Geraint Rees
University College London
H-index: 110
Europe-United Kingdom
Description
Geraint Rees, With an exceptional h-index of 110 and a recent h-index of 66 (since 2020), a distinguished researcher at University College London, specializes in the field of Cognitive Neuroscience, Functional MRI, Consciousness.
His recent articles reflect a diverse array of research interests and contributions to the field:
Differential default-mode network effective connectivity in young-onset Alzheimer's disease variants
Individualised prescriptive inference in ischaemic stroke
21st century medicine and emerging biotechnological syndromes: a cross-disciplinary systematic review of novel patient presentations in the age of technology
Predicting mortality in acutely hospitalised older patients: the impact of model dimensionality
The legibility of the imaged human brain
Neural representation of perceptually grouped shapes with & without awareness
The minimal computational substrate of fluid intelligence
The role of awareness in shaping responses in human visual cortex
Professor Information
University | University College London |
---|---|
Position | Institute of Cognitive Neuroscience |
Citations(all) | 49926 |
Citations(since 2020) | 20284 |
Cited By | 38378 |
hIndex(all) | 110 |
hIndex(since 2020) | 66 |
i10Index(all) | 309 |
i10Index(since 2020) | 258 |
University Profile Page | University College London |
Research & Interests List
Cognitive Neuroscience
Functional MRI
Consciousness
Top articles of Geraint Rees
Differential default-mode network effective connectivity in young-onset Alzheimer's disease variants
Young-onset Alzheimer's Disease(AD) is a rare form of AD characterized by early symptom onset (< 65 years) and heterogeneous clinical phenotypes. Previous studies have consistently shown that patients with late-onset AD exhibit alterations in the default mode network-a large-scale brain network associated with self-related processing and autobiographical memory. However, the functional organization of the default-mode network is far less clear in young-onset AD. Here, we assessed default-mode network effective connectivity in two common young-onset AD variants (i.e., typical amnestic variant and posterior cortical atrophy) and healthy participants to identify disease- and variant-specific differences in the default-mode network. This case-control study was conducted with thirty-nine young-onset AD patients, including typical amnestic (n = 26, 15 females, mean age = 61) and posterior cortical atrophy (n = 13; 8 females, mean age = 61.8), and 24 age-matched healthy participants (13 females, mean age=60.1). All participants underwent resting-state functional MRI and extensive neuropsychological testing. Spectral dynamic causal modelling was performed to quantify resting-state effective connectivity between default-mode network regions. Parametric empirical Bayes analysis was then performed to characterise group differences in effective connectivity. Our results showed that patients with typical AD variant showed increased connectivity from medial prefrontal cortex to posterior default-mode network nodes as well as reduced inhibitory connectivity from hippocampus to other default-mode network nodes, relative to healthy controls …
Authors
Seda Sacu,Catherine F Slattery,Karl J Friston,Ross W Paterson,Alexander JM Foulkes,Keir Yong,Sebastian Crutch,Jonathan M Schott,Adeel Razi
Journal
medRxiv
Published Date
2024
Individualised prescriptive inference in ischaemic stroke
The gold standard in the treatment of ischaemic stroke is set by evidence from randomised controlled trials. Yet the manifest complexities of the brain's functional, connective and vascular architectures introduce heterogeneity in treatment susceptibility that violates the premises of the underlying statistical framework, plausibly leading to substantial errors at both individual and population levels. The counterfactual nature of therapeutic inference has made quantifying the impact of this defect difficult. Employing large-scale lesion, connective, functional, genetic expression, and receptor distribution data, here we conduct a comprehensive series of semi-synthetic virtual interventional trials, quantifying the fidelity of the traditional approach in inferring individual treatment effects against biologically plausible, empirically informed ground truths. We compare the performance of machine learning models flexible enough to capture the observed heterogeneity, and find that the richness of the modelled lesion representation is decisive in determining individual-level fidelity, even where freedom from treatment allocation bias cannot be guaranteed. We are compelled to conclude that complex modelling of richly represented data is critical to individualised prescriptive inference in ischaemic stroke.
Authors
Dominic Giles,Tianbo Xu,Chris Foulon,Robert Gray,Sebastien Ourselin,Jorge Cardoso,Hans Rolf Jäger,Geraint Rees,Ashwani Jha,Parashkev Nachev
Journal
arXiv preprint arXiv:2301.10748
Published Date
2023/1/25
21st century medicine and emerging biotechnological syndromes: a cross-disciplinary systematic review of novel patient presentations in the age of technology
BackgroundBiotechnological syndromes refer to the illnesses that arise at the intersection of human physiology and digital technology. Now that we experience health and illness through so much technology (e.g. wearables, telemedicine, implanted devices), the medium is redefining our expression of symptoms, the observable signs of pathology and the range of diseases that may occur. Here, we systematically review all case reports describing illnesses related to digital technology in the past ten years, in order to identify novel biotechnological syndromes, map out new causal pathways of disease, and identify gaps in care that have disadvantaged a community of patients suffering from these digital complaints.MethodsPubMed, MEDLINE, Scopus, Cochrane Library and Web of Science were searched for case reports and case series that described patient cases involving biotechnological syndromes from 01/01 …
Authors
Isabel Straw,Geraint Rees,Parashkev Nachev
Published Date
2023/10/17
Predicting mortality in acutely hospitalised older patients: the impact of model dimensionality
BackgroundThe prediction of long-term mortality following acute illness can be unreliable for older patients, inhibiting the delivery of targeted clinical interventions. The difficulty plausibly arises from the complex, multifactorial nature of the underlying biology in this population, which flexible, multimodal models based on machine learning may overcome. Here, we test this hypothesis by quantifying the comparative predictive fidelity of such models in a large consecutive sample of older patients acutely admitted to hospital and characterise their biological support.MethodsA set of 804 admission episodes involving 616 unique patients with a mean age of 84.5 years consecutively admitted to the Acute Geriatric service at University College Hospital were identified, in whom clinical diagnoses, blood tests, cognitive status, computed tomography of the head, and mortality within 600 days after admission were available. We …
Authors
Alex Tsui,Petru-Daniel Tudosiu,Mikael Brudfors,Ashwani Jha,Jorge Cardoso,Sebastien Ourselin,John Ashburner,Geraint Rees,Daniel Davis,Parashkev Nachev
Journal
BMC medicine
Published Date
2023/1/8
The legibility of the imaged human brain
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 clinical applications, and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including multilayer perceptrons of demographic, psychological, serological, chronic morbidity, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted individual psychology better than the coincidence of common chronic morbidity (p<0.05). Serology predicted common morbidity (p …
Authors
James K Ruffle,Robert J Gray,Samia Mohinta,Guilherme Pombo,Chaitanya Kaul,Harpreet Hyare,Geraint Rees,Parashkev Nachev
Journal
arXiv preprint arXiv:2309.07096
Published Date
2023/8/23
Neural representation of perceptually grouped shapes with & without awareness
Study on how the visual brain groups fragmented stimuli into coherent shapes & the role of stimulus awareness.
Authors
D Sam Schwarzkopf,Zien Huang,Poutasi WB Urale,Catherine Morgan,Geraint Rees
Published Date
2023/1
The minimal computational substrate of fluid intelligence
The quantification of cognitive powers rests on identifying a behavioural task that depends on them. Such dependence cannot be assured, for the powers a task invokes cannot be experimentally controlled or constrained a priori, resulting in unknown vulnerability to failure of specificity and generalisability. Evaluating a compact version of Raven's Advanced Progressive Matrices (RAPM), a widely used clinical test of fluid intelligence, we show that LaMa, a self-supervised artificial neural network trained solely on the completion of partially masked images of natural environmental scenes, achieves human-level test scores a prima vista, without any task-specific inductive bias or training. Compared with cohorts of healthy and focally lesioned participants, LaMa exhibits human-like variation with item difficulty, and produces errors characteristic of right frontal lobe damage under degradation of its ability to integrate global spatial patterns. LaMa's narrow training and limited capacity -- comparable to the nervous system of the fruit fly -- suggest RAPM may be open to computationally simple solutions that need not necessarily invoke abstract reasoning.
Authors
Amy PK Nelson,Joe Mole,Guilherme Pombo,Robert J Gray,James K Ruffle,Edgar Chan,Geraint E Rees,Lisa Cipolotti,Parashkev Nachev
Journal
arXiv preprint arXiv:2308.07039
Published Date
2023/8/14
The role of awareness in shaping responses in human visual cortex
The visual cortex contains information about stimuli even when they are not consciously perceived. However, it remains unknown whether the visual system integrates local features into global objects without awareness. Here, we tested this by measuring brain activity in human observers viewing fragmented shapes that were either visible or rendered invisible by fast counterphase flicker. We then projected measured neural responses to these stimuli back into visual space. Visible stimuli caused robust responses reflecting the positions of their component fragments. Their neural representations also strongly resembled one another regardless of local features. By contrast, representations of invisible stimuli differed from one another and, crucially, also from visible stimuli. Our results demonstrate that even the early visual cortex encodes unconscious visual information differently from conscious information …
Authors
Zien Huang,Poutasi WB Urale,Catherine A Morgan,Geraint Rees,D Samuel Schwarzkopf
Journal
Royal Society Open Science
Published Date
2023/8/9
Professor FAQs
What is Geraint Rees's h-index at University College London?
The h-index of Geraint Rees has been 66 since 2020 and 110 in total.
What are Geraint Rees's top articles?
The articles with the titles of
Differential default-mode network effective connectivity in young-onset Alzheimer's disease variants
Individualised prescriptive inference in ischaemic stroke
21st century medicine and emerging biotechnological syndromes: a cross-disciplinary systematic review of novel patient presentations in the age of technology
Predicting mortality in acutely hospitalised older patients: the impact of model dimensionality
The legibility of the imaged human brain
Neural representation of perceptually grouped shapes with & without awareness
The minimal computational substrate of fluid intelligence
The role of awareness in shaping responses in human visual cortex
...
are the top articles of Geraint Rees at University College London.
What are Geraint Rees's research interests?
The research interests of Geraint Rees are: Cognitive Neuroscience, Functional MRI, Consciousness
What is Geraint Rees's total number of citations?
Geraint Rees has 49,926 citations in total.
What are the co-authors of Geraint Rees?
The co-authors of Geraint Rees are Karl Friston, Chris Frith, Sarah J Tabrizi, Masud Husain, Vincent Walsh, Nilli Lavie.