MIST: A multi-resolution parcellation of functional networks

Functional brain connectomics investigates functional connectivity between distinct brain parcels. There is an increasing interest to investigate connectivity across several levels of spatial resolution, from networks down to localized areas. Here we present the Multiresolution Intrinsic Segmentation Template (MIST), a multi-resolution parcellation of the cortical, subcortical and cerebellar gray matter. We provide annotated functional parcellations at nine resolutions from 7 to 444 functional parcels. The MIST parcellations compare well with prior work in terms of homogeneity and generalizability. We found that parcels at higher resolutions largely fell within the boundaries of larger parcels at lower resolutions. This allowed us to provide an overlap based pseudo-hierarchical decomposition tree that relates parcels across resolutions in a meaningful way. We provide an interactive web interface to explore the MIST parcellations and also made it accessible in the neuroimaging library nilearn. We believe that the MIST parcellation will facilitate future investigations of the multiresolution basis of brain function.功能脑连接组学（functional brain connectomics）旨在探究不同脑分区（brain parcels）之间的功能连接（functional connectivity）。当前，学界针对从宏观脑网络到局域脑区的多空间分辨率级别开展连接性研究的兴趣与日俱增。本文提出多分辨率内在分割模板（Multiresolution Intrinsic Segmentation Template，MIST）——一种覆盖大脑皮层、皮层下及小脑灰质的多分辨率脑分区方案。我们提供了9种分辨率的带注释功能分区，分区数量从7个至444个不等。在同质性与泛化性方面，MIST分区方案与既往研究成果表现相当。我们发现，高分辨率下的脑分区大多处于低分辨率下的较大脑分区边界范围内，据此构建了基于重叠关系的伪层级分解树，可有效关联不同分辨率下的脑分区。此外，我们搭建了交互式网页界面以供探索MIST分区方案，同时将其集成至神经影像工具库nilearn中。我们认为，MIST分区方案将助力未来针对脑功能多分辨率基础的相关研究。