Node Occlusion Analysis

Importance is inferred from the size of the system-level change after a node is removed or masked. If occlusion produces a large reduction in network efficiency, information flow, or model performance, that node may function as a hub, bottleneck, or critical contributor. The analysis therefore connects a local perturbation with consequences for overall neural organization or computation.

Different measures capture different consequences of the same perturbation. Network efficiency reflects changes in overall connectivity performance, information flow indicates effects on communication, and model performance shows whether a computational neural network can still produce its expected results. Comparing these outcomes helps distinguish nodes important for structural organization from those essential to a specific computational function.

The magnitude of change after occlusion provides an indication of network sensitivity to that node. Small changes suggest that the system maintains its organization or performance despite the perturbation, whereas large changes identify locations associated with greater vulnerability. In neuroscience, this contrast supports examination of brain-network resilience and the consequences of disruptions.

Researchers first evaluate the intact network or model using a selected outcome, such as efficiency, information flow, or performance. They then temporarily remove or mask one node at a time and recompute the same outcome after each perturbation. Comparing each altered result with the intact condition produces a node-specific estimate of the effect of occlusion.

In brain connectivity graphs, the method can identify regions whose perturbation produces substantial changes in network-level organization. These results help investigate how brain networks withstand disruption, how lesion-like effects alter connectivity, and which regions may contribute disproportionately to coordinated neural function. The findings relate individual brain regions to broader system-level consequences.

For computational neural networks, researchers can mask or remove individual units and then examine the resulting change in model performance. Units associated with large performance losses may make important contributions to the model's computation. This provides an interpretability framework that links particular units to functional outcomes rather than evaluating model behavior only at the whole-network level.