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An automated task for the training and assessment of distal forelimb function in a mouse model of ischemic stroke.

ABSTRACT: Behavioral models relevant to stroke research seek to capture important aspects of motor skills typically impaired in human patients, such as coordination of distal musculature. Such models may focus on mice since many genetic tools are available for use only in that species and since the training and behavioral demands of mice can differ from rats even for superficially similar behavioral readouts. However, current mouse assays are time consuming to train and score, especially in a manner producing continuous quantification. An automated assay of mouse forelimb function may provide advantages for quantification and speed, and may be useful for many applications including stroke research.We present an automated assay of distal forelimb function. In this task, mice reach forward, grip and pull an isometric handle with a prescribed force. The apparatus partially automates the training process so that mice can be trained quickly and simultaneously.Using this apparatus, it is possible to measure long-lasting impairment in success rate, force pulled, latency to pull, and latency to success up to 22 weeks following photothrombotic cortical strokes in mice.This assessment measures forelimb function as do pellet reach tasks, but it utilizes a different motion and provides automatic measures that can ease and augment the research process.This high-throughput behavioral assay can detect long-lasting motor impairments, eliminates the need for subjective scoring, and produces a rich, continuous data set from which many aspects of the reach and grasp motion can be automatically extracted.

SUBMITTER: Becker AM

PROVIDER: S-EPMC5029438 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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An automated task for the training and assessment of distal forelimb function in a mouse model of ischemic stroke.

Becker April M AM Meyers Eric E Sloan Andrew A Rennaker Robert R Kilgard Michael M Goldberg Mark P MP

Journal of neuroscience methods 20151017

<h4>Background</h4>Behavioral models relevant to stroke research seek to capture important aspects of motor skills typically impaired in human patients, such as coordination of distal musculature. Such models may focus on mice since many genetic tools are available for use only in that species and since the training and behavioral demands of mice can differ from rats even for superficially similar behavioral readouts. However, current mouse assays are time consuming to train and score, especiall ...[more]

PMID: 26484787

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Project description:We are developing a system for long term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We present a cyber-human methodology used by the SARAH system for automated assessment of upper extremity stroke rehabilitation at the home. We propose a hierarchical model for automatically segmenting stroke survivor's movements and generating training task performance assessment scores during rehabilitation. The hierarchical model fuses expert therapist knowledge-based approaches with data-driven techniques. The expert knowledge is more observable in the higher layers of the hierarchy (task and segment) and therefore more accessible to algorithms incorporating high level constraints relating to activity structure (i.e., type and order of segments per task). We utilize an HMM and a Decision Tree model to connect these high level priors to data driven analysis. The lower layers (RGB images and raw kinematics) need to be addressed primarily through data driven techniques. We use a transformer based architecture operating on low-level action features (tracking of individual body joints and objects) and a Multi-Stage Temporal Convolutional Network(MS-TCN) operating on raw RGB images. We develop a sequence combining these complimentary algorithms effectively, thus encoding the information from different layers of the movement hierarchy. Through this combination, we produce a robust segmentation and task assessment results on noisy, variable and limited data, which is characteristic of low cost video capture of rehabilitation at the home. Our proposed approach achieves 85% accuracy in per-frame labeling, 99% accuracy in segment classification and 93% accuracy in task completion assessment. Although the methodology proposed in this paper applies to upper extremity rehabilitation using the SARAH system, it can potentially be used, with minor alterations, to assist automation in many other movement rehabilitation contexts (i.e., lower extremity training for neurological accidents).

Project description:BackgroundRepetitive task training (RTT) involves the active practice of task-specific motor activities and is a component of current therapy approaches in stroke rehabilitation.ObjectivesPrimary objective: To determine if RTT improves upper limb function/reach and lower limb function/balance in adults after stroke. Secondary objectives: 1) To determine the effect of RTT on secondary outcome measures including activities of daily living, global motor function, quality of life/health status and adverse events. 2) To determine the factors that could influence primary and secondary outcome measures, including the effect of 'dose' of task practice; type of task (whole therapy, mixed or single task); timing of the intervention and type of intervention.Search methodsWe searched the Cochrane Stroke Group Trials Register (4 March 2016); the Cochrane Central Register of Controlled Trials (CENTRAL) (the Cochrane Library 2016, Issue 5: 1 October 2006 to 24 June 2016); MEDLINE (1 October 2006 to 8 March 2016); Embase (1 October 2006 to 8 March 2016); CINAHL (2006 to 23 June 2016); AMED (2006 to 21 June 2016) and SPORTSDiscus (2006 to 21 June 2016).Selection criteriaRandomised/quasi-randomised trials in adults after stroke, where the intervention was an active motor sequence performed repetitively within a single training session, aimed towards a clear functional goal.Data collection and analysisTwo review authors independently screened abstracts, extracted data and appraised trials. We determined the quality of evidence within each study and outcome group using the Cochrane 'Risk of bias' tool and GRADE (Grades of Recommendation, Assessment, Development and Evaluation) criteria. We did not assess follow-up outcome data using GRADE. We contacted trial authors for additional information.Main resultsWe included 33 trials with 36 intervention-control pairs and 1853 participants. The risk of bias present in many studies was unclear due to poor reporting; the evidence has therefore been rated 'moderate' or 'low' when using the GRADE system. There is low-quality evidence that RTT improves arm function (standardised mean difference (SMD) 0.25, 95% confidence interval (CI) 0.01 to 0.49; 11 studies, number of participants analysed = 749), hand function (SMD 0.25, 95% CI 0.00 to 0.51; eight studies, number of participants analysed = 619), and lower limb functional measures (SMD 0.29, 95% CI 0.10 to 0.48; five trials, number of participants analysed = 419). There is moderate-quality evidence that RTT improves walking distance (mean difference (MD) 34.80, 95% CI 18.19 to 51.41; nine studies, number of participants analysed = 610) and functional ambulation (SMD 0.35, 95% CI 0.04 to 0.66; eight studies, number of participants analysed = 525). We found significant differences between groups for both upper-limb (SMD 0.92, 95% CI 0.58 to 1.26; three studies, number of participants analysed = 153) and lower-limb (SMD 0.34, 95% CI 0.16 to 0.52; eight studies, number of participants analysed = 471) outcomes up to six months post treatment but not after six months. Effects were not modified by intervention type, dosage of task practice or time since stroke for upper or lower limb. There was insufficient evidence to be certain about the risk of adverse events.Authors' conclusionsThere is low- to moderate-quality evidence that RTT improves upper and lower limb function; improvements were sustained up to six months post treatment. Further research should focus on the type and amount of training, including ways of measuring the number of repetitions actually performed by participants. The definition of RTT will need revisiting prior to further updates of this review in order to ensure it remains clinically meaningful and distinguishable from other interventions.

Dataset Information

An automated task for the training and assessment of distal forelimb function in a mouse model of ischemic stroke.

Publications

An automated task for the training and assessment of distal forelimb function in a mouse model of ischemic stroke.

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