Department of Electrical Engineering, Princeton University, Princeton, NJ, USA; Department of Biomedical Engineering, Columbia University, New York, NY, USA.
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
Neuroimage. 2013 Nov 1;81:400-411. doi: 10.1016/j.neuroimage.2013.05.009. Epub 2013 May 14.
Inter-subject alignment of functional MRI (fMRI) data is necessary for group analyses. The standard approach to this problem matches anatomical features of the brain, such as major anatomical landmarks or cortical curvature. Precise alignment of functional cortical topographies, however, cannot be derived using only anatomical features. We propose a new inter-subject registration algorithm that aligns intra-subject patterns of functional connectivity across subjects. We derive functional connectivity patterns by correlating fMRI BOLD time-series, measured during movie viewing, between spatially remote cortical regions. We validate our technique extensively on real fMRI experimental data and compare our method to two state-of-the-art inter-subject registration algorithms. By cross-validating our method on independent datasets, we show that the derived alignment generalizes well to other experimental paradigms.
功能磁共振成像 (fMRI) 数据的受试者间配准对于组分析是必要的。解决这个问题的标准方法是匹配大脑的解剖特征，如主要解剖标志或皮质曲率。然而，仅使用解剖特征无法得出功能皮质拓扑的精确配准。我们提出了一种新的受试者间配准算法，该算法可以跨受试者对齐功能连接的受试者内模式。我们通过在空间上远程的皮质区域之间，对在观看电影期间测量的 fMRI BOLD 时间序列进行相关，得出功能连接模式。我们在真实的 fMRI 实验数据上广泛验证了我们的技术，并将我们的方法与两种最先进的受试者间配准算法进行了比较。通过在独立数据集上对我们的方法进行交叉验证，我们表明所得到的对齐效果很好地推广到其他实验范式。
