Age accelerating condition Healthy control p-value Age acceleration Blood data C h r o n o lo g ic a l A g e CpG site Dataset Averaging across cells ... ... ... ... ... CpG N CpG ...Figure 1: ComputAgeBench: the first comprehensive framework for benchmarking various epigenetic aging clock models.For a dataset 𝑋 , obtained by profiling DNA methylation at CpG sites in bulk blood samples, an aging clock model 𝑓 is trained to distinguish healthy individuals from those with pre-defined aging-accelerating conditions.

Adaptive deep brain stimulation (aDBS) has emerged as a promising treatment for Parkinson's disease (PD).In aDBS, a surgically placed electrode sends dynamically altered stimuli to the brain based on neurophysiological feedback: an invasive gadget that limits the amount of data one could collect for optimizing the control offline.As a consequence, a plethora of synthetic models of PD and those of the control algorithms have been proposed.Herein, we introduce the first neurophysiologically realistic benchmark for comparing said models.Specifically, our methodology covers not only conventional basal ganglia circuit dynamics and pathological oscillations, but also captures 15 previously dismissed physiological attributes, such as signal instabilities and noise, neural drift, electrode conductance changes and individual variability -all modeled as spatially distributed and temporally registered features via beta-band activity in the brain and a feedback.Furthermore, we purposely built our framework as a structured environment for training and evaluating deep reinforcement learning (RL) algorithms, opening new possibilities for optimizing aDBS control strategies and inviting the machine learning community to contribute to the emerging field of intelligent neurostimulation interfaces.Code repository

Image corruption by motion artifacts is an ingrained problem in Magnetic Resonance Imaging (MRI). In this work, we propose a neural network-based regularization term to enhance Autofocusing, a classic optimization-based method to remove motion artifacts. The method takes the best of both worlds: the optimization-based routine iteratively executes the blind demotion and deep learning-based prior penalizes for unrealistic restorations and speeds up the convergence. We validate the method on three models of motion trajectories, using synthetic and real noisy data. The method proves resilient to noise and anatomic structure variation, outperforming the state-of-the-art demotion methods.