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Brown RB, Tozer DJ, Egle M, Tuladhar AM, de Leeuw FE, Markus HS. How often does white matter hyperintensity volume regress in cerebral small vessel disease? Int J Stroke 2023; 18:937-947. [PMID: 36988075 PMCID: PMC10507994 DOI: 10.1177/17474930231169132] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/14/2023] [Indexed: 03/30/2023]
Abstract
BACKGROUND AND OBJECTIVES It has been suggested that white matter hyperintensity lesions (WMHs), which typically progress over time, can also regress, and that this might be associated with favorable cognitive performance. We determined the prevalence of WMH regression in patients with cerebral small vessel disease (SVD) and examined which demographic, clinical, and radiological markers were associated with this regression. METHODS We used semi-automated lesion marking methods to quantify WMH volume at multiple timepoints in three cohorts with symptomatic SVD; two with moderate-to-severe symptomatic SVD (the SCANS observational cohort and the control arm of the PRESERVE interventional trial) and one with mild-to-moderate SVD (the RUN DMC observational cohort). Mixed-effects ordered logistic regression models were used to test which factors predicted participants to show WMH regression. RESULTS No participants (0/98) in SCANS, 6/42 (14.3%) participants in PRESERVE, and 6/276 (2.2%) in RUN DMC showed WMH regression. On multivariate analysis, only lower WMH volume (OR: 0.36, 95% CI: 0.23-0.56) and better white matter microstructural integrity assessed by fractional anisotropy using diffusion tensor imaging (OR: 1.55, 95% CI: 1.07-2.24) predicted participant classification as regressor versus stable or progressor. DISCUSSION Only a small proportion of participants demonstrated WMH regression across the three cohorts, when a blinded standardized assessment method was used. Subjects who showed regression had less severe imaging markers of disease at baseline. Our results show that lesion regression is uncommon in SVD and unlikely to be a major factor affecting the use of WMH quantification as an outcome for clinical trials.
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Affiliation(s)
- Robin B Brown
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Daniel J Tozer
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Marco Egle
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Anil M Tuladhar
- Department of Neurology, Centre for Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Centre for Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Hugh S Markus
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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2
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Otoki Y, Yu D, Shen Q, Sahlas DJ, Ramirez J, Gao F, Masellis M, Swartz RH, Chan PC, Pettersen JA, Kato S, Nakagawa K, Black SE, Swardfager W, Taha AY. Quantitative Lipidomic Analysis of Serum Phospholipids Reveals Dissociable Markers of Alzheimer's Disease and Subcortical Cerebrovascular Disease. J Alzheimers Dis 2023; 93:665-682. [PMID: 37092220 DOI: 10.3233/jad-220795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
BACKGROUND Circulating phospholipid species have been shown to predict Alzheimer's disease (AD) prognosis but the link between phospholipid disturbances and subcortical small vessel cerebrovascular disease (CeVD) common in AD patients is not known. OBJECTIVE This study used quantitative lipidomics to measure serum diacyl, alkenyl (ether), alkyl, and lyso phospholipid species in individuals with extensive CeVD (n = 29), AD with minimal CeVD (n = 16), and AD with extensive CeVD (n = 14), and compared them to age-matched controls (n = 27). Memory was assessed using the California Verbal Learning Test. 3.0T MRI was used to assess hippocampal volume, atrophy, and white matter hyperintensity (WMH) volumes as manifestations of CeVD. RESULTS AD was associated with significantly higher concentrations of choline plasmalogen 18:0_18:1 and alkyl-phosphocholine 18:1. CeVD was associated with significantly lower lysophospholipids containing 16:0. Phospholipids containing arachidonic acid (AA) were associated with poorer memory in controls, whereas docosahexaenoic acid (DHA)-containing phospholipids were associated with better memory in individuals with AD+CeVD. In controls, DHA-containing phospholipids were associated with more atrophy and phospholipids containing linoleic acid and AA were associated with less atrophy. Lysophospholipids containing 16:0, 18:0, and 18:1 were correlated with less atrophy in controls, and of these, alkyl-phosphocholine 18:1 was correlated with smaller WMH volumes. Conversely, 16:0_18:1 choline plasmalogen was correlated with greater WMH volumes in controls. CONCLUSION This study demonstrates discernable differences in circulating phospholipids in individuals with AD and CeVD, as well as new associations between phospholipid species with memory and brain structure that were specific to contexts of commonly comorbid vascular and neurodegenerative pathologies.
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Affiliation(s)
- Yurika Otoki
- Department of Food Science and Technology, College of Agriculture and Environmental Sciences, University of California, Davis, CA, USA
- Laboratory of Food Function Analysis, Graduate School of Agricultural Science, Tohoku University, Sendai, Miyagi, Japan
| | - Di Yu
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Canada
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, Canada
- Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, Toronto, Canada
- LC Campbell Cognitive Neurology Unit, Sunnybrook Research Institute, Toronto, Canada
| | - Qing Shen
- Department of Food Science and Technology, College of Agriculture and Environmental Sciences, University of California, Davis, CA, USA
| | - Demetrios J Sahlas
- Department of Medicine (Neurology Division), McMaster University, Hamilton, Canada
| | - Joel Ramirez
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Canada
| | - Fuqiang Gao
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Canada
| | - Mario Masellis
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Canada
- Department of Medicine (Neurology Division) and the Northern Medical Program, University of British Columbia, Vancouver, Canada
| | - Richard H Swartz
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Pak Cheung Chan
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
- Department of Laboratory Medicine and Molecular Diagnostics, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Jacqueline A Pettersen
- Department of Medicine (Neurology Division) and the Northern Medical Program, University of British Columbia, Vancouver, Canada
| | - Shunji Kato
- Laboratory of Food Function Analysis, Graduate School of Agricultural Science, Tohoku University, Sendai, Miyagi, Japan
| | - Kiyotaka Nakagawa
- Laboratory of Food Function Analysis, Graduate School of Agricultural Science, Tohoku University, Sendai, Miyagi, Japan
| | - Sandra E Black
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Canada
- Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, Toronto, Canada
- LC Campbell Cognitive Neurology Unit, Sunnybrook Research Institute, Toronto, Canada
- Department of Medicine (Neurology Division), University of Toronto, Toronto, Canada
| | - Walter Swardfager
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Canada
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, Canada
- Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, Toronto, Canada
- LC Campbell Cognitive Neurology Unit, Sunnybrook Research Institute, Toronto, Canada
- University Health Network Toronto Rehabilitation Institute, Toronto, Canada
| | - Ameer Y Taha
- Department of Food Science and Technology, College of Agriculture and Environmental Sciences, University of California, Davis, CA, USA
- West Coast Metabolomics Center, Genome Center, University of California - Davis, Davis, CA, USA
- Center for Neuroscience, University of California - Davis, Davis, CA, USA
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3
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Haddad SMH, Scott CJM, Ozzoude M, Berezuk C, Holmes M, Adamo S, Ramirez J, Arnott SR, Nanayakkara ND, Binns M, Beaton D, Lou W, Sunderland K, Sujanthan S, Lawrence J, Kwan D, Tan B, Casaubon L, Mandzia J, Sahlas D, Saposnik G, Hassan A, Levine B, McLaughlin P, Orange JB, Roberts A, Troyer A, Black SE, Dowlatshahi D, Strother SC, Swartz RH, Symons S, Montero-Odasso M, ONDRI Investigators, Bartha R. Comparison of Diffusion Tensor Imaging Metrics in Normal-Appearing White Matter to Cerebrovascular Lesions and Correlation with Cerebrovascular Disease Risk Factors and Severity. Int J Biomed Imaging 2022; 2022:5860364. [PMID: 36313789 PMCID: PMC9616672 DOI: 10.1155/2022/5860364] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 04/21/2022] [Accepted: 06/01/2022] [Indexed: 11/13/2023] Open
Abstract
Alterations in tissue microstructure in normal-appearing white matter (NAWM), specifically measured by diffusion tensor imaging (DTI) fractional anisotropy (FA), have been associated with cognitive outcomes following stroke. The purpose of this study was to comprehensively compare conventional DTI measures of tissue microstructure in NAWM to diverse vascular brain lesions in people with cerebrovascular disease (CVD) and to examine associations between FA in NAWM and cerebrovascular risk factors. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were measured in cerebral tissues and cerebrovascular anomalies from 152 people with CVD participating in the Ontario Neurodegenerative Disease Research Initiative (ONDRI). Ten cerebral tissue types were segmented including NAWM, and vascular lesions including stroke, periventricular and deep white matter hyperintensities, periventricular and deep lacunar infarcts, and perivascular spaces (PVS) using T1-weighted, proton density-weighted, T2-weighted, and fluid attenuated inversion recovery MRI scans. Mean DTI metrics were measured in each tissue region using a previously developed DTI processing pipeline and compared between tissues using multivariate analysis of covariance. Associations between FA in NAWM and several CVD risk factors were also examined. DTI metrics in vascular lesions differed significantly from healthy tissue. Specifically, all tissue types had significantly different MD values, while FA was also found to be different in most tissue types. FA in NAWM was inversely related to hypertension and modified Rankin scale (mRS). This study demonstrated the differences between conventional DTI metrics, FA, MD, AD, and RD, in cerebral vascular lesions and healthy tissue types. Therefore, incorporating DTI to characterize the integrity of the tissue microstructure could help to define the extent and severity of various brain vascular anomalies. The association between FA within NAWM and clinical evaluation of hypertension and disability provides further evidence that white matter microstructural integrity is impacted by cerebrovascular function.
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Affiliation(s)
- Seyyed M. H. Haddad
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada
| | - Christopher J. M. Scott
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
| | - Miracle Ozzoude
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
| | | | - Melissa Holmes
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
| | - Sabrina Adamo
- Clinical Neurosciences, University of Toronto, Toronto, Canada
| | - Joel Ramirez
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
| | - Stephen R. Arnott
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Nuwan D. Nanayakkara
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada
| | - Malcolm Binns
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Derek Beaton
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Wendy Lou
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Kelly Sunderland
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | | | - Jane Lawrence
- Thunder Bay Regional Health Research Institute, Thunder Bay, Canada
| | | | - Brian Tan
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Leanne Casaubon
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Jennifer Mandzia
- Department of Medicine, Division of Neurology, University of Western Ontario, London, Canada
| | - Demetrios Sahlas
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | | | - Ayman Hassan
- Thunder Bay Regional Research Institute, Thunder Bay, Canada
| | - Brian Levine
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | | | - J. B. Orange
- School of Communication Sciences and Disorders, Western University, London, Canada
| | - Angela Roberts
- Roxelyn and Richard Pepper Department of Communication Sciences and Disorder, Northwestern University, Evanston, USA
| | - Angela Troyer
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Sandra E. Black
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
- Sunnybrook Health Sciences Centre, University of Toronto, Stroke Research Program, Toronto, Canada
| | | | - Stephen C. Strother
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Richard H. Swartz
- Sunnybrook Health Sciences Centre, University of Toronto, Stroke Research Program, Toronto, Canada
| | - Sean Symons
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Manuel Montero-Odasso
- Department of Medicine, Division of Geriatric Medicine, Parkwood Hospital, St. Joseph's Health Care London, London, Canada
| | - ONDRI Investigators
- Ontario Neurodegenerative Disease Initiative, Ontario Brain Institute, Toronto, Canada
| | - Robert Bartha
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada
- Department of Medical Biophysics, University of Western Ontario, London, Canada
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4
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Ferris JK, Greeley B, Vavasour IM, Kraeutner SN, Rinat S, Ramirez J, Black SE, Boyd LA. In vivo myelin imaging and tissue microstructure in white matter hyperintensities and perilesional white matter. Brain Commun 2022; 4:fcac142. [PMID: 35694147 PMCID: PMC9178967 DOI: 10.1093/braincomms/fcac142] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 03/28/2022] [Accepted: 05/26/2022] [Indexed: 11/12/2022] Open
Abstract
White matter hyperintensities negatively impact white matter structure and relate to cognitive decline in aging. Diffusion tensor imaging detects changes to white matter microstructure, both within the white matter hyperintensity and extending into surrounding (perilesional) normal-appearing white matter. However, diffusion tensor imaging markers are not specific to tissue components, complicating the interpretation of previous microstructural findings. Myelin water imaging is a novel imaging technique that provides specific markers of myelin content (myelin water fraction) and interstitial fluid (geometric mean T2). Here we combined diffusion tensor imaging and myelin water imaging to examine tissue characteristics in white matter hyperintensities and perilesional white matter in 80 individuals (47 older adults and 33 individuals with chronic stroke). To measure perilesional normal-appearing white matter, white matter hyperintensity masks were dilated in 2 mm segments up to 10 mm in distance from the white matter hyperintensity. Fractional anisotropy, mean diffusivity, myelin water fraction, and geometric mean T2 were extracted from white matter hyperintensities and perilesional white matter. We observed a spatial gradient of higher mean diffusivity and geometric mean T2, and lower fractional anisotropy, in the white matter hyperintensity and perilesional white matter. In the chronic stroke group, myelin water fraction was reduced in the white matter hyperintensity but did not show a spatial gradient in perilesional white matter. Across the entire sample, white matter metrics within the white matter hyperintensity related to whole-brain white matter hyperintensity volume; with increasing white matter hyperintensity volume there was increased mean diffusivity and geometric mean T2, and decreased myelin water fraction in the white matter hyperintensity. Normal-appearing white matter adjacent to white matter hyperintensities exhibits characteristics of a transitional stage between healthy white matter and white matter hyperintensities. This effect was observed in markers sensitive to interstitial fluid, but not in myelin water fraction, the specific marker of myelin concentration. Within the white matter hyperintensity, interstitial fluid was higher and myelin concentration was lower in individuals with more severe cerebrovascular disease. Our data suggests white matter hyperintensities have penumbra-like effects in perilesional white matter that specifically reflect increased interstitial fluid, with no changes to myelin concentration. In contrast, within the white matter hyperintensity there are varying levels of demyelination, which vary based on the severity of cerebrovascular disease. Diffusion tensor imaging and myelin imaging may be useful clinical markers to predict white matter hyperintensity formation, and to stage neuronal damage within white matter hyperintensities.
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Affiliation(s)
- Jennifer K. Ferris
- University of British Columbia Graduate Programs in Rehabilitation Sciences, , Vancouver, Canada
| | - Brian Greeley
- University of British Columbia Department of Physical Therapy, , Vancouver, Canada
| | - Irene M. Vavasour
- The University of British Columbia Department of Radiology, , Vancouver, Canada
- University of British Columbia UBC MRI Research Centre, Faculty of Medicine, , Vancouver, Canada
| | - Sarah N. Kraeutner
- University of British Columbia Department of Psychology, , Okanagan, Kelowna, Canada
| | - Shie Rinat
- University of British Columbia Graduate Programs in Rehabilitation Sciences, , Vancouver, Canada
| | - Joel Ramirez
- LC Campbell Cognitive Neurology Research Unit, Dr Sandra Black Centre for Brain Resilience and Recovery , Toronto, Canada
- Sunnybrook Research Institute, University of Toronto Hurvitz Brain Sciences Research Program, , Toronto, Canada
| | - Sandra E. Black
- LC Campbell Cognitive Neurology Research Unit, Dr Sandra Black Centre for Brain Resilience and Recovery , Toronto, Canada
- Sunnybrook Research Institute, University of Toronto Hurvitz Brain Sciences Research Program, , Toronto, Canada
| | - Lara A. Boyd
- University of British Columbia Graduate Programs in Rehabilitation Sciences, , Vancouver, Canada
- University of British Columbia Department of Physical Therapy, , Vancouver, Canada
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5
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Clancy U, Ramirez J, Chappell FM, Doubal FN, Wardlaw JM, Black SE. Neuropsychiatric symptoms as a sign of small vessel disease progression in cognitive impairment. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2022; 3:100041. [PMID: 36324402 PMCID: PMC9616231 DOI: 10.1016/j.cccb.2022.100041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/12/2022] [Accepted: 01/16/2022] [Indexed: 11/27/2022]
Abstract
Background Neuropsychiatric symptoms associate cross-sectionally with cerebral small vessel disease but it is not clear whether these symptoms could act as early clinical markers of small vessel disease progression. We investigated whether longitudinal change in Neuropsychiatric Inventory (NPI) scores associated with white matter hyperintensity (WMH) progression in a memory clinic population. Material and methods We included participants from the prospective Sunnybrook Dementia Study with Alzheimer's disease and vascular subtypes of mild cognitive impairment and dementia with two MRI and ≥ 1 NPI. We conducted linear mixed-effects analyses, adjusting for age, atrophy, vascular risk factors, cognition, function, and interscan interval. Results At baseline (n=124), greater atrophy, age, vascular risk factors and total NPI score were associated with higher baseline WMH volume, while longitudinally, all but vascular risk factors were associated. Change in total NPI score was associated with change in WMH volume, χ2 = 7.18, p = 0.007, whereby a one-point change in NPI score from baseline to follow-up was associated with a 0.0017 change in normalized WMH volume [expressed as cube root of (WMH volume cm³ as % intracranial volume)], after adjusting for age, atrophy, vascular risk factors and interscan interval. Conclusions In memory clinic patients, WMH progression over 1-2 years associated with worsening neuropsychiatric symptoms, while WMH volume remained unchanged in those with stable NPI scores in this population with low background WMH burden.
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Affiliation(s)
- Una Clancy
- Brain Research Imaging Centre, Division of Neuroimaging Sciences, Centre for Clinical Brain Sciences, UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Joel Ramirez
- Dr. Sandra Black Centre for Brain Resilience & Recovery, LC Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, 2075 Bayview Avenue, Room A4 21, Toronto, ON M4N 3M5, Canada,Corresponding author.
| | - Francesca M. Chappell
- Brain Research Imaging Centre, Division of Neuroimaging Sciences, Centre for Clinical Brain Sciences, UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Fergus N. Doubal
- Brain Research Imaging Centre, Division of Neuroimaging Sciences, Centre for Clinical Brain Sciences, UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Joanna M. Wardlaw
- Brain Research Imaging Centre, Division of Neuroimaging Sciences, Centre for Clinical Brain Sciences, UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Sandra E. Black
- Dr. Sandra Black Centre for Brain Resilience & Recovery, LC Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, 2075 Bayview Avenue, Room A4 21, Toronto, ON M4N 3M5, Canada,Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
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6
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Mirza SS, Saeed U, Ramirez J, Herrmann N, Stuss DT, Black SE, Masellis M. Effects of white matter hyperintensities, neuropsychiatric symptoms, and cognition on activities of daily living: Differences between Alzheimer's disease and dementia with Lewy bodies. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2022; 14:e12306. [PMID: 35510093 PMCID: PMC9060552 DOI: 10.1002/dad2.12306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 02/25/2022] [Accepted: 03/06/2022] [Indexed: 11/10/2022]
Affiliation(s)
- Saira Saeed Mirza
- Division of Neurology, Department of Medicine University of Toronto Toronto Ontario Canada
- Hurvitz Brain Sciences Research Program Sunnybrook Research Institute University of Toronto Toronto Ontario Canada
| | - Usman Saeed
- LC Campbell Cognitive Neurology Research Unit Sunnybrook Research Institute University of Toronto Toronto Ontario Canada
| | - Joel Ramirez
- Hurvitz Brain Sciences Research Program Sunnybrook Research Institute University of Toronto Toronto Ontario Canada
- LC Campbell Cognitive Neurology Research Unit Sunnybrook Research Institute University of Toronto Toronto Ontario Canada
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery Sunnybrook Health Sciences Centre University of Toronto Toronto Ontario Canada
| | - Nathan Herrmann
- Hurvitz Brain Sciences Research Program Sunnybrook Research Institute University of Toronto Toronto Ontario Canada
- Department of Psychiatry Faculty of Medicine University of Toronto Toronto Ontario Canada
| | - Donald T. Stuss
- Department of Psychology, Faculty of Arts and Science Department of Medicine (Neurology) Faculty of Medicine University of Toronto Toronto Ontario Canada
| | - Sandra E. Black
- Division of Neurology, Department of Medicine University of Toronto Toronto Ontario Canada
- Hurvitz Brain Sciences Research Program Sunnybrook Research Institute University of Toronto Toronto Ontario Canada
- LC Campbell Cognitive Neurology Research Unit Sunnybrook Research Institute University of Toronto Toronto Ontario Canada
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery Sunnybrook Health Sciences Centre University of Toronto Toronto Ontario Canada
| | - Mario Masellis
- Division of Neurology, Department of Medicine University of Toronto Toronto Ontario Canada
- Hurvitz Brain Sciences Research Program Sunnybrook Research Institute University of Toronto Toronto Ontario Canada
- LC Campbell Cognitive Neurology Research Unit Sunnybrook Research Institute University of Toronto Toronto Ontario Canada
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Melazzini L, Vitali P, Olivieri E, Bolchini M, Zanardo M, Savoldi F, Di Leo G, Griffanti L, Baselli G, Sardanelli F, Codari M. White Matter Hyperintensities Quantification in Healthy Adults: A Systematic Review and Meta-Analysis. J Magn Reson Imaging 2020; 53:1732-1743. [PMID: 33345393 DOI: 10.1002/jmri.27479] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 12/02/2020] [Accepted: 12/03/2020] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Although white matter hyperintensities (WMH) volumetric assessment is now customary in research studies, inconsistent WMH measures among homogenous populations may prevent the clinical usability of this biomarker. PURPOSE To determine whether a point estimate and reference standard for WMH volume in the healthy aging population could be determined. STUDY TYPE Systematic review and meta-analysis. POPULATION In all, 9716 adult subjects from 38 studies reporting WMH volume were retrieved following a systematic search on EMBASE. FIELD STRENGTH/SEQUENCE 1.0T, 1.5T, or 3.0T/fluid-attenuated inversion recovery (FLAIR) and/or proton density/T2 -weighted fast spin echo sequences or gradient echo T1 -weighted sequences. ASSESSMENT After a literature search, sample size, demographics, magnetic field strength, MRI sequences, level of automation in WMH assessment, study population, and WMH volume were extracted. STATISTICAL TESTS The pooled WMH volume with 95% confidence interval (CI) was calculated using the random-effect model. The I2 statistic was calculated as a measure of heterogeneity across studies. Meta-regression analysis of WMH volume on age was performed. RESULTS Of the 38 studies analyzed, 17 reported WMH volume as the mean and standard deviation (SD) and were included in the meta-analysis. Mean and SD of age was 66.11 ± 10.92 years (percentage of men 50.45% ± 21.48%). Heterogeneity was very high (I2 = 99%). The pooled WMH volume was 4.70 cm3 (95% CI: 3.88-5.53 cm3 ). At meta-regression analysis, WMH volume was positively associated with subjects' age (β = 0.358 cm3 per year, P < 0.05, R2 = 0.27). DATA CONCLUSION The lack of standardization in the definition of WMH together with the high technical variability in assessment may explain a large component of the observed heterogeneity. Currently, volumes of WMH in healthy subjects are not comparable between studies and an estimate and reference interval could not be determined. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Luca Melazzini
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
| | - Paolo Vitali
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Emanuele Olivieri
- Medicine and Surgery Medical School, Università degli Studi di Milano, Milano, Italy
| | - Marco Bolchini
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, Italy
| | - Moreno Zanardo
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Filippo Savoldi
- Postgraduate School in Radiology, Università degli Studi di Milano, Milano, Italy
| | - Giovanni Di Leo
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Ludovica Griffanti
- Department of Psychiatry, Wellcome Centre for Integrative Neuroimaging (WIN), University of Oxford, Oxford, UK
| | - Giuseppe Baselli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy.,Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Marina Codari
- Department of Radiology, Stanford University School of Medicine, Stanford, California, USA
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8
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Ramirez J, Holmes MF, Scott CJM, Ozzoude M, Adamo S, Szilagyi GM, Goubran M, Gao F, Arnott SR, Lawrence-Dewar JM, Beaton D, Strother SC, Munoz DP, Masellis M, Swartz RH, Bartha R, Symons S, Black SE. Ontario Neurodegenerative Disease Research Initiative (ONDRI): Structural MRI Methods and Outcome Measures. Front Neurol 2020; 11:847. [PMID: 32849254 PMCID: PMC7431907 DOI: 10.3389/fneur.2020.00847] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 07/07/2020] [Indexed: 01/18/2023] Open
Abstract
The Ontario Neurodegenerative Research Initiative (ONDRI) is a 3 years multi-site prospective cohort study that has acquired comprehensive multiple assessment platform data, including 3T structural MRI, from neurodegenerative patients with Alzheimer's disease, mild cognitive impairment, Parkinson's disease, amyotrophic lateral sclerosis, frontotemporal dementia, and cerebrovascular disease. This heterogeneous cross-section of patients with complex neurodegenerative and neurovascular pathologies pose significant challenges for standard neuroimaging tools. To effectively quantify regional measures of normal and pathological brain tissue volumes, the ONDRI neuroimaging platform implemented a semi-automated MRI processing pipeline that was able to address many of the challenges resulting from this heterogeneity. The purpose of this paper is to serve as a reference and conceptual overview of the comprehensive neuroimaging pipeline used to generate regional brain tissue volumes and neurovascular marker data that will be made publicly available online.
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Affiliation(s)
- Joel Ramirez
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Melissa F Holmes
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Christopher J M Scott
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Miracle Ozzoude
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Sabrina Adamo
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Gregory M Szilagyi
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Maged Goubran
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Fuqiang Gao
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | | | | | - Derek Beaton
- Rotman Research Institute, Baycrest, Toronto, ON, Canada
| | - Stephen C Strother
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Rotman Research Institute, Baycrest, Toronto, ON, Canada
| | - Douglas P Munoz
- Centre for Neuroscience Studies, Queen's University, Kingston, ON, Canada
| | - Mario Masellis
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, ON, Canada
| | - Richard H Swartz
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, ON, Canada
| | - Robert Bartha
- Department of Medical Biophysics, Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Sean Symons
- Department of Medical Imaging, University of Toronto, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Sandra E Black
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, ON, Canada
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9
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Haddad SMH, Scott CJM, Ozzoude M, Holmes MF, Arnott SR, Nanayakkara ND, Ramirez J, Black SE, Dowlatshahi D, Strother SC, Swartz RH, Symons S, Montero-Odasso M, Bartha R. Comparison of quality control methods for automated diffusion tensor imaging analysis pipelines. PLoS One 2019; 14:e0226715. [PMID: 31860686 PMCID: PMC6924651 DOI: 10.1371/journal.pone.0226715] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 12/02/2019] [Indexed: 12/29/2022] Open
Abstract
The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data.
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Affiliation(s)
- Seyyed M. H. Haddad
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Christopher J. M. Scott
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Miracle Ozzoude
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Melissa F. Holmes
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Stephen R. Arnott
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada
| | - Nuwan D. Nanayakkara
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Joel Ramirez
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Sandra E. Black
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
- Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre, and University of Toronto, Toronto, Ontario, Canada
| | | | - Stephen C. Strother
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Richard H. Swartz
- Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre, and University of Toronto, Toronto, Ontario, Canada
- Sunnybrook Health Sciences Centre, University of Toronto, Stroke Research Program, Toronto, Ontario, Canada
| | - Sean Symons
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Manuel Montero-Odasso
- Department of Medicine, Division of Geriatric Medicine, Parkwood Hospital, University of Western Ontario, London, Ontario, Canada
| | | | - Robert Bartha
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada
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10
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Bahrani AA, Al-Janabi OM, Abner EL, Bardach SH, Kryscio RJ, Wilcock DM, Smith CD, Jicha GA. Post-acquisition processing confounds in brain volumetric quantification of white matter hyperintensities. J Neurosci Methods 2019; 327:108391. [PMID: 31408649 DOI: 10.1016/j.jneumeth.2019.108391] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 08/03/2019] [Accepted: 08/03/2019] [Indexed: 01/18/2023]
Abstract
BACKGROUND Disparate research sites using identical or near-identical magnetic resonance imaging (MRI) acquisition techniques often produce results that demonstrate significant variability regarding volumetric quantification of white matter hyperintensities (WMH) in the aging population. The sources of such variability have not previously been fully explored. NEW METHOD 3D FLAIR sequences from a group of randomly selected aged subjects were analyzed to identify sources-of-variability in post-acquisition processing that can be problematic when comparing WMH volumetric data across disparate sites. The methods developed focused on standardizing post-acquisition protocol processing methods to develop a protocol with less than 0.5% inter-rater variance. RESULTS A series of experiments using standard MRI acquisition sequences explored post-acquisition sources-of-variability in the quantification of WMH volumetric data. Sources-of-variability included: the choice of image center, software suite and version, thresholding selection, and manual editing procedures (when used). Controlling for the identified sources-of-variability led to a protocol with less than 0.5% variability between independent raters in post-acquisition WMH volumetric quantification. COMPARISON WITH EXISTING METHOD(S) Post-acquisition processing techniques can introduce an average variance approaching 15% in WMH volume quantification despite identical scan acquisitions. Understanding and controlling for such sources-of-variability can reduce post-acquisition quantitative image processing variance to less than 0.5%. DISCUSSION Considerations of potential sources-of-variability in MRI volume quantification techniques and reduction in such variability is imperative to allow for reliable cross-site and cross-study comparisons.
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Affiliation(s)
- Ahmed A Bahrani
- Department of Biomedical Engineering, College of Engineering, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
| | - Omar M Al-Janabi
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Erin L Abner
- Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Shoshana H Bardach
- Department of Gerontology, College of Public Health, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Richard J Kryscio
- Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY, 40506, United States; Department of Statistics, College of Arts and Science, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Donna M Wilcock
- Department of Physiology, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Charles D Smith
- Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Magnetic Resonance Imaging and Spectroscopy Center (MRISC), College of Medicine, University of Kentucky, Lexington, KY, 40506, United States
| | - Gregory A Jicha
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Department of Neurology, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States; Sanders-Brown Center on Aging, College of Medicine, University of Kentucky, Lexington, KY, 40506, United States.
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11
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Guo C, Niu K, Luo Y, Shi L, Wang Z, Zhao M, Wang D, Zhu W, Zhang H, Sun L. Intra-Scanner and Inter-Scanner Reproducibility of Automatic White Matter Hyperintensities Quantification. Front Neurosci 2019; 13:679. [PMID: 31354406 PMCID: PMC6635556 DOI: 10.3389/fnins.2019.00679] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 06/13/2019] [Indexed: 11/13/2022] Open
Abstract
Objectives: To evaluate white matter hyperintensities (WMH) quantification reproducibility from multiple aspects of view and examine the effects of scan-rescan procedure, types of scanner, imaging protocols, scanner software upgrade, and automatic segmentation tools on WMH quantification results using magnetic resonance imaging (MRI). Methods: Six post-stroke subjects (4 males; mean age = 62.8, range = 58-72 years) were scanned and rescanned with both 3D T1-weighted, 2D and 3D T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) MRI across four different MRI scanners within 12 h. Two automated WMH segmentation and quantification tools were used to measure WMH volume based on each MR scan. Robustness was assessed using the coefficient of variation (CV), Dice similarity coefficient (DSC), and intra-class correlation (ICC). Results: Experimental results show that the best reproducibility was achieved by using 3D T2-FLAIR MRI under intra-scanner setting with CV ranging from 2.69 to 2.97%, while the largest variability resulted from comparing WMH volumes measured based on 2D T2-FLAIR MRI with those of 3D T2-FLAIR MRI, with CV values in the range of 15.62%-29.33%. The WMH quantification variability based on 2D MRIs is larger than 3D MRIs due to their large slice thickness. The DSC of WMH segmentation labels between intra-scanner MRIs ranges from 0.63 to 0.77, while that for inter-scanner MRIs is in the range of 0.63-0.65. In addition to image acquisition, the choice of automatic WMH segmentation tool also has a large impact on WMH quantification. Conclusion: WMH reproducibility is one of the primary issues to be considered in multicenter and longitudinal studies. The study provides solid guidance in assisting multicenter and longitudinal study design to achieve meaningful results with enough power.
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Affiliation(s)
- Chunjie Guo
- Department of Radiology, The First Hospital of Jilin University, Changchun, China
| | - Kai Niu
- Department of Otorhinolaryngology Head and Neck Surgery, The First Hospital of Jilin University, Changchun, China
| | - Yishan Luo
- BrainNow Medical Technology Limited, Sha Tin, Hong Kong
| | - Lin Shi
- BrainNow Medical Technology Limited, Sha Tin, Hong Kong.,Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Sha Tin, Hong Kong
| | - Zhuo Wang
- Department of Radiology, The First Hospital of Jilin University, Changchun, China
| | - Meng Zhao
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, China
| | - Defeng Wang
- Department of Radiology, The First Hospital of Jilin University, Changchun, China.,Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Sha Tin, Hong Kong
| | - Wan'an Zhu
- Department of Radiology, The First Hospital of Jilin University, Changchun, China
| | - Huimao Zhang
- Department of Radiology, The First Hospital of Jilin University, Changchun, China
| | - Li Sun
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, China
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12
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Shellikeri S, Myers M, Black SE, Abrahao A, Zinman L, Yunusova Y. Speech network regional involvement in bulbar ALS: a multimodal structural MRI study. Amyotroph Lateral Scler Frontotemporal Degener 2019; 20:385-395. [PMID: 31088163 DOI: 10.1080/21678421.2019.1612920] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Objective: To examine gray (GM) and white matter (WM) structural changes in regions of the speech network (SpN) in ALS patients with varying degree of bulbar disease. Methods: T1 and DTI images were obtained for 19 ALS participants and 13 neurologically-intact controls. Surface-based, volumetric, and DTI metrics were obtained for 6 regions-of-interest (ROIs) including the primary motor cortex (PMC), pars triangularis (parsT), pars opercularis (ParsO), posterior superior temporal gyrus (pSTG), and transverse temporal (TT). Disease-effects and brain-behavioral correlates between neuroanatomy and clinical measures of bulbar, limb, and overall disability were examined using linear models. Results: Structural changes were observed in the right oral and limb PMC and left ParsT, TT, and pSTG in ALS. Bulbar motor dysfunction was associated with WM abnormalities in the right oral PMC and left pSTG, and GM changes in bilateral TT. In contrast, symptom progression rate predicted GM and WM changes in bilateral pars opercularis (part of Broca's area). Grip strength and disease duration models were non-significant. Conclusions: The findings suggested that regions of the left-dominant SpN may be implicated in ALS and degeneration of these areas are related to bulbar disease severity. Involvement of regions that overlap across multiple connectomes such as Broca's area, however, may be dependent on the rate of disease progression. The work contributes to our understanding of bulbar ALS subtype, which is crucial for predicting disease progression, delivering targeted clinical care, and appropriate recruitment into clinical trials.
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Affiliation(s)
- Sanjana Shellikeri
- a Department of Speech Language Pathology , University of Toronto , Ontario , Canada.,b Hurvitz Brain Sciences Program , Sunnybrook Research Institute , Ontario , Canada
| | - Matthew Myers
- b Hurvitz Brain Sciences Program , Sunnybrook Research Institute , Ontario , Canada
| | - Sandra E Black
- b Hurvitz Brain Sciences Program , Sunnybrook Research Institute , Ontario , Canada.,c L.C. Campbell Cognitive Neurology Research Unit , Sunnybrook Research Institute, University of Toronto , Toronto , Canada.,d Department of Medicine, Division of Neurology , Sunnybrook Health Sciences Centre , Toronto , Canada.,e Rotman Research Institute, Baycrest , Toronto , Canada , and
| | - Agessandro Abrahao
- b Hurvitz Brain Sciences Program , Sunnybrook Research Institute , Ontario , Canada.,d Department of Medicine, Division of Neurology , Sunnybrook Health Sciences Centre , Toronto , Canada
| | - Lorne Zinman
- b Hurvitz Brain Sciences Program , Sunnybrook Research Institute , Ontario , Canada.,c L.C. Campbell Cognitive Neurology Research Unit , Sunnybrook Research Institute, University of Toronto , Toronto , Canada.,d Department of Medicine, Division of Neurology , Sunnybrook Health Sciences Centre , Toronto , Canada
| | - Yana Yunusova
- a Department of Speech Language Pathology , University of Toronto , Ontario , Canada.,b Hurvitz Brain Sciences Program , Sunnybrook Research Institute , Ontario , Canada.,f University Health Network, Toronto Rehabilitation Institute , Ontario , Canada
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13
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Frontal Anatomical Correlates of Cognitive and Speech Motor Deficits in Amyotrophic Lateral Sclerosis. Behav Neurol 2019; 2019:9518309. [PMID: 31001362 PMCID: PMC6436339 DOI: 10.1155/2019/9518309] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 10/25/2018] [Accepted: 12/11/2018] [Indexed: 01/15/2023] Open
Abstract
The goal of this study was to identify neurostructural frontal lobe correlates of cognitive and speaking rate changes in amyotrophic lateral sclerosis (ALS). 17 patients diagnosed with ALS and 12 matched controls underwent clinical, bulbar, and neuropsychological assessment and structural neuroimaging. Neuropsychological testing was performed via a novel computerized frontal battery (ALS-CFB), based on a validated theoretical model of frontal lobe functions, and focused on testing energization, executive function, emotion processing, theory of mind, and behavioral inhibition via antisaccades. The measure of speaking rate represented bulbar motor changes. Neuroanatomical assessment was performed using volumetric analyses focused on frontal lobe regions, postcentral gyrus, and occipital lobes as controls. Partial least square regressions (PLS) were used to predict behavioral (cognitive and speech rate) outcomes using volumetric measures. The data supported the overall hypothesis that distinct behavioral changes in cognition and speaking rate in ALS were related to specific regional neurostructural brain changes. These changes did not support a notion of a general dysexecutive syndrome in ALS. The observed specificity of behavior-brain changes can begin to provide a framework for subtyping of ALS. The data also support a more integrative framework for clinical assessment of frontal lobe functioning in ALS, which requires both behavioral testing and neuroimaging.
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14
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Soluble Epoxide Hydrolase-Derived Linoleic Acid Oxylipins in Serum Are Associated with Periventricular White Matter Hyperintensities and Vascular Cognitive Impairment. Transl Stroke Res 2018; 10:522-533. [DOI: 10.1007/s12975-018-0672-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 10/15/2018] [Accepted: 10/18/2018] [Indexed: 12/27/2022]
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15
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Cover KS, van Schijndel RA, Bosco P, Damangir S, Redolfi A. Can measuring hippocampal atrophy with a fully automatic method be substantially less noisy than manual segmentation over both 1 and 3 years? Psychiatry Res Neuroimaging 2018; 280:39-47. [PMID: 30149361 DOI: 10.1016/j.pscychresns.2018.06.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Revised: 06/26/2018] [Accepted: 06/27/2018] [Indexed: 01/20/2023]
Abstract
To quantify the "segmentation noise" of several widely used fully automatic methods for measuring longitudinal hippocampal atrophy in Alzheimer's disease and compare the results to the segmentation noise of manual segmentation over both 1 and 3 years. The segmentation noise of 5 longitudinal hippocampal atrophy measurement methods was quantified, including checking its Gaussianity, using 264 subjects from the ADNI1 back-to-back (BTB) data set over both 1 year and 3 year intervals. The segmentation methods were FreeSurfer 5.3.0 both cross sectional and longitudinal, FreeSurfer 6.0.0 longitudinal, MAPS-HBSI and FSL/FIRST 5.0.8. The BTB manual segmentation of 75 ADNI subjects from a previous study provided the manual distributions for comparison. All methods, including the manual segmentation, violated the Gaussianity assumption. Two methods, FreeSurfer 6.0.0 and MAPS-HBSI, had a segmentation noise substantially less than a surrogate for manual segmentation. FreeSurfer 5.3.0 longitudinal was confirmed as a surrogate for manual segmentation. The violation of the Gaussian assumption by the segmentation methods assessed, including manual, suggests results of previous studies that assumed Gaussian statistics without confirmation may need review. Fully automatic FreeSurfer 6.0.0 and MAPS-HBSI both have lower segmentation noise than manual requiring less than two thirds of the subjects to detect the same treatment effect.
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Affiliation(s)
- Keith S Cover
- Amsterdam University Medical Center, Amsterdam, The Netherlands.
| | | | - Paolo Bosco
- National Institute for Nuclear Physics, Pisa, Italy
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16
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Swardfager W, Cogo-Moreira H, Masellis M, Ramirez J, Herrmann N, Edwards JD, Saleem M, Chan P, Yu D, Nestor SM, Scott CJM, Holmes MF, Sahlas DJ, Kiss A, Oh PI, Strother SC, Gao F, Stefanovic B, Keith J, Symons S, Swartz RH, Lanctôt KL, Stuss DT, Black SE. The effect of white matter hyperintensities on verbal memory: Mediation by temporal lobe atrophy. Neurology 2018; 90:e673-e682. [PMID: 29374101 DOI: 10.1212/wnl.0000000000004983] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Accepted: 11/27/2017] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To determine the relationship between white matter hyperintensities (WMH) presumed to indicate disease of the cerebral small vessels, temporal lobe atrophy, and verbal memory deficits in Alzheimer disease (AD) and other dementias. METHODS We recruited groups of participants with and without AD, including strata with extensive WMH and minimal WMH, into a cross-sectional proof-of-principle study (n = 118). A consecutive case series from a memory clinic was used as an independent validation sample (n = 702; Sunnybrook Dementia Study; NCT01800214). We assessed WMH volume and left temporal lobe atrophy (measured as the brain parenchymal fraction) using structural MRI and verbal memory using the California Verbal Learning Test. Using path modeling with an inferential bootstrapping procedure, we tested an indirect effect of WMH on verbal recall that depends sequentially on temporal lobe atrophy and verbal learning. RESULTS In both samples, WMH predicted poorer verbal recall, specifically due to temporal lobe atrophy and poorer verbal learning (proof-of-principle -1.53, 95% bootstrap confidence interval [CI] -2.45 to -0.88; and confirmation -0.66, 95% CI [-0.95 to -0.41] words). This pathway was significant in subgroups with (-0.20, 95% CI [-0.38 to -0.07] words, n = 363) and without (-0.71, 95% CI [-1.12 to -0.37] words, n = 339) AD. Via the identical pathway, WMH contributed to deficits in recognition memory (-1.82%, 95% CI [-2.64% to -1.11%]), a sensitive and specific sign of AD. CONCLUSIONS Across dementia syndromes, WMH contribute indirectly to verbal memory deficits considered pathognomonic of Alzheimer disease, specifically by contributing to temporal lobe atrophy.
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Affiliation(s)
- Walter Swardfager
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada.
| | - Hugo Cogo-Moreira
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Mario Masellis
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Joel Ramirez
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Nathan Herrmann
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Jodi D Edwards
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Mahwesh Saleem
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Parco Chan
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Di Yu
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Sean M Nestor
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Christopher J M Scott
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Melissa F Holmes
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Demetrios J Sahlas
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Alexander Kiss
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Paul I Oh
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Stephen C Strother
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Fuqiang Gao
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Bojana Stefanovic
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Julia Keith
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Sean Symons
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Richard H Swartz
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Krista L Lanctôt
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Donald T Stuss
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
| | - Sandra E Black
- From Sunnybrook Research Institute (W.S., M.M., J.R., N.H., J.D.E., M.S., P.C., D.Y., S.M.N., C.J.M.S., M.F.H., A.K., P.I.O., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.), Toronto; University of Toronto (W.S., M.M., N.H., M.S., P.C., D.Y., S.M.N., A.K., P.I.O., S.C.S., F.G., B.S., J.K., S.S., R.H.S., K.L.L., D.T.S., S.E.B.); University Health Network Toronto Rehabilitation Institute (W.S., P.I.O., K.L.L.), Canada; Universidade Federal de São Paulo (H.C.-M.), Brazil; McMaster University (D.J.S., S.C.S.), Hamilton; and Rotman Research Institute (D.T.S.), Baycrest, Toronto, Canada
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17
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Swardfager W, Yu D, Scola G, Cogo-Moreira H, Chan P, Zou Y, Herrmann N, Lanctôt KL, Ramirez J, Gao F, Masellis M, Swartz RH, Sahlas DJ, Chan PC, Ojeda-Lopez C, Milan-Tomas A, Pettersen JA, Andreazza AC, Black SE. Peripheral lipid oxidative stress markers are related to vascular risk factors and subcortical small vessel disease. Neurobiol Aging 2017; 59:91-97. [DOI: 10.1016/j.neurobiolaging.2017.06.029] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Revised: 06/19/2017] [Accepted: 06/30/2017] [Indexed: 11/28/2022]
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18
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De Guio F, Jouvent E, Biessels GJ, Black SE, Brayne C, Chen C, Cordonnier C, De Leeuw FE, Dichgans M, Doubal F, Duering M, Dufouil C, Duzel E, Fazekas F, Hachinski V, Ikram MA, Linn J, Matthews PM, Mazoyer B, Mok V, Norrving B, O'Brien JT, Pantoni L, Ropele S, Sachdev P, Schmidt R, Seshadri S, Smith EE, Sposato LA, Stephan B, Swartz RH, Tzourio C, van Buchem M, van der Lugt A, van Oostenbrugge R, Vernooij MW, Viswanathan A, Werring D, Wollenweber F, Wardlaw JM, Chabriat H. Reproducibility and variability of quantitative magnetic resonance imaging markers in cerebral small vessel disease. J Cereb Blood Flow Metab 2016; 36:1319-37. [PMID: 27170700 PMCID: PMC4976752 DOI: 10.1177/0271678x16647396] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Accepted: 03/20/2016] [Indexed: 12/11/2022]
Abstract
Brain imaging is essential for the diagnosis and characterization of cerebral small vessel disease. Several magnetic resonance imaging markers have therefore emerged, providing new information on the diagnosis, progression, and mechanisms of small vessel disease. Yet, the reproducibility of these small vessel disease markers has received little attention despite being widely used in cross-sectional and longitudinal studies. This review focuses on the main small vessel disease-related markers on magnetic resonance imaging including: white matter hyperintensities, lacunes, dilated perivascular spaces, microbleeds, and brain volume. The aim is to summarize, for each marker, what is currently known about: (1) its reproducibility in studies with a scan-rescan procedure either in single or multicenter settings; (2) the acquisition-related sources of variability; and, (3) the techniques used to minimize this variability. Based on the results, we discuss technical and other challenges that need to be overcome in order for these markers to be reliably used as outcome measures in future clinical trials. We also highlight the key points that need to be considered when designing multicenter magnetic resonance imaging studies of small vessel disease.
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Affiliation(s)
- François De Guio
- University Paris Diderot, Sorbonne Paris Cité, UMRS 1161 INSERM, Paris, France DHU NeuroVasc, Sorbonne Paris Cité, Paris, France
| | - Eric Jouvent
- University Paris Diderot, Sorbonne Paris Cité, UMRS 1161 INSERM, Paris, France DHU NeuroVasc, Sorbonne Paris Cité, Paris, France Department of Neurology, AP-HP, Lariboisière Hospital, Paris, France
| | - Geert Jan Biessels
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sandra E Black
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Carol Brayne
- Department of Public Health and Primary Care, Cambridge University, Cambridge, UK
| | - Christopher Chen
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | | | - Frank-Eric De Leeuw
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Department of Neurology, Nijmegen, The Netherlands
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilian-University (LMU), Munich, Germany Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Fergus Doubal
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Marco Duering
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilian-University (LMU), Munich, Germany
| | | | - Emrah Duzel
- Department of Cognitive Neurology and Dementia Research, University of Magdeburg, Magdeburg, Germany
| | - Franz Fazekas
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Vladimir Hachinski
- Department of Clinical Neurological Sciences, University of Western Ontario, London, Canada
| | - M Arfan Ikram
- Department of Radiology and Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands Department of Neurology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jennifer Linn
- Department of Neuroradiology, University Hospital Munich, Munich, Germany
| | - Paul M Matthews
- Department of Medicine, Division of Brain Sciences, Imperial College London, London, UK
| | | | - Vincent Mok
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Bo Norrving
- Department of Clinical Sciences, Neurology, Lund University, Lund, Sweden
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | | | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Perminder Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Reinhold Schmidt
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Sudha Seshadri
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Eric E Smith
- Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Luciano A Sposato
- Department of Clinical Neurological Sciences, University of Western Ontario, London, Canada
| | - Blossom Stephan
- Institute of Health and Society, Newcastle University Institute of Ageing, Newcastle University, Newcastle, UK
| | - Richard H Swartz
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | | | - Mark van Buchem
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Aad van der Lugt
- Department of Radiology and Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | | | - Meike W Vernooij
- Department of Radiology and Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Anand Viswanathan
- Department of Neurology, J. Philip Kistler Stroke Research Center, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - David Werring
- Department of Brain Repair and Rehabilitation, Stroke Research Group, UCL, London, UK
| | - Frank Wollenweber
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilian-University (LMU), Munich, Germany
| | - Joanna M Wardlaw
- Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), University of Edinburgh, Edinburgh, UK
| | - Hugues Chabriat
- University Paris Diderot, Sorbonne Paris Cité, UMRS 1161 INSERM, Paris, France DHU NeuroVasc, Sorbonne Paris Cité, Paris, France Department of Neurology, AP-HP, Lariboisière Hospital, Paris, France
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19
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Ramirez J, McNeely AA, Scott CJ, Stuss DT, Black SE. Subcortical hyperintensity volumetrics in Alzheimer's disease and normal elderly in the Sunnybrook Dementia Study: correlations with atrophy, executive function, mental processing speed, and verbal memory. ALZHEIMERS RESEARCH & THERAPY 2014; 6:49. [PMID: 25478020 PMCID: PMC4255416 DOI: 10.1186/alzrt279] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2013] [Accepted: 07/15/2014] [Indexed: 01/18/2023]
Abstract
INTRODUCTION Subcortical hyperintensities (SHs) are radiological entities commonly observed on magnetic resonance imaging (MRI) of patients with Alzheimer's disease (AD) and normal elderly controls. Although the presence of SH is believed to indicate some form of subcortical vasculopathy, pathological heterogeneity, methodological differences, and the contribution of brain atrophy associated with AD pathology have yielded inconsistent results in the literature. METHODS Using the Lesion Explorer (LE) MRI processing pipeline for SH quantification and brain atrophy, this study examined SH volumes of interest and cognitive function in a sample of patients with AD (n = 265) and normal elderly controls (n = 100) from the Sunnybrook Dementia Study. RESULTS Compared with healthy controls, patients with AD were found to have less gray matter, less white matter, and more sulcal and ventricular cerebrospinal fluid (all significant, P <0.0001). Additionally, patients with AD had greater volumes of whole-brain SH (P <0.01), periventricular SH (pvSH) (P <0.01), deep white SH (dwSH) (P <0.05), and lacunar lesions (P <0.0001). In patients with AD, regression analyses revealed a significant association between global atrophy and pvSH (P = 0.02) and ventricular atrophy with whole-brain SH (P <0.0001). Regional volumes of interest revealed significant correlations with medial middle frontal SH volume and executive function (P <0.001) in normal controls but not in patients with AD, global pvSH volume and mental processing speed (P <0.01) in patients with AD, and left temporal SH volume and memory (P <0.01) in patients with AD. CONCLUSIONS These brain-behavior relationships and correlations with brain atrophy suggest that subtle, yet measurable, signs of small vessel disease may have potential clinical relevance as targets for treatment in Alzheimer's dementia.
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Affiliation(s)
- Joel Ramirez
- LC Campbell Cognitive Neurology Research Unit, 2075 Bayview Avenue, Room A4 21, Toronto, ON M4N 3M5, Canada ; Heart & Stroke Foundation Canadian Partnership for Stroke Recovery, Toronto, ON, Canada ; Sunnybrook Health Sciences Centre, Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Alicia A McNeely
- LC Campbell Cognitive Neurology Research Unit, 2075 Bayview Avenue, Room A4 21, Toronto, ON M4N 3M5, Canada ; Heart & Stroke Foundation Canadian Partnership for Stroke Recovery, Toronto, ON, Canada ; Sunnybrook Health Sciences Centre, Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Christopher Jm Scott
- LC Campbell Cognitive Neurology Research Unit, 2075 Bayview Avenue, Room A4 21, Toronto, ON M4N 3M5, Canada ; Heart & Stroke Foundation Canadian Partnership for Stroke Recovery, Toronto, ON, Canada ; Sunnybrook Health Sciences Centre, Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Donald T Stuss
- Faculty of Medicine, Institute of Medical Science, University of Toronto, Toronto, ON, Canada ; Rotman Research Institute, Baycrest, Toronto, ON, Canada ; Ontario Brain Institute, Toronto, ON, Canada
| | - Sandra E Black
- LC Campbell Cognitive Neurology Research Unit, 2075 Bayview Avenue, Room A4 21, Toronto, ON M4N 3M5, Canada ; Heart & Stroke Foundation Canadian Partnership for Stroke Recovery, Toronto, ON, Canada ; Sunnybrook Health Sciences Centre, Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada ; Faculty of Medicine, Institute of Medical Science, University of Toronto, Toronto, ON, Canada ; Rotman Research Institute, Baycrest, Toronto, ON, Canada
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20
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Cannon TD, Sun F, McEwen SJ, Papademetris X, He G, van Erp TGM, Jacobson A, Bearden CE, Walker E, Hu X, Zhou L, Seidman LJ, Thermenos HW, Cornblatt B, Olvet DM, Perkins D, Belger A, Cadenhead K, Tsuang M, Mirzakhanian H, Addington J, Frayne R, Woods SW, McGlashan TH, Constable RT, Qiu M, Mathalon DH, Thompson P, Toga AW. Reliability of neuroanatomical measurements in a multisite longitudinal study of youth at risk for psychosis. Hum Brain Mapp 2014; 35:2424-34. [PMID: 23982962 PMCID: PMC3843968 DOI: 10.1002/hbm.22338] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2012] [Revised: 05/14/2013] [Accepted: 05/16/2013] [Indexed: 11/10/2022] Open
Abstract
Multisite longitudinal neuroimaging designs are used to identify differential brain structural change associated with onset or progression of disease. The reliability of neuroanatomical measurements over time and across sites is a crucial aspect of power in such studies. Prior work has found that while within-site reliabilities of neuroanatomical measurements are excellent, between-site reliability is generally more modest. Factors that may increase between-site reliability include standardization of scanner platform and sequence parameters and correction for between-scanner variations in gradient nonlinearities. Factors that may improve both between- and within-site reliability include use of registration algorithms that account for individual differences in cortical patterning and shape. In this study 8 healthy volunteers were scanned twice on successive days at 8 sites participating in the North American Prodrome Longitudinal Study (NAPLS). All sites employed 3 Tesla scanners and standardized acquisition parameters. Site accounted for 2 to 30% of the total variance in neuroanatomical measurements. However, site-related variations were trivial (<1%) among sites using the same scanner model and 12-channel coil or when correcting for between-scanner differences in gradient nonlinearity and scaling. Adjusting for individual differences in sulcal-gyral geometries yielded measurements with greater reliabilities than those obtained using an automated approach. Neuroimaging can be performed across multiple sites at the same level of reliability as at a single site, achieving within- and between-site reliabilities of 0.95 or greater for gray matter density in the majority of voxels in the prefrontal and temporal cortical surfaces as well as for the volumes of most subcortical structures.
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21
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Craik FIM, Barense MD, Rathbone CJ, Grusec JE, Stuss DT, Gao F, Scott CJM, Black SE. VL: a further case of erroneous recollection. Neuropsychologia 2014; 56:367-80. [PMID: 24560915 DOI: 10.1016/j.neuropsychologia.2014.02.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2013] [Revised: 02/06/2014] [Accepted: 02/08/2014] [Indexed: 11/19/2022]
Abstract
We report a single-case study of a female patient (VL) who exhibited frequent episodes of erroneous recollections triggered by everyday events. Based on neuropsychological testing, VL was classified as suffering from mild to moderate dementia (MMSE=18) and was given a diagnosis of probable Alzheimer׳s disease. Her memory functions were uniformly impaired but her verbal abilities were generally well preserved. A structural MRI scan showed extensive areas of gray matter atrophy particularly in frontal and medial-temporal (MTL) areas. Results of experimental recognition tests showed that VL had very high false alarm rates on tests using pictures, faces and auditory stimuli, but lower false alarm rates on verbal tests. We provide a speculative account of her erroneous recollections in terms of her MTL and frontal pathology. In outline, we suggest that owing to binding failures in MTL regions, VL׳s recognition processes were forced to rely on earlier than normal stages of analysis. Environmental features on a given recognition trial may have combined with fragments persisting from previous trials resulting in erroneous feelings of familiarity and of recollection that were not discounted or edited out, due to her impaired frontal processes.
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Affiliation(s)
- Fergus I M Craik
- Rotman Research Institute, Toronto, ON, Canada M6A 2E1; University of Toronto, ON, Canada.
| | - Morgan D Barense
- Rotman Research Institute, Toronto, ON, Canada M6A 2E1; University of Toronto, ON, Canada
| | - Clare J Rathbone
- Rotman Research Institute, Toronto, ON, Canada M6A 2E1; Oxford Brookes University, Oxford, UK
| | | | - Donald T Stuss
- Rotman Research Institute, Toronto, ON, Canada M6A 2E1; University of Toronto, ON, Canada
| | - Fuqiang Gao
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | | | - Sandra E Black
- Rotman Research Institute, Toronto, ON, Canada M6A 2E1; University of Toronto, ON, Canada; Sunnybrook Health Sciences Centre, Toronto, ON, Canada
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