Tahmasebi Amir M, Abolmaesumi Purang, Zheng Zane Z, Munhall Kevin G, Johnsrude Ingrid S
School of Computing, Queen's University, Kingston, ON, Canada.
Neuroimage. 2009 Oct 1;47(4):1522-31. doi: 10.1016/j.neuroimage.2009.05.047. Epub 2009 May 27.
Conventional group analysis of functional MRI (fMRI) data usually involves spatial alignment of anatomy across participants by registering every brain image to an anatomical reference image. Due to the high degree of inter-subject anatomical variability, a low-resolution average anatomical model is typically used as the target template, and/or smoothing kernels are applied to the fMRI data to increase the overlap among subjects' image data. However, such smoothing can make it difficult to resolve small regions such as subregions of auditory cortex when anatomical morphology varies among subjects. Here, we use data from an auditory fMRI study to show that using a high-dimensional registration technique (HAMMER) results in an enhanced functional signal-to-noise ratio (fSNR) for functional data analysis within auditory regions, with more localized activation patterns. The technique is validated against DARTEL, a high-dimensional diffeomorphic registration, as well as against commonly used low-dimensional normalization techniques such as the techniques provided with SPM2 (cosine basis functions) and SPM5 (unified segmentation) software packages. We also systematically examine how spatial resolution of the template image and spatial smoothing of the functional data affect the results. Only the high-dimensional technique (HAMMER) appears to be able to capitalize on the excellent anatomical resolution of a single-subject reference template, and, as expected, smoothing increased fSNR, but at the cost of spatial resolution. In general, results demonstrate significant improvement in fSNR using HAMMER compared to analysis after normalization using DARTEL, or conventional normalization such as cosine basis function and unified segmentation in SPM, with more precisely localized activation foci, at least for activation in the region of auditory cortex.
功能磁共振成像(fMRI)数据的传统组分析通常涉及通过将每个脑图像配准到解剖学参考图像来对参与者的解剖结构进行空间对齐。由于个体间解剖结构的高度变异性,通常使用低分辨率的平均解剖模型作为目标模板,和/或对fMRI数据应用平滑核以增加受试者图像数据之间的重叠。然而,当个体间解剖形态不同时,这种平滑可能难以分辨诸如听觉皮层子区域等小区域。在这里,我们使用一项听觉fMRI研究的数据表明,使用高维配准技术(HAMMER)可提高听觉区域内功能数据分析的功能信噪比(fSNR),并具有更局部化的激活模式。该技术针对DARTEL(一种高维微分同胚配准)以及常用的低维归一化技术(如SPM2软件包提供的技术(余弦基函数)和SPM5软件包提供的技术(统一分割))进行了验证。我们还系统地研究了模板图像的空间分辨率和功能数据的空间平滑如何影响结果。只有高维技术(HAMMER)似乎能够利用单受试者参考模板的出色解剖分辨率,并且正如预期的那样,平滑提高了fSNR,但代价是空间分辨率。总体而言,结果表明,与使用DARTEL进行归一化后的分析或SPM中的传统归一化(如余弦基函数和统一分割)相比,使用HAMMER在fSNR方面有显著提高,激活焦点定位更精确,至少对于听觉皮层区域的激活是如此。