpha peak findings from ERS/ERD studies. With a streamlined protocol, these results replicate previous findings of EEG alpha and theta activation obtained during Math tasks with different signal analysis techniques and in different time frames. The efficient application to real-world, noisy EEG data with a low-resolution stochastic MP dictionary shows that this technique is very encouraging. These results provide support for studies of mathematical cognition with mobile EEG and matching pursuit.

Sleep modelled as a continuous and dynamic process predicts healthy ageing better than traditional sleep scoring.

BACKGROUND: In current clinical practice, sleep is manually scored in discrete stages of 30-s duration. We hypothesize that modelling sleep automatically as continuous and dynamic process predicts healthy ageing better than traditional scoring. METHODS: Sleep electroencephalography of 15 young healthy subjects (aged ≤40 years) was used to train the modelling method. Each 3-s sleep mini-epoch was modelled as a probabilistic combination of wakefulness, light and deep sleep. For 79 healthy sleepers (aged 20-77 years), 15 sleep features were derived from manual traditional scoring (manual features), 7 from the automatic modelling (automatic features) and 24 from a combination of automatic modelling with traditional scoring (combined features). Age was predicted with seven multiple linear regression models with i) manual, ii) automatic, iii) combined, iv) manual + automatic, v) manual + combined, vi) automatic + combined, and vii) manual + automatic + combined sleep features. Using the same seven sleep feature groups, two support vector machine and one random forest classifiers were used to discriminate younger (aged <47 years) from older subjects with fivefold cross-validation. Adjusted coefficients of determination (adj-R2) and average validation accuracy (ACC) were used to compare the linear models and the classifiers. RESULTS: The linear model and the classifiers using only manual features achieved the lowest values of adjusted coefficient of determination and classification validation accuracy (adj-R2 = 0.295, ACC = 63.00% ± 16.22%) compared to the ones using automatic (adj-R2 = 0.354, ACC = 65.83% ± 9.39%), combined (adj-R2 = 0.321, ACC = 63.42% ± 8.78%), manual + automatic (adj-R2 = 0.416, ACC = 67.00% ± 8.60%), manual + combined (adj-R2 = 0.355, ACC = 72.17% ± 12.90%), automatic + combined (adj-R2 = 0.448, ACC = 65.92% ± 7.97%), and manual + automatic + combined sleep features (adj-R2 = 0.464, ACC = 70.92% ± 10.33%). CONCLUSIONS: Continuous and dynamic sleep modelling captures healthy ageing better than traditional sleep scoring.

Loss of sleep spindle frequency deceleration in Obstructive Sleep Apnea.

OBJECTIVE: Sleep spindles have been suggested as surrogates of thalamo-cortical activity. Internal frequency modulation within a spindle's time frame has been demonstrated in healthy subjects, showing that spindles tend to decelerate their frequency before termination. We investigated internal frequency modulation of slow and fast spindles according to Obstructive Sleep Apnea (OSA) severity and brain topography. METHODS: Seven non-OSA subjects and 21 patients with OSA contributed with 30min of Non-REM sleep stage 2, subjected to a Matching pursuit procedure with Gabor chirplet functions for automatic detection of sleep spindles and quantification of sleep spindle internal frequency modulation (chirp rate). RESULTS: Moderate OSA patients showed an inferior percentage of slow spindles with deceleration when compared to Mild and Non-OSA groups in frontal and parietal regions. In parietal regions, the percentage of slow spindles with deceleration was negatively correlated with global apnea-hypopnea index (rs=-0.519, p=0.005). DISCUSSION: Loss of physiological sleep spindle deceleration may either represent a disruption of thalamo-cortical loops generating spindle oscillations or some compensatory mechanism, an interesting venue for future research in the context of cognitive dysfunction in OSA. SIGNIFICANCE: Quantification of internal frequency modulation (chirp rate) is proposed as a promising approach to advance description of sleep spindle dynamics in brain pathology.

Topography-specific spindle frequency changes in obstructive sleep apnea.

BACKGROUND: Sleep spindles, as detected on scalp electroencephalography (EEG), are considered to be markers of thalamo-cortical network integrity. Since obstructive sleep apnea (OSA) is a known cause of brain dysfunction, the aim of this study was to investigate sleep spindle frequency distribution in OSA. Seven non-OSA subjects and 21 patients with OSA (11 mild and 10 moderate) were studied. A matching pursuit procedure was used for automatic detection of fast (≥13 Hz) and slow (<13 Hz) spindles obtained from 30 min samples of NREM sleep stage 2 taken from initial, middle and final night thirds (sections I, II and III) of frontal, central and parietal scalp regions. RESULTS: Compared to non-OSA subjects, Moderate OSA patients had higher central and parietal slow spindle percentage (SSP) in all night sections studied, and higher frontal SSP in sections II and III. As the night progressed, there was a reduction in central and parietal SSP, while frontal SSP remained high. Frontal slow spindle percentage in night section III predicted OSA with good accuracy, with OSA likelihood increased by 12.1%for every SSP unit increase (OR 1.121, 95% CI 1.013-1.239, p=0.027). CONCLUSIONS: These results are consistent with diffuse, predominantly frontal thalamo-cortical dysfunction during sleep in OSA, as more posterior brain regions appear to maintain some physiological spindle frequency modulation across the night. Displaying changes in an opposite direction to what is expected from the aging process itself, spindle frequency appears to be informative in OSA even with small sample sizes, and to represent a sensitive electrophysiological marker of brain dysfunction in OSA.

Can observers link dream content to behaviours in rapid eye movement sleep behaviour disorder? A cross-sectional experimental pilot study.

Motor activity in rapid eye movement (REM) sleep behaviour disorder (RBD) has been linked to dream content. Systematic and controlled sleep laboratory studies directly assessing the relation between RBD behaviours and experienced dream content are, however, largely lacking. We aimed to investigate whether a link can be established between RBD behaviours and dream content when both are systematically sampled in a controlled setting. We investigated six patients with Parkinson syndrome and RBD who underwent 2-3 nights of video-polysomnographic recording during which they were awakened from REM sleep (10 min after the onset of the second and successive REM periods). Spontaneous free-worded dream reports and a structured dream questionnaire were obtained. Video recordings of motor manifestations were each combined with four dream reports, and seven judges had to match the video clip with the correctly reported dream content from a choice of four possibilities. Of the 35 REM sleep awakenings performed, a total of 17 (48.6%) motor-behavioural episodes with recalled dream content were obtained. The mean of correctly identified video-dream pairs was 39.5% (range 0-100%). Our data showed that reported dream content can be linked to motor behaviours above chance level. Matching accuracy was affected mainly by the clarity of dream reports and the specific nature of movements manifest in video recordings.

Quantifying chirp in sleep spindles.

Sleep spindles are considered as a marker of integrity for thalamo-cortical circuits. Recently, attention has been given to internal frequency variation in sleep spindles. In this study, a procedure based on matching pursuit with a Gabor-chirplet dictionary was applied in order to measure chirp rate in atoms representing sleep spindles, also categorized into negative, positive or zero chirp types. The sample comprised 707 EEG segments containing visual sleep spindles, labeled TP, obtained from nine healthy male volunteers (aged 20-34, average 24.6 y). Control datasets were 333 non-REM (NREM) sleep background segments and 287 REM sleep intervals, each with 16s duration. Analyses were carried out on the C3-A2 EEG channel. In TP and NREM groups, the proportion of non-null chirp types was non-random and total chirp distribution was asymmetrical towards negative values, in contrast to REM. Median negative chirp rate in the TP and NREM groups was significantly lower than in REM (-0.4 Hz/s vs -0.3 Hz/s, P < 0.05). Negative chirp atoms outnumbered positives by 50% in TP, while in NREM and REM, they were, respectively, only 22% and 12% more prevalent. TP negative chirp atoms were significantly higher in amplitude compared to positive or zero types. Considering individual subjects, 88.9% had a TP negative/positive chirp ratio above 1 (mean ± sd=1.64 ± 0.65). We propose there is increasing evidence, corroborated by the present study, favoring systematic measurement of sleep spindle chirp rate or internal frequency variation. Preferential occurrence of negatively chirping spindles is consistent with the hypothesis of electrophysiological modulation of neocortical memory consolidation.

Motor disturbances during non-REM and REM sleep in narcolepsy-cataplexy: a video-polysomnographic analysis.

Motor events during sleep can be frequently observed in patients with narcolepsy-cataplexy. We hypothesized that increased motor events and related arousals contribute to sleep fragmentation in this disease. We aimed to perform a detailed whole-night video-polysomnographic analysis of all motor events during non-rapid eye movement and rapid eye movement sleep in a group of narcolepsy-cataplexy patients and matched controls, and to assess the association with arousals. Video-polysomnographic registrations of six narcolepsy-cataplexy patients and six sex- and age-matched controls were analysed. Each motor event in the video was classified according to topography, number of involved body parts, duration and its association with arousals. The mean motor activity index was 59.9 ± 23.0 h(-1) in patients with narcolepsy-cataplexy compared with 15.4 ± 9.2 h(-1) in controls (P = 0.004). Distribution of motor events was similar in non-rapid eye movement and rapid eye movement sleep in the patient group (P = 0.219). In narcolepsy-cataplexy, motor events involved significantly more body parts (≥ 2 body regions: 38.2 ± 15.6 versus 14.9 ± 10.0; P = 0.011). In addition, the proportion of motor events lasting longer than 1 s was higher in patients than controls (88% versus 44.4%; P < 0.001). Both total and motor activity-related arousal indices were increased in narcolepsy-cataplexy (total arousal index: 21.6 ± 9.0 versus 8.7 ± 3.5; P = 0.004; motor activity-related arousal index: 17.6 ± 9.8 versus 5.9 ± 2.3; P = 0.002). Motor activity and motor activity-related arousal indices are increased in both non-rapid eye movement and rapid eye movement sleep in narcolepsy-cataplexy compared with controls. This supports the concept of a general sleep motor dysregulation in narcolepsy-cataplexy, which potentially contributes to or even underlies sleep fragmentation in this disease.

Benchmarking matching pursuit to find sleep spindles.

The aim of this study is to evaluate performance of Matching Pursuit (MP) algorithm against visual analysis for automatic sleep spindle (SS) detection in a sample of sleep stages 2-4 and REM pertaining to nine healthy young subjects. MP-SS voltage, frequency and duration characteristics were investigated for the amplitude threshold (AT) that maximized yield between test sensitivity and specificity. Parameter distribution curves were also built for correctly detected (true positive) and false-positive events. For sleep stage 2, MP reached 80.6% sensitivity and specificity for an AT value of 58.8. For all stages together, 81.2% sensitivity and specificity were reached for an AT value of 46.6. Specificity curves were adequate for all stages; sensitivity was lower for S3+4. Sigma frequency range activity with atypical characteristics was detected within REM sleep. Prevalence indexes obtained with MP were much higher than visual prevalence indexes for all stages; similar voltage, frequency and duration distribution curves were obtained for true positive and false positive events. For this sample of young male healthy subjects, the free-ware MP algorithm showed satisfactory performance for SS detection in sleep stage 2 as reported earlier, acceptable performance in sleep stages 3+4, although with lowered sensitivity, and sigma frequency range activity within REM sleep that needs better understanding. Within NREM sleep, correspondence between the MP automatic and the visual method was supported.