ORCID Profile
0000-0002-4516-5103
Current Organisation
University of Oxford
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Publisher: Springer Science and Business Media LLC
Date: 02-2016
DOI: 10.1038/NN.4228
Publisher: Elsevier BV
Date: 04-2014
Publisher: Elsevier BV
Date: 07-2014
Publisher: Public Library of Science (PLoS)
Date: 14-07-2022
DOI: 10.1371/JOURNAL.PMED.1004039
Abstract: Brain iron deposition has been linked to several neurodegenerative conditions and reported in alcohol dependence. Whether iron accumulation occurs in moderate drinkers is unknown. Our objectives were to investigate evidence in support of causal relationships between alcohol consumption and brain iron levels and to examine whether higher brain iron represents a potential pathway to alcohol-related cognitive deficits. Observational associations between brain iron markers and alcohol consumption ( n = 20,729 UK Biobank participants) were compared with associations with genetically predicted alcohol intake and alcohol use disorder from 2-s le mendelian randomization (MR). Alcohol intake was self-reported via a touchscreen questionnaire at baseline (2006 to 2010). Participants with complete data were included. Multiorgan susceptibility-weighted magnetic resonance imaging (9.60 ± 1.10 years after baseline) was used to ascertain iron content of each brain region (quantitative susceptibility mapping (QSM) and T2*) and liver tissues (T2*), a marker of systemic iron. Main outcomes were susceptibility (χ) and T2*, measures used as indices of iron deposition. Brain regions of interest included putamen, caudate, hippoc i, thalami, and substantia nigra. Potential pathways to alcohol-related iron brain accumulation through elevated systemic iron stores (liver) were explored in causal mediation analysis. Cognition was assessed at the scan and in online follow-up (5.82 ± 0.86 years after baseline). Executive function was assessed with the trail-making test, fluid intelligence with puzzle tasks, and reaction time by a task based on the “Snap” card game. Mean age was 54.8 ± 7.4 years and 48.6% were female. Weekly alcohol consumption was 17.7 ± 15.9 units and never drinkers comprised 2.7% of the s le. Alcohol consumption was associated with markers of higher iron (χ) in putamen (β = 0.08 standard deviation (SD) [95% confidence interval (CI) 0.06 to 0.09], p 0.001), caudate (β = 0.05 [0.04 to 0.07], p 0.001), and substantia nigra (β = 0.03 [0.02 to 0.05], p 0.001) and lower iron in the thalami (β = −0.06 [−0.07 to −0.04], p 0.001). Quintile-based analyses found these associations in those consuming units (56 g) alcohol weekly. MR analyses provided weak evidence these relationships are causal. Genetically predicted alcoholic drinks weekly positively associated with putamen and hippoc us susceptibility however, these associations did not survive multiple testing corrections. Weak evidence for a causal relationship between genetically predicted alcohol use disorder and higher putamen susceptibility was observed however, this was not robust to multiple comparisons correction. Genetically predicted alcohol use disorder was associated with serum iron and transferrin saturation. Elevated liver iron was observed at just units (88 g) alcohol weekly c.f. units (56 g). Systemic iron levels partially mediated associations of alcohol intake with brain iron. Markers of higher basal ganglia iron associated with slower executive function, lower fluid intelligence, and slower reaction times. The main limitations of the study include that χ and T2* can reflect changes in myelin as well as iron, alcohol use was self-reported, and MR estimates can be influenced by genetic pleiotropy. To the best of our knowledge, this study represents the largest investigation of moderate alcohol consumption and iron homeostasis to date. Alcohol consumption above 7 units weekly associated with higher brain iron. Iron accumulation represents a potential mechanism for alcohol-related cognitive decline.
Publisher: Elsevier BV
Date: 11-2023
Publisher: Springer Science and Business Media LLC
Date: 08-01-2014
Publisher: IEEE
Date: 04-2014
Publisher: Elsevier BV
Date: 05-2015
Publisher: Springer Science and Business Media LLC
Date: 18-01-2017
DOI: 10.1038/NCOMMS13624
Abstract: The hippoc al formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippoc al volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippoc al structure here we perform a genome-wide association study (GWAS) of 33,536 in iduals and discover six independent loci significantly associated with hippoc al volume, four of them novel. Of the novel loci, three lie within genes ( ASTN2 , DPP4 and MAST4 ) and one is found 200 kb upstream of SHH . A hippoc al subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippoc al volume are also associated with increased risk for Alzheimer’s disease ( r g =−0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippoc al volume and risk for neuropsychiatric illness.
Publisher: Elsevier BV
Date: 07-2006
DOI: 10.1016/J.NEUROIMAGE.2006.02.024
Abstract: There has been much recent interest in using magnetic resonance diffusion imaging to provide information about anatomical connectivity in the brain, by measuring the anisotropic diffusion of water in white matter tracts. One of the measures most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies how strongly directional the local tract structure is. Many imaging studies are starting to use FA images in voxelwise statistical analyses, in order to localise brain changes related to development, degeneration and disease. However, optimal analysis is compromised by the use of standard registration algorithms there has not to date been a satisfactory solution to the question of how to align FA images from multiple subjects in a way that allows for valid conclusions to be drawn from the subsequent voxelwise analysis. Furthermore, the arbitrariness of the choice of spatial smoothing extent has not yet been resolved. In this paper, we present a new method that aims to solve these issues via (a) carefully tuned non-linear registration, followed by (b) projection onto an alignment-invariant tract representation (the "mean FA skeleton"). We refer to this new approach as Tract-Based Spatial Statistics (TBSS). TBSS aims to improve the sensitivity, objectivity and interpretability of analysis of multi-subject diffusion imaging studies. We describe TBSS in detail and present ex le TBSS results from several diffusion imaging studies.
Publisher: Wiley
Date: 29-11-2018
DOI: 10.1002/HBM.24479
Publisher: Wiley
Date: 29-04-2019
DOI: 10.1002/HBM.24611
Publisher: Springer Science and Business Media LLC
Date: 20-05-2020
Publisher: Springer Science and Business Media LLC
Date: 03-2007
Abstract: There is much interest in using magnetic resonance diffusion imaging to provide information on anatomical connectivity in the brain by measuring the diffusion of water in white matter tracts. Among the measures, the most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies local tract directionality and integrity. Many multi-subject imaging studies are using FA images to localize brain changes related to development, degeneration and disease. In a recent paper, we presented a new approach, tract-based spatial statistics (TBSS), which aims to solve crucial issues of cross-subject data alignment, allowing localized cross-subject statistical analysis. This works by transforming the data from the centers of the tracts that are consistent across a study's subjects into a common space. In this protocol, we describe the MRI data acquisition and analysis protocols required for TBSS studies of localized change in brain connectivity across multiple subjects.
Publisher: Springer Science and Business Media LLC
Date: 21-01-2015
DOI: 10.1038/NATURE14101
Publisher: Elsevier BV
Date: 12-2013
Publisher: SPIE
Date: 11-03-2005
DOI: 10.1117/12.600607
Publisher: Springer Science and Business Media LLC
Date: 07-09-2020
DOI: 10.1038/S41467-020-18201-5
Abstract: Healthy cognitive ageing is a societal and public health priority. Cerebrovascular risk factors increase the likelihood of dementia in older people but their impact on cognitive ageing in younger, healthy brains is less clear. The UK Biobank provides cognition and brain imaging measures in the largest population cohort studied to date. Here we show that cognitive abilities of healthy in iduals (N = 22,059) in this s le are detrimentally affected by cerebrovascular risk factors. Structural equation modelling revealed that cerebrovascular risk is associated with reduced cerebral grey matter and white matter integrity within a fronto-parietal brain network underlying executive function. Notably, higher systolic blood pressure was associated with worse executive cognitive function in mid-life (44–69 years), but not in late-life ( years). During mid-life this association did not occur in the systolic range of 110–140 mmHg. These findings suggest cerebrovascular risk factors impact on brain structure and cognitive function in healthy people.
Publisher: Frontiers Media SA
Date: 12-03-2019
Publisher: IEEE
Date: 2007
Publisher: Springer Science and Business Media LLC
Date: 03-10-2016
DOI: 10.1038/NN.4398
Publisher: Springer Science and Business Media LLC
Date: 2014
Publisher: Public Library of Science (PLoS)
Date: 14-08-2014
Publisher: Elsevier BV
Date: 12-2014
Publisher: Elsevier BV
Date: 11-2013
Publisher: Cold Spring Harbor Laboratory
Date: 15-11-2019
DOI: 10.1101/843193
Abstract: Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed.
Publisher: Springer Science and Business Media LLC
Date: 21-12-2022
DOI: 10.1038/S41467-022-35321-2
Abstract: Medical imaging provides numerous insights into the subclinical changes that precede serious diseases such as heart disease and dementia. However, most imaging research either describes a single organ system or draws on clinical cohorts with small s le sizes. In this study, we use state-of-the-art multi-organ magnetic resonance imaging phenotypes to investigate cross-sectional relationships across the heart-brain-liver axis in 30,444 UK Biobank participants. Despite controlling for an extensive range of demographic and clinical covariates, we find significant associations between imaging-derived phenotypes of the heart (left ventricular structure, function and aortic distensibility), brain (brain volumes, white matter hyperintensities and white matter microstructure), and liver (liver fat, liver iron and fibroinflammation). Simultaneous three-organ modelling identifies differentially important pathways across the heart-brain-liver axis with evidence of both direct and indirect associations. This study describes a potentially cumulative burden of multiple-organ dysfunction and provides essential insight into multi-organ disease prevention.
Publisher: Springer International Publishing
Date: 25-11-2013
Publisher: Cold Spring Harbor Laboratory
Date: 03-11-2021
DOI: 10.1101/2021.11.01.466751
Abstract: Motivated by a brain lesion application, we introduce penalized generalized estimating equations for relative risk regression for modelling correlated binary data. Brain lesions can have varying incidence across the brain and result in both rare and high incidence outcomes. As a result, odds ratios estimated from generalized estimating equations with logistic regression structures are not necessarily directly interpretable as relative risks. On the other hand, use of log-link regression structures with the binomial variance function may lead to estimation instabilities when event probabilities are close to 1. To circumvent such issues, we use generalized estimating equations with log-link regression structures with identity variance function and unknown dispersion parameter. Even in this setting, parameter estimates can be infinite, which we address by penalizing the generalized estimating functions with the gradient of the Jeffreys prior. Our findings from extensive simulation studies show significant improvement over the standard log-link generalized estimating equations by providing finite estimates and achieving convergence when boundary estimates occur. The real data application on UK Biobank brain lesion maps further reveals the instabilities of the standard log-link generalized estimating equations for a large-scale data set and demonstrates the clear interpretation of relative risk in clinical applications.
Publisher: Springer Science and Business Media LLC
Date: 15-04-2012
DOI: 10.1038/NG.2250
Publisher: Elsevier BV
Date: 2017
Publisher: Elsevier BV
Date: 12-2018
Publisher: Elsevier BV
Date: 11-2013
Publisher: Cold Spring Harbor Laboratory
Date: 13-04-2016
DOI: 10.1101/041798
Abstract: Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: pecs/nidm-results.html .
Location: United States of America
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Thomas E. Nichols.