NEURO IMAGING & STROKE REHABILITATION PROGNOSIS

本项目通过评估脑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康复预后中的作用。这对基于生理测量为患者管理和康复策略提供理论基础，包括使用促进性和补偿性康复技术具有潜在的意义。主要的重点是确定卒中半影区活动减少的失语成分，定义为梗死区外局部神经代谢活动的减少，不是由于血流动力学血管限制，而是可能由于卒中的去传入效应。由于非梗死性神经组织的数量确定了有可能恢复的受影响脑组织的数量，因此可以假设，失语的体积量化将与卒中恢复预后高度相关。将通过测定血流量减少的脑组织的体积超过测量的解剖梗塞大小来识别失语成分。这种过量的体积(不是由于缺血)被推测是由于失语引起的新陈代谢的反应性降低。此外，还将研究由神经错乱和脑缺血联合引起的神经代谢变化。将进行T1和T2MRI检查，以确定梗死灶外的脑体积，以解剖定位半暗带。将使用一种参考系方法，允许解剖学与功能成像的准确关联。

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.