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Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models

机译:皮质的分组结构分割:基于声音的方法   Logistic模型

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摘要

Current theories hold that brain function is highly related to long-rangephysical connections through axonal bundles, namely extrinsic connectiv-ity.However, obtaining a groupwise cortical parcellation based on extrinsicconnectivity remains challenging. Current parcellation methods arecompu-tationally expensive; need tuning of several parameters or rely on ad-hocconstraints. Furthermore, none of these methods present a model for thecortical extrinsic connectivity of the cortex. To tackle these problems, wepropose a parsimonious model for the extrinsic connectivity and an efficientparceling technique based on clustering of tractograms. Our technique allowsthe creation of single subject and groupwise parcellations of the whole cortex.The parcellations obtained with our technique are in agreement with structuraland functional parcellations in the literature. In particular, the motor andsensory cortex are subdivided in agreement with the human ho-munculus ofPenfield. We illustrate this by comparing our resulting parcels with the motorstrip mapping included in the Human Connectome Project data.
机译:当前的理论认为,脑功能与通过轴突束的远距离物理连接高度相关,即外在连接性。然而,基于外在连接性获得成组的皮质细胞分裂仍然具有挑战性。当前的分割方法通常非常昂贵。需要调整几个参数或依靠临时约束。此外,这些方法均未提出皮质皮质外在连通性的模型。为了解决这些问题,我们提出了一种外在连通性的简化模型,以及一种基于束状图聚类的有效包裹技术。我们的技术允许创建整个皮质的单个主题和成组的分割。用我们的技术获得的分割与文献中的结构和功能分割一致。尤其是,运动皮层和感觉皮层与彭菲尔德(Penfield)的人类相一致。我们通过将生成的包裹与Human Connectome Project数据中包含的Motorstrip映射进行比较来说明这一点。

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