Golestan University of Medical Sciences Repository

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

Ryait, H. and Bermudez-Contreras, E. and Harvey, M. and Faraji, J. and Mirza Agha, B. and Gomez-Palacio Schjetnan, A. and Gruber, A. and Doan, J. and Mohajerani, M. and Metz, G.A.S. and Whishaw, I.Q. and Luczak, A. (2019) Data-driven analyses of motor impairments in animal models of neurological disorders. PLoS biology, 17 (11). e3000516.

Full text not available from this repository.

Abstract

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.

Item Type: Article
Additional Information: cited By 0
Subjects: سیستم عصبی WL
QS آناتومی انسان
QTفیزیولوژی
مقالات نمایه شده محققین دانشگاه در سایت ,Web of Science ,Scopus
Divisions: معاونت تحقیقات و فناوری
Depositing User: GOUMS
Date Deposited: 10 Dec 2019 08:11
Last Modified: 10 Dec 2019 08:11
URI: http://eprints.goums.ac.ir/id/eprint/10358

Actions (login required)

View Item View Item