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

PLoS Biol. 2019 Nov 21;17(11):e3000516. doi: 10.1371/journal.pbio.3000516. eCollection 2019 Nov.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Disease Models, Animal
  • Forelimb
  • Image Processing, Computer-Assisted*
  • Male
  • Motor Activity
  • Motor Disorders / physiopathology
  • Motor Skills
  • Movement
  • Nervous System Diseases / physiopathology*
  • Neural Networks, Computer*
  • Rats
  • Stroke / physiopathology

Grant support