Human fronto-parietal response scattering subserves vigilance at night

The nucleus accumbens functional connectivity in patients with insomnia using resting-state fMRI

Background

The aim of this study was to investigate the functional abnormalities between the nucleus accumbens (NAc) and the whole brain in individuals with Insomnia Disorder (ID) using resting-state functional magnetic resonance imaging (fMRI). Additionally, the study aimed to explore the underlying neural mechanisms of ID.

Methods

We enrolled 18 participants with ID and 16 normal controls (NC). Resting-state functional connectivity (FC) between the NAc and the whole brain voxels was calculated and compared between the two groups to identify differential brain region. Receiver operating characteristic (ROC) curve analysis was employed to assess the ability of differential features to distinguish between groups. Furthermore, Pearson correlation analysis was performed to examine the relationship between neurocognitive scores and differential features.

Results

The ID group exhibited significantly reduced FC values in several brain regions, including the right supplementary motor area, the bilateral middle frontal gyrus, the bilateral median cingulate and paracingulate gyri and the left precuneus. The area under the curve (AUC) of the classification model based on FC in these brain regions was 83.3%. Additionally, the abnormal functional changes observed in ID patients were positively correlated with the Fatigue Severity Scale (R = 0.650, p = 0.004).

Conclusion

These findings suggest that the NAc may play a crucial role in the diagnosis of ID and could serve as a potential imaging biomarker, providing insights into the underlying neural mechanisms of the disorder.

Driving Fatigue Detection with Three Non-Hair-Bearing EEG Channels and Modified Transformer Model

Driving fatigue is the main cause of traffic accidents, which seriously affects people's life and property safety. Many researchers have applied electroencephalogram (EEG) signals for driving fatigue detection to reduce negative effects. The main challenges are the practicality and accuracy of the EEG-based driving fatigue detection method when it is applied on the real road. In our previous study, we attempted to improve the practicality of fatigue detection based on the proposed non-hair-bearing (NHB) montage with fewer EEG channels, but the recognition accuracy was only 76.47% with the random forest (RF) model. In order to improve the accuracy with NHB montage, this study proposed an improved transformer architecture for one-dimensional feature vector classification based on introducing the Gated Linear Unit (GLU) in the Attention sub-block and Feed-Forward Networks (FFN) sub-block of a transformer, called GLU-Oneformer. Moreover, we constructed an NHB-EEG-based feature set, including the same EEG features (power ratio, approximate entropy, and mutual information (MI)) in our previous study, and the lateralization features of the power ratio and approximate entropy based on the strategy of brain lateralization. The results indicated that our GLU-Oneformer method significantly improved the recognition performance and achieved an accuracy of 86.97%. Our framework demonstrated that the combination of the NHB montage and the proposed GLU-Oneformer model could well support driving fatigue detection.

Sleep and anxiety: From mechanisms to interventions

Anxiety is the most common mental health problem worldwide. Epidemiological studies show that sleep disturbances, particularly insomnia, affect ∼50% of individuals with anxiety, and that insufficient sleep can instigate or further exacerbate it. This review outlines brain mechanisms underlying sleep and anxiety, by addressing recent human functional/structural imaging studies on brain networks underlying the anxiogenic impact of sleep loss, and the beneficial effect of sleep on these brain networks. We discuss recent developments from human molecular imaging studies that highlight the role of specific brain neurotransmitter mechanisms, such as the adenosinergic receptor system, on anxiety, arousal, and sleep. This review further discusses frontline sleep interventions aimed at enhancing sleep in individuals experiencing anxiety, such as nonbenzodiazepines/antidepressants, lifestyle and sleep interventions and cognitive behavioral therapy for insomnia. Notwithstanding therapeutic success, up to ∼30% of individuals with anxiety can be nonresponsive to frontline treatments. Thus, we address novel non-invasive brain stimulation techniques that can enhance electroencephalographic slow waves, and might help alleviate sleep and anxiety symptoms. Collectively, these findings contribute to an emerging biological framework that elucidates the interrelationship between sleep and anxiety, and highlight the prospect of slow wave sleep as a potential therapeutic target for reducing anxiety.

Altered Hypothalamic Functional Connectivity Following Total Sleep Deprivation in Young Adult Males

Background: Sleep deprivation can markedly influence vigilant attention that is essential to complex cognitive processes. The hypothalamus plays a critical role in arousal and attention regulation. However, the functional involvement of the hypothalamus in attentional impairments after total sleep deprivation (TSD) remains unclear. The purpose of this study is to investigate the alterations in hypothalamic functional connectivity and its association with the attentional performance following TSD. Methods: Thirty healthy adult males were recruited in the study. Participants underwent two resting-state functional magnetic resonance imaging (rs-fMRI) scans, once in rested wakefulness (RW) and once after 36 h of TSD. Seed-based functional connectivity analysis was performed using rs-fMRI for the left and right hypothalamus. Vigilant attention was measured using a psychomotor vigilance test (PVT). Furthermore, Pearson correlation analysis was conducted to investigate the relationship between altered hypothalamic functional connectivity and PVT performance after TSD. Results: After TSD, enhanced functional connectivity was observed between the left hypothalamus and bilateral thalamus, bilateral anterior cingulate cortex, right amygdala, and right insula, while reduced functional connectivity was observed between the left hypothalamus and bilateral middle frontal gyrus (AlphaSim corrected, P < 0.01). However, significant correlation between altered hypothalamic functional connectivity and PVT performance was not observed after Bonferroni correction (P > 0.05). Conclusion: Our results suggest that TSD can lead to disrupted hypothalamic circuits, which may provide new insight into neural mechanisms of attention impairments following sleep deprivation.

Disrupted Small-world Networks are Associated with Decreased Vigilant Attention after Total Sleep Deprivation

Sleep deprivation critically affects vigilant attention. Previous neuroimaging studies have revealed altered inter-regional functional connectivity after sleep deprivation, which may disrupt topological properties of brain functional networks. However, little is known about alterations in the topology of intrinsic connectivity and its involvement in attention performance after sleep deprivation. In the current study, we investigated the topological properties of brain networks derived from resting-state functional magnetic resonance imaging of 26 healthy men in rested wakefulness (RW) state and after 36 h of total sleep deprivation (TSD). In the predefined sparsity threshold range, both global and nodal network properties were evaluated based on graph theory analysis. Vigilant attention was assessed using the psychomotor vigilance test (PVT) before and after TSD. Furthermore, Pearson's correlation analyses were conducted to explore the association between altered network properties and changed PVT performance after TSD. At the global level, the brain functional networks in the TSD state showed a significantly lower small-world coefficient than RW, with decreased global efficiency. At the nodal level, the altered regions were selectively distributed in frontoparietal networks, sensorimotor networks, temporal regions, and salience networks. More specifically, the altered clustering coefficient in the posterior superior temporal sulcus (pSTS) and insula, and altered local efficiency in pSTS were further associated with PVT performance after TSD. Our results suggest that the topological properties of brain functional networks are disrupted, and aberrant topology of temporal networks and salience networks may act as neural signatures underlying the vigilant attention impairments after TSD.

A quantitative assessment of the indirect impacts of human-elephant conflict

Human-wildlife conflict has direct and indirect consequences for human communities. Understanding how both types of conflict affect communities is crucial to developing comprehensive and sustainable mitigation strategies. We conducted an interview survey of 381 participants in two rural areas in Myanmar where communities were exposed to human-elephant conflict (HEC). In addition to documenting and quantifying the types of direct and indirect impacts experienced by participants, we evaluated how HEC influences people’s attitudes towards elephant conservation. We found that 99% of participants suffered from some type of indirect impact from HEC, including fear for personal and family safety from elephants and fear that elephants will destroy their home. Despite experiencing moderate levels of indirect impacts from HEC at the community level, participants expressed attitudes consistent with supporting future elephant conservation programs.

Increased cortical excitability but stable effective connectivity index during attentional lapses

Modern lifestyle curtails sleep and increases nighttime work and leisure activities. This has a deleterious impact on vigilance and attention, exacerbating chances of committing attentional lapses, with potential dramatic outcomes. Here, we investigated the brain signature of attentional lapses and assessed whether cortical excitability and brain response propagation were modified during lapses and whether these modifications changed with aging. We compared electroencephalogram (EEG) responses to transcranial magnetic stimulation (TMS) during lapse and no-lapse periods while performing a continuous attentional/vigilance task at night, after usual bedtime. Data were collected in healthy younger (N = 12; 18-30 years) and older individuals (N = 12; 50-70 years) of both sexes. The amplitude and slope of the first component of the TMS-evoked potential were larger during lapses. In contrast, TMS response scattering over the cortical surface, as well as EEG response complexity, did not significantly vary between lapse and no-lapse periods. Importantly, despite qualitative differences, age did not significantly affect any of the TMS-EEG measures. These results demonstrate that attentional lapses are associated with a transient increase of cortical excitability. This initial change is not associated with detectable changes in subsequent effective connectivity-as indexed by response propagation-and are not markedly different between younger and older adults. These findings could contribute to develop models aimed to predicting and preventing lapses in real-life situations.

Toward practical driving fatigue detection using three frontal EEG channels: a proof-of-concept study

Objective. Although various driving fatigue detection strategies have been introduced, the limited practicability is still an obstacle for the real application of these technologies. This study is based on the newly proposed non-hair-bearing (NHB) method to achieve practical driving fatigue detection with fewer channels from NHB areas and more efficient electroencephalogram (EEG) features.Approach. EEG data were recorded from 20 healthy subjects (15 males, age = 22.2 ± 3.2 years) in a 90 min simulated driving task using a remote wireless cap. Behaviorally, subjects demonstrated a salient fatigue effect, as reflected by a monotonic increase in reaction time. Using a sliding-window approach, we determined the vigilant and fatigued states at individual level to reduce the inter-subject differences in behavioral impairment and brain activity. Multiple EEG features, including power-spectrum density (PSD), functional connectivity (FC), and entropy, were estimated in a pairwise manner, which were set as input for fatigue classification.Main results. Intriguingly, this data-driven approach showed that the best classification performance was achieved using three EEG channel pairs located in the NHB area. The mixed features of the frontal NHB area lead to the high within-subject detection rate of driving fatigue (92.7% ± 0.92%) with satisfactory generalizability for fatigue classification across different subjects (77.13% ± 0.85%). Moreover, we found the most prominent contributing features were PSD of different frequency bands within the frontal NHB area and FC within the frontal NHB area and between frontal and parietal areas.Significance. In summary, the current work provided objective evidence to support the effectiveness of the NHB method and further improved the performance, thereby moving a step forward towards practical driving fatigue detection in real-world scenarios.

Changes in EEG permutation entropy in the evening and in the transition from wake to sleep

Quantifying the complexity of the EEG signal during prolonged wakefulness and during sleep is gaining interest as an additional mean to characterize the mechanisms associated with sleep and wakefulness regulation. Here, we characterized how EEG complexity, as indexed by Multiscale Permutation Entropy (MSPE), changed progressively in the evening prior to light off and during the transition from wakefulness to sleep. We further explored whether MSPE was able to discriminate between wakefulness and sleep around sleep onset and whether MSPE changes were correlated with spectral measures of the EEG related to sleep need during concomitant wakefulness (theta power-Ptheta: 4-8 Hz). To address these questions, we took advantage of large datasets of several hundred of ambulatory EEG recordings of individual of both sexes aged 25-101 years. Results show that MSPE significantly decreases before light off (i.e. before sleep time) and in the transition from wakefulness to sleep onset. Furthermore, MSPE allows for an excellent discrimination between pre-sleep wakefulness and early sleep. Finally, we show that MSPE is correlated with concomitant Ptheta. Yet, the direction of the latter correlation changed from before light-off to the transition to sleep. Given the association between EEG complexity and consciousness, MSPE may track efficiently putative changes in consciousness preceding sleep onset. An MSPE stands as a comprehensive measure that is not limited to a given frequency band and reflects a progressive change brain state associated with sleep and wakefulness regulation. It may be an effective mean to detect when the brain is in a state close to sleep onset.

Decoding Analysis of Alpha Oscillation Networks on Maintaining Driver Alertness

The countermeasure of driver fatigue is valuable for reducing the risk of accidents caused by vigilance failure during prolonged driving. Listening to the radio (RADIO) has been proven to be a relatively effective “in-car” countermeasure. However, the connectivity analysis, which can be used to investigate its alerting effect, is subject to the issue of signal mixing. In this study, we propose a novel framework based on clustering and entropy to improve the performance of the connectivity analysis to reveal the effect of RADIO to maintain driver alertness. Regardless of reducing signal mixing, we introduce clustering algorithm to classify the functional connections with their nodes into different categories to mine the effective information of the alerting effect. Differential entropy (DE) is employed to measure the information content in different brain regions after clustering. Compared with the Louvain-based community detection method, the proposed method shows more superior ability to present RADIO effectin confused functional connection matrices. Our experimental results reveal that the active connection clusters distinguished by the proposed method gradually move from frontal region to parieto-occipital regionwith the progress of fatigue, consistent with the alpha energy changes in these two brain areas. The active class of the clusters in parieto-occipital region significantly decreases and the most active clusters remain in the frontal region when RADIO is taken. The estimation results of DE confirm the significant change (p < 0.05) of information content due to the cluster movements. Hence, preventing the movement of the active clusters from frontal region to parieto-occipital region may correlate with maintaining driver alertness. The revelation of alerting effect is helpful for the targeted upgrade of fatigue countermeasures.

Spatio-temporal properties of sleep slow waves and implications for development

Objective sleep quality can be measured by electroencephalography (EEG), a non-invasive technique to quantify electrical activity generated by the brain. With EEG, sleep depth is measured by appearance and an increase in slow wave activity (scalp-SWA). EEG slow waves (scalp-SW) are the manifestation of underlying synchronous membrane potential transitions between silent (DOWN) and active (UP) states. This bistable periodic rhythm is defined as slow oscillation (SO). During its "silent state" cortical neurons are hyperpolarized and appear inactive, while during its "active state" cortical neurons are depolarized, fire spikes and exhibit continuous synaptic activity, excitatory and inhibitory. In adults, data from high-density EEG revealed that scalp-SW propagate across the cortical mantle in complex patterns. However, scalp-SW propagation undergoes modifications across development. We present novel data from children, indicating that scalp-SW originate centro-parietally, and emerge more frontally by adolescence. Based on the concept that SO and SW could actively modify neuronal connectivity, we discuss whether they fulfill a key purpose in brain development by actively conveying modifications of the maturing brain.

Steady-State Pupil Size Varies with Circadian Phase and Sleep Homeostasis in Healthy Young Men

Pupil size informs about sympathovagal balance as well as cognitive and affective processes, and perception. It is also directly linked to phasic activity of the brainstem locus coeruleus, so that pupil measures have gained recent attention. Steady-state pupil size and its variability have been directly linked to sleep homeostasis and circadian phase, but results have been inconsistent. Here, we report robust changes in steady-state pupil size during 29 h of continuous wakefulness in healthy young men (N = 20; 18–30 years old) maintained in dim-light in strictly controlled constant routine conditions. These variations were associated with variations in motivation and sustained attention performance. Pupil size variability did not significantly change during the protocol. Yet, pupil size variability was linearly associated with subjective fatigue, sociability, and anguish. No associations were found between neither steady-state pupil size nor pupil size variability, and objective EEG measure of alertness and subjective sleepiness. Our data support therefore the notion that, compared with its variability, steady-state pupil size is strongly influenced by the concomitant changes in sleep need and circadian phase. In addition, steady-state pupil size appears to be related to motivation and attention, while its variability may be related to separate affective dimensions and subjective fatigue.