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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 €

EC-Contrib. €

Partnership

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Outcomes and
results

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.

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

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 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.	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

List of publications.
year	authors and title	journal	last update
2019	Martin K. Madsen, Patrick M. Fisher, Daniel Burmester, Agnete Dyssegaard, Dea S. StenbÃ¦k, 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 Iâ€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 NÃ¸rgaard, 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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