脑亚区功能连接

各位老师,我在关注脑亚区功能连接的文献中,遇到了一些问题,望建议,多谢!
以一篇文献为例:
Bai et al. Aberrant Hippocampal Subregion Networks Associated with the Classifications of aMCI Subjects: A Longitudinal Resting-State Study. Plos one. 2011.
先简要介绍一下该研究:
目的:观察MCI患者海马的三个亚区CA (CA1-CA3)、DG (fascia dentate and CA4)、SUB (prosubiculum, subiculum proper, presubiculum and parasubiculum)与全脑其他区域的功能连接,随访20个月,旨在比较MCI患者与正常对照的海马亚区功能连接的纵向变化程度。
4 Subject Groups ( Basline and 20 months: MCI group and Control group)
Data analysis:
1、 常规preprocessing
2、 ROI definition for hippocampal subregions: SPM Anantomy Toolbox
3、 Group-level analyses:
Ø Within group: one sample t-test得出各亚区的功能连接pattern,FDR(0.05 corrected),选取positive functional connectivity制作mask,用于组间对比。
Ø Between groups:ANOVA and Post hoc tests,得出两组海马亚区功能连接变化程度的差异。FDR(0.05 corrected)。Voxel wise灰质校正。
4、Classification analysis
To avoid circular analysis, namely the use of the same data for selection and selective analysis will result in distorted descriptive statistics and invalid statistical inference whenever the test statistics are not inherently independent of the selection criteria under the null hypothesis [34]. Therefore, firstly, the overlap of the longitudinal changes in respective network of CA, DG and SUB identified via aMCI group-1 and controls group-1 was extracted as ROI. Secondly, we examined unrelated groups of baseline 30 subjects (aMCI group-2, n= 12; aMCI-converters who subsequently developed AD, n= 6; controls group-2, n =12) and replicated the aforementioned analyses of hippocampus-subregion networks. Finally, the ability of ROI (mean Z values of overlap regions) to separate these subjects (aMCI group-2, aMCI-converters and controls group-2) was computed using Receiver Operating Characteristic (ROC) [35]. Area under the ROC curve (AUC) and best cutoff values were extracted, generating sensitivity and specificity values; the values distinguishing aMCI-converters from controls group-2, and aMCI-converters from aMCI group-2 were examined. The ROC was calculated using Medcalc software.
 
Questions:
1、 第四部分Classification analysis看不太明白,做这个的意义何在?我个人觉得前面已经做了组间比较,得出了想要观察的差异,已经比较完整了,是否可以不做这歩?
2、 假如我想做患者与对照两组海马亚区功能连接的对比,仅进行该文1-3的步骤,依次做以下分析是否完整?
Ø 常规预处理;
Ø 定义海马亚区;
Ø 得出海马亚区的FC patterns,FDR校正;
Ø 在REST中分别比较两组海马各亚区的功能连接图(全脑灰质mask>0.2),然后在单样本结果中明确差异peak点相关性及方向。以灰质图为协变量,采用REST中的Alphasim校正估计。
3、 这类亚区分析组间比较的校正是否还跟将其作为一个整体区域的校正准则相同?
4、 另外,如果做小脑亚区的功能连接,按照AAL atlas将其中的26个小脑亚区归类为7个ROI:the anterior and posterior vermis, the bilateral anterior and posterior cerebellar hemispheres and the flocculo-nodular lobe,然后按照2所列步骤进行得出两组差异,分析上是否完整?

Re

1. 可以不做。有些人进一步做分类,是为了想看看是否有应用到疾病诊断的潜力。
2. 同意。
3. 理论上来说,由于你有多个种子点,这样增加了多重比较次数,应该进行相应的多重比较校正(比如说Bonferroni校正0.05/ROI数)。但目前我所看见的大多数文章,都没有进行这一步校正。
4. 做法可以。但最好确定一下分类来源,以及此分类的可接受程度。

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