Aberrant neural oscillations hallmark numerous brain disorders. Here, we first report a method to track the phase of neural oscillations in real-time via endpoint-corrected Hilbert transform (ecHT) that mitigates the characteristic Gibbs distortion. We then used ecHT to show that the aberrant neural oscillation that hallmarks essential tremor (ET) syndrome, the most common adult movement disorder, can be transiently suppressed via transcranial electrical stimulation of the cerebellum phase-locked to the tremor. The tremor suppression is sustained shortly after the end of the stimulation and can be phenomenologically predicted. Finally, we use feature-based statistical-learning and neurophysiological-modelling to show that the suppression of ET is mechanistically attributed to a disruption of the temporal coherence of the aberrant oscillations in the olivocerebellar loop, thus establishing its causal role. The suppression of aberrant neural oscillation via phase-locked driven disruption of temporal coherence may in the future represent a powerful neuromodulatory strategy to treat brain disorders.

Sleep is a fundamental part of our lives; yet, how our brain falls asleep remains one of the most enduring mysteries of neuroscience. Here we report a new conceptual framework to analyze and model this phenomenon. The framework represents the changes in brain electroencephalogram activity during the transition into sleep as a trajectory in a normalized feature space. We use the framework to show that the brain’s wake-to-sleep transition follows bifurcation dynamics with a distinct tipping point preceded by a critical slowing down. We validate the bifurcation dynamics in two independent datasets, which include more than 1,000 human participants. Finally, we demonstrate the framework’s utility by predicting a person’s progression into sleep in real time with seconds temporal resolution and over 0.95 average accuracy.

Amyloid-β pathology and neurofibrillary tangles lead to glial activation and neurodegeneration in Alzheimer’s disease. In this study, we investigated the relationships between the levels of amyloid-β oligomers, amyloid-β plaques, glial activation and markers related to neurodegeneration in the App^NL-G-F triple mutation mouse line and in a knock-in line homozygous for the common human amyloid precursor protein (App^hu mouse). The relationships between neuropathological features were characterized with immunohistochemistry and imaging mass cytometry. Markers assessing human amyloid-β proteins, microglial and astrocytic activation and neuronal and synaptic densities were used in mice between 2.5 and 12 months of age. We found that amyloid-β oligomers were abundant in the brains of App^hu mice in the absence of classical amyloid-β plaques. These brains showed morphological changes consistent with astrocyte activation but no evidence of microglial activation or synaptic or neuronal pathology. In contrast, both high levels of amyloid-β oligomers and numerous plaques accumulated in App^NL-G-F mice in association with substantial astrocytic and microglial activation. The increase in amyloid-β oligomers over time was more strongly correlated with astrocytic than with microglia activation. Spatial analyses suggested that activated microglia were more closely associated with amyloid-β oligomers than with amyloid-β plaques in App^NL-G-F mice, which also showed age-dependent decreases in neuronal and synaptic density markers. A comparative study of the two models highlighted the dependence of glial and neuronal pathology on the nature and aggregation state of the amyloid-β peptide. Astrocyte activation and neuronal pathology appeared to be more strongly associated with amyloid-β oligomers than with amyloid-β plaques, although amyloid-β plaques were associated with microglia activation.

With the widespread use of plastic products globally, the issue of microplastics as environmental pollutants has become increasingly severe. Due to their small size, large surface area, and hydrophobic properties, microplastics are capable of adsorbing various pollutants, particularly radionuclides, which, in turn, can impact the stability of ecosystems. This laboratory study investigates the adsorption capacity of microplastics (PVC) for radionuclides (Ra-226, Cs-137, and K-40) under controlled conditions, examining the effects of spatial distribution and particle size. The laboratory experiment results indicate that the adsorption of Ra-226 by microplastics was significantly higher in the bottom water compared to the surface layer, with concentrations of 13.29 mBq/kg on microplastics mixed with the bottom water and 1.65 mBq/kg in the surface layer. The concentration of Cs-137 on microplastics mixed with the bottom water was 6.99 mBq/kg, while on microplastics mixed with the surface water, the concentration was 1.31 mBq/kg. In contrast, the adsorption of K-40 was lower, with concentrations of 2.1 mBq/kg and 0.35 mBq/kg on microplastics mixed with the bottom and surface water, respectively. Furthermore, microplastics with smaller particle sizes exhibited stronger adsorption capacities. The adsorption concentrations of Ra-226 and Cs-137 by 50 µm microplastics were 13.29 mBq/kg and 6.99 mBq/kg, respectively, while the concentrations for 100 µm and 150 µm particles decreased to 3.14 mBq/kg and 1.39 mBq/kg, and 2.2 mBq/kg and 0.35 mBq/kg, respectively. These findings suggest that the adsorption capacity of microplastics is significantly influenced by particle size and sediment depth, highlighting the potential risk of exacerbating the spread of radioactive pollutants in marine ecosystems.