The Emergence of Stereotyped Kinematic Synergies when Mice Reach to Grasp Following Stroke

Refined movement analysis in the staircase test reveals differential motor deficits in mouse models of stroke

Accurate assessment of post-stroke deficits is crucial in translational research. Recent advances in machine learning offer precise quantification of rodent motor behavior post-stroke, yet detecting lesion-specific upper extremity deficits remains unclear. Employing proximal middle cerebral artery occlusion (MCAO) and cortical photothrombosis (PT) in mice, we assessed post-stroke impairments via the Staircase test. Lesion locations were identified using 7 T-MRI. Machine learning was applied to reconstruct forepaw kinematic trajectories and feature analysis was achieved with MouseReach, a new data-processing toolbox. Lesion reconstructions pinpointed ischemic centers in the striatum (MCAO) and sensorimotor cortex (PT). Pellet retrieval alterations were observed, but were unrelated to overall stroke volume. Instead, forepaw slips and relative reaching success correlated with increasing cortical lesion size in both models. Striatal lesion size after MCAO was associated with prolonged reach durations that occurred with delayed symptom onset. Further analysis on the impact of selective serotonin reuptake inhibitors in the PT model revealed no clear treatment effects but replicated strong effect sizes of slips for post-stroke deficit detection. In summary, refined movement analysis unveiled specific deficits in two widely-used mouse stroke models, emphasizing the value of deep behavioral profiling in preclinical stroke research to enhance model validity for clinical translation.

ReachingBot: an automated and scalable benchtop device for highly parallel Single Pellet Reach-and-Grasp training and assessment in mice

BACKGROUND

The single pellet reaching and grasp (SPRG) task is a behavioural assay widely used to study motor learning, control and recovery after nervous system injury in animals. The manual training and assessment of the SPRG is labour intensive and time consuming and has led to the development of multiple devices which automate the SPRG task.

NEW METHOD

Here, using robotics, computer vision, and machine learning analysis of videos, we describe a device that can be left unattended, presents pellets to mice, and, using two supervised learning algorithms, classifies the outcome of each trial with an accuracy of greater than 94% without the use of graphical processing units (GPUs). Our devices can also be operated using our cross-platform Graphical User Interface (GUI).

RESULTS

We show that these devices train and assess mice in parallel. 21 out of 30 mice retrieved >40% of pellets successfully following the training period. Following ischaemic stroke; some mice showed large persistent deficits whilst others showed only transient deficits. This highlights the heterogeneity in reaching outcomes following stroke.

COMPARISON WITH EXISTING METHOD(S)

Current state-of-the-art desktop methods either still require supervision, manual classification of trial outcome, or expensive locally-installed hardware such as graphical processing units (GPUs).

CONCLUSIONS

ReachingBots successfully automated SPRG training and assessment and revealed the heterogeneity in reaching outcomes following stroke. We conjecture that reach-and-grasp is represented in motor cortex bilaterally but with greater asymmetry in some mice than in others.

Brain’s Energy After Stroke: From a Cellular Perspective Toward Behavior

Stroke is a neurological condition that impacts activity performance and quality of life for survivors. While neurological impairments after the event explain the performance of patients in specific activities, the origin of such impairments has traditionally been explained as a consequence of structural and functional damage to the nervous system. However, there are important mechanisms related to energy efficiency (trade-off between biological functions and energy consumption) at different levels that can be related to these impairments and restrictions: first, at the neuronal level, where the availability of energy resources is the initial cause of the event, as well as determines the possibilities of spontaneous recovery. Second, at the level of neural networks, where the “small world” operation of the network is compromised after the stroke, implicating a high energetic cost and inefficiency in the information transfer, which is related to the neurological recovery and clinical status. Finally, at the behavioral level, the performance limitations are related to the highest cost of energy or augmented energy expenditure during the tasks to maintain the stability of the segment, system, body, and finally, the behavior of the patients. In other words, the postural homeostasis. In this way, we intend to provide a synthetic vision of the energy impact of stroke, from the particularities of the operation of the nervous system, its implications, as one of the determinant factors in the possibilities of neurological, functional, and behavioral recovery of our patients.

Post-Stroke Hemiplegic Rodent Evaluation: A Framework for Assessing Forelimb Movement Quality Using Kinematics

Kinematics is the gold-standard method for measuring detailed joint motions. Recent research demonstrates that post-stroke kinematic analysis in rats reveals reaching abnormalities similar to those seen in humans after stroke. Nonetheless, behavioral neuroscientists have failed to incorporate kinematic methods for assessing movement quality in stroke models. The availability of a user-friendly method to assess multi-segment forelimb kinematics models should greatly increase uptake of this approach. Here, we present a framework for multi-segment forelimb analysis in rodents after stroke. This method greatly enhances the understanding of post-stroke forelimb motor recovery by including several movement quality metrics often used in human clinical work, such as upper-limb linear and angular kinematics, movement smoothness and kinetics, abnormal synergies, and compensations. These metrics may constitute a preclinical surrogate for the Fugl-Meyer assessment of hemiplegic patients. The data obtained using this method are 83 outputs of linear and angular kinematics and kinetics. The outputs also include 24 time series of continuous data, which afford a graphical representation of the kinematics and kinetics of the reaching cycle. We show that post-stroke rodents displayed many features resembling those seen in humans after stroke that are evident only when multi-segment kinematics models are considered. This method expands the knowledge derived from methods constrained to paw movements to a multi-segment forelimb movement quality framework. Moreover, it highlights the need for preclinical work to consider more sensitive measures of sensorimotor impairment and recovery as a means to enhance the interpretation of true recovery and compensation. © 2022 Wiley Periodicals LLC. Basic Protocol: Recording and data analysis of rodents performing the Montoya staircase task.