neuroimagen:altdti
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Table of Contents
experimento DTI
pruebas
Siguiendo los pasos de la pipeline 3.0 procesamos las imagenes DTI en intentamos hallar una red que parta de una parte especifica del cortex.
El archivo dti_track.seed se fabrica con 42 regiones distintas,
grep ctx /usr/local/freesurfer/FreeSurferColorLUT.txt | grep 'parietal\|frontal' | awk {'print $1'} > /nas/data/facehbi/dti_track.seed
Estas regiones son,
1003 ctx-lh-caudalmiddlefrontal 1008 ctx-lh-inferiorparietal 1012 ctx-lh-lateralorbitofrontal 1014 ctx-lh-medialorbitofrontal 1027 ctx-lh-rostralmiddlefrontal 1028 ctx-lh-superiorfrontal 1029 ctx-lh-superiorparietal 1032 ctx-lh-frontalpole 2003 ctx-rh-caudalmiddlefrontal 2008 ctx-rh-inferiorparietal 2012 ctx-rh-lateralorbitofrontal 2014 ctx-rh-medialorbitofrontal 2027 ctx-rh-rostralmiddlefrontal 2028 ctx-rh-superiorfrontal 2029 ctx-rh-superiorparietal 2032 ctx-rh-frontalpole 1106 ctx-lh-G_frontal_inf-Opercular_part 1107 ctx-lh-G_frontal_inf-Orbital_part 1108 ctx-lh-G_frontal_inf-Triangular_part 1109 ctx-lh-G_frontal_middle 1110 ctx-lh-G_frontal_superior 1122 ctx-lh-G_parietal_inferior-Angular_part 1123 ctx-lh-G_parietal_inferior-Supramarginal_part 1124 ctx-lh-G_parietal_superior 1154 ctx-lh-S_frontal_inferior 1155 ctx-lh-S_frontal_middle 1156 ctx-lh-S_frontal_superior 1159 ctx-lh-S_intraparietal-and_Parietal_transverse 1177 ctx-lh-S_subparietal 2106 ctx-rh-G_frontal_inf-Opercular_part 2107 ctx-rh-G_frontal_inf-Orbital_part 2108 ctx-rh-G_frontal_inf-Triangular_part 2109 ctx-rh-G_frontal_middle 2110 ctx-rh-G_frontal_superior 2122 ctx-rh-G_parietal_inferior-Angular_part 2123 ctx-rh-G_parietal_inferior-Supramarginal_part 2124 ctx-rh-G_parietal_superior 2154 ctx-rh-S_frontal_inferior 2155 ctx-rh-S_frontal_middle 2156 ctx-rh-S_frontal_superior 2159 ctx-rh-S_intraparietal-and_Parietal_transverse 2177 ctx-rh-S_subparietal
El resultado de probtrackx es una red bastante extensa,
Ahora voy a quitar los giros y demas y quedarme solo con las 16 primeras lineas.
$ cat dti_track.seed 1003 1008 1012 1014 1027 1028 1029 1032 2003 2008 2012 2014 2027 2028 2029 2032
La red que obtengo es ahora bastante similar,
Aplicando esta red como mascara (25%) saco los datos de FA en la red y los puedo comparar con otros datos . Ejemplo, SUVR en la misma visita,
Aislando el cortex
Voy a intentar aislar todas las regiones del cortex para hacer una lista de lo que puedo parcelar
$ grep ctx /usr/local/freesurfer/FreeSurferColorLUT.txt | grep -v "G\|S\|_\|nknown\|#" | awk {'if($1<3000) print $1,$2'} | grep lh | sed 's/-lh//' | awk {'print $2'}
y me quedo con los nombres utiles para que un neurologo me edite la lista,
ctx-bankssts ctx-caudalanteriorcingulate ctx-caudalmiddlefrontal ctx-corpuscallosum ctx-cuneus ctx-entorhinal ctx-fusiform ctx-inferiorparietal ctx-inferiortemporal ctx-isthmuscingulate ctx-lateraloccipital ctx-lateralorbitofrontal ctx-lingual ctx-medialorbitofrontal ctx-middletemporal ctx-parahippocampal ctx-paracentral ctx-parsopercularis ctx-parsorbitalis ctx-parstriangularis ctx-pericalcarine ctx-postcentral ctx-posteriorcingulate ctx-precentral ctx-precuneus ctx-rostralanteriorcingulate ctx-rostralmiddlefrontal ctx-superiorfrontal ctx-superiorparietal ctx-superiortemporal ctx-supramarginal ctx-frontalpole ctx-temporalpole ctx-transversetemporal ctx-insula
Mapa FP MB
Las regiones escogidas son,
ctx-caudalmiddlefrontal ctx-inferiorparietal ctx-middletemporal ctx-parsopercularis ctx-parstriangularis ctx-postcentral ctx-precentral ctx-superiorfrontal ctx-superiorparietal ctx-superiortemporal ctx-supramarginal
Voy a quedarme con lo que necesito ahora,
$ grep ctx /usr/local/freesurfer/FreeSurferColorLUT.txt | grep -v "G\|S\|_\|nknown\|#" | awk {'if($1<3000) print $1,$2'} > tofp.txt $ cat tocut.txt caudalmiddlefrontal inferiorparietal middletemporal parsopercularis parstriangularis postcentral precentral superiorfrontal superiorparietal superiortemporal supramarginal $ grep -f tocut.txt tofp.txt | awk {'print $1'} > dti_track.seed $ cat dti_track.seed 1003 1008 1015 1018 1020 1022 1024 1028 1029 1030 1031 2003 2008 2015 2018 2020 2022 2024 2028 2029 2030 2031
con estas seeds ya puedo correr el experimento.
$ dti_track.pl facehbi ... 3dias ... $ cd /nas/data/facehbi $ for x in `ls -d working/*_probtrack_out`; do mv $x `echo $x | sed 's/out/FPCustom/'`;done $ dti_metrics_alt.pl -path FPCustom facehbi $ dti_track.pl v2MriPet ... 3dias ... $ cd /nas/data/v2MriPet $ for x in `ls -d working/*_probtrack_out`; do mv $x `echo $x | sed 's/out/FPCustom/'`;done $ dti_metrics_alt.pl -path FPCustom v2MriPet
Comparando con DMN
Sorprendentemente, aunque estan relacionados, hay bastante diferencia entre la FA medida en la DMN y la medida en la nueva red,
> summary(v1m)
Call:
lm(formula = DMN_FA_v1 ~ FPCustom_FA_v1, data = dti_c)
Residuals:
Min 1Q Median 3Q Max
-0.031888 -0.006546 0.001095 0.006806 0.026302
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.06472 0.01262 5.129 8.74e-07 ***
FPCustom_FA_v1 0.89746 0.04285 20.943 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.01099 on 152 degrees of freedom
Multiple R-squared: 0.7426, Adjusted R-squared: 0.741
F-statistic: 438.6 on 1 and 152 DF, p-value: < 2.2e-16
> summary(v2m)
Call:
lm(formula = DMN_FA_v2 ~ FPCustom_FA_v2, data = dti_c)
Residuals:
Min 1Q Median 3Q Max
-0.036853 -0.005949 0.000129 0.006172 0.024941
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.07965 0.01236 6.443 1.46e-09 ***
FPCustom_FA_v2 0.83691 0.04172 20.058 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.01042 on 152 degrees of freedom
Multiple R-squared: 0.7258, Adjusted R-squared: 0.724
F-statistic: 402.3 on 1 and 152 DF, p-value: < 2.2e-16
Modelo mixto
Nada raro, la relacion con el SUVR va mas o menos igual.
> model.a <- lm(Custom_FA ~ SUVR , data=idata)
> summary(model.a)
Call:
lm(formula = Custom_FA ~ SUVR, data = idata)
Residuals:
Min 1Q Median 3Q Max
-0.055969 -0.015556 -0.001559 0.015067 0.055285
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.308468 0.007587 40.659 <2e-16 ***
SUVR -0.011063 0.006002 -1.843 0.0662 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.02038 on 306 degrees of freedom
Multiple R-squared: 0.01098, Adjusted R-squared: 0.00775
F-statistic: 3.398 on 1 and 306 DF, p-value: 0.06625
> model.c <- lme(Custom_FA ~ SUVR, random = ~ 1| Subject, data=idata)
> summary(model.c)
Linear mixed-effects model fit by REML
Data: idata
AIC BIC logLik
-1566.58 -1551.686 787.29
Random effects:
Formula: ~1 | Subject
(Intercept) Residual
StdDev: 0.01580869 0.0129124
Fixed effects: Custom_FA ~ SUVR
Value Std.Error DF t-value p-value
(Intercept) 0.30835579 0.008911474 153 34.60211 0.0000
SUVR -0.01097326 0.007035937 153 -1.55960 0.1209
Correlation:
(Intr)
SUVR -0.986
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.10991932 -0.50342918 -0.01151992 0.50847071 1.97546782
Number of Observations: 308
Number of Groups: 154
> anova(model.c, model.a)
Model df AIC BIC logLik Test L.Ratio p-value
model.c 1 4 -1566.580 -1551.686 787.2900
model.a 2 3 -1500.089 -1488.918 753.0443 1 vs 2 68.49128 <.0001
> model.b <- lmer(Custom_FA ~ SUVR + (1| Subject), data=idata)
> summary(model.b)
Linear mixed model fit by REML ['lmerMod']
Formula: Custom_FA ~ SUVR + (1 | Subject)
Data: idata
REML criterion at convergence: -1574.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.10992 -0.50343 -0.01152 0.50847 1.97547
Random effects:
Groups Name Variance Std.Dev.
Subject (Intercept) 0.0002499 0.01581
Residual 0.0001667 0.01291
Number of obs: 308, groups: Subject, 154
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.308356 0.008911 34.60
SUVR -0.010973 0.007036 -1.56
Correlation of Fixed Effects:
(Intr)
SUVR -0.986
> anova(model.b, model.a)
refitting model(s) with ML (instead of REML)
Data: idata
Models:
model.a: Custom_FA ~ SUVR
model.b: Custom_FA ~ SUVR + (1 | Subject)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
model.a 3 -1520.2 -1509 763.08 -1526.2
model.b 4 -1585.9 -1571 796.94 -1593.9 67.704 1 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Modelos
Estratificando por APOE
Tenemos los genotipos de APOE en un CSV,
> head updateAPOE_FACEHBI_051218.csv code_facehbi;Interno;APOE F079;20120457;e3e3 F103;20121210;e3e3 F080;20121207;e2e2 F097;20130137;e3e3 F018;20130447;e3e4 F096;20130432;e3e4 F002;20131084;e3e3 F113;20131063;e3e4 F027;20131385;e3e4
Limpiamos un poco esto,
> awk -F";" {'print $1,$3'} updateAPOE_FACEHBI_051218.csv | sed 's/F//; s/ /,/; s/code_facehbi/Subject/' | sort -n > facehbi_apoe.csv > head facehbi_apoe.csv Subject,APOE 001,e3e4 002,e3e3 003,e3e3 004,e3e3 005,e2e3 006,e3e4 007,e3e3 008,e3e3 009,e3e3
Lo separamos para estratificar,
> sed 's/e2e2\|e2e3/0/; s/e2e4\|e3e3/1/; s/e3e4\|e4e4/2/' facehbi_apoe.csv > facehbi_apoe_strats.csv > head facehbi_apoe_strats.csv Subject,APOE 001,2 002,1 003,1 004,1 005,0 006,2 007,1 008,1 009,1
Esto lo importo en R (mas o menos),
> idatawnp <- read.csv("facehbi_dmn_fa_suvr_np_tmp_v1.csv", sep=";", header=TRUE)
> iapoe <-read.csv("facehbi_apoe_strats.csv", sep=";", header= TRUE)
> idatacustom <- read.csv("facehbi_suvr_dmnfa_customfa_v12.csv", sep=";", header=TRUE)
> idatatmp <-merge(idatawnp, idatacustom, by="Subject")
> idata <-merge(idatatmp, iapoe, by="Subject")
> idata_apoe0 <- idata[idata$APOE == "0",]
Un monton de data aqui pero nos centramos en la visita basal,
> m0 <- lm(idata_apoe0$FPCustom_FA_v1 ~ idata_apoe0$Global_v1.x)
> summary(m0)
Call:
lm(formula = idata_apoe0$FPCustom_FA_v1 ~ idata_apoe0$Global_v1.x)
Residuals:
Min 1Q Median 3Q Max
-0.051447 -0.010969 0.001284 0.009302 0.040698
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.18674 0.07971 2.343 0.0278 *
idata_apoe0$Global_v1.x 0.09175 0.06785 1.352 0.1889
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.02192 on 24 degrees of freedom
Multiple R-squared: 0.07078, Adjusted R-squared: 0.03207
F-statistic: 1.828 on 1 and 24 DF, p-value: 0.1889
OK. ahi no hay nada pero esto solo era para ver si funcionaba la cosa. Vamos a organizar un poco la tabla,
> colnames(idata) [1] "Subject" "DMN_FA_v1.x" "Global_v1.x" [4] "Escolaridad" "male" "LL_Namingtotal_NP" [7] "LL_total_NP" "total_NP" "Piramides_Y_Palmeras_Palab_FAC" [10] "Piramides_Y_Plameras_Imag_FAC" "Kissing_Dancing_Imagenes_FAC" "Kissing_Dancing_Palabras_FAC" [13] "FNAME_TOTAL_FAC" "Action_Namimg_Libre_FAC" "Boston_Libre_FAC" [16] "Boston_Total_FAC" "Global_v1.y" "Global_v2" [19] "DMN_FA_v1.y" "DMN_FA_v2" "FPCustom_FA_v1" [22] "FPCustom_FA_v2" "APOE"
Con esos nombres me voy a equivocar seguro, asi que
> colnames(idata)[colnames(idata) == "Piramides_Y_Plameras_Imag_FAC"] <- "PyP_i" > colnames(idata)[colnames(idata) == "Piramides_Y_Palmeras_Palab_FAC"] <- "PyP_p" > colnames(idata)[colnames(idata) == "Kissing_Dancing_Imagenes_FAC"] <- "KD_i" > colnames(idata)[colnames(idata) == "Kissing_Dancing_Palabras_FAC"] <- "KD_p" > colnames(idata)[colnames(idata) == "LL_Namingtotal_NP"] <- "LL_Naming" > colnames(idata)[colnames(idata) == "LL_total_NP"] <- "LL" > colnames(idata)[colnames(idata) == "total_NP"] <- "NP" > colnames(idata)[colnames(idata) == "FNAME_TOTAL_FAC"] <- "FName" > colnames(idata)[colnames(idata) == "Action_Namimg_Libre_FAC"] <- "ANaming" > colnames(idata)[colnames(idata) == "Boston_Libre_FAC"] <- "Boston_Libre" > colnames(idata)[colnames(idata) == "Boston_Total_FAC"] <- "Boston" > colnames(idata) [1] "Subject" "DMN_FA_v1.x" "Global_v1.x" "Escolaridad" "male" "LL_Naming" [7] "LL" "NP" "PyP_p" "PyP_i" "KD_i" "KD_p" [13] "FName" "ANaming" "Boston_Libre" "Boston" "Global_v1.y" "Global_v2" [19] "DMN_FA_v1.y" "DMN_FA_v2" "FPCustom_FA_v1" "FPCustom_FA_v2" "APOE"
y tambien,
> colnames(idata)[colnames(idata) == "DMN_FA_v1.x"] <- "DMN"
> colnames(idata)[colnames(idata) == "Global_v1.x"] <- "SUVR"
> colnames(idata)[colnames(idata) == "FPCustom_FA_v1"] <- "FPCustom"
> drops <- c("Global_v1.y", "Global_v2", "DMN_FA_v1.y", "DMN_FA_v2", "FPCustom_FA_v2")
> idata[ , !(names(idata) %in% drops)]
> okdata <- idata[ , !(names(idata) %in% drops)]
con lo cual queda mucho mas potable esto,
> colnames(okdata) [1] "Subject" "DMN" "SUVR" "Escolaridad" "male" "LL_Naming" "LL" [8] "NP" "PyP_p" "PyP_i" "KD_i" "KD_p" "FName" "ANaming" [15] "Boston_Libre" "Boston" "FPCustom" "APOE"
Ahora, el objetivo es estudiar las variables neurosicologicas como funcion de la FA en las redes DTI (DMN y FPCustom), estratificando por APOE y usando como covariables SUVR, Genero, Escolaridad y Edad. 
joer q me falta la edad.
> awk -F";" '{print $2,$8}' faceHBI_matriuREF_14-1-19-1.csv | sed 's/ /;/; s/edat/Edad/; s/subject/Subject/' > edad.csv > scp -P 20022 edad.csv detritus.fundacioace.org:facehbi/dti_model/ edad.csv 100% 1312 200.6KB/s 00:00 .
Vale,la meto
> edad_dlc <-read.csv("edad.csv", sep=";", header=TRUE)
> okdata <- merge(okdata, edad_dlc, by = "Subject")
> colnames(okdata)
[1] "Subject" "DMN" "SUVR" "Escolaridad" "male" "LL_Naming" "LL"
[8] "NP" "PyP_p" "PyP_i" "KD_i" "KD_p" "FName" "ANaming"
[15] "Boston_Libre" "Boston" "FPCustom" "APOE" "Edad"
Ahora si. Pero sigue siendo una puñeta. Mejor me hago un script que corra a traves de los modelos 
. Venga, primero a lo bruto, just in case,
library(QuantPsyc)
x<-read.csv("facehbi_dti_np.csv")
Color=c("red","blue")
scan("npvars.names", what = character())->np
scan("nivars.names", what = character())->ni
sink(file = "facehbi_dti_np_models.txt", append = TRUE, type = "output", split = TRUE)
for(i in 1:length(np)){
for(j in 1:length(ni)){
y.data <- x[c(ni[j], np[i], "male", "Edad", "Escolaridad", "SUVR")]
y.data <- y.data[complete.cases(y.data),]
a <- lm( paste ('y.data$', np[i], ' ~ y.data$', ni[j], ' + y.data$male + y.data$Edad + y.data$Escolaridad + y.data$SUVR'))
writeLines(paste("NP: ", np[i], " NI: ", ni[j]))
writeLines(paste("R2: ", summary(a)$adj.r.squared, " p-value: ", summary(a)$coef[2,4]))
beta <- lm.beta(a)
for(k in 1:length(beta)){
writeLines(paste(names(beta[k]), ": ", beta[k]))
}
writeLines(paste("-------"))
}
}
sink()
a ver,
> write.csv(okdata, "facehbi_dti_np.csv")
> source("get_lms.r")
Read 11 items
Read 2 items
Un vistazo a la salida, y effectivamente, no hay nada que hacer aqui. el mas prometedor es este,
NP: NP NI: FPCustom R2: 0.245411609394026 p-value: 0.0332379182003353
Puaf. Vamos a estratificar aver,
okdata0 <- okdata[okdata$APOE == "0",]
> write.csv(okdata0, "facehbi_dti_np.csv")
> source("get_lms.r")
Read 11 items
Read 2 items
Poca cosa aqui,
NP: NP NI: DMN R2: 0.283699960037635 p-value: 0.624010265461206 NP: FName NI: DMN R2: 0.260050114900356 p-value: 0.141503076825646
Seguimos,
okdata1 <- okdata[okdata$APOE == "1",]
> write.csv(okdata1, "facehbi_dti_np.csv")
> source("get_lms.r")
Read 11 items
Read 2 items
Nop.
okdata2 <- okdata[okdata$APOE == "2",]
> write.csv(okdata2, "facehbi_dti_np.csv")
> source("get_lms.r")
Read 11 items
Read 2 items
grrrrrrr…
NP: LL_Naming NI: DMN R2: 0.338308455461783 p-value: 0.00172399799381871 NP: NP NI: FPCustom R2: 0.423468586516873 p-value: 0.344571425163692 NP: KD_p NI: FPCustom R2: 0.300292939478652 p-value: 0.382224137819204 NP: KD_i NI: FPCustom R2: 0.3278128869582 p-value: 0.511816038951647 NP: FName NI: FPCustom R2: 0.377022589868983 p-value: 0.457838263496454 NP: Boston NI: FPCustom R2: 0.305029202245965 p-value: 0.251505671812935 NP: Boston_Libre NI: FPCustom R2: 0.349670089711 p-value: 0.127585742382962
Odio estas cosas, todo porqueria que hay que mirar.
> m0 <- lm(okdata2$LL_Naming ~ okdata2$DMN + okdata2$Escolaridad + okdata2$Edad+ okdata2$SUVR +okdata2$male)
> summary(m0)
Call:
lm(formula = okdata2$LL_Naming ~ okdata2$DMN + okdata2$Escolaridad +
okdata2$Edad + okdata2$SUVR + okdata2$male)
Residuals:
Min 1Q Median 3Q Max
-1.0747 -0.2115 0.1003 0.2248 0.4808
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 20.55919 1.58086 13.005 2.56e-14 ***
okdata2$DMN -13.72502 4.01223 -3.421 0.00172 **
okdata2$Escolaridad 0.04295 0.01474 2.913 0.00648 **
okdata2$Edad -0.03229 0.00971 -3.326 0.00222 **
okdata2$SUVR 0.16477 0.26430 0.623 0.53743
okdata2$male 0.02292 0.13024 0.176 0.86142
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3551 on 32 degrees of freedom
Multiple R-squared: 0.4277, Adjusted R-squared: 0.3383
F-statistic: 4.783 on 5 and 32 DF, p-value: 0.002238
> m0 <- lm(okdata2$LL_Naming ~ okdata2$DMN + okdata2$Escolaridad + okdata2$Edad)
> summary(m0)
Call:
lm(formula = okdata2$LL_Naming ~ okdata2$DMN + okdata2$Escolaridad +
okdata2$Edad)
Residuals:
Min 1Q Median 3Q Max
-1.13733 -0.21729 0.09543 0.23242 0.42227
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 20.529979 1.521191 13.496 3.25e-15 ***
okdata2$DMN -13.286680 3.840520 -3.460 0.00148 **
okdata2$Escolaridad 0.041280 0.014099 2.928 0.00605 **
okdata2$Edad -0.030158 0.008949 -3.370 0.00188 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3469 on 34 degrees of freedom
Multiple R-squared: 0.4198, Adjusted R-squared: 0.3686
F-statistic: 8.201 on 3 and 34 DF, p-value: 0.0003054
Baaaah, pero he escrito papers con menos. Esta es la variable que se llama en la tabla LLNaming_total_NP, a saber que sera.
Seguimos, la segunda asociacion no dice nada,
> m0 <- lm(okdata2$NP ~ okdata2$FPCustom + okdata2$Escolaridad + okdata2$Edad+ okdata2$SUVR + okdata2$male)
> summary(m0)
Call:
lm(formula = okdata2$NP ~ okdata2$FPCustom + okdata2$Escolaridad +
okdata2$Edad + okdata2$SUVR + okdata2$male)
Residuals:
Min 1Q Median 3Q Max
-26.514 -7.122 1.120 5.021 32.711
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 176.2271 44.0144 4.004 0.000346 ***
okdata2$FPCustom 121.2372 126.3732 0.959 0.344571
okdata2$Escolaridad 1.2925 0.5144 2.513 0.017215 *
okdata2$Edad -0.7723 0.3146 -2.455 0.019701 *
okdata2$SUVR -5.1581 9.0583 -0.569 0.573036
okdata2$male -7.5469 4.4865 -1.682 0.102280
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 12.25 on 32 degrees of freedom
Multiple R-squared: 0.5014, Adjusted R-squared: 0.4235
F-statistic: 6.435 on 5 and 32 DF, p-value: 0.0003044
> m0 <- lm(okdata2$NP ~ okdata2$Escolaridad + okdata2$Edad)
> summary(m0)
Call:
lm(formula = okdata2$NP ~ okdata2$Escolaridad + okdata2$Edad)
Residuals:
Min 1Q Median 3Q Max
-27.939 -8.188 -0.075 6.659 37.356
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 209.0838 23.1923 9.015 1.19e-10 ***
okdata2$Escolaridad 1.4459 0.4692 3.082 0.00400 **
okdata2$Edad -0.9078 0.3010 -3.016 0.00475 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 12.41 on 35 degrees of freedom
Multiple R-squared: 0.4404, Adjusted R-squared: 0.4084
F-statistic: 13.77 on 2 and 35 DF, p-value: 3.87e-05
la variable NP esta asociada casi exclusivamente, en este grupo a la edad y la escolaridad del sujeto. Lomismo vale para el resto,
> m0 <- lm(okdata2$KD_p ~ okdata2$FPCustom + okdata2$Escolaridad + okdata2$Edad+ okdata2$SUVR + okdata2$male)
> summary(m0)
Call:
lm(formula = okdata2$KD_p ~ okdata2$FPCustom + okdata2$Escolaridad +
okdata2$Edad + okdata2$SUVR + okdata2$male)
Residuals:
Min 1Q Median 3Q Max
-4.0610 -0.7722 0.1195 0.6817 2.1740
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 59.01916 8.95276 6.592 6.19e-06 ***
okdata2$FPCustom -23.02432 25.62433 -0.899 0.3822
okdata2$Escolaridad 0.27985 0.09696 2.886 0.0107 *
okdata2$Edad -0.08073 0.05345 -1.511 0.1504
okdata2$SUVR -0.74099 1.30206 -0.569 0.5772
okdata2$male -0.11501 0.77994 -0.147 0.8846
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.562 on 16 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.4669, Adjusted R-squared: 0.3003
F-statistic: 2.803 on 5 and 16 DF, p-value: 0.05284
> m0 <- lm(okdata2$KD_i ~ okdata2$FPCustom + okdata2$Escolaridad + okdata2$Edad+ okdata2$SUVR + okdata2$male)
> summary(m0)
Call:
lm(formula = okdata2$KD_i ~ okdata2$FPCustom + okdata2$Escolaridad +
okdata2$Edad + okdata2$SUVR + okdata2$male)
Residuals:
Min 1Q Median 3Q Max
-4.9060 -0.8693 0.0712 1.3062 3.7389
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 67.92106 14.52237 4.677 0.000217 ***
okdata2$FPCustom -27.68934 41.32332 -0.670 0.511816
okdata2$Escolaridad 0.31847 0.15604 2.041 0.057094 .
okdata2$Edad -0.25900 0.08683 -2.983 0.008356 **
okdata2$SUVR 1.23577 2.07456 0.596 0.559237
okdata2$male 1.14538 1.26034 0.909 0.376169
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.539 on 17 degrees of freedom
(15 observations deleted due to missingness)
Multiple R-squared: 0.4806, Adjusted R-squared: 0.3278
F-statistic: 3.146 on 5 and 17 DF, p-value: 0.03432
> m0 <- lm(okdata2$FName ~ okdata2$FPCustom + okdata2$Escolaridad + okdata2$Edad+ okdata2$SUVR + okdata2$male)
> summary(m0)
Call:
lm(formula = okdata2$FName ~ okdata2$FPCustom + okdata2$Escolaridad +
okdata2$Edad + okdata2$SUVR + okdata2$male)
Residuals:
Min 1Q Median 3Q Max
-44.333 -7.319 -1.934 6.399 33.950
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 143.2128 55.3000 2.590 0.01434 *
okdata2$FPCustom -119.3217 158.7761 -0.752 0.45784
okdata2$Escolaridad 1.3655 0.6463 2.113 0.04251 *
okdata2$Edad -1.1730 0.3952 -2.968 0.00564 **
okdata2$SUVR -11.9182 11.3809 -1.047 0.30285
okdata2$male -7.9011 5.6369 -1.402 0.17064
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 15.39 on 32 degrees of freedom
Multiple R-squared: 0.4612, Adjusted R-squared: 0.377
F-statistic: 5.478 on 5 and 32 DF, p-value: 0.0009417
> m0 <- lm(okdata2$Boston ~ okdata2$FPCustom + okdata2$Escolaridad + okdata2$Edad+ okdata2$SUVR + okdata2$male)
> summary(m0)
Call:
lm(formula = okdata2$Boston ~ okdata2$FPCustom + okdata2$Escolaridad +
okdata2$Edad + okdata2$SUVR + okdata2$male)
Residuals:
Min 1Q Median 3Q Max
-15.5752 -1.7398 0.0496 2.4670 7.4385
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 72.9727 15.7856 4.623 6.31e-05 ***
okdata2$FPCustom -52.9013 45.2722 -1.169 0.25151
okdata2$Escolaridad 0.5936 0.1842 3.223 0.00298 **
okdata2$Edad -0.2660 0.1129 -2.357 0.02493 *
okdata2$SUVR 2.6940 3.2501 0.829 0.41350
okdata2$male -0.4492 1.6135 -0.278 0.78257
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.386 on 31 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.4016, Adjusted R-squared: 0.305
F-statistic: 4.16 on 5 and 31 DF, p-value: 0.005237
> m0 <- lm(okdata2$Boston_Libre ~ okdata2$FPCustom + okdata2$Escolaridad + okdata2$Edad+ okdata2$SUVR + okdata2$male)
> summary(m0)
Call:
lm(formula = okdata2$Boston_Libre ~ okdata2$FPCustom + okdata2$Escolaridad +
okdata2$Edad + okdata2$SUVR + okdata2$male)
Residuals:
Min 1Q Median 3Q Max
-9.6225 -1.9833 -0.2538 1.9214 6.9440
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 76.69651 12.78225 6.000 1.08e-06 ***
okdata2$FPCustom -57.40968 36.70011 -1.564 0.12759
okdata2$Escolaridad 0.49401 0.14938 3.307 0.00234 **
okdata2$Edad -0.26234 0.09136 -2.872 0.00719 **
okdata2$SUVR 1.92228 2.63062 0.731 0.47026
okdata2$male -0.14670 1.30294 -0.113 0.91106
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.558 on 32 degrees of freedom
Multiple R-squared: 0.4376, Adjusted R-squared: 0.3497
F-statistic: 4.979 on 5 and 32 DF, p-value: 0.001748
Resumiendo, En los sujetos con el alelo $\epsilon$-4 presente, hay una ligera asociacion entre la variable LLNaming_total_NP y la fraccion de anisotropia en la Default Mode Network, covariada por edady escolaridad de los sujetos de estudio. Todos los demas efectos que puedan observarse son debido al efecto de asociacion de los resultados de los test con la edad y escolaridad de los sujetos. 
Cosas raras
Si hacemos un modelo con todo observamos una cosa curiosa,
> m1 <- lm(okdata$LL_Naming ~ okdata$FPCustom + okdata$SUVR + okdata$Escolaridad + okdata$male +okdata$Edad + okdata$APOE)
> summary(m1)
Call:
lm(formula = okdata$LL_Naming ~ okdata$FPCustom + okdata$SUVR +
okdata$Escolaridad + okdata$male + okdata$Edad + okdata$APOE)
Residuals:
Min 1Q Median 3Q Max
-1.75246 0.02246 0.08062 0.14430 0.30217
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.118960 0.507667 31.751 <2e-16 ***
okdata$FPCustom -2.409148 1.269032 -1.898 0.0596 .
okdata$SUVR -0.173373 0.179192 -0.968 0.3349
okdata$Escolaridad 0.005906 0.005947 0.993 0.3223
okdata$male -0.006072 0.056222 -0.108 0.9141
okdata$Edad -0.005047 0.003865 -1.306 0.1936
okdata$APOE -0.054227 0.045219 -1.199 0.2324
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3216 on 147 degrees of freedom
Multiple R-squared: 0.06926, Adjusted R-squared: 0.03127
F-statistic: 1.823 on 6 and 147 DF, p-value: 0.09836
El modelo es una porqueria pero los residuales indican que hay un estratificacion para la variable LL_Naming,
 |  |
 |
|
Y tal y como indica el ajuste,
> m1 <- lm(okdata$LL_Naming ~ okdata$FPCustom)
> summary(m1)
Call:
lm(formula = okdata$LL_Naming ~ okdata$FPCustom)
Residuals:
Min 1Q Median 3Q Max
-1.88482 0.05739 0.08721 0.12847 0.22546
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.5248 0.3728 41.647 <2e-16 ***
okdata$FPCustom -2.1407 1.2661 -1.691 0.0929 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3248 on 152 degrees of freedom
Multiple R-squared: 0.01846, Adjusted R-squared: 0.012
F-statistic: 2.859 on 1 and 152 DF, p-value: 0.09294
 |  |
 |  |
Nota: Estas variables estan muy sesgadas por los valores dados en la clinica, de ahi este comportamiento raro.
Composites
Vamos a intentar el procedimiento utilizando los composites de NP. Las variables de los composites son,
funcioExecutiva_fluencia funcioExecutiva_velocprocess_IM funcioExecutiva_atencio memoria_fnameProf memoria_fnameNom memoria_wms memoria_rbans gnosia praxia
Saco las variables que necesito,
> awk -F";" '{print $2,$8,$9,$11,$338,$340,$341,$342,$343,$344,$345,$346,$347,$350,$412}' faceHBI_matriuREF_14-1-19-1.csv | sed 's/ /;/g; s/edat/Edad/; s/subject/Subject/; s/Anyos_Escolaridad_FAC/Escolaridad/; s/Sex_1H_0M/female/; s/Global_v1/SUVR/' > facehbi_data.csv > awk -F";" {'print $1,$3'} updateAPOE_FACEHBI_051218.csv | sed 's/F//; s/ /,/; s/code_facehbi/Subject/' | sort -n > facehbi_apoe.csv > sed 's/e2e2\|e2e3/0/; s/e2e4\|e3e3/1/; s/e3e4\|e4e4/2/; s/,/;/' facehbi_apoe.csv > facehbi_apoe_strats.csv > scp -P 20022 facehbi_apoe_strats.csv detritus.fundacioace.org:facehbi/dti_model/ facehbi_apoe_strats.csv > scp -P 20022 facehbi_data.csv detritus.fundacioace.org:facehbi/dti_model/ ............ facehbi_[osotolongo@detritus dti_model]$ awk -F";" '{print $1,$4,$6}' facehbi_suvr_dmnfa_customfa_v12.csv | sed 's/ /;/g; s/DMN_FA_v1/DMN/; s/FPCustom_FA_v1/FPCustom/' > facehbi_dti.csv data.csv
La importo en R,
> fdata <-read.csv("facehbi_data.csv", sep = ";", header=TRUE)
> fapoe <-read.csv("facehbi_apoe_strats.csv", sep = ";", header=TRUE)
> fdti <- read.csv("facehbi_dti.csv", sep=";", header=TRUE)
> okdata <- merge(fdata, fapoe, by = "Subject")
> okdata <- merge(okdata, fdti, by = "Subject")
preparo la lista de variables,
[osotolongo@detritus dti_model]$ cat npvars.names funcioExecutiva_fluencia funcioExecutiva_velocprocess_IM funcioExecutiva_atencio memoria_fnameProf memoria_fnameNom memoria_wms memoria_rbans gnosia praxia [osotolongo@detritus dti_model]$ cat nivars.names DMN FPCustom
y hacemos la primera prueba,
> write.csv(okdata, file="facehbi_dti_np.csv")
> source("get_lms.r")
Read 10 items
Read 2 items
No muy bien hasta ahora,
NP: funcioExecutiva_velocprocess_IM NI: FPCustom R2: 0.315511475678145 p-value: 0.240278609939977
Estratificamos ahora,
> okdata0 <- okdata[okdata$APOE == "0",]
> write.csv(okdata0, file="facehbi_dti_np.csv")
> source("get_lms.r")
Read 10 items
Read 2 items
> okdata1 <- okdata[okdata$APOE == "1",]
> write.csv(okdata1, file="facehbi_dti_np.csv")
> source("get_lms.r")
Read 10 items
Read 2 items
> okdata2 <- okdata[okdata$APOE == "2",]
> write.csv(okdata2, file="facehbi_dti_np.csv")
> source("get_lms.r")
Read 10 items
Read 2 items
Pero sigue el mismo estilo
APOE 0
NP: funcioExecutiva_fluencia NI: DMN R2: 0.243867525131838 p-value: 0.362338135897475 NP: memoria_fnameNom NI: DMN R2: 0.390194941103106 p-value: 0.247812414116152 NP: memoria_wms NI: FPCustom R2: 0.346181066305969 p-value: 0.61110081152783
APOE 1
NP: funcioExecutiva_velocprocess_IM NI: FPCustom R2: 0.312707319581829 p-value: 0.159750199653709
APOE 2
NP: funcioExecutiva_velocprocess_IM NI: DMN R2: 0.438816661720517 p-value: 0.07768386755793 NP: memoria_fnameNom NI: DMN R2: 0.360648103744986 p-value: 0.680280233588883 NP: llenguatge_denom_IM NI: FPCustom R2: 0.301108942185496 p-value: 0.168274379413938
y eso es lo mejorcito. Vamos a mirar un poco mejor los APOE 2 (N=38) 
, que tienen la mejor asociacion.
> m1 <- lm(okdata2$funcioExecutiva_velocprocess_IM ~ okdata2$DMN + okdata2$Edad + okdata2$Escolaridad + okdata2$female + okdata2$SUVR)
> summary(m1)
Call:
lm(formula = okdata2$funcioExecutiva_velocprocess_IM ~ okdata2$DMN +
okdata2$Edad + okdata2$Escolaridad + okdata2$female + okdata2$SUVR)
Residuals:
Min 1Q Median 3Q Max
-1.56334 -0.50510 0.08154 0.34458 2.03163
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.41530 3.18490 0.444 0.65976
okdata2$DMN -14.74565 8.08940 -1.823 0.07768 .
okdata2$Edad 0.02381 0.01956 1.218 0.23226
okdata2$Escolaridad -0.01873 0.02974 -0.630 0.53326
okdata2$female -0.45115 0.26240 -1.719 0.09521 .
okdata2$SUVR 1.68436 0.53322 3.159 0.00345 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7156 on 32 degrees of freedom
Multiple R-squared: 0.5147, Adjusted R-squared: 0.4388
F-statistic: 6.786 on 5 and 32 DF, p-value: 0.0002047
Voy a limpiar un poco a ver,
> m1 <- lm(okdata2$funcioExecutiva_velocprocess_IM ~ okdata2$DMN + okdata2$female + okdata2$SUVR)
> summary(m1)
Call:
lm(formula = okdata2$funcioExecutiva_velocprocess_IM ~ okdata2$DMN +
okdata2$female + okdata2$SUVR)
Residuals:
Min 1Q Median 3Q Max
-1.72431 -0.45826 0.02665 0.49161 1.97888
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.2864 2.3684 1.810 0.079169 .
okdata2$DMN -20.8448 6.7607 -3.083 0.004047 **
okdata2$female -0.4298 0.2608 -1.648 0.108592
okdata2$SUVR 1.9746 0.4927 4.008 0.000317 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7159 on 34 degrees of freedom
Multiple R-squared: 0.4838, Adjusted R-squared: 0.4382
F-statistic: 10.62 on 3 and 34 DF, p-value: 4.464e-05
un poco mas,
> m1 <- lm(okdata2$funcioExecutiva_velocprocess_IM ~ okdata2$DMN + okdata2$SUVR)
> summary(m1)
Call:
lm(formula = okdata2$funcioExecutiva_velocprocess_IM ~ okdata2$DMN +
okdata2$SUVR)
Residuals:
Min 1Q Median 3Q Max
-1.60347 -0.37573 -0.05682 0.41557 2.11191
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.8843 2.3971 2.038 0.049206 *
okdata2$DMN -22.6775 6.8300 -3.320 0.002111 **
okdata2$SUVR 1.8831 0.5014 3.756 0.000629 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7333 on 35 degrees of freedom
Multiple R-squared: 0.4426, Adjusted R-squared: 0.4107
F-statistic: 13.89 on 2 and 35 DF, p-value: 3.617e-05
Vaya, no esta tan mal.
A ver si encajo esto de alguna manera,
APOE 0
> m1 <- lm(okdata0$funcioExecutiva_velocprocess_IM ~ okdata0$DMN + okdata0$Escolaridad + okdata0$SUVR)
> summary(m1)
Call:
lm(formula = okdata0$funcioExecutiva_velocprocess_IM ~ okdata0$DMN +
okdata0$Escolaridad + okdata0$SUVR)
Residuals:
Min 1Q Median 3Q Max
-0.88656 -0.46872 0.04536 0.22426 1.55510
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.64148 2.60484 1.014 0.32157
okdata0$DMN 7.16412 5.08213 1.410 0.17262
okdata0$Escolaridad -0.11401 0.03127 -3.645 0.00143 **
okdata0$SUVR -2.79148 2.09112 -1.335 0.19555
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.6454 on 22 degrees of freedom
Multiple R-squared: 0.3911, Adjusted R-squared: 0.3081
F-statistic: 4.71 on 3 and 22 DF, p-value: 0.01096
APOE 1
> m1 <- lm(okdata1$funcioExecutiva_velocprocess_IM ~ okdata1$DMN + okdata1$Escolaridad + okdata1$SUVR)
> summary(m1)
Call:
lm(formula = okdata1$funcioExecutiva_velocprocess_IM ~ okdata1$DMN +
okdata1$Escolaridad + okdata1$SUVR)
Residuals:
Min 1Q Median 3Q Max
-1.3510 -0.6597 -0.1832 0.3824 4.8138
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8490 2.0202 0.420 0.675360
okdata1$DMN -1.6852 4.8278 -0.349 0.727896
okdata1$Escolaridad -0.0851 0.0217 -3.922 0.000176 ***
okdata1$SUVR 0.8174 0.9136 0.895 0.373455
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9785 on 86 degrees of freedom
Multiple R-squared: 0.1598, Adjusted R-squared: 0.1305
F-statistic: 5.452 on 3 and 86 DF, p-value: 0.001767
APOE 2
> m1 <- lm(okdata2$funcioExecutiva_velocprocess_IM ~ okdata2$DMN + okdata2$Escolaridad + okdata2$SUVR)
> summary(m1)
Call:
lm(formula = okdata2$funcioExecutiva_velocprocess_IM ~ okdata2$DMN +
okdata2$Escolaridad + okdata2$SUVR)
Residuals:
Min 1Q Median 3Q Max
-1.62197 -0.38893 -0.06813 0.44468 2.06993
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.49262 2.45738 1.828 0.07630 .
okdata2$DMN -19.91080 7.67129 -2.595 0.01385 *
okdata2$Escolaridad -0.02460 0.03046 -0.808 0.42484
okdata2$SUVR 1.78264 0.51901 3.435 0.00158 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7369 on 34 degrees of freedom
Multiple R-squared: 0.4531, Adjusted R-squared: 0.4048
F-statistic: 9.388 on 3 and 34 DF, p-value: 0.0001159
Variables de MB
Hay un interes especial en las variables de los tesst “Piramides y Palmeras” y “Kissing and Dancing”. Voy a extraer los valores,
> awk -F";" '{print $2,$8,$9,$11,$286,$287,$288,$289,$412}' faceHBI_matriuREF_14-1-19-1.csv | sed 's/ /;/g; s/edat/Edad/; s/subject/Subject/; s/Anyos_Escolaridad_FAC/Escolaridad/; s/Sex_1H_0M/female/; s/Global_v1/SUVR/; s/Piramides_Y_Palmeras_Palab_FAC/PPp/; s/Piramides_Y_Plameras_Imag_FAC/PPi/; s/Kissing_Dancing_Imagenes_FAC/KDi/; s/Kissing_Dancing_Palabras_FAC/KDp/' > facehbi_data_mb.csv > scp -P 20022 facehbi_data_mb.csv detritus.fundacioace.org:facehbi/dti_model/ facehbi_data_mb.csv 100% 5476 801.5KB/s 00:00
Los cargo, hago un composite con estas variables y miro los modelos.
> fdata <-read.csv("facehbi_data_mb.csv", sep = ";", header=TRUE)
> fapoe <-read.csv("facehbi_apoe_strats.csv", sep = ";", header=TRUE)
> fdti <- read.csv("facehbi_dti.csv", sep=";", header=TRUE)
> okdata <- merge(fdata, fapoe, by = "Subject")
> okdata <- merge(okdata, fdti, by = "Subject")
> okdata$zPPp = (okdata$PPp - mean(okdata$PPp, na.rm = TRUE))/sd(okdata$PPp, na.rm = TRUE)
> okdata$zPPi = (okdata$PPi - mean(okdata$PPi, na.rm = TRUE))/sd(okdata$PPi, na.rm = TRUE)
> okdata$zKDi = (okdata$KDi - mean(okdata$KDi, na.rm = TRUE))/sd(okdata$KDi, na.rm = TRUE)
> okdata$zKDp = (okdata$KDp - mean(okdata$KDp, na.rm = TRUE))/sd(okdata$KDp, na.rm = TRUE)
> np <- data.frame(okdata$zPPp, okdata$zPPi, okdata$zKDp, okdata$zKDi)
> fanp <- fa(np)
> okdata$scop <- fanp$scores
> m1 <- lm(okdata$scop ~ okdata$DMN + okdata$Edad + okdata$Escolaridad + okdata$female + okdata$SUVR)
> summary(m1)
Call:
lm(formula = okdata$scop ~ okdata$DMN + okdata$Edad + okdata$Escolaridad +
okdata$female + okdata$SUVR)
Residuals:
Min 1Q Median 3Q Max
-3.7621 -0.2453 0.2214 0.5313 1.0935
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.79633 2.39102 0.333 0.740
okdata$DMN 3.64776 5.39764 0.676 0.501
okdata$Edad -0.02282 0.01537 -1.484 0.142
okdata$Escolaridad 0.02556 0.02308 1.108 0.272
okdata$female -0.19461 0.23710 -0.821 0.415
okdata$SUVR -0.62693 0.58403 -1.073 0.287
Residual standard error: 0.8891 on 68 degrees of freedom
(80 observations deleted due to missingness)
Multiple R-squared: 0.1081, Adjusted R-squared: 0.04254
F-statistic: 1.649 on 5 and 68 DF, p-value: 0.1591
> m1 <- lm(okdata$scop ~ okdata$FPCustom + okdata$Edad + okdata$Escolaridad + okdata$female + okdata$SUVR)
> summary(m1)
Call:
lm(formula = okdata$scop ~ okdata$FPCustom + okdata$Edad + okdata$Escolaridad +
okdata$female + okdata$SUVR)
Residuals:
Min 1Q Median 3Q Max
-3.8339 -0.2617 0.2257 0.5570 1.1006
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.33594 2.04679 0.653 0.516
okdata$FPCustom 2.58755 4.99434 0.518 0.606
okdata$Edad -0.02482 0.01490 -1.666 0.100
okdata$Escolaridad 0.02702 0.02290 1.180 0.242
okdata$female -0.18170 0.23560 -0.771 0.443
okdata$SUVR -0.61935 0.58992 -1.050 0.297
Residual standard error: 0.8903 on 68 degrees of freedom
(80 observations deleted due to missingness)
Multiple R-squared: 0.1057, Adjusted R-squared: 0.0399
F-statistic: 1.607 on 5 and 68 DF, p-value: 0.1701
> okdata0 <- okdata[okdata$APOE == "0",]
> m1 <- lm(okdata0$scop ~ okdata0$FPCustom + okdata0$Edad + okdata0$Escolaridad + okdata0$female + okdata0$SUVR)
> summary(m1)
Call:
lm(formula = okdata0$scop ~ okdata0$FPCustom + okdata0$Edad +
okdata0$Escolaridad + okdata0$female + okdata0$SUVR)
Residuals:
19 20 21 22 23 24 25 26
0.85452 0.59106 -0.39878 -1.11895 0.04513 0.21929 -0.25185 0.05957
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.46677 14.83812 0.773 0.520
okdata0$FPCustom -24.86515 25.51080 -0.975 0.433
okdata0$Edad -0.09124 0.19281 -0.473 0.683
okdata0$Escolaridad -0.04443 0.13213 -0.336 0.769
okdata0$female 0.38851 1.52692 0.254 0.823
okdata0$SUVR 2.28677 10.61157 0.215 0.849
Residual standard error: 1.142 on 2 degrees of freedom
(18 observations deleted due to missingness)
Multiple R-squared: 0.4025, Adjusted R-squared: -1.091
F-statistic: 0.2695 on 5 and 2 DF, p-value: 0.8972
> okdata1 <- okdata[okdata$APOE == "1",]
> m1 <- lm(okdata1$scop ~ okdata1$FPCustom + okdata1$Edad + okdata1$Escolaridad + okdata1$female + okdata1$SUVR)
> summary(m1)
Call:
lm(formula = okdata1$scop ~ okdata1$FPCustom + okdata1$Edad +
okdata1$Escolaridad + okdata1$female + okdata1$SUVR)
Residuals:
Min 1Q Median 3Q Max
-1.7087 -0.3229 0.1671 0.4659 0.7777
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.709449 2.410371 -0.294 0.770
okdata1$FPCustom 1.379688 4.589883 0.301 0.765
okdata1$Edad -0.014505 0.013777 -1.053 0.299
okdata1$Escolaridad 0.006632 0.022277 0.298 0.768
okdata1$female -0.033525 0.229931 -0.146 0.885
okdata1$SUVR 1.141289 1.255969 0.909 0.369
Residual standard error: 0.6507 on 38 degrees of freedom
(46 observations deleted due to missingness)
Multiple R-squared: 0.04695, Adjusted R-squared: -0.07845
F-statistic: 0.3744 on 5 and 38 DF, p-value: 0.863
> okdata2 <- okdata[okdata$APOE == "2",]
> m1 <- lm(okdata2$scop ~ okdata2$FPCustom + okdata2$Edad + okdata2$Escolaridad + okdata2$female + okdata2$SUVR)
> summary(m1)
Call:
lm(formula = okdata2$scop ~ okdata2$FPCustom + okdata2$Edad +
okdata2$Escolaridad + okdata2$female + okdata2$SUVR)
Residuals:
Min 1Q Median 3Q Max
-3.6076 -0.3908 0.1920 0.7653 1.2746
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.67538 7.36103 0.363 0.721
okdata2$FPCustom -2.34987 21.06438 -0.112 0.913
okdata2$Edad -0.04374 0.04392 -0.996 0.334
okdata2$Escolaridad 0.10479 0.07971 1.315 0.207
okdata2$female -0.52022 0.64123 -0.811 0.429
okdata2$SUVR -0.47680 1.07041 -0.445 0.662
Residual standard error: 1.285 on 16 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.259, Adjusted R-squared: 0.02737
F-statistic: 1.118 on 5 and 16 DF, p-value: 0.3898
Con lo cual me convenzo de que esto no vale para nada.
neuroimagen/altdti.1552903884.txt.gz · Last modified: 2020/08/04 10:45 (external edit)