Opendata, web and dolomites

PredictingPain SIGNED

Deconstructing pain with predictive models: from neural architecture to pain relief

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

Project "PredictingPain" data sheet

The following table provides information about the project.

Coordinator
MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV 

Organization address
address: HOFGARTENSTRASSE 8
city: MUENCHEN
postcode: 80539
website: n.a.

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 Germany [DE]
 Total cost 1˙499˙922 €
 EC max contribution 1˙499˙922 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-STG
 Funding Scheme ERC-STG
 Starting year 2018
 Duration (year-month-day) from 2018-10-01   to  2023-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV DE (MUENCHEN) coordinator 1˙499˙922.00

Map

 Project objective

Pain is a burden for millions of people in terms of suffering, as well as for society in terms of costs. While it is well established that the perception of pain is not necessarily related to the amount of sensory input, a mechanistic framework for this is still lacking at the neurobiological level, although it would be of utmost importance for inspiring new therapeutic approaches. In PredictingPain, I will provide such a framework by applying recent insights from computational neuroscience in the form of ‘predictive coding’ to pain. Predictive coding models assume that perception is an active process in which the brain is not just passively responding to sensory inputs, but is instead constantly generating predictions about its sensory inputs.

I will employ three complementary work packages (WP) – all of which use cutting-edge neuroimaging methods in human volunteers – to answer the important question of whether predictive coding can serve as a fundamental mechanism underlying the perception of pain. In WP1, I will elucidate whether the neural architecture of the nociceptive system is capable of implementing a predictive coding scheme. With WP2, I will unravel how the subjective experience of pain is constructed from a rich array of predictive signals. Finally, in WP3 I will employ an experimental model of chronic pain to test how predictions of pain relief exert their beneficial effects when pain is ongoing. A large part of the research in PredictingPain will focus on the human spinal cord, in order to capture predictive signals at this earliest level of central nervous system pain processing, since they will exert a profound effect on processing at higher levels and the resulting pain perception.

Together, the outcomes from PredictingPain will provide a novel and mechanistic understanding of pain perception that will provide impetus for the development of new therapeutic approaches in order to reduce the suffering and financial burden that pain imposes.

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "PREDICTINGPAIN" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

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

More projects from the same programme (H2020-EU.1.1.)

PROGRESS (2019)

The Enemy of the Good: Towards a Theory of Moral Progress

Read More  

SuperH (2019)

Discovery and Characterization of Hydrogen-Based High-Temperature Superconductors

Read More  

CARBOFLOW (2020)

Streamlined carbon dioxide conversion in ionic liquids – a platform strategy for modern carbonylation chemistry

Read More