1
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Huang Y, Zhang T, Zhang S, Zhang W, Yang L, Zhu D, Liu T, Jiang X, Han J, Guo L. Genetic Influence on Gyral Peaks. Neuroimage 2023; 280:120344. [PMID: 37619794 DOI: 10.1016/j.neuroimage.2023.120344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/21/2023] [Indexed: 08/26/2023] Open
Abstract
Genetic mechanisms have been hypothesized to be a major determinant in the formation of cortical folding. Although there is an increasing number of studies examining the heritability of cortical folding, most of them focus on sulcal pits rather than gyral peaks. Gyral peaks, which reflect the highest local foci on gyri and are consistent across individuals, remain unstudied in terms of heritability. To address this knowledge gap, we used high-resolution data from the Human Connectome Project (HCP) to perform classical twin analysis and estimate the heritability of gyral peaks across various brain regions. Our results showed that the heritability of gyral peaks was heterogeneous across different cortical regions, but relatively symmetric between hemispheres. We also found that pits and peaks are different in a variety of anatomic and functional measures. Further, we explored the relationship between the levels of heritability and the formation of cortical folding by utilizing the evolutionary timeline of gyrification. Our findings indicate that the heritability estimates of both gyral peaks and sulcal pits decrease linearly with the evolution timeline of gyrification. This suggests that the cortical folds which formed earlier during gyrification are subject to stronger genetic influences than the later ones. Moreover, the pits and peaks coupled by their time of appearance are also positively correlated in respect of their heritability estimates. These results fill the knowledge gap regarding genetic influences on gyral peaks and significantly advance our understanding of how genetic factors shape the formation of cortical folding. The comparison between peaks and pits suggests that peaks are not a simple morphological mirror of pits but could help complete the understanding of folding patterns.
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Affiliation(s)
- Ying Huang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China; School of Information and Technology, Northwest University, Xi'an 710127, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China.
| | - Songyao Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Weihan Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Li Yang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Dajiang Zhu
- Computer Science & Engineering, University of Texas at Arlington, TX 76010, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, USA
| | - Xi Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
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2
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Gentile G, Jenkinson M, Griffanti L, Luchetti L, Leoncini M, Inderyas M, Mortilla M, Cortese R, De Stefano N, Battaglini M. BIANCA-MS: An optimized tool for automated multiple sclerosis lesion segmentation. Hum Brain Mapp 2023; 44:4893-4913. [PMID: 37530598 PMCID: PMC10472913 DOI: 10.1002/hbm.26424] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 05/20/2023] [Accepted: 07/06/2023] [Indexed: 08/03/2023] Open
Abstract
In this work we present BIANCA-MS, a novel tool for brain white matter lesion segmentation in multiple sclerosis (MS), able to generalize across both the wide spectrum of MRI acquisition protocols and the heterogeneity of manually labeled data. BIANCA-MS is based on the original version of BIANCA and implements two innovative elements: a harmonized setting, tested under different MRI protocols, which avoids the need to further tune algorithm parameters to each dataset; and a cleaning step developed to improve consistency in automated and manual segmentations, thus reducing unwanted variability in output segmentations and validation data. BIANCA-MS was tested on three datasets, acquired with different MRI protocols. First, we compared BIANCA-MS to other widely used tools. Second, we tested how BIANCA-MS performs in separate datasets. Finally, we evaluated BIANCA-MS performance on a pooled dataset where all MRI data were merged. We calculated the overlap using the DICE spatial similarity index (SI) as well as the number of false positive/negative clusters (nFPC/nFNC) in comparison to the manual masks processed with the cleaning step. BIANCA-MS clearly outperformed other available tools in both high- and low-resolution images and provided comparable performance across different scanning protocols, sets of modalities and image resolutions. BIANCA-MS performance on the pooled dataset (SI: 0.72 ± 0.25, nFPC: 13 ± 11, nFNC: 4 ± 8) were comparable to those achieved on each individual dataset (median across datasets SI: 0.72 ± 0.28, nFPC: 14 ± 11, nFNC: 4 ± 8). Our findings suggest that BIANCA-MS is a robust and accurate approach for automated MS lesion segmentation.
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Affiliation(s)
- Giordano Gentile
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaSienaItaly
- SIENA Imaging SRLSienaItaly
| | - Mark Jenkinson
- Welcome Centre for Integrative Neuroimaging (WIN), FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of Oxford, John Radcliffe HospitalOxfordUK
- Australian Institute of Machine Learning (AIML), School of Computer and Mathematical SciencesUniversity of AdelaideAdelaideSouth AustraliaAustralia
- South Australian Health and Medical Research Institute (SAHMRI)AdelaideSouth AustraliaAustralia
| | - Ludovica Griffanti
- Welcome Centre for Integrative Neuroimaging (WIN), FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of Oxford, John Radcliffe HospitalOxfordUK
- Welcome Centre for Integrative Neuroimaging (WIN), OHBA, Department of PsychiatryUniversity of Oxford, Warneford HospitalOxfordUK
| | - Ludovico Luchetti
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaSienaItaly
| | - Matteo Leoncini
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaSienaItaly
- SIENA Imaging SRLSienaItaly
| | - Maira Inderyas
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaSienaItaly
- SIENA Imaging SRLSienaItaly
| | | | - Rosa Cortese
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaSienaItaly
| | - Nicola De Stefano
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaSienaItaly
| | - Marco Battaglini
- Department of Medicine, Surgery and NeuroscienceUniversity of SienaSienaItaly
- SIENA Imaging SRLSienaItaly
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Manso-Ortega L, De Frutos-Sagastuy L, Gisbert-Muñoz S, Salamon N, Qiao J, Walshaw P, Quiñones I, Połczyńska MM. Grey Matter Reshaping of Language-Related Regions Depends on Tumor Lateralization. Cancers (Basel) 2023; 15:3852. [PMID: 37568668 PMCID: PMC10417790 DOI: 10.3390/cancers15153852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/12/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
A brain tumor in the left hemisphere can decrease language laterality as assessed through fMRI. However, it remains unclear whether or not this decreased language laterality is associated with a structural reshaping of the grey matter, particularly within the language network. Here, we examine if the disruption of the language hubs exclusively affects the macrostructural properties of the contralateral homologues or whether it affects both hemispheres. This study uses voxel-based morphometry applied to high-resolution MR T1-weighted MPRAGE images from 31 adult patients' left hemisphere, which is dominant for language. Eighteen patients had brain tumors in the left hemisphere, and thirteen had tumors in the right hemisphere. A cohort of 71 healthy individuals matched with respect to age and sex was used as a baseline. We defined 10 ROIs per hemisphere involved in language function. Two separate repeated-measure ANOVAs were conducted with the volume per region as the dependent variable. For the patients, tumor lateralization (right versus left) served as a between-subject factor. The current study demonstrated that the presence of a brain tumor generates global volumetric changes affecting the left language regions and their contralateral homologues. These changes are mediated by the lateralization of the lesion. Our findings suggest that functional mechanisms are supported by the rearrangement of the grey matter.
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Affiliation(s)
- Lucía Manso-Ortega
- Neurobiology of Language Group, Basque Center on Cognition, Brain and Language (BCBL), 20009 Donostia-San Sebastián, Spain; (L.D.F.-S.); (S.G.-M.)
- Department of Basque Language and Communication, University of the Basque Country, UPV/EHU, 48940 Bilbao, Spain
| | - Laura De Frutos-Sagastuy
- Neurobiology of Language Group, Basque Center on Cognition, Brain and Language (BCBL), 20009 Donostia-San Sebastián, Spain; (L.D.F.-S.); (S.G.-M.)
| | - Sandra Gisbert-Muñoz
- Neurobiology of Language Group, Basque Center on Cognition, Brain and Language (BCBL), 20009 Donostia-San Sebastián, Spain; (L.D.F.-S.); (S.G.-M.)
- Department of Basque Language and Communication, University of the Basque Country, UPV/EHU, 48940 Bilbao, Spain
| | - Noriko Salamon
- Department of Radiology, University of California, Los Angeles, CA 92093, USA; (N.S.); (J.Q.)
| | - Joe Qiao
- Department of Radiology, University of California, Los Angeles, CA 92093, USA; (N.S.); (J.Q.)
| | - Patricia Walshaw
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA 92093, USA; (P.W.); (M.M.P.)
| | - Ileana Quiñones
- Neurobiology of Language Group, Basque Center on Cognition, Brain and Language (BCBL), 20009 Donostia-San Sebastián, Spain; (L.D.F.-S.); (S.G.-M.)
- IKERBASQUE, Basque Foundation for Science, Plaza Euskadi 5, 48009 Bilbao, Spain
| | - Monika M. Połczyńska
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA 92093, USA; (P.W.); (M.M.P.)
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Hu F, Chen AA, Horng H, Bashyam V, Davatzikos C, Alexander-Bloch A, Li M, Shou H, Satterthwaite TD, Yu M, Shinohara RT. Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. Neuroimage 2023; 274:120125. [PMID: 37084926 PMCID: PMC10257347 DOI: 10.1016/j.neuroimage.2023.120125] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 04/23/2023] Open
Abstract
Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.
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Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States.
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Vishnu Bashyam
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, United States
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; The Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, United States
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
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5
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Ferris JK, Lo BP, Khlif MS, Brodtmann A, Boyd LA, Liew SL. Optimizing automated white matter hyperintensity segmentation in individuals with stroke. FRONTIERS IN NEUROIMAGING 2023; 2:1099301. [PMID: 37554631 PMCID: PMC10406248 DOI: 10.3389/fnimg.2023.1099301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 02/15/2023] [Indexed: 08/10/2023]
Abstract
White matter hyperintensities (WMHs) are a risk factor for stroke. Consequently, many individuals who suffer a stroke have comorbid WMHs. The impact of WMHs on stroke recovery is an active area of research. Automated WMH segmentation methods are often employed as they require minimal user input and reduce risk of rater bias; however, these automated methods have not been specifically validated for use in individuals with stroke. Here, we present methodological validation of automated WMH segmentation methods in individuals with stroke. We first optimized parameters for FSL's publicly available WMH segmentation software BIANCA in two independent (multi-site) datasets. Our optimized BIANCA protocol achieved good performance within each independent dataset, when the BIANCA model was trained and tested in the same dataset or trained on mixed-sample data. BIANCA segmentation failed when generalizing a trained model to a new testing dataset. We therefore contrasted BIANCA's performance with SAMSEG, an unsupervised WMH segmentation tool available through FreeSurfer. SAMSEG does not require prior WMH masks for model training and was more robust to handling multi-site data. However, SAMSEG performance was slightly lower than BIANCA when data from a single site were tested. This manuscript will serve as a guide for the development and utilization of WMH analysis pipelines for individuals with stroke.
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Affiliation(s)
- Jennifer K. Ferris
- Graduate Program in Rehabilitation Sciences, University of British Columbia, Vancouver, BC, Canada
- Gerontology Research Centre, Simon Fraser University, Vancouver, BC, Canada
| | - Bethany P. Lo
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
| | - Mohamed Salah Khlif
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Amy Brodtmann
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Lara A. Boyd
- Graduate Program in Rehabilitation Sciences, University of British Columbia, Vancouver, BC, Canada
- Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Sook-Lei Liew
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
- Department of Neurology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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6
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Manso-Ortega L, De Frutos-Sagastuy L, Gisbert- Muñoz S, Salamon N, Qiao J, Walshaw P, Quiñones I, Połczyńska MM. Grey matter reshaping of language-related regions depends on tumor lateralization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.02.526219. [PMID: 36778417 PMCID: PMC9915653 DOI: 10.1101/2023.02.02.526219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
A brain tumor in the left hemisphere can decrease language laterality as assessed with fMRI. However, it remains unclear whether or not this decreased language laterality is associated with a structural reshaping of the grey matter, particularly within the language network. Here, we examine if the disruption of language hubs exclusively affects macrostructural properties of contralateral homologues (as suggested by previous research), or whether it affects both hemispheres. This study uses voxel-based morphometry applied to high-resolution MR T1-weighted MPRAGE images from 31 adult patients left-dominant for language. Eighteen patients had brain tumors in the left hemisphere, and 13 had tumors in the right hemisphere. A cohort of 71 healthy individuals matched on age and sex was used as a baseline. We defined 10 ROIs per hemisphere known to subserve language function. Two separate repeated-measures ANOVAs were conducted with the volume per region as the dependent variables. For the patients, tumor lateralization (right versus left) served as a between-subject factor. The current study demonstrated that the presence of a brain tumor generates a global volumetric change affecting left language regions and their contralateral homologues. These changes are mediated by the lateralization of the lesion. Our findings suggest that compensatory functional mechanisms are supported by the rearrangement of the grey matter, although future longitudinal research should determine the temporal course of such changes.
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Affiliation(s)
- Lucia Manso-Ortega
- Basque Center on Cognition, Brain and Language, BCBL, Donostia-San Sebastian, Spain
- University of the Basque Country, Bilbao, Spain
| | | | - Sandra Gisbert- Muñoz
- Basque Center on Cognition, Brain and Language, BCBL, Donostia-San Sebastian, Spain
- University of the Basque Country, Bilbao, Spain
| | - Noriko Salamon
- Dept. of Radiology, University of California, Los Angeles, USA
| | - Joe Qiao
- Dept. of Radiology, University of California, Los Angeles, USA
| | - Patricia Walshaw
- Dept. of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, USA
| | - Ileana Quiñones
- Basque Center on Cognition, Brain and Language, BCBL, Donostia-San Sebastian, Spain
| | - Monika M. Połczyńska
- Dept. of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, USA
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7
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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] [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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Cai M, Jacob MA, van Loenen MR, Bergkamp M, Marques J, Norris DG, Duering M, Tuladhar AM, de Leeuw FE. Determinants and Temporal Dynamics of Cerebral Small Vessel Disease: 14-Year Follow-Up. Stroke 2022; 53:2789-2798. [PMID: 35506383 PMCID: PMC9389939 DOI: 10.1161/strokeaha.121.038099] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND The aim of this study is to investigate the temporal dynamics of small vessel disease (SVD) and the effect of vascular risk factors and baseline SVD burden on progression of SVD with 4 neuroimaging assessments over 14 years in patients with SVD. METHODS Five hundred three patients with sporadic SVD (50-85 years) from the ongoing prospective cohort study (RUN DMC [Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort]) underwent baseline assessment in 2006 and follow-up in 2011, 2015, and 2020. Vascular risk factors and magnetic resonance imaging markers of SVD were evaluated. Linear mixed-effects model and negative binomial regression model were used to examine the determinants of temporal dynamics of SVD markers. RESULTS A total of 382 SVD patients (mean [SD] 64.1 [8.4]; 219 men and 163 women) who underwent at least 2 serial brain magnetic resonance imaging scans were included, with mean (SD) follow-up of 11.15 (3.32) years. We found a highly variable temporal course of SVD. Mean (SD) WMH progression rate was 0.6 (0.74) mL/y (range, 0.02-4.73 mL/y) and 13.6% of patients had incident lacunes (1.03%/y) over the 14-year follow-up. About 4% showed net WMH regression over 14 years, whereas 38 out of 361 (10.5%), 5 out of 296 (2%), and 61 out of 231 (26%) patients showed WMH regression for the intervals 2006 to 2011, 2011 to 2015, and 2015 to 2020, respectively. Of these, 29 (76%), 5 (100%), and 57 (93%) showed overall progression across the 14-year follow-up, and the net overall WMH change between first and last scan considering all participants was a net average WMH progression over the 14-year period. Older age was a strong predictor for faster WMH progression and incident lacunes. Patients with mild baseline WMH rarely progressed to severe WMH. In addition, both baseline burden of SVD lesions and vascular risk factors independently and synergistically predicted WMH progression, whereas only baseline SVD burden predicted incident lacunes over the 14-year follow-up. CONCLUSIONS SVD shows pronounced progression over time, but mild WMH rarely progresses to clinically severe WMH. WMH regression is noteworthy during some magnetic resonance imaging intervals, although it could be overall compensated by progression over the long follow-up.
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Affiliation(s)
- Mengfei Cai
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour; Nijmegen, the Netherlands. (M.C., M.A.J., M.B., A.M.T., F.-E.d.L.)
| | - Mina A Jacob
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour; Nijmegen, the Netherlands. (M.C., M.A.J., M.B., A.M.T., F.-E.d.L.)
| | - Mark R van Loenen
- Center for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour; Nijmegen, the Netherlands. (M.R.v.L., J.M., D.G.N.)
| | - Mayra Bergkamp
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour; Nijmegen, the Netherlands. (M.C., M.A.J., M.B., A.M.T., F.-E.d.L.)
| | - José Marques
- Center for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour; Nijmegen, the Netherlands. (M.R.v.L., J.M., D.G.N.)
| | - David G Norris
- Center for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour; Nijmegen, the Netherlands. (M.R.v.L., J.M., D.G.N.)
| | - Marco Duering
- Medical Image Analysis Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of Basel, Switzerland (M.D.)
| | - Anil M Tuladhar
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour; Nijmegen, the Netherlands. (M.C., M.A.J., M.B., A.M.T., F.-E.d.L.)
| | - Frank-Erik de Leeuw
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour; Nijmegen, the Netherlands. (M.C., M.A.J., M.B., A.M.T., F.-E.d.L.)
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Jansen MG, Griffanti L, Mackay CE, Anatürk M, Melazzini L, Lange AMGD, Filippini N, Zsoldos E, Wiegertjes K, Leeuw FED, Singh-Manoux A, Kivimäki M, Ebmeier KP, Suri S. Association of cerebral small vessel disease burden with brain structure and cognitive and vascular risk trajectories in mid-to-late life. J Cereb Blood Flow Metab 2022; 42:600-612. [PMID: 34610763 PMCID: PMC8943617 DOI: 10.1177/0271678x211048411] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We characterize the associations of total cerebral small vessel disease (SVD) burden with brain structure, trajectories of vascular risk factors, and cognitive functions in mid-to-late life. Participants were 623 community-dwelling adults from the Whitehall II Imaging Sub-study with multi-modal MRI (mean age 69.96, SD = 5.18, 79% men). We used linear mixed-effects models to investigate associations of SVD burden with up to 25-year retrospective trajectories of vascular risk and cognitive performance. General linear modelling was used to investigate concurrent associations with grey matter (GM) density and white matter (WM) microstructure, and whether these associations were modified by cognitive status (Montreal Cognitive Asessment [MoCA] scores of < 26 vs. ≥ 26). Severe SVD burden in older age was associated with higher mean arterial pressure throughout midlife (β = 3.36, 95% CI [0.42-6.30]), and faster cognitive decline in letter fluency (β = -0.07, 95% CI [-0.13--0.01]), and verbal reasoning (β = -0.05, 95% CI [-0.11--0.001]). Moreover, SVD burden was related to lower GM volumes in 9.7% of total GM, and widespread WM microstructural decline (FWE-corrected p < 0.05). The latter association was most pronounced in individuals who demonstrated cognitive impairments on MoCA (MoCA < 26; F3,608 = 2.14, p = 0.007). These findings highlight the importance of managing midlife vascular health to preserve brain structure and cognitive function in old age.
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Affiliation(s)
- Michelle G Jansen
- Donders Centre for Cognition, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.,Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Ludovica Griffanti
- Department of Psychiatry, 6396University of Oxford, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroimaging (Oxford Centres for Functional MRI of the Brain & Human Brain Activity) University of Oxford, Oxford, UK
| | - Clare E Mackay
- Department of Psychiatry, 6396University of Oxford, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroimaging (Oxford Centres for Functional MRI of the Brain & Human Brain Activity) University of Oxford, Oxford, UK
| | - Melis Anatürk
- Department of Psychiatry, 6396University of Oxford, University of Oxford, Oxford, UK.,Centre for Medical Image Computing, Department of Computer Science, 4919University College London, University College London, London, UK
| | - Luca Melazzini
- Wellcome Centre for Integrative Neuroimaging (Oxford Centres for Functional MRI of the Brain & Human Brain Activity) University of Oxford, Oxford, UK.,Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
| | - Ann-Marie G de Lange
- Department of Psychiatry, 6396University of Oxford, University of Oxford, Oxford, UK.,Department of Psychology, 6305University of Oslo, University of Oslo, Oslo, Norway
| | | | - Enikő Zsoldos
- Department of Psychiatry, 6396University of Oxford, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroimaging (Oxford Centres for Functional MRI of the Brain & Human Brain Activity) University of Oxford, Oxford, UK
| | - Kim Wiegertjes
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Archana Singh-Manoux
- Department of Epidemiology and Public Health, 4919University College London, University College London, London, UK.,INSERM, Epidemiology of Ageing and Neurogenerative Diseases, Université de Paris, Paris, France
| | - Mika Kivimäki
- Department of Epidemiology and Public Health, 4919University College London, University College London, London, UK
| | - Klaus P Ebmeier
- Department of Psychiatry, 6396University of Oxford, University of Oxford, Oxford, UK
| | - Sana Suri
- Department of Psychiatry, 6396University of Oxford, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroimaging (Oxford Centres for Functional MRI of the Brain & Human Brain Activity) University of Oxford, Oxford, UK
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