Data-driven analyses of motor impairments in animal models of neurological disorders.

Autor: Hardeep Ryait, Edgar Bermudez-Contreras, Matthew Harvey, Jamshid Faraji, Behroo Mirza Agha, Andrea Gomez-Palacio Schjetnan, Aaron Gruber, Jon Doan, Majid Mohajerani, Gerlinde A S Metz, Ian Q Whishaw, Artur Luczak
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: PLoS Biology, Vol 17, Iss 11, p e3000516 (2019)
Druh dokumentu: article
ISSN: 1544-9173
1545-7885
DOI: 10.1371/journal.pbio.3000516
Popis: 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.
Databáze: Directory of Open Access Journals
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