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Structural graph-based morphometry: A multiscale searchlight framework based on sulcal pits

机译：基于结构图的形态学：基于硫型凹坑的多尺度探照灯框架

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Studying the topography of the cortex has proved valuable in order to characterize populations of subjects. In particular, the recent interest towards the deepest parts of the cortical sulci-the so-called sulcal pits-has opened new avenues in that regard. In this paper, we introduce the first fully automatic brain morphometry method based on the study of the spatial organization of sulcal pits-Structural Graph Based Morphometry (SGBM). Our framework uses attributed graphs to model local patterns of sulcal pits, and further relies on three original contributions. First, a graph kernel is defined to provide a new similarity measure between pit-graphs, with few parameters that can be efficiently estimated from the data. Secondly, we present the first searchlight scheme dedicated to brain morphometry, yielding dense information maps covering the full cortical surface. Finally, a multi-scale inference strategy is designed to jointly analyze the searchlight information maps obtained at different spatial scales. We demonstrate the effectiveness of our framework by studying gender differences and cortical asymmetries: we show that SGBM can both localize informative regions and estimate their spatial scales, while providing results which are consistent with the literature. Thanks to the modular design of our kernel and the vast array of available kernel methods, SGBM can easily be extended to include a more detailed description of the sulcal patterns and solve different statistical problems. Therefore, we suggest that our SGBM framework should be useful for both reaching a better understanding of the normal brain and defining imaging biomarkers in clinical settings. (C) 2016 Elsevier B.V. All rights reserved.
机译：研究皮层的形貌已经证明了有价值的是，以表征受试者的群体。特别是，近期朝着皮质硫基 - 所谓的硫型坑的最深部分的兴趣 - 在这方面开辟了新的途径。本文介绍了基于硫型结构图的形态学（SGBM）的空间组织研究的第一个全自动脑形态学方法。我们的框架使用归属图来模拟硫型凹坑的本地模式，并进一步依赖于三个原始贡献。首先，定义图形内核以在PIT图之间提供新的相似性度量，并且可以从数据有效地估计很少的参数。其次，我们介绍了专用于脑形态学的第一个探照灯方案，产生覆盖完整皮质表面的密集信息图。最后，设计了一种多尺度推断策略，旨在共同分析在不同空间尺度上获得的探照灯信息映射。我们通过研究性别差异和皮质不对称，展示了我们框架的有效性：我们表明SGBM都可以本地化信息区域，并估计其空间尺度，同时提供与文献一致的结果。由于我们内核的模块化设计和广泛的可用内核方法，可以轻松扩展SGBM以包括更详细地描述硫的模式并解决不同的统计问题。因此，我们建议我们的SGBM框架对于临床环境中的对正常大脑和定义成像生物标志物来说，我们的SGBM框架应该有用。 （c）2016年Elsevier B.v.保留所有权利。

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