MIST: A multi-resolution parcellation of functional brain networks

S. Urchs, J. Armoza, C. Moreau, Yassine Benhajali, J. St-Aubin, P. Orban, Pierre Bellec
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引用次数: 57

Abstract

The functional architecture of the brain is organized across multiple levels of spatial resolutions, from distributed networks to the localized areas they are made of. A brain parcellation that defines functional nodes at multiple resolutions is required to investigate the functional connectome across these scales. Here we present the Multiresolution Intrinsic Segmentation Template (MIST), a multi-resolution group level parcellation of the cortical, subcortical and cerebellar gray matter. The individual MIST parcellations match other published group parcellations in internal homogeneity and reproducibility and perform very well in real-world application benchmarks. In addition, the MIST parcellations are fully annotated and provide a hierarchical decomposition of functional brain networks across nine resolutions (7 to 444 functional parcels). We hope that the MIST parcellation will accelerate research in brain connectivity across resolutions. Because visualizing multiresolution parcellations is challenging, we provide an interactive web interface to explore the MIST. The MIST is also available through the popular nilearn toolbox.
MIST:功能性脑网络的多分辨率分割
大脑的功能架构是跨多个空间分辨率级别组织的,从分布式网络到由它们组成的局部区域。在多个分辨率上定义功能节点的脑分割需要在这些尺度上研究功能连接体。在这里,我们提出了多分辨率固有分割模板(MIST),这是一种多分辨率组水平的皮层,皮层下和小脑灰质的分割。单个MIST分组在内部同质性和可再现性方面与其他发布的组分组相匹配,并且在实际应用程序基准测试中表现非常好。此外,MIST包被完全注释,并提供跨9个分辨率(7到444个功能包)的功能脑网络的分层分解。我们希望MIST将加速跨分辨率大脑连接的研究。由于可视化多分辨率分组具有挑战性,我们提供了一个交互式web界面来探索MIST。MIST也可以通过流行的nilearn工具箱获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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