Welcome to the MANIFOLD Lab
About the lab
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 UCL Hawkes Institute, formerly the Centre for Medical Image Computing (CMIC), and the Dementia Research Centre (DRC) at the Queen Square Institute of Neurology.
Mission Statement
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.
Meet the Team
Researchers
Francesca Biondo
Senior Research Fellow
Neuroimaging, Neuropsychology, Developmental & Cognitive psychology, Ageing & Dementia, Machine Learning & Artificial Intelligence, Ultra-low-field neuroimaging
James H Cole
Professor of Neuroimage Computing
Neuroscience, Neuroimaging, Ageing, Dementia, Neurodegenerative diseases, Artificial Intelligence, Machine Learning, Information Processing
Maitrei Kohli
Research Fellow
Neuroimaging, Neurodegenrative diseases, Machine Learning & Artificial Intelligence, Computational Modelling, Behavioual Genetics
Anthi Papouli
Research Assistant
Artificial Intelligence, Neuroscience, Neuroimaging, Dementia, Bioinformatics, Multimodal Data Integration
Serena Verdi
Research Fellow
Neuroimaging, Ageing, Dementia, Neuroscience, Public engagement of science, Translational research
Mariam Zabihi
Research Fellow
Normative Modeling, Machine Learning, Neuroimaging, Ageing, Neurodegenerative diseases
PhD Students
Rohan Bhome
PhD Student
Neuroimaging, Neuroscience, Dementia, Neurodegeneration, Dementia with Lewy Bodies, Neuropsychiatry
Jen Cox
PhD student
Neuroimaging, Autoimmune diseases, Systemic inflammation, Imaging biomarkers, Translational imaging
Ayodeji Ijishakin
PhD Student
Neuroimaging, Generative Modelling, Mechanistic Interpretability, Amyotrophic Lateral Sclerosis
Sophie Martin
PhD Student
Deep Learning, Interpretability, Neuroimaging, Dementia, Computational Modelling, Physics
Alumni
Melis Anatürk
Honorary Research Fellow
Multimodal MRI, Ageing, Dementia, Machine Learning, Epidemiology, Public Engagement, Open Science
Visitors
Lab expertise
News
Projects
BARCODE
Predicting risk of dementia in adults with subjective or mild cognitive impairment using the brain-age paradigm.
ENIGMA Brain Age
James Cole chairs the ENIGMA Brain Age working group.
Interpretable Dementia Prediction
Developing a generalisable and interpretable framework for dementia prediction.
Motor Neurone Disease
Predicting disease progression in motor neurone disease
Multimodality MRI brain age prediction
Using the UK Biobank to predict brain age from multiple modalities of MRI data, including structural, diffusion and functional scans.
Neurodevelopment
Predicting risk of poor educational outcomes from MRI measurements of the brain during infancy and early childhood
Normative Modelling
Mapping individual differences in the neuroanatomy of dementia
