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Cortical Connectome Registration Using Spherical Demons

机译:使用球形恶魔的皮质Connectome注册

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We present an algorithm to align cortical surface models based on structural connectivity. We follow the continuous connectivity approach,1'2 assigning a dense connectivity to every surface point-pair. We adapt and modify an approach for aligning low-rank functional networks based on eigenvalue decomposition of individual connectomes.3 The spherical demons framework then provides a natural setting for inter-subject connectivity alignment, enforcing a smooth, anatomically plausible correspondence, and allowing us to incorporate anatomical as well as connectivity information. We apply our algorithm to 98 diffusion MRI images in an Alzheimer's Disease study, and 731 healthy subjects from the Human Connectome Project. Our method consistently reduces connectome variability due to misalignment. Further, the approach reveals subtle disease effects on structural connectivity which are not seen when registering only cortical anatomy.
机译:我们提出了一种基于结构连通性来对齐皮质表面模型的算法。我们遵循连续连通性方法,1'2为每个表面点对分配密集的连通性。我们基于单个连接体的特征值分解对低等级功能网络进行对齐的方法进行了修改和修改。3然后,球形恶魔框架为对象间的连接对齐提供了自然的环境,从而实现了平滑,解剖学上合理的对应关系,并使我们能够包含解剖以及连接信息。我们将我们的算法应用于阿尔茨海默氏病研究中的98张扩散MRI图像,以及来自人类Connectome项目的731名健康受试者。我们的方法不断降低由于错位导致的连接组变异性。此外,该方法揭示了疾病对结构连接性的细微影响,而仅在皮质解剖结构上未发现这种影响。

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