Tractografia

METRICAS (eixo y da tratometria) ad - difusividade axial ADt - difusividade axial corrigida por água livre fa - anisotropia fracionada FAt - anisotropia fracionada corrigida por água livre FW - água livre md - difusividade média MDt - difusividade média corrigida por água livre rd - difusividade radial RDt - difusividade radial corrigida por água livre nufo - número de orientações de fibras afd_total - densidade de fibra aparente (para voxel) afd_fixel - densidade de fibra aparente (para fixel)

TBSS - Tract-Based Spatial Statistics (análise baseada no voxel)

Pré Processamento

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.5     ✓ dplyr   1.0.7
## ✓ tibble  3.1.3     ✓ stringr 1.4.0
## ✓ readr   2.0.1     ✓ forcats 0.5.1
## ✓ purrr   0.3.4

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

## 
## Attaching package: 'rstatix'

## The following object is masked from 'package:stats':
## 
##     filter

## 'data.frame':    83 obs. of  48 variables:
##  $ id                           : chr  "AAM06101971" "ABA12011990" "ACC21111991" "ACD20031998" ...
##  $ idade_x                      : int  49 30 28 22 46 39 40 30 56 52 ...
##  $ grupo_moca                   : int  1 1 1 2 2 2 1 1 2 2 ...
##  $ grupo_fadiga                 : chr  "COVID.Fatigue+" NA "COVID.Fatigue+" "Control" ...
##  $ escolaridade                 : int  16 14 18 13 15 15 16 13 17 11 ...
##  $ gender                       : Factor w/ 2 levels "F","M": 1 2 1 1 2 2 1 2 2 2 ...
##  $ cfq_11_mental_fatigue_score  : int  8 NA 7 11 0 3 8 0 6 0 ...
##  $ cfq_11_physical_fatigue_score: int  14 NA 13 15 0 0 12 0 7 0 ...
##  $ pdcrs_relogio_total          : int  8 9 10 10 10 10 10 10 10 10 ...
##  $ cube_total_error_score       : int  0 0 0 0 8 0 4 0 0 3 ...
##  $ swmbe                        : int  25 10 21 14 23 0 30 0 22 31 ...
##  $ af_l                         : num  0.266 0.267 0.251 0.254 0.263 ...
##  $ af_r                         : num  0.28 0.285 0.274 0.262 0.256 ...
##  $ cc_1                         : num  0.255 0.219 0.231 0.231 0.229 ...
##  $ cc_2a                        : num  0.239 0.229 0.218 0.209 0.225 ...
##  $ cc_2b                        : num  0.254 0.254 0.239 0.231 0.245 ...
##  $ cc_3                         : num  0.278 0.282 0.259 0.256 0.265 ...
##  $ cc_4                         : num  0.29 0.3 0.285 0.274 0.289 ...
##  $ cc_5                         : num  0.293 0.31 0.302 0.294 0.304 ...
##  $ cc_6                         : num  0.283 0.299 0.287 0.278 0.297 ...
##  $ cc_7                         : num  0.289 0.294 0.29 0.281 0.295 ...
##  $ cg_l                         : num  0.282 0.279 0.265 0.264 0.267 ...
##  $ cg_r                         : num  0.281 0.285 0.266 0.269 0.275 ...
##  $ cr_l                         : num  0.287 0.299 0.282 0.285 0.282 ...
##  $ cr_r                         : num  0.291 0.317 0.305 0.294 0.283 ...
##  $ cst_l                        : num  0.293 0.306 0.282 0.292 0.292 ...
##  $ cst_r                        : num  0.298 0.324 0.306 0.298 0.291 ...
##  $ ifof_l                       : num  0.272 0.264 0.249 0.246 0.256 ...
##  $ ifof_r                       : num  0.277 0.271 0.27 0.259 0.252 ...
##  $ ilf_l                        : num  0.266 0.257 0.243 0.242 0.26 ...
##  $ ilf_r                        : num  0.276 0.269 0.263 0.256 0.247 ...
##  $ mcp                          : num  0.294 0.306 0.285 0.269 0.259 ...
##  $ or_l                         : num  0.269 0.265 0.254 0.248 0.264 ...
##  $ or_r                         : num  0.281 0.285 0.281 0.276 0.263 ...
##  $ scp_l                        : num  0.316 0.307 0.315 0.3 0.297 ...
##  $ scp_r                        : num  0.33 0.321 0.344 0.31 0.295 ...
##  $ slf_1_l                      : num  0.277 0.276 0.264 0.259 0.289 ...
##  $ slf_1_r                      : num  0.28 0.288 0.278 0.273 0.285 ...
##  $ slf_2_l                      : num  0.276 0.284 0.257 0.259 0.286 ...
##  $ slf_2_r                      : num  0.286 0.299 0.278 0.27 0.269 ...
##  $ slf_3_l                      : num  0.264 0.266 0.245 0.247 0.258 ...
##  $ slf_3_r                      : num  0.269 0.28 0.263 0.253 0.251 ...
##  $ uf_l                         : num  0.266 0.258 0.253 0.246 0.244 ...
##  $ uf_r                         : num  0.259 0.255 0.257 0.24 0.252 ...
##  $ grupo_moca_cat               : Factor w/ 2 levels "control","covid": 2 2 2 1 1 1 2 2 1 1 ...
##  $ grupo_fadiga_cat             : Factor w/ 3 levels "Control","COVID.Fatigue-",..: 3 NA 3 1 1 1 3 2 1 1 ...
##  $ sex                          : num  0 1 0 0 1 1 0 1 1 1 ...
##  $ centered_age                 : num  11.51 -7.49 -9.49 -15.49 8.51 ...

Grupo COVID

Modelo GLM

## # A tibble: 4 × 8
##   source  term       estimate std.error statistic p.value p.value.adj signf 
##   <chr>   <chr>         <dbl>     <dbl>     <dbl>   <dbl>       <dbl> <chr> 
## 1 af_l    grupo_moca  0.00662   0.00278      2.38 0.0197      0.0456  0.0456
## 2 cc_5    grupo_moca  0.00685   0.00289      2.37 0.0203      0.0462  0.0462
## 3 slf_1_l grupo_moca  0.00905   0.00291      3.11 0.00261     0.00906 0.0091
## 4 slf_2_l grupo_moca  0.00821   0.00282      2.91 0.00468     0.0147  0.0147

Modelo PERMUTAÇÃO

Obtemos os mesmos resultados usando o método de permutação abaixo:

## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"

## # A tibble: 4 × 8
##   source  term       estimate std.error statistic p.value p.value.adj signf 
##   <chr>   <chr>         <dbl>     <dbl>     <dbl>   <dbl>       <dbl> <chr> 
## 1 af_l    grupo_moca  0.00662   0.00278      2.38 0.0197      0.0456  0.0456
## 2 cc_5    grupo_moca  0.00685   0.00289      2.37 0.0203      0.0462  0.0462
## 3 slf_1_l grupo_moca  0.00905   0.00291      3.11 0.00261     0.00906 0.0091
## 4 slf_2_l grupo_moca  0.00821   0.00282      2.91 0.00468     0.0147  0.0147

Variáveis significativas para o grupo COVID

## `summarise()` has grouped output by 'grupo_moca_cat'. You can override using the `.groups` argument.

## # A tibble: 4 × 4
## # Groups:   grupo_moca_cat [2]
##   grupo_moca_cat gender  mean     sd
##   <fct>          <fct>  <dbl>  <dbl>
## 1 control        F      0.279 0.0202
## 2 control        M      0.288 0.0196
## 3 covid          F      0.272 0.0190
## 4 covid          M      0.278 0.0166

## Registered S3 methods overwritten by 'parameters':
##   method                           from      
##   as.double.parameters_kurtosis    datawizard
##   as.double.parameters_skewness    datawizard
##   as.double.parameters_smoothness  datawizard
##   as.numeric.parameters_kurtosis   datawizard
##   as.numeric.parameters_skewness   datawizard
##   as.numeric.parameters_smoothness datawizard
##   print.parameters_distribution    datawizard
##   print.parameters_kurtosis        datawizard
##   print.parameters_skewness        datawizard
##   summary.parameters_kurtosis      datawizard
##   summary.parameters_skewness      datawizard

## You can cite this package as:
##      Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach.
##      Journal of Open Source Software, 6(61), 3167, doi:10.21105/joss.03167

Grupo FADIGA

## # A tibble: 4 × 8
##   source  term            estimate std.error statistic p.value p.value.adj signf
##   <chr>   <chr>              <dbl>     <dbl>     <dbl>   <dbl>       <dbl> <chr>
## 1 cc_5    grupo_fadigaCO… -0.0101    0.00336     -2.99 0.00372     0.0175  0.01…
## 2 cc_6    grupo_fadigaCO… -0.00792   0.00286     -2.77 0.00710     0.0286  0.02…
## 3 slf_1_l grupo_fadigaCO… -0.0110    0.00343     -3.20 0.00203     0.00984 0.00…
## 4 slf_2_l grupo_fadigaCO… -0.00867   0.00334     -2.59 0.0114      0.0399  0.03…

No método de permutação abaixo, somente duas variáveis SLF.1.L e SLF.2.L foram significativas.

## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"
## [1] "Settings:  unique SS : numeric variables centered"

## # A tibble: 2 × 8
##   source  term          estimate std.error statistic p.value p.value.adj signf 
##   <chr>   <chr>            <dbl>     <dbl>     <dbl>   <dbl>       <dbl> <chr> 
## 1 slf_1_l grupo_fadiga1  0.00593   0.00198      3.00 0.00371      0.0180 0.018 
## 2 slf_2_l grupo_fadiga1  0.00549   0.00192      2.86 0.00551      0.0239 0.0239

## `summarise()` has grouped output by 'grupo_fadiga_cat'. You can override using the `.groups` argument.

## # A tibble: 6 × 4
## # Groups:   grupo_fadiga_cat [3]
##   grupo_fadiga_cat gender  mean     sd
##   <fct>            <fct>  <dbl>  <dbl>
## 1 Control          F      0.281 0.0188
## 2 Control          M      0.292 0.0179
## 3 COVID.Fatigue-   F      0.279 0.0183
## 4 COVID.Fatigue-   M      0.287 0.0134
## 5 COVID.Fatigue+   F      0.277 0.0157
## 6 COVID.Fatigue+   M      0.278 0.0126