563 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University College LondonGB43 papers
- UCL Queen Square Institute of NeurologyGB15 papers
- King's College LondonGB11 papers
- Wellcome Centre for Human NeuroimagingGB8 papers
- Wellcome / EPSRC Centre for Interventional and Surgical SciencesGB8 papers
- Queen Mary University of LondonGB7 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- Sorbonne UniversitéFR4 papers
- Assistance Publique – Hôpitaux de ParisFR3 papers
- Harvard UniversityUS3 papers
- InsermFR3 papers
- Massachusetts General HospitalUS3 papers
43 papers
Evaluation of Uncertainty-Aware Multi-Software Ensembles for Hippocampal Segmentation
Gabriel Oliveira-Stahl, Anna Schroder, James Moggridge +6
Accurate hippocampal segmentation can be a useful tool for diagnosing and monitoring neurological conditions such as Alzheimer's disease and epilepsy. While numerous automated segm…
Parallelism in Neurodegenerative Biomarker Tests: Hidden Errors and the Risk of Misconduct
Axel Petzold, Joachim Pum, David P Crabb
Biomarkers are critical tools in the diagnosis and monitoring of neurodegenerative diseases. Reliable quantification depends on assay validity, especially the demonstration of para…
Dissociating model architectures from inference computations
Noor Sajid, Johan Medrano
Parr et al., 2025 examines how auto-regressive and deep temporal models differ in their treatment of non-Markovian sequence modelling. Building on this, we highlight the need for d…
KMT2B-related disorders: expansion of the phenotypic spectrum and long-term efficacy of deep brain stimulation
L Cif, D Demailly, JP Lin +112
Heterozygous mutations in KMT2B are associated with an early-onset, progressive, and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor feat…
VASARI-auto: equitable, efficient, and economical featurisation of glioma MRI
James K Ruffle, Samia Mohinta, Kelly Pegoretti Baruteau +5
The VASARI MRI feature set is a quantitative system designed to standardise glioma imaging descriptions. Though effective, deriving VASARI is time-consuming and seldom used in clin…
Bayesian sparsification for deep neural networks with Bayesian model reduction
Dimitrije Marković, Karl J. Friston, Stefan J. Kiebel
Deep learning's immense capabilities are often constrained by the complexity of its models, leading to an increasing demand for effective sparsification techniques. Bayesian sparsi…
Compressed representation of brain genetic transcription
James K Ruffle, Henry Watkins, Robert J Gray +3
The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space.…
Computational limits to the legibility of the imaged human brain
James K Ruffle, Robert J Gray, Samia Mohinta +5
Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clin…
Linking fast and slow: the case for generative models
Johan Medrano, Karl J. Friston, Peter Zeidman
A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent…
Individualized prescriptive inference in ischaemic stroke
Dominic Giles, Chris Foulon, Guilherme Pombo +8
The gold standard in the treatment of ischaemic stroke is set by evidence from randomized controlled trials, based on simple descriptions of presumptively homogeneous populations.…
Brain tumour genetic network signatures of survival
James K Ruffle, Samia Mohinta, Guilherme Pombo +6
Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology. Gliomas are characterised by distinct combinations of genetic and…
Focal and Connectomic Mapping of Transiently Disrupted Brain Function
Michael S. Elmalem, Hanna Moody, James K. Ruffle +5
The distributed nature of the neural substrate, and the difficulty of establishing necessity from correlative data, combine to render the mapping of brain function a far harder tas…
Brain tumour segmentation with incomplete imaging data
James K Ruffle, Samia Mohinta, Robert J Gray +2
The complex heterogeneity of brain tumours is increasingly recognized to demand data of magnitudes and richness only fully-inclusive, large-scale collections drawn from routine cli…
Motor neuron pathology in CANVAS due to RFC1 expansions
Vincent Huin, Giulia Coarelli, Clément Guemy +22
CANVAS caused by RFC1 biallelic expansions is a major cause of inherited sensory neuronopathy. Detection of RFC1 expansion is challenging and CANVAS can be associated with atypical…
pymdp: A Python library for active inference in discrete state spaces
Conor Heins, Beren Millidge, Daphne Demekas +4
Active inference is an account of cognition and behavior in complex systems which brings together action, perception, and learning under the theoretical mantle of Bayesian inferenc…
Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures
Fernando Pérez-García, Catherine Scott, Rachel Sparks +2
Detailed analysis of seizure semiology, the symptoms and signs which occur during a seizure, is critical for management of epilepsy patients. Inter-rater reliability using qualitat…
A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections
Fernando Pérez-García, Reuben Dorent, Michele Rizzi +9
Accurate segmentation of brain resection cavities (RCs) aids in postoperative analysis and determining follow-up treatment. Convolutional neural networks (CNNs) are the state-of-th…
Machine Learning and Glioblastoma: Treatment Response Monitoring Biomarkers in 2021
Thomas Booth, Bernice Akpinar, Andrei Roman +10
The aim of the systematic review was to assess recently published studies on diagnostic test accuracy of glioblastoma treatment response monitoring biomarkers in adults, developed…
Low Stress Ion Conductance Microscopy of Sub-Cellular Stiffness
Richard W. Clarke, Pavel Novak, Alexander Zhukov +7
Directly examining subcellular mechanics whilst avoiding excessive strain of a live cell requires the precise control of light stress on very small areas, which is fundamentally di…
Modelling the Multiwavelength Variability of Mrk 335 using Gaussian Processes
Ryan-Rhys Griffiths, Jiachen Jiang, Douglas J. K. Buisson +12
The optical and UV variability of the majority of AGN may be related to the reprocessing of rapidly-changing X-ray emission from a more compact region near the central black hole.…
Model-based multi-parameter mapping
Yael Balbastre, Mikael Brudfors, Michela Azzarito +3
Quantitative MR imaging is increasingly favoured for its richer information content and standardised measures. However, computing quantitative parameter maps, such as those encodin…
Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation
Vien Ngoc Dang, Francesco Galati, Rosa Cortese +7
Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors…
Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy
Nishant Sinha, Natalie Peternell, Gabrielle M. Schroeder +6
Objective: To identify if whole-brain structural network alterations in patients with temporal lobe epilepsy (TLE) and focal to bilateral tonic-clonic seizures (FBTCS) differ from…
Nonlinear computations in spiking neural networks through multiplicative synapses
Michele Nardin, James W Phillips, William F Podlaski +1
The brain efficiently performs nonlinear computations through its intricate networks of spiking neurons, but how this is done remains elusive. While nonlinear computations can be i…