Yates Shona L, Barach Alice, Gingell Sarah, Whalley Heather C, Job Dominic, Johnstone Eve C, Best Jonathan J K, Lawrie Stephen M
Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5HF, UK.
Psychiatry Res. 2006 Oct 30;147(2-3):197-212. doi: 10.1016/j.pscychresns.2006.01.012. Epub 2006 Aug 1.
A number of reliable techniques have been described that can parcellate temporal neo-cortex from MRI images to preserve topographical characteristics of individual brains, but these tend to use in-house software. We describe here an adaptation of the methods previously described by Kim et al. [Kim, J.J., Crespo-Facorro, B., Andreasen, N.C., O'Leary, D.S., Zhang, B., Harris, G., Magnotta, V.A., 2000. An MRI-based parcellation method for the temporal lobe. Neuroimage 11, 271-288], but utilising commercially and, therefore, generally available software. Using Analyze, we traced individual sulci and identified coronal bounding planes, and used a combination of three orthogonal plane views, manual limit tracing and semi-automated edge detection to parcellate 13 sub-regions of temporal neo-cortex from sets of serial coronal slices. We applied this technique to the baseline scans of the first seven subjects in the Edinburgh High Risk Study (EHRS) who developed schizophrenia, and a matched group of healthy controls, to see if temporal lobe sub-regional volumes could predict the onset of schizophrenia. Two relatively inexperienced raters developed these techniques in a short time period, and intra-rater intra-class correlation coefficients (ICC) ranged from 0.56 to 0.99, while the mean inter-rater ICC was 0.90 (range 0.55-0.99). There were, however, no significant differences in temporal lobe sub-regional volumes between the two groups we examined. We have, therefore, developed a reliable parcellation technique that requires relatively little training. It is, however, a laborious process, and it remains uncertain whether it is more sensitive to early disease processes in pre-schizophrenia than are other image-analysis techniques.
已经描述了许多可靠的技术，这些技术可以从MRI图像中分割颞叶新皮质，以保留个体大脑的地形特征，但这些技术往往使用内部软件。我们在此描述了对Kim等人先前描述的方法的一种改编[Kim, J.J., Crespo-Facorro, B., Andreasen, N.C., O'Leary, D.S., Zhang, B., Harris, G., Magnotta, V.A., 2000. 一种基于MRI的颞叶分割方法。《神经影像学》11, 271 - 288]，但使用的是商业上因而普遍可用的软件。使用Analyze软件，我们追踪了个体脑沟并确定了冠状边界平面，并结合三个正交平面视图、手动边界追踪和半自动边缘检测，从一系列冠状切片中分割出颞叶新皮质的13个亚区域。我们将这项技术应用于爱丁堡高危研究（EHRS）中最初7名患精神分裂症的受试者的基线扫描，以及一组匹配的健康对照，以观察颞叶亚区域体积是否能预测精神分裂症的发病。两名经验相对不足的评估者在短时间内开发了这些技术，评估者内组内相关系数（ICC）范围为0.56至0.99，而评估者间平均ICC为0.90（范围0.55 - 0.99）。然而，我们检查的两组之间颞叶亚区域体积没有显著差异。因此，我们开发了一种可靠的分割技术，该技术所需培训相对较少。然而，这是一个费力的过程，并且与其他图像分析技术相比，它对精神分裂症前期的早期疾病过程是否更敏感仍不确定。
