PLoS biology, 2019-11, Vol.17 (11), p.e3000516-e3000516
2019
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- Autor(en) / Beteiligte
- Titel
- Data-driven analyses of motor impairments in animal models of neurological disorders
- Ist Teil von
- PLoS biology, 2019-11, Vol.17 (11), p.e3000516-e3000516
- Ort / Verlag
- United States: Public Library of Science
- Erscheinungsjahr
- 2019
- Quelle
- MEDLINE
- Beschreibungen/Notizen
- Behavior provides important insights into neuronal processes. For example, analysis of reaching movements can give a reliable indication of the degree of impairment in neurological disorders such as stroke, Parkinson disease, or Huntington disease. The analysis of such movement abnormalities is notoriously difficult and requires a trained evaluator. Here, we show that a deep neural network is able to score behavioral impairments with expert accuracy in rodent models of stroke. The same network was also trained to successfully score movements in a variety of other behavioral tasks. The neural network also uncovered novel movement alterations related to stroke, which had higher predictive power of stroke volume than the movement components defined by human experts. Moreover, when the regression network was trained only on categorical information (control = 0; stroke = 1), it generated predictions with intermediate values between 0 and 1 that matched the human expert scores of stroke severity. The network thus offers a new data-driven approach to automatically derive ratings of motor impairments. Altogether, this network can provide a reliable neurological assessment and can assist the design of behavioral indices to diagnose and monitor neurological disorders.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 1545-7885, 1544-9173eISSN: 1545-7885DOI: 10.1371/journal.pbio.3000516Titel-ID: cdi_plos_journals_2327537825
- Format
- –
- Schlagworte
- Abnormalities, Animal behavior, Animal models, Animals, Artificial neural networks, Automation, Behavior, Biology and Life Sciences, Computer and Information Sciences, Disease Models, Animal, Disorders, Food, Forelimb, Funding, Human motion, Image Processing, Computer-Assisted, Laboratories, Male, Medicine and Health Sciences, Methods, Methods and Resources, Model accuracy, Motor Activity, Motor Disorders - physiopathology, Motor Skills, Motor task performance, Movement, Movement disorders, Nervous System Diseases - physiopathology, Neural networks, Neural Networks, Computer, Neurodegenerative diseases, Neurological complications, Neurological diseases, Neurological disorders, Neurosciences, Parkinson's disease, Physical Sciences, Predictions, Rats, Regression analysis, Research and Analysis Methods, Social Sciences, Software, Stroke, Stroke - physiopathology, Stroke volume, Supervision