A Within-Animal Comparison of Skilled Forelimb Assessments in Rats

Abstract

A variety of skilled reaching tasks have been developed to evaluate forelimb function in rodent models. The single pellet skilled reaching task and pasta matrix task have provided valuable insight into recovery of forelimb function in models of neurological injury and disease. Recently, several automated measures have been developed to reduce the cost and time burden of forelimb assessment in rodents. Here, we provide a within-subject comparison of three common forelimb assessments to allow direct evaluation of sensitivity and efficiency across tasks. Rats were trained to perform the single pellet skilled reaching task, the pasta matrix task, and the isometric pull task. Once proficient on all three tasks, rats received an ischemic lesion of motor cortex and striatum to impair use of the trained limb. On the second week post-lesion, all three tasks measured a significant deficit in forelimb function. Performance was well-correlated across tasks. By the sixth week post-lesion, only the isometric pull task measured a significant deficit in forelimb function, suggesting that this task is more sensitive to chronic impairments. The number of training days required to reach asymptotic performance was longer for the isometric pull task, but the total experimenter time required to collect and analyze data was substantially lower. These findings suggest that the isometric pull task represents an efficient, sensitive measure of forelimb function to facilitate preclinical evaluation in models of neurological injury and disease.

Fig 1. Illustrations of the behavioral apparatus are shown for the (A) Isometric Pull, (B) Skilled Reaching, and (C) Pasta Matrix tasks.

Sequential images captured from videos of rats performing the tasks are shown in (D), (E), and (F), respectively. A timeline showing the task schedule prior to and following ischemic lesion is shown in (G).

Fig 2. Time to criterion proficiency measured by trials with a peak force exceeding 120 gm and pasta count are shown for the isometric pull (A) and pasta matrix (B) tasks, respectively.

Subjects which failed to reach criterion proficiency on all three tasks are marked with an “x”. Error bars show standard error of the mean.

Fig 3. Primary performance measures of the percent of trials with a peak force exceeding 120 gm for the isometric pull task (A), hit rate for skilled reaching task (B), and pasta count for the pasta matrix task (C) are shown from 1 week prior to 6 weeks following unilateral motor cortex lesion.

Error bars show standard error of the mean. Significant difference from pre-lesion performance are indicated with asterisks (paired t-test; *, p < 0.05; **, p < 0.001). One point for which only 9 subjects’ measures could be collected is marked with a ◊. Individual subjects’ performance on each task is shown in (D), (E), and (F), respectively.

Fig 4. Normalized peak force per trial, relative to subjects’ pre-lesion baselines, for the isometric pull task is plotted against normalized hit rate for the skilled reaching task for post-lesion weeks 2 (A) and 6 (D) and plotted against pasta counts for the pasta matrix task for weeks 2 (B) and 6 (E).

Skilled reaching normalized hit rates are plotted against normalized pasta matrix counts for weeks 2 (C) and 6 (F). Dotted trendlines were fitted by linear regression and R and p values were calculated using Spearman’s rank correlation.

Fig 5

A) Mean peak force per trial (A) and number of trials per day (B) are shown for the isometric pull task (A). Distance to the farthest broken pasta (C) and iso-value contours of hit rate for individual pasta pieces (D) area shown for the pasta matrix task. Error bars show standard error of the mean. Significant difference from pre-lesion performance is indicated with asterisks (paired t-test; *, p < 0.05; **, p < 0.001).

Fig 6. Total lesion volume is plotted against post-lesion Week 2 normalized peak force deficit for the isometric pull task (A), normalized hit rate deficit for the skilled reaching task (B), and normalized pasta count deficit for the pasta matrix task (C).

All deficit values are normalized to individual subjects’ pre-lesion baselines. Dotted trendlines were fitted by linear regression and R and p values were calculated using Spearman’s rank correlation.