ABIDE data

Nick Tustison

November 23, 2014

Demographics

Preprocessing: subset and matching

results <- read.csv( './Data/labelresultsAnts.csv' )
resultsSubset <- results[which( results$gender == 1 & results$age >= 6 & results$age <= 35 & results$handedness == 'R' ),]
drops <- c( "area.5th.ventricle", "area.left.lesion", "area.right.lesion", "area.left.vessel", "area.right.vessel", "area.optic.chiasm",
"volume.5th.ventricle", "volume.left.lesion", "volume.right.lesion", "volume.left.vessel", "volume.right.vessel", "volume.optic.chiasm",
"jacobian.5th.ventricle", "jacobian.left.lesion", "jacobian.right.lesion", "jacobian.left.vessel", "jacobian.right.vessel", "jacobian.optic.chiasm" )
resultsSubset <- resultsSubset[, !( names( resultsSubset ) %in% drops )]

# match the data based on age and fiq
library( MatchIt )

## Loading required package: MASS

resultsDf <- data.frame( subject.id = resultsSubset$subject.id,
                         dx.group = resultsSubset$dx.group,
                         age = resultsSubset$age,
                         fiq = resultsSubset$fiq
                       )
resultsDf$dx.group <- as.numeric( resultsDf$dx.group ) - 1
resultsDf <- na.omit( resultsDf )
resultsDf <- match.data( matchit( dx.group ~ age + fiq, data = resultsDf, method = "nearest", discard = "both" ) )

## Warning: Fewer control than treated units and matching without
## replacement.  Not all treated units will receive a match.  Treated units
## will be matched in the order specified by m.order: largest

resultsMatched <- resultsSubset[which( is.element( resultsSubset$subject.id, resultsDf$subject.id ) ), ]
write.csv( resultsMatched, './Data/labelresultsANTsSubset.csv', quote = FALSE, row.names = FALSE )

All sites

Total number of studies = 450

males = 450, females = 0

autism = 225, controls = 225

rights = 450, lefts = 0, ambis = 0

Age distribution

Gross results

Brain volume distribution

CSF volume distribution

Gray matter volume distribution

White matter volume distribution

Deep gray matter volume distribution

Total mean thickness distribution

Per site demographics

Site demographics

CALTECH: n = 11 (males = 11, females = 0) CMU: n = 15 (males = 15, females = 0) KKI: n = 27 (males = 27, females = 0) LEUVEN_1: n = 25 (males = 25, females = 0) MAX_MUN: n = 38 (males = 38, females = 0) OHSU: n = 19 (males = 19, females = 0) OLIN: n = 22 (males = 22, females = 0) PITT: n = 36 (males = 36, females = 0) SDSU: n = 22 (males = 22, females = 0) STANFORD: n = 18 (males = 18, females = 0)

Site demographics (continued)

TRINITY: n = 43 (males = 43, females = 0) UCLA_1: n = 53 (males = 53, females = 0) UCLA_2: n = 18 (males = 18, females = 0) UM_1: n = 50 (males = 50, females = 0) UM_2: n = 27 (males = 27, females = 0) YALE: n = 26 (males = 26, females = 0)

Age

Per site gross results

Brain volume

CSF volume

Gray matter volume

White matter volume

Deep gray matter volume

Total mean thickness

# Paper results

Results: Intracranial volume

All site results: Intracranial volume

library( xtable )
results <- read.csv( './Data/labelresultsAntsSubset.csv' )
fit <- aov( total.volume ~ dx.group + site + age + fiq, data = results )
print( xtable( anova( fit ) ), type = "html" )

Df Sum Sq Mean Sq F value Pr(>F) dx.group 1 635506407.47 635506407.47 0.04 0.8445 site 15 2744732545213.81 182982169680.92 11.09 0.0000 age 1 454992242535.23 454992242535.23 27.58 0.0000 fiq 1 2332337220.80 2332337220.80 0.14 0.7071 Residuals 431 7111246085396.22 16499410870.99

Per site results: Intracranial volume

total.volume ~ dx.group + age + fiq

site dx.Pvalue age.Pvalue fiq.Pvalue CALTECH 0.18 0.17 0.58 CMU 0.74 0.94 0.85 KKI 0.55 0.13 0.08 LEUVEN_1 0.93 0.26 0.26 MAX_MUN 0.36 0.10 0.79 OHSU 0.59 0.22 0.55 OLIN 0.34 0.01 0.85 PITT 0.46 0.38 0.75

Per site results (cont.): Intracranial volume

total.volume ~ dx.group + age + fiq

site dx.Pvalue age.Pvalue fiq.Pvalue SDSU 0.03 0.01 0.97 STANFORD 0.08 0.80 0.57 TRINITY 0.73 0.35 0.25 UCLA_1 0.23 0.18 0.10 UCLA_2 0.51 0.39 0.03 UM_1 0.11 0.00 0.06 UM_2 0.90 0.69 0.98 YALE 0.18 0.00 0.09

Results: Amygdala volume

All site results: Amygdala volume (right)

library( xtable )
results <- read.csv( './Data/labelresultsAntsSubset.csv' )
fit <- aov( volume.right.amygdala ~ dx.group + site + age + fiq, data = results )
print( xtable( anova( fit ) ), type = "html" )

Df Sum Sq Mean Sq F value Pr(>F) dx.group 1 27789.06 27789.06 2.20 0.1391 site 15 1635102.05 109006.80 8.61 0.0000 age 1 387099.58 387099.58 30.59 0.0000 fiq 1 3068.90 3068.90 0.24 0.6227 Residuals 431 5454667.73 12655.84

Per site results: Amygdala volume (right)

volume.right.amygdala ~ dx.group + age + fiq

site dx.Pvalue age.Pvalue fiq.Pvalue CALTECH 0.56 0.13 0.77 CMU 0.88 0.44 0.37 KKI 0.91 0.05 0.40 LEUVEN_1 0.21 0.21 0.86 MAX_MUN 0.65 0.01 0.96 OHSU 0.85 0.06 0.61 OLIN 0.78 0.04 0.18 PITT 0.51 0.18 0.55

Per site results (cont.): Amygdala volume (right)

volume.right.amygdala ~ dx.group + age + fiq

site dx.Pvalue age.Pvalue fiq.Pvalue SDSU 0.77 0.03 0.22 STANFORD 0.66 0.68 0.68 TRINITY 0.40 0.66 0.49 UCLA_1 0.00 0.20 0.03 UCLA_2 0.49 0.03 0.26 UM_1 0.99 0.06 0.54 UM_2 0.59 0.14 0.42 YALE 0.10 0.04 0.39

All site results: Amygdala volume (left)

library( xtable )
results <- read.csv( './Data/labelresultsAntsSubset.csv' )
fit <- aov( volume.left.amygdala ~ dx.group + site + age + fiq, data = results )
print( xtable( anova( fit ) ), type = "html" )

Df Sum Sq Mean Sq F value Pr(>F) dx.group 1 16020.28 16020.28 1.16 0.2814 site 15 1602296.82 106819.79 7.76 0.0000 age 1 307290.75 307290.75 22.31 0.0000 fiq 1 5151.07 5151.07 0.37 0.5412 Residuals 431 5936259.02 13773.22

Per site results: Amygdala volume (left)

volume.left.amygdala ~ dx.group + age + fiq

site dx.Pvalue age.Pvalue fiq.Pvalue CALTECH 0.38 0.21 0.44 CMU 0.52 0.69 0.76 KKI 0.83 0.18 0.49 LEUVEN_1 0.42 0.80 0.95 MAX_MUN 0.36 0.01 0.97 OHSU 0.38 0.30 0.80 OLIN 0.43 0.06 0.33 PITT 0.45 0.32 0.46

Per site results (cont.): Amygdala volume (left)

volume.left.amygdala ~ dx.group + age + fiq

site dx.Pvalue age.Pvalue fiq.Pvalue SDSU 0.21 0.09 0.73 STANFORD 0.09 0.08 0.63 TRINITY 0.35 0.26 0.55 UCLA_1 0.45 0.02 0.13 UCLA_2 0.62 0.26 0.27 UM_1 0.80 0.00 0.05 UM_2 0.07 0.38 0.95 YALE 0.24 0.01 0.48

Results: CSF volume

All sites: CSF volume

library( xtable )
results <- read.csv( './Data/labelresultsAntsSubset.csv' )
fit <- aov( csf.volume ~ dx.group + site + age + fiq, data = results )
print( xtable( anova( fit ) ), type = "html" )

Df Sum Sq Mean Sq F value Pr(>F) dx.group 1 614627510.94 614627510.94 0.50 0.4808 site 15 174831189871.42 11655412658.09 9.44 0.0000 age 1 85957057583.14 85957057583.14 69.62 0.0000 fiq 1 253881444.90 253881444.90 0.21 0.6504 Residuals 431 532118740766.49 1234614247.72

Per site results: CSF volume

csf.volume ~ dx.group + age + fiq

site dx.Pvalue age.Pvalue fiq.Pvalue CALTECH 0.52 0.07 0.66 CMU 0.33 0.40 0.91 KKI 0.78 0.72 0.05 LEUVEN_1 0.75 0.05 0.22 MAX_MUN 0.21 0.00 0.97 OHSU 0.54 0.12 0.54 OLIN 0.11 0.00 0.98 PITT 0.81 0.00 0.80

Per site results (cont.): CSF volume

csf.volume ~ dx.group + age + fiq

site dx.Pvalue age.Pvalue fiq.Pvalue SDSU 0.02 0.00 0.43 STANFORD 0.15 0.95 0.84 TRINITY 0.34 0.00 0.37 UCLA_1 0.54 0.05 0.10 UCLA_2 0.73 0.33 0.05 UM_1 0.30 0.00 0.01 UM_2 0.86 0.34 0.47 YALE 0.69 0.00 0.93

Results: Gray matter volume

All sites: Gray matter volume

library( xtable )
results <- read.csv( './Data/labelresultsAntsSubset.csv' )
fit <- aov( gray.matter.volume ~ dx.group + site + age + fiq, data = results )
print( xtable( anova( fit ) ), type = "html" )

Df Sum Sq Mean Sq F value Pr(>F) dx.group 1 50521.73 50521.73 0.00 0.9971 site 15 537930385772.23 35862025718.15 9.13 0.0000 age 1 178841055.47 178841055.47 0.05 0.8311 fiq 1 12633668.06 12633668.06 0.00 0.9548 Residuals 431 1692807038204.71 3927626538.76

Per site results: Gray matter volume

gray.matter.volume ~ dx.group + age + fiq

site dx.Pvalue age.Pvalue fiq.Pvalue CALTECH 0.13 0.63 0.73 CMU 0.98 0.70 0.93 KKI 0.33 0.72 0.32 LEUVEN_1 0.92 1.00 0.23 MAX_MUN 0.66 0.68 0.74 OHSU 0.28 0.43 0.49 OLIN 0.27 0.18 0.98 PITT 0.30 0.21 0.83

Per site results (cont.): Gray matter volume

gray.matter.volume ~ dx.group + age + fiq

site dx.Pvalue age.Pvalue fiq.Pvalue SDSU 0.06 0.22 0.68 STANFORD 0.05 0.89 0.60 TRINITY 0.90 0.42 0.35 UCLA_1 0.23 0.41 0.12 UCLA_2 0.51 0.10 0.06 UM_1 0.14 0.02 0.20 UM_2 0.99 0.86 0.63 YALE 0.18 0.07 0.04

Results: White matter volume

All sites: White matter volume

library( xtable )
results <- read.csv( './Data/labelresultsAntsSubset.csv' )
fit <- aov( white.matter.volume ~ dx.group + site + age + fiq, data = results )
print( xtable( anova( fit ) ), type = "html" )

Df Sum Sq Mean Sq F value Pr(>F) dx.group 1 30934356.06 30934356.06 0.02 0.9002 site 15 329596906415.16 21973127094.34 11.19 0.0000 age 1 71081230366.45 71081230366.45 36.19 0.0000 fiq 1 455050833.05 455050833.05 0.23 0.6305 Residuals 431 846531930532.55 1964111207.73

Per site results: White matter volume

white.matter.volume ~ dx.group + age + fiq

site dx.Pvalue age.Pvalue fiq.Pvalue CALTECH 0.14 0.10 0.45 CMU 0.80 0.64 0.79 KKI 0.58 0.27 0.03 LEUVEN_1 0.74 0.11 0.31 MAX_MUN 0.20 0.01 0.85 OHSU 0.41 0.24 0.58 OLIN 0.82 0.04 0.87 PITT 0.37 0.10 0.66

Per site results (cont.): White matter volume

white.matter.volume ~ dx.group + age + fiq

site dx.Pvalue age.Pvalue fiq.Pvalue SDSU 0.05 0.01 0.86 STANFORD 0.09 0.25 0.99 TRINITY 0.63 0.24 0.26 UCLA_1 0.25 0.34 0.50 UCLA_2 0.86 0.75 0.07 UM_1 0.12 0.00 0.26 UM_2 0.78 0.75 1.00 YALE 0.20 0.01 0.05

Results: Deep gray matter volume

All sites: Deep gray matter volume

library( xtable )
results <- read.csv( './Data/labelresultsAntsSubset.csv' )
fit <- aov( deep.gray.matter.volume ~ dx.group + site + age + fiq, data = results )
print( xtable( anova( fit ) ), type = "html" )

Df Sum Sq Mean Sq F value Pr(>F) dx.group 1 4239503.15 4239503.15 0.24 0.6277 site 15 603085508.77 40205700.58 2.23 0.0051 age 1 378605809.44 378605809.44 21.04 0.0000 fiq 1 11451993.28 11451993.28 0.64 0.4255 Residuals 431 7757010099.65 17997703.25

Per site results: Deep gray matter volume

deep.gray.matter.volume ~ dx.group + age + fiq

site dx.Pvalue age.Pvalue fiq.Pvalue CALTECH 0.56 0.03 0.38 CMU 0.98 0.86 0.92 KKI 0.40 0.43 0.30 LEUVEN_1 0.58 0.14 0.90 MAX_MUN 0.47 0.01 0.82 OHSU 0.90 0.71 0.15 OLIN 0.46 0.01 0.76 PITT 0.83 0.93 0.47

Per site results (cont.): Deep gray matter volume

deep.gray.matter.volume ~ dx.group + age + fiq

site dx.Pvalue age.Pvalue fiq.Pvalue SDSU 0.30 0.04 0.83 STANFORD 0.02 0.13 0.06 TRINITY 0.28 0.81 0.05 UCLA_1 0.32 0.73 0.11 UCLA_2 0.99 0.80 0.48 UM_1 0.03 0.00 0.01 UM_2 0.92 0.81 1.00 YALE 0.01 0.32 0.47

Results: Total mean thickness

All sites: Total mean thickness

library( xtable )
results <- read.csv( './Data/labelresultsAntsSubset.csv' )
fit <- aov( total.mean.thickness ~ dx.group + site + age + fiq, data = results )
print( xtable( anova( fit ) ), type = "html" )

Df Sum Sq Mean Sq F value Pr(>F) dx.group 1 0.11 0.11 3.20 0.0742 site 15 14.56 0.97 27.69 0.0000 age 1 1.48 1.48 42.29 0.0000 fiq 1 0.01 0.01 0.43 0.5136 Residuals 431 15.10 0.04

Per site results: Total mean thickness

total.mean.thickness ~ dx.group + age + fiq

site dx.Pvalue age.Pvalue fiq.Pvalue CALTECH 0.28 0.67 0.44 CMU 0.49 0.24 0.05 KKI 0.73 0.85 0.16 LEUVEN_1 0.17 0.15 0.76 MAX_MUN 0.35 0.00 0.65 OHSU 0.19 0.50 0.67 OLIN 0.50 0.00 0.48 PITT 0.10 0.02 0.81

Per site results (cont.): Total mean thickness

total.mean.thickness ~ dx.group + age + fiq

site dx.Pvalue age.Pvalue fiq.Pvalue SDSU 0.91 0.53 0.42 STANFORD 0.63 0.56 0.33 TRINITY 0.24 0.22 0.98 UCLA_1 0.49 0.58 0.27 UCLA_2 0.35 0.01 0.33 UM_1 0.05 0.03 0.06 UM_2 0.94 0.38 0.47 YALE 0.77 0.16 0.31

New stuff

QA/QC

Is site predictive of diagnosis?

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

Pr(>|z|) (Intercept) 0.00 siteCMU 0.85 siteKKI 0.35 siteLEUVEN_1 0.70 siteMAX_MUN 0.15 siteOHSU 0.16 siteOLIN 0.33 sitePITT 0.56 siteSDSU 0.28 siteSTANFORD 0.70 siteTRINITY 0.37 siteUCLA_1 0.46 siteUCLA_2 0.30 siteUM_1 0.26 siteUM_2 0.10 siteYALE 0.29 age 0.13 fiq 0.01

Age prediction of controls: Thickness

ABIDE data – Nick Tustison – November 23, 2014

ntustison

ABIDE data – Nick Tustison – November 23, 2014

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AngerAndTelegrams

ABIDE data

Nick Tustison

November 23, 2014

Demographics

Preprocessing: subset and matching

All sites

Age distribution

Gross results

Brain volume distribution

CSF volume distribution

Gray matter volume distribution

White matter volume distribution

Deep gray matter volume distribution

Total mean thickness distribution

Per site demographics

Site demographics

Site demographics (continued)

Age

Per site gross results

Brain volume

CSF volume

Gray matter volume

White matter volume

Deep gray matter volume

Total mean thickness

Results: Intracranial volume

All site results: Intracranial volume

Per site results: Intracranial volume

Per site results (cont.): Intracranial volume

Results: Amygdala volume

All site results: Amygdala volume (right)

Per site results: Amygdala volume (right)

Per site results (cont.): Amygdala volume (right)

All site results: Amygdala volume (left)

Per site results: Amygdala volume (left)

Per site results (cont.): Amygdala volume (left)

Results: CSF volume

All sites: CSF volume

Per site results: CSF volume

Per site results (cont.): CSF volume

Results: Gray matter volume

All sites: Gray matter volume

Per site results: Gray matter volume

Per site results (cont.): Gray matter volume

Results: White matter volume

All sites: White matter volume

Per site results: White matter volume

Per site results (cont.): White matter volume

Results: Deep gray matter volume

All sites: Deep gray matter volume

Per site results: Deep gray matter volume

Per site results (cont.): Deep gray matter volume

Results: Total mean thickness

All sites: Total mean thickness

Per site results: Total mean thickness

Per site results (cont.): Total mean thickness

New stuff

QA/QC

Is site predictive of diagnosis?

Age prediction of controls: Thickness

0 0