# Topographic gradients of intrinsic dynamics across neocortex

1. McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Canada
2. School of Physics, The University of Sydney, Australia
6 figures and 5 additional files

## Figures

Figure 1
Figure 2 with 2 supplements
Figure 2—figure supplement 1
Figure 2—figure supplement 2
Figure 3 with 1 supplement
Figure 3—figure supplement 1
Figure 4 with 2 supplements
Figure 4—figure supplement 1
Figure 4—figure supplement 2
Figure 5
Figure 6

###### Supplementary file 1

Dominance analysis.

Dominance Analysis was used to quantify the distinct contributions of inter-regional Euclidean distance, structural connectivity, and functional connectivity to temporal profile similarity (Budescu, 1993; Azen and Budescu, 2003) (https://github.com/dominance-analysis/dominance-analysis). Dominance analysis is a method for assessing the relative importance of predictors in regression or classification models. The technique estimates the relative importance of predictors by constructing all possible combinations of predictors and quantifying the relative contribution of each predictor as additional variance explained (i.e. gain in $R2$) by adding that predictor to the models. Specifically, for p predictors we have $2p-1$ models that include all possible combinations of predictors. The incremental $R2$ contribution of each predictor to a given subset model of all the other predictors is then calculated as the increase in $R2$ due to the addition of that predictor to the regression model. Here we first constructed a multiple linear regression model with distance, structural connectivity and functional connectivity as independent variables and temporal profile similarity as the dependent variable to quantify the distinct contribution of each factor using dominance analysis. The total $R2$ is 0.28 for the complete model that includes all variables. The relative importance of each factor is summarized in the table, where each column corresponds to: Interactional Dominance is the incremental $R2$ contribution of the predictor to the complete model. For each variable, interactional dominance is measured as the difference between the $R2$ of the complete model and the $R2$ of the model with all other variables except that variable; Individual Dominance of a predictor is the $R2$ of the model when only that predictor is included as the independent variable in the regression; Average Partial Dominance is the average incremental $R2$ contributions of a given predictor to all possible subset of models except the complete model and the model that only includes that variable; Total Dominance is a summary measure that quantifies the additional contribution of each predictor to all subset models by averaging all the above measures for that predictor; Percentage Relative Importance is the percent value of the Total Dominance.

https://cdn.elifesciences.org/articles/62116/elife-62116-supp1-v2.docx
###### Supplementary file 2

List of terms used in Neurosynth analyses.

The overlapping terms between Neurosynth (Yarkoni et al., 2011) and Cognitive Atlas (Poldrack et al., 2011) corpuses used in the reported analyses are listed below.

https://cdn.elifesciences.org/articles/62116/elife-62116-supp2-v2.docx
###### Supplementary file 3

List of time-series features corresponding to PC1.

The complete list of features (ranked by loading), their definitions, correlations and p-values for PC1 is presented in machine-readable format.

https://cdn.elifesciences.org/articles/62116/elife-62116-supp3-v2.xls
###### Supplementary file 4

List of time-series features corresponding to PC2.

The complete list of features (ranked by loading), their definitions, correlations and p-values for PC2 is presented in machine-readable format.

https://cdn.elifesciences.org/articles/62116/elife-62116-supp4-v2.xls
###### Transparent reporting form
https://cdn.elifesciences.org/articles/62116/elife-62116-transrepform-v2.docx

A two-part list of links to download the article, or parts of the article, in various formats.