I don’t think you need to exclude any time points.  I’m a bit surprised that 
the values are not demeaned if you’ve run ICA+FIX (to make the _hp2000_clean 
dense timeseries).  The original data are scaled to a grand mean of 10000, so 
the values are right until you demean the data (which you would want to do 
before concatenating across runs for example).

Peace,

Matt.

From: 
<[email protected]<mailto:[email protected]>>
 on behalf of peipei_xiao <[email protected]<mailto:[email protected]>>
Date: Monday, November 9, 2015 at 4:46 AM
To: hcp-users 
<[email protected]<mailto:[email protected]>>
Subject: [HCP-Users] help for lifespan data

Hi,

I have some questions need help.

I download the lifespan dataset from ConnectomeDB database. Then, I used 
Pipelines-3.10.0 script to preprocessing those data. After preprocessing, I 
used workbench-command [wb_command -cifti-parcellate 
rfMRI_REST1_LR_Atlas_hp2000_clean.dtseries.nii *.LR.dlabel.nii COLUMN 
cortical.ptseries.nii] to get subjects' time-course 
data(rfMRI_REST1_LR_Atlas_hp2000_clean.dtseries.nii).
Each time-course data is a matrix, such as a 104×420 matrix. 104 represents 
there are 104 labels, and 420 represents 420 time points.

Now, I have two question?
First, In the SPM pipeline of fMRI preprocessing, we need to  exclude the first 
10 time points of the time-course data. Should I exclude the first 10 time 
points of the lifespan data?
Second, I found that the numerical value in 104×420 time-course matrix almostly 
10^5, following is a one label,420 time points time-course data. Does the 
numerical value of time course data is correct. Does there need any transform, 
such as use the function of log10 to process those data.
11669 1173011805 1184511798 1179111692 1164711687 1172911767 1178811774 
1174711755 1174711786 1181611803 1178511817 1174911742 1180911785 1174311826 
1184211833 1188611799 1180011747 1178111749 1178711780 1181411774 1181011782 
1184111863 1178311779 1185811840 1189211868 1189911865 1186111860 1182611773 
1176911696 1169411757 1175811719 1179311724 1177311724 1180211831 1181311794 
1179711741 1179211747 1172711817 1179111783 1181311798 1177011783 1175411772 
1174111779 1180611740 1177311738 1172911752 1179611816 1175411781 1178911810 
1182411830 1183511771 1181011737 1179511772 1177311908 1181911739 1173311758 
1175011731 1178111725 1176011758 1174411753 1172811682 1174311736 1177911809 
1182311883 1178411807 1184011886 1185611800 1174211731 1169311711 1172111736 
1180011805 1165611686 1175411779 1175311726 1173411768 1181011841 1173711759 
1184911857 1177611746 1176411758 1177711786 1185011741 1173411761 1178611713 
1178211759 1171611733 1173011790 1170911734 1178611784 1181311762 1178811713 
1173811758 1179311771 1181611792 1180611836 1182211872 1189211887 1187111898 
1187011834 1188211817 1180811785 1180211819 1178111763 1176411715 1174111780 
1174611755 1169811679 1173111749 1178211769 1175511709 1161911707 1170711744 
1174311739 1176111727 1168811713 1172011781 1178711794 1174411760 1177111715 
1175911686 1167611741 1175911709 1177211776 1179211788 1175711700 1170711706 
1171811747 1176611787 1178711700 1180311759 1180311808 1176111737 1171711747 
1172511780 1177111800 1180611830 1179911746 1172411699 1176711773 1185111810 
1190111884 1190211959 1185811855 1184911857 1183811732 1176311747 1172311722 
1174611681 1168511692 1173511814 1177311783 1177711848 1182211771 1176311793 
1185611820 1183611884 1185811754 1174911738 1176011791 1182711751 1172011751 
1174811795 1179411844 1188411856 1183311913 1179211796 1177111737 1170811741 
1173611806 1181011809 1179711849 1188211906 1186311816 1177911823 1184311807 
1182011722 1173511770 1173011691 1174011689 1175311745 1177611841 1179911855 
1182611754 1179911800 1180311859 1188511795 1179011694 1162811706 1179511797 
1179711795 1178911807 1176011699 1173411718 1170711663 1169211754 1180111724 
1179111764 1179211828 1179811808 1185811820 1180511784 1186011812 1188211861 
1194011895 1182011838 1179711691 1167011696 1167611669 1172411741 1170811716 
11757








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