Laboratory for Rehabilitation Neuroscience, Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL 32611, USA.
Department of Neurology, College of Medicine, University of Florida, Gainesville, FL 32611, USA.
Cereb Cortex. 2018 May 1;28(5):1685-1699. doi: 10.1093/cercor/bhx066.
The purpose of this study was to develop a high-resolution sensorimotor area tract template (SMATT) which segments corticofugal tracts based on 6 cortical regions in primary motor cortex, dorsal premotor cortex, ventral premotor cortex, supplementary motor area (SMA), pre-supplementary motor area (preSMA), and primary somatosensory cortex using diffusion tensor imaging. Individual probabilistic tractography analyses were conducted in 100 subjects using the highest resolution data currently available. Tractography results were refined using a novel algorithm to objectively determine slice level thresholds that best minimized overlap between tracts while preserving tract volume. Consistent with tracing studies in monkey and rodent, our observations show that cortical topography is generally preserved through the internal capsule, with the preSMA tract remaining most anterior and the primary somatosensory tract remaining most posterior. We combine our results into a freely available white matter template named the SMATT. We also provide a probabilistic SMATT that quantifies the extent of overlap between tracts. Finally, we assess how the SMATT operates at the individual subject level in another independent data set, and in an individual after stroke. The SMATT and probabilistic SMATT provide new tools that segment and label sensorimotor tracts at a spatial resolution not previously available.
本研究旨在开发一种高分辨率的感觉运动区束模板(SMATT),该模板使用弥散张量成像(DTI),基于初级运动皮层、背侧运动前区、腹侧运动前区、辅助运动区(SMA)、前辅助运动区(preSMA)和初级体感皮层中的 6 个皮质区,对皮质传出束进行分割。在 100 名受试者中使用目前最高分辨率的数据进行个体概率性束追踪分析。使用一种新的算法对追踪结果进行细化,以客观地确定最佳切片水平阈值,在最小化束重叠的同时保留束体积。与猴子和啮齿动物的追踪研究一致,我们的观察结果表明,皮质拓扑结构通常在内囊内保持不变,preSMA 束保持最靠前,初级体感束保持最靠后。我们将结果整合到一个名为 SMATT 的免费白质模板中。我们还提供了一个概率性的 SMATT,量化了束之间的重叠程度。最后,我们在另一个独立数据集和一个中风后的个体中评估了 SMATT 在个体水平上的表现。SMATT 和概率性 SMATT 提供了新的工具,以以前无法达到的空间分辨率分割和标记感觉运动束。