Work packages

Work Package 1 aims to ensure the successful management of the project by overseeing communication, data handling, ethics, gender balance, and risk monitoring. It focuses on facilitating effective collaboration among project partners through structured planning, scheduling, and evaluation, with special attention to regular meetings and communication infrastructure. WP1 also includes the creation of a Data Management Plan to ensure data is handled ethically, securely, and in accordance with FAIR principles (Findable, Accessible, Interoperable, Reusable). This package transcends the entire project duration and supports all other work packages.

WP1 also emphasizes ethics monitoring to ensure compliance with GDPR, fairness, and transparency in data use, as well as adherence to ethical standards throughout the project. Gender balance will be monitored, and measures will be implemented to address disparities, including training and cultural awareness activities. Scientific quality and project risks will be regularly assessed and managed through a structured quality control process, ensuring high standards in project deliverables and overall outcomes. 

The aim of this work package is to develop innovative surface-based methods for analyzing the cerebral cortex, particularly in the context of pooled longitudinal and multi-site neuroimaging datasets. Current tools, such as QDECR, are designed for single-site cross-sectional studies and lack capabilities for repeated measures or multi-site data analysis. To address this, we will implement two key innovations: a vertex-wise mixed model for pooled longitudinal and multi-site datasets, and vertex-wise analysis methods that do not require data sharing between sites through meta-analysis and federated mega-analysis of linear models. These advancements will fill the existing gap in open-source tools and provide crucial support for other work packages, including WP3.

The WP consists of three main tasks: T2.1 involves developing and optimizing vertex-wise mixed models within the QDECR package, enhancing computational efficiency for end users. T2.2 focuses on creating a meta-analysis framework within QDECR to synthesize results across sites without sharing individual-level data, accommodating legal and consent restrictions, and integrating these methods into the EBRAINS research infrastructure. Finally, T2.3 will implement federated mega-analysis, allowing for the iterative fitting of statistical models across sites while retaining individual-level data privacy and model flexibility. Together, these tasks will advance cerebral cortex analysis, enabling robust multi-site and longitudinal research in neuroimaging.

The aim of this work package is to investigate the neurobiological mechanisms linking risk factors (RPs) and mental health using advanced multimodal approaches. This project addresses the significant gap in epidemiological and psychiatric research by examining both structural and functional brain mechanisms associated with these factors. By utilizing novel methods developed in WP2 for repeated measures and cross-dataset analyses, as well as integrating outputs from WP4, the project aims to conduct a comprehensive analysis of dimensional mental health outcomes. The research will systematically leverage QDECR tools from WP2 and outputs from WP4, enhancing our understanding of how RPs influence mental health across the lifespan.

The work package consists of several tasks: T3.1 focuses on data harmonisation and behavioural analysis, harmonising data for key measures and conducting regression analyses to explore RP impacts on mental health outcomes. T3.2 involves intrinsic functional connectivity analysis using fMRI data to assess connectivity changes related to RPs, followed by correlation and mediation analyses. T3.3 and T3.4 explore structural MRI data through repeated measures and cross-dataset approaches to identify developmental trajectories and structural metrics associated with RPs and mental health outcomes. Lastly, T3.5 applies HiTOP-focused analysis to connect RPs with HiTOP dimensions, offering a proof-of-concept for the crosswalk models and structural MRI analysis. Together, these tasks provide a comprehensive approach to understanding the complex interplay between early risk factors, brain development, and mental health.

The aim of this work package is to address the limitations of traditional categorical approaches to defining and measuring psychopathological outcomes of early risk factors (RPs), which often fail to account for comorbidity, heterogeneity, and continuity between categories. This WP introduces the HiTOP model—a hierarchical, dimensional framework of psychopathology—as an innovative solution that allows existing categorical disorder categories to be translated into hierarchical dimensions, enhancing the search for neurobiological correlates of mental health processes. By empirically developing crosswalk models, this project will generate HiTOP scores from existing phenotype data, providing a more nuanced and integrated understanding of psychopathological outcomes.

The WP involves several tasks: data harmonisation, identification of anchor variables, and the construction of focused and general crosswalk models using the Healthy Brain Network dataset. Task T4.1 focuses on harmonising data by reviewing HiTOP measurement and identifying relevant measures across datasets. Task T4.2 involves identifying key anchor variables through principal component and graph analyses. Tasks T4.3 and T4.4 will build focused and general crosswalk models, respectively, generating weights to estimate HiTOP scores from anchor variables for different age cohorts. These models will form the foundation for Task T3.5 within WP3, ultimately advancing the field's understanding of psychopathological processes through a dimensional lens.

The aim of this work package is to ensure that the project's outputs, including knowledge, data, models, and software, are effectively shared with both academic and non-academic communities, maximizing the project's impact. This work package focuses on dissemination and awareness-raising activities, adhering to data protection regulations and open science policies. WP6 is crucially linked to all other work packages as it manages the sharing of their results. Key tasks include coordinating open science policies, promoting the project through communication activities, disseminating scientific findings, and sharing data, models, and software on platforms like EBRAINS.

The work package tasks are structured to support the overall objectives through dedicated activities over the project's timeline. T5.1 involves managing compliance with open science standards, including data management and privacy protection. T5.2 focuses on communication strategies, including a website, social media, and a public symposium to engage wider audiences. T5.3 is dedicated to disseminating scientific findings through conferences and publications in renowned journals. Finally, T5.4 emphasizes the dissemination of data, models, and software, ensuring that valuable resources such as anonymized data and the R package QDECR are accessible to the community, fostering broader use and collaboration.