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Wong TT, Igbinoba Z, Tokaria R, Quarterman P, Fung M, Jaramillo D, Jambawalikar SR. UTE T2* cartilage mapping in the hip: a pilot study assessing cartilage in patients with femoroacetabular impingement. Acta Radiol 2024; 65:350-358. [PMID: 38130123 DOI: 10.1177/02841851231218252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
BACKGROUND UTE T2* cartilage mapping use in patients undergoing femoroacetabular impingement (FAI) has been lacking but may allow the detection of early cartilage damage. PURPOSE To assess the reproducibility of UTE T2* cartilage mapping and determine the difference in UTE T2* values between FAI and asymptomatic patients and to evaluate the correlation between UTE T2* values and patient-reported symptoms. MATERIAL AND METHODS Prospective evaluation of both hips (7 FAI and 7 asymptomatic patients). Bilateral hip 3-T MRI scans with UTE T2* cartilage maps were acquired. A second MRI scan was acquired 1-9 months later. Cartilage was segmented into anterosuperior, superior, and posterosuperior regions. Assessment was made of UTE T2* reproducibility (ICC). Mean UTE T2* values in patients were compared (t-tests) and correlation was made with patient-reported outcomes (Spearman's). RESULTS ICCs of mean UTE T2* were as follows: acetabular, 0.82 (95% CI=0.50-0.95); femoral, 0.76 (95% CI=0.35-0.92). Significant strong correlation was found between mean acetabular UTE T2* values and iHOT12 (ρ = -0.63) and moderate correlation with mHHS (ρ = -0.57). There was no difference in mean UTE T2* values between affected vs. non-affected FAI hips. FAI-affected hips had significantly higher values in acetabulum vs. asymptomatic patients (13.47 vs. 12.55 ms). There was no difference in mean femoral cartilage values between the FAI-affected hips vs. asymptomatic patients. The posterosuperior femoral region had a higher mean value in non-affected FAI hips vs. asymptomatic patients (12.60 vs. 11.53 ms). CONCLUSION UTE T2* cartilage mapping had excellent reproducibility. Affected FAI hips had higher mean acetabular UTE T2* values than asymptomatic patients. Severity of patient-reported symptoms correlates with UTE T2* acetabular cartilage values.
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Affiliation(s)
- Tony T Wong
- Department of Radiology, Division of Musculoskeletal Radiology, NewYork-Presbyterian Hospital - Columbia University Medical Center, New York, NY, USA
| | - Zenas Igbinoba
- Department of Radiology, Division of Musculoskeletal Radiology, NewYork-Presbyterian Hospital - Columbia University Medical Center, New York, NY, USA
| | - Rumana Tokaria
- Department of Radiology, Division of Musculoskeletal Radiology, NewYork-Presbyterian Hospital - Columbia University Medical Center, New York, NY, USA
| | | | - Maggie Fung
- General Electric (GE) Healthcare, New York, NY, USA
| | - Diego Jaramillo
- Department of Radiology, Division of Pediatric Radiology, NewYork-Presbyterian Hospital - Columbia University Medical Center, New York, NY, USA
| | - Sachin R Jambawalikar
- Department of Radiology, Division of Physics, NewYork-Presbyterian Hospital - Columbia University Medical Center, New York, NY, USA
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James SN, Manning EN, Storey M, Nicholas JM, Coath W, Keuss SE, Cash DM, Lane CA, Parker T, Keshavan A, Buchanan SM, Wagen A, Harris M, Malone I, Lu K, Needham LP, Street R, Thomas D, Dickson J, Murray-Smith H, Wong A, Freiberger T, Crutch SJ, Fox NC, Richards M, Barkhof F, Sudre CH, Barnes J, Schott JM. Neuroimaging, clinical and life course correlates of normal-appearing white matter integrity in 70-year-olds. Brain Commun 2023; 5:fcad225. [PMID: 37680671 PMCID: PMC10481255 DOI: 10.1093/braincomms/fcad225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 05/30/2023] [Accepted: 08/17/2023] [Indexed: 09/09/2023] Open
Abstract
We investigate associations between normal-appearing white matter microstructural integrity in cognitively normal ∼70-year-olds and concurrently measured brain health and cognition, demographics, genetics and life course cardiovascular health. Participants born in the same week in March 1946 (British 1946 birth cohort) underwent PET-MRI around age 70. Mean standardized normal-appearing white matter integrity metrics (fractional anisotropy, mean diffusivity, neurite density index and orientation dispersion index) were derived from diffusion MRI. Linear regression was used to test associations between normal-appearing white matter metrics and (i) concurrent measures, including whole brain volume, white matter hyperintensity volume, PET amyloid and cognition; (ii) the influence of demographic and genetic predictors, including sex, childhood cognition, education, socio-economic position and genetic risk for Alzheimer's disease (APOE-ɛ4); (iii) systolic and diastolic blood pressure and cardiovascular health (Framingham Heart Study Cardiovascular Risk Score) across adulthood. Sex interactions were tested. Statistical significance included false discovery rate correction (5%). Three hundred and sixty-two participants met inclusion criteria (mean age 70, 49% female). Higher white matter hyperintensity volume was associated with lower fractional anisotropy [b = -0.09 (95% confidence interval: -0.11, -0.06), P < 0.01], neurite density index [b = -0.17 (-0.22, -0.12), P < 0.01] and higher mean diffusivity [b = 0.14 (-0.10, -0.17), P < 0.01]; amyloid (in men) was associated with lower fractional anisotropy [b = -0.04 (-0.08, -0.01), P = 0.03)] and higher mean diffusivity [b = 0.06 (0.01, 0.11), P = 0.02]. Framingham Heart Study Cardiovascular Risk Score in later-life (age 69) was associated with normal-appearing white matter {lower fractional anisotropy [b = -0.06 (-0.09, -0.02) P < 0.01], neurite density index [b = -0.10 (-0.17, -0.03), P < 0.01] and higher mean diffusivity [b = 0.09 (0.04, 0.14), P < 0.01]}. Significant sex interactions (P < 0.05) emerged for midlife cardiovascular health (age 53) and normal-appearing white matter at 70: marginal effect plots demonstrated, in women only, normal-appearing white matter was associated with higher midlife Framingham Heart Study Cardiovascular Risk Score (lower fractional anisotropy and neurite density index), midlife systolic (lower fractional anisotropy, neurite density index and higher mean diffusivity) and diastolic (lower fractional anisotropy and neurite density index) blood pressure and greater blood pressure change between 43 and 53 years (lower fractional anisotropy and neurite density index), independently of white matter hyperintensity volume. In summary, poorer normal-appearing white matter microstructural integrity in ∼70-year-olds was associated with measures of cerebral small vessel disease, amyloid (in males) and later-life cardiovascular health, demonstrating how normal-appearing white matter can provide additional information to overt white matter disease. Our findings further show that greater 'midlife' cardiovascular risk and higher blood pressure were associated with poorer normal-appearing white matter microstructural integrity in females only, suggesting that women's brains may be more susceptible to the effects of midlife blood pressure and cardiovascular health.
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Affiliation(s)
- Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Emily N Manning
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Mathew Storey
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jennifer M Nicholas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah E Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Thomas Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ashvini Keshavan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah M Buchanan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Aaron Wagen
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Mathew Harris
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ian Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Kirsty Lu
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Louisa P Needham
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
| | - Rebecca Street
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - David Thomas
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - John Dickson
- Institute of Nuclear Medicine, University College London Hospitals Foundation Trust, London, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
| | - Tamar Freiberger
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sebastian J Crutch
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
| | - Frederik Barkhof
- Centre for Medical Image Computing, University College London, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
- School of Biomedical Engineering, King’s College, London, UK
| | - Josephine Barnes
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jonathan M Schott
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
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Marchand F, Moreau C, Kuchcinski G, Huin V, Defebvre L, Devos D. Conservative Iron Chelation for Neuroferritinopathy. Mov Disord 2022; 37:1948-1952. [PMID: 35996824 PMCID: PMC10360136 DOI: 10.1002/mds.29145] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 06/03/2022] [Accepted: 06/15/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Neuroferritinopathy is a rare inherited neurodegenerative disease with brain iron accumulation characterized by brain iron overload resulting in progressive movement disorders. No treatment is currently available. OBJECTIVE We assessed conservative iron chelation with deferiprone at 30 mg/kg/day on the disease progression with controlled periods of discontinuation. METHODS Four patients with confirmed molecular diagnosis of neuroferritinopathy were given deferiprone at different stages of disease progression and with clinical and biological monitoring to control benefit and risk. RESULTS The four patients showed slight to high improvement. In one case, we managed to stabilize disease progression for more than 11 years. In another case, we were able to reverse symptoms after a few months of treatment. The earliest the treatment was started, the most efficient it was on disease progression. CONCLUSIONS Conservative iron chelation should be further assessed in neuroferritinopathy. © 2022 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Felix Marchand
- Department of Neurology, Neurogenetic Center, Univ. Lille, CHU Lille, Lille Neurosciences and Cognition Inserm UMR-S-U1172, Lille, France.,Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neurosciences and Cognition, Lille, France
| | - Caroline Moreau
- Department of Neurology, Neurogenetic Center, Univ. Lille, CHU Lille, Lille Neurosciences and Cognition Inserm UMR-S-U1172, Lille, France.,Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neurosciences and Cognition, Lille, France
| | | | - Vincent Huin
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neurosciences and Cognition, Lille, France.,Department of Molecular Biology, CHU Lille, Lille, France
| | - Luc Defebvre
- Department of Neurology, Neurogenetic Center, Univ. Lille, CHU Lille, Lille Neurosciences and Cognition Inserm UMR-S-U1172, Lille, France.,Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neurosciences and Cognition, Lille, France
| | - David Devos
- Department of Neurology, Neurogenetic Center, Univ. Lille, CHU Lille, Lille Neurosciences and Cognition Inserm UMR-S-U1172, Lille, France.,Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neurosciences and Cognition, Lille, France.,Department of Medical Pharmacology, CHU Lille, Lille, France
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4
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Wong TT, Quarterman P, Lynch TS, Rasiej MJ, Jaramillo D, Jambawalikar SR. Feasibility of ultrashort echo time (UTE) T2* cartilage mapping in the hip: a pilot study. Acta Radiol 2022; 63:760-766. [PMID: 33926266 DOI: 10.1177/02841851211011563] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Ultrashort echo time (UTE) T2* is sensitive to molecular changes within the deep calcified layer of cartilage. Feasibility of its use in the hip needs to be established to determine suitability for clinical use. PURPOSE To establish feasibility of UTE T2* cartilage mapping in the hip and determine if differences in regional values exist. MATERIAL AND METHODS MRI scans with UTE T2* cartilage maps were prospectively acquired on eight hips. Hip cartilage was segmented into whole and deep layers in anterosuperior, superior, and posterosuperior regions. Quantitative UTE T2* maps were analyzed (independent one-way ANOVA) and reliability was calculated (ICC). RESULTS UTE T2* mean values (anterosuperior, superior, posterosuperior): full femoral layer (19.55, 18.43, 16.84 ms) (P=0.004), full acetabular layer (19.37, 17.50, 16.73 ms) (P=0.013), deep femoral layer (18.68, 17.90, 15.74 ms) (P=0.010), and deep acetabular layer (17.81, 16.18, 15.31 ms) (P=0.007). Values were higher in anterosuperior compared to posterosuperior regions (mean difference; 95% confidence interval [CI]): full femur layer (2.71 ms; 95% CI 0.91-4.51: P=0.003), deep femur layer (2.94 ms; 95% CI 0.69-5.19; P=0.009), full acetabular layer (2.63 ms 95% CI 0.55-4.72; P=0.012), and deep acetabular layer (2.50 ms; 95% CI 0.69-4.30; P=0.006). Intra-reader (ICC 0.89-0.99) and inter-reader reliability (ICC 0.63-0.96) were good to excellent for the majority of cartilage layers. CONCLUSION UTE T2* cartilage mapping was feasible in the hip with mean values in the range of 16.84-19.55 ms in the femur and 16.73-19.37 ms in the acetabulum. Significantly higher values were present in the anterosuperior region compared to the posterosuperior region.
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Affiliation(s)
- Tony T Wong
- Department of Radiology, Division of Musculoskeletal Radiology, New York Presbyterian Hospital – Columbia University Medical Center, New York, NY, USA
| | | | - Thomas S Lynch
- Department of Orthopedics, The Center for Shoulder, Elbow, and Sports Medicine, New York Presbyterian Hospital – Columbia University Medical Center, New York, NY, USA
| | - Michael J Rasiej
- Department of Radiology, Division of Musculoskeletal Radiology, New York Presbyterian Hospital – Columbia University Medical Center, New York, NY, USA
| | - Diego Jaramillo
- Department of Radiology, Division of Pediatric Radiology, New York Presbyterian Hospital – Columbia University Medical Center, New York, NY, USA
| | - Sachin R Jambawalikar
- Department of Radiology, Division of Physics, New York Presbyterian Hospital – Columbia University Medical Center, New York, NY, USA
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Alsaedi AF, Thomas DL, De Vita E, Panovska-Griffiths J, Bisdas S, Golay X. Repeatability of perfusion measurements in adult gliomas using pulsed and pseudo-continuous arterial spin labelling MRI. MAGMA (NEW YORK, N.Y.) 2022; 35:113-125. [PMID: 34817780 DOI: 10.1007/s10334-021-00975-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 10/30/2021] [Accepted: 11/05/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVES To investigate the repeatability of perfusion measures in gliomas using pulsed- and pseudo-continuous-arterial spin labelling (PASL, PCASL) techniques, and evaluate different regions-of-interest (ROIs) for relative tumour blood flow (rTBF) normalisation. MATERIALS AND METHODS Repeatability of cerebral blood flow (CBF) was measured in the Contralateral Normal Appearing Hemisphere (CNAH) and in brain tumours (aTBF). rTBF was normalised using both large/small ROIs from the CNAH. Repeatability was evaluated with intra-class-correlation-coefficient (ICC), Within-Coefficient-of-Variation (WCoV) and Coefficient-of-Repeatability (CR). RESULTS PASL and PCASL demonstrated high reliability (ICC > 0.9) for CNAH-CBF, aTBF and rTBF. PCASL demonstrated a more stable signal-to-noise ratio (SNR) with a lower WCoV of the SNR than that of PASL (10.9-42.5% vs. 12.3-29.2%). PASL and PCASL showed higher WCoV in aTBF and rTBF than in CNAH CBF in WM and GM but not in the caudate, and higher WCoV for rTBF than for aTBF when normalised using a small ROI (PASL 8.1% vs. 4.7%, PCASL 10.9% vs. 7.9%, respectively). The lowest CR was observed for rTBF normalised with a large ROI. DISCUSSION PASL and PCASL showed similar repeatability for the assessment of perfusion parameters in patients with primary brain tumours as previous studies based on volunteers. Both methods displayed reasonable WCoV in the tumour area and CNAH. PCASL's more stable SNR in small areas (caudate) is likely to be due to the longer post-labelling delays.
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Affiliation(s)
- Amirah Faisal Alsaedi
- Department of Radiology Technology, Taibah University, Medina, Kingdom of Saudi Arabia.
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - David Lee Thomas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Leonard Wolfson Experimental Neurology Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Enrico De Vita
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College Hospitals NHS Trust, London, UK
| | - Jasmina Panovska-Griffiths
- Nuffield Department of Medicine, The Big Data Institute, University of Oxford, Oxford, UK
- The Queen's College, University of Oxford, Oxford, UK
| | - Sotirios Bisdas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College Hospitals NHS Trust, London, UK
| | - Xavier Golay
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College Hospitals NHS Trust, London, UK
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6
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Betrouni N, Moreau C, Rolland AS, Carrière N, Viard R, Lopes R, Kuchcinski G, Eusebio A, Thobois S, Hainque E, Hubsch C, Rascol O, Brefel C, Drapier S, Giordana C, Durif F, Maltête D, Guehl D, Hopes L, Rouaud T, Jarraya B, Benatru I, Tranchant C, Tir M, Chupin M, Bardinet E, Defebvre L, Corvol JC, Devos D. Can Dopamine Responsiveness Be Predicted in Parkinson's Disease Without an Acute Administration Test? JOURNAL OF PARKINSON'S DISEASE 2022; 12:2179-2190. [PMID: 35871363 DOI: 10.3233/jpd-223334] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
BACKGROUND Dopamine responsiveness (dopa-sensitivity) is an important parameter in the management of patients with Parkinson's disease (PD). For quantification of this parameter, patients undergo a challenge test with acute Levodopa administration after drug withdrawal, which may lead to patient discomfort and use of significant resources. OBJECTIVE Our objective was to develop a predictive model combining clinical scores and imaging. METHODS 350 patients, recruited by 13 specialist French centers and considered for deep brain stimulation, underwent an acute L-dopa challenge (dopa-sensitivity > 30%), full assessment, and MRI investigations, including T1w and R2* images. Data were randomly divided into a learning base from 10 centers and data from the remaining centers for testing. A machine selection approach was applied to choose the optimal variables and these were then used in regression modeling. Complexity of the modelling was incremental, while the first model considered only clinical variables, the subsequent included imaging features. The performances were evaluated by comparing the estimated values and actual valuesResults:Whatever the model, the variables age, sex, disease duration, and motor scores were selected as contributors. The first model used them and the coefficients of determination (R2) was 0.60 for the testing set and 0.69 in the learning set (p < 0.001). The models that added imaging features enhanced the performances: with T1w (R2 = 0.65 and 0.76, p < 0.001) and with R2* (R2 = 0.60 and 0.72, p < 0.001). CONCLUSION These results suggest that modeling is potentially a simple way to estimate dopa-sensitivity, but requires confirmation in a larger population, including patients with dopa-sensitivity < 30.
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Affiliation(s)
- Nacim Betrouni
- University Lille, INSERM, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, LICEND, Lille, France
| | - Caroline Moreau
- University Lille, INSERM, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, LICEND, Lille, France
- CHU Lille, Neurology and Movement Disorders Department, Reference Center for Parkinson's Disease, Lille, France; NS-Park French Network
| | - Anne-Sophie Rolland
- University Lille, INSERM, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, LICEND, Lille, France
| | - Nicolas Carrière
- University Lille, INSERM, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, LICEND, Lille, France
- CHU Lille, Neurology and Movement Disorders Department, Reference Center for Parkinson's Disease, Lille, France; NS-Park French Network
- University Lille, CNRS, INSERM, CHU Lille, Institut Pasteur de Lille, US 41 - UMS 2014 - PLBS, Lille, France; NS-Park French Network
| | - Romain Viard
- University Lille, INSERM, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, LICEND, Lille, France
- University Lille, CNRS, INSERM, CHU Lille, Institut Pasteur de Lille, US 41 - UMS 2014 - PLBS, Lille, France; NS-Park French Network
| | - Renaud Lopes
- University Lille, INSERM, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, LICEND, Lille, France
- University Lille, CNRS, INSERM, CHU Lille, Institut Pasteur de Lille, US 41 - UMS 2014 - PLBS, Lille, France; NS-Park French Network
| | - Gregory Kuchcinski
- University Lille, INSERM, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, LICEND, Lille, France
- CHU Lille, Neuroradioloy Department, Lille, France
| | - Alexandre Eusebio
- Aix Marseille Universitë, AP-HM, Hôpital de La Timone, Service de Neurologie et Pathologie du Mouvement, UMR CNRS 7289, Institut de Neuroscience de La Timone, Marseille, France; NS-Park French Network
| | - Stephane Thobois
- Hospices Civils de Lyon, Hôpital Neurologique Pierre Wertheimer, Neurologie C, Bron, France
| | - Elodie Hainque
- Dëpartement de Neurologie, Hôpital Pitië-Salpêtrière, AP-HP, Paris, France; NS-Park French Network
| | - Cecile Hubsch
- Fondation Ophtalmologique A de Rothschild, Unitë James Parkinson, Paris, France; NS-Park French Network
| | - Olivier Rascol
- University of Toulouse 3, University Hospital of Toulouse, INSERM, Departments of Neuroscience and Clinical Pharmacology, Clinical Investigation Center CIC 1436, Toulouse Parkinson Expert Center, NS-NeuroToul Center of Excellence for Neurodegenerative Disorders (COEN), Toulouse, France; NS-Park French Network
| | - Christine Brefel
- University of Toulouse 3, University Hospital of Toulouse, INSERM, Departments of Neuroscience and Clinical Pharmacology, Clinical Investigation Center CIC 1436, Toulouse Parkinson Expert Center, NS-NeuroToul Center of Excellence for Neurodegenerative Disorders (COEN), Toulouse, France; NS-Park French Network
| | - Sophie Drapier
- Service de Neurologie, CHU Pont Chaillou, 2 rue Henri le Guilloux, Rennes cedex, France; NS-Park French Network
| | - Caroline Giordana
- Universitë Clermont Auvergne, EA7280, Clermont-Ferrand University Hospital, Neurology Department, Clermont-Ferrand, France; NS-Park French Network
| | - Franck Durif
- Universitë Clermont Auvergne, EA7280, Clermont-Ferrand University Hospital, Neurology Department, Clermont-Ferrand, France; NS-Park French Network
| | - David Maltête
- Department of Neurology, Rouen University Hospital and University of Rouen, France; INSERM U1239, Laboratory of Neuronal and Neuroendocrine Differentiation and Communication, Mont-Saint-Aignan, France; NS-Park French Network
| | - Dominique Guehl
- Service d'Explorations Fonctionnelles du Système Nerveux, Institut des Maladies Neurodëgënëratives Cliniques, CHU de Bordeaux, Bordeaux, France; NS-Park French Network
| | - Lucie Hopes
- Neurology Department, Nancy University Hospital, Nancy, France; NS-Park French Network
| | - Tiphaine Rouaud
- Clinique Neurologique, Hôpital Guillaume et Renë Laennec, Boulevard Jacques Monod, Nantes Cedex, France; NS-Park French Network
| | - Bechir Jarraya
- Movement Disorders Unit, Foch Hospital, Universitë Paris-Saclay (UVSQ), INSERM U992, NeuroSpin, CEA Paris-Saclay, Suresnes, France; NS-Park French Network
| | - Isabelle Benatru
- Service de Neurologie, Centre Expert Parkinson, CIC-INSERM 1402, CHU Poitiers, Poitiers, France; NS-Park French Network
| | - Christine Tranchant
- Service de Neurologie, Hôpitaux Universitaires de Strasbourg, Strasbourg, France; Institut de Gënëtique et de Biologie Molëculaire et Cellulaire (IGBMC), INSERM-U964/CNRS-UMR7104/Universitë de Strasbourg, Illkirch, France; Fëdëration de Mëdecine Translationnelle de Strasbourg (FMTS), Universitë de Strasbourg, Strasbourg, France; NS-Park French Network
| | - Melissa Tir
- Department of Neurosurgery, Amiens University Hospital, Amiens, France; Medical Imaging Unit, Amiens University Hospital, Amiens, France; BioFlowImage Research Group, Jules Verne University of Picardie, Amiens, France; NS-Park French Network
| | - Marie Chupin
- CATI, Institut du Cerveau et de le Moelle Epinière, ICM, INSERM U1127, CNRS UMR7225, Sorbonne Universitë, Paris, France
| | - Eric Bardinet
- Institut du Cerveau et de le Moelle Epinière, ICM, INSERM U1127, CNRS UMR7225, Sorbonne Universitë, Paris, France
| | - Luc Defebvre
- University Lille, INSERM, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, LICEND, Lille, France
- CHU Lille, Neurology and Movement Disorders Department, Reference Center for Parkinson's Disease, Lille, France; NS-Park French Network
| | - Jean-Christophe Corvol
- Dëpartement de Neurologie, Hôpital Pitië-Salpêtrière, AP-HP, Paris, France; NS-Park French Network
- Facultë de Mëdecine de Sorbonne Universitë, UMR S 1127, INSERM U 1127, and CNRS UMR 7225, and Institut du Cerveau et de la Moëlle Epinière, Paris, France; NS-Park French Network
| | - David Devos
- University Lille, INSERM, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, LICEND, Lille, France
- CHU Lille, Neurology and Movement Disorders Department, Reference Center for Parkinson's Disease, Lille, France; NS-Park French Network
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7
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Veale T, Malone IB, Poole T, Parker TD, Slattery CF, Paterson RW, Foulkes AJM, Thomas DL, Schott JM, Zhang H, Fox NC, Cash DM. Loss and dispersion of superficial white matter in Alzheimer's disease: a diffusion MRI study. Brain Commun 2021; 3:fcab272. [PMID: 34859218 PMCID: PMC8633427 DOI: 10.1093/braincomms/fcab272] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 09/24/2021] [Accepted: 10/18/2021] [Indexed: 11/22/2022] Open
Abstract
Pathological cerebral white matter changes in Alzheimer's disease have been shown using diffusion tensor imaging. Superficial white matter changes are relatively understudied despite their importance in cortico-cortical connections. Measuring superficial white matter degeneration using diffusion tensor imaging is challenging due to its complex organizational structure and proximity to the cortex. To overcome this, we investigated diffusion MRI changes in young-onset Alzheimer's disease using standard diffusion tensor imaging and Neurite Orientation Dispersion and Density Imaging to distinguish between disease-related changes that are degenerative (e.g. loss of myelinated fibres) and organizational (e.g. increased fibre dispersion). Twenty-nine young-onset Alzheimer's disease patients and 22 healthy controls had both single-shell and multi-shell diffusion MRI. We calculated fractional anisotropy, mean diffusivity, neurite density index, orientation dispersion index and tissue fraction (1-free water fraction). Diffusion metrics were sampled in 15 a priori regions of interest at four points along the cortical profile: cortical grey matter, grey/white boundary, superficial white matter (1 mm below grey/white boundary) and superficial/deeper white matter (2 mm below grey/white boundary). To estimate cross-sectional group differences, we used average marginal effects from linear mixed effect models of participants' diffusion metrics along the cortical profile. The superficial white matter of young-onset Alzheimer's disease individuals had lower neurite density index compared to controls in five regions (superior and inferior parietal, precuneus, entorhinal and parahippocampus) (all P < 0.05), and higher orientation dispersion index in three regions (fusiform, entorhinal and parahippocampus) (all P < 0.05). Young-onset Alzheimer's disease individuals had lower fractional anisotropy in the entorhinal and parahippocampus regions (both P < 0.05) and higher fractional anisotropy within the postcentral region (P < 0.05). Mean diffusivity was higher in the young-onset Alzheimer's disease group in the parahippocampal region (P < 0.05) and lower in the postcentral, precentral and superior temporal regions (all P < 0.05). In the overlying grey matter, disease-related changes were largely consistent with superficial white matter findings when using neurite density index and fractional anisotropy, but appeared at odds with orientation dispersion and mean diffusivity. Tissue fraction was significantly lower across all grey matter regions in young-onset Alzheimer's disease individuals (all P < 0.001) but group differences reduced in magnitude and coverage when moving towards the superficial white matter. These results show that microstructural changes occur within superficial white matter and along the cortical profile in individuals with young-onset Alzheimer's disease. Lower neurite density and higher orientation dispersion suggests underlying fibres undergo neurodegeneration and organizational changes, two effects previously indiscernible using standard diffusion tensor metrics in superficial white matter.
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Affiliation(s)
- Thomas Veale
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - Ian B Malone
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Teresa Poole
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Thomas D Parker
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Catherine F Slattery
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Ross W Paterson
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - Alexander J M Foulkes
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - David L Thomas
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - Jonathan M Schott
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image Computing, UCL, London, UK
| | - Nick C Fox
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - David M Cash
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
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8
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A Pilot Study on Feasibility of Ultrashort Echo Time T2* Cartilage Mapping in the Sacroiliac Joints. J Comput Assist Tomogr 2021; 45:717-721. [PMID: 34347705 DOI: 10.1097/rct.0000000000001206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
PURPOSE Assess feasibility of ultrashort echo time (UTE) T2* cartilage mapping in sacroiliac (SI) joints. METHODS Prospective magnetic resonance imagings with UTE T2* cartilage maps obtained on 20 SI joints in 10 subjects. Each joint was segmented into thirds by 2 radiologists. The UTE T2* maps were analyzed; reliability and differences in UTE T2* values between radiologists were assessed. RESULTS Mean UTE T2* value was 10.44 ± 0.60 ms. No difference between right/left SI joints (median, 10.52 vs 10.45 ms; P = 0.940), men/women (median, 10.34 vs. 10.57 ms; P = 0.174), or different anatomic regions (median range 10.55-10.69 ms; P = 0.805). Intraclass correlation coefficients were 0.94 to 0.99 (intraobserver) and 0.91 to 0.96 (interobserver). Mean bias ± standard deviation on Bland-Altman was -0.137 ± 0.196 ms (limits of agreement -0.521 and 0.247) without proportional bias (β = 0.148, P = 0.534). CONCLUSIONS The UTE T2* cartilage mapping in the SI joints is feasible with high reader reliability.
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9
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On the use of multicompartment models of diffusion and relaxation for placental imaging. Placenta 2021; 112:197-203. [PMID: 34392172 DOI: 10.1016/j.placenta.2021.07.302] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 04/27/2021] [Accepted: 07/27/2021] [Indexed: 12/14/2022]
Abstract
Multi-compartment models of diffusion and relaxation are ubiquitous in magnetic resonance research especially applied to neuroimaging applications. These models are increasingly making their way into the world of placental imaging. This review provides a framework for their motivation and implementation and describes some of the outstanding questions that need to be answered before they can be routinely adopted.
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10
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Flouri D, Darby JRT, Holman SL, Perumal SR, David AL, Morrison JL, Melbourne A. Magnetic resonance imaging of placentome development in the pregnant Ewe. Placenta 2021; 105:61-69. [PMID: 33549925 PMCID: PMC7611430 DOI: 10.1016/j.placenta.2021.01.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 01/08/2021] [Accepted: 01/15/2021] [Indexed: 12/28/2022]
Abstract
INTRODUCTION Novel imaging measurements of placental development are difficult to validate due to the invasive nature of gold-standard procedures. Animal studies have been important in validation of magnetic resonance imaging (MRI) measurements in invasive preclinical studies, as they allow for controlled experiments and analysis of multiple time-points during pregnancy. This study characterises the longitudinal diffusion and perfusion properties of sheep placentomes using MRI, measurements that are required for future validation studies. METHODS Pregnant ewes were anaesthetised for a MRI session on a 3T scanner. Placental MRI was used to classify placentomes morphologically into three types based on their shape and size at two gestational ages. To validate classification accuracy, placentome type derived from MRI data were compared with placentome categorisation results after delivery. Diffusion-Weighted MRI and T2-relaxometry were used to measure a broad range of biophysical properties of the placentomes. RESULTS MRI morphological classification results showed consistent gestational age changes in placentome shape, as supported by post-delivery gold standard data. The mean apparent diffusion coefficient was significantly higher at 110 days gestation than at late gestation (~140 days; term, 150 days). Mean T2 was higher at mid gestation (152.2 ± 58.1 ms) compared to late gestation (127.8 ms ± 52.0). Significantly higher perfusion fraction was measured in late gestation placentomes that also had a significantly higher fractional anisotropy when compared to the earlier gestational age. DISCUSSION We report baseline measurements of techniques common in placental MRI for the sheep placenta. These measurements are essential to support future validation measurements of placental MRI techniques.
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Affiliation(s)
- Dimitra Flouri
- School of Biomedical Engineering and Imaging Sciences, Kings College London, London, United Kingdom; Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.
| | - Jack R T Darby
- Early Origins of Adult Health Research Group, Health and Biomedical Innovation, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, Australia
| | - Stacey L Holman
- Early Origins of Adult Health Research Group, Health and Biomedical Innovation, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, Australia
| | - Sunthara R Perumal
- South Australian Health & Medical Research Institute, Preclinical, Imaging & Research Laboratories, Adelaide, Australia
| | - Anna L David
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, London, United Kingdom; NIHR Biomedical Research Centre, University College London Hospitals, London, United Kingdom
| | - Janna L Morrison
- Early Origins of Adult Health Research Group, Health and Biomedical Innovation, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, Australia
| | - Andrew Melbourne
- School of Biomedical Engineering and Imaging Sciences, Kings College London, London, United Kingdom; Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; Early Origins of Adult Health Research Group, Health and Biomedical Innovation, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, Australia
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11
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Weston PSJ, Poole T, Nicholas JM, Toussaint N, Simpson IJA, Modat M, Ryan NS, Liang Y, Rossor MN, Schott JM, Ourselin S, Zhang H, Fox NC. Measuring cortical mean diffusivity to assess early microstructural cortical change in presymptomatic familial Alzheimer's disease. ALZHEIMERS RESEARCH & THERAPY 2020; 12:112. [PMID: 32943095 PMCID: PMC7499910 DOI: 10.1186/s13195-020-00679-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 09/04/2020] [Indexed: 12/21/2022]
Abstract
Background There is increasing interest in improving understanding of the timing and nature of early neurodegeneration in Alzheimer’s disease (AD) and developing methods to measure this in vivo. Autosomal dominant familial Alzheimer’s disease (FAD) provides the opportunity for investigation of presymptomatic change. We assessed early microstructural breakdown of cortical grey matter in FAD with diffusion-weighted MRI. Methods Diffusion-weighted and T1-weighed MRI were acquired in 38 FAD mutation carriers (17 symptomatic, 21 presymptomatic) and 39 controls. Mean diffusivity (MD) was calculated for six cortical regions previously identified as being particularly vulnerable to FAD-related neurodegeneration. Linear regression compared MD between symptomatic and presymptomatic carriers and controls, adjusting for age and sex. Spearman coefficients assessed associations between cortical MD and cortical thickness. Spearman coefficients also assessed associations between cortical MD and estimated years to/from onset (EYO). Across mutation carriers, linear regression assessed associations between MD and EYO, adjusting for cortical thickness. Results Compared with controls, cortical MD was higher in symptomatic mutation carriers (mean ± SD CDR = 0.88 ± 0.39) for all six regions (p < 0.001). In late presymptomatic carriers (within 8.1 years of predicted symptom onset), MD was higher in the precuneus (p = 0.04) and inferior parietal cortex (p = 0.003) compared with controls. Across all presymptomatic carriers, MD in the precuneus correlated with EYO (p = 0.04). Across all mutation carriers, there was strong evidence (p < 0.001) of association between MD and cortical thickness in all regions except entorhinal cortex. After adjusting for cortical thickness, there remained an association (p < 0.05) in mutation carriers between MD and EYO in all regions except entorhinal cortex. Conclusions Cortical MD measurement detects microstructural breakdown in presymptomatic FAD and correlates with proximity to symptom onset independently of cortical thickness. Cortical MD may thus be a feasible biomarker of early AD-related neurodegeneration, offering additional/complementary information to conventional MRI measures.
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Affiliation(s)
- Philip S J Weston
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK.
| | - Teresa Poole
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK.,Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Jennifer M Nicholas
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK.,Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Nicolas Toussaint
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK.,Transitional Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Ivor J A Simpson
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK.,Transitional Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Marc Modat
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK.,Department of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Natalie S Ryan
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK
| | - Yuying Liang
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK
| | - Martin N Rossor
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK
| | - Jonathan M Schott
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK
| | - Sebastien Ourselin
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK.,Department of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Hui Zhang
- Microstructure Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK
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12
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Mutsaerts HJMM, Petr J, Groot P, Vandemaele P, Ingala S, Robertson AD, Václavů L, Groote I, Kuijf H, Zelaya F, O'Daly O, Hilal S, Wink AM, Kant I, Caan MWA, Morgan C, de Bresser J, Lysvik E, Schrantee A, Bjørnebekk A, Clement P, Shirzadi Z, Kuijer JPA, Wottschel V, Anazodo UC, Pajkrt D, Richard E, Bokkers RPH, Reneman L, Masellis M, Günther M, MacIntosh BJ, Achten E, Chappell MA, van Osch MJP, Golay X, Thomas DL, De Vita E, Bjørnerud A, Nederveen A, Hendrikse J, Asllani I, Barkhof F. ExploreASL: An image processing pipeline for multi-center ASL perfusion MRI studies. Neuroimage 2020; 219:117031. [PMID: 32526385 DOI: 10.1016/j.neuroimage.2020.117031] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 05/29/2020] [Accepted: 06/04/2020] [Indexed: 01/01/2023] Open
Abstract
Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.
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Affiliation(s)
- Henk J M M Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands; Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands; Radiology, University Medical Center Utrecht, Utrecht, the Netherlands; Kate Gleason College of Engineering, Rochester Institute of Technology, NY, USA; Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium.
| | - Jan Petr
- Kate Gleason College of Engineering, Rochester Institute of Technology, NY, USA; Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
| | - Paul Groot
- Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Pieter Vandemaele
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands
| | - Andrew D Robertson
- Schlegel-UW Research Institute for Aging, University of Waterloo, Waterloo, Ontario, Canada
| | - Lena Václavů
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Inge Groote
- Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway
| | - Hugo Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Fernando Zelaya
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Owen O'Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Saima Hilal
- Department of Pharmacology, National University of Singapore, Singapore; Memory Aging and Cognition Center, National University Health System, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands
| | - Ilse Kant
- Radiology, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Intensive Care, University Medical Centre, Utrecht, the Netherlands
| | - Matthan W A Caan
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Location Academic Medical Center, Amsterdam, the Netherlands
| | - Catherine Morgan
- School of Psychology and Centre for Brain Research, University of Auckland, Auckland, New Zealand
| | - Jeroen de Bresser
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Elisabeth Lysvik
- Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway
| | - Anouk Schrantee
- Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Astrid Bjørnebekk
- The Anabolic Androgenic Steroid Research Group, National Advisory Unit on Substance Use Disorder Treatment, Oslo University Hospital, Oslo, Norway
| | - Patricia Clement
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Zahra Shirzadi
- Sunnybrook Research Institute, University of Toronto, Toronto, Canada
| | - Joost P A Kuijer
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands
| | - Viktor Wottschel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands
| | - Udunna C Anazodo
- Department of Medical Biophysics, University of Western Ontario, London, Canada; Imaging Division, Lawson Health Research Institute, London, Canada
| | - Dasja Pajkrt
- Department of Pediatric Infectious Diseases, Emma Children's Hospital, Amsterdam University Medical Centre, Location Academic Medical Center, Amsterdam, the Netherlands
| | - Edo Richard
- Department of Neurology, Donders Institute for Brain, Behavior and Cognition, Radboud University Medical Centre, Nijmegen, the Netherlands; Neurology, Amsterdam University Medical Center, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Reinoud P H Bokkers
- Department of Radiology, Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Liesbeth Reneman
- Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Mario Masellis
- Sunnybrook Research Institute, University of Toronto, Toronto, Canada
| | - Matthias Günther
- Fraunhofer MEVIS, Bremen, Germany; University of Bremen, Bremen, Germany; Mediri GmbH, Heidelberg, Germany
| | | | - Eric Achten
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Michael A Chappell
- Institute of Biomedical Engineering, Department of Engineering Science & Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - Matthias J P van Osch
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Xavier Golay
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - David L Thomas
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Enrico De Vita
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, UK
| | - Atle Bjørnerud
- Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Norway
| | - Aart Nederveen
- Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Jeroen Hendrikse
- Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Iris Asllani
- Kate Gleason College of Engineering, Rochester Institute of Technology, NY, USA; Clinical Imaging Sciences Centre, Department of Neuroscience, Brighton and Sussex Medical School, Brighton, UK
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, Location VUmc, Amsterdam, the Netherlands; UCL Queen Square Institute of Neurology, University College London, London, UK; Centre for Medical Image Computing (CMIC), Faculty of Engineering Science, University College London, London, UK
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13
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Solanky BS, Prados F, Tur C, Yiannakas MC, Kanber B, Cawley N, Brownlee W, Ourselin S, Golay X, Ciccarelli O, Gandini Wheeler-Kingshott CAM. Sodium in the Relapsing-Remitting Multiple Sclerosis Spinal Cord: Increased Concentrations and Associations With Microstructural Tissue Anisotropy. J Magn Reson Imaging 2020; 52:1429-1438. [PMID: 32476227 DOI: 10.1002/jmri.27201] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 05/05/2020] [Accepted: 05/06/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Associations between brain total sodium concentration, disability, and disease progression have recently been reported in multiple sclerosis. However, such measures in spinal cord have not been reported. PURPOSE To measure total sodium concentration (TSC) alterations in the cervical spinal cord of people with relapsing-remitting multiple sclerosis (RRMS) and a control cohort using sodium MR spectroscopy (MRS). STUDY TYPE Retrospective cohort. SUBJECTS Nineteen people with RRMS and 21 healthy controls. FIELD STRENGTH/SEQUENCE 3 T sodium MRS, diffusion tensor imaging, and 3D gradient echo. ASSESSMENT Quantification of total sodium concentration in the cervical cord using a reference phantom. Measures of spinal cord cross-sectional area, fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity from 1 H MRI. Clinical assessments of 9-Hole Peg Test, 25-Foot Timed walk test, Paced Auditory Serial Addition Test with 3-second intervals, grip strength, vibration sensitivity, and posturography were performed on the RRMS cohort as well as reporting lesions in the C2/3 area. STATISTICAL TESTS Multiple linear regression models were run between sodium and clinical scores, cross-sectional area, and diffusion metrics to establish any correlations. RESULTS A significant increase in spinal cord total sodium concentration was found in people with RRMS relative to healthy controls (57.6 ± 18 mmol and 38.0 ± 8.6 mmol, respectively, P < 0.001). Increased TSC correlated with reduced fractional anisotropy (P = 0.034) and clinically with decreased mediolateral stability assessed with posturography (P = 0.045). DATA CONCLUSION Total sodium concentration in the cervical spinal cord is elevated in RRMS. This alteration is associated with reduced fractional anisotropy, which may be due to changes in tissue microstructure and, hence, in the integrity of spinal cord tissue. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Bhavana S Solanky
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Ferran Prados
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Carmen Tur
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Marios C Yiannakas
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Baris Kanber
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Niamh Cawley
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Wallace Brownlee
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Sebastien Ourselin
- Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Xavier Golay
- Brain Repair and Rehabilitation, Queen Square Institute of Neurology, University College London, London, UK
| | - Olga Ciccarelli
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
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14
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Aughwane R, Ingram E, Johnstone ED, Salomon LJ, David AL, Melbourne A. Placental MRI and its application to fetal intervention. Prenat Diagn 2020; 40:38-48. [PMID: 31306507 PMCID: PMC7027916 DOI: 10.1002/pd.5526] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 06/18/2019] [Accepted: 07/08/2019] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Magnetic resonance imaging (MRI) of placental invasion has been part of clinical practice for many years. The possibility of being better able to assess placental vascularization and function using MRI has multiple potential applications. This review summarises up-to-date research on placental function using different MRI modalities. METHOD We discuss how combinations of these MRI techniques have much to contribute to fetal conditions amenable for therapy such as singletons at high risk for fetal growth restriction (FGR) and monochorionic twin pregnancies for planning surgery and counselling for selective growth restriction and transfusion conditions. RESULTS The whole placenta can easily be visualized on MRI, with a clear boundary against the amniotic fluid, and a less clear placental-uterine boundary. Contrasts such as diffusion weighted imaging, relaxometry, blood oxygenation level dependent MRI and flow and metabolite measurement by dynamic contrast enhanced MRI, arterial spin labeling, or spectroscopic techniques are contributing to our wider understanding of placental function. CONCLUSION The future of placental MRI is exciting, with the increasing availability of multiple contrasts and new models that will boost the capability of MRI to measure oxygen saturation and placental exchange, enabling examination of placental function in complicated pregnancies.
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Affiliation(s)
| | - Emma Ingram
- Division of Developmental Biology & MedicineUniversity of ManchesterManchesterUK
| | - Edward D. Johnstone
- Division of Developmental Biology & MedicineUniversity of ManchesterManchesterUK
| | - Laurent J. Salomon
- Hôpital Necker‐Enfants Malades, AP‐HP, EHU PACT and LUMIERE PlatformUniversité Paris DescartesParisFrance
| | - Anna L. David
- Institute for Women's HealthUniversity College LondonLondonUK
- National Institute for Health ResearchUniversity College London Hospitals Biomedical Research CentreLondonUK
| | - Andrew Melbourne
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
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15
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Scott CJ, Jiao J, Melbourne A, Burgos N, Cash DM, De Vita E, Markiewicz PJ, O'Connor A, Thomas DL, Weston PS, Schott JM, Hutton BF, Ourselin S. Reduced acquisition time PET pharmacokinetic modelling using simultaneous ASL-MRI: proof of concept. J Cereb Blood Flow Metab 2019; 39:2419-2432. [PMID: 30182792 PMCID: PMC6891000 DOI: 10.1177/0271678x18797343] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Pharmacokinetic modelling on dynamic positron emission tomography (PET) data is a quantitative technique. However, the long acquisition time is prohibitive for routine clinical use. Instead, the semi-quantitative standardised uptake value ratio (SUVR) from a shorter static acquisition is used, despite its sensitivity to blood flow confounding longitudinal analysis. A method has been proposed to reduce the dynamic acquisition time for quantification by incorporating cerebral blood flow (CBF) information from arterial spin labelling (ASL) magnetic resonance imaging (MRI) into the pharmacokinetic modelling. In this work, we optimise and validate this framework for a study of ageing and preclinical Alzheimer's disease. This methodology adapts the simplified reference tissue model (SRTM) for a reduced acquisition time (RT-SRTM) and is applied to [18F]-florbetapir PET data for amyloid-β quantification. Evaluation shows that the optimised RT-SRTM can achieve amyloid burden estimation from a 30-min PET/MR acquisition which is comparable with the gold standard SRTM applied to 60 min of PET data. Conversely, SUVR showed a significantly higher error and bias, and a statistically significant correlation with tracer delivery due to the influence of blood flow. The optimised RT-SRTM produced amyloid burden estimates which were uncorrelated with tracer delivery indicating its suitability for longitudinal studies.
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Affiliation(s)
- Catherine J Scott
- Translational Imaging Group, CMIC, University College London, London, UK
| | - Jieqing Jiao
- Translational Imaging Group, CMIC, University College London, London, UK
| | - Andrew Melbourne
- Translational Imaging Group, CMIC, University College London, London, UK
| | - Ninon Burgos
- Translational Imaging Group, CMIC, University College London, London, UK.,Inria, Aramis project-team, Institut du Cerveau et de la Moelle épinière, Inserm, CNRS, Sorbonne Université, Paris, France
| | - David M Cash
- Translational Imaging Group, CMIC, University College London, London, UK.,Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Enrico De Vita
- Neuroradiological Academic Unit, UCL Institute of Neurology, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCL Hospitals Foundation Trust, London, UK.,Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, UK
| | - Pawel J Markiewicz
- Translational Imaging Group, CMIC, University College London, London, UK
| | - Antoinette O'Connor
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - David L Thomas
- Translational Imaging Group, CMIC, University College London, London, UK.,Neuroradiological Academic Unit, UCL Institute of Neurology, London, UK.,Leonard Wolfson Experimental Neurology Centre, UCL Institute of Neurology London, UK
| | - Philip Sj Weston
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Brian F Hutton
- Institute of Nuclear Medicine, University College London, London, UK.,Centre for Medical Radiation Physics, University of Wollongong, NSW, Australia
| | - Sébastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
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16
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Canas LS, Sudre CH, De Vita E, Nihat A, Mok TH, Slattery CF, Paterson RW, Foulkes AJM, Hyare H, Cardoso MJ, Thornton J, Schott JM, Barkhof F, Collinge J, Ourselin S, Mead S, Modat M. Prion disease diagnosis using subject-specific imaging biomarkers within a multi-kernel Gaussian process. Neuroimage Clin 2019; 24:102051. [PMID: 31734530 PMCID: PMC6978211 DOI: 10.1016/j.nicl.2019.102051] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 09/25/2019] [Accepted: 10/21/2019] [Indexed: 02/01/2023]
Abstract
Prion diseases are a group of rare neurodegenerative conditions characterised by a high rate of progression and highly heterogeneous phenotypes. Whilst the most common form of prion disease occurs sporadically (sporadic Creutzfeldt-Jakob disease, sCJD), other forms are caused by prion protein gene mutations, or exposure to prions in the diet or by medical procedures, such us surgeries. To date, there are no accurate quantitative imaging biomarkers that can be used to predict the future clinical diagnosis of a healthy subject, or to quantify the progression of symptoms over time. Besides, CJD is commonly mistaken for other forms of dementia. Due to the heterogeneity of phenotypes and the lack of a consistent geometrical pattern of disease progression, the approaches used to study other types of neurodegenerative diseases are not satisfactory to capture the progression of human form of prion disease. In this paper, using a tailored framework, we aim to classify and stratify patients with prion disease, according to the severity of their illness. The framework is initialised with the extraction of subject-specific imaging biomarkers. The extracted biomakers are then combined with genetic and demographic information within a Gaussian Process classifier, used to calculate the probability of a subject to be diagnosed with prion disease in the next year. We evaluate the effectiveness of the proposed method in a cohort of patients with inherited and sporadic forms of prion disease. The model has shown to be effective in the prediction of both inherited CJD (92% of accuracy) and sporadic CJD (95% of accuracy). However the model has shown to be less effective when used to stratify the different stages of the disease, in which the average accuracy is 85%, whilst the recall is 59%. Finally, our framework was extended as a differential diagnosis tool to identify both forms of CJD among another neurodegenerative disease. In summary we have developed a novel method for prion disease diagnosis and prediction of clinical onset using multiple sources of features, which may have use in other disorders with heterogeneous imaging features.
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Affiliation(s)
- Liane S Canas
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom.
| | - Carole H Sudre
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom; Dementia Research Centre, UCL Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Enrico De Vita
- Institute of Neurology, University College London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - Akin Nihat
- MRC Prion Unit at UCL, UCL Institute of Prion Diseases, London, United Kingdom; NHS National Prion Clinic, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Tze How Mok
- MRC Prion Unit at UCL, UCL Institute of Prion Diseases, London, United Kingdom; NHS National Prion Clinic, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Catherine F Slattery
- Dementia Research Centre, UCL Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Ross W Paterson
- Dementia Research Centre, UCL Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Alexander J M Foulkes
- Dementia Research Centre, UCL Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Harpreet Hyare
- NHS National Prion Clinic, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - M Jorge Cardoso
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - John Thornton
- Institute of Neurology, University College London, United Kingdom
| | - Jonathan M Schott
- Dementia Research Centre, UCL Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Frederik Barkhof
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - John Collinge
- MRC Prion Unit at UCL, UCL Institute of Prion Diseases, London, United Kingdom; NHS National Prion Clinic, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Sébastien Ourselin
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - Simon Mead
- MRC Prion Unit at UCL, UCL Institute of Prion Diseases, London, United Kingdom; NHS National Prion Clinic, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Marc Modat
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, United Kingdom
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17
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Smith LA, Melbourne A, Owen D, Cardoso MJ, Sudre CH, Tillin T, Sokolska M, Atkinson D, Chaturvedi N, Ourselin S, Hughes AD, Barkhof F, Jäger HR. Cortical cerebral blood flow in ageing: effects of haematocrit, sex, ethnicity and diabetes. Eur Radiol 2019; 29:5549-5558. [PMID: 30887200 PMCID: PMC6719435 DOI: 10.1007/s00330-019-06096-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 12/24/2018] [Accepted: 02/11/2019] [Indexed: 12/31/2022]
Abstract
OBJECTIVES Cerebral blood flow (CBF) estimates from arterial spin labelling (ASL) show unexplained variability in older populations. We studied the impact of variation of haematocrit (Hct) on CBF estimates in a tri-ethnic elderly population. MATERIALS AND METHODS Approval for the study was obtained from the Fulham Research Ethics Committee and participants gave written informed consent. Pseudo-continuous arterial spin labelling was performed on 493 subjects (age 55-90) from a tri-ethnic community-based cohort recruited in London. CBF was estimated using a simplified Buxton equation, with and without correction for Hct measured from blood samples. Differences in perfusion were compared, stratified by sex, ethnicity and diabetes. Results of Student's t tests were reported with effect size. RESULTS Hct adjustment decreased CBF estimates in all categories except white European men. The decrease for women was 2.7 (3.0, 2.4) mL/100 g/min) (mean (95% confidence interval (CI)), p < 0.001 d = 0.38. The effect size differed by ethnicity with estimated mean perfusion in South Asian and African Caribbean women found to be lower by 3.0 (3.6, 2.5) mL/100 g/min, p < 0.001 d = 0.56 and 3.1 (3.6, 2.5) mL/100 g/min), p < 0.001 d = 0.48, respectively. Estimates of perfusion in subjects with diabetes decreased by 1.8 (2.3, 1.4) mL/100 g/min, p < 0.001 d = 0.23) following Hct correction. Correction for individual Hct altered sample frequency distributions of CBF values, especially in women of non-European ethnicity. CONCLUSION ASL-derived CBF values in women, non-European ethnicities and individuals with diabetes are overestimated if calculations are not appropriately adjusted for individual Hct. KEY POINTS • CBF quantification from ASL using a fixed Hct of 43.5%, as recommended in the ISMRM white paper, may lead to erroneous CBF estimations particularly in non-European and female subjects. • Individually measured Hct values improve the accuracy of CBF estimation and, if these are not available, an adjusted value according to gender, ethnicity or diabetes status should be considered. • Hct-corrected ASL could be potentially important for CBF threshold decision making in the fields of neurodegenerative disease and neuro-oncology.
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Affiliation(s)
- Lorna A Smith
- MRC Unit for Lifelong Health and Ageing, Department of Population Science & Experimental Medicine, University College London, WC1E 6HX, London, UK. .,Centre for Medical Imaging, Division of Medicine, University College London, 2nd Floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
| | - Andrew Melbourne
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK.,Department of Medical Physics and Biomedical Engineering, University College London, London, NW1 2BU, UK
| | - David Owen
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK.,Department of Medical Physics and Biomedical Engineering, University College London, London, NW1 2BU, UK
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK.,Dementia Research Centre, UCL Institute of Neurology, London, Wc1N 3BG, UK
| | - Carole H Sudre
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK.,Department of Medical Physics and Biomedical Engineering, University College London, London, NW1 2BU, UK.,Dementia Research Centre, UCL Institute of Neurology, London, Wc1N 3BG, UK
| | - Therese Tillin
- MRC Unit for Lifelong Health and Ageing, Department of Population Science & Experimental Medicine, University College London, WC1E 6HX, London, UK
| | - Magdalena Sokolska
- Institute of Healthcare Engineering, University College London, London, UK
| | - David Atkinson
- Centre for Medical Imaging, Division of Medicine, University College London, 2nd Floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Nish Chaturvedi
- MRC Unit for Lifelong Health and Ageing, Department of Population Science & Experimental Medicine, University College London, WC1E 6HX, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Alun D Hughes
- MRC Unit for Lifelong Health and Ageing, Department of Population Science & Experimental Medicine, University College London, WC1E 6HX, London, UK
| | - Frederik Barkhof
- Department of Medical Physics and Biomedical Engineering, University College London, London, NW1 2BU, UK.,Dementia Research Centre, UCL Institute of Neurology, London, Wc1N 3BG, UK.,Department of Radiology & Nuclear Medicine, VU University Medical Centre, Amsterdam, Netherlands
| | - H R Jäger
- Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, WC1N 3BG, UK.,Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London, London, WCN1 3BG, UK
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18
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19
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Leeuwis AE, Smith LA, Melbourne A, Hughes AD, Richards M, Prins ND, Sokolska M, Atkinson D, Tillin T, Jäger HR, Chaturvedi N, van der Flier WM, Barkhof F. Cerebral Blood Flow and Cognitive Functioning in a Community-Based, Multi-Ethnic Cohort: The SABRE Study. Front Aging Neurosci 2018; 10:279. [PMID: 30279656 PMCID: PMC6154257 DOI: 10.3389/fnagi.2018.00279] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 08/28/2018] [Indexed: 11/13/2022] Open
Abstract
Introduction: Lower cerebral blood flow (CBF) is associated with cardiovascular disease and vascular risk factors, and is increasingly acknowledged as an important contributor to cognitive decline and dementia. In this cross-sectional study, we examined the association between CBF and cognitive functioning in a community-based, multi-ethnic cohort. Methods: From the SABRE (Southall and Brent Revisited) study, we included 214 European, 151 South Asian and 87 African Caribbean participants (71 ± 5 years; 39%F). We used 3T pseudo-continuous arterial spin labeling to estimate whole-brain, hematocrit corrected CBF. We measured global cognition and three cognitive domains (memory, executive functioning/attention and language) with a neuropsychological test battery. Associations were investigated using linear regression analyses, adjusted for demographic variables, vascular risk factors and MRI measures. Results: Across groups, we found an association between higher CBF and better performance on executive functioning/attention (standardized ß [stß] = 0.11, p < 0.05). Stratification for ethnicity showed associations between higher CBF and better performance on memory and executive functioning/attention in the white European group (stß = 0.14; p < 0.05 and stß = 0.18; p < 0.01 respectively), associations were weaker in the South Asian and African Caribbean groups. Conclusions: In a multi-ethnic community-based cohort we showed modest associations between CBF and cognitive functioning. In particular, we found an association between higher CBF and better performance on executive functioning/attention and memory in the white European group. The observations are consistent with the proposed role of cerebral hemodynamics in cognitive decline.
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Affiliation(s)
- Anna E Leeuwis
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam Amsterdam UMC, Amsterdam, Netherlands
| | - Lorna A Smith
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science University College London, London, United Kingdom
| | - Andrew Melbourne
- Translational Imaging Group, Department of Medical Physics and Biomedical Engineering University College London, London, United Kingdom
| | - Alun D Hughes
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science University College London, London, United Kingdom.,MRC Unit for Lifelong Health and Ageing University College London, London, United Kingdom
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing University College London, London, United Kingdom
| | - Niels D Prins
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam Amsterdam UMC, Amsterdam, Netherlands
| | - Magdalena Sokolska
- Department of Medical Physics and Biomedical Engineering University College London, London, United Kingdom
| | - David Atkinson
- Centre for Medical Imaging University College London, London, United Kingdom
| | - Therese Tillin
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science University College London, London, United Kingdom
| | - Hans R Jäger
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation UCL Institute of Neurology, London, United Kingdom
| | - Nish Chaturvedi
- Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science University College London, London, United Kingdom.,MRC Unit for Lifelong Health and Ageing University College London, London, United Kingdom
| | - Wiesje M van der Flier
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam Amsterdam UMC, Amsterdam, Netherlands.,Department of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam Amsterdam UMC, Amsterdam, Netherlands
| | - Frederik Barkhof
- Institutes of Neurology and Healthcare Engineering University College London, London, United Kingdom.,Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam Amsterdam UMC, Amsterdam, Netherlands
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20
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Kehoe PG, Blair PS, Howden B, Thomas DL, Malone IB, Horwood J, Clement C, Selman LE, Baber H, Lane A, Coulthard E, Passmore AP, Fox NC, Wilkinson IB, Ben-Shlomo Y. The Rationale and Design of the Reducing Pathology in Alzheimer's Disease through Angiotensin TaRgeting (RADAR) Trial. J Alzheimers Dis 2018; 61:803-814. [PMID: 29226862 DOI: 10.3233/jad-170101] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
BACKGROUND Anti-hypertensives that modify the renin angiotensin system may reduce Alzheimer's disease (AD) pathology and reduce the rate of disease progression. OBJECTIVE To conduct a phase II, two arm, double-blind, placebo-controlled, randomized trial of losartan to test the efficacy of Reducing pathology in Alzheimer's Disease through Angiotensin TaRgeting (RADAR). METHODS Men and women aged at least 55 years with mild-to-moderate AD will be randomly allocated 100 mg encapsulated generic losartan or placebo once daily for 12 months after successful completion of a 2-week open-label phase and 2-week placebo washout to establish drug tolerability. 228 participants will provide at least 182 subjects with final assessments to provide 84% power to detect a 25% difference in atrophy rate (therapeutic benefit) change over 12 months at an alpha level of 0.05. We will use intention-to-treat analysis, estimating between-group differences in outcomes derived from appropriate (linear or logistic) multivariable regression models adjusting for minimization variables. RESULTS The primary outcome will be rate of whole brain atrophy as a surrogate measure of disease progression. Secondary outcomes will include changes to 1) white matter hyperintensity volume and cerebral blood flow; 2) performance on a standard series of assessments of memory, cognitive function, activities of daily living, and quality of life. Major assessments (for all outcomes) and relevant safety monitoring of blood pressure and bloods will be at baseline and 12 months. Additional cognitive assessment will also be conducted at 6 months along with safety blood pressure and blood monitoring. Monitoring of blood pressure, bloods, and self-reported side effects will occur during the open-label phase and during the majority of the post-randomization dispensing visits. CONCLUSION This study will identify whether losartan is efficacious in the treatment of AD and whether definitive Phase III trials are warranted.
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Affiliation(s)
- Patrick G Kehoe
- Dementia Research Group, Translational Health Sciences, Bristol Medical School, University of Bristol, Faculty of Health Sciences, Level 1 Learning and Research>, Southmead Hospital, Bristol, UK
| | - Peter S Blair
- Bristol Randomised Trials Collaboration (BRTC), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Beth Howden
- Bristol Randomised Trials Collaboration (BRTC), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - David L Thomas
- Leonard Wolfson Experimental Neurology Centre, UCL Institute of Neurology, Queen Square, London, UK
- Dementia Research Centre (DRC), Institute of Neurology, University College London, Queen Square, London, UK
| | - Ian B Malone
- Dementia Research Centre (DRC), Institute of Neurology, University College London, Queen Square, London, UK
| | - Jeremy Horwood
- Bristol Randomised Trials Collaboration (BRTC), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Clare Clement
- Bristol Randomised Trials Collaboration (BRTC), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Lucy E Selman
- Bristol Randomised Trials Collaboration (BRTC), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Hannah Baber
- Bristol Randomised Trials Collaboration (BRTC), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Athene Lane
- Bristol Randomised Trials Collaboration (BRTC), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Elizabeth Coulthard
- ReMemBr Group, Translational Health Sciences, Bristol Medical School, University of Bristol, Faculty of Health Sciences, Brain Centre, Southmead Hospital, Bristol, UK
| | - Anthony Peter Passmore
- Institute of Clinical Sciences, Queens University Belfast, Royal Victoria Hospital, Belfast, UK
| | - Nick C Fox
- Dementia Research Centre (DRC), Institute of Neurology, University College London, Queen Square, London, UK
| | - Ian B Wilkinson
- Division of Experimental Medicine and Immunotherapeutics, School of Clinical Medicine, University of Cambridge, and Clinical Trials Unit, Addenbrookes Hospital, Cambridge, UK
| | - Yoav Ben-Shlomo
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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21
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Lane CA, Parker TD, Cash DM, Macpherson K, Donnachie E, Murray-Smith H, Barnes A, Barker S, Beasley DG, Bras J, Brown D, Burgos N, Byford M, Jorge Cardoso M, Carvalho A, Collins J, De Vita E, Dickson JC, Epie N, Espak M, Henley SMD, Hoskote C, Hutel M, Klimova J, Malone IB, Markiewicz P, Melbourne A, Modat M, Schrag A, Shah S, Sharma N, Sudre CH, Thomas DL, Wong A, Zhang H, Hardy J, Zetterberg H, Ourselin S, Crutch SJ, Kuh D, Richards M, Fox NC, Schott JM. Study protocol: Insight 46 - a neuroscience sub-study of the MRC National Survey of Health and Development. BMC Neurol 2017; 17:75. [PMID: 28420323 PMCID: PMC5395844 DOI: 10.1186/s12883-017-0846-x] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 03/21/2017] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Increasing age is the biggest risk factor for dementia, of which Alzheimer's disease is the commonest cause. The pathological changes underpinning Alzheimer's disease are thought to develop at least a decade prior to the onset of symptoms. Molecular positron emission tomography and multi-modal magnetic resonance imaging allow key pathological processes underpinning cognitive impairment - including β-amyloid depostion, vascular disease, network breakdown and atrophy - to be assessed repeatedly and non-invasively. This enables potential determinants of dementia to be delineated earlier, and therefore opens a pre-symptomatic window where intervention may prevent the onset of cognitive symptoms. METHODS/DESIGN This paper outlines the clinical, cognitive and imaging protocol of "Insight 46", a neuroscience sub-study of the MRC National Survey of Health and Development. This is one of the oldest British birth cohort studies and has followed 5362 individuals since their birth in England, Scotland and Wales during one week in March 1946. These individuals have been tracked in 24 waves of data collection incorporating a wide range of health and functional measures, including repeat measures of cognitive function. Now aged 71 years, a small fraction have overt dementia, but estimates suggest that ~1/3 of individuals in this age group may be in the preclinical stages of Alzheimer's disease. Insight 46 is recruiting 500 study members selected at random from those who attended a clinical visit at 60-64 years and on whom relevant lifecourse data are available. We describe the sub-study design and protocol which involves a prospective two time-point (0, 24 month) data collection covering clinical, neuropsychological, β-amyloid positron emission tomography and magnetic resonance imaging, biomarker and genetic information. Data collection started in 2015 (age 69) and aims to be completed in 2019 (age 73). DISCUSSION Through the integration of data on the socioeconomic environment and on physical, psychological and cognitive function from 0 to 69 years, coupled with genetics, structural and molecular imaging, and intensive cognitive and neurological phenotyping, Insight 46 aims to identify lifetime factors which influence brain health and cognitive ageing, with particular focus on Alzheimer's disease and cerebrovascular disease. This will provide an evidence base for the rational design of disease-modifying trials.
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Affiliation(s)
- Christopher A. Lane
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Thomas D. Parker
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Dave M. Cash
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Kirsty Macpherson
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Elizabeth Donnachie
- Leonard Wolfson Experimental Neurology Centre, Institute of Neurology, University College London, London, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Anna Barnes
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Suzie Barker
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Daniel G. Beasley
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Jose Bras
- Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK
- Department of Medical Sciences and Institute of Biomedicine - iBiMED, University of Aveiro, Aveiro, Portugal
| | - David Brown
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Ninon Burgos
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | | | - M. Jorge Cardoso
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Ana Carvalho
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Jessica Collins
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Enrico De Vita
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - John C. Dickson
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Norah Epie
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Miklos Espak
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Susie M. D. Henley
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Chandrashekar Hoskote
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Michael Hutel
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Jana Klimova
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Ian B. Malone
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Pawel Markiewicz
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Andrew Melbourne
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Marc Modat
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Anette Schrag
- Department of Clinical Neuroscience, Institute of Neurology, University College London, London, UK
| | - Sachit Shah
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Nikhil Sharma
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
- National Hospital for Neurology and Neurosurgery, London, UK
| | - Carole H. Sudre
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - David L. Thomas
- Leonard Wolfson Experimental Neurology Centre, Institute of Neurology, University College London, London, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image Computing, University College London, London, UK
| | - John Hardy
- Reta Lila Weston Research Laboratories, Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK
| | - Henrik Zetterberg
- Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Sebastien Ourselin
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Sebastian J. Crutch
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | | | - Nick C. Fox
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Jonathan M. Schott
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
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