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NEUROMODEL SIGNED

Statistical Modelling for relating multimodal neuroimaging to clinical outcomes in order to predict patient response to depression therapy.

Total Cost €

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EC-Contrib. €

0

Partnership

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 NEUROMODEL project word cloud

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

deal    personalized    brain    forms    host    ssri    exist    15    lvm    mdd    integrate    uniquely    linear    biomarkers    tomography    depressed    identification    followed    psychological    individualized    adjusted    medicine    patients    neurobiology    model    depression    inhibitor    cohort    situated    data    combination    individuals    imaging    suffer    resolution    serotonin    treatment    resonance    reuptake    types    ultimate    gaussian    therapy    magnetic    treatments    variables    chosen    functional    fmri    posit    outcome    single    statistical    europeans    positron    disability    insufficient    50    later    heterogeneous    images    neuropsychological    combines    environmental    latter    life    relationships    selective    expand    acquired    drug    pet    genetic    flexible    healthy    every    patient    predict    adapt    complexity    emission    multimodal    longitudinal    perfectly    latent    dimensional    outcomes    intervention    involve    database    foundation   

Project "NEUROMODEL" data sheet

The following table provides information about the project.

Coordinator
REGION HOVEDSTADEN 

Organization address
address: KONGENS VAENGE 2
city: HILLEROD
postcode: 3400
website: www.regionh.dk

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 Denmark [DK]
 Project website https://bozenne.github.io/Funding/
 Total cost 200˙194 €
 EC max contribution 200˙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-2016
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2017
 Duration (year-month-day) from 2017-06-01   to  2019-05-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    REGION HOVEDSTADEN DK (HILLEROD) coordinator 200˙194.00

Map

 Project objective

Every year, 1 out of 15 Europeans suffer from major depression (MDD) and MDD is the third cause of Disability-adjusted life-years. Today, the available treatments are clearly insufficient; only about 50% of MDD patients respond to drug intervention. We here posit that identification of biomarkers that can predict treatment response is needed to adapt a personalized medicine approach, and most likely this will involve not a single outcome but a combination of multimodal brain imaging outcomes, psychological, genetic, and environmental data. The complexity of such data requires a complex statistical model that currently does not exist. Thus, my aim is to develop a new flexible statistical method that can take into account heterogeneous types of data. More specifically, I will develop a fully flexible Latent Variable Model (LVM) that can deal with high dimensional measurements (e.g. images), non-Gaussian variables, and non-linear relationships. I will apply this flexible LVM on existing data from depressed and healthy individuals and later expand the application to predict treatment outcomes. The latter data are currently acquired and includes a cohort of MDD patients treated with a selective serotonin reuptake inhibitor (SSRI), followed in a longitudinal design. The chosen host institution is perfectly situated to this project, as they have an established unique database including, e.g., functional Magnetic Resonance Imaging (fMRI), high resolution Positron Emission Tomography (PET), and neuropsychological test outcomes. This research project uniquely combines advanced statistical modelling of rich data sets with the ultimate aim to establish individualized depression therapy. Moreover, it forms a foundation for a more general approach to integrate brain neurobiology in terms of imaging outcomes with other patient-specific data.

 Publications

year authors and title journal last update
List of publications.
2019 Martin K. Madsen, Patrick M. Fisher, Daniel Burmester, Agnete Dyssegaard, Dea S. Stenbæk, Sara Kristiansen, Sys S. Johansen, Sczabolz Lehel, Kristian Linnet, Claus Svarer, David Erritzoe, Brice Ozenne, Gitte M. Knudsen
Psychedelic effects of psilocybin correlate with serotonin 2A receptor occupancy and plasma psilocin levels
published pages: 1328-1334, ISSN: 0893-133X, DOI: 10.1038/s41386-019-0324-9
Neuropsychopharmacology 44/7 2019-09-02
2019 S. E. Ebert, P. Jensen, B. Ozenne, S. Armand, C. Svarer, D. S. Stenbaek, K. Moeller, A. Dyssegaard, G. Thomsen, J. Steinmetz, B. H. Forchhammer, G. M. Knudsen, L. H. Pinborg
Molecular imaging of neuroinflammation in patients after mild traumatic brain injury: a longitudinal 123 I‐CLINDE single photon emission computed tomography study
published pages: , ISSN: 1351-5101, DOI: 10.1111/ene.13971
European Journal of Neurology 2019-09-02
2019 Martin Nørgaard, Brice Ozenne, Claus Svarer, Stephen C. Strother, Vibe G. Frokjaer, Gitte M. Knudsen, and Melanie Ganz
Preprocessing, prediction and significance: Framework and application to brain imaging
published pages: , ISSN: , DOI:
2019-09-02

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