SI_Tables_Figures_Results2.Rmd

---
title: "Manuscript 2 from Fernanda Hansen P. de Moraes Thesis - Cortical folding alterations in humans due to aging and diseases"
author: "Fernanda Hansen Pacheco de Moraes"
date: "18 jan 2022"
output:
  html_document: 
    fig_caption: yes
    fig_width: 8
    number_sections: yes
    theme: paper
    toc: yes
editor_options: 
  chunk_output_type: inline
---

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](mailto:fernandahmoraes@gmail.com){.email})
in case any help is needed, our aim is to improve the code as needed!

# RMARKDOWN AND R SETUP
```{r setup, include=FALSE}
knitr::opts_chunk$set(
	echo = TRUE,
	message = FALSE,
	warning = FALSE,
	cache = TRUE
)
```

```{r working directory}
setwd("~/GitHub/Typical-values")
```

```{r functions, message=FALSE, warning=FALSE}
## 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")
```

```{r call packages}
library(readr)
library(tidyverse)
library(lubridate)
library(ggpubr)
library(kableExtra)
library(broom)
library(lme4)
library(lsmeans)
library(MuMIn)
library(arm)
library(effects)

```

```{r} 
# 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 for random process

```{r}
set.seed(1)
```

```{r import files}
# dados_datasetscomp <- read_csv("dados_datasetscomp2.csv")
dados_datasetscomp <- read_csv("data_typical_values.csv")

dados_datasetscomp <- dados_datasetscomp %>%
  filter(Sample != "HCP500r", Diagnostic != "MCI")

```

# DATA PREPARATION
```{r create new variables}
# 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)
```

```{r data cleanup}
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")

```

# Deaging

```{r deaging}
# 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"))
```

## GM volume

```{r}
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)
  )
```

## AvgThickness

```{r}
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)
  )

```

## TotalArea

```{r}
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)
  )

```

## ExposedArea

```{r}
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)
  )

```

## Lobes correction factor

```{r}
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)
  )

```

```{r}
# ----
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)
  )
```

# RESULTS

## Data description
**Table 1**
```{r, echo=FALSE, message=FALSE, warning=FALSE}
filter(dados_datasetscomp,
       Sample != "Mota&Houzel2015",
       ROI == "hemisphere") %>%
  group_by(Sample, Diagnostic) %>%
  summarise(
    N = n_distinct(SUBJ),
    age = paste(signif(mean(Age), 2), "±", signif(sd(Age), 2)),
    age_range = paste(signif(min(Age), 2), "; ", signif(max(Age), 2)),
    T =  paste(signif(mean(AvgThickness), 2), "±", signif(sd(AvgThickness), 2)),
    AT =  paste(signif(mean(TotalArea), 2), "±", signif(sd(TotalArea), 2)),
    AE =  paste(signif(mean(ExposedArea), 2), "±", signif(sd(ExposedArea), 2)),
    K =  paste(signif(mean(K), 2), "±", signif(sd(K), 2)),
    S =  paste(signif(mean(S), 2), "±", signif(sd(S), 2)),
    I =  paste(signif(mean(I), 2), "±", signif(sd(I), 2))
  ) %>%
  kable(digits = 2) %>%
  kable_styling()

```

## Linear mixed models summary

### GM volume \~ Age

```{r cache=TRUE}
m.1 <- lme4::lmer(GMvolume ~ Age * Diagnostic +(1|Sample) + (1|Sample:Diagnostic) , data = filter(dados_datasetscomp, ROI == "hemisphere"))
```

Model summary and R squared:
```{r}
summary(m.1)
r.squaredGLMM(m.1)
```
### AvgThickness \~ Age

```{r cache=TRUE}
m.1 <- lme4::lmer(AvgThickness ~ Age * Diagnostic +(1|Sample) + (1|Sample:Diagnostic) , data = filter(dados_datasetscomp, ROI == "hemisphere"))
```

Model summary and R squared:
```{r}
summary(m.1)
r.squaredGLMM(m.1)
```

### TotalArea \~ Age

```{r cache=TRUE}
m.1 <- lme4::lmer(TotalArea ~ Age * Diagnostic +(1|Sample) + (1|Sample:Diagnostic) , data = filter(dados_datasetscomp, ROI == "hemisphere"))
```

Model summary and R squared:
```{r}
summary(m.1)
r.squaredGLMM(m.1)
```

### ExposedArea \~ Age

```{r cache=TRUE}
m.1 <- lme4::lmer(ExposedArea ~ Age * Diagnostic +(1|Sample) + (1|Sample:Diagnostic) , data = filter(dados_datasetscomp, ROI == "hemisphere"))
```

Model summary and R squared:
```{r}
summary(m.1)
r.squaredGLMM(m.1)
```
### GI \~ Age

```{r cache=TRUE}
m.1 <- lme4::lmer(localGI ~ Age * Diagnostic +(1|Sample) + (1|Sample:Diagnostic) , data = filter(dados_datasetscomp, ROI == "hemisphere"))
```

Model summary and R squared:
```{r}
summary(m.1)
r.squaredGLMM(m.1)
```

### K \~ Age

```{r cache=TRUE}
m.1 <- lme4::lmer(K ~ Age * Diagnostic +(1|Sample) + (1|Sample:Diagnostic) , data = filter(dados_datasetscomp, ROI == "hemisphere"))
```

Model summary and R squared:
```{r}
summary(m.1)
r.squaredGLMM(m.1)
```

### S \~ Age

```{r cache=TRUE}
m.1 <- lme4::lmer(S ~ Age * Diagnostic +(1|Sample) + (1|Sample:Diagnostic) , data = filter(dados_datasetscomp, ROI == "hemisphere"))
```

Model summary and R squared:
```{r}
summary(m.1)
r.squaredGLMM(m.1)
```

### I \~ Age

```{r cache=TRUE}
m.1 <- lme4::lmer(I ~ Age * Diagnostic +(1|Sample) + (1|Sample:Diagnostic) , data = filter(dados_datasetscomp, ROI == "hemisphere"))
```

Model summary and R squared:
```{r}
summary(m.1)
r.squaredGLMM(m.1)
```