Brain Connectivity Modeler

To choose between functional and effective connectivity for fMRI data, identify whether your study aims to measure statistical dependencies or directed causal influences. This framework selection guides whether you apply undirected network analyses or causal modeling techniques.

Use DCM versus PPI for task-based fMRI connectivity analysis based on your theoretical assumptions: DCM tests directed causal influences with model comparison strategies, while PPI examines task-modulated correlations between regions.

Applying Granger causality to resting-state fMRI data involves selecting it as an effective connectivity method to determine directed temporal influences between brain regions. This requires establishing recommended thresholds and interpreting results against theoretical assumptions.

Graph-theoretic approaches apply to both resting-state and task-based fMRI by modeling brain region interactions as networks. This framework identifies topological properties and network structures using ICA-based network analyses.

Setting thresholds and model comparison strategies for DCM analyses involves applying methodological guidance to evaluate effective connectivity models. This process uses established literature references to validate theoretical assumptions and select the optimal model.

Choosing the wrong brain connectivity framework affects fMRI interpretation because functional, effective, and graph-theoretic methods carry distinct theoretical assumptions. Mismatched methods yield misleading thresholds and invalid causal inferences about brain interactions.