Grova C, Makni S, Flandin G, Ciuciu P, Gotman J, Poline J B
Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, EEG department, Room 009d, Quebec, Canada H3A 2B4.
Neuroimage. 2006 Jul 15;31(4):1475-86. doi: 10.1016/j.neuroimage.2006.02.049. Epub 2006 May 2.
Analyzing functional magnetic resonance imaging (fMRI) data restricted to the cortical surface is of particular interest for two reasons: (1) to increase detection sensitivity using anatomical constraints and (2) to compare or use fMRI results in the context of source localization from magneto/electro-encephalography (MEEG) data, which requires data to be projected on the same spatial support. Designing an optimal scheme to interpolate fMRI raw data or resulting activation maps on the cortical surface relies on a trade-off between choosing large enough interpolation kernels, because of the distributed nature of the hemodynamic response, and avoiding mixing data issued from different anatomical structures. We propose an original method that automatically adjusts the level of such a trade-off, by defining interpolation kernels around each vertex of the cortical surface using a geodesic Voronoï diagram. This Voronoï-based interpolation method was evaluated using simulated fMRI activation maps, manually generated on an anatomical MRI, and compared with a more standard approach where interpolation kernels were defined as local spheres of radius r=3 or 5 mm. Several validation parameters were considered: the spatial resolution of the simulated activation map, the spatial resolution of the cortical mesh, the level of anatomical/functional data misregistration and the location of the vertices within the gray matter ribbon. Using an activation map at the spatial resolution of standard fMRI data, robustness to misregistration errors was observed for both methods, whereas only the Voronoï-based approach was insensitive to the position of the vertices within the gray matter ribbon.
分析局限于皮质表面的功能磁共振成像（fMRI）数据具有特殊意义，原因有二：（1）利用解剖学限制提高检测灵敏度；（2）在源自脑磁图/脑电图（MEEG）数据的源定位背景下比较或使用fMRI结果，这要求数据投影在相同的空间支持上。设计一种在皮质表面上对fMRI原始数据或所得激活图进行插值的最佳方案，依赖于在选择足够大的插值内核（由于血液动力学反应的分布特性）与避免混合来自不同解剖结构的数据之间进行权衡。我们提出了一种原始方法，通过使用测地Voronoi图在皮质表面的每个顶点周围定义插值内核，自动调整这种权衡的程度。这种基于Voronoi的插值方法使用在解剖MRI上手动生成的模拟fMRI激活图进行评估，并与一种更标准的方法进行比较，在该方法中，插值内核被定义为半径r = 3或5 mm的局部球体。考虑了几个验证参数：模拟激活图的空间分辨率、皮质网格的空间分辨率、解剖学/功能数据配准错误的程度以及顶点在灰质带内的位置。使用标准fMRI数据空间分辨率下的激活图，两种方法都观察到了对配准错误的鲁棒性，而只有基于Voronoi的方法对顶点在灰质带内的位置不敏感。
