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Thompson AG, Taschler B, Smith SM, Turner MR. Premorbid brain structure influences risk of amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry 2024; 95:360-365. [PMID: 38050140 PMCID: PMC10958375 DOI: 10.1136/jnnp-2023-332322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 09/25/2023] [Indexed: 12/06/2023]
Abstract
BACKGROUND Amyotrophic lateral sclerosis (ALS) is a disease of the motor network associated with brain structure and functional connectivity alterations that are implicated in disease progression. Whether such changes have a causal role in ALS, fitting with a postulated influence of premorbid cerebral architecture on the phenotypes associated with neurodegenerative disorders is not known. METHODS This study considered causal effects and shared genetic risk of 2240 structural and functional MRI brain scan imaging-derived phenotypes (IDPs) on ALS using two sample Mendelian randomisation, with putative associations further examined with extensive sensitivity analysis. Shared genetic predisposition between IDPs and ALS was explored using genetic correlation analysis. RESULTS Increased white matter volume in the cerebral hemispheres was causally associated with ALS. Weaker causal associations were observed for brain stem grey matter volume, parieto-occipital white matter surface and volume of the left thalamic ventral anterior nucleus. Genetic correlation was observed between ALS and intracellular volume fraction and isotropic free water volume fraction within the posterior limb of the internal capsule. CONCLUSIONS This study provides evidence that premorbid brain structure, in particular white matter volume, contributes to the risk of ALS.
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Affiliation(s)
| | - Bernd Taschler
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Stephen M Smith
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Martin R Turner
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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2
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Leonardsen EH, Vidal-Piñeiro D, Roe JM, Frei O, Shadrin AA, Iakunchykova O, de Lange AMG, Kaufmann T, Taschler B, Smith SM, Andreassen OA, Wolfers T, Westlye LT, Wang Y. Genetic architecture of brain age and its causal relations with brain and mental disorders. Mol Psychiatry 2023; 28:3111-3120. [PMID: 37165155 PMCID: PMC10615751 DOI: 10.1038/s41380-023-02087-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/18/2023] [Accepted: 04/19/2023] [Indexed: 05/12/2023]
Abstract
The difference between chronological age and the apparent age of the brain estimated from brain imaging data-the brain age gap (BAG)-is widely considered a general indicator of brain health. Converging evidence supports that BAG is sensitive to an array of genetic and nongenetic traits and diseases, yet few studies have examined the genetic architecture and its corresponding causal relationships with common brain disorders. Here, we estimate BAG using state-of-the-art neural networks trained on brain scans from 53,542 individuals (age range 3-95 years). A genome-wide association analysis across 28,104 individuals (40-84 years) from the UK Biobank revealed eight independent genomic regions significantly associated with BAG (p < 5 × 10-8) implicating neurological, metabolic, and immunological pathways - among which seven are novel. No significant genetic correlations or causal relationships with BAG were found for Parkinson's disease, major depressive disorder, or schizophrenia, but two-sample Mendelian randomization indicated a causal influence of AD (p = 7.9 × 10-4) and bipolar disorder (p = 1.35 × 10-2) on BAG. These results emphasize the polygenic architecture of brain age and provide insights into the causal relationship between selected neurological and neuropsychiatric disorders and BAG.
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Affiliation(s)
- Esten H Leonardsen
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
| | - Didac Vidal-Piñeiro
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - James M Roe
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
| | - Alexey A Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Olena Iakunchykova
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Ann-Marie G de Lange
- Department of Psychology, University of Oslo, 0317, Oslo, Norway
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, 1015, Lausanne, Switzerland
- Department of Psychiatry, University of Oxford, OX1 2JD, Oxford, UK
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, 72074, Tübingen, Germany
| | - Bernd Taschler
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, OX3 9DU, Oxford, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, OX3 9DU, Oxford, United Kingdom
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- Department of Psychology, University of Oslo, 0317, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, 72074, Tübingen, Germany
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Yunpeng Wang
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway.
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Topiwala A, Nichols TE, Williams LZJ, Robinson EC, Alfaro-Almagro F, Taschler B, Wang C, Nelson CP, Miller KL, Codd V, Samani NJ, Smith SM. Telomere length and brain imaging phenotypes in UK Biobank. PLoS One 2023; 18:e0282363. [PMID: 36947528 PMCID: PMC10032499 DOI: 10.1371/journal.pone.0282363] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 02/13/2023] [Indexed: 03/23/2023] Open
Abstract
Telomeres form protective caps at the ends of chromosomes, and their attrition is a marker of biological aging. Short telomeres are associated with an increased risk of neurological and psychiatric disorders including dementia. The mechanism underlying this risk is unclear, and may involve brain structure and function. However, the relationship between telomere length and neuroimaging markers is poorly characterized. Here we show that leucocyte telomere length (LTL) is associated with multi-modal MRI phenotypes in 31,661 UK Biobank participants. Longer LTL is associated with: i) larger global and subcortical grey matter volumes including the hippocampus, ii) lower T1-weighted grey-white tissue contrast in sensory cortices, iii) white-matter microstructure measures in corpus callosum and association fibres, iv) lower volume of white matter hyperintensities, and v) lower basal ganglia iron. Longer LTL was protective against certain related clinical manifestations, namely all-cause dementia (HR 0.93, 95% CI: 0.91-0.96), but not stroke or Parkinson's disease. LTL is associated with multiple MRI endophenotypes of neurodegenerative disease, suggesting a pathway by which longer LTL may confer protective against dementia.
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Affiliation(s)
- Anya Topiwala
- Nuffield Department Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Thomas E. Nichols
- Nuffield Department Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, Oxford, United Kingdom
| | - Logan Z. J. Williams
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Emma C. Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Fidel Alfaro-Almagro
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Bernd Taschler
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
| | - Christopher P. Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
| | - Veryan Codd
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Nilesh J. Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
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4
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Lee S, Bijsterbosch JD, Almagro FA, Elliott L, McCarthy P, Taschler B, Sala-Llonch R, Beckmann CF, Duff EP, Smith SM, Douaud G. Amplitudes of resting-state functional networks - investigation into their correlates and biophysical properties. Neuroimage 2023; 265:119779. [PMID: 36462729 PMCID: PMC10933815 DOI: 10.1016/j.neuroimage.2022.119779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 10/31/2022] [Accepted: 11/21/2022] [Indexed: 12/05/2022] Open
Abstract
Resting-state fMRI studies have shown that multiple functional networks, which consist of distributed brain regions that share synchronised spontaneous activity, co-exist in the brain. As these resting-state networks (RSNs) have been thought to reflect the brain's intrinsic functional organization, intersubject variability in the networks' spontaneous fluctuations may be associated with individuals' clinical, physiological, cognitive, and genetic traits. Here, we investigated resting-state fMRI data along with extensive clinical, lifestyle, and genetic data collected from 37,842 UK Biobank participants, with the object of elucidating intersubject variability in the fluctuation amplitudes of RSNs. Functional properties of the RSN amplitudes were first examined by analyzing correlations with the well-established between-network functional connectivity. It was found that a network amplitude is highly correlated with the mean strength of the functional connectivity that the network has with the other networks. Intersubject clustering analysis showed the amplitudes are most strongly correlated with age, cardiovascular factors, body composition, blood cell counts, lung function, and sex, with some differences in the correlation strengths between sensory and cognitive RSNs. Genome-wide association studies (GWASs) of RSN amplitudes identified several significant genetic variants reported in previous GWASs for their implications in sleep duration. We provide insight into key factors determining RSN amplitudes and demonstrate that intersubject variability of the amplitudes primarily originates from differences in temporal synchrony between functionally linked brain regions, rather than differences in the magnitude of raw voxelwise BOLD signal changes. This finding additionally revealed intriguing differences between sensory and cognitive RSNs with respect to sex effects on temporal synchrony and provided evidence suggesting that synchronous coactivations of functionally linked brain regions, and magnitudes of BOLD signal changes, may be related to different genetic mechanisms. These results underscore that intersubject variability of the amplitudes in health and disease need to be interpreted largely as a measure of the sum of within-network temporal synchrony and amplitudes of BOLD signals, with a dominant contribution from the former.
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Affiliation(s)
- Soojin Lee
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Pacific Parkinson's Research Institute, University of British Columbia, Canada.
| | - Janine D Bijsterbosch
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Mallinckrodt Institute of Radiology, Washington University Medical School, Washington University in St Louis, USA
| | - Fidel Alfaro Almagro
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Lloyd Elliott
- Department of Statistics and Actuarial Science, Simon Fraser University (SFU), Canada
| | - Paul McCarthy
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Bernd Taschler
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Roser Sala-Llonch
- Department of Biomedicine, Institute of Neurosciences, University of Barcelona, Spain
| | - Christian F Beckmann
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Eugene P Duff
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Department of Brain Sciences, Imperial College London, UK Dementia Research Institute, London UK
| | - Stephen M Smith
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Gwenaëlle Douaud
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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5
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Topiwala A, Taschler B, Ebmeier KP, Smith S, Zhou H, Levey DF, Codd V, Samani NJ, Gelernter J, Nichols TE, Burgess S. Alcohol consumption and telomere length: Mendelian randomization clarifies alcohol's effects. Mol Psychiatry 2022; 27:4001-4008. [PMID: 35879401 PMCID: PMC9718662 DOI: 10.1038/s41380-022-01690-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 06/23/2022] [Accepted: 06/29/2022] [Indexed: 02/07/2023]
Abstract
Alcohol's impact on telomere length, a proposed marker of biological aging, is unclear. We performed the largest observational study to date (in n = 245,354 UK Biobank participants) and compared findings with Mendelian randomization (MR) estimates. Two-sample MR used data from 472,174 participants in a recent genome-wide association study (GWAS) of telomere length. Genetic variants were selected on the basis of associations with alcohol consumption (n = 941,280) and alcohol use disorder (AUD) (n = 57,564 cases). Non-linear MR employed UK Biobank individual data. MR analyses suggested a causal relationship between alcohol traits, more strongly for AUD, and telomere length. Higher genetically-predicted AUD (inverse variance-weighted (IVW) β = -0.06, 95% confidence interval (CI): -0.10 to -0.02, p = 0.001) was associated with shorter telomere length. There was a weaker association with genetically-predicted alcoholic drinks weekly (IVW β = -0.07, CI: -0.14 to -0.01, p = 0.03). Results were consistent across methods and independent from smoking. Non-linear analyses indicated a potential threshold relationship between alcohol and telomere length. Our findings indicate that alcohol consumption may shorten telomere length. There are implications for age-related diseases.
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Affiliation(s)
- A Topiwala
- Nuffield Department Population Health, Big Data Institute, University of Oxford, Oxford, OX3 7LF, UK.
| | - B Taschler
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - K P Ebmeier
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
| | - S Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - H Zhou
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - D F Levey
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - V Codd
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - N J Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - J Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - T E Nichols
- Nuffield Department Population Health, Big Data Institute, University of Oxford, Oxford, OX3 7LF, UK
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - S Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SR, UK
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, CB1 8RN, UK
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6
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Duff E, Zelaya F, Almagro FA, Miller KL, Martin N, Nichols TE, Taschler B, Griffanti L, Arthofer C, Douaud G, Wang C, Okell TW, Bethlehem RAI, Eickel K, Günther M, Menon DK, Williams G, Facer B, Lythgoe DJ, Dell’Acqua F, Wood GK, Williams SCR, Houston G, Keller SS, Holden C, Hartmann M, George L, Breen G, Michael BD, Jezzard P, Smith SM, Bullmore ET. Reliability of multi-site UK Biobank MRI brain phenotypes for the assessment of neuropsychiatric complications of SARS-CoV-2 infection: The COVID-CNS travelling heads study. PLoS One 2022; 17:e0273704. [PMID: 36173949 PMCID: PMC9522299 DOI: 10.1371/journal.pone.0273704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 08/11/2022] [Indexed: 12/29/2022] Open
Abstract
INTRODUCTION Magnetic resonance imaging (MRI) of the brain could be a key diagnostic and research tool for understanding the neuropsychiatric complications of COVID-19. For maximum impact, multi-modal MRI protocols will be needed to measure the effects of SARS-CoV-2 infection on the brain by diverse potentially pathogenic mechanisms, and with high reliability across multiple sites and scanner manufacturers. Here we describe the development of such a protocol, based upon the UK Biobank, and its validation with a travelling heads study. A multi-modal brain MRI protocol comprising sequences for T1-weighted MRI, T2-FLAIR, diffusion MRI (dMRI), resting-state functional MRI (fMRI), susceptibility-weighted imaging (swMRI), and arterial spin labelling (ASL), was defined in close approximation to prior UK Biobank (UKB) and C-MORE protocols for Siemens 3T systems. We iteratively defined a comparable set of sequences for General Electric (GE) 3T systems. To assess multi-site feasibility and between-site variability of this protocol, N = 8 healthy participants were each scanned at 4 UK sites: 3 using Siemens PRISMA scanners (Cambridge, Liverpool, Oxford) and 1 using a GE scanner (King's College London). Over 2,000 Imaging Derived Phenotypes (IDPs), measuring both data quality and regional image properties of interest, were automatically estimated by customised UKB image processing pipelines (S2 File). Components of variance and intra-class correlations (ICCs) were estimated for each IDP by linear mixed effects models and benchmarked by comparison to repeated measurements of the same IDPs from UKB participants. Intra-class correlations for many IDPs indicated good-to-excellent between-site reliability. Considering only data from the Siemens sites, between-site reliability generally matched the high levels of test-retest reliability of the same IDPs estimated in repeated, within-site, within-subject scans from UK Biobank. Inclusion of the GE site resulted in good-to-excellent reliability for many IDPs, although there were significant between-site differences in mean and scaling, and reduced ICCs, for some classes of IDP, especially T1 contrast and some dMRI-derived measures. We also identified high reliability of quantitative susceptibility mapping (QSM) IDPs derived from swMRI images, multi-network ICA-based IDPs from resting-state fMRI, and olfactory bulb structure IDPs from T1, T2-FLAIR and dMRI data. CONCLUSION These results give confidence that large, multi-site MRI datasets can be collected reliably at different sites across the diverse range of MRI modalities and IDPs that could be mechanistically informative in COVID brain research. We discuss limitations of the study and strategies for further harmonisation of data collected from sites using scanners supplied by different manufacturers. These acquisition and analysis protocols are now in use for MRI assessments of post-COVID patients (N = 700) as part of the ongoing COVID-CNS study.
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Affiliation(s)
- Eugene Duff
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
- Department of Paediatrics, University of Oxford, Oxford, United Kingdom
- Department of Brain Sciences, UK Dementia Research Institute, Imperial College London, London, United Kingdom
| | - Fernando Zelaya
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Fidel Alfaro Almagro
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Naomi Martin
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Thomas E. Nichols
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Bernd Taschler
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Christoph Arthofer
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Gwenaëlle Douaud
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Thomas W. Okell
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | | | | | - Matthias Günther
- mediri GmbH, Heidelberg, Germany
- University of Bremen, Bremen, Germany
- Fraunhofer MEVIS, Bremen, Germany
| | - David K. Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
| | - Guy Williams
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Bethany Facer
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - David J. Lythgoe
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Flavio Dell’Acqua
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- NatBrainLab, Department of Forensics and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry Psychology and Neuroscience, King’s College London, United Kingdom
| | - Greta K. Wood
- Clinical Infection Microbiology and Immunology, Institute of Infection, Veterinary and Ecological Sciences, Liverpool, United Kingdom
| | - Steven C. R. Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Gavin Houston
- GE Healthcare, Global Research Organisation, United Kingdom
| | - Simon S. Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Catherine Holden
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Monika Hartmann
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Lily George
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Benedict D. Michael
- Clinical Infection Microbiology and Immunology, Institute of Infection, Veterinary and Ecological Sciences, Liverpool, United Kingdom
| | - Peter Jezzard
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Edward T. Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
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7
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Taschler B, Smith SM, Nichols TE. Causal inference on neuroimaging data with Mendelian randomisation. Neuroimage 2022; 258:119385. [PMID: 35714886 PMCID: PMC10933777 DOI: 10.1016/j.neuroimage.2022.119385] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 05/30/2022] [Accepted: 06/12/2022] [Indexed: 10/18/2022] Open
Abstract
While population-scale neuroimaging studies offer the promise of discovery and characterisation of subtle risk factors, massive sample sizes increase the power for both meaningful associations and those attributable to confounds. This motivates the need for causal modelling of observational data that goes beyond statements of association and towards deeper understanding of complex relationships between individual traits and phenotypes, clinical biomarkers, genetic variation, and brain-related measures of health. Mendelian randomisation (MR) presents a way to obtain causal inference on the basis of genetic data and explicit assumptions about the relationship between genetic variables, exposure and outcome. In this work, we provide an introduction to and overview of causal inference methods based on Mendelian randomisation, with examples involving imaging-derived phenotypes from UK Biobank to make these methods accessible to neuroimaging researchers. We motivate the use of MR techniques, lay out the underlying assumptions, introduce common MR methods and focus on several scenarios in which modelling assumptions are potentially violated, resulting in biased effect estimates. Importantly, we give a detailed account of necessary steps to increase the reliability of MR results with rigorous sensitivity analyses.
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Affiliation(s)
- Bernd Taschler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, City Oxford, UK
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Douaud G, Lee S, Alfaro-Almagro F, Arthofer C, Wang C, McCarthy P, Lange F, Andersson JLR, Griffanti L, Duff E, Jbabdi S, Taschler B, Keating P, Winkler AM, Collins R, Matthews PM, Allen N, Miller KL, Nichols TE, Smith SM. SARS-CoV-2 is associated with changes in brain structure in UK Biobank. Nature 2022. [DOI: 10.1038/s41586-022-04569-5 3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
AbstractThere is strong evidence of brain-related abnormalities in COVID-191–13. However, it remains unknown whether the impact of SARS-CoV-2 infection can be detected in milder cases, and whether this can reveal possible mechanisms contributing to brain pathology. Here we investigated brain changes in 785 participants of UK Biobank (aged 51–81 years) who were imaged twice using magnetic resonance imaging, including 401 cases who tested positive for infection with SARS-CoV-2 between their two scans—with 141 days on average separating their diagnosis and the second scan—as well as 384 controls. The availability of pre-infection imaging data reduces the likelihood of pre-existing risk factors being misinterpreted as disease effects. We identified significant longitudinal effects when comparing the two groups, including (1) a greater reduction in grey matter thickness and tissue contrast in the orbitofrontal cortex and parahippocampal gyrus; (2) greater changes in markers of tissue damage in regions that are functionally connected to the primary olfactory cortex; and (3) a greater reduction in global brain size in the SARS-CoV-2 cases. The participants who were infected with SARS-CoV-2 also showed on average a greater cognitive decline between the two time points. Importantly, these imaging and cognitive longitudinal effects were still observed after excluding the 15 patients who had been hospitalised. These mainly limbic brain imaging results may be the in vivo hallmarks of a degenerative spread of the disease through olfactory pathways, of neuroinflammatory events, or of the loss of sensory input due to anosmia. Whether this deleterious effect can be partially reversed, or whether these effects will persist in the long term, remains to be investigated with additional follow-up.
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9
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Douaud G, Lee S, Alfaro-Almagro F, Arthofer C, Wang C, McCarthy P, Lange F, Andersson JLR, Griffanti L, Duff E, Jbabdi S, Taschler B, Keating P, Winkler AM, Collins R, Matthews PM, Allen N, Miller KL, Nichols TE, Smith SM. SARS-CoV-2 is associated with changes in brain structure in UK Biobank. Nature 2022; 604:697-707. [PMID: 35255491 PMCID: PMC9046077 DOI: 10.1038/s41586-022-04569-5] [Citation(s) in RCA: 661] [Impact Index Per Article: 330.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 02/21/2022] [Indexed: 01/01/2023]
Abstract
There is strong evidence of brain-related abnormalities in COVID-191-13. However, it remains unknown whether the impact of SARS-CoV-2 infection can be detected in milder cases, and whether this can reveal possible mechanisms contributing to brain pathology. Here we investigated brain changes in 785 participants of UK Biobank (aged 51-81 years) who were imaged twice using magnetic resonance imaging, including 401 cases who tested positive for infection with SARS-CoV-2 between their two scans-with 141 days on average separating their diagnosis and the second scan-as well as 384 controls. The availability of pre-infection imaging data reduces the likelihood of pre-existing risk factors being misinterpreted as disease effects. We identified significant longitudinal effects when comparing the two groups, including (1) a greater reduction in grey matter thickness and tissue contrast in the orbitofrontal cortex and parahippocampal gyrus; (2) greater changes in markers of tissue damage in regions that are functionally connected to the primary olfactory cortex; and (3) a greater reduction in global brain size in the SARS-CoV-2 cases. The participants who were infected with SARS-CoV-2 also showed on average a greater cognitive decline between the two time points. Importantly, these imaging and cognitive longitudinal effects were still observed after excluding the 15 patients who had been hospitalised. These mainly limbic brain imaging results may be the in vivo hallmarks of a degenerative spread of the disease through olfactory pathways, of neuroinflammatory events, or of the loss of sensory input due to anosmia. Whether this deleterious effect can be partially reversed, or whether these effects will persist in the long term, remains to be investigated with additional follow-up.
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Affiliation(s)
- Gwenaëlle Douaud
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Soojin Lee
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Fidel Alfaro-Almagro
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Christoph Arthofer
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Chaoyue Wang
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Paul McCarthy
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Frederik Lange
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Jesper L R Andersson
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Ludovica Griffanti
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- OHBA, Wellcome Centre for Integrative Neuroimaging (WIN), Department of Psychiatry, University of Oxford, Oxford, UK
| | - Eugene Duff
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Department of Paediatrics, University of Oxford, Oxford, UK
| | - Saad Jbabdi
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Bernd Taschler
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Peter Keating
- Ear Institute, University College London, London, UK
| | - Anderson M Winkler
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Rory Collins
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Paul M Matthews
- UK Dementia Research Institute and Department of Brain Sciences, Imperial College, London, UK
| | - Naomi Allen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Karla L Miller
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Stephen M Smith
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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10
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Douaud G, Lee S, Alfaro-Almagro F, Arthofer C, Wang C, McCarthy P, Lange F, Andersson JLR, Griffanti L, Duff E, Jbabdi S, Taschler B, Keating P, Winkler AM, Collins R, Matthews PM, Allen N, Miller KL, Nichols TE, Smith SM. SARS-CoV-2 is associated with changes in brain structure in UK Biobank. medRxiv 2022:2021.06.11.21258690. [PMID: 34189535 PMCID: PMC8240690 DOI: 10.1101/2021.06.11.21258690] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
There is strong evidence for brain-related abnormalities in COVID-19 1-13 . It remains unknown however whether the impact of SARS-CoV-2 infection can be detected in milder cases, and whether this can reveal possible mechanisms contributing to brain pathology. Here, we investigated brain changes in 785 UK Biobank participants (aged 51-81) imaged twice, including 401 cases who tested positive for infection with SARS-CoV-2 between their two scans, with 141 days on average separating their diagnosis and second scan, and 384 controls. The availability of pre-infection imaging data reduces the likelihood of pre-existing risk factors being misinterpreted as disease effects. We identified significant longitudinal effects when comparing the two groups, including: (i) greater reduction in grey matter thickness and tissue-contrast in the orbitofrontal cortex and parahippocampal gyrus, (ii) greater changes in markers of tissue damage in regions functionally-connected to the primary olfactory cortex, and (iii) greater reduction in global brain size. The infected participants also showed on average larger cognitive decline between the two timepoints. Importantly, these imaging and cognitive longitudinal effects were still seen after excluding the 15 cases who had been hospitalised. These mainly limbic brain imaging results may be the in vivo hallmarks of a degenerative spread of the disease via olfactory pathways, of neuroinflammatory events, or of the loss of sensory input due to anosmia. Whether this deleterious impact can be partially reversed, or whether these effects will persist in the long term, remains to be investigated with additional follow up.
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11
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Bendfeldt K, Taschler B, Gaetano L, Madoerin P, Kuster P, Mueller-Lenke N, Amann M, Vrenken H, Wottschel V, Barkhof F, Borgwardt S, Klöppel S, Wicklein EM, Kappos L, Edan G, Freedman MS, Montalbán X, Hartung HP, Pohl C, Sandbrink R, Sprenger T, Radue EW, Wuerfel J, Nichols TE. MRI-based prediction of conversion from clinically isolated syndrome to clinically definite multiple sclerosis using SVM and lesion geometry. Brain Imaging Behav 2020; 13:1361-1374. [PMID: 30155789 DOI: 10.1007/s11682-018-9942-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Neuroanatomical pattern classification using support vector machines (SVMs) has shown promising results in classifying Multiple Sclerosis (MS) patients based on individual structural magnetic resonance images (MRI). To determine whether pattern classification using SVMs facilitates predicting conversion to clinically definite multiple sclerosis (CDMS) from clinically isolated syndrome (CIS). We used baseline MRI data from 364 patients with CIS, randomised to interferon beta-1b or placebo. Non-linear SVMs and 10-fold cross-validation were applied to predict converters/non-converters (175/189) at two years follow-up based on clinical and demographic data, lesion-specific quantitative geometric features and grey-matter-to-whole-brain volume ratios. We applied linear SVM analysis and leave-one-out cross-validation to subgroups of converters (n = 25) and non-converters (n = 44) based on cortical grey matter segmentations. Highest prediction accuracies of 70.4% (p = 8e-5) were reached with a combination of lesion-specific geometric (image-based) and demographic/clinical features. Cortical grey matter was informative for the placebo group (acc.: 64.6%, p = 0.002) but not for the interferon group. Classification based on demographic/clinical covariates only resulted in an accuracy of 56% (p = 0.05). Overall, lesion geometry was more informative in the interferon group, EDSS and sex were more important for the placebo cohort. Alongside standard demographic and clinical measures, both lesion geometry and grey matter based information can aid prediction of conversion to CDMS.
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Affiliation(s)
- Kerstin Bendfeldt
- Medical Image Analysis Center (MIAC AG), Mittlere Str. 83, CH-4031, Basel, Switzerland.
| | - Bernd Taschler
- German Center for Neurodegenerative Diseases, Bonn, Germany.,Department of Statistics, University of Warwick, Coventry, UK
| | - Laura Gaetano
- Medical Image Analysis Center (MIAC AG), Mittlere Str. 83, CH-4031, Basel, Switzerland.,Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Philip Madoerin
- Medical Image Analysis Center (MIAC AG), Mittlere Str. 83, CH-4031, Basel, Switzerland
| | - Pascal Kuster
- Medical Image Analysis Center (MIAC AG), Mittlere Str. 83, CH-4031, Basel, Switzerland
| | - Nicole Mueller-Lenke
- Medical Image Analysis Center (MIAC AG), Mittlere Str. 83, CH-4031, Basel, Switzerland
| | - Michael Amann
- Medical Image Analysis Center (MIAC AG), Mittlere Str. 83, CH-4031, Basel, Switzerland.,Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Hugo Vrenken
- VU University Medical Center, Amsterdam, The Netherlands
| | | | - Frederik Barkhof
- VU University Medical Center, Amsterdam, The Netherlands.,Institutes of Neurology and Healthcare Engineering, UCL, London, UK
| | - Stefan Borgwardt
- Medical Image Analysis Center (MIAC AG), Mittlere Str. 83, CH-4031, Basel, Switzerland.,Department of Psychiatry (1), University of Basel, Basel, Switzerland.,King's College London, Department of Psychosis Studies, Institute of Psychiatry, London, UK
| | - Stefan Klöppel
- Department of Psychiatry and Psychotherapy, Freiburg Brain Imaging, University Medical Center Freiburg, Freiburg, Germany
| | | | - Ludwig Kappos
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | | | - Mark S Freedman
- University of Ottawa and Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | | | - Hans-Peter Hartung
- Department of Neurology, Heinrich-Heine Universität, Düsseldorf, Germany
| | - Christoph Pohl
- Bayer Pharma AG, Berlin, Germany.,Charité University Medicine Berlin, Berlin, Germany
| | - Rupert Sandbrink
- Bayer Pharma AG, Berlin, Germany.,Department of Neurology, Heinrich-Heine Universität, Düsseldorf, Germany
| | - Till Sprenger
- Medical Image Analysis Center (MIAC AG), Mittlere Str. 83, CH-4031, Basel, Switzerland.,Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Ernst-Wilhelm Radue
- Medical Image Analysis Center (MIAC AG), Mittlere Str. 83, CH-4031, Basel, Switzerland
| | - Jens Wuerfel
- Medical Image Analysis Center (MIAC AG), Mittlere Str. 83, CH-4031, Basel, Switzerland.,Charité University Medicine Berlin, Berlin, Germany
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12
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Bendfeldt K, Smieskova R, Koutsouleris N, Klöppel S, Schmidt A, Walter A, Harrisberger F, Wrege J, Simon A, Taschler B, Nichols T, Riecher-Rössler A, Lang UE, Radue EW, Borgwardt S. Classifying individuals at high-risk for psychosis based on functional brain activity during working memory processing. Neuroimage Clin 2015; 9:555-63. [PMID: 26640767 PMCID: PMC4625212 DOI: 10.1016/j.nicl.2015.09.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Revised: 09/22/2015] [Accepted: 09/23/2015] [Indexed: 11/04/2022]
Abstract
The psychosis high-risk state is accompanied by alterations in functional brain activity during working memory processing. We used binary automatic pattern-classification to discriminate between the at-risk mental state (ARMS), first episode psychosis (FEP) and healthy controls (HCs) based on n-back WM-induced brain activity. Linear support vector machines and leave-one-out-cross-validation were applied to fMRI data of matched ARMS, FEP and HC (19 subjects/group). The HC and ARMS were correctly classified, with an accuracy of 76.2% (sensitivity 89.5%, specificity 63.2%, p = 0.01) using a verbal working memory network mask. Only 50% and 47.4% of individuals were classified correctly for HC vs. FEP (p = 0.46) or ARMS vs. FEP (p = 0.62), respectively. Without mask, accuracy was 65.8% for HC vs. ARMS (p = 0.03) and 65.8% for HC vs. FEP (p = 0.0047), and 57.9% for ARMS vs. FEP (p = 0.18). Regions in the medial frontal, paracingulate, cingulate, inferior frontal and superior frontal gyri, inferior and superior parietal lobules, and precuneus were particularly important for group separation. These results suggest that FEP and HC or FEP and ARMS cannot be accurately separated in small samples under these conditions. However, ARMS can be identified with very high sensitivity in comparison to HC. This might aid classification and help to predict transition in the ARMS. The ARMS was accurately identified based on an individual patient's response within a WM network. Regional cortical activations were particularly important for group separation. Based on WM alterations, FEP and HC or FEP and ARMS could not be accurately separated in small samples.
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Affiliation(s)
- Kerstin Bendfeldt
- Medical Image Analysis Centre, University Hospital Basel, Mittlere Strasse 83, Basel 4031, Switzerland
| | - Renata Smieskova
- Medical Image Analysis Centre, University Hospital Basel, Mittlere Strasse 83, Basel 4031, Switzerland ; Department of Psychiatry, University of Basel, Wilhelm Klein-Strasse, 27, Basel 4056, Switzerland
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Nussbaumstr. 7, Munich 80336, Germany
| | - Stefan Klöppel
- Department of Psychiatry and Psychotherapy, University Medical Center, Freiburg, Freiburg, Germany
| | - André Schmidt
- Medical Image Analysis Centre, University Hospital Basel, Mittlere Strasse 83, Basel 4031, Switzerland ; Department of Psychiatry, University of Basel, Wilhelm Klein-Strasse, 27, Basel 4056, Switzerland
| | - Anna Walter
- Department of Psychiatry, University of Basel, Wilhelm Klein-Strasse, 27, Basel 4056, Switzerland
| | - Fabienne Harrisberger
- Medical Image Analysis Centre, University Hospital Basel, Mittlere Strasse 83, Basel 4031, Switzerland ; Department of Psychiatry, University of Basel, Wilhelm Klein-Strasse, 27, Basel 4056, Switzerland
| | - Johannes Wrege
- Department of Psychiatry, University of Basel, Wilhelm Klein-Strasse, 27, Basel 4056, Switzerland
| | - Andor Simon
- University Hospital of Psychiatry, University of Bern, Bern 3010, Switzerland
| | - Bernd Taschler
- Dept. of Statistics, University of Warwick, Coventry, UK
| | - Thomas Nichols
- Dept. of Statistics, University of Warwick, Coventry, UK
| | - Anita Riecher-Rössler
- Department of Psychiatry, University of Basel, Wilhelm Klein-Strasse, 27, Basel 4056, Switzerland
| | - Undine E Lang
- Department of Psychiatry, University of Basel, Wilhelm Klein-Strasse, 27, Basel 4056, Switzerland
| | - Ernst-Wilhelm Radue
- Medical Image Analysis Centre, University Hospital Basel, Mittlere Strasse 83, Basel 4031, Switzerland
| | - Stefan Borgwardt
- Medical Image Analysis Centre, University Hospital Basel, Mittlere Strasse 83, Basel 4031, Switzerland ; Department of Psychiatry, University of Basel, Wilhelm Klein-Strasse, 27, Basel 4056, Switzerland ; Department of Psychosis Studies, King's College London, Institute of Psychiatry, De Crespigny Park 16, London SE58AF, UK
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13
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Taschler B, Ge T, Bendfeldt K, Müller-Lenke N, Johnson TD, Nichols TE. Spatial modeling of multiple sclerosis for disease subtype prediction. Med Image Comput Comput Assist Interv 2014; 17:797-804. [PMID: 25485453 DOI: 10.1007/978-3-319-10470-6_99] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Magnetic resonance imaging (MRI) has become an essential tool in the diagnosis and managing of Multiple Sclerosis (MS). Currently, the assessment of MS is based on a combination of clinical scores and subjective rating of lesion images by clinicians. In this work we present an objective 5-way classification of MS disease subtype as well as a comparison between three different approaches. First we propose two spatially informed models, a Bayesian Spatial Generalized Linear Mixed Model (BSGLMM) and a Log Gaussian Cox Process (LGCP). The BSGLMM accounts for the binary nature of lesion maps and the spatial dependence between neighboring voxels, and the LGCP accounts for the random spatial variation in lesion location. Both improve upon mass univariate analyses that ignore spatial dependence and rely on some level of arbitrarily defined smoothing of the data. As a comparison, we consider a machine learning approach based on multi-class support vector machine (SVM). For the SVM classification scheme, unlike previous work, we use a large number of quantitative features derived from three MRI sequences in addition to traditional demographic and clinical measures. We show that the spatial models outperform standard approaches with average prediction accuracies of up to 85%.
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