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Brain network based stratification of mental illness

Cel

To reduce the burden of mental disorders it is a formidable aim to identify widely applicable disease markers based on
neural processes, which predict psychopathology and allow for targeted interventions. We will generate a neurobehavioural
framework for stratification of psychopathology by characterising links between network properties of brain function and
structure and reinforcement–related behaviours, which are fundamental components of some of the most prevalent mental
disorders, major depression, alcohol use disorder and ADHD. We will assess if network configurations define subtypes
within and if they correspond to comorbidity across these diagnoses. We will identify discriminative data modalities and
characterize predictors of future psychopathology.
To identify specific neurobehavioural clusters we will carry out precision phenotyping of 900 patients with major
depression, ADHD and alcohol use disorders and 300 controls, which we will investigate with innovative deep machine
learning methods derived from artifical intelligence research. Development of these methods will optimize exploitation of a
wide range of assessment modalities, including functional and structural neuroimaging, cognitive, emotional as well as
environmental measures. The neurobehavioural clusters resulting from this analysis will be validated in a longitudinal
population-based imaging genomics cohort, the IMAGEN sample of over 2000 participants spanning the period from
adolescence to adulthood and integrated with information generated from genomic and imaging-genomic meta-analyses of
>300.000 individuals.
By targeting specific neural processes the resulting stratification markers will serve as paradigmatic examples for a
diagnostic classification, which is based upon quantifiable neurobiological measures, thus enabling targetted early
intervention, identification of novel pharmaceutical targets and the establishment of neurobehaviourally informed endpoints
for clinical trials.

Dziedzina nauki

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Zaproszenie do składania wniosków

ERC-2015-AdG

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System finansowania

ERC-ADG - Advanced Grant

Instytucja przyjmująca

CHARITE - UNIVERSITAETSMEDIZIN BERLIN
Wkład UE netto
€ 988 895,34
Adres
Chariteplatz 1
10117 Berlin
Niemcy

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Region
Berlin Berlin Berlin
Rodzaj działalności
Higher or Secondary Education Establishments
Linki
Koszt całkowity
€ 988 895,34

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Ostatnia aktualizacja: 23 Lutego 2024

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Permalink: https://cordis.europa.eu/project/id/695313/pl

European Union, 2024