MPRAGE Master Sheet
|
SubID
|
Session
|
MPRAGE_cnr
|
MPRAGE_fwhm_avg
|
MPRAGE_snr_total
|
MPRAGE_snrd_total
|
|
8001
|
MR2
|
1.7978
|
4.0485
|
10.3095
|
55.3185
|
|
8002
|
MR2
|
1.7212
|
4.0225
|
9.6513
|
47.8284
|
|
8003
|
MR2
|
1.7825
|
4.0336
|
10.4999
|
57.2361
|
|
8005
|
MR2
|
1.9747
|
4.1300
|
10.1854
|
59.3610
|
|
8006
|
MR2
|
1.7470
|
4.1339
|
10.2053
|
50.0911
|
|
8007
|
MR2
|
1.7348
|
3.9592
|
10.4699
|
58.5797
|
|
8021
|
MR2
|
1.5382
|
4.1032
|
9.6307
|
46.2845
|
|
8031
|
MR2
|
1.4865
|
4.1731
|
9.4858
|
48.3871
|
|
8035
|
MR2
|
1.4054
|
4.0961
|
9.8942
|
47.4210
|
Resting State - MRIQC Master Sheet
|
SubID
|
Session
|
Task
|
dvars_nstd
|
fd_mean
|
fwhm_avg
|
gcor
|
snr
|
tsnr
|
|
8001
|
MR2
|
rest
|
65.7456
|
0.5249
|
2.5893
|
0.0096
|
2.2065
|
22.3766
|
|
8002
|
MR2
|
rest
|
50.5306
|
0.4665
|
2.4910
|
0.0060
|
3.0218
|
27.4149
|
|
8003
|
MR2
|
rest
|
42.5416
|
0.2429
|
2.5509
|
0.0144
|
2.7152
|
33.5972
|
|
8005
|
MR2
|
rest
|
42.0742
|
0.3352
|
2.6355
|
0.0149
|
2.1813
|
20.9488
|
|
8006
|
MR2
|
rest
|
44.3410
|
0.3267
|
2.6917
|
0.0037
|
2.1273
|
24.7758
|
|
8007
|
MR2
|
rest
|
79.5323
|
0.8069
|
2.5869
|
0.0122
|
2.1866
|
23.3300
|
|
8021
|
MR2
|
rest
|
43.9344
|
0.2223
|
2.5472
|
0.0052
|
2.1974
|
26.4887
|
|
8031
|
MR2
|
rest
|
45.8054
|
0.3392
|
2.5183
|
0.0027
|
2.3546
|
32.8890
|
|
8035
|
MR2
|
rest
|
49.9841
|
0.3487
|
2.6666
|
0.0109
|
2.5676
|
28.3374
|
N-Back Task - MRIQC Master Sheet
|
SubID
|
Session
|
Task
|
dvars_nstd
|
fd_mean
|
fwhm_avg
|
gcor
|
snr
|
tsnr
|
|
8001
|
MR2
|
nback
|
55.4829
|
0.2735
|
2.3820
|
0.0073
|
1.9906
|
30.1386
|
|
8002
|
MR2
|
nback
|
35.2801
|
0.1308
|
2.2357
|
0.0065
|
2.7955
|
37.2530
|
|
8003
|
MR2
|
nback
|
47.5065
|
0.2128
|
2.3353
|
0.0145
|
2.5190
|
39.5204
|
|
8005
|
MR2
|
nback
|
41.0660
|
0.1221
|
2.4448
|
0.0073
|
1.9486
|
27.6815
|
|
8006
|
MR2
|
nback
|
49.7094
|
0.2302
|
2.4114
|
0.0140
|
2.0038
|
33.3648
|
|
8007
|
MR2
|
nback
|
70.2438
|
0.2814
|
2.3998
|
0.0123
|
1.8841
|
31.3449
|
|
8021
|
MR2
|
nback
|
48.9313
|
0.3056
|
2.3827
|
0.0063
|
1.9637
|
34.7086
|
|
8031
|
MR2
|
nback
|
51.1096
|
0.3140
|
2.3465
|
0.0069
|
2.1515
|
36.9407
|
|
8035
|
MR2
|
nback
|
55.0200
|
0.2228
|
2.4725
|
0.0131
|
2.4351
|
31.2309
|
DTI Master Sheet
|
SubID
|
Session
|
meanFA
|
meanRELrms_b1500
|
tsnr_b1500
|
outmean_b1500
|
meanRELrms_b3000
|
tsnr_b3000
|
outmean_b3000
|
|
8005
|
MR2
|
0.2805
|
0.2408
|
7.6133
|
143.7340
|
0.4481
|
5.1966
|
309.7810
|
|
8001
|
MR2
|
0.3032
|
0.2975
|
6.8567
|
196.5310
|
0.5307
|
5.0585
|
454.4510
|
|
8006
|
MR2
|
0.3095
|
0.2866
|
6.8763
|
155.5310
|
0.5461
|
4.7479
|
318.9380
|
|
8002
|
MR2
|
0.2803
|
0.2808
|
7.0332
|
132.4690
|
0.5115
|
4.9669
|
299.8910
|
|
8007
|
MR2
|
0.2546
|
0.3280
|
6.5940
|
201.9380
|
0.4438
|
5.1935
|
382.7190
|
|
8003
|
MR2
|
0.2796
|
0.2773
|
7.0813
|
119.2500
|
0.5389
|
4.9242
|
254.4380
|
|
8031
|
MR2
|
0.2725
|
0.2696
|
7.2935
|
134.8120
|
0.5111
|
5.1570
|
304.5000
|
|
8021
|
MR2
|
0.2695
|
0.3788
|
6.8619
|
194.9690
|
0.4695
|
5.2196
|
334.5000
|
|
8035
|
MR2
|
0.3046
|
0.2479
|
6.9798
|
193.2500
|
0.3850
|
5.0133
|
403.5000
|
pCASL Master Sheet
|
SubID
|
Session
|
tsnr
|
outmean
|
|
8005
|
MR2
|
1.2797
|
5895.5000
|
|
8006
|
MR2
|
1.3171
|
5608.0500
|
|
8002
|
MR2
|
1.9512
|
6278.2500
|
|
8007
|
MR2
|
1.6594
|
6469.0000
|
|
8003
|
MR2
|
1.6843
|
5624.3000
|
|
8031
|
MR2
|
2.0728
|
6583.4500
|
|
8021
|
MR2
|
1.3442
|
6004.5500
|
|
8035
|
MR2
|
1.2918
|
5640.7500
|
T2* Flair Master Sheet
|
SubID
|
Session
|
snr
|
|
8005
|
MR2
|
2.3417
|
|
8006
|
MR2
|
2.4246
|
|
8002
|
MR2
|
2.0530
|
|
8007
|
MR2
|
1.7085
|
|
8003
|
MR2
|
2.0287
|
|
8031
|
MR2
|
2.4123
|
|
8021
|
MR2
|
2.4556
|
|
8035
|
MR2
|
2.4511
|
RedCap_Report<-read.csv("/Volumes/REACT2/MRI/QC_Output/Reports/RedCap/PRE/REACT_RedCap_FitDemos_3_17_20.csv")
corrplot<-c("SubID", "fit_date" ,"fit_vo2_age" ,"fit_vo2_gender" ,"fit_bmi" ,"fit_wc_avg", "arm_circ_avg","fit_rest_sbp_avg" ,"fit_rest_dbp_avg" ,"fit_rest_hr_avg" ,"fit_bp_med" ,"fit_bp_med_num", "demo_edu_years", "history_cig_history", "demo_smoke", "ses_earnings", "ses_family_income" ,"psqi_global")
RedCap_for_corrplot<-RedCap_Report[corrplot]
RedCap_for_corrplot[,2:18]<-lapply(RedCap_for_corrplot[,2:18], FUN = as.numeric)
RedCap_for_corrplot<-RedCap_for_corrplot[complete.cases(RedCap_for_corrplot$psqi_global),]
RedCap_for_corrplot<-RedCap_for_corrplot[,-c(12)]
#RedCap_for_corrplot[,2:17]<-lapply(RedCap_for_corrplot[,2:17], FUN = scale)
RedCap_for_corrplot<-RedCap_for_corrplot[,c(1,(5:17))]
MPRAGE_for_corrplot<-MPRAGE.IQMs.df
MPRAGE_for_corrplot$SubID<-as.character(MPRAGE_for_corrplot$SubID)
RedCap_for_corrplot$SubID<-as.character(RedCap_for_corrplot$SubID)
df_for_corrplot<-left_join(MPRAGE_for_corrplot, RedCap_for_corrplot)
## Joining, by = "SubID"
corrplot(cor(df_for_corrplot[,3:19]) ,order="FPC", tl.cex = .75)
MPRAGE IQMs + DHQ RedCap
RedCap_Report<-read.csv("/Volumes/REACT2/MRI/QC_Output/Reports/RedCap/PRE/REACT_RedCap_DHQ_03_17_20.csv")
corrplot<-c("SubID", "nci_sex", "nci_age" , "fit_bmi" , "fit_wt_avg" , "fit_wc_avg" , "nci_energy", "nci_energy_fat" , "nci_energy_carb" , "nci_energy_protein" , "nci_energy_alcohol" , "nci_total_hei" , "nci_hei_calories_fat" , "nci_hei_calories_sugar")
RedCap_for_corrplot<-RedCap_Report[corrplot]
RedCap_for_corrplot[,3:14]<-lapply(RedCap_for_corrplot[,3:14], FUN = as.numeric)
RedCap_for_corrplot<-RedCap_for_corrplot[complete.cases(RedCap_for_corrplot$nci_hei_calories_fat),]
#RedCap_for_corrplot[,2:17]<-lapply(RedCap_for_corrplot[,2:17], FUN = scale)
#RedCap_for_corrplot<-RedCap_for_corrplot[,c(1,(5:17))]
#RedCap_for_corrplot<-RedCap_Report[corrplot]
#RedCap_for_corrplot[,2:18]<-lapply(RedCap_for_corrplot[,2:18], FUN = as.numeric)
#RedCap_for_corrplot<-RedCap_for_corrplot[complete.cases(RedCap_for_corrplot$psqi_global),]
#RedCap_for_corrplot<-RedCap_for_corrplot[,-c(12)]
#RedCap_for_corrplot[,2:17]<-lapply(RedCap_for_corrplot[,2:17], FUN = scale)
#RedCap_for_corrplot<-RedCap_for_corrplot[,c(1,(5:17))]
RedCap_for_corrplot<-RedCap_for_corrplot[,-c(2)]
MPRAGE_for_corrplot<-MPRAGE.IQMs.df
MPRAGE_for_corrplot$SubID<-as.character(MPRAGE_for_corrplot$SubID)
RedCap_for_corrplot$SubID<-as.character(RedCap_for_corrplot$SubID)
df_for_corrplot<-left_join(MPRAGE_for_corrplot, RedCap_for_corrplot)
df_for_corrplot<-df_for_corrplot[complete.cases(df_for_corrplot),]
corrplot(cor(df_for_corrplot[,3:18]) ,order="hclust", tl.cex = .75)
MPRAGE IQMs + Cognitive RedCap
RedCap_Report<-read.csv("/Volumes/REACT2/MRI/QC_Output/Reports/RedCap/PRE/REACT_RecCap_Cognitive_03_17_20.csv")
corrplot<-c("SubID", "rbans_17", "rbans_18" , "rbans_19" , "rbans_20" , "rbans_23" , "rbans_24" , "rbans_25", "rbans_26", "rbans_27", "rbans_28" , "rbans_29" , "rbans_30" , "rbans_34", "rbans_35" , "rbans_36", "cog2_swm", "time_2item", "time_3item","time_4item","accur_2item", "accur_3item", "accur_4item", "wtar_raw", "igt_total_raw", "raw_dccs", "ls_raw", "raw_tvpt", "raw_fic" , "cog2_stroop" , "congruent_rt_computer" , "congruent_accur_computer" , "incong_rt_computer" , "incong_accur_computer", "neutral_rt_computer","neutral_accur_computer", "a_raw", "cog1_trailmaking","b_raw")
RedCap_for_corrplot<-RedCap_Report[corrplot]
RedCap_for_corrplot[,2:39]<-lapply(RedCap_for_corrplot[,2:39], FUN = as.numeric)
#RedCap_for_corrplot<-RedCap_for_corrplot[complete.cases(RedCap_for_corrplot$),]
#RedCap_for_corrplot<-RedCap_for_corrplot[,-c(12)]
#RedCap_for_corrplot[,2:17]<-lapply(RedCap_for_corrplot[,2:17], FUN = scale)
#RedCap_for_corrplot<-RedCap_for_corrplot[,c(1,(5:17))]
#RedCap_for_corrplot<-RedCap_Report[corrplot]
#RedCap_for_corrplot[,2:18]<-lapply(RedCap_for_corrplot[,2:18], FUN = as.numeric)
#RedCap_for_corrplot<-RedCap_for_corrplot[complete.cases(RedCap_for_corrplot$psqi_global),]
#RedCap_for_corrplot<-RedCap_for_corrplot[,-c(12)]
#RedCap_for_corrplot[,2:17]<-lapply(RedCap_for_corrplot[,2:17], FUN = scale)
#RedCap_for_corrplot<-RedCap_for_corrplot[,c(1,(5:17))]
Rbanz_for_corrplot<-RedCap_for_corrplot[, c(1, 2:16)]
Rbanz_for_corrplot<-Rbanz_for_corrplot[complete.cases(Rbanz_for_corrplot),]
MPRAGE_for_corrplot<-MPRAGE.IQMs.df
MPRAGE_for_corrplot$SubID<-as.character(MPRAGE_for_corrplot$SubID)
Rbanz_for_corrplot$SubID<-as.character(Rbanz_for_corrplot$SubID)
RBANZ_MRI_for_corrplot<-left_join(Rbanz_for_corrplot, MPRAGE_for_corrplot)
RBANZ_MRI_for_corrplot<-RBANZ_MRI_for_corrplot[,-c(17)]
RBANZ_MRI_for_corrplot[,2:20]<-lapply(RBANZ_MRI_for_corrplot[,2:20], FUN = as.numeric)
RBANZ_MRI_for_corrplot<-RBANZ_MRI_for_corrplot[complete.cases(RBANZ_MRI_for_corrplot),]
COG2_swm_for_corrplot<-RedCap_for_corrplot[, c(1, 17:23)]
COG2_swm_for_corrplot<-COG2_swm_for_corrplot[complete.cases(COG2_swm_for_corrplot),]
COG2_swm_for_corrplot$SubID<-as.character(COG2_swm_for_corrplot$SubID)
COG2_swm_MRI_for_corrplot<-left_join(COG2_swm_for_corrplot, MPRAGE_for_corrplot)
COG2_swm_MRI_for_corrplot<-COG2_swm_MRI_for_corrplot[,-c(2,9)]
COG2_swm_MRI_for_corrplot[,2:11]<-lapply(COG2_swm_MRI_for_corrplot[,2:11], FUN = as.numeric)
COG2_swm_MRI_for_corrplot<-COG2_swm_MRI_for_corrplot[complete.cases(COG2_swm_MRI_for_corrplot),]
COG2_others_for_corrplot<-RedCap_for_corrplot[, c(1, 24:29)]
COG2_others_for_corrplot<-COG2_others_for_corrplot[complete.cases(COG2_others_for_corrplot),]
COG2_others_for_corrplot$SubID<-as.character(COG2_others_for_corrplot$SubID)
COG2_others_MRI_for_corrplot<-left_join(COG2_others_for_corrplot, MPRAGE_for_corrplot)
COG2_others_MRI_for_corrplot<-COG2_others_MRI_for_corrplot[,-c(8)]
COG2_others_MRI_for_corrplot[,2:11]<-lapply(COG2_others_MRI_for_corrplot[,2:11], FUN = as.numeric)
COG2_others_MRI_for_corrplot<-COG2_others_MRI_for_corrplot[complete.cases(COG2_others_MRI_for_corrplot),]
COG2_stroop_for_corrplot<-RedCap_for_corrplot[, c(1, 30:36)]
COG2_stroop_for_corrplot<-COG2_stroop_for_corrplot[complete.cases(COG2_stroop_for_corrplot),]
COG2_stroop_for_corrplot$SubID<-as.character(COG2_stroop_for_corrplot$SubID)
COG2_stroop_MRI_for_corrplot<-left_join(COG2_stroop_for_corrplot, MPRAGE_for_corrplot)
COG2_stroop_MRI_for_corrplot<-COG2_stroop_MRI_for_corrplot[,-c(2,9)]
COG2_stroop_MRI_for_corrplot[,2:11]<-lapply(COG2_stroop_MRI_for_corrplot[,2:11], FUN = as.numeric)
COG2_stroop_MRI_for_corrplot<-COG2_stroop_MRI_for_corrplot[complete.cases(COG2_stroop_MRI_for_corrplot),]
COG1_Trail_for_corrplot<-RedCap_for_corrplot[, c(1, 37:39)]
COG1_Trail_for_corrplot<-COG1_Trail_for_corrplot[complete.cases(COG1_Trail_for_corrplot),]
MPRAGE IQMs + RBANZ RedCap
corrplot(cor(RBANZ_MRI_for_corrplot[,2:20]) ,order="hclust", tl.cex = .75)
MPRAGE IQMs + Cog2_swm RedCap
corrplot(cor(COG2_swm_MRI_for_corrplot[,2:11]) ,order="hclust", tl.cex = .75)
MPRAGE IQMs + Cog2 Stroop RedCap
corrplot(cor(COG2_stroop_MRI_for_corrplot[,2:11]) ,order="hclust", tl.cex = .75)
MPRAGE IQMs + Cog2 OTHERS RedCap
corrplot(cor(COG2_others_MRI_for_corrplot[,2:11]) ,order="hclust", tl.cex = .75)
