3 research outputs found
Unsupervised Behaviour Analysis and Magnification (uBAM) using Deep Learning
Motor behaviour analysis is essential to biomedical research and clinical
diagnostics as it provides a non-invasive strategy for identifying motor
impairment and its change caused by interventions. State-of-the-art
instrumented movement analysis is time- and cost-intensive, since it requires
placing physical or virtual markers. Besides the effort required for marking
keypoints or annotations necessary for training or finetuning a detector, users
need to know the interesting behaviour beforehand to provide meaningful
keypoints. We introduce unsupervised behaviour analysis and magnification
(uBAM), an automatic deep learning algorithm for analysing behaviour by
discovering and magnifying deviations. A central aspect is unsupervised
learning of posture and behaviour representations to enable an objective
comparison of movement. Besides discovering and quantifying deviations in
behaviour, we also propose a generative model for visually magnifying subtle
behaviour differences directly in a video without requiring a detour via
keypoints or annotations. Essential for this magnification of deviations even
across different individuals is a disentangling of appearance and behaviour.
Evaluations on rodents and human patients with neurological diseases
demonstrate the wide applicability of our approach. Moreover, combining
optogenetic stimulation with our unsupervised behaviour analysis shows its
suitability as a non-invasive diagnostic tool correlating function to brain
plasticity.Comment: Published in Nature Machine Intelligence (2021),
https://rdcu.be/ch6p
Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats
The majority of stroke patients develop post-stroke fatigue, a symptom which impairs motivation and diminishes the success of rehabilitative interventions. We show that large cortical strokes acutely reduce activity levels in rats for 1-2 weeks as a physiological response paralleled by signs of systemic inflammation. Rats were exposed early (1-2 weeks) or late (3-4 weeks after stroke) to an individually monitored enriched environment to stimulate self-controlled high-intensity sensorimotor training. A group of animals received Anti-Nogo antibodies for the first two weeks after stroke, a neuronal growth promoting immunotherapy already in clinical trials. Early exposure to the enriched environment resulted in poor outcome: Training intensity was correlated to enhanced systemic inflammation and functional impairment. In contrast, animals starting intense sensorimotor training two weeks after stroke preceded by the immunotherapy revealed better recovery with functional outcome positively correlated to the training intensity and the extent of re-innervation of the stroke denervated cervical hemi-cord. Our results suggest stroke-induced fatigue as a biological purposeful reaction of the organism during neuronal remodeling, enabling new circuit formation which will then be stabilized or pruned in the subsequent rehabilitative training phase. However, intense training too early may lead to wrong connections and is thus less effective