EPICC rsFC BMI

https://npsy.wordpress.com/fsl-and-imaging-how-tos/9-2/

September 06 2020

Import Baseline Data

library(xtable)
library(dplyr)
library(foreign)
library(png)
library(knitr)
library(dplyr)
library(kableExtra)
library(readr)
library(tidyverse)
library(summarytools)
library(data.table)
library(corrplot)
library(readxl)

VO2.HR.df <- readxl::read_excel("/Users/alyshagilmore/Downloads/restingstate_crew/CREATE_SUB_LIST/EPICC Data.xlsx", skip = 1, na = "<NA>")

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VO2.HR.df<-VO2.HR.df[(VO2.HR.df$`Pre / Post`=="PRE"),]
VO2.HR.df<-VO2.HR.df<-VO2.HR.df[!(is.na(VO2.HR.df$`LAB ID`)),]
VO2.HR.df$Rest_HR<-VO2.HR.df$`REST HR`
VO2.HR.df$Rest.Sys<-VO2.HR.df$`REST SYST. BP`
VO2.HR.df$Rest.Dia<-VO2.HR.df$`REST DIAST. BP`
VO2.HR.df$Rest_MAP<- ((2*VO2.HR.df$Rest.Dia) +VO2.HR.df$Rest.Sys) / 3
VO2.HR.df <- VO2.HR.df %>% select(`LAB ID`,Rest_HR , Rest.Sys, Rest.Dia ,Rest_MAP)
VO2.HR.df$ID<-as.character(VO2.HR.df$`LAB ID`)

# Import VO2 data
VO2.df<-readxl::read_xlsx("/Users/alyshagilmore/Downloads/restingstate_crew/CREATE_SUB_LIST/EPICC Data.xlsx", sheet = "VO2 Data")

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VO2.df$Session<-as.character(VO2.df$`Pre / Post`)
VO2.df$Session<-as.character(VO2.df$`Pre / Post`)
VO2.df<-VO2.df[(VO2.df$Session=="PRE"),]
VO2.df<-VO2.df[,-c(3:4)]
VO2.df<-VO2.df[complete.cases(VO2.df$`Pre / Post`),]
VO2.df$ID<-as.character(VO2.df$`LAB ID`)
# Define variables as numeric values (because R sometimes likes to make them character strings).
VO2.df$VO2_END_REASON_NOTES<-as.character(VO2.df$`TEST END REASONS`)
VO2.df$CRFrel<-as.numeric(VO2.df$`PEAK VO2/KG`)
VO2.df$CRFabs<-as.numeric(VO2.df$`PEAK VO2`)
VO2.df$BMI<-as.numeric(VO2.df$BMI)
VO2.df$WEIGHT_kg<-VO2.df$`WEIGHT (KG)`
VO2.df$ID<-as.character(VO2.df$`LAB ID`)
VO2.df$VO2_END_factor<-as.numeric(VO2.df$`TEST END REASON?`)
VO2.df$VO2_END_REASON<-as.factor(VO2.df$`TEST END REASON?`)
VO2.df$AP_VO2MAX<-as.numeric(VO2.df$`AGE PERCENTILE OF VO2 MAX`)
VO2.df$VO2_END_REASON <- factor(VO2.df$VO2_END_REASON,
levels = c(1 ,2 ,3 , 4 ,5 , 6 ),
labels = c("Achieved HR Goal","Subject Quit", "Safety Concern", "Equipment Malfunction",
           "Beta-Blocked- Reached RPE Goal",
           "Other"))
VO2.df$BetaBlocker<-(grepl(VO2.df$VO2_END_REASON_NOTES, pattern = "beta", ignore.case = TRUE))
VO2.df$BetaBlocker<-if_else(VO2.df$BetaBlocker==TRUE, 1,0)
TEXT1<-c("Beta-Blocked- Achieved HR goal")
VO2_END_REASON_char<-as.character(VO2.df$VO2_END_REASON)
VO2.df$VO2_END_REASON<-if_else((VO2.df$BetaBlocker==1 & VO2.df$VO2_END_REASON=="Achieved HR Goal"), TEXT1, VO2_END_REASON_char)
VO2.df$VO2_END_REASON<-as.factor(VO2.df$VO2_END_REASON)
VO2.df$BetaBlocker<-as.factor(VO2.df$BetaBlocker)
VO2.df<-left_join(VO2.HR.df,  VO2.df )
VO2.df$RESTING_HR<-VO2.df$`REST HR`
VO2.df$BetaBlocker<-ifelse(VO2.df$BetaBlocker==1, "YES", "NO")
VO2.df$BetaBlocker<-as.factor(VO2.df$BetaBlocker)
VO2.df<-VO2.df %>%  select(ID, AGE, BetaBlocker, WEIGHT_kg, BMI , CRFrel, CRFabs,Rest_HR,Rest.Sys, Rest.Dia, Rest_MAP,VO2_END_REASON)
VO2.df<-VO2.df[!(VO2.df$AGE<30),]
VO2.df<-VO2.df[!(VO2.df$VO2_END_REASON=="Subject Quit"),]
#VO2.df<-VO2.df[!(VO2.df$VO2_END_REASON=="Other"),]
VO2.df$VO2_END_REASON<- droplevels(VO2.df$VO2_END_REASON)
print(dfSummary(VO2.df[,2:12], plain.ascii = TRUE,
                justify="c", graph.magnif = .50, valid.col = TRUE, na.col = FALSE,
                labels.col = TRUE, 
                varnumbers = FALSE, tmp.img.dir = "/tmp"), 
      method = 'render' )

Data Frame Summary

VO2.df

Dimensions: 95 x 11
Duplicates: 0

Variable	Stats / Values	Freqs (% of Valid)	Graph	Valid
AGE [numeric]	Mean (sd) : 63.6 (6.6) min < med < max: 47 < 64 < 77 IQR (CV) : 8.5 (0.1)	27 distinct values		95 (100%)
BetaBlocker [factor]	1. NO 2. YES	88 ( 92.6% ) 7 ( 7.4% )		95 (100%)
WEIGHT_kg [numeric]	Mean (sd) : 83.1 (17.2) min < med < max: 53.7 < 82.5 < 137.5 IQR (CV) : 24.2 (0.2)	92 distinct values		95 (100%)
BMI [numeric]	Mean (sd) : 31.6 (6.4) min < med < max: 20.2 < 30.3 < 51.6 IQR (CV) : 8.5 (0.2)	95 distinct values		95 (100%)
CRFrel [numeric]	Mean (sd) : 17 (3.6) min < med < max: 7.3 < 16.6 < 26 IQR (CV) : 4 (0.2)	95 distinct values		95 (100%)
CRFabs [numeric]	Mean (sd) : 1.4 (0.3) min < med < max: 0.5 < 1.4 < 2.1 IQR (CV) : 0.4 (0.2)	95 distinct values		95 (100%)
Rest_HR [numeric]	Mean (sd) : 72.9 (10.4) min < med < max: 52 < 73 < 97 IQR (CV) : 13.5 (0.1)	37 distinct values		95 (100%)
Rest.Sys [numeric]	Mean (sd) : 141.5 (17.5) min < med < max: 108 < 140 < 190 IQR (CV) : 23 (0.1)	53 distinct values		95 (100%)
Rest.Dia [numeric]	Mean (sd) : 83 (8.6) min < med < max: 57 < 85 < 99 IQR (CV) : 11 (0.1)	31 distinct values		95 (100%)
Rest_MAP [numeric]	Mean (sd) : 102.5 (9.5) min < med < max: 80 < 102.3 < 126.7 IQR (CV) : 11.7 (0.1)	69 distinct values		95 (100%)
VO2_END_REASON [factor]	1. Achieved HR Goal 2. Beta-Blocked- Achieved HR 3. Beta-Blocked- Reached RPE 4. Other	85 ( 89.5% ) 2 ( 2.1% ) 5 ( 5.3% ) 3 ( 3.2% )		95 (100%)

Generated by summarytools 0.9.6 (R version 3.6.1)
2020-09-06

# Import SPSS Database
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)
EPICC.MASTER<-EPIC_vars
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$Age<-EPIC_vars$BDH001 
EPIC_vars$EDU<-EPIC_vars$BDH004   #Rename variables for exported sublist
EPIC_vars$Smoker<-EPIC_vars$SBD003   #Rename variables for exported sublist
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")
EPIC_vars$FIRST.bilat.Hippo<-EPIC_vars$collapsed_hippocampus
EPIC_vars<-EPIC_vars[ , -which(names(EPIC_vars) %in% c("BMI"))] ## works as expected
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)
#MAP
EPIC_vars$T1_Rest_MAP<- ((2*EPIC_vars$T1_Rest_Diast_BP) +EPIC_vars$T1_Rest_Syst_BP) / 3
# Clean up a few things T1
EPIC_vars$T1_Moderate_AVGhrs_perday<-as.numeric(EPIC_vars$T1_Moderate_AVG)
EPIC_vars$T1_Moderate_Total_hrs<-as.numeric(EPIC_vars$T1_Moderate_Total)
EPIC_vars$T1_ACC_weartime_hrs<-as.numeric(EPIC_vars$T1_Total_Hrs)
# Weekly minutes of MVPA
EPIC_vars$WeeklyMVPA_min <-EPIC_vars$T1_Moderate_Total*60
## Self-Reported Symptoms Scale
TRI_data<- EPIC_vars %>%  select(ID, starts_with("tri_") )
TRI_data<- TRI_data %>%  select(ID, ends_with("score") )
#table(complete.cases(TRI_data$tri_totscore))
#shapiro.test(TRI_data$tri_totscore)
EPIC_vars<-full_join(EPIC_vars,VO2.df,   by="ID")
EPIC_vars<-EPIC_vars[!(EPIC_vars$VO2_END_REASON=="Subject Quit"),]
#EPIC_vars<-EPIC_vars[!(EPIC_vars$VO2_END_REASON=="Other"),]
EPIC_vars$BetaBlocker<-ifelse(EPIC_vars$BetaBlocker==1, "YES", "NO")
EPIC_vars$BetaBlocker<-as.factor(EPIC_vars$BetaBlocker)
EPIC_vars<-EPIC_vars[!(duplicated(EPIC_vars$ID)),]
EPIC_vars<-EPIC_vars[!(is.na(EPIC_vars$ID)),]

# Import FreeSurfer Volumetric Data:  
ASEG<-read.delim("/Users/alyshagilmore/Downloads/restingstate_crew/FreeSurfer_Results_sub7001_to_7063/aseg_stats.txt")
x<-ASEG$Measure.volume
x<-as.character(x)
tmp<-strsplit(x, "_")   #strsplit: splits a charachter string in a fixed spot. in this case at' '[space]
mat  <- matrix(unlist(tmp), ncol=2, byrow=TRUE) #this breaks the char string into 2 sep columns.
df<-as.data.frame(mat) #make a data frame
df$ID<-as.character(df$V1) #make var ID
ASEG$ID<-df$ID
Brain_vols<-ASEG %>% 
  select(ID, EstimatedTotalIntraCranialVol, CSF, MaskVol,
         BrainSegVol,BrainSegVolNotVentSurf,BrainSegVolNotVent, 
         SubCortGrayVol,
         TotalGrayVol ,CorticalWhiteMatterVol, Brain.Stem, 
         Right.Hippocampus,Left.Hippocampus,
         Left.Amygdala ,Right.Amygdala,
         Right.Putamen,Left.Putamen,
         Right.Caudate, Left.Caudate, 
         Right.Accumbens.area, Left.Accumbens.area,
         Left.VentralDC, Right.VentralDC,
         Left.Pallidum,Right.Pallidum,
         Left.Thalamus.Proper,Right.Thalamus.Proper,
         Left.Cerebellum.Cortex, Right.Cerebellum.Cortex,
         Left.Cerebellum.White.Matter, Right.Cerebellum.White.Matter,
         Right.vessel,Left.vessel,
         Left.choroid.plexus,Right.choroid.plexus)
# Sum Total Volume
Brain_vols$TOT_HIPP_VOL<-Brain_vols$Left.Hippocampus+Brain_vols$Right.Hippocampus
Brain_vols$TOT_VentralDC<-Brain_vols$Left.VentralDC+Brain_vols$Right.VentralDC
Brain_vols$TOT_Amygdala<-Brain_vols$Left.Amygdala+Brain_vols$Left.Amygdala
Brain_vols$TOT_Accumbens<-Brain_vols$Left.Accumbens.area+Brain_vols$Right.Accumbens.area
Brain_vols$TOT_Thalamus.Proper<-Brain_vols$Left.Thalamus.Proper+Brain_vols$Right.Thalamus.Proper
Brain_vols$TOT_Pallidum<-Brain_vols$Left.Pallidum+Brain_vols$Right.Pallidum
Brain_vols$TOT_Caudate<-Brain_vols$Left.Caudate+Brain_vols$Right.Caudate
Brain_vols$TOT_Putamen<-Brain_vols$Left.Putamen+Brain_vols$Right.Putamen
## Merge it all together and write out (check-point)
EPIC_vars<-full_join(EPIC_vars, Brain_vols , by="ID")

#**CRF Relative GLM - Extract Z-Stat for % BOLD Change for each participant**  
library(stringr)
DATA=EPIC_vars
setwd("/Users/alyshagilmore/Downloads/restingstate_crew/ROIoutput/1")
PSC<-list.files("/Users/alyshagilmore/Downloads/restingstate_crew/ROIoutput/1", pattern = glob2rx("*-psc.txt"))
i=1
for (i in PSC) {
featquery<-read.delim(i , sep = " ", header = FALSE)
colnames(featquery)<-c("SubID", "ROI_CLUSTER_NAME", "Type",  "L_R" , "POS_NEG","meanZ", "medianZ", "maxZ", "X", "Y", "Z")
ROI_CLUSTER_NAME<-paste(as.character(featquery[2,2]), "PBC", sep = "_")
featquery[,1:11]<-lapply(featquery[,1:11], as.character)
featquery<-featquery %>%select(SubID, meanZ )
names(featquery)<-c("ID", ROI_CLUSTER_NAME)
featquery$ID <-as.character(featquery$ID)
featquery$ID<-str_sub(featquery$ID, 1, 4)
DATA <-left_join(DATA, featquery, by="ID")
}
Z<-list.files("/Users/alyshagilmore/Downloads/restingstate_crew/ROIoutput/1", pattern = glob2rx("*-zscr.txt"))
for (i in Z) {
featquery<-read.delim(i , sep = " ", header = FALSE)
colnames(featquery)<-c("ID", "ROI_CLUSTER_NAME", "Type",  "L_R" , "POS_NEG","meanZ", "medianZ", "maxZ", "X", "Y", "Z")
featquery[,1:11]<-lapply(featquery[,1:11], as.character)
ROI_CLUSTER_NAME<-paste(as.character(featquery[2,2]), "Z", sep = "_")
featquery[,1:11]<-lapply(featquery[,1:11], as.character)
featquery<-featquery %>%select(ID, meanZ )
names(featquery)<-c("ID", ROI_CLUSTER_NAME)
featquery$ID <-as.character(featquery$ID)
featquery$ID<-str_sub(featquery$ID, 1, 4)
DATA <-left_join(DATA, featquery, by="ID")
}
DATA<-DATA[!(duplicated(DATA$ID)),]
DATA<-DATA[!(is.na(DATA$ID)),]

#**BMI GLM - Extract Z-Stat for % BOLD Change for each participant**  
library(stringr)
setwd("/Users/alyshagilmore/Downloads/restingstate_crew/ROIoutput/2")
PSC<-list.files("/Users/alyshagilmore/Downloads/restingstate_crew/ROIoutput/2", pattern = glob2rx("*-psc.txt"))
i=1
for (i in PSC) {
featquery<-read.delim(i , sep = " ", header = FALSE)
colnames(featquery)<-c("SubID", "ROI_CLUSTER_NAME", "Type",  "L_R" , "POS_NEG","meanZ", "medianZ", "maxZ", "X", "Y", "Z")
ROI_CLUSTER_NAME<-paste(as.character(featquery[2,2]), "PBC", sep = "_")
featquery[,1:11]<-lapply(featquery[,1:11], as.character)
featquery<-featquery %>%select(SubID, meanZ )
names(featquery)<-c("ID", ROI_CLUSTER_NAME)
featquery$ID <-as.character(featquery$ID)
featquery$ID<-str_sub(featquery$ID, 1, 4)
DATA <-left_join(DATA, featquery, by="ID")
}
Z<-list.files("/Users/alyshagilmore/Downloads/restingstate_crew/ROIoutput/2", pattern = glob2rx("*-zscr.txt"))
for (i in Z) {
featquery<-read.delim(i , sep = " ", header = FALSE)
colnames(featquery)<-c("ID", "ROI_CLUSTER_NAME", "Type",  "L_R" , "POS_NEG","meanZ", "medianZ", "maxZ", "X", "Y", "Z")
featquery[,1:11]<-lapply(featquery[,1:11], as.character)
ROI_CLUSTER_NAME<-paste(as.character(featquery[2,2]), "Z", sep = "_")
featquery[,1:11]<-lapply(featquery[,1:11], as.character)
featquery<-featquery %>%select(ID, meanZ )
names(featquery)<-c("ID", ROI_CLUSTER_NAME)
featquery$ID <-as.character(featquery$ID)
featquery$ID<-str_sub(featquery$ID, 1, 4)
DATA <-left_join(DATA, featquery, by="ID")
}
DATA<-DATA[!(duplicated(DATA$ID)),]

MeanFD<-read.delim2("/Users/alyshagilmore/Downloads/restingstate_crew/meanFD.txt", header = F) #Not sure why, but R imports loads of duplicates in txt file. 
MeanFD<-MeanFD[!duplicated(MeanFD),] # To remove duplicates
MeanFD<-data.frame(MeanFD) #as dataframe with 34 rows and no duplicates.
#view(MeanFD)
#Step 12b:** Next, we use a few commands to split the subject ids that we have (e.g., 7001_1) into subject ids that can be merged with other databases (i.e., 7001). Same as Step 7c
x<-MeanFD$MeanFD
x<-as.character(x)
tmp<-strsplit(x, " ")   #strsplit: splits a charachter string in a fixed spot. in this case at' '[space]
mat  <- matrix(unlist(tmp), ncol=2, byrow=TRUE) #this breaks the char string into 2 sep columns.
df<-as.data.frame(mat) #make a data frame
df$ID<-as.character(df$V1) #make var ID
df$meanFD<-as.character(df$V2) #make var session
MeanFD<-df %>% select(ID, meanFD) #extract ID & session only (e.i., ignore identical colummns V1 & V2)
rm(x, mat,tmp, df)
# Now again, but for '_1'
x<-MeanFD$ID 
x<-as.character(x)
tmp<-strsplit(x, "_")   #strsplit: splits a charachter string in a fixed spot. in this case at'_1'
mat  <- matrix(unlist(tmp), ncol=2, byrow=TRUE) #this breaks the char string into 2 sep columns.
df<-as.data.frame(mat) #make a data frame
df$ID<-as.character(df$V1) #make var ID
df<-df %>% select(ID) #extract ID & session only (e.i., ignore identical colummns V1 & V2)
rm(mat,tmp)
MeanFD.df<-cbind(df, MeanFD) #bind/merge with orginal dataframe 
MeanFD.df<-MeanFD.df[,-c(2)] # Remove duplicate col
# Step 12c:** Mean center MeanFD values, just in case we'll need them later.  Same as Step 11b.
  MeanFD.df$meanFD<-as.numeric(MeanFD.df$meanFD)
  MeanFD.df$meanFD_mc<-scale(MeanFD.df$meanFD, scale = FALSE)
  
FULL_DATA<-left_join(DATA,MeanFD.df,  by="ID")
FULL_DATA$SURF.bilat.Hippo<-FULL_DATA$TOT_HIPP_VOL
FULL_DATA$SURF.bilat.Hippo.eiv<-FULL_DATA$TOT_HIPP_VOL/DATA$EstimatedTotalIntraCranialVol
DATA<-FULL_DATA[complete.cases(FULL_DATA$meanFD),]
Constricted_DAT<-DATA

Constricted Sample: N=34

Data Frame Summary

T1_DEMOS_df

Dimensions: 34 x 18
Duplicates: 0

Variable	Stats / Values	Freqs (% of Valid)	Graph	Valid	Missing
AGE [numeric]	Mean (sd) : 63.6 (5.7) min < med < max: 51 < 64 < 76 IQR (CV) : 7.8 (0.1)	18 distinct values		34 (100%)	0 (0%)
WEIGHT_kg [numeric]	Mean (sd) : 84.5 (17) min < med < max: 53.7 < 84 < 119 IQR (CV) : 20.1 (0.2)	34 distinct values		34 (100%)	0 (0%)
BMI [numeric]	Mean (sd) : 31.8 (6.6) min < med < max: 20.2 < 31.4 < 43.3 IQR (CV) : 10.7 (0.2)	34 distinct values		34 (100%)	0 (0%)
meanFD [numeric]	Mean (sd) : 0.1 (0) min < med < max: 0 < 0.1 < 0.2 IQR (CV) : 0.1 (0.4)	34 distinct values		34 (100%)	0 (0%)
Rest.Sys [numeric]	Mean (sd) : 137.8 (13.7) min < med < max: 110 < 138 < 165 IQR (CV) : 18.5 (0.1)	28 distinct values		34 (100%)	0 (0%)
Rest.Dia [numeric]	Mean (sd) : 82.9 (9.2) min < med < max: 59 < 84 < 99 IQR (CV) : 12.2 (0.1)	22 distinct values		34 (100%)	0 (0%)
Rest_MAP [numeric]	Mean (sd) : 101.2 (8.7) min < med < max: 81.3 < 102.3 < 116 IQR (CV) : 10.2 (0.1)	32 distinct values		34 (100%)	0 (0%)
Rest_HR [numeric]	Mean (sd) : 71.7 (10) min < med < max: 52 < 74 < 97 IQR (CV) : 14.8 (0.1)	24 distinct values		34 (100%)	0 (0%)
ncomorbidities [numeric]	Mean (sd) : 4.9 (3.1) min < med < max: 0 < 5.5 < 11 IQR (CV) : 4 (0.6)	12 distinct values		34 (100%)	0 (0%)
radiation_n [numeric]	Mean (sd) : 14 (6.4) min < med < max: 0 < 16 < 21 IQR (CV) : 8.8 (0.5)	17 distinct values		34 (100%)	0 (0%)
Race_10 [factor]	1. Caucasian/White 2. Other	24 ( 85.7% ) 4 ( 14.3% )		28 (82.35%)	6 (17.65%)
Smoker [factor]	1. Yes 2. No	1 ( 2.9% ) 33 ( 97.1% )		34 (100%)	0 (0%)
Handedness [factor]	1. Right 2. Left	22 ( 78.6% ) 6 ( 21.4% )		28 (82.35%)	6 (17.65%)
EDU [numeric]	Mean (sd) : 15.8 (2.8) min < med < max: 12 < 16 < 23 IQR (CV) : 4 (0.2)	9 distinct values		28 (82.35%)	6 (17.65%)
bdito [numeric]	Mean (sd) : 5.2 (4.3) min < med < max: 0 < 4.5 < 17 IQR (CV) : 4.2 (0.8)	12 distinct values		28 (82.35%)	6 (17.65%)
EMO_tscore [numeric]	Mean (sd) : 46.5 (9.4) min < med < max: 37.1 < 45.9 < 66.6 IQR (CV) : 17.5 (0.2)	12 distinct values		28 (82.35%)	6 (17.65%)
T1_Moderate_Total_hrs [numeric]	Mean (sd) : 4.1 (3.4) min < med < max: 0.7 < 2.5 < 12.2 IQR (CV) : 3.8 (0.8)	27 distinct values		29 (85.29%)	5 (14.71%)
FIRST.bilat.Hippo [numeric]	Mean (sd) : 7223.7 (777) min < med < max: 5352 < 7082.9 < 9635 IQR (CV) : 903.1 (0.1)	29 distinct values		29 (85.29%)	5 (14.71%)

Generated by summarytools 0.9.6 (R version 3.6.1)
2020-09-06

Body mass index is associated with resting state functional connectivity of the hippocampus among women newly diagnosed with breast cancer.

Shannon D. Donofry, PhD¹, Alina Lesnovskaya, MS¹, Alysha D. Gilmore, BS¹, Jermon A. Drake, BA¹, Hayley S. Ripperger, BA¹, Patrick T. Donahue, MS^1,2, Mary Crisafio, MS^1,3, George Grove, MS¹, Amanda L. Gentry, MPH⁴, Susan M. Sereika, PhD^4,5, Catherine M. Bender, PhD, RN, FAAN^4,6, Kirk I. Erickson, PhD^1,7

¹Department of Psychology, University of Pittsburgh, Pittsburgh, PA

²Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD

³Department of Health and Exercise Sciences, Colorado State University, Fort Collins, CO

⁴School of Nursing, University of Pittsburgh, Pittsburgh, PA

⁵Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA

⁶Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA

⁷Center for the Neural Basis of Cognition, University of Pittsburgh & Carnegie Mellon University, Pittsburgh, PA

Background: Overweight and obesity (body mass index [BMI] ≥25kg/m2) are associated with poorer prognosis after diagnosis with breast cancer, and weight gain is a common during treatment. Symptoms of depression and anxiety are also highly prevalent in women with breast cancer and may be exacerbated by post-diagnosis weight gain. Prior research suggests that changes in hippocampal structure and function may underlie psychological impairments in breast cancer and obesity. Thus, the aim of the present study was to examine the relationship between BMI, psychological functioning, and resting state connectivity of the hippocampus among women diagnosed with breast cancer.

Methods: The sample included 34 postmenopausal women newly diagnosed with Stage I-IIIa breast cancer (age M=63.59±5.73) who were enrolled in a six-month randomized clinical trial of aerobic exercise. At baseline, prior to the start of the exercise program, participants completed a resting state functional MRI scan. Using the hippocampus as a seed, whole-brain functional connectivity analyses were performed to examine the relationship between BMI and hippocampal connectivity at rest. Connectivity values from significant clusters were then extracted and correlated with scores on the Beck Depression Inventory (BDI) and PROMIS Anxiety scale. Whole-brain models included age, education, and framewise displacement as covariates.

Particpants were removed from the sample if..
1. They quit the fittness test prior to meeting one of the criteria listed above.
2. Equipment malfunction or safety concerns prevented fitness test completion.

Resting State MRI Aquistion Details found in EPICC Protocol.

Preprocessing. After skull stripping, the structural image was spatially normalized to MNI space. All rsfMRI frames were aligned to correct for head motion during the scan, co-registered to each participant’s structural image, and spatially normalized to MNI space. The rsfMRI timecourses were then band-pass filtered (....) to attenuate respiration and other physiological noise. In addition, six affine transformation parameters from the alignment process, as well as the mean time courses from the brain parenchyma including white matter tissue and ventricles were included as covariates in order to further account for motion and physiological noise. The data were of high quality in this healthy young adult sample, and no subjects were eliminated due to excessive motion (mean framewise displacement ranged from .04 to .26mm; M+SD=.09+ .03) or physiological noise. The residualized parameter estimate maps were converted to z scores (via Fishers r to z transform) to achieve normality and were entered into higher level analyses.

Seed Creation For the functional connectivity and volumetric analysis of the hippocampus and control (caudate nucleus) seeds, we employed FMRIB’s Integrated Registration and Segmentation Tool (FIRST) in FMRIB’s Software Library (FSL) version 5.0. FIRST is a semi-automated model-based subcortical segmentation tool which uses a Bayesian framework from shape and appearance models obtained from manually segmented images from the Center for Morphometric Analysis, Massachusetts General Hospital, Boston, MA, USA (see Patenaude, Smith, Kennedy, & Jenkinson, 2011 for further description of this method). Briefly, FIRST runs a two-stage affine registration to a standard space template (MNI space) with 1mm resolution using 12 degrees of freedom and uses a subcortical mask to exclude voxels outside subcortical regions. Second, subcortical regions, including hippocampus, are segmented (both hemispheres separately). Manual volumetric region labels are parameterized as surface meshes and modeled as a point distribution model. Collapsed HIPP TBH

Sample Demographics

	normal (N=5)	overweight (N=10)	obese (N=12)	severe obese (N=7)	Overall (N=34)
AGE
Mean (SD)	66.0 (7.31)	63.9 (3.93)	63.8 (6.48)	61.0 (5.66)	63.6 (5.73)
Median [Min, Max]	64.0 [57.0, 74.0]	64.0 [58.0, 69.0]	65.0 [54.0, 76.0]	63.0 [51.0, 67.0]	64.0 [51.0, 76.0]
BMI
Mean (SD)	21.9 (1.37)	27.8 (1.63)	33.9 (2.84)	41.2 (1.03)	31.8 (6.62)
Median [Min, Max]	21.5 [20.2, 23.7]	27.9 [25.2, 29.9]	33.0 [30.3, 39.7]	41.1 [40.3, 43.3]	31.4 [20.2, 43.3]
Race
black	1 (20.0%)	2 (20.0%)	0 (0%)	1 (14.3%)	4 (11.8%)
white	3 (60.0%)	5 (50.0%)	10 (83.3%)	6 (85.7%)	24 (70.6%)
Missing	1 (20.0%)	3 (30.0%)	2 (16.7%)	0 (0%)	6 (17.6%)
EDU
Mean (SD)	14.8 (1.50)	14.9 (2.97)	15.7 (2.75)	17.4 (3.15)	15.8 (2.83)
Median [Min, Max]	15.0 [13.0, 16.0]	14.0 [12.0, 20.0]	16.0 [12.0, 20.0]	16.0 [14.0, 23.0]	16.0 [12.0, 23.0]
Missing	1 (20.0%)	3 (30.0%)	2 (16.7%)	0 (0%)	6 (17.6%)
Smoker
Yes	0 (0%)	0 (0%)	1 (8.3%)	0 (0%)	1 (2.9%)
No	5 (100%)	10 (100%)	11 (91.7%)	7 (100%)	33 (97.1%)
VO2_END_REASON
Achieved HR Goal	4 (80.0%)	8 (80.0%)	12 (100%)	7 (100%)	31 (91.2%)
Beta-Blocked- Achieved HR goal	0 (0%)	1 (10.0%)	0 (0%)	0 (0%)	1 (2.9%)
Beta-Blocked- Reached RPE Goal	0 (0%)	1 (10.0%)	0 (0%)	0 (0%)	1 (2.9%)
Other	1 (20.0%)	0 (0%)	0 (0%)	0 (0%)	1 (2.9%)
bdito
Mean (SD)	3.25 (3.59)	6.57 (5.53)	4.10 (3.00)	6.71 (4.82)	5.25 (4.30)
Median [Min, Max]	2.50 [0, 8.00]	5.00 [0, 14.0]	3.50 [0, 10.0]	5.00 [3.00, 17.0]	4.50 [0, 17.0]
Missing	1 (20.0%)	3 (30.0%)	2 (16.7%)	0 (0%)	6 (17.6%)
EMO_tscore
Mean (SD)	46.4 (7.71)	46.4 (10.0)	44.7 (9.97)	49.4 (10.3)	46.5 (9.43)
Median [Min, Max]	47.0 [37.1, 54.3]	43.2 [37.1, 59.4]	41.5 [37.1, 66.6]	47.8 [37.1, 61.4]	45.9 [37.1, 66.6]
Missing	1 (20.0%)	3 (30.0%)	2 (16.7%)	0 (0%)	6 (17.6%)
meanFD
Mean (SD)	0.0679 (0.0173)	0.0822 (0.0247)	0.116 (0.0285)	0.132 (0.0547)	0.102 (0.0396)
Median [Min, Max]	0.0731 [0.0426, 0.0846]	0.0790 [0.0474, 0.124]	0.111 [0.0527, 0.161]	0.136 [0.0591, 0.219]	0.0999 [0.0426, 0.219]
Rest.Sys
Mean (SD)	132 (18.6)	140 (14.1)	138 (12.0)	139 (14.1)	138 (13.7)
Median [Min, Max]	141 [110, 148]	138 [121, 164]	140 [118, 159]	131 [128, 165]	138 [110, 165]
Rest.Dia
Mean (SD)	78.8 (14.2)	84.3 (4.99)	86.6 (8.24)	77.6 (9.55)	82.9 (9.20)
Median [Min, Max]	71.0 [67.0, 98.0]	85.0 [76.0, 92.0]	85.5 [74.0, 99.0]	81.0 [59.0, 88.0]	84.0 [59.0, 99.0]
Rest_MAP
Mean (SD)	96.4 (14.6)	103 (6.77)	104 (6.65)	98.1 (8.71)	101 (8.69)
Median [Min, Max]	92.3 [81.3, 115]	102 [94.0, 116]	103 [93.7, 115]	99.0 [82.3, 106]	102 [81.3, 116]
Rest_HR
Mean (SD)	73.4 (5.50)	65.9 (10.1)	73.2 (10.9)	76.4 (8.04)	71.7 (9.96)
Median [Min, Max]	75.0 [65.0, 80.0]	65.0 [52.0, 84.0]	74.0 [55.0, 97.0]	79.0 [63.0, 83.0]	74.0 [52.0, 97.0]
T1_Moderate_Total_hrs
Mean (SD)	5.88 (3.54)	6.14 (4.52)	3.09 (2.72)	2.15 (1.27)	4.08 (3.43)
Median [Min, Max]	7.03 [1.78, 10.2]	5.47 [1.68, 12.3]	2.11 [1.48, 10.4]	2.13 [0.730, 4.38]	2.50 [0.730, 12.3]
Missing	0 (0%)	3 (30.0%)	2 (16.7%)	0 (0%)	5 (14.7%)
CRFrel
Mean (SD)	18.8 (3.61)	17.7 (2.03)	16.5 (2.14)	14.5 (2.41)	16.8 (2.70)
Median [Min, Max]	18.5 [14.3, 24.0]	17.7 [14.8, 21.0]	16.2 [13.8, 19.6]	14.2 [11.4, 18.4]	16.8 [11.4, 24.0]

Results

Results: Mean BMI of the sample was in the obese range (M=31.83±6.62). BMI was negatively associated with resting hippocampal connectivity to a single cluster encompassing regions in the prefrontal cortex (PFC), including the superior frontal gyrus (SFG), medial frontal gyrus, dorsomedial PFC, and dorsolateral PFC (Z-max=3.71, p=0.04). Hippocampal connectivity with this cluster was positively correlated with BDI (r=0.24) and PROMIS Anxiety (r=0.09) scores, but these correlation values were not significant (ps>0.2). BMI was unrelated to depression or anxiety (ps>0.14).

include_graphics("~/Downloads/restingstate_crew/SBM1/Figure1.png")

Figure 1:

include_graphics("~/Downloads/restingstate_crew/SBM1/Figure2.png" )

Figure 2:

include_graphics("~/Downloads/restingstate_crew/SBM1/Figure3.png")

Figure 3:

                                    ───────────────────────────────────────────────────
                                                    Model 1      Model 2      Model 3  
                                                  ─────────────────────────────────────
                                      (Intercept)   -20.67     37.52 ***    37.29 ***  
                                                    (11.84)    (1.05)       (1.08)     
                                      Age             0.29     -0.36 ***    -0.36 ***  
                                                     (0.18)    (0.01)       (0.01)     
                                      EDU            -0.01      0.30 ***     0.30 ***  
                                                     (0.36)    (0.02)       (0.02)     
                                      Racewhite      -3.11     -0.04         0.00      
                                                     (2.87)    (0.18)       (0.19)     
                                      meanFD         46.06    132.14 ***   130.93 ***  
                                                    (26.22)    (1.97)       (2.29)     
                                      BMI                      -1.04 ***    -1.03 ***  
                                                               (0.01)       (0.01)     
                                      bdito                                  0.02      
                                                                            (0.02)     
                                                  ─────────────────────────────────────
                                      N              28        28           28         
                                      R2              0.20      1.00         1.00      
                                    ───────────────────────────────────────────────────
                                      *** p < 0.001; ** p < 0.01; * p <                
                                      0.05.                                            

Column names: names, Model 1, Model 2, Model 3

Conclusions: Higher BMI was associated with reduced hippocampal connectivity to regions of PFC that have been implicated in cognitive control and emotion regulation such as the SFG and dorsolateral PFC. Although hippocampal connectivity was not significantly related to psychological functioning, it is possible that BMI-related differences in hippocampal connectivity following a recent breast cancer diagnosis may relate to future worsening of psychological functioning during treatment and remission. Additional longitudinal research exploring this hypothesis is warranted.

References:

Gentry AL, Erickson KI, Sereika SM, et al. Protocol for Exercise Program in Cancer and Cognition (EPICC): A randomized controlled trial of the effects of aerobic exercise on cognitive function in postmenopausal women with breast cancer receiving aromatase inhibitor therapy. Contemp Clin Trials. 2018;67:109-115. doi:10.1016/j.cct.2018.02.012

Patenaude, B., Smith, S.M., Kennedy, D., and Jenkinson M. A Bayesian Model of Shape and Appearance for Subcortical Brain NeuroImage, 56(3):907-922, 2011.

Beck AT, Steer RA, Brown GK. Beck Depression Inventory-II. San Antonio: The Psychological Corporation; 1996.

Assessment. 2011 Sep; 18(3):263-83. [Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): depression, anxiety, and anger].

Liden CB, Wolowicz M, Stivoric JM, et al. Accuracy and reliability of the Sensewear™ armband as an energy expenditure assessment device. BodyMedia Inc White Papers. 2002;12:1–15.

• Pilkonis PA, Choi SW, Reise SP, Stover AM, Riley WT, Cella D, PROMIS Cooperative Group.

• Woolrich, M. W., Ripley, B. D., Brady, M., & Smith, S. M. (2001). Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data. NeuroImage, 14(6), 1370–1386. http://doi.org/10.1006/nimg.2001.0931

BMI Total Hippocampus .glm n28

sig.level thresh (for colors): 0.05

               ─────────────────────────────────────────────────────────────────────────────────────────────
                                     Model 1     Model 2      Model 3      Model 4    Model 5      Model 6  
                                ────────────────────────────────────────────────────────────────────────────
                 (Intercept)       37.52 ***   -37.31 **    -2.20        22.30 **     -7.48     -19.87 *    
                                   (1.05)      (12.23)      (4.27)       (7.22)      (14.46)     (7.49)     
                 Age               -0.36 ***     0.29       -0.31 ***    -0.18         0.15      -0.04      
                                   (0.01)       (0.16)      (0.06)       (0.10)       (0.20)     (0.10)     
                 EDU                0.30 ***     0.03        0.41 ***     0.42 *       0.12       0.60 **   
                                   (0.02)       (0.32)      (0.10)       (0.17)       (0.36)     (0.19)     
                 Racewhite         -0.04        -3.45       -0.60        -1.79        -2.56      -3.55 *    
                                   (0.18)       (2.55)      (0.77)       (1.34)       (2.82)     (1.43)     
                 meanFD           132.14 ***    59.73 *    125.89 ***   100.33 ***    54.55 *    85.11 ***  
                                   (1.97)      (23.85)      (8.24)      (13.54)      (26.13)    (13.78)     
                 BMI               -1.04 ***                                                                
                                   (0.01)                                                                   
                 CRFrel                          0.90 *      2.06 ***                                       
                                                (0.34)      (0.12)                                          
                 CRFrel:BMI                                 -0.06 ***                                       
                                                            (0.00)                                          
                 WEIGHT_kg                                               -0.31 ***                          
                                                                         (0.03)                             
                 CRFabs                                                               -5.62      26.24 ***  
                                                                                      (3.73)     (4.39)     
                 CRFabs:WEIGHT_                                                                  -0.24 ***  
                 kg                                                                                         
                                                                                                 (0.03)     
                                ────────────────────────────────────────────────────────────────────────────
                 N                 28           28          28           28           28         28         
                 R2                 1.00         0.40        0.95         0.84         0.28       0.82      
               ─────────────────────────────────────────────────────────────────────────────────────────────
                 *** p < 0.001; ** p < 0.01; * p < 0.05.                                                    

Column names: names, Model 1, Model 2, Model 3, Model 4, Model 5, Model 6
MODEL INFO:
Observations: 28 (6 missing obs. deleted)
Dependent Variable: C1Z3_BMI.full
Type: OLS linear regression 

MODEL FIT:
F(6,21) = 66.86, p = 0.00
R² = 0.95
Adj. R² = 0.94 

Standard errors: OLS
-------------------------------------------------
                      Est.   S.E.   t val.      p
----------------- -------- ------ -------- ------
(Intercept)          -2.20   4.27    -0.52   0.61
Age                  -0.31   0.06    -5.07   0.00
EDU                   0.41   0.10     4.22   0.00
Racewhite            -0.60   0.77    -0.77   0.45
meanFD              125.89   8.24    15.28   0.00
CRFrel                2.06   0.12    16.50   0.00
CRFrel:BMI           -0.06   0.00   -15.28   0.00
-------------------------------------------------

Model BMI ~ Bilateral Hippocampal Volume

Mod<-(lm(BMI~FIRST.bilat.Hippo, DATA))
Mod1<-(lm(BMI~SURF.bilat.Hippo, DATA))
Mod2<-(lm(BMI~SURF.bilat.Hippo+EstimatedTotalIntraCranialVol, DATA))
jtools::export_summs(Mod,Mod1,Mod2)

                              ────────────────────────────────────────────────────────────────
                                                               Model 1    Model 2    Model 3  
                                                            ──────────────────────────────────
                                (Intercept)                     4.38       9.29      25.40    
                                                              (11.20)    (10.57)    (14.24)   
                                FIRST.bilat.Hippo               0.00 *                        
                                                               (0.00)                         
                                SURF.bilat.Hippo                           0.00 *     0.00 *  
                                                                          (0.00)     (0.00)   
                                EstimatedTotalIntraCranialV                          -0.00    
                                ol                                                            
                                                                                     (0.00)   
                                                            ──────────────────────────────────
                                N                              29         27         27       
                                R2                              0.18       0.16       0.25    
                              ────────────────────────────────────────────────────────────────
                                *** p < 0.001; ** p < 0.01; * p < 0.05.                       

Column names: names, Model 1, Model 2, Model 3

jtools::effect_plot(Mod2,pred = SURF.bilat.Hippo )

                                  ────────────────────────────────────────────────────────
                                                                       Model 1   Model 2  
                                                                    ──────────────────────
                                    (Intercept)                         9.53     11.98    
                                                                      (10.97)    (9.64)   
                                    AGE                                -0.13     -0.13    
                                                                       (0.14)    (0.12)   
                                    Racewhite                          -1.61     -2.05    
                                                                       (2.18)    (1.91)   
                                    SmokerNo                           -2.80     -2.39    
                                                                       (4.10)    (3.59)   
                                    EDU                                 0.15      0.12    
                                                                       (0.28)    (0.24)   
                                    C1Z3BMI_MFGSFG_Z                    0.42 *   -0.85    
                                                                       (0.20)    (0.50)   
                                    ncomorbidities                      0.82 *    0.58    
                                                                       (0.32)    (0.29)   
                                    C1Z3BMI_MFGSFG_Z:ncomorbidities               0.22 *  
                                                                                 (0.08)   
                                                                    ──────────────────────
                                    N                                  28        28       
                                    R2                                  0.40      0.57    
                                  ────────────────────────────────────────────────────────
                                    *** p < 0.001; ** p < 0.01; * p < 0.05.               

Column names: names, Model 1, Model 2

 

Other interesting finds...

Physical Activity Surveillance & Physiological Monitoring Participants wore SenseWear Armband (BodyMedia, Inc.). Physical activity and galvanic skin response (GSR) data was objectively collected using the SenseWear Armband (BodyMedia, Inc.). The device was placed on the participant’s upper arm and she is instructed to wear it at all times (other than showering, swimming, or bathing) for 7 days. It utilizes a 2-axis accelerometer, heat flux sensor, galvanic skin response sensor (GSR), skin temperature sensor, and a near-body ambient temperature sensor to capture data leading to the calculation of energy expenditure.

Call:
lm(formula = T1_AVG_GSR ~ AGE + BMI + C1Z3BMI_MFGFP_Z + BMI, 
    data = DATA)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.089924 -0.032875  0.002289  0.021398  0.124626 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)   
(Intercept)     -0.192703   0.138361  -1.393  0.17596   
AGE              0.001924   0.001817   1.059  0.29989   
BMI              0.005430   0.001460   3.719  0.00102 **
C1Z3BMI_MFGFP_Z -0.005005   0.002036  -2.458  0.02125 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.04985 on 25 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.4516,    Adjusted R-squared:  0.3858 
F-statistic: 6.863 on 3 and 25 DF,  p-value: 0.001578


Call:
lm(formula = T1_AVG_GSR ~ AGE + BMI * C1Z3BMI_MFGFP_Z, data = DATA)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.092488 -0.027968  0.004099  0.024680  0.082284 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)   
(Intercept)         -0.1318332  0.1186083  -1.112  0.27736   
AGE                  0.0017113  0.0015399   1.111  0.27746   
BMI                  0.0038509  0.0013260   2.904  0.00778 **
C1Z3BMI_MFGFP_Z      0.0154342  0.0064360   2.398  0.02461 * 
BMI:C1Z3BMI_MFGFP_Z -0.0007086  0.0002150  -3.296  0.00304 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.04222 on 24 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.6225,    Adjusted R-squared:  0.5596 
F-statistic: 9.895 on 4 and 24 DF,  p-value: 7.091e-05

summary(lm( T1_AVG_GSR   ~ AGE+CRFrel+C1Z3BMI_MFGFP_Z , DATA))

Call:
lm(formula = T1_AVG_GSR ~ AGE + CRFrel + C1Z3BMI_MFGFP_Z, data = DATA)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.093936 -0.027915  0.000245  0.026652  0.153865 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)  
(Intercept)      0.2606655  0.1478371   1.763   0.0901 .
AGE             -0.0007099  0.0020108  -0.353   0.7270  
CRFrel          -0.0068080  0.0039039  -1.744   0.0935 .
C1Z3BMI_MFGFP_Z -0.0056180  0.0024082  -2.333   0.0280 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.05867 on 25 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.2407,    Adjusted R-squared:  0.1495 
F-statistic: 2.641 on 3 and 25 DF,  p-value: 0.07143

summary(lm( T1_AVG_GSR   ~ AGE+CRFrel*C1Z3BMI_MFGFP_Z , DATA))

Call:
lm(formula = T1_AVG_GSR ~ AGE + CRFrel * C1Z3BMI_MFGFP_Z, data = DATA)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.099293 -0.019698  0.003473  0.029693  0.087432 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)   
(Intercept)             0.230067   0.128951   1.784  0.08705 . 
AGE                    -0.001161   0.001755  -0.662  0.51449   
CRFrel                 -0.003179   0.003602  -0.883  0.38624   
C1Z3BMI_MFGFP_Z        -0.044650   0.013133  -3.400  0.00236 **
CRFrel:C1Z3BMI_MFGFP_Z  0.002222   0.000738   3.011  0.00605 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.05101 on 24 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.4488,    Adjusted R-squared:  0.357 
F-statistic: 4.886 on 4 and 24 DF,  p-value: 0.00502

Model2<-(lm(T1_AVG_GSR   ~ AGE+CRFrel*C1Z3BMI_MFGFP_Z , DATA))
visreg::visreg(Model2,"CRFrel" , by="C1Z3BMI_MFGFP_Z", overlay=TRUE )

DATA$C1Z3BMI_RdlPFC_PBC<-as.numeric( DATA$C1Z3BMI_RdlPFC_PBC)
summary(lm(C1Z3BMI_RdlPFC_PBC ~ AGE+Rest.Dia+BMI+collapsed_hippocampus, DATA))

Call:
lm(formula = C1Z3BMI_RdlPFC_PBC ~ AGE + Rest.Dia + BMI + collapsed_hippocampus, 
    data = DATA)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.75101 -0.11093  0.05099  0.12708  0.58981 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)   
(Intercept)           -1.180e+00  9.581e-01  -1.232  0.22996   
AGE                   -5.230e-03  9.161e-03  -0.571  0.57336   
Rest.Dia               1.491e-02  5.206e-03   2.865  0.00853 **
BMI                   -7.105e-03  7.857e-03  -0.904  0.37485   
collapsed_hippocampus  5.132e-05  7.174e-05   0.715  0.48130   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2461 on 24 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.2672,    Adjusted R-squared:  0.1451 
F-statistic: 2.188 on 4 and 24 DF,  p-value: 0.1008

summary(lm(C1Z3BMI_RdlPFC_PBC ~ AGE+Rest.Dia*BMI, DATA))

Call:
lm(formula = C1Z3BMI_RdlPFC_PBC ~ AGE + Rest.Dia * BMI, data = DATA)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.47655 -0.12869  0.01136  0.14925  0.41515 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)   
(Intercept)   3.5276661  1.4807042   2.382  0.02397 * 
AGE          -0.0013186  0.0065880  -0.200  0.84276   
Rest.Dia     -0.0460790  0.0180355  -2.555  0.01613 * 
BMI          -0.1499329  0.0452204  -3.316  0.00247 **
Rest.Dia:BMI  0.0019032  0.0005721   3.327  0.00240 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2053 on 29 degrees of freedom
Multiple R-squared:  0.4215,    Adjusted R-squared:  0.3417 
F-statistic: 5.282 on 4 and 29 DF,  p-value: 0.002543

MODEL<-(lm(C1Z3BMI_RdlPFC_PBC ~ AGE+Rest.Dia*BMI, DATA))
visreg::visreg(MODEL,"BMI" , by="Rest.Dia", overlay=TRUE )

summary(lm(C1Z3BMI_RdlPFC_PBC ~ Rest.Dia*BMI+collapsed_hippocampus, DATA))

Call:
lm(formula = C1Z3BMI_RdlPFC_PBC ~ Rest.Dia * BMI + collapsed_hippocampus, 
    data = DATA)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.45369 -0.09998 -0.00248  0.16744  0.44857 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)   
(Intercept)            3.117e+00  1.657e+00   1.881  0.07223 . 
Rest.Dia              -4.251e-02  1.928e-02  -2.205  0.03731 * 
BMI                   -1.468e-01  4.697e-02  -3.126  0.00459 **
collapsed_hippocampus  2.168e-05  6.220e-05   0.349  0.73050   
Rest.Dia:BMI           1.820e-03  6.003e-04   3.031  0.00576 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2107 on 24 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.4629,    Adjusted R-squared:  0.3734 
F-statistic: 5.171 on 4 and 24 DF,  p-value: 0.003777

 

 

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