Developing a “mechanism-based taxonomy” of Alzheimer´s and Parkinson´s Disease.

Currently, the established disease classification systems such as ICD (international classification of disease) make use of phenotypes measured clinically or using standard laboratory and imaging techniques to establish major types and subtypes of diseases.

In contrast to the established disease classification systems, a “mechanism-based taxonomy” is based upon the knowledge about the biological pathways involved in the aetiology of a disease to guide the classification of disease classes and subclasses.

A specific challenge we face in the course of the AETIONOMY project lies in the fact that for most neurodegenerative diseases the dysfunctional biological pathways underlying the disease are not known. AETIONOMY will therefore have to first define new routes towards the identification of the underlying disease mechanisms before organising these and proposing a rational disease taxonomy for Alzheimer’s and Parkinson’s disease. Moreover, we will validate the mechanism-based taxonomy at least partially in the course of a prospective clinical study.

The Virtual Dementia Cohort (VDC): A platform for in-silico testing and validation of candidate mechanisms

Access to clinical datasets is usually restricted by data owners. This results in lengthy legal/ethical approvals and additional data processing efforts. AETIONOMY proposed a way out of this dilemma by the generation of free, virtual patient cohorts.

Our rationale is as follows: Ultimately, all clinical studies are just data. For many of the variables measured in studies like ADNI and PPMI we actually know the distribution of values; for other variables we may quantify our a priori knowledge and assume a distribution around its known mean. The data will be introduced into forward modeling approaches (integrative cause-and-effect modeling informed by biology) and linked to generative brain network models (The Virtual Brain) to create individual Virtual Patient brain models, which produce the entire range of human brain imaging data using Monte Carlo simulations (or related methods). The virtual cohort can by challenged by real-world data any time; it can be “optimised” to fit observational data or distributions observed in ADNI or PPMI or any other cohort better, if desired. Randomizer functions, however, can be used to generate diversity in variables wherever we believe that this should be helpful and lead to better representation of what we know about real patients.


AETIONOMY's new Research Services


  • AETIONOMY Knowledge base – the unification point of the knowledge and data management on Neurodegeneration with a main focus on Alzheimer’s and Parkinson’s diseases. Please check the 'AETIONOMY prospective study' – Organising mechanistic knowledge about neurodegenerative diseases for the improvement of drug development and therapy – to get access to this unification point.
  • NeuroMMSig – Mechanistic interpretation of multiscale, multimodal clinical data, representing essential pathophysiology mechanisms of neurodegenerative diseases.

Monthly Bulletins and Newsletters


The AETIONOMY team generated the 5th AETIONOMY Newsletter, which is focussing on disease modelling, multimodal mechanistic signatures, disease hypotheses and approaches for validation.

About the Innovative Medicines Initiative (IMI)

This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking AETIONOMY (grant n° 115568).