Opendata, web and dolomites


Decoding impairments of gait and balance from local field potentials in patients with Parkinson's disease.

Total Cost €


EC-Contrib. €






 gaitDCODE project word cloud

Explore the words cloud of the gaitDCODE project. It provides you a very rough idea of what is the project "gaitDCODE" about.

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Project "gaitDCODE" data sheet

The following table provides information about the project.


Organization address
address: RUE DU BUGNON 21
postcode: 1011

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Switzerland [CH]
 Project website
 Total cost 175˙419 €
 EC max contribution 175˙419 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2017
 Funding Scheme MSCA-IF-EF-SE
 Starting year 2018
 Duration (year-month-day) from 2018-04-01   to  2020-03-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

Parkinson's disease (PD) is one of the most prevalent neurodegenerative disorders, affecting more than ten million people worldwide. Over the past decades, dopaminergic and surgical therapies have been optimized to treat upper-limb motor symptoms such as tremor, bradykinesia or rigidity with remarkable outcomes. However, these interventions are poorly effective to alleviate gait and balance deficits, which remain amongst of the most incapacitating symptoms affecting everyday mobility and quality of life in subjects with PD. Alternative stimulation strategies and novel surgical targets showed promises to improve gait. However, the lack of mechanistic insights and reliable readouts capturing the relationships between neural activity and leg motor dysfunction have prevented the development of effective, evidence-based therapies. Project gaitDCODE seeks to address these limitations. The objective of gaitDCODE is (i) to uncover the relationships between detailed patterns of gait deficits and the corresponding neural activity recorded from cortical networks and subthalamus nucleus in subjects with PD, and (ii) to leverage this knowledge to develop decoding algorithms that automatically predict these impairments in real-time. To enable these discoveries, gaitDCODE will build on two key innovations. First, it will exploit the conceptual and methodological expertise that the candidate gained through his work on gait rehabilitation after spinal cord injury, including high-resolution statistical approaches to capture the multi-faceted and time-varying characteristics of gait. Second, it will leverage the latest developments in implantable technology to sense and decode brain activity remotely and in real-time over extensive periods of time. In the long term, gaitDCODE will open up important avenues for the design of patient-specific, closed-loop neuromodulation therapies that address gait and balance deficits in PD using evidence-based models of locomotor deficits.

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The information about "GAITDCODE" are provided by the European Opendata Portal: CORDIS opendata.

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