Included Decision Trees

Five decision trees are currently distributed with tedana.

meica is the decision tree that is based on MEICA version 2.5 and tedana_orig is very similar and has been included with tedana since the start of this project. An explanation of why these both exist is in the FAQ While multiple publications have used and benefited from this decision, tree, but it includes many steps with arbitrary thresholds and, when components seem misclassified, it’s often hard to understand why.

minimal is a simplified version of that decision tree with fewer steps and arbitrary thresholds. Minimal is designed to be more stable and comprehensible, but it has not yet be extensively validated and parts of the tree may change in response to additional tests on a wider range of data sets.

With the addition of options to fit external regressors to components, there are two demonstration decision trees that implement this new functionality. While these might work well, since they have not yet been validated on data, they are labeled demo. decision_tree_demo_external_regressors_single_model demonstrates fitting all nuisance regressors to a single model. decision_tree_demo_external_regressors_motion_task_models demonstrates fitting nuisance regressors to a model, partial tests and tagging for components that fit Motion or CSF regressors, and retention of some components that fit task regressors.

Flowcharts describing the steps in these trees are below. As documented more in Understanding and building a component selection process, the input to each tree is a table with metrics, like $\kappa$ or $\rho$ , for each component. Each step or node in the decision tree either calculates new values or changes component classifications based on these metrics. When a component classification changes to accept or reject, a classification_tag is also assigned which may help understand why a component was given a specific classification.

Each step in the flow chart is labeled with a node number. If tedana is run using one of these trees, those node numbers will match the numbers in the ICA status table and the ICA decision tree that are described in Output filename descriptions. These node numbers can be used to see when in the process a component’s classifiation changed.

meica decision tree

Nodes 1-5 reject components that are very unlikely to be BOLD. In nodes 9-10 components where $\kappa$ > $\kappa$ elbow and $\rho$ < $\rho$ elbow are classified as provisional accept. A non-obvious aspect of this decision tree is that no decision node below this point distinguishes components that are provisional accept from components that are still unclassified and nothing that does not cross the $\kappa$ and $\rho$ elbow thresholds is inherantly rejected. The number of provisional accept components is used to see if the process should be restarted (node 11) and calculate other thresholds (nodes 12-16 & 20), but nothing is directly accepted or rejected based on the elbow thresholds. Several additional criteria are used to reject components (nodes 17, 21, & 22). In older versions of tedana components were classified as ignored. This meant too small/minor to lose a degree of freedom by rejecting so treat like the accepted components. This was widely confusing to many users so they are now classified as accepted but with classification tags low variance (node 18) or accept borderline (nodes 24 & 25).

LaTeX file to generate the meica decision tree flow chart

tedana_orig decision tree

Identical to the meica decision tree until node 21. In the tedana tree, components that cross the threshold criteria in nodes 21 and 22 are rejected and not included in the calculation for a new variance explained threshold in node 23. In the meica tree, those components are provisionally rejected, but still included in the new variance explained calculation and can be potentially accepted in nodes 24 and 25. This means tedana will give the same results as meica or reject additional components base on those two final decision criteria.

LaTeX file to generate the tedana_orig decision tree flow chart

Minimal decision tree

The minimal tree starts similarly to the other trees by rejecting components that are very unlikely to be BOLD (nodes 1-5). Then all components where $\kappa$ > $\kappa$ elbow and $\rho$ < $\rho$ elbow are provisional accept and otherwise are provisional reject (nodes 8 & 10). The only expection to this is if $\kappa$ > $\kappa$ elbow and $\kappa$ > 2* $\rho$ than it is provisional accept regardless of the $\rho$ elbow under the assumption that there is enough T2* weighted signal the component should not be rejected even if it also contains noise (node 9). If provisional reject components have very low variance they are accepted rather than losing degrees of freedom, but no more than 1% of the total variance can be accepted this way (node 11). After that point, everything that is provisional accept is accepted (node 12) and everything that is provisional reject is rejected (node 13)

LaTeX file to generate the minimal decision tree flow chart

Demo external regressors single model

This tree is similar to the minimal tree except there is an added node (node 11) where components are rejected if they significantly fit a model of external nuisance regressor time series and the fit models a substantial amount of the total variance. Unlike the minimal tree, components that would be accepted based on $\kappa$ & $\rho$ criteria can be rejected based on a fit to external regressors. This is called a “demo” tree because it is demonstrating how fits to external regressors can be used. It might be a good decision tree to use, but results have not yet been tested and validated.

External Decision Tree With Motion and Task Models Flow Chart

Demo external regressors, motion task models

This is based on the minimal tree, but multiple nodes were added to demonstrate how to use external regressors for fits. Unlike the minimal tree, components that would be accepted based on $\kappa$ & $\rho$ criteria can be rejected based on a fit to external regressors. Components are rejected if they significantly fit a model of external nuisance regressor time series and the fit models a substantial amount of the total variance (node 10). For rejected components, if they also fit a partial model of motion external regressors (node 11), or CSF external regressors (node 12), the outputs are also tagged to say they fit those groups of regressors. Additionally, if a rejected component fits the task design and has $\kappa$ > $\kappa$ elbow, then it is accepted under the conservative assumption to retain task fitting components with some $T_2^*`$ signal even if those components also contain potentially rejection-worthy noise (node 13). This is called a “demo” tree because it is demonstrating how fits to external regressors can be used. It might be a good decision tree to use, but results have not yet been tested and validated.