de Boer Renske, Schaap Michiel, van der Lijn Fedde, Vrooman Henri A, de Groot Marius, Vernooij Meike W, Ikram M Arfan, van Velsen Evert F S, van der Lugt Aad, Breteler Monique M B, Niessen Wiro J
Biomedical Imaging Group Rotterdam, Department of Radiology, Erasmus MC, Rotterdam, The Netherlands.
Med Image Comput Comput Assist Interv. 2010;13(Pt 2):101-8. doi: 10.1007/978-3-642-15745-5_13.
We present a framework for statistical analysis in large cohorts of structural brain connectivity, derived from diffusion weighted MRI. A brain network is defined between subcortical gray matter structures and a cortical parcellation obtained with FreeSurfer. Connectivity is established through minimum cost paths with an anisotropic local cost function and is quantified per connection. The connectivity network potentially encodes important information about brain structure, and can be analyzed using multivariate regression methods. The proposed framework can be used to study the relation between connectivity and e.g. brain function or neurodegenerative disease. As a proof of principle, we perform principal component regression in order to predict age and gender, based on the connectivity networks of 979 middle-aged and elderly subjects, in a 10-fold cross-validation. The results are compared to predictions based on fractional anisotropy and mean diffusivity averaged over the white matter and over the corpus callosum. Additionally, the predictions are performed based on the best predicting connection in the network. Principal component regression outperformed all other prediction models, demonstrating the age and gender information encoded in the connectivity network.
我们提出了一个用于对源自扩散加权磁共振成像的大脑结构连接性大型队列进行统计分析的框架。大脑网络定义于皮质下灰质结构与通过FreeSurfer获得的皮质分区之间。通过具有各向异性局部成本函数的最小成本路径建立连接，并对每个连接进行量化。连接性网络可能编码有关大脑结构的重要信息，并且可以使用多元回归方法进行分析。所提出的框架可用于研究连接性与例如脑功能或神经退行性疾病之间的关系。作为原理验证，我们基于979名中老年受试者的连接性网络，在10折交叉验证中进行主成分回归以预测年龄和性别。将结果与基于白质和胼胝体平均分数各向异性和平均扩散率的预测进行比较。此外，基于网络中最佳预测连接进行预测。主成分回归优于所有其他预测模型，证明了连接性网络中编码的年龄和性别信息。
