Image Sciences Institute, University Medical Centre Utrecht, Utrecht, The Netherlands.
Neuroimage. 2010 Jan 1;49(1):587-96. doi: 10.1016/j.neuroimage.2009.07.018. Epub 2009 Jul 18.
Voxel-based morphometry (VBM) and automated lobar region of interest (ROI) volumetry are comprehensive and fast methods to detect differences in overall brain anatomy on magnetic resonance images. However, VBM and automated lobar ROI volumetry have detected dissimilar gray matter differences within identical image sets in our own experience and in previous reports. To gain more insight into how diverging results arise and to attempt to establish whether one method is superior to the other, we investigated how differences in spatial scale and in the need to statistically correct for multiple spatial comparisons influence the relative sensitivity of either technique to group differences in gray matter volumes. We assessed the performance of both techniques on a small dataset containing simulated gray matter deficits and additionally on a dataset of 22q11-deletion syndrome patients with schizophrenia (22q11DS-SZ) vs. matched controls. VBM was more sensitive to simulated focal deficits compared to automated ROI volumetry, and could detect global cortical deficits equally well. Moreover, theoretical calculations of VBM and ROI detection sensitivities to focal deficits showed that at increasing ROI size, ROI volumetry suffers more from loss in sensitivity than VBM. Furthermore, VBM and automated ROI found corresponding GM deficits in 22q11DS-SZ patients, except in the parietal lobe. Here, automated lobar ROI volumetry found a significant deficit only after a smaller sub-region of interest was employed. Thus, sensitivity to focal differences is impaired relatively more by averaging over larger volumes in automated ROI methods than by the correction for multiple comparisons in VBM. These findings indicate that VBM is to be preferred over automated lobar-scale ROI volumetry for assessing gray matter volume differences between groups.
体素基形态测量学 (VBM) 和自动脑叶 ROI 容积测量是在磁共振图像上检测整体大脑解剖差异的全面且快速的方法。然而，在我们自己的经验和以前的报告中，VBM 和自动脑叶 ROI 容积测量已经检测到了相同图像集中相似的灰质差异。为了更深入地了解分歧结果的产生原因，并尝试确定一种方法是否优于另一种方法，我们研究了空间尺度的差异以及是否需要对多个空间比较进行统计校正，如何影响这两种技术对灰质体积组间差异的相对敏感性。我们评估了这两种技术在一个包含模拟灰质缺陷的小数据集上的性能，此外还在一个 22q11 缺失综合征伴精神分裂症患者（22q11DS-SZ）与匹配对照的数据集上评估了这两种技术的性能。与自动 ROI 容积测量相比，VBM 对模拟局灶性缺陷更敏感，并且可以同样好地检测到全皮质缺陷。此外，对 VBM 和 ROI 检测局灶性缺陷敏感性的理论计算表明，随着 ROI 大小的增加，ROI 容积测量的敏感性损失比 VBM 更严重。此外，VBM 和自动 ROI 在 22q11DS-SZ 患者中发现了相应的 GM 缺陷，除了在顶叶。在这里，仅在使用较小的感兴趣区域亚区后，自动脑叶 ROI 容积测量才发现了显著的缺陷。因此，与 VBM 相比，在自动 ROI 方法中对更大体积进行平均会相对更多地损害对局灶性差异的敏感性，而在 VBM 中校正多个比较会相对更多地损害对局灶性差异的敏感性。这些发现表明，在评估组间灰质体积差异时，VBM 优于自动脑叶尺度 ROI 容积测量。
