How could the basal ganglia control sleep?

Sleep Promotion by 3-Hydroxy-4-Iminobutyric Acid in Walnut Diaphragma juglandis Fructus

Insufficient sleep can produce a multitude of deleterious repercussions on various domains of human well-being. Concomitantly, the walnut (Juglans mandshurica) confers numerous salutary biological activities pertaining to sleep. Nevertheless, the sedative and hypnotic capacities of walnut's functional constituents remain obscure. In this investigation, we analyzed the sedative and hypnotic components of the walnut Diaphragma juglandis fructus and innovatively discovered a compound, defined as 3-hydroxy-4-iminobutyric acid (HIBA), which disrupts motor activity and enhances sleep duration by regulating the neurotransmitters (GABA, DA, etc.) within the brain and serum of mice. Subsequently, a metabolomics approach of the serum, basal ganglia, hypothalamus, and hippocampus as well as the gut microbiota was undertaken to unravel the underlying molecular mechanisms of sleep promotion. Our data reveal that HIBA can regulate the metabolism of basal ganglia (sphingolipids, acylcarnitines, etc.), possibly in relation to HIBA's influence on the gut microbiome (Muribaculum, Bacteroides, Lactobacillus, etc.). Therefore, we introduce a novel natural product, HIBA, and explicate the modulation of sleep promotion in mice based on the microbiota-gut-brain axis. This study contributes fresh insights toward natural product-based sleep research.

Arousal Regulation by the External Globus Pallidus: A New Node for the Mesocircuit Hypothesis

In the decade since its debut, the Mesocircuit Hypothesis (MH) has provided researchers a scaffolding for interpreting their findings by associating subcortical-cortical dysfunction with the loss and recovery of consciousness following severe brain injury. Here, we leverage new findings from human and rodent lesions, as well as chemo/optogenetic, tractography, and stimulation studies to propose the external segment of the globus pallidus (GPe) as an additional node in the MH, in hopes of increasing its explanatory power. Specifically, we discuss the anatomical and molecular mechanisms involving the GPe in sleep-wake control and propose a plausible mechanistic model explaining how the GPe can modulate cortical activity through its direct connections with the prefrontal cortex and thalamic reticular nucleus to initiate and maintain sleep. The inclusion of the GPe in the arousal circuitry has implications for understanding a range of phenomena, such as the effects of the adenosine (A2A) and dopamine (D2) receptors on sleep-wake cycles, the paradoxical effects of zolpidem in disorders of consciousness, and sleep disturbances in conditions such as Parkinson's Disease.

Parkinsonian daytime sleep-wake classification using deep brain stimulation lead recordings

Excessive daytime sleepiness is a recognized non-motor symptom that adversely impacts the quality of life of people with Parkinson's disease (PD), yet effective treatment options remain limited. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for PD motor signs. Reliable daytime sleep-wake classification using local field potentials (LFPs) recorded from DBS leads implanted in STN can inform the development of closed-loop DBS approaches for prompt detection and disruption of sleep-related neural oscillations. We performed STN DBS lead recordings in three nonhuman primates rendered parkinsonian by administrating neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Reference sleep-wake states were determined on a second-by-second basis by video monitoring of eyes (eyes-open, wake and eyes-closed, sleep). The spectral power in delta (1-4 Hz), theta (4-8 Hz), low-beta (8-20 Hz), high-beta (20-35 Hz), gamma (35-90 Hz), and high-frequency (200-400 Hz) bands were extracted from each wake and sleep epochs for training (70% data) and testing (30% data) a support vector machines classifier for each subject independently. The spectral features yielded reasonable daytime sleep-wake classification (sensitivity: 90.68 ± 1.28; specificity: 88.16 ± 1.08; accuracy: 89.42 ± 0.68; positive predictive value; 88.70 ± 0.89, n = 3). Our findings support the plausibility of monitoring daytime sleep-wake states using DBS lead recordings. These results could have future clinical implications in informing the development of closed-loop DBS approaches for automatic detection and disruption of sleep-related neural oscillations in people with PD to promote wakefulness.

Sleep disturbance-related neuroimaging features as potential biomarkers for the diagnosis of major depressive disorder: A multicenter study based on machine learning

BACKGROUND

Objective biomarkers are crucial for overcoming the clinical dilemma in major depressive disorder (MDD), and the individualized diagnosis is essential to facilitate the precise medicine for MDD.

METHODS

Sleep disturbance-related magnetic resonance imaging (MRI) features was identified in the internal dataset (92 MDD patients) using the relevance vector regression algorithm, which was further verified in 460 MDD patients of an independent, multicenter dataset. Subsequently, using these MRI features, the eXtreme Gradient Boosting classification model was constructed in the current multicenter dataset (460 MDD patients and 470 normal controls). Meanwhile, the association between classification outputs and the severity of depressive symptoms was also investigated.

RESULTS

In MDD patients, the combination of gray matter density and fractional amplitude of low-frequency fluctuation can accurately predict individual sleep disturbance score that was calculated by the sum of item 4 score, item 5 score, and item 6 score of the 17-Item Hamilton Rating Scale for Depression (HAMD-17) (R² = 0.158 in the internal dataset; R² = 0.110 in multicenter dataset). Furthermore, the classification model based on these MRI features distinguished MDD patients from normal controls with 86.3% accuracy (area under the curve = 0.937). Importantly, the classification outputs significantly correlated with HAMD-17 scores in MDD patients.

LIMITATION

Lacking some specialized tools to assess the personal sleep quality, e.g. Pittsburgh Sleep Quality Index.

CONCLUSION

Neuroimaging features can reflect accurately individual sleep disturbance manifestation and serve as potential diagnostic biomarkers of MDD.

Chronic severe methanol intoxication after repeated mask cleansing due to fear of COVID-19: A new risk of coronaphobia

Background

Disproportionate fear of contracting COVID‐19 (coronaphobia) may result in inappropriate use of preventive measures that could, in turn, result in severe harm to the patient.

Objective

To describe a patient with subacute parkinsonism and cognitive dysfunction and magnetic resonance imaging (MRI) evidence of bilateral deep white matter and basal ganglia damage.

Case presentation

A 56‐year‐old female presented with a 4‐week history of insomnia, cognitive decline, and parkinsonism. Brain MRI revealed a bilateral lesion of both globus pallidus, deep white matter, and cerebellar hemispheres. Her son reported that, for the previous month, she had been cleaning her facial mask three times a day with a pure methanol solution as a disinfectant due to an intense fear of acquiring COVID‐19. Previously, she had used 97% isopropyl alcohol and had inadvertently switched to methanol. After the exposure ended, she slowly improved but 4 months later she remains severely disabled.

Conclusions

The repeated exposure to methanol vapor, the MRI findings, and the absence of other etiologies for her cognitive and parkinsonian features led to the diagnosis of chronic methanol intoxication with severe central nervous system damage. Misinformation is a likely contributory factor to such scenario. Efforts should be made by the scientific community to educate the general public on avoiding self‐damaging behaviors as a result of coronaphobia.