Understanding the molecular mechanism of glaucoma and development of neuroprotectants are significantly hindered by the lack of a reliable animal model that accurately recapitulates human glaucoma. Here we sought to develop a mouse model for the secondary glaucoma that is often observed in humans after silicone oil (SO) blocks the pupil or migrates into the anterior chamber following vitreoretinal surgery. We observed significant intraocular pressure (IOP) elevation after intracameral injection of SO, and that SO removal allows IOP to return quickly to normal. This simple, inducible and reversible mouse ocular hypertension model shows dynamic changes of visual function that correlate with progressive RGC loss and axon degeneration. It may be applicable with only minor modifications to a range of animal species in which it will generate stable, robust IOP elevation and significant neurodegeneration that will facilitate selection of neuroprotectants and investigating the pathogenesis of ocular hypertension-induced glaucoma.
All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for all the figures.
- Yang Hu
- Yang Hu
- Yang Hu
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#32093) of the Stanford University.
- Jeremy Nathans, Johns Hopkins University School of Medicine, United States
© 2019, Zhang et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
During deep anesthesia, the electroencephalographic (EEG) signal of the brain alternates between bursts of activity and periods of relative silence (suppressions). The origin of burst-suppression and its distribution across the brain remain matters of debate. In this work, we used functional magnetic resonance imaging (fMRI) to map the brain areas involved in anesthesia-induced burst-suppression across four mammalian species: humans, long-tailed macaques, common marmosets, and rats. At first, we determined the fMRI signatures of burst-suppression in human EEG-fMRI data. Applying this method to animal fMRI datasets, we found distinct burst-suppression signatures in all species. The burst-suppression maps revealed a marked inter-species difference: in rats, the entire neocortex engaged in burst-suppression, while in primates most sensory areas were excluded—predominantly the primary visual cortex. We anticipate that the identified species-specific fMRI signatures and whole-brain maps will guide future targeted studies investigating the cellular and molecular mechanisms of burst-suppression in unconscious states.
In humans, ageing is characterized by decreased brain signal variability and increased behavioral variability. To understand how reduced brain variability segregates with increased behavioral variability, we investigated the association between reaction time variability, evoked brain responses and ongoing brain signal dynamics, in young (N=36) and older adults (N=39). We studied the electroencephalogram (EEG) and pupil size fluctuations to characterize the cortical and arousal responses elicited by a cued go/no-go task. Evoked responses were strongly modulated by slow (<2 Hz) fluctuations of the ongoing signals, which presented reduced power in the older participants. Although variability of the evoked responses was lower in the older participants, once we adjusted for the effect of the ongoing signal fluctuations, evoked responses were equally variable in both groups. Moreover, the modulation of the evoked responses caused by the ongoing signal fluctuations had no impact on reaction time, thereby explaining why although ongoing brain signal variability is decreased in older individuals, behavioral variability is not. Finally, we showed that adjusting for the effect of the ongoing signal was critical to unmask the link between neural responses and behavior as well as the link between task-related evoked EEG and pupil responses.