Age-associated DNA methylation changes in Xenopus frogs.

Age-associated changes in DNA methylation have been characterized across various animals, but not yet in amphibians, which are of particular interest because they include widely studied model organisms. In this study, we present clear evidence that the aquatic vertebrate species Xenopus tropicalis displays patterns of age-associated changes in DNA methylation. We have generated whole-genome bisulfite sequencing (WGBS) profiles from skin samples of nine frogs representing young, mature, and old adults and characterized the gene- and chromosome-scale DNA methylation changes with age. Many of the methylation features and changes we observe are consistent with what is known in mammalian species, suggesting that the mechanism of age-related changes is conserved. Moreover, we selected a few thousand age-associated CpG sites to build an assay based on targeted DNA methylation analysis (TBSseq) to expand our findings in future studies involving larger cohorts of individuals. Preliminary results of a pilot TBSeq experiment recapitulate the findings obtained with WGBS setting the basis for the development of an epigenetic clock assay. The results of this study will allow us to leverage the unique resources available for Xenopus to study how DNA methylation relates to other hallmarks of ageing.

A CNN and LSTM Network for Eye-Blink Classification from MRI Scanner Monitoring Videos.

Eye closure changes brain activity, so eye-blink tracking of subjects undergoing resting-state functional magnetic resonance imaging (fMRI) is relevant for identifying when a subject blinks, falls asleep, or keeps their eyes closed. Existing MRI eye-tracking solutions use commercially available MR-compatible video cameras with tracking software that can fail on low-quality videos. In this paper, we propose a two-stage convolutional recurrent neural network to classify open and closed eyes from frames of MRI eye-tracking videos under variable camera conditions. The model extracts visual features from each video frame using a convolutional neural network based on the Inception-v3 model, then uses a long short-term memory network to incorporate temporal information encoded in the sequence of visual features over time. Our model is implemented in Keras and demonstrated on a dataset of MRI eye-tracking videos from the Human Connectome Project. We manually labelled frames from the dataset for training and evaluation. The network was able to classify eye-blink states with a precision of 0.739 and recall of 0.835 on a previously unseen holdout dataset under varying camera conditions, eye position, and video quality.Clinical relevance- Functional mapping studies in psychiatry and neuro-development which rely on a resting state fMRI protocol may yield divergent results depending on whether the subject keeps their eyes closed or open or whether the subject falls asleep. The clinical relevance of this work is to introduce the eye state (closed or open) in brain imaging studies as a prospective covariate, and as a feature that can potentially control for sleep state as a confounding factor.