Complexity of Wake Electroencephalography Correlates With Slow Wave Activity After Sleep Onset

Cerebral blood flow in sleep: A systematic review and meta-analysis

Sleep plays an essential role in physiology, allowing the brain and body to restore itself. Despite its critical role, our understanding of the underlying processes in the sleeping human brain is still limited. Sleep comprises several distinct stages with varying depths and temporal compositions. Cerebral blood flow (CBF), which delivers essential nutrients and oxygen to the brain, varies across brain regions throughout these sleep stages, reflecting changes in neuronal function and regulation. This systematic review and meta-analysis assesses global and regional CBF across sleep stages. We included, appraised, and summarized all 38 published sleep studies on CBF in healthy humans that were not or only slightly (<24 h) sleep deprived. Our main findings are that CBF varies with sleep stage and depth, being generally lowest in NREM sleep and highest in REM sleep. These changes appear to stem from sleep stage-specific regional brain activities that serve particular functions, such as alterations in consciousness and emotional processing.

Classification of Sleep Quality and Aging as a Function of Brain Complexity: A Multiband Non-Linear EEG Analysis

Understanding and classifying brain states as a function of sleep quality and age has important implications for developing lifestyle-based interventions involving sleep hygiene. Current studies use an algorithm that captures non-linear features of brain complexity to differentiate awake electroencephalography (EEG) states, as a function of age and sleep quality. Fifty-eight participants were assessed using the Pittsburgh Sleep Quality Inventory (PSQI) and awake resting state EEG. Groups were formed based on age and sleep quality (younger adults n = 24, mean age = 24.7 years, SD = 3.43, good sleepers n = 11; older adults n = 34, mean age = 72.87; SD = 4.18, good sleepers n = 9). Ten non-linear features were extracted from multiband EEG analysis to feed several classifiers followed by a leave-one-out cross-validation. Brain state complexity accurately predicted (i) age in good sleepers, with 75% mean accuracy (across all channels) for lower frequencies (alpha, theta, and delta) and 95% accuracy at specific channels (temporal, parietal); and (ii) sleep quality in older groups with moderate accuracy (70 and 72%) across sub-bands with some regions showing greater differences. It also differentiated younger good sleepers from older poor sleepers with 85% mean accuracy across all sub-bands, and 92% at specific channels. Lower accuracy levels (<50%) were achieved in predicting sleep quality in younger adults. The algorithm discriminated older vs. younger groups excellently and could be used to explore intragroup differences in older adults to predict sleep intervention efficiency depending on their brain complexity.

Can Daytime Transcranial Direct Current Stimulation Treatment Change the Sleep Electroencephalogram Complexity of REM Sleep in Depressed Patients? A Double-Blinded, Randomized, Placebo-Controlled Trial

STUDY OBJECTIVES

The purpose of this study was to determine the effects of daytime transcranial direct current stimulation (tDCS) on sleep electroencephalogram (EEG) in patients with depression.

METHODS

The study was a double-blinded, randomized, controlled clinical trial. A total of 37 patients diagnosed with a major depression were recruited; 19 patients (13 females and 6 males mean age 44.79 ± 15.25 years) received tDCS active stimulation and 18 patients (9 females and 9 males; mean age 43.61 ± 11.89 years) received sham stimulation. Ten sessions of daytime tDCS were administered with the anode over F3 and the cathode over F4. Each session delivered a 2 mA current for 30 min per 10 working days. Hamilton-24 and Montgomery scales were used to assess the severity of depression, and polysomnography (PSG) was used to assess sleep structure and EEG complexity. Eight intrinsic mode functions (IMFs) were computed from each EEG signal in a channel. The sample entropy of the cumulative sum of the IMFs were computed to acquire high-dimensional multi-scale complexity information of EEG signals.

RESULTS

The complexity of Rapid Eye Movement (REM) EEG signals significantly decreased intrinsic multi-scale entropy (iMSE) (1.732 ± 0.057 vs. 1.605 ± 0.046, P = 0.0004 in the case of the C4 channel, IMF 1:4 and scale 7) after tDCS active stimulation. The complexity of the REM EEG signals significantly increased iMSE (1.464 ± 0.101 vs. 1.611 ± 0.085, P = 0.001 for C4 channel, IMF 1:4 and scale 7) after tDCS sham stimulation. There was no significant difference in the Hamilton-24 (P = 0.988), Montgomery scale score (P = 0.726), and sleep structure (N1% P = 0.383; N2% P = 0.716; N3% P = 0.772) between the two groups after treatment.

CONCLUSION

Daytime tDCS changed the complexity of sleep in the REM stage, and presented as decreased intrinsic multi-scale entropy, while no changes in sleep structure occurred. This finding indicated that daytime tDCS may be an effective method to improve sleep quality in depressed patients. Trial registration This trial has been registered at the ClinicalTrials.gov (protocol ID: TCHIRB-10409114, in progress).

Automatic classification of excitation location of snoring sounds

STUDY OBJECTIVES

For surgical treatment of patients with obstructive sleep apnea-hypopnea syndrome, it is crucial to locate accurately the obstructive sites in the upper airway; however, noninvasive methods for locating the obstructive sites have not been well explored. Snoring, as the cardinal symptom of obstructive sleep apnea-hypopnea syndrome, should contain information that reflects the state of the upper airway. Through the classification of snores produced at four different locations, this study aimed to test the hypothesis that snores generated by various obstructive sites differ.

METHODS

We trained and tested our model on a public data set that comprised 219 participants. For each snore episode, an acoustic and a physiological feature were extracted and concatenated, forming a 59-dimensional fusion feature. A principal component analysis and a support machine vector were used for dimensional reduction and snore classification. The performance of the proposed model was evaluated using several metrics: sensitivity, precision, specificity, area under the receiver operating characteristic curve, and F1 score.

RESULTS

The unweighted average values of sensitivity, precision, specificity, area under the curve, and F1 were 86.36%, 89.09%, 96.4%, 87.9%, and 87.63%, respectively. The model achieved 98.04%, 80.56%, 72.73%, and 94.12% sensitivity for types V (velum), O (oropharyngeal), T (tongue), and E (epiglottis) snores.

CONCLUSIONS

The characteristics of snores are related to the state of the upper airway. The machine-learning-based model can be used to locate the vibration sites in the upper airway.

Electrocardiogram-based sleep analysis for sleep apnea screening and diagnosis

PURPOSE

Despite the increasing number of research studies of cardiopulmonary coupling (CPC) analysis, an electrocardiogram-based technique, the use of CPC in underserved population remains underexplored. This study aimed to first evaluate the reliability of CPC analysis for the detection of obstructive sleep apnea (OSA) by comparing with polysomnography (PSG)-derived sleep outcomes.

METHODS

Two hundred five PSG data (149 males, age 46.8 ± 12.8 years) were used for the evaluation of CPC regarding the detection of OSA. Automated CPC analyses were based on ECG signals only. Respiratory event index (REI) derived from CPC and apnea-hypopnea index (AHI) derived from PSG were compared for agreement tests.

RESULTS

CPC-REI positively correlated with PSG-AHI (r = 0.851, p < 0.001). After adjusting for age and gender, CPC-REI and PSG-AHI were still significantly correlated (r = 0.840, p < 0.001). The overall results of sensitivity and specificity of CPC-REI were good.

CONCLUSION

Compared with the gold standard PSG, CPC approach yielded acceptable results among OSA patients. ECG recording can be used for the screening or diagnosis of OSA in the general population.

Predictors of Clinically Important Changes in Actual and Perceived Functional Arm Use of the Affected Upper Limb After Rehabilitative Therapy in Chronic Stroke

OBJECTIVE

To identify the predictors of minimal clinically important changes in actual and perceived functional arm use of the affected upper limb after rehabilitative therapy.

DESIGN

Retrospective, observational cohort study.

SETTING

Outpatient rehabilitation settings.

PARTICIPANTS

A cohort of 94 patients with chronic stroke.

INTERVENTIONS

Patients received robot-assisted therapy, mirror therapy, or combined therapy for 4 weeks.

MAIN OUTCOME MEASURES

The primary outcome measures, assessed pre- and post intervention, included actual functional arm use measured by an accelerometer and perceived functional arm use measured by the Motor Activity Log (MAL). Candidate predictors included age, sex, time after stroke, side of stroke, and scores on the Fugl-Meyer Assessment, Modified Ashworth Scale, Medical Research Council scale, Wolf Motor Function Test, MAL (quality of movement), and Nottingham Extended Activities of Daily Living.

RESULTS

Being male (odds ratio [OR], 3.17; 95% CI, 1.13-8.87) and having a higher than median Medical Research Council score (OR, 2.68; 95% CI, 1.12-6.41) significantly predicted minimal clinically important changes assessed by an accelerometer. Fugl-Meyer Assessment scores (odds ratio, 1.06; 95% CI, 1.02-1.11) were a significant predictor of achieving clinically important changes in MAL amount of use. Wolf Motor Function Test (quality) scores (OR, 3.05; 95% CI, 1.38-6.77) could predict clinically important improvements in MAL quality of movement.

CONCLUSIONS

Predictors of clinically important changes in the use of the affected upper limb after robot-assisted therapy, mirror therapy, or combined therapy in patients with chronic stroke for 4 weeks differ for actual vs perceived use. Further studies are recommended to validate these findings in a larger sample.