Using data analysis and multi-scale modelling, we aim to bridge the levels of the structure and function of the brain–from genes to circuits to behaviour, including wakefulness and sleep.
Our growing team will conduct research across these pillars:
- Artificial Intelligence and Machine Learning: Researchers will employ machine learning and artificial intelligence (including topological data analysis, deep learning and SVM) to analyze, categorize and predict genetic, epigenetic, MRI, DTI, fMRI, hd-EEG and clinical data.
- Computational Genomics: Bioinformatics researchers, computational biologists and computer scientists will employ and develop state-of-the-art genetic, epigenetic and transcriptomic analyses and predictive models to provide new insights into mental health and brain disorders in a data-driven way, making outcome predictors and identifying optimal intervention opportunities.
- Brain Circuit Modelling: Researchers will lead the development, simulation and analysis of large-scale computational models of biophysically detailed brain circuitry (for both preclinical and human brains), integrating genetic, epigenetic and transcriptomic data, and ion channel, cellular and synaptic biophysics. The team will work in close collaboration with other groups involved in detailed biophysical modelling. The team will also curate and maintain data, literature and models to enable data-driven modelling of brain circuitry in health and disease.
- Whole Brain Modelling: Researchers will develop computational models of the human brain, in health and mental illness, incorporating multimodal neuroimaging and expression data including structural, functional, diffusion and spectrometry MRI, PET/CT and EEG. The team will also construct atlases, develop analysis approaches, and identify biomarkers to define mental health and brain disorders in a data-driven manner.
- Whole Person Modelling: Researchers will develop and apply predictive modelling approaches for individual citizen, patient and population trajectories and outcomes. This includes tools, pipelines and approaches for the exploration, visualization and analysis of complex high-dimensional data, with the aim of the data-driven definition of mental health and brain disorders, and the establishment of outcome predictors and identification of optimal intervention opportunities.
- Knowledge Management: Researchers will organize and disseminate data, models and literature related to the definition and treatment of mental health disorders to support both researchers and clinicians. This team will maintain and curate ontologies of brain disorders and link to data, analyses, visualizations, literature and models supporting their definition. This clinical support platform will support reasoning and inference across definitions and datasets to bridge psychiatric and neurological disorders.
- Data Management: The Centre will structure and map clinical and research data into a unified schema to develop a knowledge graph. This will lay the technical foundation to support supervised and unsupervised content discovery, enable data mining, and facilitate interactive modes of engagement with our data. Data provenance will be maintained to ensure scientific value, quality assessment and attribution.
We are committed to the values of:
- fostering an open, team-science environment
- conducting reproducible science
- adhering to the FAIR data principles
- establishing global collaborations
- disseminating knowledge
- training the next generation of scientists
- being an ethical, responsible incubator for mental health technologies