NEURO IMAGING & STROKE REHABILITATION PROGNOSIS

该项目通过评估血流和光谱成像方法如何分离和量化中风后的结构，血管和神经代谢异常来研究脑成像在中风恢复预后中的作用。 这具有潜在的意义，提供了一个合理的，基于生理测量，病人的管理和康复策略，包括使用促进与补偿康复技术。 主要的重点是识别中风半影区活动减少的神经联系不能成分，定义为梗死区以外的区域神经代谢活动减少，不是由于血流动力学血管限制，可能是由于中风的传入阻滞效应。 由于非梗死神经组织的量确定了具有恢复潜力的受影响脑的量，因此假设神经机能不能的体积定量与中风恢复预后高度相关。将通过确定血流减少的脑组织体积（超过测量的解剖梗死尺寸）来识别神经联系不能成分。 这种过量的体积（不是由于缺血）被认为是由于神经机能不能引起的代谢反应性减少。 神经机能不能和局部缺血引起的神经代谢变化也将被研究。 将进行T1和T2 MRI以确定梗死外的脑体积，从而解剖定位半暗带。 将使用允许解剖与功能成像准确相关的参考系统方法。

This project investigates the role of cerebral imaging in prognosis of stroke recovery by evaluating how blood flow and spectroscopic imaging methods can separate and quantify structural, vascular, and neuro-metabolic abnormalities post-stroke. This has potential significance to provide a rationale, based on physiological measurements, for patient management and rehabilitation stragegies, including the use of facilitatory versus compensatory rehabilitation techniques. The primary emphasis is on identifying the diaschisis component of reduced activity in the stroke penumbra, defined as a reductioin in regional neurometabolic activity outside the infarct not due to hemodynamic vascular constraints, and presumably due to deafferentation effects of the stroke. Since the amount of non-infarcted neural tissue identifies the amount of affected brain that has potential for recovery, it is hypothesized that volumetric quantitation of diaschisis will be highly correlated with stroke recovery prognosis. The diaschisis component will be identified by determining the volume of brain tissue with reduced blood flow that is in excess of the measured anatomic infarction size. This excessive volume (which is not due to ischemia) is presumed to be due to the reactive reduction of metabolism from diaschisis. Neuro-metabolic changes caused by combined diaschisis and ischemia will also be studied. T1 and T2 MRI will be conducted to determine the volume of braiin outside the infarction to anatomically localize the penumbra. A reference system method that permits accurate correlation of anatomic with functional imaging will be utilized.