Traditional clinical approaches diagnose disorders of the nervous system using standardized observational criteria. Although aiming for homogeneity of symptoms, this method often results in highly heterogeneous disorders. A standing question thus is how to automatically stratify a given random cohort of the population, such that treatment can be better tailored to each cluster’s symptoms, and severity of any given group forecasted to provide neuroprotective therapies. In this work we introduce new methods to automatically stratify a random cohort of the population composed of healthy controls of different ages and patients with different disorders of the nervous systems. Using a simple walking task and measuring micro-fluctuations in their biorhythmic motions, we show that gait is compromised in healthy aging and that in young FMR1 premutation carriers, gait forecasts, even by 15 years ahead, symptoms resembling those of elderly with Parkinson’s disease. Our methods combine non-linear causal network connectivity analyses in the temporal and frequency domains with stochastic mapping, defining a new type of internal motor timings amenable to create personalized clinical interventions. We frame our results using the principle of reafference and operationalize them using causal prediction, thus renovating the theory of internal models for the study of neuromotor control.