In neuroscience research, the refined analysis of rodent locomotion is complex and cumbersome, and access to the technique is limited because of the necessity for expensive equipment. In this study, we implemented a new deep learning-based open-source toolbox for Automated Limb Motion Analysis (ALMA) that requires only basic behavioral equipment and an inexpensive camera. The ALMA toolbox enables the consistent and comprehensive analyses of locomotor kinematics and paw placement and can be applied to neurological conditions affecting the brain and spinal cord. We demonstrated that the ALMA toolbox can (1) robustly track the evolution of locomotor deficits after spinal cord injury, (2) sensitively detect locomotor abnormalities after traumatic brain injury, and (3) correctly predict disease onset in a multiple sclerosis model. We, therefore, established a broadly applicable automated and standardized approach that requires minimal financial and time commitments to facilitate the comprehensive analysis of locomotion in rodent disease models.

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Functional recovery after incomplete spinal cord injury depends on the effective rewiring of neuronal circuits. Here, we show that selective chemogenetic activation of either corticospinal projection neurons or intraspinal relay neurons alone led to anatomically restricted plasticity and little functional recovery. In contrast, coordinated stimulation of both supraspinal centers and spinal relay stations resulted in marked and circuit-specific enhancement of neuronal rewiring, shortened EMG latencies, and improved locomotor recovery.