Welcome to the website of the Machine-learning Artificial Intelligence Neuro Imaging Focusing on Longevity & Dementia (MANIFOLD) Laboratory. We are based at University College London, part of the Centre for Medical Image Computing (CMIC) and the Dementia Research Centre (DRC) at the Queen Square Institute of Neurology.
Our goal is to further our understanding of how the brain ages and how this affects risk of cognitive decline, neurodegenerative diseases and dementia. We do this using advanced statistics, machine learning and AI methods to analyse neuroimaging data, alongside genetic, cognitive, clinical, biological and behavioural information – taking a big-data science approach to help translate computational methods into the clinic for people with age-associated cognitive decline, dementia and related conditions.
Neuroscience, Neuroimaging, Ageing & Dementia, Machine Learning & Artificial Intelligence, Neuropsychology, Developmental & Cognitive psychology
Neuroscience, Neuroimaging, Ageing, Dementia, Neurodegenerative diseases, Artificial Intelligence, Machine Learning, Information Processing
Normative Modeling, Machine Learning, Neuroimaging, Ageing, Neurodegenerative diseases
Neuroimaging, Deep learning, Motor Neurone Disease, Computational neuroscience
Multi-modal data fusion methods, Neuroimaging, Deep Learning, Generative modelling, ALS
Neuroimaging, Autoimmune diseases, Systemic inflammation, Imaging biomarkers, Translational imaging
Neuroimaging, Neuroscience, Dementia, Neurodegeneration, Dementia with Lewy Bodies, Neuropsychiatry
Neuroimaging, Ageing, Dementia, Neuroscience, Public engagement of science, Translational research
Deep Learning, Interpretability, Neuroimaging, Dementia, Computational Modelling, Physics
Multimodal MRI, Ageing, Dementia, Machine Learning, Epidemiology, Public Engagement, Open Science
Predicting risk of dementia in adults with subjective or mild cognitive impairment using the brain-age paradigm.
Developing a generalisable and interpretable framework for dementia prediction.
Using the UK Biobank to predict brain age from multiple modalities of MRI data, including structural, diffusion and functional scans.
Predicting risk of poor educational outcomes from MRI measurements of the brain during infancy and early childhood