1
|
Godoy-Corchuelo JM, Ali Z, Brito Armas JM, Martins-Bach AB, García-Toledo I, Fernández-Beltrán LC, López-Carbonero JI, Bascuñana P, Spring S, Jimenez-Coca I, Muñoz de Bustillo Alfaro RA, Sánchez-Barrena MJ, Nair RR, Nieman BJ, Lerch JP, Miller KL, Ozdinler HP, Fisher EMC, Cunningham TJ, Acevedo-Arozena A, Corrochano S. TDP-43-M323K causes abnormal brain development and progressive cognitive and motor deficits associated with mislocalised and increased levels of TDP-43. Neurobiol Dis 2024; 193:106437. [PMID: 38367882 PMCID: PMC10988218 DOI: 10.1016/j.nbd.2024.106437] [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: 12/15/2023] [Revised: 02/02/2024] [Accepted: 02/08/2024] [Indexed: 02/19/2024] Open
Abstract
TDP-43 pathology is found in several neurodegenerative disorders, collectively referred to as "TDP-43 proteinopathies". Aggregates of TDP-43 are present in the brains and spinal cords of >97% of amyotrophic lateral sclerosis (ALS), and in brains of ∼50% of frontotemporal dementia (FTD) patients. While mutations in the TDP-43 gene (TARDBP) are usually associated with ALS, many clinical reports have linked these mutations to cognitive impairments and/or FTD, but also to other neurodegenerative disorders including Parkinsonism (PD) or progressive supranuclear palsy (PSP). TDP-43 is a ubiquitously expressed, highly conserved RNA-binding protein that is involved in many cellular processes, mainly RNA metabolism. To investigate systemic pathological mechanisms in TDP-43 proteinopathies, aiming to capture the pleiotropic effects of TDP-43 mutations, we have further characterised a mouse model carrying a point mutation (M323K) within the endogenous Tardbp gene. Homozygous mutant mice developed cognitive and behavioural deficits as early as 3 months of age. This was coupled with significant brain structural abnormalities, mainly in the cortex, hippocampus, and white matter fibres, together with progressive cortical interneuron degeneration and neuroinflammation. At the motor level, progressive phenotypes appeared around 6 months of age. Thus, cognitive phenotypes appeared to be of a developmental origin with a mild associated progressive neurodegeneration, while the motor and neuromuscular phenotypes seemed neurodegenerative, underlined by a progressive loss of upper and lower motor neurons as well as distal denervation. This is accompanied by progressive elevated TDP-43 protein and mRNA levels in cortex and spinal cord of homozygous mutant mice from 3 months of age, together with increased cytoplasmic TDP-43 mislocalisation in cortex, hippocampus, hypothalamus, and spinal cord at 12 months of age. In conclusion, we find that Tardbp M323K homozygous mutant mice model many aspects of human TDP-43 proteinopathies, evidencing a dual role for TDP-43 in brain morphogenesis as well as in the maintenance of the motor system, making them an ideal in vivo model system to study the complex biology of TDP-43.
Collapse
Affiliation(s)
- Juan M Godoy-Corchuelo
- Neurological Disorders Group, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdiSSC), Madrid 28040, Spain
| | - Zeinab Ali
- Neurological Disorders Group, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdiSSC), Madrid 28040, Spain; MRC Harwell Institute, Oxfordshire, UK
| | - Jose M Brito Armas
- Unidad de Investigación, Hospital Universitario de Canarias, ITB-ULL and CIBERNED, La Laguna, Spain
| | | | - Irene García-Toledo
- Neurological Disorders Group, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdiSSC), Madrid 28040, Spain
| | - Luis C Fernández-Beltrán
- Neurological Disorders Group, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdiSSC), Madrid 28040, Spain; Department of Medicine, Universidad Complutense de Madrid, Madrid, Spain
| | - Juan I López-Carbonero
- Neurological Disorders Group, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdiSSC), Madrid 28040, Spain
| | - Pablo Bascuñana
- Brain Mapping Group, Hospital Clínico San Carlos, IdISSC, Madrid, Spain
| | - Shoshana Spring
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, ON, Canada
| | - Irene Jimenez-Coca
- Neurological Disorders Group, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdiSSC), Madrid 28040, Spain
| | | | - Maria J Sánchez-Barrena
- Department of Crystallography and Structural Biology, Institute of Physical Chemistry "Blas Cabrera", CSIC, Madrid, Spain
| | - Remya R Nair
- MRC Harwell Institute, Oxfordshire, UK; Nucleic Acid Therapy Accelerator (NATA), Harwell Campus, Oxfordshire, UK
| | - Brian J Nieman
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, ON, Canada
| | - Jason P Lerch
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Hande P Ozdinler
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Elizabeth M C Fisher
- Department of Neuromuscular Diseases, and UCL Queen Square Motor Neuron Disease Centre, UCL, Institute of Neurology, London, UK
| | - Thomas J Cunningham
- MRC Harwell Institute, Oxfordshire, UK; MRC Prion Unit at UCL, UCL Institute of Prion Diseases, London, UK
| | - Abraham Acevedo-Arozena
- Unidad de Investigación, Hospital Universitario de Canarias, ITB-ULL and CIBERNED, La Laguna, Spain.
| | - Silvia Corrochano
- Neurological Disorders Group, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdiSSC), Madrid 28040, Spain; MRC Harwell Institute, Oxfordshire, UK.
| |
Collapse
|
2
|
Seifert AC, Xu J, Kong Y, Eippert F, Miller KL, Tracey I, Vannesjo SJ. Thermal stimulus task fMRI in the cervical spinal cord at 7 Tesla. Hum Brain Mapp 2024; 45:e26597. [PMID: 38375948 PMCID: PMC10877664 DOI: 10.1002/hbm.26597] [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: 04/24/2023] [Revised: 12/15/2023] [Accepted: 01/04/2024] [Indexed: 02/21/2024] Open
Abstract
Although functional magnetic resonance imaging (fMRI) is widely applied in the brain, fMRI of the spinal cord is more technically demanding. Proximity to the vertebral column and lungs results in strong spatial inhomogeneity and temporal fluctuations in B0 . Increasing field strength enables higher spatial resolution and improved sensitivity to blood oxygenation level-dependent (BOLD) signal, but amplifies the effects of B0 inhomogeneity. In this work, we present the first task fMRI in the spinal cord at 7 T. Further, we compare the performance of single-shot and multi-shot 2D echo-planar imaging (EPI) protocols, which differ in sensitivity to spatial and temporal B0 inhomogeneity. The cervical spinal cords of 11 healthy volunteers were scanned at 7 T using single-shot 2D EPI at 0.75 mm in-plane resolution and multi-shot 2D EPI at 0.75 and 0.6 mm in-plane resolutions. All protocols used 3 mm slice thickness. For each protocol, the BOLD response to 13 10-s noxious thermal stimuli applied to the right thumb was acquired in a 10-min fMRI run. Image quality, temporal signal to noise ratio (SNR), and BOLD activation (percent signal change and z-stat) at both individual- and group-level were evaluated between the protocols. Temporal SNR was highest in single-shot and multi-shot 0.75 mm protocols. In group-level analyses, activation clusters appeared in all protocols in the ipsilateral dorsal quadrant at the expected C6 neurological level. In individual-level analyses, activation clusters at the expected level were detected in some, but not all subjects and protocols. Single-shot 0.75 mm generally produced the highest mean z-statistic, while multi-shot 0.60 mm produced the best-localized activation clusters and the least geometric distortion. Larger than expected within-subject segmental variation of BOLD activation along the cord was observed. Group-level sensory task fMRI of the cervical spinal cord is feasible at 7 T with single-shot or multi-shot EPI. The best choice of protocol will likely depend on the relative importance of sensitivity to activation versus spatial localization of activation for a given experiment. PRACTITIONER POINTS: First stimulus task fMRI results in the spinal cord at 7 T. Single-shot 0.75 mm 2D EPI produced the highest mean z-statistic. Multi-shot 0.60 mm 2D EPI provided the best-localized activation and least distortion.
Collapse
Affiliation(s)
- Alan C. Seifert
- Biomedical Engineering and Imaging InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Diagnostic, Molecular, and Interventional RadiologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Graduate School of Biomedical SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Junqian Xu
- Department of RadiologyBaylor College of MedicineHoustonTexasUSA
- Department of PsychiatryBaylor College of MedicineHoustonTexasUSA
| | - Yazhuo Kong
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- Institute of PsychologyChinese Academy of SciencesBeijingChina
| | - Falk Eippert
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- Max Planck Research Group Pain PerceptionMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Irene Tracey
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - S. Johanna Vannesjo
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- Department of PhysicsNorwegian University of Science and Technology (NTNU)TrondheimNorway
| |
Collapse
|
3
|
Steffen MJA, McCoy KD, Mengeling MA, Miller KL, Davila H, Wardyn SE, Shibli-Rahhal A, Farukhi I, Solimeo SL. National Survey of the Bone Densitometry Evaluation Process Within an Integrated Healthcare System. J Clin Densitom 2024; 27:101459. [PMID: 38118352 DOI: 10.1016/j.jocd.2023.101459] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 12/22/2023]
Abstract
BACKGROUND To assess the current state of bone mineral density evaluation services via dual energy x-ray absorptiometry (DXA) provided to Veterans with fracture risk through the development and administration of a nationwide survey of facilities in the Veterans Health Administration. METHODOLOGY The Bone Densitometry Survey was developed by convening a Work Group of individuals with expertise in bone densitometry and engaging the Work Group in an iterative drafting and revision process. Once completed, the survey was beta tested, administered through REDCap, and sent via e-mail to points of contact at 178 VHA facilities. RESULTS Facility response rate was 31 % (56/178). Most DXA centers reported positively to markers of readiness for their bone densitometers: less than 10 years old (n=35; 63 %); in "excellent" or "good" condition (n=44; 78 %, 32 % and 46 %, respectively); and perform phantom calibration (n=43; 77 %). Forty-one DXA centers (73 %) use intake processes that have been shown to reduce errors. Thirty-seven DXA centers (66 %) reported their technologists receive specialized training in DXA, while 14 (25 %) indicated they receive accredited training. Seventeen DXA centers (30 %) reported performing routine precision assessment. CONCLUSIONS Many DXA centers reported using practices that meet minimal standards for DXA reporting and preparation; however, the lack of standardization, even within an integrated healthcare system, indicates an opportunity for quality improvement to ensure consistent high quality bone mineral density evaluation of Veterans.
Collapse
Affiliation(s)
- Melissa J A Steffen
- VA Office of Rural Health, Veterans Rural Health Resource Center-Iowa City (VRHRC-IC), Department of Veterans Affairs, Iowa City VA Healthcare System, Iowa City, IA, 52246, United States; Center for Access & Delivery Research and Evaluation (CADRE), Department of Veterans Affairs, Iowa City VA Healthcare System, Iowa City, IA, United States; Primary Care Analytics Team Iowa City (PCAT-IC), Department of Veterans Affairs, Iowa City VA Healthcare System, Iowa City, IA, United States.
| | - Kimberly D McCoy
- VA Office of Rural Health, Veterans Rural Health Resource Center-Iowa City (VRHRC-IC), Department of Veterans Affairs, Iowa City VA Healthcare System, Iowa City, IA, 52246, United States; Center for Access & Delivery Research and Evaluation (CADRE), Department of Veterans Affairs, Iowa City VA Healthcare System, Iowa City, IA, United States
| | - Michelle A Mengeling
- VA Office of Rural Health, Veterans Rural Health Resource Center-Iowa City (VRHRC-IC), Department of Veterans Affairs, Iowa City VA Healthcare System, Iowa City, IA, 52246, United States; Center for Access & Delivery Research and Evaluation (CADRE), Department of Veterans Affairs, Iowa City VA Healthcare System, Iowa City, IA, United States; Division of General Internal Medicine, Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, United States
| | - Karla L Miller
- VA Office of Rural Health, Veterans Rural Health Resource Center-Salt Lake City (VRHRC-SLC); VA Salt Lake City Healthcare System, Salt Lake City, UT, United States; Department of Internal Medicine, Rheumatology Section, VA Salt Lake City Healthcare System, Salt Lake City, UT, United States; Division of Rheumatology, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Heather Davila
- VA Office of Rural Health, Veterans Rural Health Resource Center-Iowa City (VRHRC-IC), Department of Veterans Affairs, Iowa City VA Healthcare System, Iowa City, IA, 52246, United States; Center for Access & Delivery Research and Evaluation (CADRE), Department of Veterans Affairs, Iowa City VA Healthcare System, Iowa City, IA, United States
| | - Shylo E Wardyn
- VA Office of Rural Health, Veterans Rural Health Resource Center-Iowa City (VRHRC-IC), Department of Veterans Affairs, Iowa City VA Healthcare System, Iowa City, IA, 52246, United States; Center for Access & Delivery Research and Evaluation (CADRE), Department of Veterans Affairs, Iowa City VA Healthcare System, Iowa City, IA, United States
| | - Amal Shibli-Rahhal
- Division of General Internal Medicine, Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, United States
| | - Irfan Farukhi
- Nuclear Medicine Service, VA Texas Health Care System, Dallas VA Medical Center, Dallas, TX, United States
| | - Samantha L Solimeo
- VA Office of Rural Health, Veterans Rural Health Resource Center-Iowa City (VRHRC-IC), Department of Veterans Affairs, Iowa City VA Healthcare System, Iowa City, IA, 52246, United States; Division of General Internal Medicine, Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, United States
| |
Collapse
|
4
|
Smart A, Tisca C, Huszar IN, Kor D, Ansorge O, Tachrount M, Smart S, Lerch JP, Miller KL, Martins-Bach AB. Protocol for tissue processing and paraffin embedding of mouse brains following ex vivo MRI. STAR Protoc 2023; 4:102681. [PMID: 37948184 PMCID: PMC10658376 DOI: 10.1016/j.xpro.2023.102681] [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: 06/01/2023] [Revised: 08/31/2023] [Accepted: 10/09/2023] [Indexed: 11/12/2023] Open
Abstract
Combining histology and ex vivo MRI from the same mouse brain is a powerful way to study brain microstructure. Mouse brains prepared for ex vivo MRI are often kept in storage solution for months, potentially becoming brittle and showing reduced antigenicity. Here, we describe a protocol for mouse brain dissection, tissue processing, paraffin embedding, sectioning, and staining. We then detail registration of histology to ex vivo MRI data from the same sample and extraction of quantitative histological measurements.
Collapse
Affiliation(s)
- Adele Smart
- Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU Oxford, UK; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU Oxford, UK.
| | - Cristiana Tisca
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU Oxford, UK
| | - Istvan N Huszar
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU Oxford, UK
| | - Daniel Kor
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU Oxford, UK
| | - Olaf Ansorge
- Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU Oxford, UK
| | - Mohamed Tachrount
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU Oxford, UK
| | - Sean Smart
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU Oxford, UK
| | - Jason P Lerch
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU Oxford, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU Oxford, UK
| | - Aurea B Martins-Bach
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU Oxford, UK
| |
Collapse
|
5
|
Rieger SW, Hess A, Ji Y, Rodgers CT, Jezzard P, Miller KL, Wu W. A temperature-controlled cooling system for accurate quantitative post-mortem MRI. Magn Reson Med 2023; 90:2643-2652. [PMID: 37529979 PMCID: PMC10952464 DOI: 10.1002/mrm.29816] [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: 03/29/2023] [Revised: 06/16/2023] [Accepted: 07/13/2023] [Indexed: 08/03/2023]
Abstract
PURPOSE To develop a temperature-controlled cooling system to facilitate accurate quantitative post-mortem MRI and enable scanning of unfixed tissue. METHODS A water cooling system was built and integrated with a 7T scanner to minimize temperature drift during MRI scans. The system was optimized for operational convenience and rapid deployment to ensure efficient workflow, which is critical for scanning unfixed post-mortem samples. The performance of the system was evaluated using a 7-h diffusion MRI protocol at 7T with a porcine tissue sample. Quantitative T1 , T2 , and ADC maps were interspersed with the diffusion scans at seven different time points to investigate the temperature dependence of MRI tissue parameters. The impact of temperature changes on biophysical model fitting of diffusion MRI data was investigated using simulation. RESULTS Tissue T1 , T2 , and ADC values remained stable throughout the diffusion MRI scan using the developed cooling system, but varied substantially using a conventional scan setup without temperature control. The cooling system enabled accurate estimation of biophysical model parameters by stabilizing the tissue temperature throughout the diffusion scan, while the conventional setup showed evidence of significantly biased estimation. CONCLUSION A temperature-controlled cooling system was developed to tackle the challenge of heating in post-mortem imaging, which shows potential to improve the accuracy and reliability of quantitative post-mortem imaging and enables long scans of unfixed tissue.
Collapse
Affiliation(s)
- Sebastian W. Rieger
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of PsychiatryUniversity of OxfordOxfordUK
| | - Aaron Hess
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Yang Ji
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Christopher T. Rodgers
- Wolfson Brain Imaging Centre, Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
| | - Peter Jezzard
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| |
Collapse
|
6
|
Seifert AC, Xu J, Kong Y, Eippert F, Miller KL, Tracey I, Vannesjo SJ. Thermal Stimulus Task fMRI in the Cervical Spinal Cord at 7 Tesla. bioRxiv 2023:2023.01.31.526451. [PMID: 36778391 PMCID: PMC9915652 DOI: 10.1101/2023.01.31.526451] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE Although functional MRI is widely applied in the brain, fMRI of the spinal cord is more technically demanding. Proximity to the vertebral column and lungs results in strong spatial inhomogeneity and temporal fluctuations in B0. Increasing field strength enables higher spatial resolution and improved sensitivity to BOLD signal, but amplifies the effects of B0 inhomogeneity. In this work, we present the first stimulus task fMRI in the spinal cord at 7 T. Further, we compare the performance of single-shot and multi-shot 2D EPI protocols, as they differ in sensitivity to spatial and temporal B0 inhomogeneity. METHODS The cervical spinal cords of 11 healthy volunteers were scanned at 7 T using single-shot 2D EPI at 0.75 mm in-plane resolution and multi-shot 2D EPI at 0.75 and 0.6 mm in-plane resolutions. For each protocol, the BOLD response to thirteen 10-second noxious thermal stimuli applied to the right thumb was acquired in a 10-minute fMRI run. Image quality, temporal SNR, and BOLD activation (percent signal change and z-stat) at both individual- and group-level were evaluated between the protocols. RESULTS Temporal SNR was highest in single-shot and multi-shot 0.75 mm protocols. In group-level analyses, activation clusters appeared in all protocols in the ipsilateral dorsal quadrant at the expected C6 neurological level. In individual-level analyses, activation clusters at the expected level were detected in some, but not all subjects and protocols. Single-shot 0.75 mm generally produced the highest mean z-statistic, while multi-shot 0.60 mm produced the best-localized activation clusters and the least geometric distortion. Larger than expected within-subject segmental variation of BOLD activation along the cord was observed. CONCLUSION Group-level sensory task fMRI of the cervical spinal cord is feasible at 7 T with single-shot or multi-shot EPI. The best choice of protocol will likely depend on the relative importance of sensitivity to activation versus spatial localization of activation for a given experiment.
Collapse
|
7
|
Ali Z, Godoy-Corchuelo JM, Martins-Bach AB, Garcia-Toledo I, Fernández-Beltrán LC, Nair RR, Spring S, Nieman BJ, Jimenez-Coca I, Bains RS, Forrest H, Lerch JP, Miller KL, Fisher EMC, Cunningham TJ, Corrochano S. Mutation in the FUS nuclear localisation signal domain causes neurodevelopmental and systemic metabolic alterations. Dis Model Mech 2023; 16:dmm050200. [PMID: 37772684 PMCID: PMC10642611 DOI: 10.1242/dmm.050200] [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: 03/21/2023] [Accepted: 08/29/2023] [Indexed: 09/30/2023] Open
Abstract
Variants in the ubiquitously expressed DNA/RNA-binding protein FUS cause aggressive juvenile forms of amyotrophic lateral sclerosis (ALS). Most FUS mutation studies have focused on motor neuron degeneration; little is known about wider systemic or developmental effects. We studied pleiotropic phenotypes in a physiological knock-in mouse model carrying the pathogenic FUSDelta14 mutation in homozygosity. RNA sequencing of multiple organs aimed to identify pathways altered by the mutant protein in the systemic transcriptome, including metabolic tissues, given the link between ALS-frontotemporal dementia and altered metabolism. Few genes were commonly altered across all tissues, and most genes and pathways affected were generally tissue specific. Phenotypic assessment of mice revealed systemic metabolic alterations related to the pathway changes identified. Magnetic resonance imaging brain scans and histological characterisation revealed that homozygous FUSDelta14 brains were smaller than heterozygous and wild-type brains and displayed significant morphological alterations, including a thinner cortex, reduced neuronal number and increased gliosis, which correlated with early cognitive impairment and fatal seizures. These findings show that the disease aetiology of FUS variants can include both neurodevelopmental and systemic alterations.
Collapse
Affiliation(s)
- Zeinab Ali
- Neurological Disorders Group, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdiSSC), Madrid 28040, Spain
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK
- Mammalian Genetics Unit, MRC Harwell Institute, Didcot, Oxfordshire OX11 ORD, UK
| | - Juan M. Godoy-Corchuelo
- Neurological Disorders Group, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdiSSC), Madrid 28040, Spain
| | - Aurea B. Martins-Bach
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9D, UK
| | - Irene Garcia-Toledo
- Neurological Disorders Group, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdiSSC), Madrid 28040, Spain
| | - Luis C. Fernández-Beltrán
- Neurological Disorders Group, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdiSSC), Madrid 28040, Spain
- Department of Medicine, Universidad Complutense de Madrid, Madrid 28040, Spain
| | - Remya R. Nair
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK
- Mammalian Genetics Unit, MRC Harwell Institute, Didcot, Oxfordshire OX11 ORD, UK
| | - Shoshana Spring
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, ON M57 3H7, Canada
| | - Brian J. Nieman
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, ON M57 3H7, Canada
| | - Irene Jimenez-Coca
- Neurological Disorders Group, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdiSSC), Madrid 28040, Spain
| | - Rasneer S. Bains
- Mary Lyon Centre at MRC Harwell, Didcot, Oxfordshire OX11 ORD, UK
| | - Hamish Forrest
- Mary Lyon Centre at MRC Harwell, Didcot, Oxfordshire OX11 ORD, UK
| | - Jason P. Lerch
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9D, UK
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9D, UK
| | - Elizabeth M. C. Fisher
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Thomas J. Cunningham
- Mammalian Genetics Unit, MRC Harwell Institute, Didcot, Oxfordshire OX11 ORD, UK
- MRC Prion Unit at UCL, UCL Institute of Prion Diseases, University College London, London W1W 7FF, UK
| | - Silvia Corrochano
- Neurological Disorders Group, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdiSSC), Madrid 28040, Spain
- Mammalian Genetics Unit, MRC Harwell Institute, Didcot, Oxfordshire OX11 ORD, UK
| |
Collapse
|
8
|
Li Z, Miller KL, Andersson JLR, Zhang J, Liu S, Guo H, Wu W. Sampling strategies and integrated reconstruction for reducing distortion and boundary slice aliasing in high-resolution 3D diffusion MRI. Magn Reson Med 2023; 90:1484-1501. [PMID: 37317708 PMCID: PMC10952965 DOI: 10.1002/mrm.29741] [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: 01/11/2023] [Revised: 04/14/2023] [Accepted: 05/17/2023] [Indexed: 06/16/2023]
Abstract
PURPOSE To develop a new method for high-fidelity, high-resolution 3D multi-slab diffusion MRI with minimal distortion and boundary slice aliasing. METHODS Our method modifies 3D multi-slab imaging to integrate blip-reversed acquisitions for distortion correction and oversampling in the slice direction (kz ) for reducing boundary slice aliasing. Our aim is to achieve robust acceleration to keep the scan time the same as conventional 3D multi-slab acquisitions, in which data are acquired with a single direction of blip traversal and without kz -oversampling. We employ a two-stage reconstruction. In the first stage, the blip-up/down images are respectively reconstructed and analyzed to produce a field map for each diffusion direction. In the second stage, the blip-reversed data and the field map are incorporated into a joint reconstruction to produce images that are corrected for distortion and boundary slice aliasing. RESULTS We conducted experiments at 7T in six healthy subjects. Stage 1 reconstruction produces images from highly under-sampled data (R = 7.2) with sufficient quality to provide accurate field map estimation. Stage 2 joint reconstruction substantially reduces distortion artifacts with comparable quality to fully-sampled blip-reversed results (2.4× scan time). Whole-brain in-vivo results acquired at 1.22 mm and 1.05 mm isotropic resolutions demonstrate improved anatomical fidelity compared to conventional 3D multi-slab imaging. Data demonstrate good reliability and reproducibility of the proposed method over multiple subjects. CONCLUSION The proposed acquisition and reconstruction framework provide major reductions in distortion and boundary slice aliasing for 3D multi-slab diffusion MRI without increasing the scan time, which can potentially produce high-quality, high-resolution diffusion MRI.
Collapse
Affiliation(s)
- Ziyu Li
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Jesper L. R. Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Jieying Zhang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of MedicineTsinghua UniversityBeijingChina
| | - Simin Liu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of MedicineTsinghua UniversityBeijingChina
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of MedicineTsinghua UniversityBeijingChina
| | - Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| |
Collapse
|
9
|
Howard AFD, Huszar IN, Smart A, Cottaar M, Daubney G, Hanayik T, Khrapitchev AA, Mars RB, Mollink J, Scott C, Sibson NR, Sallet J, Jbabdi S, Miller KL. An open resource combining multi-contrast MRI and microscopy in the macaque brain. Nat Commun 2023; 14:4320. [PMID: 37468455 DOI: 10.1038/s41467-023-39916-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 07/03/2023] [Indexed: 07/21/2023] Open
Abstract
Understanding brain structure and function often requires combining data across different modalities and scales to link microscale cellular structures to macroscale features of whole brain organisation. Here we introduce the BigMac dataset, a resource combining in vivo MRI, extensive postmortem MRI and multi-contrast microscopy for multimodal characterisation of a single whole macaque brain. The data spans modalities (MRI and microscopy), tissue states (in vivo and postmortem), and four orders of spatial magnitude, from microscopy images with micrometre or sub-micrometre resolution, to MRI signals on the order of millimetres. Crucially, the MRI and microscopy images are carefully co-registered together to facilitate quantitative multimodal analyses. Here we detail the acquisition, curation, and first release of the data, that together make BigMac a unique, openly-disseminated resource available to researchers worldwide. Further, we demonstrate example analyses and opportunities afforded by the data, including improvement of connectivity estimates from ultra-high angular resolution diffusion MRI, neuroanatomical insight provided by polarised light imaging and myelin-stained histology, and the joint analysis of MRI and microscopy data for reconstruction of the microscopy-inspired connectome. All data and code are made openly available.
Collapse
Affiliation(s)
- Amy F D Howard
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Istvan N Huszar
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Adele Smart
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Michiel Cottaar
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Greg Daubney
- Wellcome Centre for Integrative Neuroimaging, Experimental Psychology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Taylor Hanayik
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Jeroen Mollink
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Connor Scott
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging, Experimental Psychology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| |
Collapse
|
10
|
Sundaresan V, Arthofer C, Zamboni G, Murchison AG, Dineen RA, Rothwell PM, Auer DP, Wang C, Miller KL, Tendler BC, Alfaro-Almagro F, Sotiropoulos SN, Sprigg N, Griffanti L, Jenkinson M. Automated detection of cerebral microbleeds on MR images using knowledge distillation framework. Front Neuroinform 2023; 17:1204186. [PMID: 37492242 PMCID: PMC10363739 DOI: 10.3389/fninf.2023.1204186] [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] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/19/2023] [Indexed: 07/27/2023] Open
Abstract
Introduction Cerebral microbleeds (CMBs) are associated with white matter damage, and various neurodegenerative and cerebrovascular diseases. CMBs occur as small, circular hypointense lesions on T2*-weighted gradient recalled echo (GRE) and susceptibility-weighted imaging (SWI) images, and hyperintense on quantitative susceptibility mapping (QSM) images due to their paramagnetic nature. Accurate automated detection of CMBs would help to determine quantitative imaging biomarkers (e.g., CMB count) on large datasets. In this work, we propose a fully automated, deep learning-based, 3-step algorithm, using structural and anatomical properties of CMBs from any single input image modality (e.g., GRE/SWI/QSM) for their accurate detections. Methods In our method, the first step consists of an initial candidate detection step that detects CMBs with high sensitivity. In the second step, candidate discrimination step is performed using a knowledge distillation framework, with a multi-tasking teacher network that guides the student network to classify CMB and non-CMB instances in an offline manner. Finally, a morphological clean-up step further reduces false positives using anatomical constraints. We used four datasets consisting of different modalities specified above, acquired using various protocols and with a variety of pathological and demographic characteristics. Results On cross-validation within datasets, our method achieved a cluster-wise true positive rate (TPR) of over 90% with an average of <2 false positives per subject. The knowledge distillation framework improves the cluster-wise TPR of the student model by 15%. Our method is flexible in terms of the input modality and provides comparable cluster-wise TPR and better cluster-wise precision compared to existing state-of-the-art methods. When evaluating across different datasets, our method showed good generalizability with a cluster-wise TPR >80 % with different modalities. The python implementation of the proposed method is openly available.
Collapse
Affiliation(s)
- Vaanathi Sundaresan
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, Karnataka, India
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Christoph Arthofer
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- National Institute for Health and Care Research (NIHR) Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - Giovanna Zamboni
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Dipartimento di Scienze Biomediche, Metaboliche e Neuroscienze, Universitá di Modena e Reggio Emilia, Modena, Italy
| | - Andrew G. Murchison
- Department of Neuroradiology, Oxford University Hospitals National Health Service (NHS) Foundation Trust, Oxford, United Kingdom
| | - Robert A. Dineen
- National Institute for Health and Care Research (NIHR) Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
- Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Peter M. Rothwell
- Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Dorothee P. Auer
- National Institute for Health and Care Research (NIHR) Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
- Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Benjamin C. Tendler
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Stamatios N. Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- National Institute for Health and Care Research (NIHR) Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - Nikola Sprigg
- Stroke Trials Unit, Mental Health and Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom
| | - Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, 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
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
- Australian Institute for Machine Learning, School of Computer Science, The University of Adelaide, Adelaide, SA, Australia
| |
Collapse
|
11
|
Tendler BC, Welland M, Miller KL. Why every lab needs a handbook. eLife 2023; 12:e88853. [PMID: 37395453 DOI: 10.7554/elife.88853] [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] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 06/26/2023] [Indexed: 07/04/2023] Open
Abstract
A lab handbook is a flexible document that outlines the ethos of a research lab or group. A good handbook will outline the different roles within the lab, explain what is expected of all lab members, provide an overview of the culture the lab aims to create, and describe how the lab supports its members so that they can develop as researchers. Here we describe how we wrote a lab handbook for a large research group, and provide resources to help other labs write their own handbooks.
Collapse
Affiliation(s)
- Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Maddie Welland
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
12
|
Li Z, Fan Q, Bilgic B, Wang G, Wu W, Polimeni JR, Miller KL, Huang SY, Tian Q. Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat). Med Image Anal 2023; 86:102744. [PMID: 36867912 PMCID: PMC10517382 DOI: 10.1016/j.media.2023.102744] [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: 03/01/2022] [Revised: 12/25/2022] [Accepted: 01/05/2023] [Indexed: 01/20/2023]
Abstract
Diffusion MRI is a useful neuroimaging tool for non-invasive mapping of human brain microstructure and structural connections. The analysis of diffusion MRI data often requires brain segmentation, including volumetric segmentation and cerebral cortical surfaces, from additional high-resolution T1-weighted (T1w) anatomical MRI data, which may be unacquired, corrupted by subject motion or hardware failure, or cannot be accurately co-registered to the diffusion data that are not corrected for susceptibility-induced geometric distortion. To address these challenges, this study proposes to synthesize high-quality T1w anatomical images directly from diffusion data using convolutional neural networks (CNNs) (entitled "DeepAnat"), including a U-Net and a hybrid generative adversarial network (GAN), and perform brain segmentation on synthesized T1w images or assist the co-registration using synthesized T1w images. The quantitative and systematic evaluations using data of 60 young subjects provided by the Human Connectome Project (HCP) show that the synthesized T1w images and results for brain segmentation and comprehensive diffusion analysis tasks are highly similar to those from native T1w data. The brain segmentation accuracy is slightly higher for the U-Net than the GAN. The efficacy of DeepAnat is further validated on a larger dataset of 300 more elderly subjects provided by the UK Biobank. Moreover, the U-Nets trained and validated on the HCP and UK Biobank data are shown to be highly generalizable to the diffusion data from Massachusetts General Hospital Connectome Diffusion Microstructure Dataset (MGH CDMD) acquired with different hardware systems and imaging protocols and therefore can be used directly without retraining or with fine-tuning for further improved performance. Finally, it is quantitatively demonstrated that the alignment between native T1w images and diffusion images uncorrected for geometric distortion assisted by synthesized T1w images substantially improves upon that by directly co-registering the diffusion and T1w images using the data of 20 subjects from MGH CDMD. In summary, our study demonstrates the benefits and practical feasibility of DeepAnat for assisting various diffusion MRI data analyses and supports its use in neuroscientific applications.
Collapse
Affiliation(s)
- Ziyu Li
- Department of Biomedical Engineering, Tsinghua University, Beijing, China; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Guangzhi Wang
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Qiyuan Tian
- Department of Biomedical Engineering, Tsinghua University, Beijing, China; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, United States.
| |
Collapse
|
13
|
Kelly FE, Frerk C, Bailey CR, Cook TM, Ferguson K, Flin R, Fong K, Groom P, John C, Lang AR, Meek T, Miller KL, Richmond L, Sevdalis N, Stacey MR. Human factors in anaesthesia: a narrative review. Anaesthesia 2023; 78:479-490. [PMID: 36630729 DOI: 10.1111/anae.15920] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/08/2022] [Indexed: 01/12/2023]
Abstract
Healthcare relies on high levels of human performance, as described by the 'human as the hero' concept. However, human performance varies and is recognised to fall in high-pressure situations, meaning that it is not a reliable method of ensuring safety. Other safety-critical industries embed human factors principles into all aspects of their organisations to improve safety and reduce reliance on exceptional human performance; there is potential to do the same in anaesthesia. Human factors is a broad-based scientific discipline which aims to make it as easy as possible for workers to do things correctly. The human factors strategies most likely to be effective are those which 'design out' the chance of an error or adverse event occurring. When errors or adverse events do happen, barriers are in place to trap them and reduce the risk of progression to patient and/or worker harm. If errors or adverse events are not trapped by these barriers, mitigations are in place to minimise the consequences. Non-technical skills form an important part of human factors barriers and mitigation strategies and include: situation awareness; decision-making; task management; and team working. Human factors principles are not a substitute for proper investment and appropriate staffing levels. Although applying human factors science has the potential to save money in the long term, its proper implementation may require investment before reward can be reaped. This narrative review describes what is known about human factors in anaesthesia to date.
Collapse
Affiliation(s)
- F E Kelly
- Department of Anaesthesia and Intensive Care Medicine, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK
| | - C Frerk
- Department of Anaesthesia and Critical Care, Northampton General Hospital, Northampton, UK.,College of Life Sciences/Leicester Medical School, University of Leicester, UK
| | - C R Bailey
- Department of Anaesthetics, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - T M Cook
- Department of Anaesthesia and Intensive Care Medicine, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK.,School of Medicine, Bristol University, Bristol, UK
| | - K Ferguson
- Department of Anaesthesia, Aberdeen Royal Infirmary, Aberdeen, UK
| | - R Flin
- School of Psychology, Aberdeen Business School, Robert Gordon University, Aberdeen, UK
| | - K Fong
- Department of Anaesthesia, University College London Hospitals NHS Foundation Trust, London, UK.,Department of Science, Technology, Engineering and Public Policy, University College London, UK
| | - P Groom
- Department of Anaesthesia, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - C John
- Department of Anaesthesia, University College Hospital's NHS Foundation Trust, London, UK
| | - A R Lang
- Human Factors Research Group, Faculty of Engineering, University of Nottingham, UK
| | - T Meek
- Department of Anaesthesia, James Cook University Hospital, Middlesbrough, UK
| | - K L Miller
- Department of Anaesthesia, Birmingham Women's and Children's Hospital NHS Foundation Trust, Birmingham, UK
| | - L Richmond
- Department of Anaesthesia, Swansea Bay University Health Board, Swansea, UK
| | - N Sevdalis
- Centre for Implementation Science, King's College London, UK
| | - M R Stacey
- Department of Anaesthetics, Intensive Care and Pain Medicine, University Hospital of Wales, Cardiff, UK
| |
Collapse
|
14
|
Kelly FE, Frerk C, Bailey CR, Cook TM, Ferguson K, Flin R, Fong K, Groom P, John C, Lang AR, Meek T, Miller KL, Richmond L, Sevdalis N, Stacey MR. Implementing human factors in anaesthesia: guidance for clinicians, departments and hospitals: Guidelines from the Difficult Airway Society and the Association of Anaesthetists: Guidelines from the Difficult Airway Society and the Association of Anaesthetists. Anaesthesia 2023; 78:458-478. [PMID: 36630725 DOI: 10.1111/anae.15941] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/14/2022] [Indexed: 01/12/2023]
Abstract
Human factors is an evidence-based scientific discipline used in safety critical industries to improve safety and worker well-being. The implementation of human factors strategies in anaesthesia has the potential to reduce the reliance on exceptional personal and team performance to provide safe and high-quality patient care. To encourage the adoption of human factors science in anaesthesia, the Difficult Airway Society and the Association of Anaesthetists established a Working Party, including anaesthetists and operating theatre team members with human factors expertise and/or interest, plus a human factors scientist, an industrial psychologist and an experimental psychologist/implementation scientist. A three-stage Delphi process was used to formulate a set of 12 recommendations: these are described using a 'hierarchy of controls' model and classified into design, barriers, mitigations and education and training strategies. Although most anaesthetic knowledge of human factors concerns non-technical skills, such as teamwork and communication, human factors is a broad-based scientific discipline with many other additional aspects that are just as important. Indeed, the human factors strategies most likely to have the greatest impact are those related to the design of safe working environments, equipment and systems. While our recommendations are primarily provided for anaesthetists and the teams they work with, there are likely to be lessons for others working in healthcare beyond the speciality of anaesthesia.
Collapse
Affiliation(s)
- F E Kelly
- Department of Anaesthesia and Intensive Care Medicine, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK
| | - C Frerk
- Department of Anaesthesia and Critical Care, Northampton General Hospital, Northampton, UK.,University of Leicester, College of Life Sciences/Leicester Medical School, Leicester, UK
| | - C R Bailey
- Department of Anaesthetics, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - T M Cook
- Department of Anaesthesia and Intensive Care Medicine, Royal United Hospitals Bath NHS Foundation Trust, Bath, UK.,Bristol University, Bristol, UK
| | - K Ferguson
- Department of Anaesthesia, Aberdeen Royal Infirmary, Aberdeen, UK
| | - R Flin
- Aberdeen Business School, Robert Gordon University, Aberdeen, UK
| | - K Fong
- Department of Anaesthesia, University College London Hospitals NHS Foundation Trust, London, UK.,Department of Science, Technology, Engineering and Public Policy, University College London, UK
| | - P Groom
- Department of Anaesthesia, Liverpool University Hospitals NHS Foundation Trust, Aintree, Liverpool, UK
| | - C John
- University College Hospital's NHS Foundation Trust, London, UK
| | - A R Lang
- Human Factors Research Group, Faculty of Engineering, University of Nottingham, UK
| | - T Meek
- Department of Anaesthesia, James Cook University Hospital, Middlesbrough, UK
| | - K L Miller
- Department of Paediatric Anaesthesia, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | - L Richmond
- Department of Anaesthesia, Swansea Bay University Health Board, Swansea, UK
| | - N Sevdalis
- Centre for Implementation Science, King's College London, UK
| | - M R Stacey
- Department of Anaesthetics, Intensive Care and Pain Medicine, University Hospital of Wales, Cardiff, UK
| |
Collapse
|
15
|
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.
Collapse
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
| |
Collapse
|
16
|
Rawlings-Mortimer F, Lazari A, Tisca C, Tachrount M, Martins-Bach AB, Miller KL, Lerch JP, Johansen-Berg H. 7,8-dihydroxyflavone enhances long-term spatial memory and alters brain volume in wildtype mice. Front Syst Neurosci 2023; 17:1134594. [PMID: 37008453 PMCID: PMC10057119 DOI: 10.3389/fnsys.2023.1134594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 02/21/2023] [Indexed: 03/17/2023] Open
Abstract
Introduction: 7,8-dihydroxyflavone (7,8-DHF) is a low molecular weight compound that can cross the blood brain barrier and has been implicated in numerous functions and behaviours. It is thought to have neuroprotective capability and has been shown to alleviate symptoms in a wide range of diseases.Methods: 7,8-DHF was administered systemically to wildtype mice during Morris water maze training. Long-term spatial memory was assessed 28 days later. Ex-vivo T2-weighted (T2w) imaging was undertaken on a subset of these mice to assess brain-wide changes in volume.Results: We found that systemic 7,8-DHF administration during the training period enhanced spatial memory 28 days later. Volumetric changes were observed in numerous brain regions associated with a broad range of functions including cognition, sensory, and motor processing.Discussion: Our findings give the first whole brain overview of long-term anatomical changes following 7,8-DHF administration providing valuable information for assessing and understanding the widespread effects this drug has been shown to have in behaviour and disease.
Collapse
|
17
|
Yao J, Tendler BC, Zhou Z, Lei H, Zhang L, Bao A, Zhong J, Miller KL, He H. Both noise-floor and tissue compartment difference in diffusivity contribute to FA dependence on b-value in diffusion MRI. Hum Brain Mapp 2023; 44:1371-1388. [PMID: 36264194 PMCID: PMC9921221 DOI: 10.1002/hbm.26121] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 08/27/2022] [Accepted: 10/09/2022] [Indexed: 11/06/2022] Open
Abstract
Noninvasive diffusion magnetic resonance imaging (dMRI) has been widely employed in both clinical and research settings to investigate brain tissue microstructure. Despite the evidence that dMRI-derived fractional anisotropy (FA) correlates with white matter properties, the metric is not specific. Recent studies have reported that FA is dependent on the b-value, and its origin has primarily been attributed to either the influence of microstructure or the noise-floor effect. A systematic investigation into the inter-relationship of these two effects is however still lacking. This study aims to quantify contributions of the reported differences in intra- and extra-neurite diffusivity to the observed changes in FA, in addition to the noise in measurements. We used in-vivo and post-mortem human brain imaging, as well as numerical simulations and histological validation, for this purpose. Our investigations reveal that the percentage difference of FA between b-values (pdFA) has significant positive associations with neurite density index (NDI), which is derived from in-vivo neurite orientation dispersion and density imaging (NODDI), or Bielschowsky's silver impregnation (BIEL) staining sections of fixed post-mortem human brain samples. Furthermore, such an association is found to be varied with Signal-to-Noise Ratio (SNR) level, indicating a nonlinear interaction effect between tissue microstructure and noise. Finally, a multicompartment model simulation revealed that these findings can be driven by differing diffusivities of intra- and extra-neurite compartments in tissue, with the noise-floor further amplifying the effect. In conclusion, both the differences in intra- and extra-neurite diffusivity and noise-floor effects significantly contribute to the FA difference associated with the b-value.
Collapse
Affiliation(s)
- Junye Yao
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Zihan Zhou
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hao Lei
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China
| | - Lei Zhang
- Department of Neurology in Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of Zhejiang Province, and Department of Neurobiology, Zhejiang University, Hangzhou, China.,National Human Brain Bank for Health and Disease, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, China
| | - Aimin Bao
- Department of Neurology in Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of Zhejiang Province, and Department of Neurobiology, Zhejiang University, Hangzhou, China.,National Human Brain Bank for Health and Disease, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Imaging Sciences, University of Rochester, Rochester, New York, USA
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| |
Collapse
|
18
|
Huszar IN, Pallebage-Gamarallage M, Bangerter-Christensen S, Brooks H, Fitzgibbon S, Foxley S, Hiemstra M, Howard AFD, Jbabdi S, Kor DZL, Leonte A, Mollink J, Smart A, Tendler BC, Turner MR, Ansorge O, Miller KL, Jenkinson M. Tensor image registration library: Deformable registration of stand-alone histology images to whole-brain post-mortem MRI data. Neuroimage 2023; 265:119792. [PMID: 36509214 PMCID: PMC10933796 DOI: 10.1016/j.neuroimage.2022.119792] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [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: 07/27/2022] [Revised: 10/26/2022] [Accepted: 12/04/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Accurate registration between microscopy and MRI data is necessary for validating imaging biomarkers against neuropathology, and to disentangle complex signal dependencies in microstructural MRI. Existing registration methods often rely on serial histological sampling or significant manual input, providing limited scope to work with a large number of stand-alone histology sections. Here we present a customisable pipeline to assist the registration of stand-alone histology sections to whole-brain MRI data. METHODS Our pipeline registers stained histology sections to whole-brain post-mortem MRI in 4 stages, with the help of two photographic intermediaries: a block face image (to undistort histology sections) and coronal brain slab photographs (to insert them into MRI space). Each registration stage is implemented as a configurable stand-alone Python script using our novel platform, Tensor Image Registration Library (TIRL), which provides flexibility for wider adaptation. We report our experience of registering 87 PLP-stained histology sections from 14 subjects and perform various experiments to assess the accuracy and robustness of each stage of the pipeline. RESULTS All 87 histology sections were successfully registered to MRI. Histology-to-block registration (Stage 1) achieved 0.2-0.4 mm accuracy, better than commonly used existing methods. Block-to-slice matching (Stage 2) showed great robustness in automatically identifying and inserting small tissue blocks into whole brain slices with 0.2 mm accuracy. Simulations demonstrated sub-voxel level accuracy (0.13 mm) of the slice-to-volume registration (Stage 3) algorithm, which was observed in over 200 actual brain slice registrations, compensating 3D slice deformations up to 6.5 mm. Stage 4 combined the previous stages and generated refined pixelwise aligned multi-modal histology-MRI stacks. CONCLUSIONS Our open-source pipeline provides robust automation tools for registering stand-alone histology sections to MRI data with sub-voxel level precision, and the underlying framework makes it readily adaptable to a diverse range of microscopy-MRI studies.
Collapse
Affiliation(s)
- Istvan N Huszar
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | | | - Sarah Bangerter-Christensen
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Brigham Young University, Provo, UT, USA
| | - Hannah Brooks
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Sean Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Sean Foxley
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Radiology, University of Chicago, Chicago, IL, USA
| | - Marlies Hiemstra
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Anatomy, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Amy F D Howard
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Daniel Z L Kor
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Anna Leonte
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Neuroscience, University of Groningen, Groningen, the Netherlands
| | - Jeroen Mollink
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Department of Anatomy, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Adele Smart
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Martin R Turner
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Olaf Ansorge
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| |
Collapse
|
19
|
Kor DZL, Jbabdi S, Huszar IN, Mollink J, Tendler BC, Foxley S, Wang C, Scott C, Smart A, Ansorge O, Pallebage-Gamarallage M, Miller KL, Howard AFD. An automated pipeline for extracting histological stain area fraction for voxelwise quantitative MRI-histology comparisons. Neuroimage 2022; 264:119726. [PMID: 36368503 PMCID: PMC10933753 DOI: 10.1016/j.neuroimage.2022.119726] [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: 07/06/2022] [Revised: 10/27/2022] [Accepted: 10/31/2022] [Indexed: 11/11/2022] Open
Abstract
The acquisition of MRI and histology in the same post-mortem tissue sample enables direct correlation between MRI and histologically-derived parameters. However, there still lacks a standardised automated pipeline to process histology data, with most studies relying on manual intervention. Here, we introduce an automated pipeline to extract a quantitative histological measure for staining density (stain area fraction, SAF) from multiple immunohistochemical (IHC) stains. The pipeline is designed to directly address key IHC artefacts related to tissue staining and slide digitisation. Here, the pipeline was applied to post-mortem human brain data from multiple subjects, relating MRI parameters (FA, MD, RD, AD, R2*, R1) to IHC slides stained for myelin, neurofilaments, microglia and activated microglia. Utilising high-quality MRI-histology co-registrations, we then performed whole-slide voxelwise comparisons (simple correlations, partial correlations and multiple regression analyses) between multimodal MRI- and IHC-derived parameters. The pipeline was found to be reproducible, robust to artefacts and generalisable across multiple IHC stains. Our partial correlation results suggest that some simple MRI-SAF correlations should be interpreted with caution, due to the co-localisation of other tissue features (e.g., myelin and neurofilaments). Further, we find activated microglia-a generic biomarker of inflammation-to consistently be the strongest predictor of high DTI FA and low RD, which may suggest sensitivity of diffusion MRI to aspects of neuroinflammation related to microglial activation, even after accounting for other microstructural changes (demyelination, axonal loss and general microglia infiltration). Together, these results show the utility of this approach in carefully curating IHC data and performing multimodal analyses to better understand microstructural relationships with MRI.
Collapse
Affiliation(s)
- Daniel Z L Kor
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom.
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom
| | - Istvan N Huszar
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom
| | - Jeroen Mollink
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom
| | - Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom
| | - Sean Foxley
- Department of Radiology, University of Chicago, Chicago, IL, United States of America
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom
| | - Connor Scott
- Academic Unit of Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Adele Smart
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom; Academic Unit of Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Olaf Ansorge
- Academic Unit of Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Menuka Pallebage-Gamarallage
- Academic Unit of Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom
| | - Amy F D Howard
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom
| |
Collapse
|
20
|
Howard AF, Cottaar M, Drakesmith M, Fan Q, Huang SY, Jones DK, Lange FJ, Mollink J, Rudrapatna SU, Tian Q, Miller KL, Jbabdi S. Estimating axial diffusivity in the NODDI model. Neuroimage 2022; 262:119535. [PMID: 35931306 PMCID: PMC9802007 DOI: 10.1016/j.neuroimage.2022.119535] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/20/2022] [Accepted: 08/01/2022] [Indexed: 01/03/2023] Open
Abstract
To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d∥=1.7μm2/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ∼2-2.5μm2/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.
Collapse
Affiliation(s)
- Amy Fd Howard
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
| | - Michiel Cottaar
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Mark Drakesmith
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States; Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China; Academy of Medical Engineering and Translational Medicine, Medical College, Tianjin University, Tianjin, China
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Frederik J Lange
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jeroen Mollink
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Suryanarayana Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Philips Innovation Campus, Bangalore, India
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States
| | - Karla L Miller
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Saad Jbabdi
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
21
|
Miller KL, Mccoy K, Richards C, Seaman A, Solimeo SL. Engagement in Primary Prevention Program among Rural Veterans With Osteoporosis Risk. JBMR Plus 2022; 6:e10682. [PMID: 36248271 PMCID: PMC9549732 DOI: 10.1002/jbm4.10682] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/24/2022] [Accepted: 09/05/2022] [Indexed: 02/05/2023] Open
Abstract
A primary osteoporosis prevention program using a virtual bone health team (BHT) was implemented to comanage the care of rural veterans in the Mountain West region of the United States. The BHT identified, screened, and treated rural veterans at risk for osteoporosis using telephone and United States Postal Service communications. Eligibility was determined by regular use of Veterans Health Administration primary care, age 50 or older, and evidence of fracture risk. This study was conducted to identify demographic and clinical factors associated with the acceptance of osteoporosis screening and the initiation of medication where indicated. A cross-sectional cohort design (N = 6985) was utilized with a generalized estimating equation and logit link function to account for facility-level clustering. Fully saturated and reduced models were fitted using backward selection. Less than a quarter of eligible veterans enrolled in BHT's program and completed screening. Factors associated with a lower likelihood of clinic enrollment included being of older age, unmarried, greater distance from VHA services, having a copayment, prior fracture, or history of rheumatoid arthritis. A majority of veterans with treatment indication started medication therapy (N = 453). In this subpopulation, Fisher's exact test showed a significant association between osteoporosis treatment uptake and a history of two or more falls in the prior year, self-reported parental history of fracture, current smoking, and weight-bearing exercise. The BHT was designed to reduce barriers to screening; however, for this population cost and travel continue to limit engagement. The remarkable rate of medication initiation notwithstanding, low enrollment reduces the impact of this primary prevention program, and findings pertaining to fracture, smoking, and exercise imply that health beliefs are an important contributing factor. Efforts to identify and address barriers to osteoporosis screening and treatment, such as clinical factors, social determinants of health, and health beliefs, may pave the way for effective implementation of population bone health care delivery systems. Published 2022. This article is a U.S. Government work and is in the public domain in the USA. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
Collapse
Affiliation(s)
- Karla L. Miller
- VHA Office of Rural Health, Veterans Rural Health Resource Center‐Salt Lake City, Department of Internal Medicine, Rheumatology SectionVeterans Affairs Salt Lake City Health Care SystemSalt Lake CityUtahUSA,Associate Professor (Clinical) of Medicine, Division of RheumatologyUniversity of Utah School of MedicineSalt Lake CityUtahUSA
| | - Kimberly Mccoy
- VHA Office of Rural Health, Veterans Rural Health Resource Center‐Iowa City (VRHRC‐IC), Center for Access & Delivery Research and Evaluation (CADRE)Veterans Affairs Iowa City VHA Health Care SystemIowa CityIowaUSA
| | - Chris Richards
- VHA Office of Rural Health, Veterans Rural Health Resource Center‐Iowa City (VRHRC‐IC), Center for Access & Delivery Research and Evaluation (CADRE)Department of Veterans Affairs Iowa City VHA Health Care SystemIowa CityIowaUSA
| | - Aaron Seaman
- VHA Office of Rural Health, Veterans Rural Health Resource Center‐Iowa City (VRHRC‐IC)Veterans Affairs Iowa City VHA Health Care SystemIowa CityIowaUSA,Division of General Internal Medicine, Department of Internal Medicine, Carver College of MedicineUniversity of IowaIowa CityIowaUSA
| | - Samantha L. Solimeo
- VHA Office of Rural Health, Veterans Rural Health Resource Center‐Iowa City (VRHRC‐IC), Center for Access & Delivery Research and Evaluation (CADRE), Primary Care Analytics Team Iowa City (PCAT‐IC)Veterans Affairs Iowa City VHA Health Care SystemIowa CityIowaUSA,Division of General Internal Medicine, Department of Internal Medicine, Carver College of MedicineUniversity of IowaIowa CityIowaUSA
| |
Collapse
|
22
|
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.
Collapse
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
| | | |
Collapse
|
23
|
Graedel NN, Miller KL, Chiew M. Ultrahigh Resolution fMRI at 7T Using Radial-Cartesian TURBINE Sampling. Magn Reson Med 2022; 88:2058-2073. [PMID: 35785429 PMCID: PMC9546489 DOI: 10.1002/mrm.29359] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 04/21/2022] [Accepted: 05/23/2022] [Indexed: 12/05/2022]
Abstract
Purpose We investigate the use of TURBINE, a 3D radial‐Cartesian acquisition scheme in which EPI planes are rotated about the phase‐encoding axis to acquire a cylindrical k‐space for high‐fidelity ultrahigh isotropic resolution fMRI at 7 Tesla with minimal distortion and blurring. Methods An improved, completely self‐navigated version of the TURBINE sampling scheme was designed for fMRI at 7 Telsa. To demonstrate the image quality and spatial specificity of the acquisition, thin‐slab visual and motor BOLD fMRI at 0.67 mm isotropic resolution (16 mm slab, TRvol = 2.32 s), and 0.8 × 0.8 × 2.0 mm (whole‐brain, TRvol = 2.4 s) data were acquired. To prioritize the high spatial fidelity, we employed a temporally regularized reconstruction to improve sensitivity without any spatial bias. Results TURBINE images provide high structural fidelity with almost no distortion, dropout, or T2* blurring for the thin‐slab acquisitions compared to conventional 3D EPI owing to the radial sampling in‐plane and the short echo train used. This results in activation that can be localized to pre‐ and postcentral gyri in a motor task, for example, with excellent correspondence to brain structure measured by a T1‐MPRAGE. The benefits of TURBINE (low distortion, dropout, blurring) are reduced for the whole‐brain acquisition due to the longer EPI train. We demonstrate robust BOLD activation at 0.67 mm isotropic resolution (thin‐slab) and also anisotropic 0.8 × 0.8 × 2.0 mm (whole‐brain) acquisitions. Conclusion TURBINE is a promising acquisition approach for high‐resolution, minimally distorted fMRI at 7 Tesla and could be particularly useful for fMRI in areas of high B0 inhomogeneity. Click here for author‐reader discussions
Collapse
Affiliation(s)
- Nadine N Graedel
- Wellcome Centre for Integrative Neuroscience, FMRIB Centre, University of Oxford, Oxford, United Kingdom.,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroscience, FMRIB Centre, University of Oxford, Oxford, United Kingdom
| | - Mark Chiew
- Wellcome Centre for Integrative Neuroscience, FMRIB Centre, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
24
|
Topiwala A, Wang C, Ebmeier KP, Burgess S, Bell S, Levey DF, Zhou H, McCracken C, Roca-Fernández A, Petersen SE, Raman B, Husain M, Gelernter J, Miller KL, Smith SM, Nichols TE. Associations between moderate alcohol consumption, brain iron, and cognition in UK Biobank participants: Observational and mendelian randomization analyses. PLoS Med 2022; 19:e1004039. [PMID: 35834561 PMCID: PMC9282660 DOI: 10.1371/journal.pmed.1004039] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 06/01/2022] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Brain iron deposition has been linked to several neurodegenerative conditions and reported in alcohol dependence. Whether iron accumulation occurs in moderate drinkers is unknown. Our objectives were to investigate evidence in support of causal relationships between alcohol consumption and brain iron levels and to examine whether higher brain iron represents a potential pathway to alcohol-related cognitive deficits. METHODS AND FINDINGS Observational associations between brain iron markers and alcohol consumption (n = 20,729 UK Biobank participants) were compared with associations with genetically predicted alcohol intake and alcohol use disorder from 2-sample mendelian randomization (MR). Alcohol intake was self-reported via a touchscreen questionnaire at baseline (2006 to 2010). Participants with complete data were included. Multiorgan susceptibility-weighted magnetic resonance imaging (9.60 ± 1.10 years after baseline) was used to ascertain iron content of each brain region (quantitative susceptibility mapping (QSM) and T2*) and liver tissues (T2*), a marker of systemic iron. Main outcomes were susceptibility (χ) and T2*, measures used as indices of iron deposition. Brain regions of interest included putamen, caudate, hippocampi, thalami, and substantia nigra. Potential pathways to alcohol-related iron brain accumulation through elevated systemic iron stores (liver) were explored in causal mediation analysis. Cognition was assessed at the scan and in online follow-up (5.82 ± 0.86 years after baseline). Executive function was assessed with the trail-making test, fluid intelligence with puzzle tasks, and reaction time by a task based on the "Snap" card game. Mean age was 54.8 ± 7.4 years and 48.6% were female. Weekly alcohol consumption was 17.7 ± 15.9 units and never drinkers comprised 2.7% of the sample. Alcohol consumption was associated with markers of higher iron (χ) in putamen (β = 0.08 standard deviation (SD) [95% confidence interval (CI) 0.06 to 0.09], p < 0.001), caudate (β = 0.05 [0.04 to 0.07], p < 0.001), and substantia nigra (β = 0.03 [0.02 to 0.05], p < 0.001) and lower iron in the thalami (β = -0.06 [-0.07 to -0.04], p < 0.001). Quintile-based analyses found these associations in those consuming >7 units (56 g) alcohol weekly. MR analyses provided weak evidence these relationships are causal. Genetically predicted alcoholic drinks weekly positively associated with putamen and hippocampus susceptibility; however, these associations did not survive multiple testing corrections. Weak evidence for a causal relationship between genetically predicted alcohol use disorder and higher putamen susceptibility was observed; however, this was not robust to multiple comparisons correction. Genetically predicted alcohol use disorder was associated with serum iron and transferrin saturation. Elevated liver iron was observed at just >11 units (88 g) alcohol weekly c.f. <7 units (56 g). Systemic iron levels partially mediated associations of alcohol intake with brain iron. Markers of higher basal ganglia iron associated with slower executive function, lower fluid intelligence, and slower reaction times. The main limitations of the study include that χ and T2* can reflect changes in myelin as well as iron, alcohol use was self-reported, and MR estimates can be influenced by genetic pleiotropy. CONCLUSIONS To the best of our knowledge, this study represents the largest investigation of moderate alcohol consumption and iron homeostasis to date. Alcohol consumption above 7 units weekly associated with higher brain iron. Iron accumulation represents a potential mechanism for alcohol-related cognitive decline.
Collapse
Affiliation(s)
- Anya Topiwala
- Nuffield Department Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
| | - Klaus P. Ebmeier
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Steven Bell
- Department of Clinical Neurosciences, University of Cambridge, United Kingdom
| | - Daniel F. Levey
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, United States of America
| | - Hang Zhou
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, United States of America
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | | | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, United Kingdom
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- Alan Turing Institute, London, United Kingdom
| | - Betty Raman
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Masud Husain
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
- Division of Clinical Neurology, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford, United Kingdom
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut, United States of America
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
| | - Thomas E. Nichols
- Nuffield Department Population Health, Big Data Institute, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Oxford University, Oxford, United Kingdom
| |
Collapse
|
25
|
Tang-Wright K, Smith JET, Bridge H, Miller KL, Dyrby TB, Ahmed B, Reislev NL, Sallet J, Parker AJ, Krug K. Intra-Areal Visual Topography in Primate Brains Mapped with Probabilistic Tractography of Diffusion-Weighted Imaging. Cereb Cortex 2022; 32:2555-2574. [PMID: 34730185 PMCID: PMC9201591 DOI: 10.1093/cercor/bhab364] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/28/2021] [Accepted: 08/29/2021] [Indexed: 11/24/2022] Open
Abstract
Noninvasive diffusion-weighted magnetic resonance imaging (dMRI) can be used to map the neural connectivity between distinct areas in the intact brain, but the standard resolution achieved fundamentally limits the sensitivity of such maps. We investigated the sensitivity and specificity of high-resolution postmortem dMRI and probabilistic tractography in rhesus macaque brains to produce retinotopic maps of the lateral geniculate nucleus (LGN) and extrastriate cortical visual area V5/MT based on their topographic connections with the previously established functional retinotopic map of primary visual cortex (V1). We also replicated the differential connectivity of magnocellular and parvocellular LGN compartments with V1 across visual field positions. Predicted topographic maps based on dMRI data largely matched the established retinotopy of both LGN and V5/MT. Furthermore, tractography based on in vivo dMRI data from the same macaque brains acquired at standard field strength (3T) yielded comparable topographic maps in many cases. We conclude that tractography based on dMRI is sensitive enough to reveal the intrinsic organization of ordered connections between topographically organized neural structures and their resultant functional organization.
Collapse
Affiliation(s)
- K Tang-Wright
- Department of Physiology Anatomy and Genetics, University of Oxford, Oxford, OX1 3PT, UK
| | - J E T Smith
- Department of Physiology Anatomy and Genetics, University of Oxford, Oxford, OX1 3PT, UK
- Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with Max Planck Society, 60528 Frankfurt, Germany
| | - H Bridge
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - K L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - T B Dyrby
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Amager & Hvidovre, 2650 Hvidovre, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - B Ahmed
- Department of Physiology Anatomy and Genetics, University of Oxford, Oxford, OX1 3PT, UK
| | - N L Reislev
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Amager & Hvidovre, 2650 Hvidovre, Denmark
| | - J Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX1 3UD, UK
- Université Lyon 1, INSERM, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - A J Parker
- Department of Physiology Anatomy and Genetics, University of Oxford, Oxford, OX1 3PT, UK
- Institute of Biology, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany
- Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
| | - K Krug
- Department of Physiology Anatomy and Genetics, University of Oxford, Oxford, OX1 3PT, UK
- Institute of Biology, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany
- Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
- Centre for Behavioral Brain Sciences, Otto-von-Guericke-University Magdeburg, 39106 Magdeburg, Germany
| |
Collapse
|
26
|
Wang C, Martins-Bach AB, Alfaro-Almagro F, Douaud G, Klein JC, Llera A, Fiscone C, Bowtell R, Elliott LT, Smith SM, Tendler BC, Miller KL. Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging. Nat Neurosci 2022; 25:818-831. [PMID: 35606419 PMCID: PMC9174052 DOI: 10.1038/s41593-022-01074-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [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: 08/11/2021] [Accepted: 04/11/2022] [Indexed: 12/17/2022]
Abstract
A key aim in epidemiological neuroscience is identification of markers to assess brain health and monitor therapeutic interventions. Quantitative susceptibility mapping (QSM) is an emerging magnetic resonance imaging technique that measures tissue magnetic susceptibility and has been shown to detect pathological changes in tissue iron, myelin and calcification. We present an open resource of QSM-based imaging measures of multiple brain structures in 35,273 individuals from the UK Biobank prospective epidemiological study. We identify statistically significant associations of 251 phenotypes with magnetic susceptibility that include body iron, disease, diet and alcohol consumption. Genome-wide associations relate magnetic susceptibility to 76 replicating clusters of genetic variants with biological functions involving iron, calcium, myelin and extracellular matrix. These patterns of associations include relationships that are unique to QSM, in particular being complementary to T2* signal decay time measures. These new imaging phenotypes are being integrated into the core UK Biobank measures provided to researchers worldwide, creating the potential to discover new, non-invasive markers of brain health.
Collapse
Affiliation(s)
- Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Aurea B Martins-Bach
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Gwenaëlle Douaud
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Johannes C Klein
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Oxford Parkinson's Disease Centre, University of Oxford, Oxford, UK
| | - Alberto Llera
- Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Nijmegen, the Netherlands
| | - Cristiana Fiscone
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Lloyd T Elliott
- Department of Statistics and Actuarial Science, Simon Fraser University, Vancouver, British Columbia, Canada
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| |
Collapse
|
27
|
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.
Collapse
|
28
|
Testard C, Brent LJN, Andersson J, Chiou KL, Negron-Del Valle JE, DeCasien AR, Acevedo-Ithier A, Stock MK, Antón SC, Gonzalez O, Walker CS, Foxley S, Compo NR, Bauman S, Ruiz-Lambides AV, Martinez MI, Skene JHP, Horvath JE, Unit CBR, Higham JP, Miller KL, Snyder-Mackler N, Montague MJ, Platt ML, Sallet J. Social connections predict brain structure in a multidimensional free-ranging primate society. Sci Adv 2022; 8:eabl5794. [PMID: 35417242 PMCID: PMC9007502 DOI: 10.1126/sciadv.abl5794] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 02/23/2022] [Indexed: 06/14/2023]
Abstract
Reproduction and survival in most primate species reflects management of both competitive and cooperative relationships. Here, we investigated the links between neuroanatomy and sociality in free-ranging rhesus macaques. In adults, the number of social partners predicted the volume of the mid-superior temporal sulcus and ventral-dysgranular insula, implicated in social decision-making and empathy, respectively. We found no link between brain structure and other key social variables such as social status or indirect connectedness in adults, nor between maternal social networks or status and dependent infant brain structure. Our findings demonstrate that the size of specific brain structures varies with the number of direct affiliative social connections and suggest that this relationship may arise during development. These results reinforce proposed links between social network size, biological success, and the expansion of specific brain circuits.
Collapse
Affiliation(s)
- Camille Testard
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
| | - Lauren J. N. Brent
- Centre for Research in Animal Behaviour, University of Exeter, Exeter, UK
| | | | - Kenneth L. Chiou
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Josue E. Negron-Del Valle
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Alex R. DeCasien
- Department of Anthropology, New York University, New York, NY, USA
- New York Consortium in Evolutionary Primatology, NYCEP, New York, NY, USA
- Section on Developmental Neurogenomics, National Institute of Mental Health, Washington, DC, USA
| | | | - Michala K. Stock
- Department of Sociology and Anthropology, Metropolitan State University of Denver, Denver, CO, USA
| | - Susan C. Antón
- Department of Anthropology, New York University, New York, NY, USA
- New York Consortium in Evolutionary Primatology, NYCEP, New York, NY, USA
| | - Olga Gonzalez
- Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Christopher S. Walker
- Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA
| | - Sean Foxley
- Wellcome Integrative Neuroimaging Centre, fMRIB, Oxford, UK
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | - Nicole R. Compo
- Caribbean Primate Research Center, University of Puerto Rico, Sabana Seca, Puerto Rico
- Comparative Medicine, University of South Florida, Tampa, FL, USA
| | - Samuel Bauman
- Caribbean Primate Research Center, University of Puerto Rico, Sabana Seca, Puerto Rico
| | | | - Melween I. Martinez
- Caribbean Primate Research Center, University of Puerto Rico, Sabana Seca, Puerto Rico
| | - J. H. Pate Skene
- Department of Neurobiology, Duke University, Durham, NC, USA
- Institute of Cognitive Science, University of Colorado, Boulder, CO, USA
| | - Julie E. Horvath
- Department of Biological and Biomedical Sciences, North Carolina Central University, Durham, NC 27707, USA
- Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
- North Carolina Museum of Natural Sciences, Raleigh, NC 27601, USA
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
| | | | - James P. Higham
- Department of Anthropology, New York University, New York, NY, USA
- New York Consortium in Evolutionary Primatology, NYCEP, New York, NY, USA
| | | | - Noah Snyder-Mackler
- Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, USA
| | - Michael J. Montague
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael L. Platt
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
- Marketing Department, University of Pennsylvania, Philadelphia, PA, USA
| | - Jérôme Sallet
- Department of Experimental Psychology, Wellcome Integrative Neuroimaging Centre, Oxford, UK
- Stem Cell and Brain Research Institute, Inserm, Université Lyon 1, Bron U1208, France
| |
Collapse
|
29
|
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.
Collapse
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
| |
Collapse
|
30
|
Tendler BC, Hanayik T, Ansorge O, Bangerter-Christensen S, Berns GS, Bertelsen MF, Bryant KL, Foxley S, van den Heuvel MP, Howard AFD, Huszar IN, Khrapitchev AA, Leonte A, Manger PR, Menke RAL, Mollink J, Mortimer D, Pallebage-Gamarallage M, Roumazeilles L, Sallet J, Scholtens LH, Scott C, Smart A, Turner MR, Wang C, Jbabdi S, Mars RB, Miller KL. The Digital Brain Bank, an open access platform for post-mortem imaging datasets. eLife 2022; 11:e73153. [PMID: 35297760 PMCID: PMC9042233 DOI: 10.7554/elife.73153] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [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: 03/17/2022] [Indexed: 11/13/2022] Open
Abstract
Post-mortem magnetic resonance imaging (MRI) provides the opportunity to acquire high-resolution datasets to investigate neuroanatomy and validate the origins of image contrast through microscopy comparisons. We introduce the Digital Brain Bank (open.win.ox.ac.uk/DigitalBrainBank), a data release platform providing open access to curated, multimodal post-mortem neuroimaging datasets. Datasets span three themes-Digital Neuroanatomist: datasets for detailed neuroanatomical investigations; Digital Brain Zoo: datasets for comparative neuroanatomy; and Digital Pathologist: datasets for neuropathology investigations. The first Digital Brain Bank data release includes 21 distinctive whole-brain diffusion MRI datasets for structural connectivity investigations, alongside microscopy and complementary MRI modalities. This includes one of the highest-resolution whole-brain human diffusion MRI datasets ever acquired, whole-brain diffusion MRI in fourteen nonhuman primate species, and one of the largest post-mortem whole-brain cohort imaging studies in neurodegeneration. The Digital Brain Bank is the culmination of our lab's investment into post-mortem MRI methodology and MRI-microscopy analysis techniques. This manuscript provides a detailed overview of our work with post-mortem imaging to date, including the development of diffusion MRI methods to image large post-mortem samples, including whole, human brains. Taken together, the Digital Brain Bank provides cross-scale, cross-species datasets facilitating the incorporation of post-mortem data into neuroimaging studies.
Collapse
Affiliation(s)
- Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Taylor Hanayik
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Olaf Ansorge
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Sarah Bangerter-Christensen
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | | | - Mads F Bertelsen
- Centre for Zoo and Wild Animal Health, Copenhagen ZooFrederiksbergDenmark
| | - Katherine L Bryant
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Sean Foxley
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
- Department of Radiology, University of ChicagoChicagoUnited States
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
- Department of Child Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Amy FD Howard
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Istvan N Huszar
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Alexandre A Khrapitchev
- Medical Research Council Oxford Institute for Radiation Oncology, University of OxfordOxfordUnited Kingdom
| | - Anna Leonte
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Paul R Manger
- School of Anatomical Sciences, Faculty of Health Sciences, University of the WitwatersrandJohannesburgSouth Africa
| | - Ricarda AL Menke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Jeroen Mollink
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Duncan Mortimer
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Menuka Pallebage-Gamarallage
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Lea Roumazeilles
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
| | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
- Stem Cell and Brain Research Institute, Université Lyon 1, INSERMBronFrance
| | - Lianne H Scholtens
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Connor Scott
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Adele Smart
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Martin R Turner
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
- Donders Institute for Brain, Cognition and Behaviour, Radboud University NijmegenNijmegenNetherlands
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
| |
Collapse
|
31
|
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.
Collapse
|
32
|
Shahdloo M, Schüffelgen U, Papp D, Miller KL, Chiew M. Model-based dynamic off-resonance correction for improved accelerated fMRI in awake behaving nonhuman primates. Magn Reson Med 2022; 87:2922-2932. [PMID: 35081259 PMCID: PMC9306555 DOI: 10.1002/mrm.29167] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/26/2021] [Accepted: 01/03/2022] [Indexed: 11/18/2022]
Abstract
Purpose To estimate dynamic off‐resonance due to vigorous body motion in accelerated fMRI of awake behaving nonhuman primates (NHPs) using the echo‐planar imaging reference navigator, in order to attenuate the effects of time‐varying off‐resonance on the reconstruction. Methods In NHP fMRI, the animal’s head is usually head‐posted, and the dynamic off‐resonance is mainly caused by motion in body parts that are distant from the brain and have low spatial frequency. Hence, off‐resonance at each frame can be approximated as a spatially linear perturbation of the off‐resonance at a reference frame, and is manifested as a relative linear shift in k‐space. Using GRAPPA operators, we estimated these shifts by comparing the navigator at each time frame with that at the reference frame. Estimated shifts were then used to correct the data at each frame. The proposed method was evaluated in phantom scans, simulations, and in vivo data. Results The proposed method is shown to successfully estimate spatially low‐order dynamic off‐resonance perturbations, including induced linear off‐resonance perturbations in phantoms, and is able to correct retrospectively corrupted data in simulations. Finally, it is shown to reduce ghosting artifacts and geometric distortions by up to 20% in simultaneous multislice in vivo acquisitions in awake‐behaving NHPs. Conclusion A method is proposed that does not need sequence modification or extra acquisitions and makes accelerated awake behaving NHP imaging more robust and reliable, reducing the gap between what is possible with NHP protocols and state‐of‐the‐art human imaging.
Collapse
Affiliation(s)
- Mo Shahdloo
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Urs Schüffelgen
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Daniel Papp
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,NeuroPoly Lab, Electrical Engineering Department, Polytechnique Montréal, Montreal, Canada
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Mark Chiew
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| |
Collapse
|
33
|
Sundaresan V, Arthofer C, Zamboni G, Dineen RA, Rothwell PM, Sotiropoulos SN, Auer DP, Tozer DJ, Markus HS, Miller KL, Dragonu I, Sprigg N, Alfaro-Almagro F, Jenkinson M, Griffanti L. Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning. Front Neuroinform 2022; 15:777828. [PMID: 35126079 PMCID: PMC8811357 DOI: 10.3389/fninf.2021.777828] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 12/23/2021] [Indexed: 11/21/2022] Open
Abstract
Cerebral microbleeds (CMBs) appear as small, circular, well defined hypointense lesions of a few mm in size on T2*-weighted gradient recalled echo (T2*-GRE) images and appear enhanced on susceptibility weighted images (SWI). Due to their small size, contrast variations and other mimics (e.g., blood vessels), CMBs are highly challenging to detect automatically. In large datasets (e.g., the UK Biobank dataset), exhaustively labelling CMBs manually is difficult and time consuming. Hence it would be useful to preselect candidate CMB subjects in order to focus on those for manual labelling, which is essential for training and testing automated CMB detection tools on these datasets. In this work, we aim to detect CMB candidate subjects from a larger dataset, UK Biobank, using a machine learning-based, computationally light pipeline. For our evaluation, we used 3 different datasets, with different intensity characteristics, acquired with different scanners. They include the UK Biobank dataset and two clinical datasets with different pathological conditions. We developed and evaluated our pipelines on different types of images, consisting of SWI or GRE images. We also used the UK Biobank dataset to compare our approach with alternative CMB preselection methods using non-imaging factors and/or imaging data. Finally, we evaluated the pipeline's generalisability across datasets. Our method provided subject-level detection accuracy > 80% on all the datasets (within-dataset results), and showed good generalisability across datasets, providing a consistent accuracy of over 80%, even when evaluated across different modalities.
Collapse
Affiliation(s)
- Vaanathi Sundaresan
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- Oxford-Nottingham Centre for Doctoral Training in Biomedical Imaging, University of Oxford, Oxford, United Kingdom
| | - Christoph Arthofer
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - Giovanna Zamboni
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- Wolfson Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Dipartimento di Scienze Biomediche, Metaboliche e Neuroscienze, Università di Modena e Reggio Emilia, Modena, Italy
| | - Robert A. Dineen
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
- Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Peter M. Rothwell
- Wolfson Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Stamatios N. Sotiropoulos
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - Dorothee P. Auer
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
- Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Daniel J. Tozer
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Hugh S. Markus
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Karla L. Miller
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
| | - Iulius Dragonu
- Siemens Healthcare Ltd., Research and Collaborations GB & I, Frimley, United Kingdom
| | - Nikola Sprigg
- Stroke Trials Unit, Mental Health and Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom
| | - Fidel Alfaro-Almagro
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
| | - Mark Jenkinson
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia
- Australian Institute for Machine Learning (AIML), School of Computer Science, The University of Adelaide, Adelaide, SA, Australia
| | - Ludovica Griffanti
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
- *Correspondence: Ludovica Griffanti
| |
Collapse
|
34
|
Griffanti L, Gillis G, O'Donoghue MC, Blane J, Pretorius PM, Mitchell R, Aikin N, Lindsay K, Campbell J, Semple J, Alfaro-Almagro F, Smith SM, Miller KL, Martos L, Raymont V, Mackay CE. Adapting UK Biobank imaging for use in a routine memory clinic setting: The Oxford Brain Health Clinic. Neuroimage Clin 2022; 36:103273. [PMID: 36451375 PMCID: PMC9723313 DOI: 10.1016/j.nicl.2022.103273] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/24/2022] [Accepted: 11/20/2022] [Indexed: 11/23/2022]
Abstract
The Oxford Brain Health Clinic (BHC) is a joint clinical-research service that provides memory clinic patients and clinicians access to high-quality assessments not routinely available, including brain MRI aligned with the UK Biobank imaging study (UKB). In this work we present how we 1) adapted the UKB MRI acquisition protocol to be suitable for memory clinic patients, 2) modified the imaging analysis pipeline to extract measures that are in line with radiology reports and 3) explored the alignment of measures from BHC patients to the largest brain MRI study in the world (ultimately 100,000 participants). Adaptations of the UKB acquisition protocol for BHC patients include dividing the scan into core and optional sequences (i.e., additional imaging modalities) to improve patients' tolerance for the MRI assessment. We adapted the UKB structural MRI analysis pipeline to take into account the characteristics of a memory clinic population (e.g., high amount of white matter hyperintensities and hippocampal atrophy). We then compared the imaging derived phenotypes (IDPs) extracted from the structural scans to visual ratings from radiology reports, non-imaging factors (age, cognition) and to reference distributions derived from UKB data. Of the first 108 BHC attendees (August 2020-November 2021), 92.5 % completed the clinical scans, 88.0 % consented to use of data for research, and 43.5 % completed the additional research sequences, demonstrating that the protocol is well tolerated. The high rates of consent to research makes this a valuable real-world quality research dataset routinely captured in a clinical service. Modified tissue-type segmentation with lesion masking greatly improved grey matter volume estimation. CSF-masking marginally improved hippocampal segmentation. The IDPs were in line with radiology reports and showed significant associations with age and cognitive performance, in line with the literature. Due to the age difference between memory clinic patients of the BHC (age range 65-101 years, average 78.3 years) and UKB participants (44-82 years, average 64 years), additional scans on elderly healthy controls are needed to improve reference distributions. Current and future work aims to integrate automated quantitative measures in the radiology reports and evaluate their clinical utility.
Collapse
Affiliation(s)
- Ludovica Griffanti
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom; Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom.
| | - Grace Gillis
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - M Clare O'Donoghue
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Jasmine Blane
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Pieter M Pretorius
- Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; Oxford University Hospitals NHS Trust, Oxford, United Kingdom
| | | | - Nicola Aikin
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Karen Lindsay
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Jon Campbell
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Juliet Semple
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Fidel Alfaro-Almagro
- Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Stephen M Smith
- Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Karla L Miller
- Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| | - Lola Martos
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Vanessa Raymont
- Department of Psychiatry, University of Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Clare E Mackay
- Department of Psychiatry, University of Oxford, United Kingdom; Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom
| |
Collapse
|
35
|
Seaman AT, Steffen MJA, Van Tiem JM, Wardyn S, Santana X, Miller KL, Solimeo SL. Cultivating across "pockets of excellence": challenges to sustaining efforts to improve osteoporosis care. Osteoporos Int 2022; 33:139-147. [PMID: 34414462 DOI: 10.1007/s00198-021-06098-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 08/13/2021] [Indexed: 12/01/2022]
Abstract
UNLABELLED We conducted in-depth, semi-structured interviews with clinicians involved in bone health care to understand the challenges of implementing and sustaining bone health care interventions. Participants identified individual- and system-level challenges to care delivery, implementation, and sustainment. We discuss opportunities to address challenges through a commitment to relationship- and infrastructure-building support. PURPOSE Osteoporosis and fracture-related sequalae exact significant individual and societal costs; however, identification and treatment of at-risk patients are troublingly low, especially among men. The purpose of this study was to identify challenges to implementing and sustaining bone health care delivery interventions in the Veterans Health Administration. METHODS We conducted interviews with endocrinologists, pharmacists, primary care physicians, rheumatologists, and orthopedic surgeons involved in bone health care (n = 20). Interviews were audio-recorded and transcribed verbatim. To determine thematic domains, we engaged in an iterative, qualitative content analysis of the transcripts. RESULTS Participants reported multiple barriers to delivering bone health care and to sustaining the initiatives designed to address delivery challenges. Challenges of bone health care delivery existed at both the individual level-a lack of patient and clinician awareness and competing clinical demands-and the system level-multiple points of entry to bone health care, a dispersion of patient management, and guideline variability. To address the challenges, participants developed initiatives targeting the identification of at-risk patients, clinician education, increasing communication, and care coordination. Sustaining initiatives, however, was challenged by staff turnover and the inability to achieve and maintain priority status for bone health care. CONCLUSION The multiple, multi-level barriers to bone health care affect both care delivery processes and sustainment of initiatives to improve those processes. Barriers to care delivery, while tempered by intervention, are entangled and persist alongside sustainment challenges. These challenges require relationship- and infrastructure-building support.
Collapse
Affiliation(s)
- Aaron T Seaman
- VA Office of Rural Health Veterans Rural Health Resource Center- Iowa City (VRHRC-IC), Department of Veterans Affairs, Iowa City VA Health Care System, Iowa City, IA, USA.
- Center for Access and Delivery Research and Evaluation (CADRE), Department of Veterans Affairs, Iowa City VA Health Care System, Iowa City, IA, USA.
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 280-B MRF, USA.
| | - Melissa J A Steffen
- VA Office of Rural Health Veterans Rural Health Resource Center- Iowa City (VRHRC-IC), Department of Veterans Affairs, Iowa City VA Health Care System, Iowa City, IA, USA
- Center for Access and Delivery Research and Evaluation (CADRE), Department of Veterans Affairs, Iowa City VA Health Care System, Iowa City, IA, USA
- Primary Care Analytics Team Iowa City (PCAT-IC), Department of Veterans Affairs, Iowa City VA Health Care System, Iowa City, IA, USA
| | - Jennifer M Van Tiem
- VA Office of Rural Health Veterans Rural Health Resource Center- Iowa City (VRHRC-IC), Department of Veterans Affairs, Iowa City VA Health Care System, Iowa City, IA, USA
- Center for Access and Delivery Research and Evaluation (CADRE), Department of Veterans Affairs, Iowa City VA Health Care System, Iowa City, IA, USA
| | - Shylo Wardyn
- VA Office of Rural Health Veterans Rural Health Resource Center- Iowa City (VRHRC-IC), Department of Veterans Affairs, Iowa City VA Health Care System, Iowa City, IA, USA
- Center for Access and Delivery Research and Evaluation (CADRE), Department of Veterans Affairs, Iowa City VA Health Care System, Iowa City, IA, USA
| | - Xiomara Santana
- VA Office of Rural Health Veterans Rural Health Resource Center- Iowa City (VRHRC-IC), Department of Veterans Affairs, Iowa City VA Health Care System, Iowa City, IA, USA
- Center for Access and Delivery Research and Evaluation (CADRE), Department of Veterans Affairs, Iowa City VA Health Care System, Iowa City, IA, USA
| | - Karla L Miller
- VA Office of Rural Health Veterans Rural Health Resource Center-Salt Lake City (VRHRC-SLC), Salt Lake City, UT, USA
- Department of Internal Medicine, Rheumatology Section, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
- Division of Rheumatology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Samantha L Solimeo
- VA Office of Rural Health Veterans Rural Health Resource Center- Iowa City (VRHRC-IC), Department of Veterans Affairs, Iowa City VA Health Care System, Iowa City, IA, USA
- Center for Access and Delivery Research and Evaluation (CADRE), Department of Veterans Affairs, Iowa City VA Health Care System, Iowa City, IA, USA
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 280-B MRF, USA
- Primary Care Analytics Team Iowa City (PCAT-IC), Department of Veterans Affairs, Iowa City VA Health Care System, Iowa City, IA, USA
| |
Collapse
|
36
|
Tendler BC, Qi F, Foxley S, Pallebage-Gamarallage M, Menke RAL, Ansorge O, Hurley SA, Miller KL. A method to remove the influence of fixative concentration on postmortem T 2 maps using a kinetic tensor model. Hum Brain Mapp 2021; 42:5956-5972. [PMID: 34541735 PMCID: PMC8596944 DOI: 10.1002/hbm.25661] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 08/06/2021] [Accepted: 08/30/2021] [Indexed: 12/20/2022] Open
Abstract
Formalin fixation has been shown to substantially reduce T2 estimates, primarily driven by the presence of fixative in tissue. Prior to scanning, post‐mortem samples are often placed into a fluid that has more favourable imaging properties. This study investigates whether there is evidence for a change in T2 in regions close to the tissue surface due to fixative outflux into this surrounding fluid. Furthermore, we investigate whether a simulated spatial map of fixative concentration can be used as a confound regressor to reduce T2 inhomogeneity. To achieve this, T2 maps and diffusion tensor estimates were obtained in 14 whole, formalin‐fixed post‐mortem brains placed in Fluorinert approximately 48 hr prior to scanning. Seven brains were fixed with 10% formalin and seven brains were fixed with 10% neutral buffered formalin (NBF). Fixative outflux was modelled using a proposed kinetic tensor (KT) model, which incorporates voxelwise diffusion tensor estimates to account for diffusion anisotropy and tissue‐specific diffusion coefficients. Brains fixed with 10% NBF revealed a spatial T2 pattern consistent with modelled fixative outflux. Confound regression of fixative concentration reduced T2 inhomogeneity across both white and grey matter, with the greatest reduction attributed to the KT model versus simpler models of fixative outflux. No such effect was observed in brains fixed with 10% formalin. Correlations between the transverse relaxation rate R2 and ferritin/myelin proteolipid protein (PLP) histology lead to an increased similarity for the relationship between R2 and PLP for the two fixative types after KT correction.
Collapse
Affiliation(s)
- Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford
| | - Feng Qi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford
| | - Sean Foxley
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford.,Department of Radiology, University of Chicago, Chicago, Illinois, USA
| | | | - Ricarda A L Menke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford
| | - Olaf Ansorge
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Samuel A Hurley
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford.,Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford
| |
Collapse
|
37
|
Slator PJ, Palombo M, Miller KL, Westin C, Laun F, Kim D, Haldar JP, Benjamini D, Lemberskiy G, de Almeida Martins JP, Hutter J. Combined diffusion-relaxometry microstructure imaging: Current status and future prospects. Magn Reson Med 2021; 86:2987-3011. [PMID: 34411331 PMCID: PMC8568657 DOI: 10.1002/mrm.28963] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [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: 12/07/2020] [Revised: 06/25/2021] [Accepted: 07/20/2021] [Indexed: 12/15/2022]
Abstract
Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T 1 , T 2 , and T 2 ∗ . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.
Collapse
Affiliation(s)
- Paddy J. Slator
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Marco Palombo
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Carl‐Fredrik Westin
- Department of RadiologyBrigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA
| | - Frederik Laun
- Institute of RadiologyUniversity Hospital ErlangenFriedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Daeun Kim
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Dan Benjamini
- The Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBethesdaMDUSA
- The Center for Neuroscience and Regenerative MedicineUniformed Service University of the Health SciencesBethesdaMDUSA
| | | | - Joao P. de Almeida Martins
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
- Department of Radiology and Nuclear MedicineSt. Olav’s University HospitalTrondheimNorway
| | - Jana Hutter
- Centre for Biomedical EngineeringSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
- Centre for the Developing BrainSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
| |
Collapse
|
38
|
Bryant KL, Ardesch DJ, Roumazeilles L, Scholtens LH, Khrapitchev AA, Tendler BC, Wu W, Miller KL, Sallet J, van den Heuvel MP, Mars RB. Diffusion MRI data, sulcal anatomy, and tractography for eight species from the Primate Brain Bank. Brain Struct Funct 2021; 226:2497-2509. [PMID: 34264391 PMCID: PMC8608778 DOI: 10.1007/s00429-021-02268-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [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: 01/27/2021] [Accepted: 03/26/2021] [Indexed: 12/16/2022]
Abstract
Large-scale comparative neuroscience requires data from many species and, ideally, at multiple levels of description. Here, we contribute to this endeavor by presenting diffusion and structural MRI data from eight primate species that have not or rarely been described in the literature. The selected samples from the Primate Brain Bank cover a prosimian, New and Old World monkeys, and a great ape. We present preliminary labelling of the cortical sulci and tractography of the optic radiation, dorsal part of the cingulum bundle, and dorsal parietal-frontal and ventral temporal-frontal longitudinal white matter tracts. Both dorsal and ventral association fiber systems could be observed in all samples, with the dorsal tracts occupying much less relative volume in the prosimian than in other species. We discuss the results in the context of known primate specializations and present hypotheses for further research. All data and results presented here are available online as a resource for the scientific community.
Collapse
Affiliation(s)
- Katherine L Bryant
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU, UK
| | - Dirk Jan Ardesch
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lea Roumazeilles
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Lianne H Scholtens
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Alexandre A Khrapitchev
- Department of Oncology, University of Oxford, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, Oxford, UK
| | - Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU, UK
| | - Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU, UK
| | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500, Bron, France
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU, UK.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands.
| |
Collapse
|
39
|
Griffanti L, Raman B, Alfaro-Almagro F, Filippini N, Cassar MP, Sheerin F, Okell TW, Kennedy McConnell FA, Chappell MA, Wang C, Arthofer C, Lange FJ, Andersson J, Mackay CE, Tunnicliffe EM, Rowland M, Neubauer S, Miller KL, Jezzard P, Smith SM. Adapting the UK Biobank Brain Imaging Protocol and Analysis Pipeline for the C-MORE Multi-Organ Study of COVID-19 Survivors. Front Neurol 2021; 12:753284. [PMID: 34777224 PMCID: PMC8586081 DOI: 10.3389/fneur.2021.753284] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 10/06/2021] [Indexed: 01/08/2023] Open
Abstract
SARS-CoV-2 infection has been shown to damage multiple organs, including the brain. Multiorgan MRI can provide further insight on the repercussions of COVID-19 on organ health but requires a balance between richness and quality of data acquisition and total scan duration. We adapted the UK Biobank brain MRI protocol to produce high-quality images while being suitable as part of a post-COVID-19 multiorgan MRI exam. The analysis pipeline, also adapted from UK Biobank, includes new imaging-derived phenotypes (IDPs) designed to assess the possible effects of COVID-19. A first application of the protocol and pipeline was performed in 51 COVID-19 patients post-hospital discharge and 25 controls participating in the Oxford C-MORE study. The protocol acquires high resolution T1, T2-FLAIR, diffusion weighted images, susceptibility weighted images, and arterial spin labelling data in 17 min. The automated imaging pipeline derives 1,575 IDPs, assessing brain anatomy (including olfactory bulb volume and intensity) and tissue perfusion, hyperintensities, diffusivity, and susceptibility. In the C-MORE data, IDPs related to atrophy, small vessel disease and olfactory bulbs were consistent with clinical radiology reports. Our exploratory analysis tentatively revealed some group differences between recovered COVID-19 patients and controls, across severity groups, but not across anosmia groups. Follow-up imaging in the C-MORE study is currently ongoing, and this protocol is now being used in other large-scale studies. The protocol, pipeline code and data are openly available and will further contribute to the understanding of the medium to long-term effects of COVID-19.
Collapse
Affiliation(s)
- Ludovica Griffanti
- Department of Psychiatry, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Betty Raman
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford Biomedical Research Centre (BRC) National Institute for Health Research (NIHR), University of Oxford, Oxford, United Kingdom
- Radcliffe Department of Medicine, British Heart Foundation Centre for Research Excellence, University of Oxford, Oxford, United Kingdom
| | - Fidel Alfaro-Almagro
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Nicola Filippini
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Camillo Hospital, Venice, Italy
| | - Mark Philip Cassar
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford Biomedical Research Centre (BRC) National Institute for Health Research (NIHR), University of Oxford, Oxford, United Kingdom
| | - Fintan Sheerin
- Department of Radiology, Oxford University Hospitals National Health Service (NHS) Foundation Trust, Oxford, United Kingdom
| | - Thomas W. Okell
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Flora A. Kennedy McConnell
- Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Nottingham Biomedical Research Centre, Queens Medical Centre, University of Nottingham, Nottingham, United Kingdom
| | - Michael A. Chappell
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
- Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Nottingham Biomedical Research Centre, Queens Medical Centre, University of Nottingham, Nottingham, United Kingdom
| | - Chaoyue Wang
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Christoph Arthofer
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Frederik J. Lange
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Jesper Andersson
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Clare E. Mackay
- Department of Psychiatry, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Elizabeth M. Tunnicliffe
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford Biomedical Research Centre (BRC) National Institute for Health Research (NIHR), University of Oxford, Oxford, United Kingdom
| | - Matthew Rowland
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford Biomedical Research Centre (BRC) National Institute for Health Research (NIHR), University of Oxford, Oxford, United Kingdom
- Radcliffe Department of Medicine, British Heart Foundation Centre for Research Excellence, University of Oxford, Oxford, United Kingdom
| | - Karla L. Miller
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Peter Jezzard
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| | - Stephen M. Smith
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, United Kingdom
| |
Collapse
|
40
|
Nathanson MH, Harrop-Griffiths W, Aldington DJ, Forward D, Mannion S, Kinnear-Mellor RGM, Miller KL, Ratnayake B, Wiles MD, Wolmarans MR. Regional analgesia for lower leg trauma and the risk of acute compartment syndrome: Guideline from the Association of Anaesthetists. Anaesthesia 2021; 76:1518-1525. [PMID: 34096035 PMCID: PMC9292897 DOI: 10.1111/anae.15504] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/16/2021] [Indexed: 11/30/2022]
Abstract
Pain resulting from lower leg injuries and consequent surgery can be severe. There is a range of opinion on the use of regional analgesia and its capacity to obscure the symptoms and signs of acute compartment syndrome. We offer a multi-professional, consensus opinion based on an objective review of case reports and case series. The available literature suggested that the use of neuraxial or peripheral regional techniques that result in dense blocks of long duration that significantly exceed the duration of surgery should be avoided. The literature review also suggested that single-shot or continuous peripheral nerve blocks using lower concentrations of local anaesthetic drugs without adjuncts are not associated with delays in diagnosis provided post-injury and postoperative surveillance is appropriate and effective. Post-injury and postoperative ward observations and surveillance should be able to identify the signs and symptoms of acute compartment syndrome. These observations should be made at set frequencies by healthcare staff trained in the pathology and recognition of acute compartment syndrome. The use of objective scoring charts is recommended by the Working Party. Where possible, patients at risk of acute compartment syndrome should be given a full explanation of the choice of analgesic techniques and should provide verbal consent to their chosen technique, which should be documented. Although the patient has the right to refuse any form of treatment, such as the analgesic technique offered or the surgical procedure proposed, neither the surgeon nor the anaesthetist has the right to veto a treatment recommended by the other.
Collapse
Affiliation(s)
- M H Nathanson
- Department of Anaesthesia, Nottingham University Hospitals NHS Trust, President, Association of Anaesthetists (Co-Chair), Nottingham, UK
| | - W Harrop-Griffiths
- Imperial College, Vice President, Royal College of Anaesthetists (Co-Chair), London, UK
| | - D J Aldington
- Hampshire Hospitals NHS Trust, British Pain Society, Hampshire, UK
| | - D Forward
- Department of Trauma and Orthopaedic Surgery, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - S Mannion
- Department of Anaesthesiology, South Infirmary Victoria University Hospital, Irish Standing Committee, Association of Anaesthetists, Cork, Ireland
| | - R G M Kinnear-Mellor
- Department of Anaesthesia, Nottingham University Hospitals NHS Trust, Surgeon Commander, Royal Navy; Chair, Defence Medical Services Military Pain Special Interest Group, Nottingham, UK
| | - K L Miller
- Department of Paediatric Anaesthesia, Birmingham Women's and Children's NHS Foundation Trust, Trainee Committee, Association of Anaesthetists, Birmingham, UK
| | - B Ratnayake
- Department of Anaesthesia, Kingston Hospital NHS Trust, Immediate Past President, British Society of Orthopaedic Anaesthetists, Immediate Past President, British Society of Orthopaedic Anaesthetists, Kingston-upon-Thames, UK
| | - M D Wiles
- Department of Anaesthesia and Operating Services, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - M R Wolmarans
- Department of Anaesthesia, Norfolk and Norwich University Hospital NHS Trust, Past-President, Regional Anaesthesia UK (RA-UK), Norwich, UK
| |
Collapse
|
41
|
Miller KL, Steffen MJ, McCoy KD, Cannon G, Seaman AT, Anderson ZL, Patel S, Green J, Wardyn S, Solimeo SL. Delivering fracture prevention services to rural US veterans through telemedicine: a process evaluation. Arch Osteoporos 2021; 16:27. [PMID: 33566174 PMCID: PMC7875846 DOI: 10.1007/s11657-021-00882-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 01/04/2021] [Indexed: 02/03/2023]
Abstract
An informatics-driven population bone health clinic was implemented to identify, screen, and treat rural US Veterans at risk for osteoporosis. We report the results of our implementation process evaluation which demonstrated BHT to be a feasible telehealth model for delivering preventative osteoporosis services in this setting. PURPOSE An established and growing quality gap in osteoporosis evaluation and treatment of at-risk patients has yet to be met with corresponding clinical care models addressing osteoporosis primary prevention. The rural bone health tea m (BHT) was implemented to identify, screen, and treat rural Veterans lacking evidence of bone health care and we conducted a process evaluation to understand BHT implementation feasibility. METHODS For this evaluation, we defined the primary outcome as the number of Veterans evaluated with DXA and a secondary outcome as the number of Veterans who initiated prescription therapy to reduce fracture risk. Outcomes were measured over a 15-month period and analyzed descriptively. Qualitative data to understand successful implementation were collected concurrently by conducting interviews with clinical personnel interacting with BHT and BHT staff and observations of BHT implementation processes at three site visits using the Promoting Action on Research Implementation in Health Services (PARIHS) framework. RESULTS Of 4500 at-risk, rural Veterans offered osteoporosis screening, 1081 (24%) completed screening, and of these, 37% had normal bone density, 48% osteopenia, and 15% osteoporosis. Among Veterans with pharmacotherapy indications, 90% initiated therapy. Qualitative analyses identified barriers of rural geography, rural population characteristics, and the infrastructural resource requirement. Data infrastructure, evidence base for care delivery, stakeholder buy-in, formal and informal facilitator engagement, and focus on teamwork were identified as facilitators of implementation success. CONCLUSION The BHT is a feasible population telehealth model for delivering preventative osteoporosis care to rural Veterans.
Collapse
Affiliation(s)
- Karla L. Miller
- VA Office of Rural Health, Veterans Rural Health Resource Center-Salt Lake City (VRHRC-SLC), Salt Lake City, UT USA
- Department of Internal Medicine, Rheumatology Section, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT USA
- Division of Rheumatology, University of Utah School of Medicine, Salt Lake City, UT USA
| | - Melissa J. Steffen
- VA Office of Rural Health, Veterans Rural Health Resource Center-Salt Lake City (VRHRC-SLC), Salt Lake City, UT USA
- VA Office of Rural Health, Veterans Rural Health Resource Center-Iowa City (VRHRC-IC), Salt Lake City, UT USA
- Comprehensive Access & Delivery Research and Evaluation (CADRE), Primary Care Analytics Team Iowa City (PCAT-IC), Department of Veterans Affairs, CADRE, Iowa City VA HCS, Research 152, 601 Highway 6 West, Iowa City, IA 52246 USA
| | - Kimberly D. McCoy
- VA Office of Rural Health, Veterans Rural Health Resource Center-Iowa City (VRHRC-IC), Salt Lake City, UT USA
- Comprehensive Access & Delivery Research and Evaluation (CADRE), Primary Care Analytics Team Iowa City (PCAT-IC), Department of Veterans Affairs, CADRE, Iowa City VA HCS, Research 152, 601 Highway 6 West, Iowa City, IA 52246 USA
| | - Grant Cannon
- Department of Internal Medicine, Rheumatology Section, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT USA
| | - Aaron T. Seaman
- VA Office of Rural Health, Veterans Rural Health Resource Center-Iowa City (VRHRC-IC), Salt Lake City, UT USA
- Division of Genera l Internal Medicine, Department of Internal Medicine, University of Iowa Carver College of Medicine, 200 Hawkins Drive, 52242 Iowa City, IA USA
| | - Zachary L. Anderson
- VA Office of Rural Health, Veterans Rural Health Resource Center-Salt Lake City (VRHRC-SLC), Salt Lake City, UT USA
- Department of Anesthesiology, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT USA
| | - Shardool Patel
- VA Office of Rural Health, Veterans Rural Health Resource Center-Salt Lake City (VRHRC-SLC), Salt Lake City, UT USA
- Department of Anesthesiology, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT USA
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT USA
| | - Janiel Green
- VA Office of Rural Health, Veterans Rural Health Resource Center-Salt Lake City (VRHRC-SLC), Salt Lake City, UT USA
- Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT USA
| | - Shylo Wardyn
- VA Office of Rural Health, Veterans Rural Health Resource Center-Iowa City (VRHRC-IC), Salt Lake City, UT USA
| | - Samantha L. Solimeo
- VA Office of Rural Health, Veterans Rural Health Resource Center-Iowa City (VRHRC-IC), Salt Lake City, UT USA
- Comprehensive Access & Delivery Research and Evaluation (CADRE), Primary Care Analytics Team Iowa City (PCAT-IC), Department of Veterans Affairs, CADRE, Iowa City VA HCS, Research 152, 601 Highway 6 West, Iowa City, IA 52246 USA
- Division of Genera l Internal Medicine, Department of Internal Medicine, University of Iowa Carver College of Medicine, 200 Hawkins Drive, 52242 Iowa City, IA USA
| |
Collapse
|
42
|
Miller KL, Steffen MJ, McCoy KD, Cannon G, Seaman AT, Anderson ZL, Patel S, Green J, Wardyn S, Solimeo SL. Correction to: Delivering fracture prevention services to rural US veterans through telemedicine: a process evaluation. Arch Osteoporos 2021; 16:61. [PMID: 33825995 PMCID: PMC8182872 DOI: 10.1007/s11657-021-00916-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Karla L. Miller
- VA Office of Rural Health, Veterans Rural Health Resource Center-Salt Lake City (VRHRC-SLC), Salt Lake City, UT USA ,Department of Internal Medicine, Rheumatology Section, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT USA ,grid.223827.e0000 0001 2193 0096Division of Rheumatology, University of Utah School of Medicine, Salt Lake City, UT USA
| | - Melissa J. Steffen
- VA Office of Rural Health, Veterans Rural Health Resource Center-Salt Lake City (VRHRC-SLC), Salt Lake City, UT USA ,VA Office of Rural Health, Veterans Rural Health Resource Center-Iowa City (VRHRC-IC), Salt Lake City, UT USA ,Comprehensive Access & Delivery Research and Evaluation (CADRE), Primary Care Analytics Team Iowa City (PCAT-IC), Department of Veterans Affairs, CADRE, Iowa City VA HCS, Research 152, 601 Highway 6 West, Iowa City, IA 52246 USA
| | - Kimberly D. McCoy
- VA Office of Rural Health, Veterans Rural Health Resource Center-Iowa City (VRHRC-IC), Salt Lake City, UT USA ,Comprehensive Access & Delivery Research and Evaluation (CADRE), Primary Care Analytics Team Iowa City (PCAT-IC), Department of Veterans Affairs, CADRE, Iowa City VA HCS, Research 152, 601 Highway 6 West, Iowa City, IA 52246 USA
| | - Grant Cannon
- Department of Internal Medicine, Rheumatology Section, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT USA
| | - Aaron T. Seaman
- VA Office of Rural Health, Veterans Rural Health Resource Center-Iowa City (VRHRC-IC), Salt Lake City, UT USA ,grid.214572.70000 0004 1936 8294Division of Genera l Internal Medicine, Department of Internal Medicine, University of Iowa Carver College of Medicine, 200 Hawkins Drive, Iowa City, IA 52242 USA
| | - Zachary L. Anderson
- VA Office of Rural Health, Veterans Rural Health Resource Center-Salt Lake City (VRHRC-SLC), Salt Lake City, UT USA ,Department of Anesthesiology, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT USA
| | - Shardool Patel
- VA Office of Rural Health, Veterans Rural Health Resource Center-Salt Lake City (VRHRC-SLC), Salt Lake City, UT USA ,Department of Anesthesiology, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT USA ,grid.223827.e0000 0001 2193 0096Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT USA
| | - Janiel Green
- VA Office of Rural Health, Veterans Rural Health Resource Center-Salt Lake City (VRHRC-SLC), Salt Lake City, UT USA ,Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT USA
| | - Shylo Wardyn
- VA Office of Rural Health, Veterans Rural Health Resource Center-Iowa City (VRHRC-IC), Salt Lake City, UT USA
| | - Samantha L. Solimeo
- VA Office of Rural Health, Veterans Rural Health Resource Center-Iowa City (VRHRC-IC), Salt Lake City, UT USA ,Comprehensive Access & Delivery Research and Evaluation (CADRE), Primary Care Analytics Team Iowa City (PCAT-IC), Department of Veterans Affairs, CADRE, Iowa City VA HCS, Research 152, 601 Highway 6 West, Iowa City, IA 52246 USA ,grid.214572.70000 0004 1936 8294Division of Genera l Internal Medicine, Department of Internal Medicine, University of Iowa Carver College of Medicine, 200 Hawkins Drive, Iowa City, IA 52242 USA
| |
Collapse
|
43
|
Raman B, Cassar MP, Tunnicliffe EM, Filippini N, Griffanti L, Alfaro-Almagro F, Okell T, Sheerin F, Xie C, Mahmod M, Mózes FE, Lewandowski AJ, Ohuma EO, Holdsworth D, Lamlum H, Woodman MJ, Krasopoulos C, Mills R, McConnell FAK, Wang C, Arthofer C, Lange FJ, Andersson J, Jenkinson M, Antoniades C, Channon KM, Shanmuganathan M, Ferreira VM, Piechnik SK, Klenerman P, Brightling C, Talbot NP, Petousi N, Rahman NM, Ho LP, Saunders K, Geddes JR, Harrison PJ, Pattinson K, Rowland MJ, Angus BJ, Gleeson F, Pavlides M, Koychev I, Miller KL, Mackay C, Jezzard P, Smith SM, Neubauer S. Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital discharge. EClinicalMedicine 2021; 31:100683. [PMID: 33490928 PMCID: PMC7808914 DOI: 10.1016/j.eclinm.2020.100683] [Citation(s) in RCA: 342] [Impact Index Per Article: 114.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 11/22/2020] [Accepted: 11/26/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The medium-term effects of Coronavirus disease (COVID-19) on organ health, exercise capacity, cognition, quality of life and mental health are poorly understood. METHODS Fifty-eight COVID-19 patients post-hospital discharge and 30 age, sex, body mass index comorbidity-matched controls were enrolled for multiorgan (brain, lungs, heart, liver and kidneys) magnetic resonance imaging (MRI), spirometry, six-minute walk test, cardiopulmonary exercise test (CPET), quality of life, cognitive and mental health assessments. FINDINGS At 2-3 months from disease-onset, 64% of patients experienced breathlessness and 55% reported fatigue. On MRI, abnormalities were seen in lungs (60%), heart (26%), liver (10%) and kidneys (29%). Patients exhibited changes in the thalamus, posterior thalamic radiations and sagittal stratum on brain MRI and demonstrated impaired cognitive performance, specifically in the executive and visuospatial domains. Exercise tolerance (maximal oxygen consumption and ventilatory efficiency on CPET) and six-minute walk distance were significantly reduced. The extent of extra-pulmonary MRI abnormalities and exercise intolerance correlated with serum markers of inflammation and acute illness severity. Patients had a higher burden of self-reported symptoms of depression and experienced significant impairment in all domains of quality of life compared to controls (p<0.0001 to 0.044). INTERPRETATION A significant proportion of patients discharged from hospital reported symptoms of breathlessness, fatigue, depression and had limited exercise capacity. Persistent lung and extra-pulmonary organ MRI findings are common in patients and linked to inflammation and severity of acute illness. FUNDING NIHR Oxford and Oxford Health Biomedical Research Centres, British Heart Foundation Centre for Research Excellence, UKRI, Wellcome Trust, British Heart Foundation.
Collapse
Affiliation(s)
- Betty Raman
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
- Radcliffe Department of Medicine, British Heart Foundation Centre for Research Excellence, University of Oxford, Oxford, United Kingdom
| | - Mark Philip Cassar
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Elizabeth M. Tunnicliffe
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Nicola Filippini
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Thomas Okell
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Fintan Sheerin
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Cheng Xie
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Masliza Mahmod
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Ferenc E. Mózes
- Oxford Centre for Clinical Magnetic Resonance Research, Radcliffe Department of Medicine, John Radcliffe Hospital Oxford, University of Oxford, Oxford, United Kingdom
| | - Adam J. Lewandowski
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Eric O. Ohuma
- Maternal, Adolescent, Reproductive & Child Health (MARCH) Centre, London School of Hygiene & Tropical Medicine (LSHTM), London, United Kingdom
| | - David Holdsworth
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | - Hanan Lamlum
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Myles J. Woodman
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Catherine Krasopoulos
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Rebecca Mills
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Flora A. Kennedy McConnell
- Division of Clinical Neuroscience, Faculty of Medicine & Health Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Christoph Arthofer
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Frederik J. Lange
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jesper Andersson
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Charalambos Antoniades
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
- Radcliffe Department of Medicine, British Heart Foundation Centre for Research Excellence, University of Oxford, Oxford, United Kingdom
| | - Keith M. Channon
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
- Radcliffe Department of Medicine, British Heart Foundation Centre for Research Excellence, University of Oxford, Oxford, United Kingdom
| | - Mayooran Shanmuganathan
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Vanessa M. Ferreira
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Stefan K. Piechnik
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
| | - Paul Klenerman
- Nuffield Department of Medicine, NIHR Oxford BRC, University of Oxford, Oxford, United Kingdom
| | - Christopher Brightling
- Institute for Lung Health, Department of Respiratory Sciences, NIHR Leicester BRC, University of Leicester, Leicester, United Kingdom
| | - Nick P. Talbot
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
| | - Nayia Petousi
- Nuffield Department of Medicine, NIHR Oxford BRC, University of Oxford, Oxford, United Kingdom
| | - Najib M. Rahman
- Nuffield Department of Medicine, NIHR Oxford BRC, University of Oxford, Oxford, United Kingdom
| | - Ling-Pei Ho
- Weatherall Institute of Molecular Medicine, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Kate Saunders
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - John R. Geddes
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Paul J. Harrison
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Kyle Pattinson
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
| | - Matthew J. Rowland
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
| | - Brian J. Angus
- Experimental Medicine, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Fergus Gleeson
- Department of Oncology, Medical Science Department, University of Oxford, Oxford, United Kingdom
| | - Michael Pavlides
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
- Translational Gastroenterology Unit, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Ivan Koychev
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Clare Mackay
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
| | - Peter Jezzard
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), University of Oxford, Oxford, United Kingdom
- Radcliffe Department of Medicine, British Heart Foundation Centre for Research Excellence, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
44
|
Warnert EAH, Kasper L, Meltzer CC, Lightfoote JB, Bucknor MD, Haroon H, Duggan G, Gowland P, Wald L, Miller KL, Morris EA, Anazodo UC. Resonate: Reaching Excellence Through Equity, Diversity, and Inclusion in ISMRM. J Magn Reson Imaging 2020; 53:1608-1611. [PMID: 33350020 DOI: 10.1002/jmri.27476] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 12/01/2020] [Indexed: 11/10/2022] Open
Affiliation(s)
- Esther A H Warnert
- Department of Radiology & Nuclear Medicine, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Lars Kasper
- Techna Institute, University Health Network, Toronto, Ontario, Canada.,Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland.,Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Carolyn C Meltzer
- Departments of Radiology and Imaging Sciences, Neurology, and Psychiatry and Behavioral Science, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Johnson B Lightfoote
- Chair, Commission for Women and Diversity, American College of Radiology, Reston, Virginia, USA.,Pomona Valley Hospital Medical Center, Pomona, California, USA
| | - Matthew D Bucknor
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Hamied Haroon
- Division of Neuroscience & Experimental Psychology, The University of Manchester, Manchester, UK
| | | | - Penny Gowland
- School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Larry Wald
- A.A. Martinos Center, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Karla L Miller
- Nuffield Department of Clinical Neurosciences University of Oxford, Wellcome Centre for Integrative Neuroimaging, FMRIB, Oxford, UK
| | - Elizabeth A Morris
- Department of Radiology, Memorial Sloan Kettering Cancer Center New York, New York, New York, USA
| | - Udunna C Anazodo
- Department of Medical Biophysics, Western University, London, Ontario, Canada.,Lawson Health Research Institute, St Joseph's Health Care, London, Ontario, Canada
| |
Collapse
|
45
|
Wang C, Foxley S, Ansorge O, Bangerter-Christensen S, Chiew M, Leonte A, Menke RA, Mollink J, Pallebage-Gamarallage M, Turner MR, Miller KL, Tendler BC. Methods for quantitative susceptibility and R2* mapping in whole post-mortem brains at 7T applied to amyotrophic lateral sclerosis. Neuroimage 2020; 222:117216. [PMID: 32745677 PMCID: PMC7775972 DOI: 10.1016/j.neuroimage.2020.117216] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [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: 05/01/2020] [Revised: 07/03/2020] [Accepted: 07/27/2020] [Indexed: 12/12/2022] Open
Abstract
Susceptibility weighted magnetic resonance imaging (MRI) is sensitive to the local concentration of iron and myelin. Here, we describe a robust image processing pipeline for quantitative susceptibility mapping (QSM) and R2* mapping of fixed post-mortem, whole-brain data. Using this pipeline, we compare the resulting quantitative maps in brains from patients with amyotrophic lateral sclerosis (ALS) and controls, with validation against iron and myelin histology. Twelve post-mortem brains were scanned with a multi-echo gradient echo sequence at 7T, from which susceptibility and R2* maps were generated. Semi-quantitative histological analysis for ferritin (the principal iron storage protein) and myelin proteolipid protein was performed in the primary motor, anterior cingulate and visual cortices. Magnetic susceptibility and R2* values in primary motor cortex were higher in ALS compared to control brains. Magnetic susceptibility and R2* showed positive correlations with both myelin and ferritin estimates from histology. Four out of nine ALS brains exhibited clearly visible hyperintense susceptibility and R2* values in the primary motor cortex. Our results demonstrate the potential for MRI-histology studies in whole, fixed post-mortem brains to investigate the biophysical source of susceptibility weighted MRI signals in neurodegenerative diseases like ALS.
Collapse
Affiliation(s)
- Chaoyue Wang
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, United Kingdom.
| | - Sean Foxley
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, United Kingdom; Department of Radiology, University of Chicago, United States
| | - Olaf Ansorge
- Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - Sarah Bangerter-Christensen
- Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; Brigham Young University, Provo, United States
| | - Mark Chiew
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, United Kingdom
| | - Anna Leonte
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, United Kingdom; University of Groningen,the Netherlands
| | - Ricarda Al Menke
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, United Kingdom
| | - Jeroen Mollink
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, United Kingdom; Department of Anatomy, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, the Netherlands
| | | | - Martin R Turner
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, United Kingdom; Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - Karla L Miller
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, United Kingdom
| | - Benjamin C Tendler
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, United Kingdom
| |
Collapse
|
46
|
Tendler BC, Foxley S, Hernandez-Fernandez M, Cottaar M, Scott C, Ansorge O, Miller KL, Jbabdi S. Use of multi-flip angle measurements to account for transmit inhomogeneity and non-Gaussian diffusion in DW-SSFP. Neuroimage 2020; 220:117113. [PMID: 32621975 PMCID: PMC7573656 DOI: 10.1016/j.neuroimage.2020.117113] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 11/29/2019] [Revised: 06/25/2020] [Accepted: 06/27/2020] [Indexed: 11/06/2022] Open
Abstract
Diffusion-weighted steady-state free precession (DW-SSFP) is an SNR-efficient diffusion imaging method. The improved SNR and resolution available at ultra-high field has motivated its use at 7T. However, these data tend to have severe B1 inhomogeneity, leading not only to spatially varying SNR, but also to spatially varying diffusivity estimates, confounding comparisons both between and within datasets. This study proposes the acquisition of DW-SSFP data at two-flip angles in combination with explicit modelling of non-Gaussian diffusion to address B1 inhomogeneity at 7T. Data were acquired from five fixed whole human post-mortem brains with a pair of flip angles that jointly optimize the diffusion contrast-to-noise (CNR) across the brain. We compared one- and two-flip angle DW-SSFP data using a tensor model that incorporates the full DW-SSFP Buxton signal, in addition to tractography performed over the cingulum bundle and pre-frontal cortex using a ball & sticks model. The two-flip angle DW-SSFP data produced angular uncertainty and tractography estimates close to the CNR optimal regions in the single-flip angle datasets. The two-flip angle tensor estimates were subsequently fitted using a modified DW-SSFP signal model that incorporates a gamma distribution of diffusivities. This allowed us to generate tensor maps at a single effective b-value yielding more consistent SNR across tissue, in addition to eliminating the B1 dependence on diffusion coefficients and orientation maps. Our proposed approach will allow the use of DW-SSFP at 7T to derive diffusivity estimates that have greater interpretability, both within a single dataset and between experiments.
Collapse
Affiliation(s)
- Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Sean Foxley
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | | | - Michiel Cottaar
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Connor Scott
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Olaf Ansorge
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| |
Collapse
|
47
|
Tendler BC, Foxley S, Cottaar M, Jbabdi S, Miller KL. Modeling an equivalent b-value in diffusion-weighted steady-state free precession. Magn Reson Med 2020; 84:873-884. [PMID: 31922283 PMCID: PMC7216928 DOI: 10.1002/mrm.28169] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [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: 11/11/2019] [Revised: 12/13/2019] [Accepted: 12/18/2019] [Indexed: 11/26/2022]
Abstract
PURPOSE Diffusion-weighted steady-state free precession (DW-SSFP) is shown to provide a means to probe non-Gaussian diffusion through manipulation of the flip angle. A framework is presented to define an effective b-value in DW-SSFP. THEORY The DW-SSFP signal is a summation of coherence pathways with different b-values. The relative contribution of each pathway is dictated by the flip angle. This leads to an apparent diffusion coefficient (ADC) estimate that depends on the flip angle in non-Gaussian diffusion regimes. By acquiring DW-SSFP data at multiple flip angles and modeling the variation in ADC for a given form of non-Gaussianity, the ADC can be estimated at a well-defined effective b-value. METHODS A gamma distribution is used to model non-Gaussian diffusion, embedded in the Buxton signal model for DW-SSFP. Monte-Carlo simulations of non-Gaussian diffusion in DW-SSFP and diffusion-weighted spin-echo sequences are used to verify the proposed framework. Dependence of ADC on flip angle in DW-SSFP is verified with experimental measurements in a whole, human postmortem brain. RESULTS Monte-Carlo simulations reveal excellent agreement between ADCs estimated with diffusion-weighted spin-echo and the proposed framework. Experimental ADC estimates vary as a function of flip angle over the corpus callosum of the postmortem brain, estimating the mean and standard deviation of the gamma distribution as 1.50 · 10 - 4 mm2 /s and 2.10 · 10 - 4 mm2 /s. CONCLUSION DW-SSFP can be used to investigate non-Gaussian diffusion by varying the flip angle. By fitting a model of non-Gaussian diffusion, the ADC in DW-SSFP can be estimated at an effective b-value, comparable to more conventional diffusion sequences.
Collapse
Affiliation(s)
- Benjamin C. Tendler
- Wellcome Centre for Integrative NeuroimagingFMRIBNuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - Sean Foxley
- Department of RadiologyUniversity of ChicagoChicagoIllinois
| | - Michiel Cottaar
- Wellcome Centre for Integrative NeuroimagingFMRIBNuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - Saad Jbabdi
- Wellcome Centre for Integrative NeuroimagingFMRIBNuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - Karla L. Miller
- Wellcome Centre for Integrative NeuroimagingFMRIBNuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| |
Collapse
|
48
|
Roumazeilles L, Eichert N, Bryant KL, Folloni D, Sallet J, Vijayakumar S, Foxley S, Tendler BC, Jbabdi S, Reveley C, Verhagen L, Dershowitz LB, Guthrie M, Flach E, Miller KL, Mars RB. Longitudinal connections and the organization of the temporal cortex in macaques, great apes, and humans. PLoS Biol 2020; 18:e3000810. [PMID: 32735557 PMCID: PMC7423156 DOI: 10.1371/journal.pbio.3000810] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [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: 11/22/2019] [Revised: 08/12/2020] [Accepted: 07/08/2020] [Indexed: 12/19/2022] Open
Abstract
The temporal association cortex is considered a primate specialization and is involved in complex behaviors, with some, such as language, particularly characteristic of humans. The emergence of these behaviors has been linked to major differences in temporal lobe white matter in humans compared with monkeys. It is unknown, however, how the organization of the temporal lobe differs across several anthropoid primates. Therefore, we systematically compared the organization of the major temporal lobe white matter tracts in the human, gorilla, and chimpanzee great apes and in the macaque monkey. We show that humans and great apes, in particular the chimpanzee, exhibit an expanded and more complex occipital-temporal white matter system; additionally, in humans, the invasion of dorsal tracts into the temporal lobe provides a further specialization. We demonstrate the reorganization of different tracts along the primate evolutionary tree, including distinctive connectivity of human temporal gray matter.
Collapse
Affiliation(s)
- Lea Roumazeilles
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Nicole Eichert
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Katherine L. Bryant
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Davide Folloni
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Suhas Vijayakumar
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Sean Foxley
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Benjamin C. Tendler
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Colin Reveley
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Lennart Verhagen
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Lori B. Dershowitz
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Martin Guthrie
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Edmund Flach
- Zoological Society of London, London, United Kingdom
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Rogier B. Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
| |
Collapse
|
49
|
Alfaro-Almagro F, McCarthy P, Afyouni S, Andersson JLR, Bastiani M, Miller KL, Nichols TE, Smith SM. Confound modelling in UK Biobank brain imaging. Neuroimage 2020; 224:117002. [PMID: 32502668 PMCID: PMC7610719 DOI: 10.1016/j.neuroimage.2020.117002] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 05/08/2020] [Accepted: 05/25/2020] [Indexed: 01/19/2023] Open
Abstract
Dealing with confounds is an essential step in large cohort studies to address problems such as unexplained variance and spurious correlations. UK Biobank is a powerful resource for studying associations between imaging and non-imaging measures such as lifestyle factors and health outcomes, in part because of the large subject numbers. However, the resulting high statistical power also raises the sensitivity to confound effects, which therefore have to be carefully considered. In this work we describe a set of possible confounds (including nonlinear effects and interactions that researchers may wish to consider for their studies using such data). We include descriptions of how we can estimate the confounds, and study the extent to which each of these confounds affects the data, and the spurious correlations that may arise if they are not controlled. Finally, we discuss several issues that future studies should consider when dealing with confounds.
Collapse
Affiliation(s)
- Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
| | - Paul McCarthy
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | | | - Jesper L R Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; NIHR Biomedical Research Centre, University of Nottingham, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Thomas E Nichols
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Big Data Institute, University of Oxford, UK
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| |
Collapse
|
50
|
Littlejohns TJ, Holliday J, Gibson LM, Garratt S, Oesingmann N, Alfaro-Almagro F, Bell JD, Boultwood C, Collins R, Conroy MC, Crabtree N, Doherty N, Frangi AF, Harvey NC, Leeson P, Miller KL, Neubauer S, Petersen SE, Sellors J, Sheard S, Smith SM, Sudlow CLM, Matthews PM, Allen NE. The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat Commun 2020; 11:2624. [PMID: 32457287 PMCID: PMC7250878 DOI: 10.1038/s41467-020-15948-9] [Citation(s) in RCA: 236] [Impact Index Per Article: 59.0] [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: 03/18/2019] [Accepted: 04/03/2020] [Indexed: 01/18/2023] Open
Abstract
UK Biobank is a population-based cohort of half a million participants aged 40-69 years recruited between 2006 and 2010. In 2014, UK Biobank started the world's largest multi-modal imaging study, with the aim of re-inviting 100,000 participants to undergo brain, cardiac and abdominal magnetic resonance imaging, dual-energy X-ray absorptiometry and carotid ultrasound. The combination of large-scale multi-modal imaging with extensive phenotypic and genetic data offers an unprecedented resource for scientists to conduct health-related research. This article provides an in-depth overview of the imaging enhancement, including the data collected, how it is managed and processed, and future directions.
Collapse
Affiliation(s)
| | - Jo Holliday
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Lorna M Gibson
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
- Department of Clinical Radiology, New Royal Infirmary of Edinburgh, Edinburgh, UK
| | | | | | - Fidel Alfaro-Almagro
- Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, University of Westminster, London, UK
| | | | - Rory Collins
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Megan C Conroy
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nicola Crabtree
- Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | | | - Alejandro F Frangi
- Department of Cardiovascular Sciences and Electrical Engineering, KU Leuven, Leuven, Belgium
- CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine, Schools of Computing and Medicine, University of Leeds, Leeds, UK
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Paul Leeson
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Karla L Miller
- Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Stefan Neubauer
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Steffen E Petersen
- William Harvey Research Institute, Queen Mary University of Medicine, London, UK
| | - Jonathan Sellors
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- UK Biobank Coordinating Centre, Stockport, UK
| | | | - Stephen M Smith
- Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Cathie L M Sudlow
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Paul M Matthews
- Department of Brain Sciences, Imperial College London and UK Dementia Research Institute, London, UK
| | - Naomi E Allen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| |
Collapse
|