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Brain network based stratification of mental illness

Periodic Reporting for period 3 - STRATIFY (Brain network based stratification of mental illness)

Reporting period: 2019-10-01 to 2020-12-31

STRATIFY aims to reduce the burden of mental disorders by identifying widely applicable disease markers based on neural processes, which predict psychopathology and allow for targeted interventions. STRATIFY generates 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.

We are carrying out precision phenotyping of up to 800 patients with major depression, alcohol use disorders, eating disorders and psychosis, and 300 controls, which we shall investigate with innovative biostatistical methods derived from artificial 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 the longitudinal population-based imaging genomics cohort, IMAGEN and assessed for genetic risk factors 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 targeted early intervention, identification of novel pharmaceutical targets and the establishment of neurobehaviourally informed endpoints for clinical trials.

We are also adapting our precision medicine approach to Low and Middle Income Countries (LMIC) on a global scale, as part of the Global Imaging Genetics in Adolescents Consortium (GIGA) founded by us (Schumann et al. Lancet Global Health 2019 Jan;7(1):e32). One important output of this work is a China-UK collaboration resulting in a manuscript by Xu et al. 'Satellite Imaging of Global Urbanicity relates to Adolescent Brain Development and Behavior' (Nature Medicine, in revision).
To date the STRATIFY project has published 22 papers, with additional papers still in review. Some key manuscripts are described below:

We discovered symptom clusters with shared biology (see Figure). A paper by Alex Ing et. al. 'Identifying neurobehavioural symptom groups based on shared brain mechanisms' is published in Nature Human Behaviour. This paper describes a new method to find relations between behavioral symptoms, and neuroimaging measures of brain structure and function. By characterising behavioral symptom groups based on shared neural mechanisms, the results provide a framework for developing a classification system for psychiatric illness, which is based on quantitative neurobehavioural measures. A detailed description of the method developed is being prepared for publication.

Jia et. al.'s 'Neurobehavioural characterisation of reinforcement-related behaviour', is currently under revision in Nature Human Behaviour. Here we describe the identification of stratification markers of externalising symptoms based on functional brain activity during reinforcement processes. Neural network underlying hyperactivity and inattention of ADHD while similar during reward anticipation, were distinct during motor inhibition, suggesting different neural mechanisms underlying distinct ADHD behaviours.

In a large GWAS meta-analysis by Evangelou et al., 'Novel alcohol-related genes suggest shared genetic mechanisms with neuropsychiatric disorders' , Nature Human Behaviour 2019, we investigated 480.842 cases participants to decipher the genetic architecture of alcohol intake. The study identified genetic pathways associated with alcohol consumption and suggested shared genetic mechanisms with neuropsychiatric disorders including schizophrenia.
We are currently extending our approach to include further task-based brain activation and functional dynamics, as well as multi level-omics into the model. A project by Chang et al. aims at extracting potential functional connectivity patterns associated with reward processing, and derive a series of ‘snapshots’ of brain functional connectivity patterns. The resultant spatiotemporal feature will help us to distinguish patients with reward-related dysfunction from healthy controls. We employ multi-level omics analysis to incorporate information extracted from different neuroimaging modalities, genetic and environmental data.