Inspired by Prof. Viktor Jirsa.

Brain disorders confront us with a paradox that is rooted in two opposing realities. On the one hand, we have more data than ever (from multimodal imaging to electrophysiology). On the other hand, clinical action remains constrained by population averages and by the absence of a mechanistic theory that can be trusted at the individual level—especially in neurological disorders and psychiatry, where heterogeneity and comorbidity dominate.

A central reason for this gap is mechanistic uncertainty. For most phenotypes, multiple causal mechanisms are plausible; different modeling and inference approaches often test one mechanism (or one family) at a time and obtain slightly different—sometimes incompatible—answers. This is not only a “methods” problem: in multiscale systems like the brain, different mechanisms can produce similar emergent behavior (a form of degeneracy), meaning that the same data can be explained by multiple causal stories unless we explicitly quantify uncertainty and compare hypotheses.

In a degenerate system, reliable mechanism-based prediction requires convergence across inference methods and mechanistic hypotheses toward a minimally sufficient common denominator of causality. Concretely, when inference is constrained by multimodal anatomical/functional data, we can:

• (i) Infer probability rather than single best-fit solution,
• (ii) Compare competing mechanistic hypotheses, and
• (iii) Identify what is stable across methods and models.

The result is a consensus causal core that is robust enough to support validation and clinical interpretation.