The effects of stroke can be debilitating, severely impacting people’s quality of life by impeding motor control pathways. The study examines the corticospinal tracts (CST), a neural pathway associated with voluntary fine motor skills, to understand the synaptic and structural alterations post-stroke. Poinsatte et al. invented SpinalTRAQ (Spinal cord Tomographic Registration and Automated Quantification), which serves as a pipeline to analyze CST connectivity through the cervical spinal cord. SpinalTRAQ integrated serial two-photon tomography (STPT) for 3D imaging and machine learning (ML) to capture highly detailed images of the mouse spinal cord. Mice underwent an induced focal motor cortical stroke and were compared with healthy mice at 1, 4, and 6 weeks post-stroke. The images were processed by the random forest ML model that was trained to recognize axons, synapses, white and gray matter to classify structural changes in the spinal cord. In healthy mice, the majority of CST synapses were concentrated within contralateral laminae 5 and 7, which help control movement, and ipsilateral lamina 9, which housed motor neurons. Interestingly, in post-stroke mice, the affected CST expressed axonal loss, whereas the unaffected CST exhibited region-specific reinnervation. A pattern of neural plasticity was examined even at 6 weeks post-stroke, suggesting SpinalTRAQ’s breakthrough in mapping out neural connections and subtle patterns that the human eye could easily miss.