Explore the words cloud of the MINDS project. It provides you a very rough idea of what is the project "MINDS" about.
The following table provides information about the project.
Coordinator |
DANMARKS TEKNISKE UNIVERSITET
Organization address contact info |
Coordinator Country | Denmark [DK] |
Project website | http://people.compute.dtu.dk/alvmu/minds.html |
Total cost | 212˙194 € |
EC max contribution | 212˙194 € (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-EF-ST |
Starting year | 2016 |
Duration (year-month-day) | from 2016-01-11 to 2018-01-10 |
Take a look of project's partnership.
# | ||||
---|---|---|---|---|
1 | DANMARKS TEKNISKE UNIVERSITET | DK (KGS LYNGBY) | coordinator | 212˙194.00 |
Functional magnetic resonance imaging (fMRI) is the dominating approach to research in the mapping of neural activity in the human brain. State of the art data analysis techniques employ a statistical parametric mapping (SPM) strategy to convert raw signal into interpretable images by processing data in a pipeline of task-specific modules. This approach, despite its simplicity and reliability, presents a set of inconveniences, including low interconnectivity among modules, resulting in suboptimal solutions. In this project we aim at making a major contribution to the field by replacing the step-by-step data processing pipeline by a deep neural network. We hypothesise that this will achieve better solutions by propagating the effects of module-based decisions through the network, jointly optimizing the whole processing pipeline. Moreover, fMRI low temporal resolution will be alleviated by means of a post-processing treatment, where advanced interpolation techniques will be used. We will release a freely accessible software tool that integrates with SPM, supplying an easy-to-use framework including advanced techniques for an automatic multivariate non-linear data analysis. The generated deep network solution will be applied in a multidisciplinary study in neurofeedback, where subjects will learn relaxation strategies guided by fMRI technology. At the end of the project, we expect our tool to become a useful standard practise in the field.
year | authors and title | journal | last update |
---|---|---|---|
2016 |
Albert Vilamala, Kristoffer H. Madsen, Lars K. Hansen Towards end-to-end optimisation of functional image analysis pipelines. published pages: , ISSN: , DOI: |
2019-06-18 | |
2017 |
Albert Vilamala, Kristoffer H. Madsen, Lars K. Hansen EEG Biofeedback for Relaxation using Deep Neural Networks. published pages: , ISSN: , DOI: |
2019-06-18 |
Are you the coordinator (or a participant) of this project? Plaese send me more information about the "MINDS" 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 "MINDS" are provided by the European Opendata Portal: CORDIS opendata.
Unravelling maintenance mechanisms of immune tolerance after termination of venom immunotherapy by means of clonal mast cell diseases
Read MoreDecrypting Mycobacterium cytochrome P450 (CYP) physiological functions by testing hypotheses emitted form large-scale comparative genomics analysis
Read More