Datasets description

Session N age age_range AvgT AT AE K S I
1 50 23 ± 1.7 20 ; 26 2.7 ± 0.093 110000 ± 9100 40000 ± 2700 -0.51 ± 0.017 9.1 ± 0.093 10 ± 0.078
2 50 23 ± 1.7 20 ; 26 2.7 ± 0.093 110000 ± 9000 40000 ± 2700 -0.51 ± 0.017 9.1 ± 0.095 10 ± 0.076
3 50 23 ± 1.7 20 ; 26 2.7 ± 0.094 110000 ± 9100 40000 ± 2700 -0.51 ± 0.017 9.1 ± 0.091 10 ± 0.078

Cortical folding model by Mota & Houzel (Science, 2015)

Applying the cortical folding from Mota & Houzel:

term estimate std.error statistic p.value conf.low conf.high
(Intercept) -0.42 0.26 -1.59 0.11 -0.93 0.10
logExposedArea 1.23 0.06 21.65 0.00 1.12 1.34 

## [1] "Verificando a diferenca entre o coeficiente obtido para a Sample e o teorico de 5/4, qual o valor p deste teste? 0.72983083070736"
## [1] "Total number of CTL subjects (Session 1) = 50"
## [1] "Age range CTL. Min age =  20 ; max age =  26"

## 
##  Pearson's product-moment correlation
## 
## data:  filter(dados, ROI == "hemisphere", Diagnostic == "CTL", Session == 1)$K and filter(dados, ROI == "hemisphere", Diagnostic == "CTL", Session == 1)$Age
## t = -3.0804, df = 98, p-value = 0.002683
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4663220 -0.1069411
## sample estimates:
##        cor 
## -0.2971182
## 
##  Pearson's product-moment correlation
## 
## data:  filter(dados, ROI == "hemisphere", Diagnostic == "CTL", Session == 1)$S and filter(dados, ROI == "hemisphere", Diagnostic == "CTL", Session == 1)$Age
## t = 0.53082, df = 98, p-value = 0.5967
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1443931  0.2473602
## sample estimates:
##        cor 
## 0.05354357
## 
##  Pearson's product-moment correlation
## 
## data:  filter(dados, ROI == "hemisphere", Diagnostic == "CTL", Session == 1)$I and filter(dados, ROI == "hemisphere", Diagnostic == "CTL", Session == 1)$Age
## t = -1.9857, df = 98, p-value = 0.04986
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3784688483 -0.0002629164
## sample estimates:
##        cor 
## -0.1966709

Deaged

Sessions

CTL

## [1] "Total number of CTL subjects (Session 1) = 50"
## [1] "Total number of CTL subjects (Session 2) = 50"
## [1] "Total number of CTL subjects (Session 3) = 50"
## [1] "Age range CTL. Min age =  20 ; max age =  26"

Session term estimate std.error statistic p.value conf.low conf.high
1 (Intercept) -0.42 0.26 -1.59 0.11 -0.93 0.10
1 logExposedArea 1.23 0.06 21.65 0.00 1.12 1.34
2 (Intercept) -0.37 0.26 -1.41 0.16 -0.89 0.15
2 logExposedArea 1.22 0.06 21.41 0.00 1.11 1.33
3 (Intercept) -0.49 0.27 -1.82 0.07 -1.02 0.04
3 logExposedArea 1.25 0.06 21.42 0.00 1.13 1.36 

## ANOVA Table (type III tests)
## 
##    Effect DFn DFd     F     p p<.05      ges
## 1 Session   2 198 0.481 0.619       0.000192
## ANOVA Table (type III tests)
## 
##    Effect DFn DFd     F     p p<.05      ges
## 1 Session   2 198 0.138 0.872       1.44e-05
## ANOVA Table (type III tests)
## 
##    Effect  DFn    DFd     F     p p<.05      ges
## 1 Session 1.87 184.73 0.352 0.689       7.98e-05
## ANOVA Table (type III tests)
## 
##    Effect DFn DFd     F     p p<.05      ges
## 1 Session   2 198 0.775 0.462       5.23e-05

## ANOVA Table (type III tests)
## 
##    Effect  DFn    DFd     F     p p<.05   ges
## 1 Session 1.88 186.17 0.196 0.809       1e-05

SUBJ variance

meanT meanK meanS meanI meanGI sd_T sd_K sd_S sd_I sd_GI SD_percent_T SD_percent_K SD_percent_S SD_percent_I SD_percent_GI
2.673792 -0.5058084 9.066871 10.47732 2.692996 0.0187468 0.0016854 0.0137674 0.0064288 0.006937 0.7011324 0.3332081 0.1518431 0.0613596 0.2575937 
## [1] "Desvio padrão médio de AvgT =  0.019 , desvio padrao do desvio padrão =  0.013"
## [1] "Desvio padrão médio de K =  0.0017 , desvio padrao do desvio padrão =  0.0012"
## [1] "Desvio padrão médio de S =  0.014 , desvio padrao do desvio padrão =  0.01"
## [1] "Desvio padrão médio de I =  0.0064 , desvio padrao do desvio padrão =  0.0043"
## [1] "Desvio padrão médio de GI =  0.0069 , desvio padrao do desvio padrão =  0.0047"

AvgCortThickness ~ SUBJ

## Analysis of Variance Table
##         npar     Sum Sq    Mean Sq F value
## Session    2 0.00049756 0.00024878  0.4626
## Linear mixed model fit by REML ['lmerMod']
## Formula: AvgThickness ~ Session + (1 | SUBJ)
##    Data: filter(dados, ROI == "hemisphere")
## 
## REML criterion at convergence: -1156.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.8173 -0.5144 -0.0243  0.5220  3.4278 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev.
##  SUBJ     (Intercept) 0.0082696 0.09094 
##  Residual             0.0005378 0.02319 
## Number of obs: 300, groups:  SUBJ, 50
## 
## Fixed effects:
##               Estimate Std. Error t value
## (Intercept)  2.6731078  0.0130679 204.555
## Session2    -0.0004355  0.0032797  -0.133
## Session3     0.0024880  0.0032797   0.759
## 
## Correlation of Fixed Effects:
##          (Intr) Sessn2
## Session2 -0.125       
## Session3 -0.125  0.500
## $sigma
## $sigma$data
## [1] 0.02319103
## 
## $sigma$SUBJ
## (Intercept) 
##  0.09093744 
## 
## 
## $cors
## $cors$data
## [1] NA
## 
## $cors$SUBJ
## [1] NA
##               R2m       R2c
## [1,] 0.0001889038 0.9389468
Session N_SUBJ mean CI
1 50 2.7 ± 0.013 2.6 ; 2.7
2 50 2.7 ± 0.013 2.6 ; 2.7
3 50 2.7 ± 0.013 2.6 ; 2.7 

##                  Estimate  Std..Error     t.value       p.z
## (Intercept)  2.6731078135 0.013067922 204.5549192 0.0000000
## Session2    -0.0004354916 0.003279707  -0.1327837 0.8943645
## Session3     0.0024880127 0.003279707   0.7586082 0.4480870

K ~ SUBJ

## Analysis of Variance Table
##         npar     Sum Sq    Mean Sq F value
## Session    2 1.1892e-06 5.9459e-07  0.0436
## Linear mixed model fit by REML ['lmerMod']
## Formula: K ~ Session + (1 | SUBJ)
##    Data: filter(dados, ROI == "hemisphere")
## 
## REML criterion at convergence: -2236.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7507 -0.5668 -0.0046  0.6140  3.5112 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev.
##  SUBJ     (Intercept) 2.675e-04 0.016356
##  Residual             1.363e-05 0.003692
## Number of obs: 300, groups:  SUBJ, 50
## 
## Fixed effects:
##               Estimate Std. Error  t value
## (Intercept) -5.059e-01  2.342e-03 -215.969
## Session2     4.939e-05  5.221e-04    0.095
## Session3     1.512e-04  5.221e-04    0.290
## 
## Correlation of Fixed Effects:
##          (Intr) Sessn2
## Session2 -0.111       
## Session3 -0.111  0.500
## $sigma
## $sigma$data
## [1] 0.003691925
## 
## $sigma$SUBJ
## (Intercept) 
##  0.01635589 
## 
## 
## $cors
## $cors$data
## [1] NA
## 
## $cors$SUBJ
## [1] NA
##               R2m       R2c
## [1,] 1.414624e-05 0.9515193
Session N_SUBJ mean CI
1 50 -0.51 ± 0.0023 -0.51 ; -0.5
2 50 -0.51 ± 0.0023 -0.51 ; -0.5
3 50 -0.51 ± 0.0023 -0.51 ; -0.5 

##                  Estimate  Std..Error       t.value       p.z
## (Intercept) -5.058753e-01 0.002342350 -215.96914123 0.0000000
## Session2     4.938863e-05 0.000522117    0.09459304 0.9246381
## Session3     1.512184e-04 0.000522117    0.28962549 0.7721028

S ~ SUBJ

## Analysis of Variance Table
##         npar     Sum Sq    Mean Sq F value
## Session    2 0.00020462 0.00010231  0.2887
## Linear mixed model fit by REML ['lmerMod']
## Formula: S ~ Session + (1 | SUBJ)
##    Data: filter(dados, ROI == "hemisphere")
## 
## REML criterion at convergence: -1260
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3529 -0.5569 -0.0341  0.6093  4.9192 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev.
##  SUBJ     (Intercept) 0.0083676 0.09147 
##  Residual             0.0003544 0.01882 
## Number of obs: 300, groups:  SUBJ, 50
## 
## Fixed effects:
##               Estimate Std. Error t value
## (Intercept)  9.0670569  0.0130727  693.59
## Session2     0.0007201  0.0026622    0.27
## Session3    -0.0012771  0.0026622   -0.48
## 
## Correlation of Fixed Effects:
##          (Intr) Sessn2
## Session2 -0.102       
## Session3 -0.102  0.500
## $sigma
## $sigma$data
## [1] 0.01882455
## 
## $sigma$SUBJ
## (Intercept) 
##  0.09147459 
## 
## 
## $cors
## $cors$data
## [1] NA
## 
## $cors$SUBJ
## [1] NA
##               R2m       R2c
## [1,] 7.845553e-05 0.9593743
Session N_SUBJ mean CI
1 50 9.1 ± 0.013 9 ; 9.1
2 50 9.1 ± 0.013 9 ; 9.1
3 50 9.1 ± 0.013 9 ; 9.1 

##                  Estimate  Std..Error     t.value       p.z
## (Intercept)  9.0670568552 0.013072706 693.5868428 0.0000000
## Session2     0.0007200822 0.002662194   0.2704845 0.7867875
## Session3    -0.0012771442 0.002662194  -0.4797337 0.6314167

I ~ SUBJ

## Analysis of Variance Table
##         npar     Sum Sq    Mean Sq F value
## Session    2 9.2936e-05 4.6468e-05  0.6882
## Linear mixed model fit by REML ['lmerMod']
## Formula: I ~ Session + (1 | SUBJ)
##    Data: filter(dados, ROI == "hemisphere")
## 
## REML criterion at convergence: -1687.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.0083 -0.5579  0.0350  0.5825  3.0375 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev.
##  SUBJ     (Intercept) 5.980e-03 0.077333
##  Residual             6.753e-05 0.008217
## Number of obs: 300, groups:  SUBJ, 50
## 
## Fixed effects:
##              Estimate Std. Error t value
## (Intercept) 1.048e+01  1.097e-02 955.272
## Session2    1.596e-04  1.162e-03   0.137
## Session3    1.252e-03  1.162e-03   1.078
## 
## Correlation of Fixed Effects:
##          (Intr) Sessn2
## Session2 -0.053       
## Session3 -0.053  0.500
## $sigma
## $sigma$data
## [1] 0.008217418
## 
## $sigma$SUBJ
## (Intercept) 
##  0.07733324 
## 
## 
## $cors
## $cors$data
## [1] NA
## 
## $cors$SUBJ
## [1] NA
##               R2m       R2c
## [1,] 5.139036e-05 0.9888355
Session N_SUBJ mean CI
1 50 10 ± 0.011 10 ; 10
2 50 10 ± 0.011 10 ; 10
3 50 10 ± 0.011 10 ; 11 

##                 Estimate  Std..Error     t.value       p.z
## (Intercept) 1.047685e+01 0.010967400 955.2723277 0.0000000
## Session2    1.596154e-04 0.001162118   0.1373486 0.8907552
## Session3    1.252383e-03 0.001162118   1.0776721 0.2811801