@article {6254,
	title = {Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats},
	journal = {Sage Journals},
	volume = { Journal of Cerebral Blood Flow \& Metabolism},
	year = {2018},
	month = {05/18},
	abstract = {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{\textendash}2 weeks as a physiological response paralleled by signs of systemic inflammation. Rats were exposed early (1{\textendash}2 weeks) or late (3{\textendash}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.},
	url = {http://journals.sagepub.com/doi/abs/10.1177/0271678X18777661},
	author = {Anna-Sophia Wahl and Erlebach, E. and Biagio Brattoli and Uta B{\"u}chler and Kaiser, J. and Ineichen, V. B. and Alice C. Mosberger and Schneeberger, S. and Imobersteg, S. and Wieckhorst, M. and Stirn, M. and Schroeter, A. and Bj{\"o}rn Ommer and M. E. Schwab}
}
@conference {buechler:CVPR:2017,
	title = {LSTM Self-Supervision for Detailed Behavior Analysis},
	booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2017},
	publisher = {(BB and UB contributed equally)},
	organization = {(BB and UB contributed equally)},
	abstract = {Behavior analysis provides a crucial non-invasive and
easily accessible diagnostic tool for biomedical research.
A detailed analysis of posture changes during skilled mo-
tor tasks can reveal distinct functional deficits and their
restoration during recovery. Our specific scenario is based
on a neuroscientific study of rodents recovering from a large
sensorimotor cortex stroke and skilled forelimb grasping is
being recorded. Given large amounts of unlabeled videos
that are recorded during such long-term studies, we seek
an approach that captures fine-grained details of posture
and its change during rehabilitation without costly manual
supervision. Therefore, we utilize self-supervision to au-
tomatically learn accurate posture and behavior represen-
tations for analyzing motor function. Learning our model
depends on the following fundamental elements: (i) limb
detection based on a fully convolutional network is ini-
tialized solely using motion information, (ii) a novel self-
supervised training of LSTMs using only temporal permu-
tation yields a detailed representation of behavior, and (iii)
back-propagation of this sequence representation also im-
proves the description of individual postures. We establish a
novel test dataset with expert annotations for evaluation of
fine-grained behavior analysis. Moreover, we demonstrate
the generality of our approach by successfully applying it to
self-supervised learning of human posture on two standard
benchmark datasets.},
	author = {Biagio Brattoli and Uta B{\"u}chler and Anna-Sophia Wahl and M. E. Schwab and Bj{\"o}rn Ommer}
}
@article {6188,
	title = {Optogenetically stimulating the intact corticospinal tract post-stroke restores motor control through regionalized functional circuit formation},
	journal = {Nature Communications},
	year = {2017},
	pages = {(ASW \& UB contributed equally; BO and MES contributed equally)},
	doi = {10.1038/s41467-017-01090-6},
	url = {https://www.nature.com/articles/s41467-017-01090-6},
	author = {Anna-Sophia Wahl and Uta B{\"u}chler and A. Br{\"a}ndli and Biagio Brattoli and S. Musall and H. Kasper and B.V. Ineichen and F. Helmchen and Bj{\"o}rn Ommer and M. E. Schwab}
}
@conference {antic:MICCAI:2015,
	title = {Spatiotemporal Parsing of Motor Kinematics for Assessing Stroke Recovery},
	booktitle = {Medical Image Computing and Computer-Assisted Intervention},
	year = {2015},
	publisher = {Springer},
	organization = {Springer},
	author = {Antic, B. and Uta B{\"u}chler and Anna-Sophia Wahl and M. E. Schwab and Bj{\"o}rn Ommer}
}
@article {Wahl:Science:2014,
	title = {Asynchronous Therapy Restores Motor Control by Rewiring of the Rat Corticospinal Tract after Stroke},
	journal = {Science},
	volume = {344},
	number = {6189},
	year = {2014},
	pages = {1250--1255},
	publisher = {American Association for The Advancement of Science},
	url = {http://www.sciencemag.org/content/344/6189/1250},
	author = {Anna-Sophia Wahl and Omlor, W. and Rubio, J. C. and Chen, J. L. and Zheng, H. and Schr{\"o}ter, A. and Gullo, M. and Weinmann, O. and Kobayashi, K. and F. Helmchen and Bj{\"o}rn Ommer and M. E. Schwab}
}
