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DecoMP_ECoG SIGNED

Decoding memory processing from experimental and spontaneous human brain activity using intracranial electrophysiological recordings and machine learning based methods.

Total Cost €

0

EC-Contrib. €

0

Partnership

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Project "DecoMP_ECoG" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITY COLLEGE LONDON 

Organization address
address: GOWER STREET
city: LONDON
postcode: WC1E 6BT
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 United Kingdom [UK]
 Project website https://sites.google.com/site/decompecog/
 Total cost 241˙169 €
 EC max contribution 241˙169 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2014
 Funding Scheme MSCA-IF-GF
 Starting year 2015
 Duration (year-month-day) from 2015-07-01   to  2018-11-21

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY COLLEGE LONDON UK (LONDON) coordinator 241˙169.00
2    BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY US (STANFORD) partner 0.00

Map

 Project objective

Despite the critical importance of memory for cognitive function and socialization, very little is known about how information is stored for later retrieval and use. Understanding how the human brain maintains and stores information would enhance research on memory dysfunction in degenerative diseases, such as the age-related dementias, which represent a large burden for European society, and could facilitate the development of strategies for improving memory. The current proposal will use intracranial electrophysiological recordings from the surface of the human brain to investigate encoding, retrieval and consolidation of category-specific information during experimental settings, as well as during spontaneous brain activity. The proposal consists in two parts: first, electrocorticographic (ECoG) data will be acquired at Stanford University, with access to high-quality recordings and modern tools for electrophysiological data analysis. Secondly, machine learning based methodologies will be developed at the Department of Computer Science, University College London (return host) to decode spontaneous brain activity in different vigilance states. Finally, all developed methods will be implemented in an open source software, ensuring the timely dissemination of state-of-the art techniques. The methodological developments considered in this project could provide means for developing computer-aided diagnostic tools for neurodegenerative diseases.

 Publications

year authors and title journal last update
List of publications.
2018 Aaron Kucyi, Jessica Schrouff, Stephan Bickel, Brett L. Foster, James M. Shine, Josef Parvizi
Intracranial electrophysiology reveals reproducible intrinsic functional connectivity within human brain networks
published pages: 0217-18, ISSN: 0270-6474, DOI: 10.1523/JNEUROSCI.0217-18.2018
The Journal of Neuroscience 2019-06-11
2018 Jessica Schrouff, J. M. Monteiro, L. Portugal, M. J. Rosa, C. Phillips, J. Mourão-Miranda
Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models
published pages: 117-143, ISSN: 1539-2791, DOI: 10.1007/s12021-017-9347-8
Neuroinformatics 16/1 2019-06-11
2016 Jessica Schrouff, Janaina Mourão-Miranda, Christophe Phillips, Josef Parvizi
Decoding intracranial EEG data with multiple kernel learning method
published pages: 19-28, ISSN: 0165-0270, DOI: 10.1016/j.jneumeth.2015.11.028
Journal of Neuroscience Methods 261 2019-06-11
2016 Amy L. Daitch, Brett L. Foster, Jessica Schrouff, Vinitha Rangarajan, Itır Kaşikçi, Sandra Gattas, Josef Parvizi
Mapping human temporal and parietal neuronal population activity and functional coupling during mathematical cognition
published pages: E7277-E7286, ISSN: 0027-8424, DOI: 10.1073/pnas.1608434113
Proceedings of the National Academy of Sciences 113/46 2019-06-11

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

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