Machine Learning-assisted Quantitative Mapping of Intracortical Axonal Plasticity Following a Focal Cortical Stroke in Rodents

Abstract

Stroke destroys neurons and their connections leading to focal neurological deficits. Although limited, many patients exhibit a certain degree of spontaneous functional recovery. Structural remodeling of the intracortical axonal connections is implicated in the reorganization of cortical motor representation maps, which is considered to be an underlying mechanism of the improvement in motor function. Therefore, an accurate assessment of intracortical axonal plasticity would be necessary to develop strategies to facilitate functional recovery following a stroke. The present study developed a machine learning-assisted image analysis tool based on multi-voxel pattern analysis in fMRI imaging. Intracortical axons originating from the rostral forelimb area (RFA) were anterogradely traced using biotinylated dextran amine (BDA) following a photothrombotic stroke in the mouse motor cortex. BDA-traced axons were visualized in tangentially sectioned cortical tissues, digitally marked, and converted to pixelated axon density maps. Application of the machine learning algorithm enabled sensitive comparison of the quantitative differences and the precise spatial mapping of the post-stroke axonal reorganization even in the regions with dense axonal projections. Using this method, we observed a substantial extent of the axonal sprouting from the RFA to the premotor cortex and the peri-infarct region caudal to the RFA. Therefore, the machine learning-assisted quantitative axonal mapping developed in this study can be utilized to discover intracortical axonal plasticity that may mediate functional restoration following stroke.