Search Results for author: John Duncan

Found 5 papers, 1 papers with code

Seizure freedom after surgical resection of diffusion-weighted MRI abnormalities

no code implementations4 Oct 2024 Jonathan Horsley, Gerard Hall, Callum Simpson, Csaba Kozma, Rhys Thomas, Yujiang Wang, Jane de Tisi, Anna Miserocchi, Andrew McEvoy, Sjoerd Vos, Gavin Winston, John Duncan, Peter Taylor

Current clinical approaches to identify structural abnormalities for surgical targeting in epilepsy do not use diffusion-weighted MRI (dMRI), despite evidence that dMRI abnormalities are present in epilepsy and may relate to the epileptogenic zone.

Status epilepticus and thinning of the entorhinal cortex

no code implementations11 Aug 2024 Jonathan Horsley, Yujiang Wang, Callum Simpson, Vyte Janiukstyte, Karoline Leiberg, Beth Little, Jane de Tisi, John Duncan, Peter N. Taylor

Individuals with TLE and SE had reduced entorhinal thickness compared to those with TLE and no history of SE.

Building artificial neural circuits for domain-general cognition: a primer on brain-inspired systems-level architecture

no code implementations21 Mar 2023 Jascha Achterberg, Danyal Akarca, Moataz Assem, Moritz Heimbach, Duncan E. Astle, John Duncan

There is a concerted effort to build domain-general artificial intelligence in the form of universal neural network models with sufficient computational flexibility to solve a wide variety of cognitive tasks but without requiring fine-tuning on individual problem spaces and domains.

Enhancing Fiber Orientation Distributions using convolutional Neural Networks

1 code implementation12 Aug 2020 Oeslle Lucena, Sjoerd B. Vos, Vejay Vakharia, John Duncan, Keyoumars Ashkan, Rachel Sparks, Sebastien Ourselin

We evaluate how well each CNN model can resolve local fiber orientation 1) when training and testing on datasets with the same dMRI acquisition protocol; 2) when testing on a dataset with a different dMRI acquisition protocol than used to train the CNN models; and 3) when testing on a dataset with a fewer number of gradient directions than used to train the CNN models.

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