Spinal cord infarction is reported to account for less than 1% of all strokes and is a relatively rare disease. In recent years, thoracic endovascular aortic repair (TEVAR) has become a common treatment for aortic aneurysms, and spinal cord ischemia is one of its complications. Most cases occur in the perioperative period; however, a few cases have been reported in the chronic stage. Here, we report a case of spinal cord infarction, 6 months after TEVAR. A 77-year-old man experienced sudden onset paraparesis following dumbbell exercises and defecation. He had a history of an infectious thoracoabdominal aortic aneurysm treated by TEVAR 6 months prior. Paralysis and disturbance of the thermal pain and tactile sensations of the lower limbs were observed, but proprioception and deep sensation were preserved. Computed tomography (CT) showed no evidence of intraspinal hemorrhage, new aortic dissection, or endoleak around the aortic stent placed from Th11 to L3. Magnetic resonance imaging (MRI) showed intramedullary hyperintensity from Th11 to the conus 2 days after onset. Anticoagulant therapy and rehabilitation were performed, and the lower-limb muscle strength gradually improved. After aortic stenting, particularly including the level of the Adamkiewicz artery, the risk of spinal cord ischemia must be monitored, because spinal circulation depends on collateral circulation.

Neuroscience is inherently interdisciplinary, rapidly expanding beyond its roots in biological sciences to many areas of the social and physical sciences. This expansion has led to more sophisticated ways of thinking about the links between brains and behavior and has inspired the development of increasingly advanced tools to characterize the activity of large populations of neurons. However, along with these advances comes a heightened risk of fostering confusion unless efforts are made to better integrate findings across different model systems and to develop a better understanding about how different measurement techniques provide mutually constraining information. Here we use selective visuospatial attention as a case study to highlight the importance of these issues, and we suggest that exploiting multiple measures can better constrain models that relate neural activity to animal behavior.