Deep Learning | MANIFOLD Lab

Investigating the Utility of Explainable Artificial Intelligence for Neuroimaging-Based Dementia Diagnosis and Prognosis

Mon, 02 Feb 2026 00:00:00 +0000

Interpretable deep learning for dementia: a systematic review

Fri, 03 Feb 2023 00:00:00 +0000

Interpretable machine learning for dementia: A systematic review

Sun, 01 Jan 2023 00:00:00 +0000

Authors: Sophie A. Martin, Florence J. Townend, Frederik Barkhof, James H. Cole

DOI: https://doi.org/10.1002/alz.12948

Interpretable Dementia Prediction

Mon, 01 Jan 0001 00:00:00 +0000

Developing a generalisable and interpretable framework for dementia prediction.

[1] Rob Matherson, “Model predicts cognitive decline due to Alzheimer’s, up to two years out” MIT News Office (2019) https://news.mit.edu/2019/model-predicts-alzheimers-decline-0802

Low-field MRI

Mon, 01 Jan 0001 00:00:00 +0000

Motor Neurone Disease

Mon, 01 Jan 0001 00:00:00 +0000

Motor neurone disease (MND) is a rare and incurable neurodegenerative condition which affects the motor neurons in the brain, leading to muscle weakness, speech difficulties, and respiratory failure. Predicting the prognosis of MND at the diagnostic appointment would help with more efficient and effective clinical care management as well as improving clinical trial recruitment. Clinical data recorded at MND-care appointments includes ratings of the patient’s functions and neurodegeneration-related markers in the blood. Brain and spinal imaging (usually MRI) is also taken at the diagnostic appointment to rule out mimic diseases.

We are combining different sources of MND data to predict disease progression through the application of deep learning data fusion techniques. Additionally, we are exploring the relationship between the brain age paradigm and MND disease progession through the use of deep generative modelling. In collaboration with Professor Andrea Malaspina and the UCL Queen Square Motor Neurone Disease Centre https://www.ucl.ac.uk/ion/research/research-centres/motor-neuron-disease-centre

Multimodal Brain Age

Mon, 01 Jan 0001 00:00:00 +0000

Neurodevelopment

Mon, 01 Jan 0001 00:00:00 +0000

Using machine and deep learning approaches, this project aims to determine whether characteristics of brain structure (as indexed by neuroimaging) measured in infancy and early childhood can help predict poor educational outcomes in later childhood.

Scottish Medical Imaging Archive

Mon, 01 Jan 0001 00:00:00 +0000

In collaboration with researchers from The University of Edinburgh and eDRIS (Public Health Scotland).