Optimal time lags from causal prediction model help stratify and forecast nervous system pathology

Theodoros Bermperidis,Richa Rai,Sunil K Agrawal,Damiano Zanotto,Elizabeth B Torres,Anil K Lalwani,Jihye Ryu

doi:10.1038/s41598-021-00156-2

Abstract

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 combine non-linear causal network connectivity analyses in the temporal and frequency domains with stochastic mapping. The methods define a new type of internal motor timings. These are amenable to create personalized clinical interventions tailored to self-emerging clusters signaling fundamentally different types of gait pathologies. 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.

Highlights

Traditional clinical approaches diagnose disorders of the nervous system using standardized observational criteria
The Fragile-X Tremor Ataxia syndrome (FXTAS) patient may receive an ASD diagnosis during childhood, when differences in communication and social interactions gain priority over motor issues[13,14] but later in life, as motor issues exacerbate, they may gain the diagnosis of patients with Parkinson’s Disease (PPD)
If the inclusion of A improves the prediction, we say that A causally predicts B (A→B) and we can assess the degree to which this is determined by the method of choice

Summary

Introduction

Traditional clinical approaches diagnose disorders of the nervous system using standardized observational criteria. Patients with Fragile-X Tremor Ataxia syndrome (FXTAS), patients with Parkinson’s Disease (PPD) and patients in the broad spectrum of Autism (ASD) my all have motor issues detectable and tractable through gait patterns at different stages of their lifespan[1,2,3,4,5,6,7,8] These disorders share some common genes[9,10,11], but their phenotypic features differ depending on the person’s age and on the evolution of various epigenetic factors[1,12]. We discuss our results within a new unifying framework to study disorders of the nervous systems across different diagnoses of disparate clinical criteria and yet, converging functional phenotypes

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Discussion

Conclusion