EPICC Resting State Crew
AG
8/16/2020
# Import SPSS Database
# Import .sav data
EPIC_vars<-read.spss("/Users/alyshagilmore/Downloads/restingstate_crew/CREATE_SUB_LIST/merged EPICC data with 1024 &1069 & 674 & 7701 & 745.sav" , use.value.labels = TRUE, trim.factor.names = FALSE, to.data.frame = TRUE)
EPIC_vars<-as.data.frame(EPIC_vars)
EPIC_vars$ncomorbidities<-as.numeric(EPIC_vars$ncomorbidities)
EPIC_vars$radiation_n<-EPIC_vars$SBD009
# Rename known variables for exported sublist
EPIC_vars$ID<-as.character(EPIC_vars$ID)
EPIC_vars$EDU<-EPIC_vars$BDH004 #Rename variables for exported sublist
EPIC_vars$Smoker<-EPIC_vars$SBD003 #Rename variables for exported sublist
EPIC_vars$Age<-EPIC_vars$BDH001
EPIC_vars$Handedness<-EPIC_vars$BDH003
EPIC_vars$English_native<-EPIC_vars$BDH006
EPIC_vars$Handedness<-as.factor(EPIC_vars$Handedness)
EPIC_vars$ASA_Status<-EPIC_vars$POF009
EPIC_vars$ASA_Physical_Status<-as.numeric(EPIC_vars$POF009)
EPIC_vars$English_native<-EPIC_vars$BDH006
EPIC_vars$BDH010A<-as.numeric(EPIC_vars$BDH010A)
EPIC_vars$BDH010A<-if_else(EPIC_vars$BDH010A==1, "White", "Other")
EPIC_vars$BDH010B<-as.numeric(EPIC_vars$BDH010B)
EPIC_vars$BDH010B<-if_else(EPIC_vars$BDH010B==1, "AA", "Other")
Race.df<-EPIC_vars %>%
select(ID, BDH010B,BDH010A)
EPIC_vars$Race<-""
EPIC_vars$Race<-if_else(Race.df$BDH010B=="AA", "black", EPIC_vars$Race )
EPIC_vars$Race<-if_else(Race.df$BDH010A=="White", "white", EPIC_vars$Race )
EPIC_vars$Race<-as.factor(EPIC_vars$Race)
EPIC_vars$Race_10<-if_else(EPIC_vars$Race=="white", "1", "0")
rm(Race.df)
EPIC_vars$BDH003<-as.character(EPIC_vars$BDH003)
EPIC_vars$Handedness_10<- ifelse(EPIC_vars$BDH003=="Right", 1, EPIC_vars$BDH003)
EPIC_vars$Handedness_10<- ifelse(EPIC_vars$Handedness=="Left", 0, EPIC_vars$Handedness_10)
EPIC_vars$Handedness_10<- ifelse(EPIC_vars$Handedness=="Both", "Both", EPIC_vars$Handedness_10)
EPIC_vars$Smoker<-EPIC_vars$SBD003
EPIC_vars$Smoker_10<-if_else(EPIC_vars$SBD003=="Yes", "1", "0")
EPIC_vars$Smoker_10<-as.factor(EPIC_vars$Smoker_10)
DEMOS<-EPIC_vars %>% select(ID,Age,EDU,Race,Race_10, Handedness, Handedness_10,Smoker, Smoker_10, ncomorbidities,ASA_Physical_Status,radiation_n)
# Time 1 Fitness Data
T1<-EPIC_vars %>% select(ID, starts_with("T1"))
T1$ID<-as.character(T1$ID)
#MAP
T1$T1_Rest_MAP<- ((2*T1$T1_Rest_Diast_BP) +T1$T1_Rest_Syst_BP) / 3
# Clean up a few things T1
T1$T1_Moderate_AVGhrs_perday<-as.numeric(T1$T1_Moderate_AVG)
T1$T1_Moderate_Total_hrs<-as.numeric(T1$T1_Moderate_Total)
T1$T1_ACC_weartime_hrs<-as.numeric(T1$T1_Total_Hrs)
# Weekly minutes of MVPA
T1$WeeklyMVPA_min <-T1$T1_Moderate_Total*60
# shapiro.test(T1$WeeklyMVPA_min) Not normally dist.
# EMOTIONAL/AFFECTIVE Data (One person with PTSD diagnosis)
EMO_data<- EPIC_vars %>% select(ID, bdiav,bdito, emo_avg,EMO_tscore, PCL_ppd, PCL_tss)
#shapiro.test(EMO_data$bdiav)
#shapiro.test(EMO_data$emo_avg)
#shapiro.test(EMO_data$PCL_tss)
#table(EMO_data$PCL_ppd)
# Sleep Regulation Data
Sleep_data<- EPIC_vars %>% select(ID, PGHglbsc,EPS_avg )
## Self-Reported Symptoms Scale
TRI_data<- EPIC_vars %>% select(ID, starts_with("tri_") )
TRI_data<- TRI_data %>% select(ID, ends_with("score") )
#table(complete.cases(TRI_data$tri_totscore))
#shapiro.test(TRI_data$tri_totscore)
# Pain & Fatigue Data
BPISF_FAT_data<-EPIC_vars %>% select(ID, FAT_avg,starts_with("BPISF"))
#shapiro.test(BPISF_FAT_data$FAT_avg) #0.02 p
# Cognitive Data
COG_data<-EPIC_vars %>%
select(ID,verbalmemory,executivefunctionv1,
psychomotorspeed, attention ,visualmemory ,
executivefunctionv2,visualworkingmemory,
concentrationv2)
# Normal Dist
#shapiro.test(COG_data$verbalmemory)
#shapiro.test(COG_data$executivefunctionv2)
#shapiro.test(COG_data$visualworkingmemory)
#shapiro.test(COG_data$attention)
#shapiro.test(COG_data$executivefunctionv1) #Nearly
# Fails Normal Dist Testing
#shapiro.test(COG_data$psychomotorspeed)
#shapiro.test(COG_data$concentrationv2)
#shapiro.test(COG_data$visualmemory)
write.csv(DEMOS, "DEMOS.csv")
write.csv(COG_data, "COG_data.csv")
write.csv(T1, "T1_FITNESS_MASTER.csv")
write.csv(EMO_data, "EMO_DATA.csv")T1_DEMOS<-left_join(DEMOS, T1)
T1_DEMOS_BRAIN_34<-left_join(BRAIN, T1_DEMOS, "ID")
T1_DEMOS_BRAIN_34<-left_join(T1_DEMOS_BRAIN_34, EMO_data, "ID")
T1_DEMOS_BRAIN_34<-left_join(T1_DEMOS_BRAIN_34, BPISF_FAT_data, "ID")
T1_DEMOS_BRAIN_34<-left_join(T1_DEMOS_BRAIN_34, COG_data, "ID")
T1_DEMOS_BRAIN_88<-left_join(T1_DEMOS, BRAIN, "ID")
T1_DEMOS_BRAIN_88<-left_join(T1_DEMOS_BRAIN_88, EMO_data,"ID")
T1_DEMOS_BRAIN_88<-left_join(T1_DEMOS_BRAIN_88, BPISF_FAT_data, "ID")
T1_DEMOS_BRAIN_88<-left_join(T1_DEMOS_BRAIN_88, COG_data, "ID")
write.csv(T1_DEMOS_BRAIN_88, "T1_DEMOS_BRAIN_88.csv")
write.csv(T1_DEMOS_BRAIN_34, "T1_DEMOS_BRAIN_34.csv")Analysis Plan for Aim 2 We will use the same analytical approach described for Aim 1 to fit models for intermediate outcomes (neuroimaging metrics of brain heath, inflammatory markers, and symptoms) and to test Aim 2 hypotheses regarding the effects of exercise on primary intermediate outcomes (neuroimaging metrics and inflammatory markers of IL-6 and CRP). The only difference will be that initial neuroimaging analyses will be conducted on a voxel-wise basis using FSL, SPM, or FreeSurfer, depending on the imaging modality and analysis (e.g., volumetric). In addition to voxel-wise analyses we may apply regions-of-interest (ROI) analyses since our primary hypotheses focus on prefrontal and hippocampal regions. These ROIs can be generated using anatomical templates from normalized MNI space or from functionally-derived estimates of activation or connectivity. We will use standard cluster-based corrections for multiple comparisons of fMRI results, threshold-free cluster enhancement, and a false discovery rate (FDR) of p<.01 for volumetric and white matter analyses.
Data Frame Summary
T1_DEMOS_df
Dimensions: 88 x 9Duplicates: 5
| Variable | Stats / Values | Freqs (% of Valid) | Graph | Valid | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age [numeric] | Mean (sd) : 63.8 (5.3) min < med < max: 51 < 64 < 77 IQR (CV) : 6.5 (0.1) | 23 distinct values |  |
72 (81.82%) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Handedness [factor] | 1. Right 2. Left 3. Both |
|
 |
72 (81.82%) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Race [factor] | 1. black 2. white |
|
 |
72 (81.82%) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| EDU [numeric] | Mean (sd) : 15.5 (2.5) min < med < max: 12 < 16 < 23 IQR (CV) : 3.2 (0.2) |
|
 |
72 (81.82%) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Smoker [factor] | 1. Yes 2. No |
|
 |
88 (100%) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| T1_Weight_Kg [numeric] | Mean (sd) : 82.2 (17.7) min < med < max: 53.7 < 81.2 < 137.5 IQR (CV) : 22.8 (0.2) | 72 distinct values |  |
74 (84.09%) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| ncomorbidities [numeric] | Mean (sd) : 4.7 (3.2) min < med < max: 0 < 5 < 11 IQR (CV) : 5 (0.7) | 12 distinct values |  |
88 (100%) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| ASA_Physical_Status [numeric] | Mean (sd) : 2.1 (0.4) min < med < max: 1 < 2 < 3 IQR (CV) : 0 (0.2) |
|
 |
74 (84.09%) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| radiation_n [numeric] | Mean (sd) : 15.3 (6.3) min < med < max: 0 < 17 < 30 IQR (CV) : 8 (0.4) | 23 distinct values |  |
87 (98.86%) |
Generated by summarytools 0.9.6 (R version 3.6.1)
2020-08-18
Cognitive and behavioral testing To assess multiple domains of cognitive function, a comprehensive neuropsychological battery of computerized and paper-and-pencil tasks is administered to participants (Table 1).27–34 Our battery includes 4 tests from the Cambridge Neuropsychological Test Automated Battery (CANTAB) Connect, a computerized battery that consists of measures documented to be sensitive to changes in cognitive function in women with breast cancer35 and that provides immediate data and detailed accuracy and response speed recording. The primary endpoint, changes in cognitive function, will be derived from normed (for age, education) composites of neuropsychological test scores (expressed as mean Z-scores), with the composite scores for the targeted cognitive function domains empirically derived via exploratory factor analysis.
## Warning: package 'knitr' was built under R version 3.6.2
Data Frame Summary
FULL_SUMMARY
Dimensions: 88 x 13Duplicates: 14
| Variable | Stats / Values | Freqs (% of Valid) | Graph | Valid |
|---|---|---|---|---|
| bdiav [numeric] | Mean (sd) : 0.3 (0.2) min < med < max: 0 < 0.2 < 1.3 IQR (CV) : 0.2 (0.9) | 18 distinct values |  |
72 (81.82%) |
| emo_avg [numeric] | Mean (sd) : 1.5 (0.7) min < med < max: 1 < 1.2 < 3.5 IQR (CV) : 0.9 (0.4) | 18 distinct values |  |
67 (76.14%) |
| FAT_avg [numeric] | Mean (sd) : 2.3 (0.9) min < med < max: 1 < 2.2 < 4.9 IQR (CV) : 1.1 (0.4) | 25 distinct values |  |
67 (76.14%) |
| BPISF_meanpainseverity [numeric] | Mean (sd) : 1.6 (1.8) min < med < max: 0 < 1.2 < 8 IQR (CV) : 2.2 (1.1) | 21 distinct values |  |
72 (81.82%) |
| BPISF_meanpaininterference [numeric] | Mean (sd) : 1 (1.4) min < med < max: 0 < 0.3 < 6.6 IQR (CV) : 1.5 (1.4) | 25 distinct values |  |
72 (81.82%) |
| verbalmemory [numeric] | Mean (sd) : -0.2 (0.7) min < med < max: -2.1 < -0.1 < 1.1 IQR (CV) : 0.9 (-3.5) | 72 distinct values |  |
72 (81.82%) |
| executivefunctionv1 [numeric] | Mean (sd) : -0.2 (0.8) min < med < max: -3 < -0.1 < 1.2 IQR (CV) : 1.3 (-4.8) | 68 distinct values |  |
72 (81.82%) |
| psychomotorspeed [numeric] | Mean (sd) : 0 (0.7) min < med < max: -2.1 < 0.1 < 1.2 IQR (CV) : 0.8 (133.3) | 71 distinct values |  |
71 (80.68%) |
| attention [numeric] | Mean (sd) : -0.1 (0.8) min < med < max: -2.2 < 0 < 1.3 IQR (CV) : 1.1 (-9.3) | 71 distinct values |  |
71 (80.68%) |
| visualmemory [numeric] | Mean (sd) : 0.1 (0.9) min < med < max: -3.2 < 0.5 < 1 IQR (CV) : 0.8 (7.6) | 59 distinct values |  |
71 (80.68%) |
| executivefunctionv2 [numeric] | Mean (sd) : 0 (0.6) min < med < max: -1.3 < -0.1 < 1.7 IQR (CV) : 0.8 (-15.8) | 68 distinct values |  |
72 (81.82%) |
| visualworkingmemory [numeric] | Mean (sd) : 0.1 (0.7) min < med < max: -1.8 < 0.1 < 1.4 IQR (CV) : 1.1 (12.1) | 73 distinct values |  |
73 (82.95%) |
| concentrationv2 [numeric] | Mean (sd) : -0.1 (0.7) min < med < max: -2 < -0.1 < 1 IQR (CV) : 0.9 (-6.4) | 72 distinct values |  |
72 (81.82%) |
Generated by summarytools 0.9.6 (R version 3.6.1)
2020-08-18
Fitness Testing & Physical Activity Data To monitor the fidelity of the intervention, cardiorespiratory fitness is measured in all participants via a submaximal graded exercise test which is often used in at-risk populations as a safer yet highly correlated (r > 0.80) alternative to a maximal VO2 test.25 This test requires walking on a treadmill between 2.0–4.0 mph as the grade increases 1% each minute. Test speed is self-selected by the participant at increments of 0.5 mph within the 2.0-4.0 mph range. Prior to the test, height and body mass are recorded and the participant is fitted with a mouthpiece and nose clip to collect expired air. Resting heart rate and resting blood pressure values are also obtained to ensure they are within a safe range to begin testing. Heart rate, blood pressure, respiratory gases and ratings of perceived exertion are continuously monitored during the test. The test is terminated when the participant reaches 85% of her age-predicted maximal heart rate (220–age), a Borg26 rating of perceived exertion of 15 or greater in participants taking a beta blocker, or volitional exhaustion. Peak oxygen consumption is analyzed using a ParvoMedics metabolic cart and data is extrapolated to estimate maximal oxygen consumption (VO2 max). Participants with an estimated VO2 max above the upper 50th percentile complete all baseline assessments, but are not selected for randomization (described below). For randomized participants, changes in aerobic capacity between baseline and follow-up testing are a marker of the efficacy of the aerobic intervention.
Data Frame Summary
T1_FIT
Dimensions: 88 x 19Duplicates: 13
| Variable | Stats / Values | Freqs (% of Valid) | Graph | Valid | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Smoker [factor] | 1. Yes 2. No |
|
 |
88 (100%) | ||||||||
| T1_Weight_Kg [numeric] | Mean (sd) : 82.2 (17.7) min < med < max: 53.7 < 81.2 < 137.5 IQR (CV) : 22.8 (0.2) | 72 distinct values |  |
74 (84.09%) | ||||||||
| T1_Rest_HR [numeric] | Mean (sd) : 72.6 (10.5) min < med < max: 52 < 73 < 97 IQR (CV) : 13.8 (0.1) | 35 distinct values |  |
74 (84.09%) | ||||||||
| T1_Rest_Syst_BP [numeric] | Mean (sd) : 141.7 (16.4) min < med < max: 109 < 140.5 < 190 IQR (CV) : 22.2 (0.1) | 41 distinct values |  |
74 (84.09%) | ||||||||
| T1_Rest_Diast_BP [numeric] | Mean (sd) : 83.6 (9.1) min < med < max: 57 < 85 < 99 IQR (CV) : 10 (0.1) | 32 distinct values |  |
74 (84.09%) | ||||||||
| T1_BMI [numeric] | Mean (sd) : 31.1 (6.6) min < med < max: 20.2 < 29.9 < 51.6 IQR (CV) : 8.6 (0.2) | 73 distinct values |  |
74 (84.09%) | ||||||||
| T1_Heart_Rate [numeric] | Mean (sd) : 132.3 (6.1) min < med < max: 111 < 133 < 146 IQR (CV) : 6.5 (0) | 25 distinct values |  |
74 (84.09%) | ||||||||
| T1_Peak_VO2_ml_kg_min [numeric] | Mean (sd) : 17.1 (3.6) min < med < max: 7.3 < 16.7 < 26 IQR (CV) : 4 (0.2) | 70 distinct values |  |
74 (84.09%) | ||||||||
| T1_Estimated_VO2max [numeric] | Mean (sd) : 21.8 (4.3) min < med < max: 13 < 21.6 < 34.5 IQR (CV) : 5.8 (0.2) | 73 distinct values |  |
73 (82.95%) | ||||||||
| T1_V_Threshold_HR [numeric] | Mean (sd) : 110.4 (11) min < med < max: 85 < 110 < 134 IQR (CV) : 15 (0.1) | 33 distinct values |  |
73 (82.95%) | ||||||||
| T1_HR_Diff [numeric] | Mean (sd) : -0.4 (4) min < med < max: -19 < 0 < 4 IQR (CV) : 1 (-11.4) | 11 distinct values |  |
74 (84.09%) | ||||||||
| T1_V_Threshold_Percent_VO2 [numeric] | Mean (sd) : 0.7 (0.1) min < med < max: 0.5 < 0.7 < 1 IQR (CV) : 0.1 (0.2) | 38 distinct values |  |
73 (82.95%) | ||||||||
| T1_Peak_RPE [numeric] | Mean (sd) : 13.1 (2.6) min < med < max: 8 < 13 < 19 IQR (CV) : 4 (0.2) | 12 distinct values |  |
74 (84.09%) | ||||||||
| T1_Peak_MET_Level [numeric] | Mean (sd) : 4.9 (1) min < med < max: 2.1 < 4.8 < 7.4 IQR (CV) : 1.1 (0.2) | 67 distinct values |  |
74 (84.09%) | ||||||||
| WeeklyMVPA_min [numeric] | Mean (sd) : 284.9 (230) min < med < max: 25.8 < 195 < 915 IQR (CV) : 301.5 (0.8) | 67 distinct values |  |
71 (80.68%) | ||||||||
| T1_Avg_Daily_Min_MVPA [numeric] | Mean (sd) : 47.1 (35.2) min < med < max: 4.1 < 35.8 < 150.9 IQR (CV) : 43.7 (0.7) | 71 distinct values |  |
71 (80.68%) | ||||||||
| T1_Moderate_Total_hrs [numeric] | Mean (sd) : 4.7 (3.8) min < med < max: 0.4 < 3.2 < 15.2 IQR (CV) : 5 (0.8) | 67 distinct values |  |
71 (80.68%) | ||||||||
| T1_AVG_AWAKE_SED_TIME_DAY_LESS_1.5 [numeric] | Mean (sd) : 12.1 (2.2) min < med < max: 7.9 < 12.2 < 18 IQR (CV) : 2.4 (0.2) | 64 distinct values |  |
71 (80.68%) | ||||||||
| T1_ACC_weartime_hrs [numeric] | Mean (sd) : 158.7 (34.8) min < med < max: 16.7 < 164 < 252.7 IQR (CV) : 11.9 (0.2) | 71 distinct values |  |
71 (80.68%) |
Generated by summarytools 0.9.6 (R version 3.6.1)
2020-08-18
Full Sample (n=88) / Complete Cases (n=64)
Constricted Sample (n=34) / Complete Cases (n=27)
FreeSurfer Volumes
CRF Bilateral Hippocamus rsFC
CRF Bilateral Hippocampus - Precentral Gyrus FC
DATA[,147:160]<-lapply(DATA[,147:160], as.numeric)
summary(lm(C1Z2CRF_pcgyrACC_PBC~ Age+Race+EDU+BPISF_meanpaininterference+T1_Estimated_VO2max, DATA))##
## Call:
## lm(formula = C1Z2CRF_pcgyrACC_PBC ~ Age + Race + EDU + BPISF_meanpaininterference +
## T1_Estimated_VO2max, data = DATA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.28891 -0.07272 0.01189 0.07457 0.22561
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.818694 0.462226 -1.771 0.0910 .
## Age 0.004170 0.006257 0.666 0.5124
## Racewhite 0.039646 0.072974 0.543 0.5927
## EDU 0.004953 0.008931 0.555 0.5850
## BPISF_meanpaininterference -0.035831 0.022241 -1.611 0.1221
## T1_Estimated_VO2max 0.018571 0.007558 2.457 0.0228 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1199 on 21 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.4592, Adjusted R-squared: 0.3305
## F-statistic: 3.566 on 5 and 21 DF, p-value: 0.0172MODEL<-(lm(C1Z2CRF_pcgyrACC_PBC~ Age+Race+EDU+BPISF_meanpaininterference*T1_Estimated_VO2max, DATA))
visreg::visreg(MODEL, "BPISF_meanpaininterference", "T1_Estimated_VO2max", overlay=TRUE)
CRF Bilateral Hippocampus -
DATA[,147:160]<-lapply(DATA[,147:160], as.numeric)
DATA$T1_Weight_Kg## [1] 68.86 60.73 94.09 119.00 86.64 60.82 80.64 93.36 98.36 81.80
## [11] 58.24 99.09 87.55 87.90 99.28 79.82 68.00 97.55 54.91 93.60
## [21] 84.70 111.00 116.70 74.90 80.70 113.20 84.40 73.40 53.70 NA
## [31] NA NA NA NAsummary(lm(C1Z2CRF_pcgyrACC_PBC~ Age+Race+EDU+T1_BMI*T1_Total_Body_Percent, DATA))##
## Call:
## lm(formula = C1Z2CRF_pcgyrACC_PBC ~ Age + Race + EDU + T1_BMI *
## T1_Total_Body_Percent, data = DATA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.29199 -0.05547 -0.01482 0.06040 0.22116
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.9010213 1.9610697 2.499 0.02082 *
## Age 0.0050218 0.0054500 0.921 0.36730
## Racewhite 0.1402691 0.0746710 1.878 0.07427 .
## EDU -0.0062171 0.0099469 -0.625 0.53868
## T1_BMI -0.1796465 0.0590601 -3.042 0.00620 **
## T1_Total_Body_Percent -0.0547811 0.0197908 -2.768 0.01153 *
## T1_BMI:T1_Total_Body_Percent 0.0018522 0.0006324 2.929 0.00803 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.122 on 21 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.4934, Adjusted R-squared: 0.3486
## F-statistic: 3.408 on 6 and 21 DF, p-value: 0.01655MODEL<-(lm(C1Z2CRF_pcgyrACC_PBC~ Age+Race+EDU+T1_BMI*T1_Total_Body_Percent, DATA))
visreg::visreg(MODEL, "T1_Total_Body_Percent", "T1_BMI", overlay=TRUE)
CRF Bilateral Hippocampus -
DATA[,147:160]<-lapply(DATA[,147:160], as.numeric)
summary(lm(C1Z2CRF_pcgyrACC_Z~ Age+Race+EDU+T1_Peak_VO2_Max_Value , DATA))##
## Call:
## lm(formula = C1Z2CRF_pcgyrACC_Z ~ Age + Race + EDU + T1_Peak_VO2_Max_Value,
## data = DATA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6977 -2.2902 0.1059 1.4698 6.3474
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -28.45354 10.48529 -2.714 0.0124 *
## Age 0.32908 0.14387 2.287 0.0317 *
## Racewhite 2.49311 2.01428 1.238 0.2283
## EDU -0.07113 0.24925 -0.285 0.7779
## T1_Peak_VO2_Max_Value 2.71002 2.64324 1.025 0.3159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.441 on 23 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.312, Adjusted R-squared: 0.1924
## F-statistic: 2.608 on 4 and 23 DF, p-value: 0.06221summary(lm(C1Z2CRF_pcgyrACC_Z~ Age+Race+EDU+T1_BMI, DATA))##
## Call:
## lm(formula = C1Z2CRF_pcgyrACC_Z ~ Age + Race + EDU + T1_BMI,
## data = DATA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.108 -1.923 -1.005 2.419 5.900
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -13.8941 11.2277 -1.237 0.228
## Age 0.1697 0.1532 1.107 0.280
## Racewhite 3.4840 2.0733 1.680 0.106
## EDU 0.1073 0.2515 0.426 0.674
## T1_BMI -0.1336 0.1258 -1.062 0.299
##
## Residual standard error: 3.436 on 23 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.3142, Adjusted R-squared: 0.1949
## F-statistic: 2.634 on 4 and 23 DF, p-value: 0.06031summary(lm(C1Z2CRF_pcgyrACC_Z~ Age+Race+EDU+T1_Peak_VO2_Max_Value+T1_BMI , DATA))##
## Call:
## lm(formula = C1Z2CRF_pcgyrACC_Z ~ Age + Race + EDU + T1_Peak_VO2_Max_Value +
## T1_BMI, data = DATA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.890 -2.088 -0.360 1.935 5.506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -19.73123 11.45240 -1.723 0.0989 .
## Age 0.22694 0.15241 1.489 0.1507
## Racewhite 3.30820 2.00854 1.647 0.1138
## EDU 0.02796 0.24827 0.113 0.9113
## T1_Peak_VO2_Max_Value 4.44152 2.76482 1.606 0.1224
## T1_BMI -0.21495 0.13182 -1.631 0.1172
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.324 on 22 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.3862, Adjusted R-squared: 0.2467
## F-statistic: 2.768 on 5 and 22 DF, p-value: 0.04363summary(lm(C1Z2CRF_pcgyrACC_Z~ Age+Race+EDU+T1_Peak_VO2_Max_Value*T1_BMI , DATA))##
## Call:
## lm(formula = C1Z2CRF_pcgyrACC_Z ~ Age + Race + EDU + T1_Peak_VO2_Max_Value *
## T1_BMI, data = DATA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4963 -2.4235 -0.3655 2.3331 5.7095
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -55.9551 21.1063 -2.651 0.0149 *
## Age 0.2836 0.1458 1.945 0.0653 .
## Racewhite 2.0580 1.9866 1.036 0.3120
## EDU 0.2421 0.2566 0.944 0.3561
## T1_Peak_VO2_Max_Value 28.4083 12.2951 2.311 0.0311 *
## T1_BMI 0.6739 0.4626 1.457 0.1599
## T1_Peak_VO2_Max_Value:T1_BMI -0.6898 0.3459 -1.994 0.0593 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.119 on 21 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.4839, Adjusted R-squared: 0.3365
## F-statistic: 3.282 on 6 and 21 DF, p-value: 0.01947MODEL<-(lm(C1Z2CRF_pcgyrACC_Z~ Age+Race+EDU+T1_Peak_VO2_Max_Value*T1_BMI , DATA))
visreg::visreg(MODEL, "T1_Peak_VO2_Max_Value", "T1_BMI", overlay=TRUE)
summary(lm(C1Z2CRF_pcgyrACC_Z~ Age+Race+EDU+T1_BMI*T1_Total_Body_Percent, DATA))##
## Call:
## lm(formula = C1Z2CRF_pcgyrACC_Z ~ Age + Race + EDU + T1_BMI *
## T1_Total_Body_Percent, data = DATA)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9944 -2.4038 -0.3667 2.2770 5.2773
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 100.50759 51.83797 1.939 0.0661 .
## Age 0.15344 0.14406 1.065 0.2989
## Racewhite 4.04802 1.97382 2.051 0.0530 .
## EDU -0.15034 0.26293 -0.572 0.5736
## T1_BMI -3.61916 1.56117 -2.318 0.0306 *
## T1_Total_Body_Percent -1.18000 0.52314 -2.256 0.0349 *
## T1_BMI:T1_Total_Body_Percent 0.03745 0.01672 2.240 0.0360 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.225 on 21 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.4483, Adjusted R-squared: 0.2907
## F-statistic: 2.844 on 6 and 21 DF, p-value: 0.03461MODEL<-(lm(C1Z2CRF_pcgyrACC_Z~ Age+Race+EDU+T1_BMI*T1_Total_Body_Percent, DATA))
visreg::visreg(MODEL, "T1_Total_Body_Percent", "T1_BMI", overlay=TRUE)
BMI Bilateral Hippocamus rsFC
