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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 2 - STRATIFY (Brain network based stratification of mental illness)

Teaser

Summary

STRATIFY aims to reduce the burden of mental disorders, which account for 28% of the disease burden of non-communicable diseases, 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 reinforcementâ€“related behaviours, which are fundamental components of some of the most prevalent mental disorders. The project aims to assess if network configurations define subtypes within and if they correspond to comorbidity across these diagnoses. It will identify discriminative data modalities and characterize predictors of future psychopathology.

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 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 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\' (submitted). We found an increased correlation of urbanicity with brain structure and functional network connectivity in Chinese compared to European participants. Urbanicity was highly correlated with a neuropsychological trait, perspective taking as well as symptoms of depression in both datasets. These correlations were mediated by the structural and functional brain changes observed. Susceptibility to urbanicity was greatest in two developmental windows during mid-childhood and adolescence.

Work performed

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 in press in Nature Human Behaviour. This paper describes a new method to find relations between behavioral symptoms, and neuroimaging measures of brain structure and function. The authors identified two clusters of behavioural symptoms, consisting of anxiety/depression and executive dysfunction symptoms respectively. These clusters correlated with distinct sets of brain regions and inter-regional connections, measured by structural and functional neuroimaging modalities. The authors found that the neural correlates of these symptom groups were present before behavioural symptoms had developed, and that these neural correlates showed case-control differences in corresponding psychiatric disorders, depression and ADHD, in independent clinical samples. 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. Brain fMRI networks of relevant tasks were correlated with externalising and internalising behaviours. Significant correlations were observed for externalising behaviours, especially in reward anticipation and successful inhibition networks. 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 addition, the highly comorbid ADHD and ODD/CD share similar neural networks during both reward anticipation and motor inhibition, hence supporting the idea of unifying ADHD and ODD/CD into a single spectrum disorder.

In a large GWAS meta-analysis by Evangelou et al., \'Novel alcohol-related genes suggest shared genetic mechanisms with neuropsychiatric disorders\' , Nature Human Behaviour (in press), 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.The GWAS on alcohol drinking identifying candidates and gene scores are to be assessed in STRATIFY.

Final results

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 paper on this topic is currently in preparation by Chang et al. The research explores how the coupling of brain hemodynamic response measured by fMRI reflects the interaction among large-scale brain regions. Recent studies suggest that brain regional interactions are temporally evolving, and may reflect ongoing mental states of subjects. To extract potential functional connectivity patterns associated with reward processing, we apply the sliding window (20-60s) approach on the timecourse of brain networks, and derive a series of â€˜snapshotsâ€™ of brain functional connectivity patterns. By comparing occurrence of connectivity patterns with stimuli onset timeline, we can identify patterns co-occurring with task performance. 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. To this end, we first reduce high-dimensional data using principal component analysis (PCA), and then apply canonical correlation analysis (CCA) to simultaneously maximise the inter-modality covariation across different layers of data. The multimodal neural signature delineates multi-facets biological underpinnings of reward-related processing, and thus can stratify groups of subjects with different symptoms profiles.