Roi Attribution Maps

The aggregation step converts many feature-level attribution scores into region-level summaries, making the model’s evidence easier to compare across brain areas. A region can be represented by the combined contribution of its voxels or signals, while the chosen anatomical or functional boundaries determine which features are grouped together. This connects computational results with brain organization.

Attribution scores indicate how strongly individual imaging features contribute to a model prediction or measured neural outcome. After these scores are assigned, researchers can determine which predefined regions carry more or less influence in the analysis. This helps distinguish broad regional patterns from isolated feature-level signals when interpreting neural decoding or machine-learning results.

ROI definitions determine the brain features included in each regional summary, so they shape how attribution patterns are interpreted. Anatomically defined regions organize contributions according to brain structure, whereas functionally defined regions organize them according to a functional designation. Comparing results across these choices can clarify how computational findings relate to different views of brain organization.

A typical workflow begins by selecting predefined anatomical or functional regions of interest and identifying the relevant imaging features, such as voxels or signals. Attribution scores are then assigned to those features and aggregated within each region. The resulting visual or quantitative representation can be examined across brain areas, conditions, or participants.

These maps support comparisons of regional contributions across experimental conditions and participants. Researchers can examine whether the same regions receive stronger or weaker attribution under different conditions, or whether contribution patterns vary between individuals. Such comparisons provide a structured way to relate model behavior to changes in neural organization associated with a study’s measured outcome.

They are useful when researchers need to interpret neural decoding or machine-learning analyses in relation to brain regions. The maps can highlight patterns associated with cognition, disease, or behavior, allowing computational results to be discussed in a neuroscience framework. They also provide a way to evaluate whether influential regions correspond with established neurobiological knowledge.

Researchers can compare the regions emphasized by attribution scores with established neurobiological knowledge and with patterns observed across conditions or participants. Agreement may support the relevance of the computational result to brain organization, while differing patterns can identify findings that require closer interpretation. This makes the maps useful for examining model behavior alongside neuroscience evidence.