Accolla Ettore A, Dukart Juergen, Helms Gunther, Weiskopf Nikolaus, Kherif Ferath, Lutti Antoine, Chowdhury Rumana, Hetzer Stefan, Haynes John-Dylan, Kühn Andrea A, Draganski Bogdan
Department of Neurology, Charité University Medicine Berlin, Berlin, Germany; LREN, Département des Neurosciences Cliniques, CHUV, Université de Lausanne, Lausanne, Switzerland; Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin, Berlin, Germany.
Hum Brain Mapp. 2014 Oct;35(10):5083-92. doi: 10.1002/hbm.22533. Epub 2014 Apr 28.
Despite advances in understanding basic organizational principles of the human basal ganglia, accurate in vivo assessment of their anatomical properties is essential to improve early diagnosis in disorders with corticosubcortical pathology and optimize target planning in deep brain stimulation. Main goal of this study was the detailed topological characterization of limbic, associative, and motor subdivisions of the subthalamic nucleus (STN) in relation to corresponding corticosubcortical circuits. To this aim, we used magnetic resonance imaging and investigated independently anatomical connectivity via white matter tracts next to brain tissue properties. On the basis of probabilistic diffusion tractography we identified STN subregions with predominantly motor, associative, and limbic connectivity. We then computed for each of the nonoverlapping STN subregions the covariance between local brain tissue properties and the rest of the brain using high-resolution maps of magnetization transfer (MT) saturation and longitudinal (R1) and transverse relaxation rate (R2*). The demonstrated spatial distribution pattern of covariance between brain tissue properties linked to myelin (R1 and MT) and iron (R2*) content clearly segregates between motor and limbic basal ganglia circuits. We interpret the demonstrated covariance pattern as evidence for shared tissue properties within a functional circuit, which is closely linked to its function. Our findings open new possibilities for investigation of changes in the established covariance pattern aiming at accurate diagnosis of basal ganglia disorders and prediction of treatment outcome.
尽管在理解人类基底神经节的基本组织原则方面取得了进展,但对其解剖学特性进行准确的体内评估对于改善皮质下皮质病变疾病的早期诊断以及优化深部脑刺激中的靶点规划至关重要。本研究的主要目标是详细描述丘脑底核(STN)的边缘、联合和运动亚区与相应皮质下皮质回路相关的拓扑特征。为此,我们使用磁共振成像,并通过脑白质束独立研究解剖连接性以及脑组织特性。基于概率性扩散张量成像,我们确定了主要具有运动、联合和边缘连接性的STN亚区。然后,我们使用磁化传递(MT)饱和度、纵向(R1)和横向弛豫率(R2*)的高分辨率图谱,计算每个不重叠的STN亚区局部脑组织特性与大脑其他部分之间的协方差。与髓磷脂(R1和MT)和铁(R2*)含量相关的脑组织特性之间协方差的空间分布模式表明,运动和边缘基底神经节回路明显不同。我们将所展示的协方差模式解释为功能回路内共享组织特性的证据,这与其功能密切相关。我们的研究结果为研究既定协方差模式的变化开辟了新的可能性,旨在准确诊断基底神经节疾病并预测治疗结果。