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sc1_sc2_imana.m
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function varargout=sc1_sc2_imana(what,varargin)
% UPDATE INFO IN SECTION 2 WITH EVERY NEW SUBJECT, SESSION
numDummys = 3; % per run
numTRs = 601; % per run (includes dummies)
%========================================================================================================================
% (1) Directories
baseDir = '/Users/maedbhking/Documents/Cerebellum_Cognition';
% baseDir = '/Volumes/Seagate Backup Plus Drive';
% baseDir = '/Volumes/MotorControl/data/super_cerebellum_new';
% baseDir = '/Users/jdiedrichsen/Data/super_cerebellum_new';
externalDir ='/Volumes/Seagate Backup Plus Drive';
studyDir{1} =fullfile(baseDir,'sc1');
studyDir{2} =fullfile(baseDir,'sc2');
restDir =fullfile(baseDir,'restingState');
behavDir ='/data';
imagingDir ='/imaging_data';
imagingDirNA ='/imaging_data_nonaggr';
imagingDirA ='/imaging_data_aggr';
imagingDirRaw ='/imaging_data_raw';
dicomDir ='/imaging_data_dicom';
anatomicalDir ='/anatomicals';
freesurferDir ='/surfaceFreesurfer';
suitDir ='/suit';
caretDir ='/surfaceCaret';
regDir ='/RegionOfInterest/';
eyeDirRaw ='/eyeTracking_raw';
eyeDir ='/eyeTracking';
physioDir ='/physio';
encodeDir ='/encoding';
%========================================================================================================================
% (2) PRE-PROCESSING: Study AND Subject AND Session specific info. EDIT W/ EVERY NEW SUBJECT AND W/ EVERY NEW
% SESSION
% Subjs 1-22 were preprocessed using this script. The naming convention for
% the dicom files changed after s22 so subjs 23-31 were preprocessed with
% sc1_sc2_imana_ch (exact same preprocessing steps)
% this script was used to process resting state data
% MANUAL INPUT REQUIRED FOR THE FOLLOWING VARIABLES:
% DicomName: find this in imaging_data_dicom
% rs_DicomName: find this in restingState
% NiiRawName: find this in imaging_data_dicom (numbers before series number)
% rs_NiiRawName: find this in restingState (numbers before series number)
% saccRun: runs to be included for saccade analysis
% anatNum: one scan per subject (collected in session 1 of study 1 - usually series 2)
subj_name = {'s01','s02','s03','s04','s05','s06','s07','s08','s09','s10','s11',...
's12','s13','s14','s15','s16','s17','s18','s19','s20','s21','s22','s23','s24',...
's25','s26','s27','s28','s29','s30','s31'};
% returnSubjs=[2,3,4,6,7,8,9,10,12,14,15,17,18,19,20,21,22,24,25,26,27,28,29,30,31];
returnSubjs=[2,3,4,6,8,9,10,12,14,15,17,18,19,20,21,22,24,25,26,27,28,29,30,31];
DicomName{1} = {'2016_03_15_HP10.MR.DIEDRICHSEN_FEB2016',...%s01
'2016_03_23_HP10.MR.DIEDRICHSEN_FEB2016',... %s01
'2016_04_13_I011.MR.DIEDRICHSEN_FEB2016',... %s02
'2016_04_156_I011.MR.DIEDRICHSEN_FEB2016',...%s02
'2016_04_18_UH11.MR.DIEDRICHSEN_FEB2016',... %s03
'2016_04_19_UH11.MR.DIEDRICHSEN_FEB2016',... %s03
'2016_04_26_CC23.MR.DIEDRICHSEN_FEB2016',... %s04
'2016_04_29_CC23.MR.DIEDRICHSEN_FEB2016',... %s04
'2016_05_18_EK05.MR.DIEDRICHSEN_FEB2016',... %s05
'2016_05_19_EK05.MR.DIEDRICHSEN_FEB2016',... %s05
'2016_05_24_M028.MR.DIEDRICHSEN_FEB2016',... %s06
'2016_05_30_M028.MR.DIEDRICHSEN_FEB2016',... %s06
'2016_05_25_HI15.MR.DIEDRICHSEN_FEB2016',... %s07
'2016_05_26_HI15.MR.DIEDRICHSEN_FEB2016',... %s07
'2016_05_31_NL20.MR.DIEDRICHSEN_FEB2016',... %s08
'2016_06_01_NL20.MR.DIEDRICHSEN_FEB2016',... %s08
'2016_06_02_OH14.MR.DIEDRICHSEN_FEB2016',... %s09
'2016_06_03_OH14.MR.DIEDRICHSEN_FEB2016',... %s09
'2016_07_07_RV14.MR.DIEDRICHSEN_FEB2016',... %s10
'2016_07_08_RV14.MR.DIEDRICHSEN_FEB2016',... %s10
'2016_07_15_AC15.MR.DIEDRICHSEN_FEB2016',... %s11
'2016_07_22_AC15.MR.DIEDRICHSEN_FEB2016',... %s11
'2016_07_20_RM20.MR.DIEDRICHSEN_FEB2016',... %s12
'2016_07_21_RM20.MR.DIEDRICHSEN_FEB2016',... %s12
'2016_07_28_AW12.MR.DIEDRICHSEN_FEB2016',... %s13
'2016_07_29_AW12.MR.DIEDRICHSEN_FEB2016',... %s13
'2016_07_28_SR12.MR.DIEDRICHSEN_FEB2016',... %s14
'2016_07_29_SR12.MR.DIEDRICHSEN_FEB2016',... %s14
'2016_08_04_RT25.MR.DIEDRICHSEN_FEB2016',... %s15
'2016_08_05_RT25.MR.DIEDRICHSEN_FEB2016',... %s15
'2016_08_04_FW03.MR.DIEDRICHSEN_FEB2016',... %s16
'2016_08_05_FW03.MR.DIEDRICHSEN_FEB2016',... %s16
'2016_08_22_AR14.MR.DIEDRICHSEN_FEB2016',... %s17
'2016_08_23_AR14.MR.DIEDRICHSEN_FEB2016',... %s17
'2017_02_14_AO21.MR.DIEDRICHSEN_MAEBDH',... %s18
'2017_02_22_AO21.MR.DIEDRICHSEN_MAEBDH',... %s18
'2017_03_09_HA31.MR.DIEDRICHSEN_MAEBDH',... %s19
'2017_03_13_HA31.MR.DIEDRICHSEN_MAEBDH',... %s19
'2017_03_15_ML24.MR.DIEDRICHSEN_MAEBDH',... %s20
'2017_03_20_ML24.MR.DIEDRICHSEN_MCC',... %s20
'2017_03_13_AZ18.MR.DIEDRICHSEN_MAEBDH',... %s21
'2017_03_20_AZ18.MR.DIEDRICHSEN_MCC',... %s21
'2017_03_15_VE14.MR.DIEDRICHSEN_MAEBDH',... %s22
'2017_03_16_VE14.MR.DIEDRICHSEN_MCC',... %s22
};
DicomName{2} = {'',...%s01
'',... %s01
'2017_05_25_IO11.MR.DIEDRICHSEN_MCC',...%s02
'2017_05_26_IO11.MR.DIEDRICHSEN_MCC',...%s02
'2017_03_27_UH11.MR.DIEDRICHSEN_MCC',...%s03
'2017_03_28_UH11.MR.DIEDRICHSEN_MCC',...%s03
'2017_06_12_CC23.MR.DIEDRICHSEN_MCC',...%s04
'2017_06_13_CC23.MR.DIEDRICHSEN_MCC',...%s04
'',...%s05
'',...%s05
'2017_04_12_MO28.MR.DIEDRICHSEN_MCC',...%s06
'2017_04_20_MO28.MR.DIEDRICHSEN_MCC',...%s06
'2017_05_16_HI15.MR.DIEDRICHSEN_MCC',...%s07
'2017_05_17_HI15.MR.DIEDRICHSEN_MCC',...%s07
'2017_04_24_NL20.MR.DIEDRICHSEN_MCC',...%s08
'2017_05_01_NL20.MR.DIEDRICHSEN_MCC',...%s08
'2017_04_19_OH14.MR.DIEDRICHSEN_MCC',...%s09
'2017_04_20_OH14.MR.DIEDRICHSEN_MCC',...%s09
'2017_04_27_RV14.MR.DIEDRICHSEN_MCC',...%s10
'2017_04_28_RV14.MR.DIEDRICHSEN_MCC',...%s10
'',...%s11
'',...%s11
'2017_04_24_RM20.MR.DIEDRICHSEN_MCC',...%s12
'2017_04_25_RM20.MR.DIEDRICHSEN_MCC',...%s12
'',...%s13
'',...%s13
'2017_06_12_SR12.MR.DIEDRICHSEN_MCC',...%s14
'2017_06_15_SR12.MR.DIEDRICHSEN_MCC',...%s14
'2017_06_14_RT25.MR.DIEDRICHSEN_MCC',...%s15
'2017_06_15_RT25.MR.DIEDRICHSEN_MCC',...%s15
'',...%s16
'',...%s16
'2017_03_24_AR14.MR.DIEDRICHSEN_MCC',...%s17
'2017_03_28_AR14.MR.DIEDRICHSEN_MCC',...%s17
'2017_05_09_AO21.MR.DIEDRICHSEN_MCC',...%s18
'2017_05_10_AO21.MR.DIEDRICHSEN_MCC',...%s18
'2017_05_25_HA31.MR.DIEDRICHSEN_MCC',...%s19
'2017_05_26_HA31.MR.DIEDRICHSEN_MCC',...%s19
'2017_04_12_ML24.MR.DIEDRICHSEN_MCC',...%s20
'2017_04_23_ML24.MR.DIEDRICHSEN_MCC',...%s20
'2017_04_25_AZ18.MR.DIEDRICHSEN_MCC',...%s21
'2017_04_28_AZ18.MR.DIEDRICHSEN_MCC',...%s21
'2017_04_26_VE14.MR.DIEDRICHSEN_MCC',...%s22
'2017_04_28_VE14.MR.DIEDRICHSEN_MCC',...%s22
};
rs_DicomName = {'',... %s01
'2017_09_07_IO11.MR.DIEDRICHSEN_MCC',...%s02
'2017_09_13_UH11.MR.DIEDRICHSEN_MCC',...%s03
'',...%s04
'',...%s05
'2017_08_24_MO28.MR.DIEDRICHSEN_MCC',...%s06
'2017_09_05_HI15.MR.DIEDRICHSEN_MCC',...%s07
'2017_09_07_NL20.MR.DIEDRICHSEN_MCC',...%s08
'',...%s09
'2017_08_15_RV14.MR.DIEDRICHSEN_MCC',...%s10
'',...%s11
'2017_08_11_RM20.MR.DIEDRICHSEN_MCC',...%s12
'',...%s13
'2017_09_07_SR12.MR.DIEDRICHSEN_MCC',...%s14
'',...%s15
'',...%s16
'',...%s17
'2017_09_07_AO21.MR.DIEDRICHSEN_MCC',...%s18
'',...%s19
'2017_08_24_ML24.MR.DIEDRICHSEN_MCC',...%s20
'',...%s21
'2017_09_07_VE14.MR.DIEDRICHSEN_MCC',...%s22
'2017_09_12_HO21.MR.Diedrichsen_MCC',...%s23
'2017_11_01_NA14.MR.Diedrichsen_MCC',...%s24
'2017_09_12_DA18.MR.Diedrichsen_MCC',...%s25
'2017_11_03_ME01.MR.Diedrichsen_MCC',...%s26
'2017_10_25_VI14.MR.Diedrichsen_MCC',...%s27
'2017_11_24_ME06.MR.Diedrichsen_MCC',...%s28
'2017_11_02_MA19.MR.Diedrichsen_MCC',...%s29
'2017_11_01_AV10.MR.Diedrichsen_MCC',...%s30
'2017_11_03_MA18.MR.Diedrichsen_MCC',...%s31
};
NiiRawName{1} = {'160315140823DST131221107524367007',...%s01
'160323140933DST131221107524367007',...%s01
'160413101535DST131221107524367007',...%s02
'160415100919DST131221107524367007',...%s02
'160418140549DST131221107524367007',...%s03
'160419152813DST131221107524367007',...%s03
'160426135807DST131221107524367007',...%s04
'160429150526DST131221107524367007',...%s04
'160518145625DST131221107524367007',...%s05
'160519140503DST131221107524367007',...%s05
'160524143451DST131221107524367007',...%s06
'160530143056DST131221107524367007',...%s06
'160525145854DST131221107524367007',...%s07
'160526150947DST131221107524367007',...%s07
'160531141052DST131221107524367007',...%s08
'160601140606DST131221107524367007',...%s08
'160602145638DST131221107524367007',...%s09
'160603144457DST131221107524367007',...%s09
'160707141119DST131221107524367007',...%s10
'160708125723DST131221107524367007',...%s10
'160715123900DST131221107524367007',...%s11
'160722143518DST131221107524367007',...%s11
'160720110707DST131221107524367007',...%s12
'160721105741DST131221107524367007',...%s12
'160728100746DST131221107524367007',...%s13
'160729100321DST131221107524367007',...%s13
'160728120942DST131221107524367007',...%s14
'160729120849DST131221107524367007',...%s14
'160804140003DST131221107524367007',...%s15
'160805130528DST131221107524367007',...%s15
'160804100807DST131221107524367007',...%s16
'160805100423DST131221107524367007',...%s16
'160822095244DST131221107524367007',...%s17
'160823100118DST131221107524367007',...%s17
'170214144242STD131221107524367007',...%s18
'170222143758STD131221107524367007',...%s18
'170309121041STD131221107524367007',...%s19
'170313120111DST131221107524367007',...%s19
'170315121648DST131221107524367007',...%s20
'170320120712DST131221107524367007',...%s20
'170313095950DST131221107524367007',...%s21
'170320100001DST131221107524367007',...%s21
'170315144047DST131221107524367007',...%s22
'170316144945DST131221107524367007',...%s22
};
NiiRawName{2} = {'',...%s01
'',... %s01
'170525100719DST131221107524367007',...%s02
'170525100719DST131221107524367007',...%s02
'170327151145DST131221107524367007',...%s03
'170328124001DST131221107524367007',...%s03
'170612140815DST131221107524367007',...%s04
'170612140815DST131221107524367007',...%s04
'',...%s05
'',...%s05
'170412101047DST131221107524367007',...%s06
'170420150758DST131221107524367007',...%s06
'2017_05_16_HI15',...%s07 (strange formatting for this subject)
'2017_05_16_HI15',...%s07
'170424100628DST131221107524367007',...%s08
'170501083431DST131221107524367007',...%s08
'170419115131DST131221107524367007',...%s09
'170420120648DST131221107524367007',...%s09
'170427131043DST131221107524367007',...%s10
'170427131043DST131221107524367007',...%s10
'',...%s11
'',...%s11
'170424140431DST131221107524367007',...%s12
'170424140431DST131221107524367007',...%s12
'',...%s13
'',...%s13
'170612120235DST131221107524367007',...%s14
'170615145716DST131221107524367007',...%s14
'170614140812DST131221107524367007',...%s15
'170614140812DST131221107524367007',...%s15
'',...%s16
'',...%s16
'170324151234DST131221107524367007',...%s17
'170328100924DST131221107524367007',...%s17
'170509140556DST131221107524367007',...%s18
'170509140556DST131221107524367007',...%s18
'170525151250DST131221107524367007',...%s19
'170525151250DST131221107524367007',...%s19
'170412121416DST131221107524367007',...%s20
'170417125335DST131221107524367007',...%s20
'170425155510DST131221107524367007',...%s21
'170425155510DST131221107524367007',...%s21
'170427102053DST131221107524367007',...%s22
'170427102053DST131221107524367007',...%s22
};
rs_NiiRawName = {'',...%s01
'IO11',...%s02
'UH11',...%s03
'',...%s04
'',...%s05
'MO28',...%s06
'2017_09_05_HI15',...%s07 (strange formatting for this subject)
'NL20',...%s08
'',...%s09
'RV14',...%s10
'',...%s11
'RM20',...%s12
'',...%s13
'SR12',...%s14
'',...%s15
'',...%s16
'',...%s17
'AO21',...%s18
'',...%s19
'ML24',...%s20
'',...%s21
'VE14',...%s22
'HO21',...%s23
'171101121712DST131221107524367007',...%s24
'DA18',...%s25
'171103143826DST131221107524367007',...%s26
'VI14',...%s27
'171124115601STD131221107524367007',...%s28
'MA19',...%s29
'171101113420DST131221107524367007',...%s30
'171103100256DST131221107524367007',...%s31
};
fscanNum{1} = {[3,4,5,6,8,9,10,11],...%s01
[2,3,4,5,6,7,8,9],... %s01
[3,4,5,6,8,10,11,12],... %s02
[2,3,4,5,6,7,8,9],... %s02
[3,4,5,6,7,8,9,10],... %s03
[3,4,5,6,8,9,10,11],... %s03
[4,7,8,9,10,11,12,13],...%s04
[2,3,4,5,6,7,8,9],... %s04
[3,4,5,6,7,8,9,10],... %s05
[2,3,4,5,6,7,8,9],... %s05
[3,4,5,6,7,8,9,10],... %s06
[2,3,4,5,6,7,8,9],... %s06
[3,4,5,6,7,8,9,10],... %s07
[2,3,4,5,6,7,8,9],... %s07
[3,4,5,6,7,8,9,10],... %s08
[2,3,4,5,6,7,8,9],... %s08
[3,4,5,6,7,8,9,10],... %s09
[2,3,4,5,6,7,8,9],... %s09
[3,4,5,6,7,8,9,10],... %s10
[2,3,4,5,6,7,8,9],... %s10
[4,5,6,7,8,9,10,11],... %s11
[3,4,5,6,7,8,9,10],... %s11
[3,4,5,7,8,9,10,11],... %s12
[2,3,4,5,6,7,8,9],... %s12
[4,5,6,7,8,9,10,11],... %s13
[2,3,4,5,6,7,9,10],... %s13
[4,5,6,7,8,9,10,11],... %s14
[2,3,4,5,6,7,8,9],... %s14
[3,4,5,6,7,8,9,10],... %s15
[2,3,4,6,7,8,9,10],... %s15
[3,4,5,6,7,8,9,10],... %s16
[2,3,4,5,6,7,8,9],... %s16
[3,4,5,6,7,8,9,10],... %s17
[2,3,4,5,6,7,8,9],... %s17
[3,4,5,6,7,8,9,10],... %s18
[2,4,5,6,7,8,10,11],... %s18
[3,4,5,6,7,8,9,10],... %s19
[2,3,4,5,6,7,8,9],... %s19
[3,4,5,6,7,8,9,10],... %s20
[2,3,4,5,6,7,8,9],... %s20
[3,4,5,6,7,9,10,11],... %s21
[3,4,5,7,8,9,10,11],... %s21
[3,4,5,6,7,8,9,10],... %s22
[2,3,4,5,6,7,8,9],... %s22
};
fscanNum{2} = {[],...%s01 functional scans (series number)
[],...%s01
[3,5,7,9,11,13,15,17],...%s02
[2,4,6,8,10,12,14,16],...%s02
[3,5,7,9,11,13,15,17],...% s03_1
[2,4,6,8,10,12,14,16],...% s03_2
[3,5,7,9,11,13,15,17],...%s04
[2,4,6,8,10,12,14,16],...%s04
[],...%s05
[],...%s05
[4,6,8,10,12,14,16,18],...%s06_1
[2,4,6,8,10,12,14,16],...%s06_2
[4,6,8,10,12,14,16,18],...%s07_1
[2,4,6,8,10,12,14,16],...% s07_2
[3,5,7,9,11,13,15,17],...% s08_1
[2,4,6,8,10,12,14,16],...% s08_2
[3,5,7,9,11,13,15,17],...% s09_1
[2,4,6,8,10,12,14,16],...% s09_2
[3,5,7,9,11,13,15,17],...% s10_1
[3,5,7,9,11,13,15,17],...% s10_2
[],...%s11
[],...%s11
[3,5,7,9,11,13,15,17],...% s12_1
[2,4,6,8,10,12,14,16],...% s12_2
[],...%s13
[],...%s13
[3,5,7,9,11,13,15,17],...%s14
[2,4,6,8,10,12,14,16],...%s14
[3,5,7,9,11,13,15,17],...%s15
[2,4,6,8,10,12,14,16],...%s15
[],...%s16
[],...%s16
[3,5,7,9,11,13,15,17],...% s17_1
[2,4,6,8,10,12,14,16],...% s17_2
[3,5,7,9,11,13,15,17],...% s18_1
[2,4,6,8,10,12,14,16],...% s18_2
[3,5,7,9,11,13,15,17],...% s19_1
[2,4,6,8,10,12,14,16],...% s19_2
[3,5,7,9,11,13,15,17],...% s20_1
[2,4,6,8,10,12,14,16],...% s20_2
[3,5,7,9,11,13,15,17],...% s21_1
[2,4,6,8,10,12,14,16],...% s21_2
[3,5,7,9,11,13,15,17],...% s22_1
[2,4,6,8,10,12,14,16],...% s22_2
};
rs_fscanNum = {[],...%s01 functional scans (series number)
[2,4],...%s02
[2,4],...%s03
[],...%s04
[],...%s05
[2,4],...%s06
[2,4],...%s07
[2,4],...%s08
[],...%s09
[2,4],...%s10
[],...%s11
[2,4],...% s12
[],...%s13
[2,4],...%s14
[],...%s15
[],...%s16
[],...%s17
[2,4],...%s18
[],...%s19
[2,4],...% s20
[],...% s21
[2,4],...% s22
[2,4],...%s23
[2,4],...%s24
[2,4],...%s25
[23,25],...%s26
[2,4],...%s27
[20,22],...%s28
[],...%s29 % need to fix this one
[3,5],...%s30
[2,4],...%s31
};
saccRun{1} = {[],...%s01
[1],... %s02
[],... %s03
[],... %s04
[1,2,3,4],...%s05
[1,2,3,4],...%s06
[3,4],... %s07
[1,2,3],... %s08
[1,2,3,4],...%s09
[3,4],... %s10
[2,3,4],... %s11
[1,2],... %s12
[2],... %s13
[1,2,3],... %s14
[1,2],... %s15
[2],... %s16
[1,2],... %s17
[1,2],... %s18
[1,2],... %s19
[1],... %s20
[1,2],... %s21
[1],... $s22
};
saccRun{2} = {[],... %s01
[],... %s02 [1,2]
[],... %s03
[],... %s04
[],... %s05
[1,2],...%s06
[1,2],...%s07
[],... %s08
[2],... %s09
[1,2],...%s10
[],... %s11
[],... %s12
[],... %s13
[1,2],...%s14
[],... %s15
[],... %s16
[],... %s17
[1,2],...%s18
[1,2],...%s19
[1,2],...%s20
[1,2],...%s21
[1,2],...%s22
};
anatNum = {7,...%s01
2,... %s02
2,... %s03
2,... %s04
2,... %s05
2,... %s06
2,... %s07
2,... %s08
2,... %s09
2,... %s10
2,... %s11
2,... %s12
2,... %s13
3,... %s14
2,... %s15
2,... %s16
2,... %s17
2,... %s18
2,... %s19
2,... %s20
2,... %s21
2,... %s22
};
% The values of loc_AC should be acquired manually prior to the preprocessing
% Step 1: open .nii file with MRIcron and manually find AC and read the xyz coordinate values
% (note: these values are not [0 0 0] in the MNI coordinate)
% Step 2: set those values into loc_AC (subtract from zero)
loc_AC = {[-108,-128,-149],...%s01
[-84, -124,-131],... %s02
[-93,-132,-151],... %s03
[-91,-136,-147],... %s04
[-89, -133,-125],... %s05
[-93,-139,-136],... %s06
[-90,-130,-149],... %s07
[-90,-132,-145],... %s08
[-98,-141,-127],... %s09
[-93,-132,-122],... %s10
[-102,-132,-137],... %s11
[-96,-146,-147],... %s12
[-95,-141,-143],... %s13
[-90,-138,-146],... %s14
[-98,-147,-142],... %s15
[-93,-126,-157],... %s16
[-92,-131,-134],... %s17
[-94,-139,-137],... %s18
[-92,-137,-127],... %s19
[-95,-140,-152],... %s20
[-94,-139,-137],... %s21
[-94,-143,-145],... %s22
};
%========================================================================================================================
% (3) GLM.
% GLM Directories - change glmNum when appropriate
funcRunNum = [51,66]; % first and last behavioural run numbers (16 runs per subject)
run = {'01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16'};
runB = [51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66]; % Behavioural labelling of runs
sess = [1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2]; % session number
runs{1}{1}=1:8; % study 1, session 1
runs{1}{2}=9:16; % study 1, session 2
runs{2}{1}=17:24; % study 2, session 1
runs{2}{2}=25:32; % study 2, session 2
PW = {'betasNW','betasUW'}; % methods of noise normalisation (none and univariate noise normalisation)
% (4) Hemisphere and Region Names
hem = {'lh','rh'};
regSide = 1:2;
atlasA = 'x';
atlasname = 'fsaverage_sym';
hemName = {'LeftHem','RightHem'};
switch(what)
case 'ANAT:dicom_import' % STEP 1.1: Import anat dicom
% STUDY 1 ONLY
% converts dicom to nifti files w/ spm_dicom_convert
% anatomical always collected in study 1, sess 1
% example: sc1_sc2_imana('ANAT:dicom_import',1)
sn=varargin{1}; % subjNum
subjs=length(sn);
for s=1:subjs,
dircheck(fullfile(studyDir{1},dicomDir,[subj_name{sn(s)},'_1']));
cd(fullfile(studyDir{1},dicomDir,[subj_name{sn(s)},'_1']));
for i=1:length(anatNum{sn(s)})
r=anatNum{sn(s)}(i);
ss=(sn(s)*2)-1;
DIR=dir(sprintf('%s.%4.4d.*.IMA',DicomName{ss},r)); % Get DICOM FILE NAMES
Names=vertcat(DIR.name);
if (~isempty(Names))
HDR=spm_dicom_headers(Names,1); % Load dicom headers
dirname{r}=sprintf('series%2.2d',r);
if (~exist(dirname{r}))
mkdir(dirname{r}); %make dir for series{r} for .nii file output
end;
dircheck(fullfile(studyDir{1},dicomDir,[subj_name{sn(s)},'_1'],dirname{r}));
cd(fullfile(studyDir{1},dicomDir,[subj_name{sn(s)},'_1'],dirname{r}));
spm_dicom_convert(HDR,'all','flat','nii');%,dirname{r}); % Convert the data to nifti
cd ..
end;
fprintf('Series %d done \n',anatNum{sn(s)}(i))
end;
fprintf('Anatomical runs have been imported for %s Rename the .nii file as ''anatomical_raw.nii'' and move to the anatomical folder.\n',subj_name{sn(s)})
end
% MAKE SURE TO FIND ANATOMICAL AFTER THIS STEP AND MOVE TO FOLDER
% Rename to 'anatomical_raw
case 'ANAT:reslice_LPI' % STEP 1.2: Reslice anatomical image within LPI coordinate systems
% STUDY 1 ONLY
sn = varargin{1}; % subjNum
% example: sc1_sc2_imana('ANAT:reslice_LPI',1)
subjs=length(sn);
for s=1:subjs,
% (1) Reslice anatomical image to set it within LPI co-ordinate frames
source = fullfile(studyDir{1},anatomicalDir,subj_name{sn(s)},['anatomical_raw','.nii']);
dest = fullfile(studyDir{1},anatomicalDir,subj_name{sn(s)},['anatomical','.nii']);
spmj_reslice_LPI(source,'name', dest);
% (2) In the resliced image, set translation to zero
V = spm_vol(dest);
dat = spm_read_vols(V);
V.mat(1:3,4) = [0 0 0];
spm_write_vol(V,dat);
display 'Manually retrieve the location of the anterior commissure (x,y,z) before continuing'
end
case 'ANAT:centre_AC' % STEP 1.3: Re-centre AC
% STUDY 1 ONLY
% Set origin of anatomical to anterior commissure (must provide
% coordinates in section (4)).
% example: sc1_imana('ANAT:centre_AC',1)
sn=varargin{1}; % subjNum
subjs=length(sn);
for s=1:subjs,
img = fullfile(studyDir{1},anatomicalDir,subj_name{sn(s)},['anatomical','.nii']);
V = spm_vol(img);
dat = spm_read_vols(V);
V.mat(1:3,4) = loc_AC{sn(s)};
spm_write_vol(V,dat);
fprintf('Done for %s',subj_name{sn(s)})
end
case 'ANAT:segmentation' % STEP 1.4: Segmentation + Normalisation
% STUDY 1 ONLY
% example: sc1_imana('ANAT:segmentation',1)
sn=varargin{1}; % subjNum
subjs=length(sn);
SPMhome=fileparts(which('spm.m'));
J=[];
for s=1:subjs,
J.channel.vols = {fullfile(studyDir{1},anatomicalDir,subj_name{sn(s)},'anatomical.nii,1')};
J.channel.biasreg = 0.001;
J.channel.biasfwhm = 60;
J.channel.write = [0 0];
J.tissue(1).tpm = {fullfile(SPMhome,'tpm/TPM.nii,1')};
J.tissue(1).ngaus = 1;
J.tissue(1).native = [1 0];
J.tissue(1).warped = [0 0];
J.tissue(2).tpm = {fullfile(SPMhome,'tpm/TPM.nii,2')};
J.tissue(2).ngaus = 1;
J.tissue(2).native = [1 0];
J.tissue(2).warped = [0 0];
J.tissue(3).tpm = {fullfile(SPMhome,'tpm/TPM.nii,3')};
J.tissue(3).ngaus = 2;
J.tissue(3).native = [1 0];
J.tissue(3).warped = [0 0];
J.tissue(4).tpm = {fullfile(SPMhome,'tpm/TPM.nii,4')};
J.tissue(4).ngaus = 3;
J.tissue(4).native = [1 0];
J.tissue(4).warped = [0 0];
J.tissue(5).tpm = {fullfile(SPMhome,'tpm/TPM.nii,5')};
J.tissue(5).ngaus = 4;
J.tissue(5).native = [1 0];
J.tissue(5).warped = [0 0];
J.tissue(6).tpm = {fullfile(SPMhome,'tpm/TPM.nii,6')};
J.tissue(6).ngaus = 2;
J.tissue(6).native = [0 0];
J.tissue(6).warped = [0 0];
J.warp.mrf = 1;
J.warp.cleanup = 1;
J.warp.reg = [0 0.001 0.5 0.05 0.2];
J.warp.affreg = 'mni';
J.warp.fwhm = 0;
J.warp.samp = 3;
J.warp.write = [1 1];
matlabbatch{1}.spm.spatial.preproc=J;
spm_jobman('run',matlabbatch);
fprintf('Check segmentation results for %s\n', subj_name{sn(s)})
end;
case 'SURF:run_all' % STEP 2.1-2.5: enter sn - runs all surface constructions (freesurfer + caret format transformation): DEPRECIATED
sn=varargin{1};
subjs=length(sn) ;
for s=1:subjs,
sc1_sc2_imana('SURF:recon_all',sn(s));
sc1_sc2_imana('SURF:xhemireg',sn(s));
sc1_sc2_imana('SURF:map_ico',sn(s));
sc1_sc2_imana('SURF:make_caret',sn(s));
end
case 'SURF:recon_all' % STEP 2.2: Calls recon_all: DEPRECIATED
% STUDY 1 ONLY
% Calls recon-all, which performs, all of the
% FreeSurfer cortical reconstruction process
% example: sc1_imana('surf_freesurfer',1)
sn=varargin{1}; % subjNum
for i=sn
freesurfer_reconall(fullfile(studyDir{1},freesurferDir),subj_name{i},fullfile(anatomicalDir,subj_name{i},['anatomical.nii']));
end
case 'SURF:xhemireg' % STEP 2.3: cross-register surfaces left / right hem: DEPRECIATED
% STUDY 1 ONLY
% surface-based interhemispheric registration
% example: sc1_imana('surf_xhemireg',1)
s=varargin{1}; % subjNum
for i=s
freesurfer_registerXhem({subj_name{i}},fullfile(studyDir{1},freesurferDir),'hemisphere',[1 2]); % For debug... [1 2] orig
end;
case 'SURF:map_ico' % STEP 2.4: Align to the new atlas surface (map icosahedron): DEPRECIATED
% STUDY 1 ONLY
% Resampels a registered subject surface to a regular isocahedron
% This allows things to happen in atlas space - each vertex number
% corresponds exactly to an anatomical location
% Makes a new folder, called ['x' subj] that contains the remapped subject
% Uses function mri_surf2surf
% mri_surf2surf: resamples one cortical surface onto another
% example: sc1_sc2_imana('SURF:map_ico',1)
sn=varargin{1}; % subjNum
for i=sn
freesurfer_mapicosahedron_xhem(subj_name{i},fullfile(studyDir{1},freesurferDir),'smoothing',1,'hemisphere',[1:2]);
end;
case 'SURF:make_caret' % STEP 2.5: Translate into caret format: DEPRECIATED
% STUDY 1 ONLY
% Imports a surface reconstruction from Freesurfer automatically into Caret
% Makes a new spec file and moves the coord-files to respond to World,
% rather than to RAS_tkreg coordinates.
% example: sc1_imana('surf_make_caret',1)
sn=varargin{1}; % subjNum
for i=sn
caret_importfreesurfer(['x' subj_name{i}],fullfile(studyDir{1},freesurferDir),fullfile(studyDir{1},caretDir));
end;
case 'FUNC:func_dicom_import' % STEP 3.1: Import func dicom
% converts dicom to nifti files w/ spm_dicom_convert
% example: sc1_sc2_imana('FUNC:func_dicom_import',1,1,1)
sn=varargin{1}; % subjNum
sess=varargin{2}; % sessNum
study=varargin{3}; % studyNum
subjs=length(sn);
for s=1:subjs,
dircheck(fullfile(studyDir{study},dicomDir,[subj_name{sn(s)},sprintf('_%d',sess)]));
cd(fullfile(studyDir{study},dicomDir,[subj_name{sn(s)},sprintf('_%d',sess)]));
if sess==1,
ss=(sn(s)*2)-1;
else
ss=(sn(s)*2);
end
for i=1:length(fscanNum{study}{ss})
r=fscanNum{study}{ss}(i);
DIR=dir(sprintf('%s.%4.4d.*.IMA',DicomName{study}{ss},r)); % Get DICOM FILE NAMES
Names=vertcat(DIR.name);
if (~isempty(Names))
HDR=spm_dicom_headers(Names,1); % Load dicom headers
dirname{r}=sprintf('series%2.2d',r);
if (~exist(dirname{r}))
mkdir(dirname{r}); % make dir for series{r} for .nii file output
end;
dircheck(fullfile(studyDir{study},dicomDir,[subj_name{sn(s)},sprintf('_%d',sess)],dirname{r}));
cd(fullfile(studyDir{study},dicomDir,[subj_name{sn(s)},sprintf('_%d',sess)],dirname{r}));
spm_dicom_convert(HDR,'all','flat','nii'); % Convert the data to nifti
cd ..
end;
display(sprintf('Series %d done \n',fscanNum{study}{ss}(i)))
end;
fprintf('Functional runs have been imported. Be sure to copy the unique .nii name for subj files and place into section (4).\n')
end
case 'FUNC:make_4dNifti' % STEP 3.2: Make 4dNifti
% merges nifti files for each image into a 4-d nifti (time is 4th
% dimension) w/ spm_file_merge
% all imaging data (raw and realigned) for both sc1 and sc2 is
% saved in sc1
% example: sc1_sc2_imana('FUNC:make_4dNifti',1,1,1)
sn=varargin{1}; % subjNum
sess=varargin{2}; % sessNum
study=varargin{3}; % studyNum
subjs=length(sn);
for s=1:subjs,
dircheck(fullfile(studyDir{1},imagingDirRaw,subj_name{sn(s)}));
if sess==1,
ss=(sn(s)*2)-1;
else
ss=(sn(s)*2);
end
for i=1:length(fscanNum{study}{ss}), % run number
outfilename = fullfile(studyDir{1},imagingDirRaw,subj_name{sn(s)},sprintf('run_%2.2d.nii',runs{study}{sess}(i)));
for j=1:numTRs-numDummys % doesn't include dummy scans in .nii file
P{j}=fullfile(studyDir{study},dicomDir,[subj_name{sn(s)},sprintf('_%d',sess)],sprintf('series%2.2d',fscanNum{study}{ss}(i)),...
sprintf('f%s-%4.4d-%5.5d-%6.6d-01.nii',NiiRawName{study}{ss},fscanNum{study}{ss}(i),j+numDummys,j+numDummys));
end;
spm_file_merge(char(P),outfilename);
fprintf('Run %d done for %s \n',runs{study}{sess}(i),subj_name{sn(s)});
end;
end
case 'FUNC:realign' % STEP 3.3: Realign functional images (both sessions)
% SPM realigns first volume in each run to first volume of first
% run, and then registers each image in that run to the first
% volume of that run. Hence also why it's often better to run
% anatomical before functional scans.
% SPM does this with 4x4 affine transformation matrix in nifti
% header (see function 'coords'). These matrices convert from voxel
% space to world space(mm). If the first image has an affine
% transformation matrix M1, and image two has one (M2), the mapping
% from 1 to 2 is: M2/M1 (map image 1 to world space-mm - and then
% mm to voxel space of image 2).
% Registration determines the 6 parameters that determine the rigid
% body transformation for each image (described above). Reslice
% conducts these transformations; resampling each image according
% to the transformation parameters. This is for functional only!
% all of the imaging data for both sc1 and sc2 are saved sc1 folder
% example: sc1_sc2_imana('FUNC:realign',1,[1:32])
sn=varargin{1}; %subjNum
runs=varargin{2}; % runNum
subjs=length(sn);
for s=1:subjs,
cd(fullfile(studyDir{1},imagingDirRaw,subj_name{sn(s)}));
spm_jobman % run this step first
data={};
for i = 1:length(runs),
for j=1:numTRs-numDummys;
data{i}{j,1}=sprintf('run_%2.2d.nii,%d',runs(i),j);
end;
end;
spmj_realign(data);
fprintf('runsrealigned for %s\n',subj_name{sn(s)});
end
case 'FUNC:move_data' % STEP 3.4: Move realigned data
% Moves image data from imaging_dicom_raw into a "working dir":
% imaging_dicom.
% example: sc1_sc2_imana('FUNC:move_data',1,[1:32])
sn=varargin{1}; % subjNum
runs=varargin{2}; % runNum
subjs=length(sn);
for s=1:subjs,
dircheck(fullfile(studyDir{1},imagingDir,subj_name{sn(s)}))
for r=1:length(runs);
% move realigned data for each run
source = fullfile(studyDir{1},imagingDirRaw,subj_name{sn(s)},sprintf('rrun_%2.2d.nii',runs(r)));
dest = fullfile(studyDir{1},imagingDir,subj_name{sn(s)},sprintf('rrun_%2.2d.nii',runs(r)));
copyfile(source,dest);
% move realignment parameter files for each run
source = fullfile(studyDir{1},imagingDirRaw,subj_name{sn(s)},sprintf('rp_run_%2.2d.txt',runs(r)));
dest = fullfile(studyDir{1},imagingDir,subj_name{sn(s)},sprintf('rp_run_%2.2d.txt',runs(r)));
copyfile(source,dest);
end;
% move mean_epis
source = fullfile(studyDir{1},imagingDirRaw,subj_name{sn(s)},'meanrun_01.nii');
dest = fullfile(studyDir{1},imagingDir,subj_name{sn(s)},'meanepi.nii');
copyfile(source,dest);
fprintf('realigned epi''s moved for %s \n',subj_name{sn(s)})
end
case 'FUNC:coreg' % STEP 3:5 STUDY 1:Adjust meanepi to anatomical image REQUIRES USER INPUT
% (1) Manually seed the functional/anatomical registration
% - Do "coregtool" on the matlab command window
% - Select anatomical image and meanepi image to overlay
% - Manually adjust meanepi image and save result as rmeanepi image
% - Coregistration is done for study 1 only
% example: sc1_sc2_imana('FUNC:coreg',1)
sn=varargin{1};% subjNum
step=varargin{2}; % 'manual' or 'auto'
cd(fullfile(studyDir{1},anatomicalDir,subj_name{sn}));
switch step,
case 'manual'
coregtool;
keyboard();
case 'auto'
% do nothing
end
% (2) Automatically co-register functional and anatomical images for study 1
J.ref = {fullfile(studyDir{1},anatomicalDir,subj_name{sn},['anatomical.nii'])};
J.source = {fullfile(studyDir{1},imagingDir,subj_name{sn},['rmeanepi.nii'])};
J.other = {''};
J.eoptions.cost_fun = 'nmi';
J.eoptions.sep = [4 2];
J.eoptions.tol = [0.02 0.02 0.02 0.001 0.001 0.001 0.01 0.01 0.01 0.001 0.001 0.001];
J.eoptions.fwhm = [7 7];
matlabbatch{1}.spm.spatial.coreg.estimate=J;
spm_jobman('run',matlabbatch);
% (3) Manually check again
% coregtool;
% keyboard();
% NOTE:
% Overwrites meanepi, unless you update in step one, which saves it
% as rmeanepi.
% Each time you click "update" in coregtool, it saves current
% alignment by appending the prefix 'r' to the current file
% So if you continually update rmeanepi, you'll end up with a file
% called r...rrrmeanepi.
case 'FUNC:make_samealign' % STEP 3.6: Align functional images to rmeanepi of study 1
% Aligns all functional images from both sessions (each study done separately)
% to rmeanepi of study 1
% example: sc1_sc2_imana('FUNC:make_samealign',1,[1:32])
sn=varargin{1}; % subjNum
runs=varargin{2}; % runNum
prefix='r';
subjs=length(sn);
for s=1:subjs,
cd(fullfile(studyDir{1},imagingDir,subj_name{sn(s)}));
% Select image for reference
% Always rmeanepi from study 1
P{1} = fullfile(studyDir{1},imagingDir,subj_name{sn(s)},'rmeanepi.nii');
% Select images to be realigned
Q={};
for r=1:numel(runs)
for i=1:numTRs-numDummys;
Q{end+1} = fullfile(studyDir{1},imagingDir,subj_name{sn(s)},...
sprintf('%srun_%2.2d.nii,%d',prefix,runs(r),i));
end;
end;
% Run spmj_makesamealign_nifti
spmj_makesamealign_nifti(char(P),char(Q));
fprintf('functional images realigned for %s',subj_name{sn(s)})
end
%spmj_checksamealign
case 'FUNC:make_maskImage' % STEP 3.7: STUDY 1:Make mask images (noskull and grey_only)
% Make maskImage meanepi
% example: sc1_sc2_imana('FUNC:make_maskImage',1)
sn=varargin{1}; % subjNum
subjs=length(sn);
for s=1:subjs,
cd(fullfile(studyDir{1},imagingDir,subj_name{sn(s)}));
nam{1} = fullfile(studyDir{1},imagingDir,subj_name{sn(s)}, 'rmeanepi.nii');
nam{2} = fullfile(studyDir{1},anatomicalDir, subj_name{sn(s)}, 'c1anatomical.nii');
nam{3} = fullfile(studyDir{1},anatomicalDir, subj_name{sn(s)}, 'c2anatomical.nii');
nam{4} = fullfile(studyDir{1},anatomicalDir, subj_name{sn(s)}, 'c3anatomical.nii');
spm_imcalc(nam, 'rmask_noskull.nii', 'i1>1 & (i2+i3+i4)>0.2')
nam={};
nam{1} = fullfile(studyDir{1},imagingDir,subj_name{sn(s)}, 'rmeanepi.nii');
nam{2} = fullfile(studyDir{1}, anatomicalDir, subj_name{sn(s)}, 'c1anatomical.nii');
spm_imcalc(nam, 'rmask_gray.nii', 'i1>2 & i2>0.4')
nam={};
nam{1} = fullfile(studyDir{1},imagingDir,subj_name{sn(s)}, 'rmeanepi.nii');
nam{2} = fullfile(studyDir{1}, anatomicalDir, subj_name{sn(s)}, 'c1anatomical.nii');
nam{3} = fullfile(studyDir{1}, anatomicalDir, subj_name{sn(s)}, 'c5anatomical.nii');
spm_imcalc(nam, 'rmask_grayEyes.nii', 'i1>2400 & i2+i3>0.4')
nam={};
nam{1} = fullfile(studyDir{1},imagingDir,subj_name{sn(s)}, 'rmeanepi.nii');
nam{2} = fullfile(studyDir{1}, anatomicalDir, subj_name{sn(s)}, 'c5anatomical.nii');
nam{3} = fullfile(studyDir{1}, anatomicalDir, subj_name{sn(s)}, 'c1anatomical.nii');
nam{4} = fullfile(studyDir{1}, anatomicalDir, subj_name{sn(s)}, 'c2anatomical.nii');
nam{5} = fullfile(studyDir{1}, anatomicalDir, subj_name{sn(s)}, 'c3anatomical.nii');
spm_imcalc(nam, 'rmask_noskullEyes.nii', 'i1>2000 & (i2+i3+i4+i5)>0.2')
end
case 'RS:func_dicom_import' % STEP 4.1: Import rs dicom
% converts dicom to nifti files w/ spm_dicom_convert
% example: sc1_sc2_imana('RS:func_dicom_import',2)
sn=varargin{1}; % subjNum
subjs=length(sn);
for s=1:subjs,