Alterations in EEG connectivity in healthy young adults provide an indicator of sleep depth

Longitudinal single-subject neuroimaging study reveals effects of daily environmental, physiological, and lifestyle factors on functional brain connectivity

Our behavior and mental states are constantly shaped by our environment and experiences. However, little is known about the response of brain functional connectivity to environmental, physiological, and behavioral changes on different timescales, from days to months. This gives rise to an urgent need for longitudinal studies that collect high-frequency data. To this end, for a single subject, we collected 133 days of behavioral data with smartphones and wearables and performed 30 functional magnetic resonance imaging (fMRI) scans measuring attention, memory, resting state, and the effects of naturalistic stimuli. We find traces of past behavior and physiology in brain connectivity that extend up as far as 15 days. While sleep and physical activity relate to brain connectivity during cognitively demanding tasks, heart rate variability and respiration rate are more relevant for resting-state connectivity and movie-watching. This unique data set is openly accessible, offering an exceptional opportunity for further discoveries. Our results demonstrate that we should not study brain connectivity in isolation, but rather acknowledge its interdependence with the dynamics of the environment, changes in lifestyle, and short-term fluctuations such as transient illnesses or restless sleep. These results reflect a prolonged and sustained relationship between external factors and neural processes. Overall, precision mapping designs such as the one employed here can help to better understand intraindividual variability, which may explain some of the observed heterogeneity in fMRI findings. The integration of brain connectivity, physiology data and environmental cues will propel future environmental neuroscience research and support precision healthcare.

Effects of gender and age on sleep EEG functional connectivity differences in subjects with mild difficulty falling asleep

Introduction

Difficulty falling asleep place an increasing burden on society. EEG-based sleep staging is fundamental to the diagnosis of sleep disorder, and the selection of features for each sleep stage is a key step in the sleep analysis. However, the differences of sleep EEG features in gender and age are not clear enough.

Methods

This study aimed to investigate the effects of age and gender on sleep EEG functional connectivity through statistical analysis of brain functional connectivity and machine learning validation. The two-overnight sleep EEG data of 78 subjects with mild difficulty falling asleep were categorized into five sleep stages using markers and segments from the "sleep-EDF" public database. First, the 78 subjects were finely grouped, and the mutual information of the six sleep EEG rhythms of δ, θ, α, β, spindle, and sawtooth wave was extracted as a functional connectivity measure. Then, one-way analysis of variance (ANOVA) was used to extract significant differences in functional connectivity of sleep rhythm waves across sleep stages with respect to age and gender. Finally, machine learning algorithms were used to investigate the effects of fine grouping of age and gender on sleep staging.

Results and discussion

The results showed that: (1) The functional connectivity of each sleep rhythm wave differed significantly across sleep stages, with delta and beta functional connectivity differing significantly across sleep stages. (2) Significant differences in functional connections among young and middle-aged groups, and among young and elderly groups, but no significant difference between middle-aged and elderly groups. (3) Female functional connectivity strength is generally higher than male at the high-frequency band of EEG, but no significant difference in the low-frequency. (4) Finer group divisions based on gender and age can indeed improve the accuracy of sleep staging, with an increase of about 3.58% by using the random forest algorithm. Our results further reveal the electrophysiological neural mechanisms of each sleep stage, and find that sleep functional connectivity differs significantly in both gender and age, providing valuable theoretical guidance for the establishment of automated sleep stage models.

EEG connectivity patterns in response to gaming and learning-based cognitive stimulations in Rett syndrome

BACKGROUND

Functional connectivity is scarcely studied in Rett syndrome (RTT). Explorations revealed associations between RTT's clinical, genetic profiles, and coherence measures, highlighting an unexplored frontier in understanding RTT's neural mechanisms and cognitive processes.

AIMS

To evaluate the effects of diverse cognitive stimulations-learning-focused versus gaming-oriented-on electroencephalography brain connectivity in RTT. The comparison with resting states aimed to uncover potential biomarkers and insights into the neural processes associated with RTT.

METHODS AND PROCEDURES

The study included 15 girls diagnosed with RTT. Throughout sessions lasting about 25 min, participants alternated between active and passive tasks, using an eyetracker device while their brain activity was recorded with a 20-channel EEG. Results revealed significant alterations during cognitive tasks, notably in delta, alpha and beta bands. Both tasks induced spectral pattern changes and connectivity shifts, hinting at enhanced neural processing. Hemispheric asymmetry decreased during tasks, suggesting more balanced neural processing. Linear and nonlinear connectivity alterations were observed in active tasks compared to resting state, while passive tasks showed no significant changes.

CONCLUSIONS AND IMPLICATIONS

Results underscores the potential of cognitive stimulation for heightened cognitive abilities, promoting enhanced brain connectivity and information flow in Rett syndrome. These findings offer valuable markers for evaluating cognitive interventions and suggest gaming-related activities as effective tools for improving learning outcomes.

A rs-fMRI study of functional connectivity changes between thalamus and postcentral gyrus in patients with neuropathic pain after brachial plexus avulsion

BACKGROUND

The neuropathic pain (NPP) after brachial plexus avulsion (BPA) is common and difficult to cure, and thalamus and postcentral gyrus have been accepted to be the key nodes of mechanisms and pathways for pain. However, little attention has been paid on the thalamus-postcentral gyrus functional connectivity changes in NP patients after BPA.

METHODS

Eighteen patients with NPP after BPA and twenty age and gender matched healthy controls were enrolled and underwent resting-state functional MRI (rs-fMRI) scans in this study. The Pearson's r-value of functional connection (bilateral thalamus and postcentral gyrus as regions of interest) was generated and examined using two sample t-test. The linear regression analysis was used to select possible related factors, and multiple linear regression of the possible predictors was used to identify the variables that significantly predicted Visual Analogue Score (VAS).

RESULTS

The standardized Pearson r-values of the left thalamus-right thalamus, left thalamus-left postcentral gyrus, left thalamus-right postcentral gyrus, right thalamus-left postcentral gyrus and right thalamus-right postcentral gyrus in the control group were 0.759 ± 0.242, 0.358 ± 0.297, 0.383 ± 0.270, 0.317 ± 0.295 and 0.333 ± 0.304, respectively. And the corresponding standardized Pearson r-values in patients group were 0.510 ± 0.224,0.305 ± 0.212,0.281 ± 0.225,0.333 ± 0.193 and 0.333 ± 0.210, respectively. The functional connectivity strength of the left thalamus-right thalamus in control group was significantly higher than that in the patients group (P < 0.05). Linear regression analysis showed that the functional connectivity strength of the left thalamus-right thalamus was negatively correlated with the patients' VAS score (P < 0.05).

CONCLUSIONS

NPP patients after BPA had a significant pain-related bilateral thalamus functional connection reorganization, with the purpose to limit the pain signal inputs within the unilateral cerebral hemisphere.

Phase-amplitude coupling of Go/Nogo task-related neuronal oscillation decreases for humans with insufficient sleep

Phase-amplitude coupling (PAC) across frequency might be associated with the long-range synchronization of brain networks, facilitating the spatiotemporal integration of multiple cell assemblies for information transmission during inhibitory control. However, sleep problems may affect these cortical information transmissions based on cross-frequency PAC, especially when humans work in environments of social isolation. This study aimed to evaluate changes in the theta-beta/gamma PAC of task-related electroencephalography (EEG) for humans with insufficient sleep. Here, we monitored the EEG signals of 60 healthy volunteers and 18 soldiers in the normal environment, performing a Go/Nogo task. Soldiers also participated in the same test in isolated cabins. These measures demonstrated theta-beta PACs between the frontal and central-parietal, and robust theta-gamma PACs between the frontal and occipital cortex. Unfortunately, these PACs significantly decreased when humans experienced insufficient sleep, which was positively correlated with the behavioral performance of inhibitory control. The evaluation of theta-beta/gamma PAC of Go/Nogo task-related EEG is necessary to help understand the different influences of sleep problems in humans.

Characterizing brain stage-dependent pupil dynamics based on lateral hypothalamic activity

Pupil dynamics presents varied correlation features with brain activity under different vigilant levels. The modulation of brain dynamic stages can arise from the lateral hypothalamus (LH), where diverse neuronal cell types contribute to arousal regulation in opposite directions via the anterior cingulate cortex (ACC). However, the relationship of the LH and pupil dynamics has seldom been investigated. Here, we performed local field potential (LFP) recordings at the LH and ACC, and whole-brain fMRI with simultaneous fiber photometry Ca2+ recording in the ACC, to evaluate their correlation with brain state-dependent pupil dynamics. Both LFP and functional magnetic resonance imaging (fMRI) data showed various correlations to pupil dynamics across trials that span negative, null, and positive correlation values, demonstrating brain state-dependent coupling features. Our results indicate that the correlation of pupil dynamics with ACC LFP and whole-brain fMRI signals depends on LH activity, suggesting a role of the latter in brain dynamic stage regulation.

Electroencephalographic assessment in patients with Rett syndrome during cognitive stimulation by means of eye tracking technology and alternative and augmentative communication systems

Rett syndrome (RTT) is considered a rare disease despite being the leading genetic disorder to cause severe intellectual disability in women. There is no cure for RTT, so the treatment is symptomatic and supporting, requiring a multidisciplinary approach. Occupational therapy can help girls and their families to improve communication, being one of the main concerns when verbal language and intentional hand movement are impaired or lost. This paper presents a pilot study of cognitive training through the combined use of eye-tracking technology (ETT) and augmentative and alternative communication (AAC) using the Peabody Picture Vocabulary Test (PPVT-IV). The objective was to evaluate brain activation by means of electroencephalography (EEG) during the stimulation of non-verbal communication. EEG data were recorded during an eyes-open resting state (EO-RS) period and during cognitive stimulation via AAC activity. To assess their effect, both signals were compared at the spectral level, focusing on frequency, brain symmetry and connectivity. During the task, a redistribution of power towards fast frequency bands was observed, as well as an improvement in the brain symmetry index (BSI) and functional synchronicity through increased coherence. Therefore, the results of the spectral analysis showed a possible deviation from the pathological pattern, manifesting a positive effect in the use of non-verbal cognitive stimulation activities. In conclusion, it was observed that it is possible to establish a cognitive training system that produces brain activation and favors communication and learning despite intentional language loss.Clinical Relevance- This manifests a method of cognitive training that would induce brain activation in RTT patients with absence of intentional communication. The evaluation system through spectral analysis could complement the standardized protocols to asses communication that are based on verbal and motor production.

Brain and brain-heart Granger causality during wakefulness and sleep

In this exploratory study we apply Granger Causality (GC) to investigate the brain-brain and brain-heart interactions during wakefulness and sleep. Our analysis includes electroencephalogram (EEG) and electrocardiogram (ECG) data during all-night polysomnographic recordings from volunteers with apnea, available from the Massachusetts General Hospital's Computational Clinical Neurophysiology Laboratory and the Clinical Data Animation Laboratory. The data is manually annotated by clinical staff at the MGH in 30 second contiguous intervals (wakefulness and sleep stages 1, 2, 3, and rapid eye movement (REM). We applied GC to 4-s non-overlapping segments of available EEG and ECG across all-night recordings of 50 randomly chosen patients. To identify differences in GC between the different sleep stages, the GC for each sleep stage was subtracted from the GC during wakefulness. Positive (negative) differences indicated that GC was greater (lower) during wakefulness compared to the specific sleep stage. The application of GC to study brain-brain and brain-heart bidirectional connections during wakefulness and sleep confirmed the importance of fronto-posterior connectivity during these two states, but has also revealed differences in ipsilateral and contralateral mechanisms of these connections. It has also confirmed the existence of bidirectional brain-heart connections that are more prominent in the direction from brain to heart. Our exploratory study has shown that GC can be successfully applied to sleep data analysis and captures the varying physiological mechanisms that are related to wakefulness and different sleep stages.

Wavelet-Based Biphase Analysis of Brain Rhythms in Automated Wake-Sleep Classification

Many studies in the field of sleep have focused on connectivity and coherence. Still, the nonstationary nature of electroencephalography (EEG) makes many of the previous methods unsuitable for automatic sleep detection. Time-frequency representations and high-order spectra are applied to nonstationary signal analysis and nonlinearity investigation, respectively. Therefore, combining wavelet and bispectrum, wavelet-based bi-phase (Wbiph) was proposed and used as a novel feature for sleep-wake classification. The results of the statistical analysis with emphasis on the importance of the gamma rhythm in sleep detection show that the Wbiph is more potent than coherence in the wake-sleep classification. The Wbiph has not been used in sleep studies before. However, the results and inherent advantages, such as the use of wavelet and bispectrum in its definition, suggest it as an excellent alternative to coherence. In the next part of this paper, a convolutional neural network (CNN) classifier was applied for the sleep-wake classification by Wbiph. The classification accuracy was 97.17% in nonLOSO and 95.48% in LOSO cross-validation, which is the best among previous studies on sleep-wake classification.

The effect of photic stimulation alone and in combination with sleep deprivation after a seizure-like event - reappraisal by using linear and nonlinear EEG methods

ObjectivesThe present study aimed to compare the effectiveness of different provocation tests used for the study of the 'susceptibility to seizure' by quantitative electroencephalography (EEG) analysis.MethodsEight subjects with a history of a seizure-like disturbed consciousness participated in this preliminary study. A routine EEG was carried out with photic stimulation (eyes closed and after eyes open) at the beginning of the investigation. Some days later, a sleep-deprived EEG was recorded with the same protocol. Selected epochs (in eyes closed condition) after the stimulations were analysed with Point(wise) Correlation Dimension (PD2i) and Synchronization Likelihood (SL) methods. The results were compared to those obtained by similar analysis of the resting state (control) epochs with Wilcoxon Signed Rank Test (p ≤ 0.05).ResultsIn our study, significantly lower grand mean PD2i and higher delta SL values were found in sleep-deprived state after stimulation with eyes closed compared to the control. Our results indicated a lower-dimensional, hypersynchronous state of the brain as a consequence of these combined provocations.DiscussionThis may correspond to a possible 'preictal' state of the brain. Accordingly, it is suggested that photic stimulation together with sleep deprivation seems to be more effective in provocation - especially when the stimulation was made with eyes closed.

Cross-participant prediction of vigilance stages through the combined use of wPLI and wSMI EEG functional connectivity metrics

Functional connectivity (FC) metrics describe brain inter-regional interactions and may complement information provided by common power-based analyses. Here, we investigated whether the FC-metrics weighted Phase Lag Index (wPLI) and weighted Symbolic Mutual Information (wSMI) may unveil functional differences across four stages of vigilance-wakefulness (W), NREM-N2, NREM-N3, and REM sleep-with respect to each other and to power-based features. Moreover, we explored their possible contribution in identifying differences between stages characterized by distinct levels of consciousness (REM+W vs. N2+N3) or sensory disconnection (REM vs. W). Overnight sleep and resting-state wakefulness recordings from 24 healthy participants (27 ± 6 years, 13F) were analyzed to extract power and FC-based features in six classical frequency bands. Cross-validated linear discriminant analyses (LDA) were applied to investigate the ability of extracted features to discriminate (1) the four vigilance stages, (2) W+REM vs. N2+N3, and (3) W vs. REM. For the four-way vigilance stages classification, combining features based on power and both connectivity metrics significantly increased accuracy relative to considering only power, wPLI, or wSMI features. Delta-power and connectivity (0.5-4 Hz) represented the most relevant features for all the tested classifications, in line with a possible involvement of slow waves in consciousness and sensory disconnection. Sigma-FC, but not sigma-power (12-16 Hz), was found to strongly contribute to the differentiation between states characterized by higher (W+REM) and lower (N2+N3) probabilities of conscious experiences. Finally, alpha-FC resulted as the most relevant FC-feature for distinguishing among wakefulness and REM sleep and may thus reflect the level of disconnection from the external environment.

Glutamatergic Neurons in the Preoptic Hypothalamus Promote Wakefulness, Destabilize NREM Sleep, Suppress REM Sleep, and Regulate Cortical Dynamics

Clinical and experimental data from the last nine decades indicate that the preoptic area of the hypothalamus is a critical node in a brain network that controls sleep onset and homeostasis. By contrast, we recently reported that a group of glutamatergic neurons in the lateral and medial preoptic area increases wakefulness, challenging the long-standing notion in sleep neurobiology that the preoptic area is exclusively somnogenic.

Clinical and experimental data from the last nine decades indicate that the preoptic area of the hypothalamus is a critical node in a brain network that controls sleep onset and homeostasis. By contrast, we recently reported that a group of glutamatergic neurons in the lateral and medial preoptic area increases wakefulness, challenging the long-standing notion in sleep neurobiology that the preoptic area is exclusively somnogenic. However, the precise role of these subcortical neurons in the control of behavioral state transitions and cortical dynamics remains unknown. Therefore, in this study, we used conditional expression of excitatory hM3Dq receptors in these preoptic glutamatergic (Vglut2⁺) neurons and show that their activation initiates wakefulness, decreases non-rapid eye movement (NREM) sleep, and causes a persistent suppression of rapid eye movement (REM) sleep. We also demonstrate, for the first time, that activation of these preoptic glutamatergic neurons causes a high degree of NREM sleep fragmentation, promotes state instability with frequent arousals from sleep, decreases body temperature, and shifts cortical dynamics (including oscillations, connectivity, and complexity) to a more wake-like state. We conclude that a subset of preoptic glutamatergic neurons can initiate, but not maintain, arousals from sleep, and their inactivation may be required for NREM stability and REM sleep generation. Further, these data provide novel empirical evidence supporting the hypothesis that the preoptic area causally contributes to the regulation of both sleep and wakefulness.

SIGNIFICANCE STATEMENT Historically, the preoptic area of the hypothalamus has been considered a key site for sleep generation. However, emerging modeling and empirical data suggest that this region might play a dual role in sleep-wake control. We demonstrate that chemogenetic stimulation of preoptic glutamatergic neurons produces brief arousals that fragment sleep, persistently suppresses REM sleep, causes hypothermia, and shifts EEG patterns toward a “lighter” NREM sleep state. We propose that preoptic glutamatergic neurons can initiate, but not maintain, arousal from sleep and gate REM sleep generation, possibly to block REM-like intrusions during NREM-to-wake transitions. In contrast to the long-standing notion in sleep neurobiology that the preoptic area is exclusively somnogenic, we provide further evidence that preoptic neurons also generate wakefulness.