tedana.selection.selection_nodes.calc_varex_kappa_ratio

calc_varex_kappa_ratio(selector, decide_comps, log_extra_info='', custom_node_label='', only_used_metrics=False)[source]

Calculate the cross-component metric “kappa_rate”.

Calculates the cross_component_metric kappa_rate for the components in decide_comps and then calculate the variance explained / kappa ratio for ALL components and adds those values to a new column in the component_table titled “varex kappa ratio”.

Parameters:

  • selector (ComponentSelector) – If only_used_metrics is False, the updated selector is returned.

  • decide_comps (str or list[str]) – What classification(s) to operate on. using default or intermediate_classification labels. For example: decide_comps=’unclassified’ means to operate only on unclassified components. Use ‘all’ to include all components.

  • log_extra_info (str) – Additional text to the information log. Default=””.

  • custom_node_label (str) – A short label to describe what happens in this step. If “” then a label is automatically generated. Default=””.

  • only_used_metrics (bool) – If True, only return the component_table metrics that would be used. Default=False.

Returns:

  • selector (ComponentSelector) – If only_used_metrics is False, the updated selector is returned.

  • used_metrics (set(str)) – If only_used_metrics is True, the names of the metrics used in the function are returned.

Note

These measures are used in the original kundu decision tree. kappa_rate = (max-min kappa values of selected components)/(max-min variance explained) varex_k. varex kappa ratio = kappa_rate * “variance explained”/”kappa” for each component. Components with larger variance and smaller kappa are more likely to be rejected. This metric sometimes causes issues with high magnitude BOLD responses such as the V1 response to a block-design flashing checkerboard