Description of the procedures and analysis present in Manuscript 2, Establishing a baseline for human cortical folding morphological variables: a multicenter study, at the Doctorate Thesis presented to the Programa de Pós-Graduação em Ciências Médicas at the Instituto D’Or de Pesquisa e Ensino as a partial requirement to obtain the Doctorate Degree.
Part of the data used here cannot be shared due to restrictions of the Ethic Committee. Data can be shared upon reasonable request to the corresponding author. To fulfill these limitation, we will generate random data to simulate the results.
Get in touch with us (fernandahmoraes@gmail.com) in case any help is needed, our aim is to improve the code as needed!
setwd("~/GitHub/Typical-values")## define functions
# test angular coeficinet versus theoretical value
test_coef <- function(reg, coefnum, val){
co <- coef(summary(reg))
tstat <- (co[coefnum,1] - val)/co[coefnum,2]
2 * pt(abs(tstat), reg$df.residual, lower.tail = FALSE)
}
# wrap text
wrapper <- function(x, ...) paste(strwrap(x, ...), collapse = "\n")library(readr)
library(tidyverse)
library(lubridate)
library(ggpubr)
library(kableExtra)
library(broom)
library(lme4)
library(lsmeans)
library(MuMIn)
library(arm)
library(effects)# COLOR BLIND PALETTE WITH BLACK
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
cbbPalette2 <- c("#D55E00", "#E69F00", "#56B4E9", "#0072B2", "#CC79A7", "#009E73", "#F0E442")set.seed(1)# dados_datasetscomp <- read_csv("dados_datasetscomp2.csv")
dados_datasetscomp <- read_csv("data_typical_values.csv")
dados_datasetscomp <- dados_datasetscomp %>%
filter(Sample != "HCP500r", Diagnostic != "MCI")# estimate cortical folding variables
dados_datasetscomp <- dados_datasetscomp %>%
mutate(
# create new variables
GMvolume = ifelse(!is.na(GMvolume),GMvolume,AvgThickness*TotalArea),
logAvgThickness = log10(AvgThickness),
logTotalArea = log10(TotalArea),
logExposedArea = log10(ExposedArea),
localGI = TotalArea / ExposedArea,
k = sqrt(AvgThickness) * TotalArea / (ExposedArea ^ 1.25),
K = 1 / 4 * log10(AvgThickness ^ 2) + log10(TotalArea) - 5 / 4 * log10(ExposedArea),
S = 3 / 2 * log10(TotalArea) + 3 / 4 * log10(ExposedArea) - 9 / 4 * log10(AvgThickness ^
2) ,
I = log10(TotalArea) + log10(ExposedArea) + log10(AvgThickness ^ 2),
# c = as.double(ifelse(
# ROI == "hemisphere", NA, 4 * pi / GaussianCurvature
# )),
TotalArea_corrected = ifelse(ROI == "hemisphere", TotalArea, TotalArea * c),
ExposedArea_corrected = ifelse(ROI == "hemisphere", ExposedArea, ExposedArea * c),
logTotalArea_corrected = log10(TotalArea_corrected),
logExposedArea_corrected = log10(ExposedArea_corrected),
localGI_corrected = ifelse(
ROI == "hemisphere",
TotalArea / ExposedArea,
TotalArea_corrected / ExposedArea_corrected
),
k_corrected = ifelse(
ROI == "hemisphere",
sqrt(AvgThickness) * log10(TotalArea) / (log10(ExposedArea) ^ 1.25),
sqrt(AvgThickness) * log10(TotalArea_corrected) / (log10(ExposedArea_corrected ^
1.25))
),
K_corrected = ifelse(
ROI == "hemisphere",
1 / 4 * log10(AvgThickness ^ 2) + log10(TotalArea) - 5 / 4 * log10(ExposedArea),
1 / 4 * log10(AvgThickness ^ 2) + log10(TotalArea_corrected) - 5 / 4 * log10(ExposedArea_corrected)
),
I_corrected = ifelse(
ROI == "hemisphere",
log10(TotalArea) + log10(ExposedArea) + log10(AvgThickness ^ 2) ,
log10(TotalArea_corrected) + log10(ExposedArea_corrected) + log10(AvgThickness ^ 2)
),
S_corrected = ifelse(
ROI == "hemisphere",
3 / 2 * log10(TotalArea) + 3 / 4 * log10(ExposedArea) - 9 / 4 * log10(AvgThickness ^ 2) ,
3 / 2 * log10(TotalArea_corrected) + 3 / 4 * log10(ExposedArea_corrected) - 9 / 4 * log10(AvgThickness ^ 2)
),
Knorm = K_corrected / sqrt(1 + (1 / 4) ^ 2 + (5 / 4) ^ 2),
Snorm = S_corrected / sqrt((3 / 2) ^ 2 + (3 / 4) ^ 2 + (9 / 4) ^ 2),
Inorm = I_corrected / sqrt(1 ^ 2 + 1 ^ 2 + 1 ^ 1)
)
# create age intervals
dados_datasetscomp$Age_interval <- cut(dados_datasetscomp$Age,
breaks = c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100),
right = FALSE,
include.lowest = TRUE)
dados_datasetscomp$Age_interval10 <- cut(dados_datasetscomp$Age,
breaks = c(0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100),
right = FALSE,
include.lowest = TRUE)dados_all <- dados_datasetscomp %>% filter(!is.na(logAvgThickness), ExposedArea != 0 | !is.na(localGI), !is.infinite(logExposedArea)) %>%
droplevels()
dados_datasetscomp <- dados_all
dados_datasetscomp$Diagnostic <- as.factor(dados_datasetscomp$Diagnostic)
dados_datasetscomp$Diagnostic <- relevel(dados_datasetscomp$Diagnostic, ref = "CTL")# define age for deaging
Age.cor = 25
# DEAGING + HARMONIZATION FULL DATA ----
dados_datasetscomp_rate <-
filter(dados_datasetscomp, Diagnostic == "CTL")
dados_datasetscomp_rate$Sample <-
as.factor(dados_datasetscomp_rate$Sample)
dados_datasetscomp_rate$ROI <-
factor(dados_datasetscomp_rate$ROI,
levels = c("hemisphere", "F", "O", "P", "T"))m.1 <-
lme4::lmer(GMvolume ~ Age * ROI + (1|Sample:ROI), data = dados_datasetscomp_rate)
re <- as_tibble(ranef(m.1)) %>%
filter(grpvar == "Sample:ROI") %>%
mutate(
GM_shift = condval,
sd_GM_shift = condsd,
Sample = str_split(grp, pattern = ":", simplify = TRUE)[, 1],
ROI = str_split(grp, pattern = ":", simplify = TRUE)[, 2]
) %>%
dplyr::select(-c(condval, grpvar, term, condsd, grp))
Age.trend <- as_tibble(lstrends(m.1, ~ ROI, var = "Age")) %>%
mutate(Age.trend_GM = Age.trend) %>%
dplyr::select(c(ROI, Age.trend_GM))
dados_datasetscomp <- full_join(dados_datasetscomp, Age.trend) %>%
full_join(re) %>%
mutate(
GMvolume_shiftc = GMvolume - GM_shift,
GMvolume_age_decay = GMvolume - Age.trend_GM * (Age - Age.cor),
GMvolume_age_decay_shiftc = GMvolume - GM_shift - Age.trend_GM *
(Age - Age.cor)
)m.1 <-
lme4::lmer(AvgThickness ~ Age * ROI + (1|Sample:ROI), data = dados_datasetscomp_rate)
re <- as_tibble(ranef(m.1)) %>%
filter(grpvar == "Sample:ROI") %>%
mutate(
T_shift = condval,
sd_T_shift = condsd,
Sample = str_split(grp, pattern = ":", simplify = TRUE)[, 1],
ROI = str_split(grp, pattern = ":", simplify = TRUE)[, 2]
) %>%
dplyr::select(-c(condval, grpvar, term, condsd, grp))
Age.trend <- as_tibble(lstrends(m.1, ~ ROI, var = "Age")) %>%
mutate(Age.trend_T = Age.trend) %>%
dplyr::select(c(ROI, Age.trend_T))
dados_datasetscomp <- full_join(dados_datasetscomp, Age.trend) %>%
full_join(re) %>%
mutate(
AvgThickness_shiftc = AvgThickness - T_shift,
AvgThickness_age_decay = AvgThickness - Age.trend_T * (Age - Age.cor),
AvgThickness_age_decay_shiftc = AvgThickness - T_shift - Age.trend_T *
(Age - Age.cor),
logAvgThickness_shiftc = log10(AvgThickness_shiftc),
logAvgThickness_age_decay = log10(AvgThickness_age_decay),
logAvgThickness_age_decay_shiftc = log10(AvgThickness_age_decay_shiftc)
)m.1 <-
lme4::lmer(TotalArea_corrected ~ Age * ROI + (1 | Sample:ROI), data = dados_datasetscomp_rate)
re <- as_tibble(ranef(m.1)) %>%
filter(grpvar == "Sample:ROI") %>%
mutate(
AT_shift = condval,
sd_AT_shift = condsd,
Sample = str_split(grp, pattern = ":", simplify = TRUE)[, 1],
ROI = str_split(grp, pattern = ":", simplify = TRUE)[, 2]
) %>%
dplyr::select(-c(condval, grpvar, term, condsd, grp))
Age.trend <-
as_tibble(lstrends(m.1, ~ ROI, var = "Age")) %>%
mutate(Age.trend_AT = Age.trend) %>%
dplyr::select(c(ROI, Age.trend_AT))
dados_datasetscomp <- full_join(dados_datasetscomp, re) %>%
full_join(Age.trend) %>%
mutate(
TotalArea_shiftc = TotalArea_corrected - AT_shift,
TotalArea_age_decay = TotalArea_corrected - Age.trend_AT * (Age - Age.cor),
TotalArea_age_decay_shiftc = TotalArea_corrected - AT_shift - Age.trend_AT *
(Age - Age.cor),
logTotalArea_shiftc = log10(TotalArea_shiftc),
logTotalArea_age_decay = log10(TotalArea_age_decay),
logTotalArea_age_decay_shiftc = log10(TotalArea_age_decay_shiftc)
)m.1 <-
lme4::lmer(
ExposedArea_corrected ~ Age * ROI + (1 | Sample:ROI), data = dados_datasetscomp_rate)
re <- as_tibble(ranef(m.1)) %>%
filter(grpvar == "Sample:ROI") %>%
mutate(
AE_shift = condval,
sd_AE_shift = condsd,
Sample = str_split(grp, pattern = ":", simplify = TRUE)[, 1],
ROI = str_split(grp, pattern = ":", simplify = TRUE)[, 2]
) %>%
dplyr::select(-c(condval, grpvar, term, condsd, grp))
Age.trend <-
as_tibble(lstrends(m.1, ~ ROI, var = "Age")) %>%
mutate(Age.trend_AE = Age.trend) %>%
dplyr::select(c(ROI, Age.trend_AE))
dados_datasetscomp <- full_join(dados_datasetscomp, re) %>%
full_join(Age.trend) %>%
mutate(
ExposedArea_shiftc = ExposedArea_corrected - AE_shift,
ExposedArea_age_decay = ExposedArea_corrected - Age.trend_AE * (Age - Age.cor),
ExposedArea_age_decay_shiftc = ExposedArea_corrected - AE_shift - Age.trend_AE * (Age - Age.cor),
logExposedArea_shiftc = log10(ExposedArea_shiftc),
logExposedArea_age_decay = log10(ExposedArea_age_decay),
logExposedArea_age_decay_shiftc = log10(ExposedArea_age_decay_shiftc)
)m.1 <-
lme4::lmer(c ~ Age * ROI + (1|Sample:ROI), data = dados_datasetscomp_rate)
re <- as_tibble(ranef(m.1)) %>%
filter(grpvar == "Sample:ROI") %>%
mutate(
c_shift = condval,
sd_c_shift = condsd,
Sample = str_split(grp, pattern = ":", simplify = TRUE)[, 1],
ROI = str_split(grp, pattern = ":", simplify = TRUE)[, 2]
) %>%
dplyr::select(-c(condval, grpvar, term, condsd, grp))
Age.trend <- as_tibble(lstrends(m.1, ~ ROI, var = "Age")) %>%
mutate(Age.trend_c = Age.trend) %>%
dplyr::select(c(ROI, Age.trend_c))
dados_datasetscomp <- full_join(dados_datasetscomp, Age.trend) %>%
full_join(re) %>%
mutate(
c_shiftc = c - c_shift,
c_age_decay = c - Age.trend_c * (Age - Age.cor),
c_age_decay_shiftc = c - c_shift - Age.trend_c *
(Age - Age.cor)
)# ----
dados_datasetscomp <- dados_datasetscomp %>%
mutate(
localGI_age_decay = TotalArea_age_decay/ExposedArea_age_decay,
localGI_shiftc = TotalArea_shiftc/ExposedArea_shiftc,
localGI_age_decay_shiftc = TotalArea_age_decay_shiftc/ExposedArea_age_decay_shiftc,
K_age_decay = log10(TotalArea_age_decay) + 1/4*log10(AvgThickness_age_decay^2) - 5/4*log10(ExposedArea_age_decay),
K_shiftc = log10(TotalArea_shiftc) + 1/4*log10(AvgThickness_shiftc^2) - 5/4*log10(ExposedArea_shiftc),
K_age_decay_shiftc = log10(TotalArea_age_decay_shiftc) + 1/4*log10(AvgThickness_age_decay_shiftc^2) - 5/4*log10(ExposedArea_age_decay_shiftc),
I_age_decay = log10(TotalArea_age_decay) + log10(ExposedArea_age_decay) + log10(AvgThickness_age_decay^2),
I_shiftc = log10(TotalArea_shiftc) + log10(ExposedArea_shiftc) + log10(AvgThickness_shiftc^2),
I_age_decay_shiftc = log10(TotalArea_age_decay_shiftc) + log10(ExposedArea_age_decay_shiftc) + log10(AvgThickness_age_decay_shiftc^2),
S_age_decay = 3/2*log10(TotalArea_age_decay) + 3/4*log10(ExposedArea_age_decay) - 9/4*log10(AvgThickness_age_decay^2),
S_shiftc = 3/2*log10(TotalArea_shiftc) + 3/4*log10(ExposedArea_shiftc) - 9/4*log10(AvgThickness_shiftc^2),
S_age_decay_shiftc = 3/2*log10(TotalArea_age_decay_shiftc) + 3/4*log10(ExposedArea_age_decay_shiftc) - 9/4*log10(AvgThickness_age_decay_shiftc^2),
Knorm_shiftc = K_shiftc / sqrt(1 + (1 / 4) ^ 2 + (5 / 2) ^ 2),
Snorm_shiftc = S_shiftc / sqrt((3 / 2) ^ 2 + (3 / 4) ^ 2 + (9 / 4) ^ 2),
Inorm_shiftc = I_shiftc / sqrt(1 ^ 2 + 1 ^ 2 + 1 ^ 2),
Knorm_age_decay = K_age_decay / sqrt(1 + (1 / 4) ^ 2 + (5 / 2) ^ 2),
Snorm_age_decay = S_age_decay / sqrt((3 / 2) ^ 2 + (3 / 4) ^ 2 + (9 / 4) ^ 2),
Inorm_age_decay = I_age_decay / sqrt(1 ^ 2 + 1 ^ 2 + 1 ^ 2),
Knorm_age_decay_shiftc = K_age_decay_shiftc / sqrt(1 + (1 / 4) ^ 2 + (5 / 2) ^ 2),
Snorm_age_decay_shiftc = S_age_decay_shiftc / sqrt((3 / 2) ^ 2 + (3 / 4) ^ 2 + (9 / 4) ^ 2),
Inorm_age_decay_shiftc = I_age_decay_shiftc / sqrt(1 ^ 2 + 1 ^ 2 + 1 ^ 2)
)| Sample | Diagnostic | N | age | age_range | T | AT | AE | K | S | I |
|---|---|---|---|---|---|---|---|---|---|---|
| ADNI | CTL | 868 | 75 ± 6.5 | 56 ; 96 | 2.4 ± 0.11 | 97000 ± 8400 | 39000 ± 2700 | -0.56 ± 0.017 | 9.2 ± 0.13 | 10 ± 0.074 |
| ADNI | AD | 542 | 75 ± 8 | 56 ; 92 | 2.2 ± 0.14 | 95000 ± 10000 | 38000 ± 3300 | -0.58 ± 0.023 | 9.4 ± 0.15 | 10 ± 0.11 |
| AHEAD | CTL | 100 | 42 ± 19 | 24 ; 76 | 2.6 ± 0.15 | 110000 ± 10000 | 39000 ± 2700 | -0.5 ± 0.023 | 9.2 ± 0.13 | 10 ± 0.094 |
| AOMICPIOP1 | CTL | 208 | 22 ± 1.8 | 18 ; 26 | 2.6 ± 0.084 | 110000 ± 10000 | 40000 ± 2700 | -0.49 ± 0.014 | 9.1 ± 0.094 | 10 ± 0.078 |
| AOMICPIOP2 | CTL | 224 | 22 ± 1.8 | 18 ; 26 | 2.6 ± 0.089 | 110000 ± 9100 | 39000 ± 2700 | -0.51 ± 0.013 | 9.1 ± 0.093 | 10 ± 0.078 |
| HCPr900 | CTL | 881 | 29 ± 3.6 | 24 ; 37 | 2.7 ± 0.09 | 110000 ± 11000 | 40000 ± 3000 | -0.5 ± 0.013 | 9.1 ± 0.11 | 10 ± 0.082 |
| IDOR | CTL | 77 | 66 ± 8.4 | 43 ; 80 | 2.5 ± 0.099 | 98000 ± 7800 | 37000 ± 2400 | -0.52 ± 0.014 | 9.1 ± 0.1 | 10 ± 0.072 |
| IDOR | AD | 13 | 77 ± 6.1 | 63 ; 86 | 2.4 ± 0.079 | 95000 ± 9300 | 37000 ± 3000 | -0.55 ± 0.015 | 9.2 ± 0.13 | 10 ± 0.069 |
| IXI-Guys | CTL | 314 | 51 ± 16 | 20 ; 86 | 2.5 ± 0.11 | 97000 ± 10000 | 39000 ± 3100 | -0.56 ± 0.018 | 9.2 ± 0.12 | 10 ± 0.093 |
| IXI-HH | CTL | 181 | 47 ± 17 | 20 ; 82 | 2.5 ± 0.18 | 97000 ± 11000 | 39000 ± 3200 | -0.55 ± 0.028 | 9.2 ± 0.16 | 10 ± 0.12 |
| IXI-IOP | CTL | 68 | 42 ± 17 | 20 ; 86 | 2.5 ± 0.13 | 92000 ± 11000 | 38000 ± 3300 | -0.56 ± 0.02 | 9.1 ± 0.14 | 10 ± 0.099 |
| NKI | CTL | 168 | 34 ± 19 | 4 ; 85 | 2.5 ± 0.14 | 110000 ± 11000 | 40000 ± 2800 | -0.52 ± 0.027 | 9.2 ± 0.12 | 10 ± 0.094 |
| OASIS | CTL | 312 | 45 ± 24 | 18 ; 94 | 2.3 ± 0.11 | 1e+05 ± 11000 | 39000 ± 2900 | -0.55 ± 0.023 | 9.3 ± 0.11 | 10 ± 0.099 |
m.1 <- lme4::lmer(GMvolume ~ Age * Diagnostic +(1|Sample) + (1|Sample:Diagnostic) , data = filter(dados_datasetscomp, ROI == "hemisphere"))Model summary and R squared:
summary(m.1)## Linear mixed model fit by REML ['lmerMod']
## Formula: GMvolume ~ Age * Diagnostic + (1 | Sample) + (1 | Sample:Diagnostic)
## Data: filter(dados_datasetscomp, ROI == "hemisphere")
##
## REML criterion at convergence: 183968.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8395 -0.6727 -0.0320 0.6298 4.5351
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample:Diagnostic (Intercept) 27722513 5265
## Sample (Intercept) 366049150 19132
## Residual 733577651 27085
## Number of obs: 7912, groups: Sample:Diagnostic, 13; Sample, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 303365.74 6119.08 49.577
## Age -1061.86 27.79 -38.216
## DiagnosticAD -67730.51 9855.71 -6.872
## Age:DiagnosticAD 725.19 105.75 6.858
##
## Correlation of Fixed Effects:
## (Intr) Age DgnsAD
## Age -0.196
## DiagnostcAD -0.083 0.195
## Ag:DgnstcAD 0.052 -0.263 -0.809r.squaredGLMM(m.1)## R2m R2c
## [1,] 0.3957362 0.6067994m.1 <- lme4::lmer(AvgThickness ~ Age * Diagnostic +(1|Sample) + (1|Sample:Diagnostic) , data = filter(dados_datasetscomp, ROI == "hemisphere"))Model summary and R squared:
summary(m.1)## Linear mixed model fit by REML ['lmerMod']
## Formula:
## AvgThickness ~ Age * Diagnostic + (1 | Sample) + (1 | Sample:Diagnostic)
## Data: filter(dados_datasetscomp, ROI == "hemisphere")
##
## REML criterion at convergence: -13111.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.8038 -0.6167 0.0104 0.6495 3.4140
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample:Diagnostic (Intercept) 0.0006026 0.02455
## Sample (Intercept) 0.0071880 0.08478
## Residual 0.0110146 0.10495
## Number of obs: 7912, groups: Sample:Diagnostic, 13; Sample, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.7038037 0.0270734 99.869
## Age -0.0044384 0.0001077 -41.194
## DiagnosticAD -0.3421007 0.0405587 -8.435
## Age:DiagnosticAD 0.0029937 0.0004098 7.305
##
## Correlation of Fixed Effects:
## (Intr) Age DgnsAD
## Age -0.172
## DiagnostcAD -0.084 0.183
## Ag:DgnstcAD 0.045 -0.263 -0.762r.squaredGLMM(m.1)## R2m R2c
## [1,] 0.4615061 0.6845916m.1 <- lme4::lmer(TotalArea ~ Age * Diagnostic +(1|Sample) + (1|Sample:Diagnostic) , data = filter(dados_datasetscomp, ROI == "hemisphere"))Model summary and R squared:
summary(m.1)## Linear mixed model fit by REML ['lmerMod']
## Formula: TotalArea ~ Age * Diagnostic + (1 | Sample) + (1 | Sample:Diagnostic)
## Data: filter(dados_datasetscomp, ROI == "hemisphere")
##
## REML criterion at convergence: 167642
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9066 -0.7162 -0.0476 0.6633 4.2777
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample:Diagnostic (Intercept) 0 0
## Sample (Intercept) 23918246 4891
## Residual 93180143 9653
## Number of obs: 7912, groups: Sample:Diagnostic, 13; Sample, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 113025.061 1543.436 73.229
## Age -244.189 9.866 -24.750
## DiagnosticAD -13184.422 2852.379 -4.622
## Age:DiagnosticAD 152.485 37.675 4.047
##
## Correlation of Fixed Effects:
## (Intr) Age DgnsAD
## Age -0.277
## DiagnostcAD -0.073 0.259
## Ag:DgnstcAD 0.072 -0.263 -0.992
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularr.squaredGLMM(m.1)## R2m R2c
## [1,] 0.2353489 0.3915348m.1 <- lme4::lmer(ExposedArea ~ Age * Diagnostic +(1|Sample) + (1|Sample:Diagnostic) , data = filter(dados_datasetscomp, ROI == "hemisphere"))Model summary and R squared:
summary(m.1)## Linear mixed model fit by REML ['lmerMod']
## Formula:
## ExposedArea ~ Age * Diagnostic + (1 | Sample) + (1 | Sample:Diagnostic)
## Data: filter(dados_datasetscomp, ROI == "hemisphere")
##
## REML criterion at convergence: 148576.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8868 -0.7093 -0.0618 0.6706 3.8432
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample:Diagnostic (Intercept) 0 0.0
## Sample (Intercept) 279141 528.3
## Residual 8382585 2895.3
## Number of obs: 7912, groups: Sample:Diagnostic, 13; Sample, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 40714.770 207.022 196.669
## Age -40.602 2.859 -14.204
## DiagnosticAD -262.931 853.269 -0.308
## Age:DiagnosticAD 1.535 11.275 0.136
##
## Correlation of Fixed Effects:
## (Intr) Age DgnsAD
## Age -0.597
## DiagnostcAD -0.151 0.249
## Ag:DgnstcAD 0.152 -0.255 -0.992
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularr.squaredGLMM(m.1)## R2m R2c
## [1,] 0.09982676 0.1288366m.1 <- lme4::lmer(localGI ~ Age * Diagnostic +(1|Sample) + (1|Sample:Diagnostic) , data = filter(dados_datasetscomp, ROI == "hemisphere"))Model summary and R squared:
summary(m.1)## Linear mixed model fit by REML ['lmerMod']
## Formula: localGI ~ Age * Diagnostic + (1 | Sample) + (1 | Sample:Diagnostic)
## Data: filter(dados_datasetscomp, ROI == "hemisphere")
##
## REML criterion at convergence: -14689.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7049 -0.6845 -0.0074 0.6784 6.4692
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample:Diagnostic (Intercept) 0.000000 0.00000
## Sample (Intercept) 0.012446 0.11156
## Residual 0.009017 0.09496
## Number of obs: 7912, groups: Sample:Diagnostic, 13; Sample, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.7763677 0.0339367 81.81
## Age -0.0034689 0.0000975 -35.58
## DiagnosticAD -0.3178661 0.0280689 -11.32
## Age:DiagnosticAD 0.0037476 0.0003707 10.11
##
## Correlation of Fixed Effects:
## (Intr) Age DgnsAD
## Age -0.124
## DiagnostcAD -0.033 0.260
## Ag:DgnstcAD 0.033 -0.264 -0.992
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularr.squaredGLMM(m.1)## R2m R2c
## [1,] 0.2623413 0.6900971m.1 <- lme4::lmer(K ~ Age * Diagnostic +(1|Sample) + (1|Sample:Diagnostic) , data = filter(dados_datasetscomp, ROI == "hemisphere"))Model summary and R squared:
summary(m.1)## Linear mixed model fit by REML ['lmerMod']
## Formula: K ~ Age * Diagnostic + (1 | Sample) + (1 | Sample:Diagnostic)
## Data: filter(dados_datasetscomp, ROI == "hemisphere")
##
## REML criterion at convergence: -42555
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.9733 -0.6065 0.0381 0.6446 5.2725
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample:Diagnostic (Intercept) 4.607e-06 0.002146
## Sample (Intercept) 4.410e-04 0.021001
## Residual 2.658e-04 0.016304
## Number of obs: 7912, groups: Sample:Diagnostic, 13; Sample, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -4.920e-01 6.412e-03 -76.73
## Age -8.612e-04 1.675e-05 -51.40
## DiagnosticAD -8.439e-02 5.444e-03 -15.50
## Age:DiagnosticAD 8.855e-04 6.366e-05 13.91
##
## Correlation of Fixed Effects:
## (Intr) Age DgnsAD
## Age -0.113
## DiagnostcAD -0.039 0.221
## Ag:DgnstcAD 0.030 -0.264 -0.882r.squaredGLMM(m.1)## R2m R2c
## [1,] 0.4372324 0.7897408m.1 <- lme4::lmer(S ~ Age * Diagnostic +(1|Sample) + (1|Sample:Diagnostic) , data = filter(dados_datasetscomp, ROI == "hemisphere"))Model summary and R squared:
summary(m.1)## Linear mixed model fit by REML ['lmerMod']
## Formula: S ~ Age * Diagnostic + (1 | Sample) + (1 | Sample:Diagnostic)
## Data: filter(dados_datasetscomp, ROI == "hemisphere")
##
## REML criterion at convergence: -10789.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5837 -0.6903 -0.0315 0.6406 5.5519
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample:Diagnostic (Intercept) 0.0003168 0.01780
## Sample (Intercept) 0.0057953 0.07613
## Residual 0.0147855 0.12160
## Number of obs: 7912, groups: Sample:Diagnostic, 13; Sample, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 9.0840260 0.0242645 374.375
## Age 0.0017030 0.0001246 13.664
## DiagnosticAD 0.1969053 0.0413152 4.766
## Age:DiagnosticAD -0.0013879 0.0004747 -2.924
##
## Correlation of Fixed Effects:
## (Intr) Age DgnsAD
## Age -0.222
## DiagnostcAD -0.079 0.212
## Ag:DgnstcAD 0.058 -0.263 -0.866r.squaredGLMM(m.1)## R2m R2c
## [1,] 0.1515722 0.399721m.1 <- lme4::lmer(I ~ Age * Diagnostic +(1|Sample) + (1|Sample:Diagnostic) , data = filter(dados_datasetscomp, ROI == "hemisphere"))Model summary and R squared:
summary(m.1)## Linear mixed model fit by REML ['lmerMod']
## Formula: I ~ Age * Diagnostic + (1 | Sample) + (1 | Sample:Diagnostic)
## Data: filter(dados_datasetscomp, ROI == "hemisphere")
##
## REML criterion at convergence: -17050
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.9306 -0.6593 -0.0005 0.6762 3.2113
##
## Random effects:
## Groups Name Variance Std.Dev.
## Sample:Diagnostic (Intercept) 0.0001859 0.01364
## Sample (Intercept) 0.0013945 0.03734
## Residual 0.0067038 0.08188
## Number of obs: 7912, groups: Sample:Diagnostic, 13; Sample, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.053e+01 1.259e-02 836.069
## Age -3.118e-03 8.371e-05 -37.254
## DiagnosticAD -1.829e-01 2.848e-02 -6.421
## Age:DiagnosticAD 1.747e-03 3.196e-04 5.467
##
## Correlation of Fixed Effects:
## (Intr) Age DgnsAD
## Age -0.287
## DiagnostcAD -0.109 0.200
## Ag:DgnstcAD 0.075 -0.262 -0.844r.squaredGLMM(m.1)## R2m R2c
## [1,] 0.4505911 0.5554086