Suggestions from Pro.Zhihao Li and Biswal

1. ROI definitions

  • ** The bigger ROIs do not work for that some ROIs overlapped with others with bigger radius.**
  • 只能采用全脑parcellation的方法定义ROIs，参考 Craddock 2012。但是这里有两种思路： 1. 对静息态的数据预处理之后进行parcellation，再应用到任务态FC分析中； 2. 直接对任务态的数据进行parcellation 不确定哪一种更合适

2. Indexs

  • No need to change the ROIs, but change the indexs. Try compute the attributes of ROI correlation matrix
  • 可以将李教授的方法和Biswal的方法相结合，使用全脑的parcellation来定义ROI，使用ROI相关矩阵属性来做因变量指标。

Operation

2.1 Resting state preprocessing

  • 删除任务态中已经被排除的被试，保持人数一致；
  • 预处理也选择dpabi，参数也保持一致；
  • pyClusterROI对数据进行分割。
  • 最终得到根据功能连接计算得到的模版

2.2 计算每个条件的拓扑属性

  • conn可以自动计算，不再赘述

2.3 是否需要画出每个条件的连接矩阵？ Get the the correlation ROI-to-ROI matrix from the conn toolbox

  • If you want to export many of those simultaneously you can also easily export those values directly to a .txt file for seed-to-voxel analyses using the approach described here https://www.nitrc.org/forum/message.php?… and for ROI-to-ROI analyses by directly accessing the file resultsROI_Condition*.mat in conn/results/firstlevel which wil contain the entire ROI-to-ROI correlation matrix for each subject.

  • You can find that information in the files: conn_*/results/firstlevel/ANALYSIS_01/resultsROI_Subject###_Condition###.mat
  • This file will contain a matrix Z with the ROI-to-ROI connectivity values (Fisher-transformed correlation coefficients). In particular the value Z(i,j) will contain the connectivity between source ROI ‘i’ and target ROI ‘j’. The names of these ROIs can be read from the variables ‘names’ and ‘names2’, respectively, from the same .mat file. In other words, names{i} is the source ROI and names{j} is the target ROI corresponding to the Z(i,j) value. Note that source ROIs are all of the ROIs that you entered as sources in the first-level analysis step (which are typically a subset of all of the ROIs entered in the original Setup step). In contrast target ROIs are all of the ROIs entered in the Setup step. The ROIs are sorted so that the square matrix Z(:,1:size(Z,1)) will contain the connectivity among the source ROIs only.
  • In addition, the file resultsROI_Condition*.mat in the same folder will contain the same values concatenated across all subjects (Z will be now a 3-dimensional matrix -ROIs by ROIs by subjects- of connectivity values).

2.4 对于任务态功能连接，是否要回归全脑信号（global signal regression）？

  • 一般而言，静息态数据分析才需要面对这个问题，任务态一般不需要去。

    paper 1

  • title: Towards a consensus regarding global signal regression for resting state functional connectivity MRI
  • authors: Kevin Murphy, Michael D. Fox
  • NeuroImage, 2016
  • on the way