Electrical brain stimulation induces dendritic stripping but improves survival of silent neurons after optic nerve damage.

Repetitive transorbital alternating current stimulation (rtACS) improves vision in patients with chronic visual impairments and an acute treatment increased survival of retinal neurons after optic nerve crush (ONC) in rodent models of visual system injury. However, despite this protection no functional recovery could be detected in rats, which was interpreted as evidence of "silent survivor" cells. We now analysed the mechanisms underlying this "silent survival" effect.

Repetitive Transcorneal Alternating Current Stimulation Reduces Brain Idling State After Long-term Vision Loss.

Background: Deafferentation of visual system structures following brain or optic nerve injury leaves cortical areas deprived of visual input. Deprived cortical areas have a reduced sensory information processing and are characterized with localized enhanced or synchronized rhythms believed to represent an "idling state".

Objective/hypothesis: We hypothesized that cortical idling can be modified with transcorneal alternating current stimulation (tACS) known to modulate cortical oscillations and thus change the functional state of the deafferented areas.

Transcorneal alternating current stimulation after severe axon damage in rats results in "long-term silent survivor" neurons.

Transcorneal alternating current stimulation (tACS) was proposed to decrease acute death of retinal ganglion cells after optic nerve transection in rats, but it is not known if cell survival is long-term and associated with functional restoration. We therefore evaluated the effects of tACS in a rat model of optic nerve crush using anatomical, electrophysiological and behavioural measures. Rats were trained in a brightness discrimination visual task and the retinal ganglion cell number was quantified with in vivo confocal neuroimaging.

Full-genome RNAi profiling of early embryogenesis in Caenorhabditis elegans.

A key challenge of functional genomics today is to generate well-annotated data sets that can be interpreted across different platforms and technologies. Large-scale functional genomics data often fail to connect to standard experimental approaches of gene characterization in individual laboratories. Furthermore, a lack of universal annotation standards for phenotypic data sets makes it difficult to compare different screening approaches.