Welcome to the MANIFOLD Lab

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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.

Lab beliefs
UCL Hawkes DRC logos

Meet the Team

Researchers

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Francesca Biondo

Senior Research Fellow

Neuroimaging, Neuropsychology, Developmental & Cognitive psychology, Ageing & Dementia, Machine Learning & Artificial Intelligence, Ultra-low-field neuroimaging

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James H Cole

Professor of Neuroimage Computing

Neuroscience, Neuroimaging, Ageing, Dementia, Neurodegenerative diseases, Artificial Intelligence, Machine Learning, Information Processing

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Maitrei Kohli

Research Fellow

Neuroimaging, Neurodegenrative diseases, Machine Learning & Artificial Intelligence, Computational Modelling, Behavioual Genetics

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Sophie Martin

Research Fellow

Deep Learning, Interpretability, Neuroimaging, Dementia, Computational Modelling, Physics

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Anthi Papouli

Research Assistant

Artificial Intelligence, Neuroscience, Neuroimaging, Dementia, Bioinformatics, Multimodal Data Integration

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Serena Verdi

Research Fellow

Neuroimaging, Ageing, Dementia, Neuroscience, Public engagement of science, Translational research

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Mariam Zabihi

Research Fellow

Normative Modeling, Machine Learning, Neuroimaging, Ageing, Neurodegenerative diseases

PhD Students

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Rohan Bhome

PhD Student

Neuroimaging, Neuroscience, Dementia, Neurodegeneration, Dementia with Lewy Bodies, Neuropsychiatry

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Ayodeji Ijishakin

PhD Student

Neuroimaging, Generative Modelling, Mechanistic Interpretability, Amyotrophic Lateral Sclerosis

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Jiongqi Qu

PhD student

Deep Learning, Generalisability, Foundation Model, Dementia, Neuroimaging

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Florence Townend

PhD Student

Multi-modal data fusion, Neuroimaging, Motor neurone disease

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Douglas Wyllie

PhD Student

Multi-modal data fusion, Neuroimaging, Deep learning, Dementia

Alumni

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Melis Anatürk

former Research Fellow

Multimodal MRI, Ageing, Dementia, Machine Learning, Epidemiology, Public Engagement, Open Science

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Jen Cox

PhD graduate

Neuroimaging, Autoimmune diseases, Systemic inflammation, Imaging biomarkers, Translational imaging

Visitors

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Michela Gravina

Research Fellow

Neuroimaging, Machine learning

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Dorian Manouvriez

PhD student

Neuroimaging, Deep learning

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Remika Mito

Research Fellow

Neuroimaging, Diffusion MRI, Tractography, Epilepsy

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Rafael Navarro

PhD student

Neuroimaging, Machine learning, Biomarkers

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Hui Xin Ng

PhD student

Neuroimaging, Ageing, Psychiatry

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Lemuel Puglisi

PhD student

Neuroimaging, Deep learning, Generative modelling

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Sivaniya Subramaniapillai

Research Fellow

Neuroimaging, Brain Health, Ageing, Dementia, Sex/Gender, Diversity

Lab expertise

Statistics
Machine Learning
Artificial Intelligence
Dementia
Ageing
Imaging

Projects

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BARCODE

BARCODE

Predicting risk of dementia in adults with subjective or mild cognitive impairment using the brain-age paradigm.

ENIGMA Brain Age

ENIGMA Brain Age

James Cole chairs the ENIGMA Brain Age working group.

Interpretable Dementia Prediction

Interpretable Dementia Prediction

Developing a generalisable and interpretable framework for dementia prediction.

Motor Neurone Disease

Motor Neurone Disease

Predicting disease progression in motor neurone disease

Multimodality MRI brain age prediction

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

Neurodevelopment

Predicting risk of poor educational outcomes from MRI measurements of the brain during infancy and early childhood

Normative Modelling

Normative Modelling

Mapping individual differences in the neuroanatomy of dementia

Contact

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