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Brain network based stratification of mental illness

Brain network based stratification of mental illness

Objective

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.
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Host institution

KING'S COLLEGE LONDON

Address

Strand
Wc2r 2ls London

United Kingdom

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 2 352 066,13

Beneficiaries (5)

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KING'S COLLEGE LONDON

United Kingdom

EU Contribution

€ 2 352 066,13

THE UNIVERSITY OF NOTTINGHAM

United Kingdom

EU Contribution

€ 314 162,50

THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN

Ireland

EU Contribution

€ 182 601,25

UNIVERSITY OF SOUTHAMPTON

United Kingdom

EU Contribution

€ 354 385

CHARITE - UNIVERSITAETSMEDIZIN BERLIN

Germany

EU Contribution

€ 191 000

Project information

Grant agreement ID: 695313

Status

Ongoing project

  • Start date

    1 October 2016

  • End date

    30 September 2021

Funded under:

H2020-EU.1.1.

  • Overall budget:

    € 3 394 214,88

  • EU contribution

    € 3 394 214,88

Hosted by:

KING'S COLLEGE LONDON

United Kingdom