Lee David S, Sahib Ashish, Wade Benjamin, Narr Katherine L, Hellemann Gerhard, Woods Roger P, Joshi Shantanu H
Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA.
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA.
Med Image Comput Comput Assist Interv. 2019 Oct;11765:373-381. doi: 10.1007/978-3-030-32245-8_42. Epub 2019 Oct 10.
We present a method for multimodal brain data registration that aligns shapes of nodal network configurations in an invertible manner. We use ideas from shape analysis to represent an individual subject data configuration as an element on a hypersphere, where geodesics have closed form solutions. The method not only performs inter-subject data registration, but also allows for the construction of a population data template to which all subject data configurations can be registered. Results show compression of data measures and significant reduction in variance after registration. We also observe increased predictive power of regions of interest (ROI) node identification, significant increases in pairwise network connectivity measures, as well as significant increases in canonical correlations with age after registration.
我们提出了一种用于多模态脑数据配准的方法，该方法以可逆的方式对齐节点网络配置的形状。我们利用形状分析的思想，将个体受试者的数据配置表示为超球面上的一个元素，其中测地线具有封闭形式的解。该方法不仅可以进行受试者间的数据配准，还允许构建一个群体数据模板，所有受试者的数据配置都可以配准到该模板上。结果表明，配准后数据度量得到压缩，方差显著降低。我们还观察到，感兴趣区域（ROI）节点识别的预测能力增强，成对网络连通性度量显著增加，以及配准后与年龄的典型相关性显著增加。
