Version

Weighted

Visual Summary

Dist+Subcort Excl

Sample Table
Sub02 Individual R2 Contributions with Distance+Subcortical Exclusion
R2_ed R2_pl R2_co Total
0.0427209 0.0230510 0.0687007 0.1344727
0.0168579 0.0228486 0.0317422 0.0714487
0.0170410 0.0062044 0.0387167 0.0619621
0.0132366 0.0076348 0.0033482 0.0242196
0.0150198 0.0794642 0.0179103 0.1123944
0.0047484 0.0152087 0.0295321 0.0494892
0.0278255 0.0057667 0.0156888 0.0492810
0.0536798 0.0072083 0.0034594 0.0643474
0.0279259 0.0080713 0.0411702 0.0771674
0.0020022 0.0042421 0.0290348 0.0352791
Plot

Dist+Subcort Excl+2SDceil

Sample Table
Sub02 Individual R2 Contributions with Distance+Subcortical Exclusion+2SD ceil
R2_ed R2_pl_2SD R2_co_2SD Total
0.0408433 0.0212981 0.0787162 0.1408575
0.0162504 0.0284549 0.0204041 0.0651095
0.0147441 0.0076970 0.0482501 0.0706912
0.0129254 0.0092687 0.0035405 0.0257346
0.0143620 0.0707902 0.0185187 0.1036708
0.0052259 0.0169639 0.0065795 0.0287693
0.0286364 0.0050666 0.0159113 0.0496143
0.0534961 0.0054738 0.0069081 0.0658781
0.0244114 0.0078634 0.0411993 0.0734741
0.0024873 0.0039568 0.0183892 0.0248333
Plot

Binarized

Individual Plots

Specific Analyses:PL-Based

0.05 is the mean R2 value across various thresholds

Functional Associations with Path Length Within Low and High R2 Category

Functional Associations with Path Length Comparing Low and High R2 Category

0.01

0.02

0.04

0.05

0.06

0.08

0.10

0.15
## Error in t.test.default(x = numeric(0), y = numeric(0), p.adjust.method = "holm", : not enough 'x' observations
## Error in t.test.default(x = numeric(0), y = numeric(0), p.adjust.method = "holm", : not enough 'x' observations

## Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]): contrasts can be applied only to factors with 2 or more levels
## Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]): contrasts can be applied only to factors with 2 or more levels
0.20

0.25

0.30

Are the two MLR models (PL_cont, PL_cat) different?

AIC analysis
  • In PL_cat model, PL==1 coded as 0, all other PLs coded as 1
  • I calculated AIC values for each model and then subtracted AIC(model with PL continuous) from AIC(model with PL categorical). If model with PL categorical is a better fit, then AIC_PLcat - AIC_Plcont should be negative (suggesting that AIC_PLcat has a smaller AIC value). Values hover around 0, with slight “leaning” toward the left (negative) for more conservative thresholds.

R2-Brain Maps

Some differences in R2 contributions are notable at the brain-map level, but overall, the images (i.e., the R2 brain maps) for the PL_continuous and PL_categorical condition look similar.

Comparison of R2 contributions for continuous versus categorical binarized path length at 0.01 threshold

 

Comparison of R2 TOTAL contributions for continuous versus categorical binarized path length at 0.01 threshold

T-test (R2Total @ 0.01 threshold)

  • The R2 contributions across all ROIs differ when considering the total R2 for an MLR model with PL_continuous versus PL_categorical. This difference was assessed by a paired, two-sided t-test.

  • t = -5.2429, df = 419, p-value = 2.513e-07

  • 95 percent confidence interval: -0.004602861 -0.002092614

  • mean of the differences: -0.003347738 (R2Total_PLcategorical - R2Total_PLcontinuous)

PL1 R2 values relative to PL2+ (Histogram Distribution)

PL1 R2 values relative to PL2+ (Boxplots, T-values)
Comparing R2 contributions relative to Multi-Hop PLs
mean comparison mean of differences p-val
R2_pl1minuspl3 ~ R2_pl2minuspl3 9e-04 0.2481
Comparing R2 contributions relative to Multi-Hop PLs
mean comparison mean of differences p-val
R2_pl1minuspl3 ~ R2_pl2minuspl3 8e-04 0.0028
Comparing R2 contributions relative to Multi-Hop PLs
mean comparison mean of differences p-val
R2_pl1minuspl3 ~ R2_pl2minuspl3 0.0015 3e-04
Comparing R2 contributions relative to Multi-Hop PLs
mean comparison mean of differences p-val
R2_pl1minuspl3 ~ R2_pl2minuspl3 0.0014 4e-04
Comparing R2 contributions relative to Multi-Hop PLs
mean comparison mean of differences p-val
R2_pl1minuspl3 ~ R2_pl2minuspl3 0.0014 4e-04
Comparing R2 contributions relative to Multi-Hop PLs
mean comparison mean of differences p-val
R2_pl1minuspl3 ~ R2_pl2minuspl3 0.0023 0
Comparing R2 contributions relative to Multi-Hop PLs
mean comparison mean of differences p-val
R2_pl1minuspl3 ~ R2_pl2minuspl3 0.0034 0
Comparing R2 contributions relative to Multi-Hop PLs
mean comparison mean of differences p-val
R2_pl1minuspl4 ~ R2_pl2minuspl4 0.0078 0.0798
Comparing R2 contributions relative to Multi-Hop PLs
mean comparison mean of differences p-val
R2_pl1minuspl4 ~ R2_pl3minuspl4 0.0037 0.3329
Comparing R2 contributions relative to Multi-Hop PLs
mean comparison mean of differences p-val
R2_pl1minuspl4 ~ R2_pl2minuspl4 0.0055 0.0014
Comparing R2 contributions relative to Multi-Hop PLs
mean comparison mean of differences p-val
R2_pl1minuspl4 ~ R2_pl3minuspl4 0.0053 0.0035
Comparing R2 contributions relative to Multi-Hop PLs
mean comparison mean of differences p-val
R2_pl1minuspl4 ~ R2_pl2minuspl4 0.007 0
Comparing R2 contributions relative to Multi-Hop PLs
mean comparison mean of differences p-val
R2_pl1minuspl4 ~ R2_pl3minuspl4 0.0067 1e-04
Comparing R2 contributions relative to Multi-Hop PLs
mean comparison mean of differences p-val
R2_pl1minuspl4 ~ R2_pl2minuspl4 0.0052 0
Comparing R2 contributions relative to Multi-Hop PLs
mean comparison mean of differences p-val
R2_pl1minuspl4 ~ R2_pl3minuspl4 0.0055 0

TractInfo

Strength of Tracts Across ROIs (relative weight > 0.4)

SUMMARY OF ANALYSES

Analytic Procedure Map