1
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Jack CR, Arani A, Borowski BJ, Cash DM, Crawford K, Das SR, DeCarli C, Fletcher E, Fox NC, Gunter JL, Ittyerah R, Harvey DJ, Jahanshad N, Maillard P, Malone IB, Nir TM, Reid RI, Reyes DA, Schwarz CG, Senjem ML, Thomas DL, Thompson PM, Tosun D, Yushkevich PA, Ward CP, Weiner MW. Overview of ADNI MRI. Alzheimers Dement 2024. [PMID: 39258539 DOI: 10.1002/alz.14166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 07/09/2024] [Accepted: 07/11/2024] [Indexed: 09/12/2024]
Abstract
The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi-platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1-weighted and fluid-attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. HIGHLIGHTS: The MRI Core provides multi-platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups. The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current. As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF-fMRI; and 2861 HighResHippo (see Table 1 for abbreviations). As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM-SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF-fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations). ADNI MRI is an underutilized resource that could be more useful to the research community.
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Affiliation(s)
- Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Arvin Arani
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Bret J Borowski
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Dave M Cash
- Dementia Research Centre, University College London Institute of Neurology, Queen Square, London, UK
| | - Karen Crawford
- Laboratory of Neuro Imaging (LONI), University of Southern California, Los Angeles, California, USA
| | - Sandhitsu R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Charles DeCarli
- Department of Neurology, University of California, Davis, California, USA
| | - Evan Fletcher
- Department of Neurology, University of California, Davis, California, USA
| | - Nick C Fox
- Dementia Research Centre, University College London Institute of Neurology, Queen Square, London, UK
| | | | - Ranjit Ittyerah
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Danielle J Harvey
- Department of Public Health Sciences, Division of Biostatistics, University of California, Davis, California, USA
| | - Neda Jahanshad
- Keck School of Medicine of USC, Los Angeles, California, USA
| | - Pauline Maillard
- Department of Neurology, University of California, Davis, California, USA
| | - Ian B Malone
- Dementia Research Centre, University College London Institute of Neurology, Queen Square, London, UK
| | - Talia M Nir
- Keck School of Medicine of USC, Los Angeles, California, USA
| | - Robert I Reid
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Denise A Reyes
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Matthew L Senjem
- Department of Information Technology, Mayo Clinic, Rochester, Minnesota, USA
| | - David L Thomas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - Paul M Thompson
- Laboratory of Neuro Imaging (LONI), University of Southern California, Los Angeles, California, USA
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Paul A Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Chadwick P Ward
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Michael W Weiner
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
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2
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James SN, Manning EN, Storey M, Nicholas JM, Coath W, Keuss SE, Cash DM, Lane CA, Parker T, Keshavan A, Buchanan SM, Wagen A, Harris M, Malone I, Lu K, Needham LP, Street R, Thomas D, Dickson J, Murray-Smith H, Wong A, Freiberger T, Crutch SJ, Fox NC, Richards M, Barkhof F, Sudre CH, Barnes J, Schott JM. Neuroimaging, clinical and life course correlates of normal-appearing white matter integrity in 70-year-olds. Brain Commun 2023; 5:fcad225. [PMID: 37680671 PMCID: PMC10481255 DOI: 10.1093/braincomms/fcad225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 05/30/2023] [Accepted: 08/17/2023] [Indexed: 09/09/2023] Open
Abstract
We investigate associations between normal-appearing white matter microstructural integrity in cognitively normal ∼70-year-olds and concurrently measured brain health and cognition, demographics, genetics and life course cardiovascular health. Participants born in the same week in March 1946 (British 1946 birth cohort) underwent PET-MRI around age 70. Mean standardized normal-appearing white matter integrity metrics (fractional anisotropy, mean diffusivity, neurite density index and orientation dispersion index) were derived from diffusion MRI. Linear regression was used to test associations between normal-appearing white matter metrics and (i) concurrent measures, including whole brain volume, white matter hyperintensity volume, PET amyloid and cognition; (ii) the influence of demographic and genetic predictors, including sex, childhood cognition, education, socio-economic position and genetic risk for Alzheimer's disease (APOE-ɛ4); (iii) systolic and diastolic blood pressure and cardiovascular health (Framingham Heart Study Cardiovascular Risk Score) across adulthood. Sex interactions were tested. Statistical significance included false discovery rate correction (5%). Three hundred and sixty-two participants met inclusion criteria (mean age 70, 49% female). Higher white matter hyperintensity volume was associated with lower fractional anisotropy [b = -0.09 (95% confidence interval: -0.11, -0.06), P < 0.01], neurite density index [b = -0.17 (-0.22, -0.12), P < 0.01] and higher mean diffusivity [b = 0.14 (-0.10, -0.17), P < 0.01]; amyloid (in men) was associated with lower fractional anisotropy [b = -0.04 (-0.08, -0.01), P = 0.03)] and higher mean diffusivity [b = 0.06 (0.01, 0.11), P = 0.02]. Framingham Heart Study Cardiovascular Risk Score in later-life (age 69) was associated with normal-appearing white matter {lower fractional anisotropy [b = -0.06 (-0.09, -0.02) P < 0.01], neurite density index [b = -0.10 (-0.17, -0.03), P < 0.01] and higher mean diffusivity [b = 0.09 (0.04, 0.14), P < 0.01]}. Significant sex interactions (P < 0.05) emerged for midlife cardiovascular health (age 53) and normal-appearing white matter at 70: marginal effect plots demonstrated, in women only, normal-appearing white matter was associated with higher midlife Framingham Heart Study Cardiovascular Risk Score (lower fractional anisotropy and neurite density index), midlife systolic (lower fractional anisotropy, neurite density index and higher mean diffusivity) and diastolic (lower fractional anisotropy and neurite density index) blood pressure and greater blood pressure change between 43 and 53 years (lower fractional anisotropy and neurite density index), independently of white matter hyperintensity volume. In summary, poorer normal-appearing white matter microstructural integrity in ∼70-year-olds was associated with measures of cerebral small vessel disease, amyloid (in males) and later-life cardiovascular health, demonstrating how normal-appearing white matter can provide additional information to overt white matter disease. Our findings further show that greater 'midlife' cardiovascular risk and higher blood pressure were associated with poorer normal-appearing white matter microstructural integrity in females only, suggesting that women's brains may be more susceptible to the effects of midlife blood pressure and cardiovascular health.
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Affiliation(s)
- Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Emily N Manning
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Mathew Storey
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jennifer M Nicholas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah E Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Thomas Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ashvini Keshavan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah M Buchanan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Aaron Wagen
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Mathew Harris
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ian Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Kirsty Lu
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Louisa P Needham
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
| | - Rebecca Street
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - David Thomas
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - John Dickson
- Institute of Nuclear Medicine, University College London Hospitals Foundation Trust, London, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
| | - Tamar Freiberger
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sebastian J Crutch
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
| | - Frederik Barkhof
- Centre for Medical Image Computing, University College London, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
- School of Biomedical Engineering, King’s College, London, UK
| | - Josephine Barnes
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jonathan M Schott
- MRC Unit for Lifelong Health and Ageing at UCL, Institute of Cardiovascular Science, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
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3
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Nadian MH, Farmani S, Ghazizadeh A. A novel methodology for exact targeting of human and non-human primate brain structures and skull implants using atlas-based 3D reconstruction. J Neurosci Methods 2023; 391:109851. [PMID: 37028519 DOI: 10.1016/j.jneumeth.2023.109851] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/18/2023] [Accepted: 04/03/2023] [Indexed: 04/09/2023]
Abstract
BACKGROUND Accurate targeting of brain areas for stimulation and/or electrophysiological recording is key in many therapeutic applications and basic neuroscience research. Nevertheless, there are currently no end-to-end packages that accommodate all steps required for exact localization, visualization and targeting regions of interest (ROIs) using standard atlases and for designing skull implants. NEW METHOD We have implemented a new processing pipeline that addresses this issue in macaques and humans including various preprocessing, registration, warping procedures and 3D reconstructions and provide a noncommercial open-source graphical software which we refer to as the MATLAB-based reconstruction for recording and stimulation (MATres). RESULTS The results of skull stripping were shown to work seamlessly in humans and monkeys. Linear and nonlinear warping of the standard atlas to the native space outperformed state-of-the-art using AFNI with improvements being more prominent in humans which had a more complex gyration geometry. The skull surface extracted by MATres using MRI images had more than 90% match with CT ground truth and could be used to design skull implants that conformed well to the skull's local curvature. COMPARISON WITH EXISTING METHOD(S) The accuracy of the various steps including skull stripping, standard atlas registration and skull reconstruction in MATres was compared with and shown to outperform the AFNI. The localization accuracy of the recording chambers designed with MATres and implanted in two macaque monkeys was further confirmed using MRI imaging. CONCLUSIONS Precise localization of ROIs offered by MATres can be used for planning electrode penetrations for recording and for shallow or deep brain stimulation (DBS).
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Affiliation(s)
- Mohammad Hossein Nadian
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
| | - Sepideh Farmani
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Ali Ghazizadeh
- Bio-intelligence Research Unit, Sharif Brain Center, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran.
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4
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Green RE, Lord J, Scelsi MA, Xu J, Wong A, Naomi-James S, Handy A, Gilchrist L, Williams DM, Parker TD, Lane CA, Malone IB, Cash DM, Sudre CH, Coath W, Thomas DL, Keuss S, Dobson R, Legido-Quigley C, Fox NC, Schott JM, Richards M, Proitsi P. Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer's disease. Alzheimers Res Ther 2023; 15:38. [PMID: 36814324 PMCID: PMC9945600 DOI: 10.1186/s13195-023-01184-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 02/08/2023] [Indexed: 02/24/2023]
Abstract
BACKGROUND Identifying blood-based signatures of brain health and preclinical pathology may offer insights into early disease mechanisms and highlight avenues for intervention. Here, we systematically profiled associations between blood metabolites and whole-brain volume, hippocampal volume, and amyloid-β status among participants of Insight 46-the neuroscience sub-study of the National Survey of Health and Development (NSHD). We additionally explored whether key metabolites were associated with polygenic risk for Alzheimer's disease (AD). METHODS Following quality control, levels of 1019 metabolites-detected with liquid chromatography-mass spectrometry-were available for 1740 participants at age 60-64. Metabolite data were subsequently clustered into modules of co-expressed metabolites using weighted coexpression network analysis. Accompanying MRI and amyloid-PET imaging data were present for 437 participants (age 69-71). Regression analyses tested relationships between metabolite measures-modules and hub metabolites-and imaging outcomes. Hub metabolites were defined as metabolites that were highly connected within significant (pFDR < 0.05) modules or were identified as a hub in a previous analysis on cognitive function in the same cohort. Regression models included adjustments for age, sex, APOE genotype, lipid medication use, childhood cognitive ability, and social factors. Finally, associations were tested between AD polygenic risk scores (PRS), including and excluding the APOE region, and metabolites and modules that significantly associated (pFDR < 0.05) with an imaging outcome (N = 1638). RESULTS In the fully adjusted model, three lipid modules were associated with a brain volume measure (pFDR < 0.05): one enriched in sphingolipids (hippocampal volume: ß = 0.14, 95% CI = [0.055,0.23]), one in several fatty acid pathways (whole-brain volume: ß = - 0.072, 95%CI = [- 0.12, - 0.026]), and another in diacylglycerols and phosphatidylethanolamines (whole-brain volume: ß = - 0.066, 95% CI = [- 0.11, - 0.020]). Twenty-two hub metabolites were associated (pFDR < 0.05) with an imaging outcome (whole-brain volume: 22; hippocampal volume: 4). Some nominal associations were reported for amyloid-β, and with an AD PRS in our genetic analysis, but none survived multiple testing correction. CONCLUSIONS Our findings highlight key metabolites, with functions in membrane integrity and cell signalling, that associated with structural brain measures in later life. Future research should focus on replicating this work and interrogating causality.
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Affiliation(s)
- Rebecca E Green
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.,UK National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre, South London and Maudsley Trust, London, UK
| | - Jodie Lord
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK
| | - Marzia A Scelsi
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK
| | - Jin Xu
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.,Institute of Pharmaceutical Science, King's College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK
| | - Sarah Naomi-James
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.,Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Alex Handy
- University College London, Institute of Health Informatics, London, UK
| | - Lachlan Gilchrist
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK
| | - Dylan M Williams
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Thomas D Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,Department of Brain Sciences, Imperial College London, London, W12 0NN, UK.,UK DRI Centre for Care Research and Technology, Imperial College London, London, W12 0BZ, UK
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Ian B Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,UK Dementia Research Institute at University College London, London, UK
| | - Carole H Sudre
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK.,MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.,Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - David L Thomas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Richard Dobson
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.,UK National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre, South London and Maudsley Trust, London, UK.,University College London, Institute of Health Informatics, London, UK.,Health Data Research UK London, University College London, London, UK.,NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, UK
| | - Cristina Legido-Quigley
- Institute of Pharmaceutical Science, King's College London, London, UK.,Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,UK Dementia Research Institute at University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.
| | - Marcus Richards
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.
| | - Petroula Proitsi
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.
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5
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Prosser L, Macdougall A, Sudre CH, Manning EN, Malone IB, Walsh P, Goodkin O, Pemberton H, Barkhof F, Biessels GJ, Cash DM, Barnes J. Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration. Neurology 2023; 100:e834-e845. [PMID: 36357185 PMCID: PMC9984210 DOI: 10.1212/wnl.0000000000201572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 09/28/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Dementia is a growing socioeconomic challenge that requires early intervention. Identifying biomarkers that reliably predict clinical progression early in the disease process would better aid selection of individuals for future trial participation. Here, we compared the ability of baseline, single time-point biomarkers (CSF amyloid 1-42, CSF ptau-181, white matter hyperintensities (WMH), cerebral microbleeds, whole-brain volume, and hippocampal volume) to predict decline in cognitively normal individuals who later converted to mild cognitive impairment (MCI) (CNtoMCI) and those with MCI who later converted to an Alzheimer disease (AD) diagnosis (MCItoAD). METHODS Standardized baseline biomarker data from AD Neuroimaging Initiative 2 (ADNI2)/GO and longitudinal diagnostic data (including ADNI3) were used. Cox regression models assessed biomarkers in relation to time to change in clinical diagnosis using all follow-up time points available. Models were fit for biomarkers univariately and together in a multivariable model. Hazard ratios (HRs) were compared to evaluate biomarkers. Analyses were performed separately in CNtoMCI and MCItoAD groups. RESULTS For CNtoMCI (n = 189), there was strong evidence that higher WMH volume (individual model: HR 1.79, p = 0.002; fully adjusted model: HR 1.98, p = 0.003) and lower hippocampal volume (individual: HR 0.54, p = 0.001; fully adjusted: HR 0.40, p < 0.001) were associated with conversion to MCI individually and independently. For MCItoAD (n = 345), lower hippocampal (individual model: HR 0.45, p < 0.001; fully adjusted model: HR 0.55, p < 0.001) and whole-brain volume (individual: HR 0.31, p < 0.001; fully adjusted: HR 0.48, p = 0.02), increased CSF ptau (individual: HR 1.88, p < 0.001; fully adjusted: HR 1.61, p < 0.001), and lower CSF amyloid (individual: HR 0.37, p < 0.001; fully adjusted: HR 0.62, p = 0.008) were most strongly associated with conversion to AD individually and independently. DISCUSSION Lower hippocampal volume was a consistent predictor of clinical conversion to MCI and AD. CSF and brain volume biomarkers were predictive of conversion to AD from MCI, whereas WMH were predictive of conversion to MCI from cognitively normal. The predictive ability of WMH in the CNtoMCI group may be interpreted as some being on a different pathologic pathway, such as vascular cognitive impairment.
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Affiliation(s)
- Lloyd Prosser
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands.
| | - Amy Macdougall
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Carole H Sudre
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Emily N Manning
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Ian B Malone
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Phoebe Walsh
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Olivia Goodkin
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Hugh Pemberton
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Frederik Barkhof
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Geert Jan Biessels
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - David M Cash
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
| | - Josephine Barnes
- From the Department of Neurodegenerative Disease (L.P., A.M., C.H.S., E.N.M., I.B.M., P.W., H.P., D.M.C., J.B.), Dementia Research Centre, UCL Queen Square Institute of Neurology, London; Medical Statistics (A.M.), London School of Hygiene and Tropical Medicine; School of Biomedical Engineering and Imaging Sciences (C.H.S.), King's College London; Centre for Medical Image Computing (C.H.S., O.G., H.P., F.B.) and Department of Population Sciences and Experimental Medicine (C.H.S.), MRC Unit for Lifelong Health and Ageing at UCL, University College London, United Kingdom; Department of Radiology and Nuclear Medicine (F.B.), VU University Medical Center, Amsterdam Neuroscience; and Department of Neurology and Neurosurgery (G.J.B.), UMC Utrecht Brain Center, University Medical Center Utrecht, the Netherlands
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6
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Nietz AK, Streng ML, Popa LS, Carter RE, Flaherty EB, Aronson JD, Ebner TJ. To be and not to be: wide-field Ca2+ imaging reveals neocortical functional segmentation combines stability and flexibility. Cereb Cortex 2023:7024718. [PMID: 36734268 DOI: 10.1093/cercor/bhac523] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/09/2022] [Accepted: 12/10/2022] [Indexed: 02/04/2023] Open
Abstract
The stability and flexibility of the functional parcellation of the cerebral cortex is fundamental to how familiar and novel information is both represented and stored. We leveraged new advances in Ca2+ sensors and microscopy to understand the dynamics of functional segmentation in the dorsal cerebral cortex. We performed wide-field Ca2+ imaging in head-fixed mice and used spatial independent component analysis (ICA) to identify independent spatial sources of Ca2+ fluorescence. The imaging data were evaluated over multiple timescales and discrete behaviors including resting, walking, and grooming. When evaluated over the entire dataset, a set of template independent components (ICs) were identified that were common across behaviors. Template ICs were present across a range of timescales, from days to 30 seconds, although with lower occurrence probability at shorter timescales, highlighting the stability of the functional segmentation. Importantly, unique ICs emerged at the shorter duration timescales that could act to transiently refine the cortical network. When data were evaluated by behavior, both common and behavior-specific ICs emerged. Each behavior is composed of unique combinations of common and behavior-specific ICs. These observations suggest that cerebral cortical functional segmentation exhibits considerable spatial stability over time and behaviors while retaining the flexibility for task-dependent reorganization.
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Affiliation(s)
- Angela K Nietz
- Department of Neuroscience, University of Minnesota, 2001 Sixth Street S.E., Minneapolis 55455, MN, United States
| | - Martha L Streng
- Department of Neuroscience, University of Minnesota, 2001 Sixth Street S.E., Minneapolis 55455, MN, United States
| | - Laurentiu S Popa
- Department of Neuroscience, University of Minnesota, 2001 Sixth Street S.E., Minneapolis 55455, MN, United States
| | - Russell E Carter
- Department of Neuroscience, University of Minnesota, 2001 Sixth Street S.E., Minneapolis 55455, MN, United States
| | - Evelyn B Flaherty
- Department of Neuroscience, University of Minnesota, 2001 Sixth Street S.E., Minneapolis 55455, MN, United States
| | - Justin D Aronson
- Department of Neuroscience, University of Minnesota, 2001 Sixth Street S.E., Minneapolis 55455, MN, United States
| | - Timothy J Ebner
- Department of Neuroscience, University of Minnesota, 2001 Sixth Street S.E., Minneapolis 55455, MN, United States
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7
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James SN, Nicholas JM, Lu K, Keshavan A, Lane CA, Parker T, Buchanan SM, Keuss SE, Murray-Smith H, Wong A, Cash DM, Malone IB, Barnes J, Sudre CH, Coath W, Modat M, Ourselin S, Crutch SJ, Kuh D, Fox NC, Schott JM, Richards M. Adulthood cognitive trajectories over 26 years and brain health at 70 years of age: findings from the 1946 British Birth Cohort. Neurobiol Aging 2023; 122:22-32. [PMID: 36470133 PMCID: PMC10564626 DOI: 10.1016/j.neurobiolaging.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 10/06/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022]
Abstract
Few studies can address how adulthood cognitive trajectories relate to brain health in 70-year-olds. Participants (n = 468, 49% female) from the 1946 British birth cohort underwent 18F-Florbetapir PET/MRI. Cognitive function was measured in childhood (age 8 years) and across adulthood (ages 43, 53, 60-64 and 69 years) and was examined in relation to brain health markers of β-amyloid (Aβ) status, whole brain and hippocampal volume, and white matter hyperintensity volume (WMHV). Taking into account key contributors of adult cognitive decline including childhood cognition, those with greater Aβ and WMHV at age 70 years had greater decline in word-list learning memory in the preceding 26 years, particularly after age 60. In contrast, those with smaller whole brain and hippocampal volume at age 70 years had greater decline in processing search speed, subtly manifest from age 50 years. Subtle changes in memory and processing speed spanning 26 years of adulthood were associated with markers of brain health at 70 years of age, consistent with detectable prodromal cognitive effects in early older age.
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Affiliation(s)
- Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK.
| | - Jennifer M Nicholas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK; Department of Medical Statistics, London School of Hygiene and Tropical Medicine, University of London, London, UK
| | - Kirsty Lu
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ashvini Keshavan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Thomas Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK; UK Dementia Research Institute Centre for Care Research and Technology, Imperial College London, UK; Department of Medicine, Division of Brain Sciences, Imperial College London
| | - Sarah M Buchanan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah E Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK; UK Dementia Research Institute at UCL, University College London, London, UK
| | - Ian B Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Josephine Barnes
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Marc Modat
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sebastien Ourselin
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sebastian J Crutch
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK; UK Dementia Research Institute at UCL, University College London, London, UK
| | - Jonathan M Schott
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK; Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK; UK Dementia Research Institute at UCL, University College London, London, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
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8
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Praveenkumar S, Kalaiselvi T, Somasundaram K. Methods of Brain Extraction from Magnetic Resonance Images of Human Head: A Review. Crit Rev Biomed Eng 2023; 51:1-40. [PMID: 37581349 DOI: 10.1615/critrevbiomedeng.2023047606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Abstract
Medical images are providing vital information to aid physicians in diagnosing a disease afflicting the organ of a human body. Magnetic resonance imaging is an important imaging modality in capturing the soft tissues of the brain. Segmenting and extracting the brain is essential in studying the structure and pathological condition of brain. There are several methods that are developed for this purpose. Researchers in brain extraction or segmentation need to know the current status of the work that have been done. Such an information is also important for improving the existing method to get more accurate results or to reduce the complexity of the algorithm. In this paper we review the classical methods and convolutional neural network-based deep learning brain extraction methods.
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Affiliation(s)
| | - T Kalaiselvi
- Department of Computer Science and Applications, Gandhigram Rural Institute, Gandhigram 624302, Tamil Nadu, India
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9
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Pei L, Murat AK, Tahon NHM, Zenkin S, Alkarawi S, Kamal A, Yilmaz M, Chen L, Er M, Nursima AK, Colen R. A general skull stripping of multiparametric brain MRIs using 3D convolutional neural network. Sci Rep 2022; 12:10826. [PMID: 35760886 PMCID: PMC9237075 DOI: 10.1038/s41598-022-14983-4] [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: 01/11/2022] [Accepted: 06/15/2022] [Indexed: 01/18/2023] Open
Abstract
Accurate skull stripping facilitates following neuro-image analysis. For computer-aided methods, the presence of brain skull in structural magnetic resonance imaging (MRI) impacts brain tissue identification, which could result in serious misjudgments, specifically for patients with brain tumors. Though there are several existing works on skull stripping in literature, most of them either focus on healthy brain MRIs or only apply for a single image modality. These methods may be not optimal for multiparametric MRI scans. In the paper, we propose an ensemble neural network (EnNet), a 3D convolutional neural network (3DCNN) based method, for brain extraction on multiparametric MRI scans (mpMRIs). We comprehensively investigate the skull stripping performance by using the proposed method on a total of 15 image modality combinations. The comparison shows that utilizing all modalities provides the best performance on skull stripping. We have collected a retrospective dataset of 815 cases with/without glioblastoma multiforme (GBM) at the University of Pittsburgh Medical Center (UPMC) and The Cancer Imaging Archive (TCIA). The ground truths of the skull stripping are verified by at least one qualified radiologist. The quantitative evaluation gives an average dice score coefficient and Hausdorff distance at the 95th percentile, respectively. We also compare the performance to the state-of-the-art methods/tools. The proposed method offers the best performance.The contributions of the work have five folds: first, the proposed method is a fully automatic end-to-end for skull stripping using a 3D deep learning method. Second, it is applicable for mpMRIs and is also easy to customize for any MRI modality combination. Third, the proposed method not only works for healthy brain mpMRIs but also pre-/post-operative brain mpMRIs with GBM. Fourth, the proposed method handles multicenter data. Finally, to the best of our knowledge, we are the first group to quantitatively compare the skull stripping performance using different modalities. All code and pre-trained model are available at: https://github.com/plmoer/skull_stripping_code_SR .
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Affiliation(s)
- Linmin Pei
- Imaging and Visualization Group, ABCS, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA.
| | - A K Murat
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, 15232, USA
| | - Nourel Hoda M Tahon
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, 15232, USA
| | - Serafettin Zenkin
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, 15232, USA
| | - Safa Alkarawi
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, 15232, USA
| | - Abdallah Kamal
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, 15232, USA
| | - Mahir Yilmaz
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, 15232, USA
| | - Lingling Chen
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, 15232, USA
| | - Mehmet Er
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, 15232, USA
| | - A K Nursima
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, 15232, USA
| | - Rivka Colen
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15260, USA. .,Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, 15232, USA.
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10
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Alam S, Eom TY, Steinberg J, Ackerman D, Schmitt JE, Akers WJ, Zakharenko SS, Khairy K. An End-To-End Pipeline for Fully Automatic Morphological Quantification of Mouse Brain Structures From MRI Imagery. FRONTIERS IN BIOINFORMATICS 2022; 2:865443. [PMID: 36304320 PMCID: PMC9580949 DOI: 10.3389/fbinf.2022.865443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 05/03/2022] [Indexed: 11/29/2022] Open
Abstract
Segmentation of mouse brain magnetic resonance images (MRI) based on anatomical and/or functional features is an important step towards morphogenetic brain structure characterization of murine models in neurobiological studies. State-of-the-art image segmentation methods register image volumes to standard presegmented templates or well-characterized highly detailed image atlases. Performance of these methods depends critically on the quality of skull-stripping, which is the digital removal of tissue signal exterior to the brain. This is, however, tedious to do manually and challenging to automate. Registration-based segmentation, in addition, performs poorly on small structures, low resolution images, weak signals, or faint boundaries, intrinsic to in vivo MRI scans. To address these issues, we developed an automated end-to-end pipeline called DeepBrainIPP (deep learning-based brain image processing pipeline) for 1) isolating brain volumes by stripping skull and tissue from T2w MRI images using an improved deep learning-based skull-stripping and data augmentation strategy, which enables segmentation of large brain regions by atlas or template registration, and 2) address segmentation of small brain structures, such as the paraflocculus, a small lobule of the cerebellum, for which DeepBrainIPP performs direct segmentation with a dedicated model, producing results superior to the skull-stripping/atlas-registration paradigm. We demonstrate our approach on data from both in vivo and ex vivo samples, using an in-house dataset of 172 images, expanded to 4,040 samples through data augmentation. Our skull stripping model produced an average Dice score of 0.96 and residual volume of 2.18%. This facilitated automatic registration of the skull-stripped brain to an atlas yielding an average cross-correlation of 0.98. For small brain structures, direct segmentation yielded an average Dice score of 0.89 and 5.32% residual volume error, well below the tolerance threshold for phenotype detection. Full pipeline execution is provided to non-expert users via a Web-based interface, which exposes analysis parameters, and is powered by a service that manages job submission, monitors job status and provides job history. Usability, reliability, and user experience of DeepBrainIPP was measured using the Customer Satisfaction Score (CSAT) and a modified PYTHEIA Scale, with a rating of excellent. DeepBrainIPP code, documentation and network weights are freely available to the research community.
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Affiliation(s)
- Shahinur Alam
- Center for Bioimage Informatics, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - Tae-Yeon Eom
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - Jeffrey Steinberg
- Center for in Vivo Imaging and Therapeutics, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - David Ackerman
- Scientific Computing, Janelia Research Campus, Ashburn, VA, United States
| | - J. Eric Schmitt
- Brain Behavior Laboratory, Departments of Psychiatry and Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Walter J. Akers
- Center for in Vivo Imaging and Therapeutics, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - Stanislav S. Zakharenko
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - Khaled Khairy
- Center for Bioimage Informatics, St. Jude Children’s Research Hospital, Memphis, TN, United States
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, United States
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11
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Ranjbar S, Singleton KW, Curtin L, Rickertsen CR, Paulson LE, Hu LS, Mitchell JR, Swanson KR. Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients. FRONTIERS IN NEUROIMAGING 2022; 1:832512. [PMID: 37555156 PMCID: PMC10406204 DOI: 10.3389/fnimg.2022.832512] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 02/21/2022] [Indexed: 08/10/2023]
Abstract
Automatic brain tumor segmentation is particularly challenging on magnetic resonance imaging (MRI) with marked pathologies, such as brain tumors, which usually cause large displacement, abnormal appearance, and deformation of brain tissue. Despite an abundance of previous literature on learning-based methodologies for MRI segmentation, few works have focused on tackling MRI skull stripping of brain tumor patient data. This gap in literature can be associated with the lack of publicly available data (due to concerns about patient identification) and the labor-intensive nature of generating ground truth labels for model training. In this retrospective study, we assessed the performance of Dense-Vnet in skull stripping brain tumor patient MRI trained on our large multi-institutional brain tumor patient dataset. Our data included pretreatment MRI of 668 patients from our in-house institutional review board-approved multi-institutional brain tumor repository. Because of the absence of ground truth, we used imperfect automatically generated training labels using SPM12 software. We trained the network using common MRI sequences in oncology: T1-weighted with gadolinium contrast, T2-weighted fluid-attenuated inversion recovery, or both. We measured model performance against 30 independent brain tumor test cases with available manual brain masks. All images were harmonized for voxel spacing and volumetric dimensions before model training. Model training was performed using the modularly structured deep learning platform NiftyNet that is tailored toward simplifying medical image analysis. Our proposed approach showed the success of a weakly supervised deep learning approach in MRI brain extraction even in the presence of pathology. Our best model achieved an average Dice score, sensitivity, and specificity of, respectively, 94.5, 96.4, and 98.5% on the multi-institutional independent brain tumor test set. To further contextualize our results within existing literature on healthy brain segmentation, we tested the model against healthy subjects from the benchmark LBPA40 dataset. For this dataset, the model achieved an average Dice score, sensitivity, and specificity of 96.2, 96.6, and 99.2%, which are, although comparable to other publications, slightly lower than the performance of models trained on healthy patients. We associate this drop in performance with the use of brain tumor data for model training and its influence on brain appearance.
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Affiliation(s)
- Sara Ranjbar
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, United States
| | - Kyle W. Singleton
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, United States
| | - Lee Curtin
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, United States
| | - Cassandra R. Rickertsen
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, United States
| | - Lisa E. Paulson
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, United States
| | - Leland S. Hu
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, United States
- Department of Diagnostic Imaging and Interventional Radiology, Mayo Clinic, Phoenix, AZ, United States
| | - Joseph Ross Mitchell
- Department of Medicine, Faculty of Medicine & Dentistry and the Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, Canada
- Provincial Clinical Excellence Portfolio, Alberta Health Services, Edmonton, AB, Canada
| | - Kristin R. Swanson
- Mathematical NeuroOncology Lab, Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, United States
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12
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ASMCNN: An efficient brain extraction using active shape model and convolutional neural networks. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.01.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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13
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Fatih N, Chaturvedi N, Lane CA, Parker TD, Lu K, Cash DM, Malone IB, Silverwood R, Wong A, Barnes J, Sudre CH, Richards M, Fox NC, Schott JM, Hughes A, James SN. Sex-related differences in whole brain volumes at age 70 in association with hyperglycemia during adult life. Neurobiol Aging 2022; 112:161-169. [PMID: 35183802 DOI: 10.1016/j.neurobiolaging.2021.09.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 09/01/2021] [Accepted: 09/04/2021] [Indexed: 01/19/2023]
Abstract
Longitudinal studies of the relationship between hyperglycemia and brain health are rare and there is limited information on sex differences in associations. We investigated whether glycosylated hemoglobin (HbA1c) measured at ages of 53, 60-64 and 69 years, and cumulative glycemic index (CGI), a measure of cumulative glycemic burden, were associated with metrics of brain health in later life. Participants were from Insight 46, a substudy of the Medical Research Council National Survey of Health and Development (NSHD) who undertook volumetric MRI, florbetapir amyloid-PET imaging and cognitive assessments at ages of 69-71. Analyses were performed using linear and logistic regression as appropriate, with adjustment for potential confounders. We observed a sex interaction between HbA1c and whole brain volume (WBV) at all 3 time points. Following stratification of our sample, we observed that HbA1c at all ages, and CGI were positively associated with lower WBV exclusively in females. HbA1c (or CGI) was not associated with amyloid status, white matter hyperintensities (WMHs), hippocampal volumes (HV) or cognitive outcomes in either sex. Higher HbA1c in adulthood is associated with smaller WBV at 69-71 years in females but not in males. This suggests that there may be preferential target organ damage in the brain for females with hyperglycemia.
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Affiliation(s)
- Nasrtullah Fatih
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom.
| | - Nish Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Christopher A Lane
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Thomas D Parker
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Kirsty Lu
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - David M Cash
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Ian B Malone
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Richard Silverwood
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Josephine Barnes
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Nick C Fox
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Jonathan M Schott
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Alun Hughes
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
| | - Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
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14
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Thakur SP, Pati S, Panchumarthy R, Karkada D, Wu J, Kurtaev D, Sako C, Shah P, Bakas S. Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments. BRAINLESION : GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES. BRAINLES (WORKSHOP) 2022; 12962:151-167. [PMID: 36331281 PMCID: PMC9627678 DOI: 10.1007/978-3-031-08999-2_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Brain extraction is an indispensable step in neuro-imaging with a direct impact on downstream analyses. Most such methods have been developed for non-pathologically affected brains, and hence tend to suffer in performance when applied on brains with pathologies, e.g., gliomas, multiple sclerosis, traumatic brain injuries. Deep Learning (DL) methodologies for healthcare have shown promising results, but their clinical translation has been limited, primarily due to these methods suffering from i) high computational cost, and ii) specific hardware requirements, e.g., DL acceleration cards. In this study, we explore the potential of mathematical optimizations, towards making DL methods amenable to application in low resource environments. We focus on both the qualitative and quantitative evaluation of such optimizations on an existing DL brain extraction method, designed for pathologically-affected brains and agnostic to the input modality. We conduct direct optimizations and quantization of the trained model (i.e., prior to inference on new data). Our results yield substantial gains, in terms of speedup, latency, through-put, and reduction in memory usage, while the segmentation performance of the initial and the optimized models remains stable, i.e., as quantified by both the Dice Similarity Coefficient and the Hausdorff Distance. These findings support post-training optimizations as a promising approach for enabling the execution of advanced DL methodologies on plain commercial-grade CPUs, and hence contributing to their translation in limited- and low- resource clinical environments.
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Affiliation(s)
- Siddhesh P Thakur
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ravi Panchumarthy
- Intel Health and Life Sciences, Intel Corporation, Santa Clara, CA, USA
| | - Deepthi Karkada
- Intel Health and Life Sciences, Intel Corporation, Santa Clara, CA, USA
| | - Junwen Wu
- Intel Health and Life Sciences, Intel Corporation, Santa Clara, CA, USA
| | - Dmitry Kurtaev
- Intel Health and Life Sciences, Intel Corporation, Santa Clara, CA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Prashant Shah
- Intel Health and Life Sciences, Intel Corporation, Santa Clara, CA, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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15
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Zhornitsky S, Chaudhary S, Le TM, Chen Y, Zhang S, Potvin S, Chao HH, van Dyck CH, Li CSR. Cognitive dysfunction and cerebral volumetric deficits in individuals with Alzheimer's disease, alcohol use disorder, and dual diagnosis. Psychiatry Res Neuroimaging 2021; 317:111380. [PMID: 34482052 PMCID: PMC8579376 DOI: 10.1016/j.pscychresns.2021.111380] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 08/22/2021] [Accepted: 08/25/2021] [Indexed: 10/20/2022]
Abstract
Epidemiological surveys suggest that excessive drinking is associated with higher risk of Alzheimer's disease (AD). The present study utilized data from the National Alzheimer's Coordinating Center to examine cognition as well as gray/white matter and ventricular volumes among participants with AD and alcohol use disorder (AD/AUD, n = 52), AD only (n = 701), AUD only (n = 67), and controls (n = 1283). AUD diagnosis was associated with higher Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) in AD than in non-AD. AD performed worse on semantic fluency and Trail Making Test A + B (TMT A + B) and showed smaller total GMV, WMV, and larger ventricular volume than non-AD. AD had smaller regional GMV in the inferior/superior parietal cortex, hippocampal formation, occipital cortex, inferior frontal gyrus, posterior cingulate cortex, and isthmus cingulate cortex than non-AD. AUD had significantly smaller somatomotor cortical GMV and showed a trend towards smaller volume in the hippocampal formation, relative to non-AUD participants. Misuse of alcohol has an additive effect on dementia severity among AD participants. Smaller hippocampal volume is a common feature of both AD and AUD. Although AD is associated with more volumetric deficits overall, AD and AUD are associated with atrophy in largely distinct brain regions.
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Affiliation(s)
- Simon Zhornitsky
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA.
| | - Shefali Chaudhary
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Thang M Le
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Yu Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Sheng Zhang
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA
| | - Stéphane Potvin
- Centre de recherche de l'Institut, Universitaire en Santé Mentale de Montréal, Montreal, QC, Canada; Department of Psychiatry, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Herta H Chao
- Department of Medicine, Yale University School of Medicine, New Haven, CT 06519, USA; VA Connecticut Healthcare System, West Haven, CT 06516, USA
| | - Christopher H van Dyck
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA; Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06520, USA; Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06519, USA; Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06520, USA; Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT 06520, USA
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16
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Sapkota S, Ramirez J, Yhap V, Masellis M, Black SE. Brain atrophy trajectories predict differential functional performance in Alzheimer's disease: Moderations with apolipoprotein E and sex. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12244. [PMID: 34692981 PMCID: PMC8515221 DOI: 10.1002/dad2.12244] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 08/09/2021] [Indexed: 12/30/2022]
Abstract
INTRODUCTION We examine whether distinct brain atrophy patterns (using brain parenchymal fraction [BPF]) differentially predict functional performance and decline in Alzheimer's disease (AD), and are independently moderated by (1) a key AD genetic risk marker (apolipoprotein E [APOE]), (2) sex, and (3) high-risk group (women APOE ɛ4 carriers). METHODS We used a 2-year longitudinal sample of AD patients (baseline N = 170; mean age = 71.3 [9.1] years) from the Sunnybrook Dementia Study. We applied latent class analysis, latent growth modeling, and path analysis. We aimed to replicate our findings (N = 184) in the Alzheimer's Disease Neuroimaging Initiative. RESULTS We observed that high brain atrophy class predicted lower functional performance and steeper decline. This association was moderated by APOE, sex, and high-risk group. Baseline findings as moderated by APOE and high-risk group were replicated. DISCUSSION Women APOE ɛ4 carriers may selectively be at a greater risk of functional impairment with higher brain atrophy.
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Affiliation(s)
- Shraddha Sapkota
- Hurvitz Brain Sciences Research ProgramSunnybrook Research InstituteSunnybrook Health Sciences CentreTorontoOntarioCanada
| | - Joel Ramirez
- Hurvitz Brain Sciences Research ProgramSunnybrook Research InstituteSunnybrook Health Sciences CentreTorontoOntarioCanada
| | - Vanessa Yhap
- Hurvitz Brain Sciences Research ProgramSunnybrook Research InstituteSunnybrook Health Sciences CentreTorontoOntarioCanada
| | - Mario Masellis
- Hurvitz Brain Sciences Research ProgramSunnybrook Research InstituteSunnybrook Health Sciences CentreTorontoOntarioCanada
- Department of Medicine (Neurology)University of TorontoTorontoOntarioCanada
| | - Sandra E. Black
- Hurvitz Brain Sciences Research ProgramSunnybrook Research InstituteSunnybrook Health Sciences CentreTorontoOntarioCanada
- Department of Medicine (Neurology)University of TorontoTorontoOntarioCanada
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17
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Fiford CM, Sudre CH, Young AL, Macdougall A, Nicholas J, Manning EN, Malone IB, Walsh P, Goodkin O, Pemberton HG, Barkhof F, Alexander DC, Cardoso MJ, Biessels GJ, Barnes J. Presumed small vessel disease, imaging and cognition markers in the Alzheimer's Disease Neuroimaging Initiative. Brain Commun 2021; 3:fcab226. [PMID: 34661106 PMCID: PMC8514859 DOI: 10.1093/braincomms/fcab226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 06/22/2021] [Accepted: 06/25/2021] [Indexed: 01/18/2023] Open
Abstract
MRI-derived features of presumed cerebral small vessel disease are frequently found in Alzheimer's disease. Influences of such markers on disease-progression measures are poorly understood. We measured markers of presumed small vessel disease (white matter hyperintensity volumes; cerebral microbleeds) on baseline images of newly enrolled individuals in the Alzheimer's Disease Neuroimaging Initiative cohort (GO and 2) and used linear mixed models to relate these to subsequent atrophy and neuropsychological score change. We also assessed heterogeneity in white matter hyperintensity positioning within biomarker abnormality sequences, driven by the data, using the Subtype and Stage Inference algorithm. This study recruited both sexes and included: controls: [n = 159, mean(SD) age = 74(6) years]; early and late mild cognitive impairment [ns = 265 and 139, respectively, mean(SD) ages =71(7) and 72(8) years, respectively]; Alzheimer's disease [n = 103, mean(SD) age = 75(8)] and significant memory concern [n = 72, mean(SD) age = 72(6) years]. Baseline demographic and vascular risk-factor data, and longitudinal cognitive scores (Mini-Mental State Examination; logical memory; and Trails A and B) were collected. Whole-brain and hippocampal volume change metrics were calculated. White matter hyperintensity volumes were associated with greater whole-brain and hippocampal volume changes independently of cerebral microbleeds (a doubling of baseline white matter hyperintensity was associated with an increase in atrophy rate of 0.3 ml/year for brain and 0.013 ml/year for hippocampus). Cerebral microbleeds were found in 15% of individuals and the presence of a microbleed, as opposed to none, was associated with increases in atrophy rate of 1.4 ml/year for whole brain and 0.021 ml/year for hippocampus. White matter hyperintensities were predictive of greater decline in all neuropsychological scores, while cerebral microbleeds were predictive of decline in logical memory (immediate recall) and Mini-Mental State Examination scores. We identified distinct groups with specific sequences of biomarker abnormality using continuous baseline measures and brain volume change. Four clusters were found; Group 1 showed early Alzheimer's pathology; Group 2 showed early neurodegeneration; Group 3 had early mixed Alzheimer's and cerebrovascular pathology; Group 4 had early neuropsychological score abnormalities. White matter hyperintensity volumes becoming abnormal was a late event for Groups 1 and 4 and an early event for 2 and 3. In summary, white matter hyperintensities and microbleeds were independently associated with progressive neurodegeneration (brain atrophy rates) and cognitive decline (change in neuropsychological scores). Mechanisms involving white matter hyperintensities and progression and microbleeds and progression may be partially separate. Distinct sequences of biomarker progression were found. White matter hyperintensity development was an early event in two sequences.
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Affiliation(s)
- Cassidy M Fiford
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Carole H Sudre
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Health Sciences, University College London, London WC1E 3HB, UK
| | - Alexandra L Young
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 3AF, UK
| | - Amy Macdougall
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Jennifer Nicholas
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Emily N Manning
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Ian B Malone
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Phoebe Walsh
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Olivia Goodkin
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK
| | - Hugh G Pemberton
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam Neuroscience, 1081 HV Amsterdam, The Netherlands
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- UCL Institute of Healthcare Engineering, London WC1E 6DH, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands
| | - Josephine Barnes
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
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18
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A comparison of automated atrophy measures across the frontotemporal dementia spectrum: Implications for trials. NEUROIMAGE-CLINICAL 2021; 32:102842. [PMID: 34626889 PMCID: PMC8503665 DOI: 10.1016/j.nicl.2021.102842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 08/13/2021] [Accepted: 09/23/2021] [Indexed: 11/22/2022]
Abstract
Background Frontotemporal dementia (FTD) is a common cause of young onset dementia, and whilst there are currently no treatments, there are several promising candidates in development and early phase trials. Comprehensive investigations of neuroimaging markers of disease progression across the full spectrum of FTD disorders are lacking and urgently needed to facilitate these trials. Objective To investigate the comparative performance of multiple automated segmentation and registration pipelines used to quantify longitudinal whole-brain atrophy across the clinical, genetic and pathological subgroups of FTD, in order to inform upcoming trials about suitable neuroimaging-based endpoints. Methods Seventeen fully automated techniques for extracting whole-brain atrophy measures were applied and directly compared in a cohort of 226 participants who had undergone longitudinal structural 3D T1-weighted imaging. Clinical diagnoses were behavioural variant FTD (n = 56) and primary progressive aphasia (PPA, n = 104), comprising semantic variant PPA (n = 38), non-fluent variant PPA (n = 42), logopenic variant PPA (n = 18), and PPA-not otherwise specified (n = 6). 49 of these patients had either a known pathogenic mutation or postmortem confirmation of their underlying pathology. 66 healthy controls were included for comparison. Sample size estimates to detect a 30% reduction in atrophy (80% power; 0.05 significance) were computed to explore the relative feasibility of these brain measures as surrogate markers of disease progression and their ability to detect putative disease-modifying treatment effects. Results Multiple automated techniques showed great promise, detecting significantly increased rates of whole-brain atrophy (p<0.001) and requiring sample sizes of substantially less than 100 patients per treatment arm. Across the different FTD subgroups, direct measures of volume change consistently outperformed their indirect counterparts, irrespective of the initial segmentation quality. Significant differences in performance were found between both techniques and patient subgroups, highlighting the importance of informed biomarker choice based on the patient population of interest. Conclusion This work expands current knowledge and builds on the limited longitudinal investigations currently available in FTD, as well as providing valuable information about the potential of fully automated neuroimaging biomarkers for sporadic and genetic FTD trials.
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19
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Dercon Q, Nicholas JM, James SN, Schott JM, Richards M. Grip strength from midlife as an indicator of later-life brain health and cognition: evidence from a British birth cohort. BMC Geriatr 2021; 21:475. [PMID: 34465287 PMCID: PMC8406895 DOI: 10.1186/s12877-021-02411-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 08/10/2021] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Grip strength is an indicator of physical function with potential predictive value for health in ageing populations. We assessed whether trends in grip strength from midlife predicted later-life brain health and cognition. METHODS 446 participants in an ongoing British birth cohort study, the National Survey of Health and Development (NSHD), had their maximum grip strength measured at ages 53, 60-64, and 69, and subsequently underwent neuroimaging as part of a neuroscience sub-study, referred to as "Insight 46", at age 69-71. A group-based trajectory model identified latent groups of individuals in the whole NSHD cohort with below- or above-average grip strength over time, plus a reference group. Group assignment, plus standardised grip strength levels and change from midlife were each related to measures of whole-brain volume (WBV) and white matter hyperintensity volume (WMHV), plus several cognitive tests. Models were adjusted for sex, body size, head size (where appropriate), sociodemographics, and behavioural and vascular risk factors. RESULTS Lower grip strength from midlife was associated with smaller WBV and lower matrix reasoning scores at age 69-71, with findings consistent between analysis of individual time points and analysis of trajectory groups. There was little evidence of an association between grip strength and other cognitive test scores. Although greater declines in grip strength showed a weak association with higher WMHV at age 69-71, trends in the opposite direction were seen at individual time points with higher grip strength at ages 60-64, and 69 associated with higher WMHV. CONCLUSIONS This study provides preliminary evidence that maximum grip strength may have value in predicting brain health. Future work should assess to what extent age-related declines in grip strength from midlife reflect concurrent changes in brain structure.
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Affiliation(s)
- Quentin Dercon
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.
| | - Jennifer M Nicholas
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, United Kingdom
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, United Kingdom
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20
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Fletcher E, DeCarli C, Fan AP, Knaack A. Convolutional Neural Net Learning Can Achieve Production-Level Brain Segmentation in Structural Magnetic Resonance Imaging. Front Neurosci 2021; 15:683426. [PMID: 34234642 PMCID: PMC8255694 DOI: 10.3389/fnins.2021.683426] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 05/27/2021] [Indexed: 01/18/2023] Open
Abstract
Deep learning implementations using convolutional neural nets have recently demonstrated promise in many areas of medical imaging. In this article we lay out the methods by which we have achieved consistently high quality, high throughput computation of intra-cranial segmentation from whole head magnetic resonance images, an essential but typically time-consuming bottleneck for brain image analysis. We refer to this output as “production-level” because it is suitable for routine use in processing pipelines. Training and testing with an extremely large archive of structural images, our segmentation algorithm performs uniformly well over a wide variety of separate national imaging cohorts, giving Dice metric scores exceeding those of other recent deep learning brain extractions. We describe the components involved to achieve this performance, including size, variety and quality of ground truth, and appropriate neural net architecture. We demonstrate the crucial role of appropriately large and varied datasets, suggesting a less prominent role for algorithm development beyond a threshold of capability.
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Affiliation(s)
- Evan Fletcher
- Department of Neurology, University of California, Davis, Davis, CA, United States
| | - Charles DeCarli
- Department of Neurology, University of California, Davis, Davis, CA, United States
| | - Audrey P Fan
- Department of Neurology, University of California, Davis, Davis, CA, United States.,Department of Biomedical Engineering, University of California, Davis, Davis, CA, United States
| | - Alexander Knaack
- Department of Neurology, University of California, Davis, Davis, CA, United States
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21
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Salzmann A, James SN, Williams DM, Richards M, Cadar D, Schott JM, Coath W, Sudre CH, Chaturvedi N, Garfield V. Investigating the Relationship Between IGF-I, IGF-II, and IGFBP-3 Concentrations and Later-Life Cognition and Brain Volume. J Clin Endocrinol Metab 2021; 106:1617-1629. [PMID: 33631000 PMCID: PMC8118585 DOI: 10.1210/clinem/dgab121] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Indexed: 01/18/2023]
Abstract
BACKGROUND The insulin/insulin-like signaling (IIS) pathways, including insulin-like growth factors (IGFs), vary with age. However, their association with late-life cognition and neuroimaging parameters is not well characterized. METHODS Using data from the British 1946 birth cohort, we investigated associations of IGF-I, IGF-II and IGF binding protein 3 (IGFBP-3; measured at 53 and 60-64 years of age) with cognitive performance [word-learning test (WLT) and visual letter search (VLS) at 60-64 years and 69 years of age] and cognitive state [Addenbrooke's Cognitive Exam III (ACE-III) at 69-71 years of age], and in a proportion, quantified neuroimaging measures [whole brain volume (WBV), white matter hyperintensity volume (WMHV), hippocampal volume (HV)]. Regression models included adjustments for demographic, lifestyle, and health factors. RESULTS Higher IGF-I and IGF-II at 53 years of age was associated with higher ACE-III scores [ß 0.07 95% confidence interval (CI) (0.02, 0.12); scoreACE-III 89.48 (88.86, 90.1), respectively). IGF-II at 53 years of age was additionally associated with higher WLT scores [scoreWLT 20 (19.35, 20.65)]. IGFBP-3 at 60 to 64 years of age was associated with favorable VLS score at 60 to 64 and 69 years of age [ß 0.07 (0.01, 0.12); ß 0.07 (0.02, 0.12), respectively], higher memory and cognitive state at 69 years of age [ß 0.07 (0.01, 0.12); ß 0.07 (0.01, 0.13), respectively], and reduced WMHV [ß -0.1 (-0.21, -0.00)]. IGF-I/IGFBP-3 at 60 to 64 years of was associated with lower VLS scores at 69 years of age [ß -0.08 (-0.15, -0.02)]. CONCLUSIONS Increased measure in IIS parameters (IGF-I, IGF-II, and IGFBP-3) relate to better cognitive state in later life. There were apparent associations with specific cognitive domains (IGF-II relating to memory; IGFBP-3 relating to memory, processing speed, and WMHV; and IGF-I/IGFBP-3 molar ratio related to slower processing speed). IGFs and IGFBP-3 are associated with favorable cognitive function outcomes.
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Affiliation(s)
- Antoine Salzmann
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Dylan M Williams
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Dorina Cadar
- Department of Behavioural Science and Health, University College London, London, UK
| | - Jonathan M Schott
- Department of Neurodegenerative Disease, The Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - William Coath
- Department of Neurodegenerative Disease, The Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Carole H Sudre
- Department of Neurodegenerative Disease, The Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Nishi Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Victoria Garfield
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
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22
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Lane CA, Barnes J, Nicholas JM, Baker JW, Sudre CH, Cash DM, Parker TD, Malone IB, Lu K, James SN, Keshavan A, Buchanan S, Keuss S, Murray-Smith H, Wong A, Gordon E, Coath W, Modat M, Thomas D, Hardy R, Richards M, Fox NC, Schott JM. Investigating the relationship between BMI across adulthood and late life brain pathologies. ALZHEIMERS RESEARCH & THERAPY 2021; 13:91. [PMID: 33941254 PMCID: PMC8091727 DOI: 10.1186/s13195-021-00830-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 04/12/2021] [Indexed: 01/01/2023]
Abstract
Background In view of reported associations between high adiposity, particularly in midlife and late-life dementia risk, we aimed to determine associations between body mass index (BMI), and BMI changes across adulthood and brain structure and pathology at age 69–71 years. Methods Four hundred sixty-five dementia-free participants from Insight 46, a sub-study of the British 1946 birth cohort, who had cross-sectional T1/FLAIR volumetric MRI, and florbetapir amyloid-PET imaging at age 69–71 years, were included in analyses. We quantified white matter hyperintensity volume (WMHV) using T1 and FLAIR 3D-MRI; β-amyloid (Aβ) positivity/negativity using a SUVR approach; and whole brain (WBV) and hippocampal volumes (HV) using 3D T1-MRI. We investigated the influence of BMI, and BMI changes at and between 36, 43, 53, 60–64, 69 and 71 years, on late-life WMHV, Aβ-status, WBV and mean HV. Analyses were repeated using overweight and obese status. Results At no time-point was BMI, change in BMI or overweight/obese status associated with WMHV or WBV at age 69–71 years. Decreasing BMI in the 1–2 years before imaging was associated with an increased odds of being β-amyloid positive (OR 1.45, 95% confidence interval 1.09, 1.92). There were associations between being overweight and larger mean HV at ages 60–64 (β = 0.073 ml, 95% CI 0.009, 0.137), 69 (β = 0.076 ml, 95% CI 0.012, 0.140) and 71 years (β = 0.101 ml, 95% CI 0.037, 0.165). A similar, albeit weaker, trend was seen with obese status. Conclusions Using WMHV, β-amyloid status and brain volumes as indicators of brain health, we do not find evidence to explain reported associations between midlife obesity and late-life dementia risk. Declining BMI in later life may reflect preclinical Alzheimer’s disease. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00830-7.
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Affiliation(s)
- Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK.,Hoffmann-La Roche UK Ltd, London, UK
| | - Josephine Barnes
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK
| | - Jennifer M Nicholas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK.,Department of Medical Statistics, London School of Hygiene and Tropical Medicine, University of London, London, UK
| | - John W Baker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK
| | - Carole H Sudre
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK
| | - Thomas D Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK
| | - Ian B Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK
| | - Kirsty Lu
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK
| | | | - Ashvini Keshavan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK
| | - Sarah Buchanan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK
| | - Sarah Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Elizabeth Gordon
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK
| | - Marc Modat
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - David Thomas
- Leonard Wolfson Experimental Neurology Centre, UCL Queen Square Institute of Neurology, University College London, London, UK.,Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Rebecca Hardy
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | | | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK.,UK Dementia Research Institute at UCL, University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Box 16, Queen Square, London, WC1N 3BG, UK. .,UK Dementia Research Institute at UCL, University College London, London, UK.
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23
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James SN, Nicholas JM, Lane CA, Parker TD, Lu K, Keshavan A, Buchanan SM, Keuss SE, Murray-Smith H, Wong A, Cash DM, Malone IB, Barnes J, Sudre CH, Coath W, Prosser L, Ourselin S, Modat M, Thomas DL, Cardoso J, Heslegrave A, Zetterberg H, Crutch SJ, Schott JM, Richards M, Fox NC. A population-based study of head injury, cognitive function and pathological markers. Ann Clin Transl Neurol 2021; 8:842-856. [PMID: 33694298 PMCID: PMC8045921 DOI: 10.1002/acn3.51331] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 02/12/2021] [Indexed: 02/01/2023] Open
Abstract
Objective To assess associations between head injury (HI) with loss of consciousness (LOC), ageing and markers of later‐life cerebral pathology; and to explore whether those effects may help explain subtle cognitive deficits in dementia‐free individuals. Methods Participants (n = 502, age = 69–71) from the 1946 British Birth Cohort underwent cognitive testing (subtests of Preclinical Alzheimer Cognitive Composite), 18F‐florbetapir Aβ‐PET and MR imaging. Measures include Aβ‐PET status, brain, hippocampal and white matter hyperintensity (WMH) volumes, normal appearing white matter (NAWM) microstructure, Alzheimer’s disease (AD)‐related cortical thickness, and serum neurofilament light chain (NFL). LOC HI metrics include HI occurring: (i) >15 years prior to the scan (ii) anytime up to age 71. Results Compared to those with no evidence of an LOC HI, only those reporting an LOC HI>15 years prior (16%, n = 80) performed worse on cognitive tests at age 69–71, taking into account premorbid cognition, particularly on the digit‐symbol substitution test (DSST). Smaller brain volume (BV) and adverse NAWM microstructural integrity explained 30% and 16% of the relationship between HI and DSST, respectively. We found no evidence that LOC HI was associated with Aβ load, hippocampal volume, WMH volume, AD‐related cortical thickness or NFL (all p > 0.01). Interpretation Having a LOC HI aged 50’s and younger was linked with lower later‐life cognitive function at age ~70 than expected. This may reflect a damaging but small impact of HI; explained in part by smaller BV and different microstructure pathways but not via pathology related to AD (amyloid, hippocampal volume, AD cortical thickness) or ongoing neurodegeneration (serum NFL).
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Affiliation(s)
- Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, United Kingdom.,Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jennifer M Nicholas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.,Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Thomas D Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Kirsty Lu
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Ashvini Keshavan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sarah M Buchanan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sarah E Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Heidi Murray-Smith
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, United Kingdom
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Ian B Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Josephine Barnes
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, United Kingdom.,Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.,School of Biomedical Engineering and Imaging Sciences, King's College London, Institute of Nuclear Medicine, University College London Hospitals, London, United Kingdom.,Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Lloyd Prosser
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, Institute of Nuclear Medicine, University College London Hospitals, London, United Kingdom
| | - Marc Modat
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.,School of Biomedical Engineering and Imaging Sciences, King's College London, Institute of Nuclear Medicine, University College London Hospitals, London, United Kingdom
| | - David L Thomas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, Institute of Nuclear Medicine, University College London Hospitals, London, United Kingdom
| | - Amanda Heslegrave
- UK Dementia Research Institute at UCL, University College London, London, United Kingdom.,Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
| | - Henrik Zetterberg
- UK Dementia Research Institute at UCL, University College London, London, United Kingdom.,Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Sebastian J Crutch
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, United Kingdom
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.,UK Dementia Research Institute at UCL, University College London, London, United Kingdom
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24
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Keshavan A, Pannee J, Karikari TK, Rodriguez JL, Ashton NJ, Nicholas JM, Cash DM, Coath W, Lane CA, Parker TD, Lu K, Buchanan SM, Keuss SE, James SN, Murray-Smith H, Wong A, Barnes A, Dickson JC, Heslegrave A, Portelius E, Richards M, Fox NC, Zetterberg H, Blennow K, Schott JM. Population-based blood screening for preclinical Alzheimer's disease in a British birth cohort at age 70. Brain 2021; 144:434-449. [PMID: 33479777 PMCID: PMC7940173 DOI: 10.1093/brain/awaa403] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 08/10/2020] [Accepted: 09/17/2020] [Indexed: 11/14/2022] Open
Abstract
Alzheimer's disease has a preclinical stage when cerebral amyloid-β deposition occurs before symptoms emerge, and when amyloid-β-targeted therapies may have maximum benefits. Existing amyloid-β status measurement techniques, including amyloid PET and CSF testing, are difficult to deploy at scale, so blood biomarkers are increasingly considered for screening. We compared three different blood-based techniques-liquid chromatography-mass spectrometry measures of plasma amyloid-β, and single molecule array (Simoa) measures of plasma amyloid-β and phospho-tau181-to detect cortical 18F-florbetapir amyloid PET positivity (defined as a standardized uptake value ratio of >0.61 between a predefined cortical region of interest and eroded subcortical white matter) in dementia-free members of Insight 46, a substudy of the population-based British 1946 birth cohort. We used logistic regression models with blood biomarkers as predictors of amyloid PET status, with or without age, sex and APOE ε4 carrier status as covariates. We generated receiver operating characteristics curves and quantified areas under the curves to compare the concordance of the different blood tests with amyloid PET. We determined blood test cut-off points using Youden's index, then estimated numbers needed to screen to obtain 100 amyloid PET-positive individuals. Of the 502 individuals assessed, 441 dementia-free individuals with complete data were included; 82 (18.6%) were amyloid PET-positive. The area under the curve for amyloid PET status using a base model comprising age, sex and APOE ε4 carrier status was 0.695 (95% confidence interval: 0.628-0.762). The two best-performing Simoa plasma biomarkers were amyloid-β42/40 (0.620; 0.548-0.691) and phospho-tau181 (0.707; 0.646-0.768), but neither outperformed the base model. Mass spectrometry plasma measures performed significantly better than any other measure (amyloid-β1-42/1-40: 0.817; 0.770-0.864 and amyloid-β composite: 0.820; 0.775-0.866). At a cut-off point of 0.095, mass spectrometry measures of amyloid-β1-42/1-40 detected amyloid PET positivity with 86.6% sensitivity and 71.9% specificity. Without screening, to obtain 100 PET-positive individuals from a population with similar amyloid PET positivity prevalence to Insight 46, 543 PET scans would need to be performed. Screening using age, sex and APOE ε4 status would require 940 individuals, of whom 266 would proceed to scan. Using mass spectrometry amyloid-β1-42/1-40 alone would reduce these numbers to 623 individuals and 243 individuals, respectively. Across a theoretical range of amyloid PET positivity prevalence of 10-50%, mass spectrometry measures of amyloid-β1-42/1-40 would consistently reduce the numbers proceeding to scans, with greater cost savings demonstrated at lower prevalence.
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Affiliation(s)
- Ashvini Keshavan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Josef Pannee
- Clinical Neurochemistry Laboratory, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Thomas K Karikari
- Clinical Neurochemistry Laboratory, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Juan Lantero Rodriguez
- Clinical Neurochemistry Laboratory, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Nicholas J Ashton
- Clinical Neurochemistry Laboratory, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital, Mölndal, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- National Institute for Health Research Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation Trust, London, UK
| | - Jennifer M Nicholas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, University of London, London, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Thomas D Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Kirsty Lu
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah M Buchanan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah E Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | | | - Heidi Murray-Smith
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Anna Barnes
- Institute of Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, UK
| | - John C Dickson
- Institute of Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, UK
| | - Amanda Heslegrave
- UK Dementia Research Institute Fluid Biomarkers Laboratory, UK DRI at UCL, London, UK
| | - Erik Portelius
- Clinical Neurochemistry Laboratory, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital, Mölndal, Sweden
| | | | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital, Mölndal, Sweden
- UK Dementia Research Institute Fluid Biomarkers Laboratory, UK DRI at UCL, London, UK
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
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25
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Lu K, Nicholas JM, Weston PSJ, Stout JC, O’Regan AM, James SN, Buchanan SM, Lane CA, Parker TD, Keuss SE, Keshavan A, Murray-Smith H, Cash DM, Sudre CH, Malone IB, Coath W, Wong A, Richards M, Henley SMD, Fox NC, Schott JM, Crutch SJ. Visuomotor integration deficits are common to familial and sporadic preclinical Alzheimer's disease. Brain Commun 2021; 3:fcab003. [PMID: 33615219 PMCID: PMC7882207 DOI: 10.1093/braincomms/fcab003] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 12/04/2020] [Accepted: 12/08/2020] [Indexed: 11/26/2022] Open
Abstract
We investigated whether subtle visuomotor deficits were detectable in familial and sporadic preclinical Alzheimer's disease. A circle-tracing task-with direct and indirect visual feedback, and dual-task subtraction-was completed by 31 individuals at 50% risk of familial Alzheimer's disease (19 presymptomatic mutation carriers; 12 non-carriers) and 390 cognitively normal older adults (members of the British 1946 Birth Cohort, all born during the same week; age range at assessment = 69-71 years), who also underwent β-amyloid-PET/MRI to derive amyloid status (positive/negative), whole-brain volume and white matter hyperintensity volume. We compared preclinical Alzheimer's groups against controls cross-sectionally (mutation carriers versus non-carriers; amyloid-positive versus amyloid-negative) on speed and accuracy of circle-tracing and subtraction. Mutation carriers (mean 7 years before expected onset) and amyloid-positive older adults traced disproportionately less accurately than controls when visual feedback was indirect, and were slower at dual-task subtraction. In the older adults, the same pattern of associations was found when considering amyloid burden as a continuous variable (Standardized Uptake Value Ratio). The effect of amyloid was independent of white matter hyperintensity and brain volumes, which themselves were associated with different aspects of performance: greater white matter hyperintensity volume was also associated with disproportionately poorer tracing accuracy when visual feedback was indirect, whereas larger brain volume was associated with faster tracing and faster subtraction. Mutation carriers also showed evidence of poorer tracing accuracy when visual feedback was direct. This study provides the first evidence of visuomotor integration deficits common to familial and sporadic preclinical Alzheimer's disease, which may precede the onset of clinical symptoms by several years.
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Affiliation(s)
- Kirsty Lu
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Jennifer M Nicholas
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
| | - Philip S J Weston
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Julie C Stout
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Alison M O’Regan
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, London, WC1E 7HB, UK
| | - Sarah M Buchanan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Thomas D Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Sarah E Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Ashvini Keshavan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
- UK Dementia Research Institute at University College London, London, UK
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL, London, WC1E 7HB, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EU, UK
- Department of Medical Physics, University College London, London, WC1E 7JE, UK
| | - Ian B Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, London, WC1E 7HB, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, London, WC1E 7HB, UK
| | - Susie M D Henley
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
- UK Dementia Research Institute at University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Sebastian J Crutch
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
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Abstract
Rodent models are increasingly important in translational neuroimaging research. In rodent neuroimaging, particularly magnetic resonance imaging (MRI) studies, brain extraction is a critical data preprocessing component. Current brain extraction methods for rodent MRI usually require manual adjustment of input parameters due to widely different image qualities and/or contrasts. Here we propose a novel method, termed SHape descriptor selected Extremal Regions after Morphologically filtering (SHERM), which only requires a brain template mask as the input and is capable of automatically and reliably extracting the brain tissue in both rat and mouse MRI images. The method identifies a set of brain mask candidates, extracted from MRI images morphologically opened and closed sequentially with multiple kernel sizes, that match the shape of the brain template. These brain mask candidates are then merged to generate the brain mask. This method, along with four other state-of-the-art rodent brain extraction methods, were benchmarked on four separate datasets including both rat and mouse MRI images. Without involving any parameter tuning, our method performed comparably to the other four methods on all datasets, and its performance was robust with stably high true positive rates and low false positive rates. Taken together, this study provides a reliable automatic brain extraction method that can contribute to the establishment of automatic pipelines for rodent neuroimaging data analysis.
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27
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Lane CA, Barnes J, Nicholas JM, Sudre CH, Cash DM, Malone IB, Parker TD, Keshavan A, Buchanan SM, Keuss SE, James SN, Lu K, Murray-Smith H, Wong A, Gordon E, Coath W, Modat M, Thomas D, Richards M, Fox NC, Schott JM. Associations Between Vascular Risk Across Adulthood and Brain Pathology in Late Life: Evidence From a British Birth Cohort. JAMA Neurol 2020; 77:175-183. [PMID: 31682678 PMCID: PMC6830432 DOI: 10.1001/jamaneurol.2019.3774] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Question When is vascular risk during adulthood (early adulthood, midlife, or late life) most strongly associated with late-life brain structure and pathology? Findings In a propective cohort of 463 participants free of dementia from the population-based Insight 46 study, higher vascular risk in early adulthood was most strongly associated with smaller whole-brain volumes and greater white matter–hyperintensity volumes at age 69 to 71 years. There were no associations at any age with amyloid status. Meaning These findings are consistent with vascular risk influencing late-life brain health via cerebral small-vessel disease and lower brain volumes but not amyloidosis; vascular risk screening and modification may need to be considered from early adulthood. Importance Midlife vascular risk burden is associated with late-life dementia. Less is known about if and how risk exposure in early adulthood influences late-life brain health. Objective To determine the associations between vascular risk in early adulthood, midlife, and late life with late-life brain structure and pathology using measures of white matter–hyperintensity volume, β-amyloid load, and whole-brain and hippocampal volumes. Design, Setting, and Participants This prospective longitudinal cohort study, Insight 46, is part of the Medical Research Council National Survey of Health and Development, which commenced in 1946. Participants had vascular risk factors evaluated at ages 36 years (early adulthood), 53 years (midlife), and 69 years (early late life). Participants were assessed with multimodal magnetic resonance imaging and florbetapir-amyloid positron emission tomography scans between May 2015 and January 2018 at University College London. Participants with at least 1 available imaging measure, vascular risk measurements at 1 or more points, and no dementia were included in analyses. Exposures Office-based Framingham Heart study–cardiovascular risk scores (FHS-CVS) were derived at ages 36, 53, and 69 years using systolic blood pressure, antihypertensive medication usage, smoking, diabetic status, and body mass index. Analysis models adjusted for age at imaging, sex, APOE genotype, socioeconomic position, and, where appropriate, total intracranial volume. Main Outcomes and Measures White matter–hyperintensity volume was generated from T1/fluid-attenuated inversion recovery scans using an automated technique and whole-brain volume and hippocampal volume were generated from automated in-house pipelines; β-amyloid status was determined using a gray matter/eroded subcortical white matter standardized uptake value ratio threshold of 0.61. Results A total of 502 participants were assessed as part of Insight 46, and 463 participants (236 male [51.0%]) with at least 1 available imaging measure (mean [SD] age at imaging, 70.7 [0.7] years; 83 β-amyloid positive [18.2%]) who fulfilled eligibility criteria were included. Among them, FHS-CVS increased with age (36 years: median [interquartile range], 2.7% [1.5%-3.6%]; 53 years: 10.9% [6.7%-15.6%]; 69 years: 24.3% [14.9%-34.9%]). At all points, these scores were associated with smaller whole-brain volumes (36 years: β coefficient per 1% increase, −3.6 [95% CI, −7.0 to −0.3]; 53 years: −0.8 [95% CI, −1.5 to −0.08]; 69 years: −0.6 [95% CI, −1.1 to −0.2]) and higher white matter–hyperintensity volume (exponentiated coefficient: 36 years, 1.09 [95% CI, 1.01-1.18]; 53 years, 1.02 [95% CI, 1.00-1.04]; 69 years, 1.01 [95% CI, 1.00-1.02]), with largest effect sizes at age 36 years. At no point were FHS-CVS results associated with β-amyloid status. Conclusions and Relevance Higher vascular risk is associated with smaller whole-brain volume and greater white matter–hyperintensity volume at age 69 to 71 years, with the strongest association seen with early adulthood vascular risk. There was no evidence that higher vascular risk influences amyloid deposition, at least up to age 71 years. Reducing vascular risk with appropriate interventions should be considered from early adulthood to maximize late-life brain health.
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Affiliation(s)
- Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Josephine Barnes
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jennifer M Nicholas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.,London School of Hygiene and Tropical Medicine, Department of Medical Statistics, University of London, London, United Kingdom
| | - Carole H Sudre
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Ian B Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Thomas D Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Ashvini Keshavan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sarah M Buchanan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sarah E Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, United Kingdom
| | - Kirsty Lu
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Heidi Murray-Smith
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, United Kingdom
| | - Elizabeth Gordon
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Marc Modat
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - David Thomas
- Leonard Wolfson Experimental Neurology Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.,Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, United Kingdom
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.,UK Dementia Research Institute at UCL, University College London, London, United Kingdom
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.,UK Dementia Research Institute at UCL, University College London, London, United Kingdom
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Hsu LM, Wang S, Ranadive P, Ban W, Chao THH, Song S, Cerri DH, Walton LR, Broadwater MA, Lee SH, Shen D, Shih YYI. Automatic Skull Stripping of Rat and Mouse Brain MRI Data Using U-Net. Front Neurosci 2020; 14:568614. [PMID: 33117118 PMCID: PMC7575753 DOI: 10.3389/fnins.2020.568614] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 08/13/2020] [Indexed: 11/13/2022] Open
Abstract
Accurate removal of magnetic resonance imaging (MRI) signal outside the brain, a.k.a., skull stripping, is a key step in the brain image pre-processing pipelines. In rodents, this is mostly achieved by manually editing a brain mask, which is time-consuming and operator dependent. Automating this step is particularly challenging in rodents as compared to humans, because of differences in brain/scalp tissue geometry, image resolution with respect to brain-scalp distance, and tissue contrast around the skull. In this study, we proposed a deep-learning-based framework, U-Net, to automatically identify the rodent brain boundaries in MR images. The U-Net method is robust against inter-subject variability and eliminates operator dependence. To benchmark the efficiency of this method, we trained and validated our model using both in-house collected and publicly available datasets. In comparison to current state-of-the-art methods, our approach achieved superior averaged Dice similarity coefficient to ground truth T2-weighted rapid acquisition with relaxation enhancement and T2∗-weighted echo planar imaging data in both rats and mice (all p < 0.05), demonstrating robust performance of our approach across various MRI protocols.
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Affiliation(s)
- Li-Ming Hsu
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Shuai Wang
- Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Paridhi Ranadive
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Woomi Ban
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Tzu-Hao Harry Chao
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Sheng Song
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Domenic Hayden Cerri
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Lindsay R Walton
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Margaret A Broadwater
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Sung-Ho Lee
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Dinggang Shen
- Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Yen-Yu Ian Shih
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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Zöllei L, Iglesias JE, Ou Y, Grant PE, Fischl B. Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years. Neuroimage 2020; 218:116946. [PMID: 32442637 PMCID: PMC7415702 DOI: 10.1016/j.neuroimage.2020.116946] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 03/03/2020] [Accepted: 05/12/2020] [Indexed: 01/23/2023] Open
Abstract
The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges in this domain due to: 1) a smaller-scaled region of interest, 2) increased motion corruption, 3) regional changes in geometry due to heterochronous growth, and 4) regional variations in contrast properties corresponding to ongoing myelination and other maturation processes. Nevertheless, there is a great need for automated image-processing tools to quantify differences between infant groups and other individuals, because aberrant cortical morphologic measurements (including volume, thickness, surface area, and curvature) have been associated with neuropsychiatric, neurologic, and developmental disorders in children. In this paper we present an automated segmentation and surface extraction pipeline designed to accommodate clinical MRI studies of infant brains in a population 0-2 year-olds. The algorithm relies on a single channel of T1-weighted MR images to achieve automated segmentation of cortical and subcortical brain areas, producing volumes of subcortical structures and surface models of the cerebral cortex. We evaluated the algorithm both qualitatively and quantitatively using manually labeled datasets, relevant comparator software solutions cited in the literature, and expert evaluations. The computational tools and atlases described in this paper will be distributed to the research community as part of the FreeSurfer image analysis package.
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Affiliation(s)
- Lilla Zöllei
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA.
| | - Juan Eugenio Iglesias
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Center for Medical Image Computing, University College London, United Kingdom; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
| | - Yangming Ou
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, USA
| | - Bruce Fischl
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
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30
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Feng J, Zhang SW, Chen L. Identification of Alzheimer's disease based on wavelet transformation energy feature of the structural MRI image and NN classifier. Artif Intell Med 2020; 108:101940. [DOI: 10.1016/j.artmed.2020.101940] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 07/01/2020] [Accepted: 08/07/2020] [Indexed: 02/07/2023]
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31
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Sanchis-Segura C, Ibañez-Gual MV, Aguirre N, Cruz-Gómez ÁJ, Forn C. Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction. Sci Rep 2020; 10:12953. [PMID: 32737332 PMCID: PMC7395772 DOI: 10.1038/s41598-020-69361-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 07/08/2020] [Indexed: 12/22/2022] Open
Abstract
Sex differences in 116 local gray matter volumes (GMVOL) were assessed in 444 males and 444 females without correcting for total intracranial volume (TIV) or after adjusting the data with the scaling, proportions, power-corrected proportions (PCP), and residuals methods. The results confirmed that only the residuals and PCP methods completely eliminate TIV-variation and result in sex-differences that are "small" (∣d∣ < 0.3). Moreover, as assessed using a totally independent sample, sex differences in PCP and residuals adjusted-data showed higher replicability ([Formula: see text] 93%) than scaling and proportions adjusted-data [Formula: see text] 68%) or raw data ([Formula: see text] 45%). The replicated effects were meta-analyzed together and confirmed that, when TIV-variation is adequately controlled, volumetric sex differences become "small" (∣d∣ < 0.3 in all cases). Finally, we assessed the utility of TIV-corrected/ TIV-uncorrected GMVOL features in predicting individuals' sex with 12 different machine learning classifiers. Sex could be reliably predicted (> 80%) when using raw local GMVOL, but also when using scaling or proportions adjusted-data or TIV as a single predictor. Conversely, after properly controlling TIV variation with the PCP and residuals' methods, prediction accuracy dropped to [Formula: see text] 60%. It is concluded that gross morphological differences account for most of the univariate and multivariate sex differences in GMVOL.
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Affiliation(s)
- Carla Sanchis-Segura
- Departament de Psicologia Bàsica, Clínica i Psicobiologia, Universitat Jaume I, Avda. Sos Baynat, SN, 12071, Castelló, Spain.
| | | | - Naiara Aguirre
- Departament de Psicologia Bàsica, Clínica i Psicobiologia, Universitat Jaume I, Avda. Sos Baynat, SN, 12071, Castelló, Spain
| | - Álvaro Javier Cruz-Gómez
- Departament de Psicologia Bàsica, Clínica i Psicobiologia, Universitat Jaume I, Avda. Sos Baynat, SN, 12071, Castelló, Spain
| | - Cristina Forn
- Departament de Psicologia Bàsica, Clínica i Psicobiologia, Universitat Jaume I, Avda. Sos Baynat, SN, 12071, Castelló, Spain
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32
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Fatima A, Shahid AR, Raza B, Madni TM, Janjua UI. State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms. J Digit Imaging 2020; 33:1443-1464. [PMID: 32666364 DOI: 10.1007/s10278-020-00367-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Several neuroimaging processing applications consider skull stripping as a crucial pre-processing step. Due to complex anatomical brain structure and intensity variations in brain magnetic resonance imaging (MRI), an appropriate skull stripping is an important part. The process of skull stripping basically deals with the removal of the skull region for clinical analysis in brain segmentation tasks, and its accuracy and efficiency are quite crucial for diagnostic purposes. It requires more accurate and detailed methods for differentiating brain regions and the skull regions and is considered as a challenging task. This paper is focused on the transition of the conventional to the machine- and deep-learning-based automated skull stripping methods for brain MRI images. It is observed in this study that deep learning approaches have outperformed conventional and machine learning techniques in many ways, but they have their limitations. It also includes the comparative analysis of the current state-of-the-art skull stripping methods, a critical discussion of some challenges, model of quantifying parameters, and future work directions.
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Affiliation(s)
- Anam Fatima
- Medical Imaging and Diagnostics (MID) Lab, National Centre of Artificial Intelligence (NCAI), Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad, 45550, Pakistan
| | - Ahmad Raza Shahid
- Medical Imaging and Diagnostics (MID) Lab, National Centre of Artificial Intelligence (NCAI), Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad, 45550, Pakistan
| | - Basit Raza
- Medical Imaging and Diagnostics (MID) Lab, National Centre of Artificial Intelligence (NCAI), Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad, 45550, Pakistan.
| | - Tahir Mustafa Madni
- Medical Imaging and Diagnostics (MID) Lab, National Centre of Artificial Intelligence (NCAI), Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad, 45550, Pakistan
| | - Uzair Iqbal Janjua
- Medical Imaging and Diagnostics (MID) Lab, National Centre of Artificial Intelligence (NCAI), Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad, 45550, Pakistan
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Fiford CM, Sudre CH, Pemberton H, Walsh P, Manning E, Malone IB, Nicholas J, Bouvy WH, Carmichael OT, Biessels GJ, Cardoso MJ, Barnes J. Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change. Neuroinformatics 2020; 18:429-449. [PMID: 32062817 PMCID: PMC7338814 DOI: 10.1007/s12021-019-09439-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Accurate, automated white matter hyperintensity (WMH) segmentations are needed for large-scale studies to understand contributions of WMH to neurological diseases. We evaluated Bayesian Model Selection (BaMoS), a hierarchical fully-unsupervised model selection framework for WMH segmentation. We compared BaMoS segmentations to semi-automated segmentations, and assessed whether they predicted longitudinal cognitive change in control, early Mild Cognitive Impairment (EMCI), late Mild Cognitive Impairment (LMCI), subjective/significant memory concern (SMC) and Alzheimer's (AD) participants. Data were downloaded from the Alzheimer's disease Neuroimaging Initiative (ADNI). Magnetic resonance images from 30 control and 30 AD participants were selected to incorporate multiple scanners, and were semi-automatically segmented by 4 raters and BaMoS. Segmentations were assessed using volume correlation, Dice score, and other spatial metrics. Linear mixed-effect models were fitted to 180 control, 107 SMC, 320 EMCI, 171 LMCI and 151 AD participants separately in each group, with the outcomes being cognitive change (e.g. mini-mental state examination; MMSE), and BaMoS WMH, age, sex, race and education used as predictors. There was a high level of agreement between BaMoS' WMH segmentation volumes and a consensus of rater segmentations, with a median Dice score of 0.74 and correlation coefficient of 0.96. BaMoS WMH predicted cognitive change in: control, EMCI, and SMC groups using MMSE; LMCI using clinical dementia rating scale; and EMCI using Alzheimer's disease assessment scale-cognitive subscale (p < 0.05, all tests). BaMoS compares well to semi-automated segmentation, is robust to different WMH loads and scanners, and can generate volumes which predict decline. BaMoS can be applicable to further large-scale studies.
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Affiliation(s)
- Cassidy M. Fiford
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Carole H. Sudre
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Hugh Pemberton
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Phoebe Walsh
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Emily Manning
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Ian B. Malone
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | | | - Willem H Bouvy
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - M. Jorge Cardoso
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Josephine Barnes
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- London School of Hygiene and Tropical Medicine, London, UK
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
- Pennington Biomedical Research Center, Baton Rouge, LA USA
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Fiford CM, Nicholas JM, Biessels GJ, Lane CA, Cardoso MJ, Barnes J. High blood pressure predicts hippocampal atrophy rate in cognitively impaired elders. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12035. [PMID: 32587882 PMCID: PMC7308793 DOI: 10.1002/dad2.12035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 02/26/2020] [Accepted: 02/26/2020] [Indexed: 11/12/2022]
Abstract
INTRODUCTION Understanding relationships among blood pressure (BP), cognition, and brain volume could inform Alzheimer's disease (AD) management. METHODS We investigated Alzheimer's Disease Neuroimaging Initiative (ADNI) participants: 200 controls, 346 mild cognitive impairment (MCI), and 154 AD. National Alzheimer's Co-ordinating Center (NACC) participants were separately analyzed: 1098 controls, 2297 MCI, and 4845 AD. Relationships between cognition and BP were assessed in both cohorts and BP and atrophy rates in ADNI. Multivariate mixed linear-regression models were fitted with joint outcomes of BP (systolic, diastolic, and pulse pressure), cognition (Mini-Mental State Examination, Logical Memory, and Digit Symbol) and atrophy rate (whole-brain, hippocampus). RESULTS ADNI MCI and AD patients with greater baseline systolic BP had higher hippocampal atrophy rates ([r, P value]; 0.2, 0.005 and 0.2, 0.04, respectively). NACC AD patients with lower systolic BP had lower cognitive scores (0.1, 0.0003). DISCUSSION Higher late-life BP may be associated with faster decline in cognitively impaired elders.
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Affiliation(s)
- Cassidy M. Fiford
- Department of Neurodegenerative Disease, Dementia Research CentreUCL Institute of NeurologyLondonUK
| | | | - Geert Jan Biessels
- Department of Neurology and NeurosurgeryBrain Center Rudolf MagnusUniversity Medical CenterUtrechtthe Netherlands
| | - Christopher A. Lane
- Department of Neurodegenerative Disease, Dementia Research CentreUCL Institute of NeurologyLondonUK
| | - M. Jorge Cardoso
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Josephine Barnes
- Department of Neurodegenerative Disease, Dementia Research CentreUCL Institute of NeurologyLondonUK
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He B, Yang Z, Fan L, Gao B, Li H, Ye C, You B, Jiang T. MonkeyCBP: A Toolbox for Connectivity-Based Parcellation of Monkey Brain. Front Neuroinform 2020; 14:14. [PMID: 32410977 PMCID: PMC7198896 DOI: 10.3389/fninf.2020.00014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 03/10/2020] [Indexed: 01/24/2023] Open
Abstract
Non-human primate models are widely used in studying the brain mechanism underlying brain development, cognitive functions, and psychiatric disorders. Neuroimaging techniques, such as magnetic resonance imaging, play an important role in the examinations of brain structure and functions. As an indispensable tool for brain imaging data analysis, brain atlases have been extensively investigated, and a variety of versions constructed. These atlases diverge in the criteria based on which they are plotted. The criteria range from cytoarchitectonic features, neurotransmitter receptor distributions, myelination fingerprints, and transcriptomic patterns to structural and functional connectomic profiles. Among them, brainnetome atlas is tightly related to brain connectome information and built by parcellating the brain on the basis of the anatomical connectivity profiles derived from structural neuroimaging data. The pipeline for building the brainnetome atlas has been published as a toolbox named ATPP (A Pipeline for Automatic Tractography-Based Brain Parcellation). In this paper, we present a variation of ATPP, which is dedicated to monkey brain parcellation, to address the significant differences in the process between the two species. The new toolbox, MonkeyCBP, has major alterations in three aspects: brain extraction, image registration, and validity indices. By parcellating two different brain regions (posterior cingulate cortex) and (frontal pole) of the rhesus monkey, we demonstrate the efficacy of these alterations. The toolbox has been made public (https://github.com/bheAI/MonkeyCBP_CLI, https://github.com/bheAI/MonkeyCBP_GUI). It is expected that the toolbox can benefit the non-human primate neuroimaging community with high-throughput computation and low labor involvement.
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Affiliation(s)
- Bin He
- School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, China
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhengyi Yang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Bin Gao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Hai Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Chuyang Ye
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bo You
- School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Chinese Institute for Brain Research, Beijing, China
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Conventional and Deep Learning Methods for Skull Stripping in Brain MRI. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10051773] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Skull stripping in brain magnetic resonance volume has recently been attracting attention due to an increased demand to develop an efficient, accurate, and general algorithm for diverse datasets of the brain. Accurate skull stripping is a critical step for neuroimaging diagnostic systems because neither the inclusion of non-brain tissues nor removal of brain parts can be corrected in subsequent steps, which results in unfixed error through subsequent analysis. The objective of this review article is to give a comprehensive overview of skull stripping approaches, including recent deep learning-based approaches. In this paper, the current methods of skull stripping have been divided into two distinct groups—conventional or classical approaches, and convolutional neural networks or deep learning approaches. The potentials of several methods are emphasized because they can be applied to standard clinical imaging protocols. Finally, current trends and future developments are addressed giving special attention to recent deep learning algorithms.
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Lane CA, Barnes J, Nicholas JM, Sudre CH, Cash DM, Parker TD, Malone IB, Lu K, James SN, Keshavan A, Murray-Smith H, Wong A, Buchanan SM, Keuss SE, Gordon E, Coath W, Barnes A, Dickson J, Modat M, Thomas D, Crutch SJ, Hardy R, Richards M, Fox NC, Schott JM. Associations between blood pressure across adulthood and late-life brain structure and pathology in the neuroscience substudy of the 1946 British birth cohort (Insight 46): an epidemiological study. Lancet Neurol 2019; 18:942-952. [PMID: 31444142 PMCID: PMC6744368 DOI: 10.1016/s1474-4422(19)30228-5] [Citation(s) in RCA: 160] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 05/08/2019] [Accepted: 05/09/2019] [Indexed: 01/06/2023]
Abstract
BACKGROUND Midlife hypertension confers increased risk for cognitive impairment in late life. The sensitive period for risk exposure and extent that risk is mediated through amyloid or vascular-related mechanisms are poorly understood. We aimed to identify if, and when, blood pressure or change in blood pressure during adulthood were associated with late-life brain structure, pathology, and cognition. METHODS Participants were from Insight 46, a neuroscience substudy of the ongoing longitudinal Medical Research Council National Survey of Health and Development, a birth cohort that initially comprised 5362 individuals born throughout mainland Britain in one week in 1946. Participants aged 69-71 years received T1 and FLAIR volumetric MRI, florbetapir amyloid-PET imaging, and cognitive assessment at University College London (London, UK); all participants were dementia-free. Blood pressure measurements had been collected at ages 36, 43, 53, 60-64, and 69 years. We also calculated blood pressure change variables between ages. Primary outcome measures were white matter hyperintensity volume (WMHV) quantified from multimodal MRI using an automated method, amyloid-β positivity or negativity using a standardised uptake value ratio approach, whole-brain and hippocampal volumes quantified from 3D-T1 MRI, and a composite cognitive score-the Preclinical Alzheimer Cognitive Composite (PACC). We investigated associations between blood pressure and blood pressure changes at and between 36, 43, 53, 60-64, and 69 years of age with WMHV using generalised linear models with a gamma distribution and log link function, amyloid-β status using logistic regression, whole-brain volume and hippocampal volumes using linear regression, and PACC score using linear regression, with adjustment for potential confounders. FINDINGS Between May 28, 2015, and Jan 10, 2018, 502 individuals were assessed as part of Insight 46. 465 participants (238 [51%] men; mean age 70·7 years [SD 0·7]; 83 [18%] amyloid-β-positive) were included in imaging analyses. Higher systolic blood pressure (SBP) and diastolic blood pressure (DBP) at age 53 years and greater increases in SBP and DBP between 43 and 53 years were positively associated with WMHV at 69-71 years of age (increase in mean WMHV per 10 mm Hg greater SBP 7%, 95% CI 1-14, p=0·024; increase in mean WMHV per 10 mm Hg greater DBP 15%, 4-27, p=0·0057; increase in mean WMHV per one SD change in SBP 15%, 3-29, p=0·012; increase in mean WMHV per 1 SD change in DBP 15%, 3-30, p=0·017). Higher DBP at 43 years of age was associated with smaller whole-brain volume at 69-71 years of age (-6·9 mL per 10 mm Hg greater DBP, -11·9 to -1·9, p=0·0068), as were greater increases in DBP between 36 and 43 years of age (-6·5 mL per 1 SD change, -11·1 to -1·9, p=0·0054). Greater increases in SBP between 36 and 43 years of age were associated with smaller hippocampal volumes at 69-71 years of age (-0·03 mL per 1 SD change, -0·06 to -0·001, p=0·043). Neither absolute blood pressure nor change in blood pressure predicted amyloid-β status or PACC score at 69-71 years of age. INTERPRETATION High and increasing blood pressure from early adulthood into midlife seems to be associated with increased WMHV and smaller brain volumes at 69-71 years of age. We found no evidence that blood pressure affected cognition or cerebral amyloid-β load at this age. Blood pressure monitoring and interventions might need to start around 40 years of age to maximise late-life brain health. FUNDING Alzheimer's Research UK, Medical Research Council, Dementias Platform UK, Wellcome Trust, Brain Research UK, Wolfson Foundation, Weston Brain Institute, Avid Radiopharmaceuticals.
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Affiliation(s)
- Christopher A Lane
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Josephine Barnes
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Jennifer M Nicholas
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK,Department of Medical Statistics, London School of Hygiene and Tropical Medicine, University of London, London, UK
| | - Carole H Sudre
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK,School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - David M Cash
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Thomas D Parker
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Ian B Malone
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Kirsty Lu
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah-Naomi James
- Medical Research Council Unit for Lifelong Health and Ageing at University College London, London, UK
| | - Ashvini Keshavan
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- Medical Research Council Unit for Lifelong Health and Ageing at University College London, London, UK
| | - Sarah M Buchanan
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah E Keuss
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Elizabeth Gordon
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - William Coath
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Anna Barnes
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - John Dickson
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Marc Modat
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK,School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - David Thomas
- Leonard Wolfson Experimental Neurology Centre and Academic Neuroradiological Unit, Department of Brain Repair and Rehabilitation, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Sebastian J Crutch
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - Rebecca Hardy
- Medical Research Council Unit for Lifelong Health and Ageing at University College London, London, UK
| | - Marcus Richards
- Medical Research Council Unit for Lifelong Health and Ageing at University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK,UK Dementia Research Institute at University College London, University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London, UK,UK Dementia Research Institute at University College London, University College London, London, UK,Correspondence to: Prof Jonathan M Schott, Dementia Research Centre, University College London Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
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38
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Noorizadeh N, Kazemi K, Danyali H, Aarabi A. Multi-atlas based neonatal brain extraction using a two-level patch-based label fusion strategy. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.101602] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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39
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Feo R, Giove F. Towards an efficient segmentation of small rodents brain: A short critical review. J Neurosci Methods 2019; 323:82-89. [DOI: 10.1016/j.jneumeth.2019.05.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 05/09/2019] [Accepted: 05/10/2019] [Indexed: 01/27/2023]
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Abstract
Skull stripping in brain magnetic resonance imaging (MRI) is an essential step to analyze images of the brain. Although manual segmentation has the highest accuracy, it is a time-consuming task. Therefore, various automatic segmentation algorithms of the brain in MRI have been devised and proposed previously. However, there is still no method that solves the entire brain extraction problem satisfactorily for diverse datasets in a generic and robust way. To address these shortcomings of existing methods, we propose the use of a 3D-UNet for skull stripping in brain MRI. The 3D-UNet was recently proposed and has been widely used for volumetric segmentation in medical images due to its outstanding performance. It is an extended version of the previously proposed 2D-UNet, which is based on a deep learning network, specifically, the convolutional neural network. We evaluated 3D-UNet skull-stripping using a publicly available brain MRI dataset and compared the results with three existing methods (BSE, ROBEX, and Kleesiek’s method; BSE and ROBEX are two conventional methods, and Kleesiek’s method is based on deep learning). The 3D-UNet outperforms two typical methods and shows comparable results with the specific deep learning-based algorithm, exhibiting a mean Dice coefficient of 0.9903, a sensitivity of 0.9853, and a specificity of 0.9953.
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41
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Lin W, Tong T, Gao Q, Guo D, Du X, Yang Y, Guo G, Xiao M, Du M, Qu X. Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer's Disease Prediction From Mild Cognitive Impairment. Front Neurosci 2018; 12:777. [PMID: 30455622 PMCID: PMC6231297 DOI: 10.3389/fnins.2018.00777] [Citation(s) in RCA: 136] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Accepted: 10/05/2018] [Indexed: 12/18/2022] Open
Abstract
Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN.
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Affiliation(s)
- Weiming Lin
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China
- Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou, China
| | - Tong Tong
- Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou, China
- Imperial Vision Technology, Fuzhou, China
| | - Qinquan Gao
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou, China
- Imperial Vision Technology, Fuzhou, China
| | - Di Guo
- School of Computer & Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Xiaofeng Du
- School of Computer & Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Yonggui Yang
- Department of Radiology, Xiamen 2nd Hospital, Xiamen, China
| | - Gang Guo
- Department of Radiology, Xiamen 2nd Hospital, Xiamen, China
| | - Min Xiao
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China
| | - Min Du
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Provincial Key Laboratory of Eco-Industrial Green Technology, Nanping, China
| | - Xiaobo Qu
- Department of Electronic Science, Xiamen University, Xiamen, China
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42
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Ou Y, Zöllei L, Da X, Retzepi K, Murphy SN, Gerstner ER, Rosen BR, Grant PE, Kalpathy-Cramer J, Gollub RL. Field of View Normalization in Multi-Site Brain MRI. Neuroinformatics 2018; 16:431-444. [PMID: 29353341 PMCID: PMC7334884 DOI: 10.1007/s12021-018-9359-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Multi-site brain MRI analysis is needed in big data neuroimaging studies, but challenging. The challenges lie in almost every analysis step including skull stripping. The diversities in multi-site brain MR images make it difficult to tune parameters specific to subjects or imaging protocols. Alternatively, using constant parameter settings often leads to inaccurate, inconsistent and even failed skull stripping results. One reason is that images scanned at different sites, under different scanners or protocols, and/or by different technicians often have very different fields of view (FOVs). Normalizing FOV is currently done manually or using ad hoc pre-processing steps, which do not always generalize well to multi-site diverse images. In this paper, we show that (a) a generic FOV normalization approach is possible in multi-site diverse images; we show experiments on images acquired from Philips, GE, Siemens scanners, from 1.0T, 1.5T, 3.0T field of strengths, and from subjects 0-90 years of ages; and (b) generic FOV normalization improves skull stripping accuracy and consistency for multiple skull stripping algorithms; we show this effect for 5 skull stripping algorithms including FSL's BET, AFNI's 3dSkullStrip, FreeSurfer's HWA, BrainSuite's BSE, and MASS. We have released our FOV normalization software at http://www.nitrc.org/projects/normalizefov .
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Affiliation(s)
- Yangming Ou
- Department of Pediatrics and Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Lilla Zöllei
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Xiao Da
- Functional Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kallirroi Retzepi
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Shawn N Murphy
- Research Computing, Partners Healthcare, Boston, MA, USA
| | - Elizabeth R Gerstner
- Neuro-Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Bruce R Rosen
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - P Ellen Grant
- Department of Pediatrics and Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Randy L Gollub
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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43
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Bachmann C, Jacobs HIL, Porta Mana P, Dillen K, Richter N, von Reutern B, Dronse J, Onur OA, Langen KJ, Fink GR, Kukolja J, Morrison A. On the Extraction and Analysis of Graphs From Resting-State fMRI to Support a Correct and Robust Diagnostic Tool for Alzheimer's Disease. Front Neurosci 2018; 12:528. [PMID: 30323734 PMCID: PMC6172342 DOI: 10.3389/fnins.2018.00528] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 07/13/2018] [Indexed: 01/28/2023] Open
Abstract
The diagnosis of Alzheimer's disease (AD), especially in the early stage, is still not very reliable and the development of new diagnosis tools is desirable. A diagnosis based on functional magnetic resonance imaging (fMRI) is a suitable candidate, since fMRI is non-invasive, readily available, and indirectly measures synaptic dysfunction, which can be observed even at the earliest stages of AD. However, the results of previous attempts to analyze graph properties of resting state fMRI data are contradictory, presumably caused by methodological differences in graph construction. This comprises two steps: clustering the voxels of the functional image to define the nodes of the graph, and calculating the graph's edge weights based on a functional connectivity measure of the average cluster activities. A variety of methods are available for each step, but the robustness of results to method choice, and the suitability of the methods to support a diagnostic tool, are largely unknown. To address this issue, we employ a range of commonly and rarely used clustering and edge definition methods and analyze their graph theoretic measures (graph weight, shortest path length, clustering coefficient, and weighted degree distribution and modularity) on a small data set of 26 healthy controls, 16 subjects with mild cognitive impairment (MCI) and 14 with Alzheimer's disease. We examine the results with respect to statistical significance of the mean difference in graph properties, the sensitivity of the results to model and parameter choices, and relative diagnostic power based on both a statistical model and support vector machines. We find that different combinations of graph construction techniques yield contradicting, but statistically significant, relations of graph properties between health conditions, explaining the discrepancy across previous studies, but casting doubt on such analyses as a method to gain insight into disease effects. The production of significant differences in mean graph properties turns out not to be a good predictor of future diagnostic capacity. Highest predictive power, expressed by largest negative surprise values, are achieved for both atlas-driven and data-driven clustering (Ward clustering), as long as graphs are small and clusters large, in combination with edge definitions based on correlations and mutual information transfer.
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Affiliation(s)
- Claudia Bachmann
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany
| | - Heidi I L Jacobs
- Faculty of Health, Medicine and Life Science, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, Netherlands.,Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States.,Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - PierGianLuca Porta Mana
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Kim Dillen
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
| | - Nils Richter
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany.,Department of Neurology, University Hospital of Cologne, Cologne, Germany
| | - Boris von Reutern
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany.,Department of Neurology, University Hospital of Cologne, Cologne, Germany
| | - Julian Dronse
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany.,Department of Neurology, University Hospital of Cologne, Cologne, Germany
| | - Oezguer A Onur
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany.,Department of Neurology, University Hospital of Cologne, Cologne, Germany
| | - Karl-Josef Langen
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-4), Jülich Research Centre, Jülich, Germany
| | - Gereon R Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany.,Department of Neurology, University Hospital of Cologne, Cologne, Germany
| | - Juraj Kukolja
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany.,Department of Neurology, University Hospital of Cologne, Cologne, Germany.,Department of Neurology, Helios University Hospital Wuppertal, Wuppertal, Germany
| | - Abigail Morrison
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany.,Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr-University Bochum, Bochum, Germany
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44
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Novosad P, Collins DL. An efficient and accurate method for robust inter-dataset brain extraction and comparisons with 9 other methods. Hum Brain Mapp 2018; 39:4241-4257. [PMID: 29972616 DOI: 10.1002/hbm.24243] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 05/17/2018] [Accepted: 05/27/2018] [Indexed: 01/18/2023] Open
Abstract
Brain extraction is an important first step in many magnetic resonance neuroimaging studies. Due to variability in brain morphology and in the appearance of the brain due to differences in scanner acquisition parameters, the development of a generally applicable brain extraction algorithm has proven challenging. Learning-based brain extraction algorithms in particular perform well when the target and training images are sufficiently similar, but often perform worse when this condition is not met. In this study, we propose a new patch-based multi-atlas segmentation method for brain extraction which is specifically developed for accurate and robust processing across datasets. Using a diverse collection of labeled images from 5 different datasets, extensive comparisons were made with 9 other commonly used brain extraction methods, both before and after applying error correction (a machine learning method for automatically correcting segmentation errors) to each method. The proposed method performed equal to or better than the other methods in each of two segmentation scenarios: a challenging inter-dataset segmentation scenario in which no dataset-specific atlases were used (mean Dice coefficient 98.57%, volumetric correlation 0.994 across datasets following error correction), and an intra-dataset segmentation scenario in which only dataset-specific atlases were used (mean Dice coefficient 99.02%, volumetric correlation 0.998 across datasets following error correction). Furthermore, combined with error correction, the proposed method runs in less than one-tenth of the time required by the other top-performing methods in the challenging inter-dataset comparisons. Validation on an independent multi-centre dataset also confirmed the excellent performance of the proposed method.
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Affiliation(s)
- Philip Novosad
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
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45
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Amiri H, de Sitter A, Bendfeldt K, Battaglini M, Gandini Wheeler-Kingshott CAM, Calabrese M, Geurts JJG, Rocca MA, Sastre-Garriga J, Enzinger C, de Stefano N, Filippi M, Rovira Á, Barkhof F, Vrenken H. Urgent challenges in quantification and interpretation of brain grey matter atrophy in individual MS patients using MRI. Neuroimage Clin 2018; 19:466-475. [PMID: 29984155 PMCID: PMC6030805 DOI: 10.1016/j.nicl.2018.04.023] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 03/28/2018] [Accepted: 04/22/2018] [Indexed: 01/18/2023]
Abstract
Atrophy of the brain grey matter (GM) is an accepted and important feature of multiple sclerosis (MS). However, its accurate measurement is hampered by various technical, pathological and physiological factors. As a consequence, it is challenging to investigate the role of GM atrophy in the disease process as well as the effect of treatments that aim to reduce neurodegeneration. In this paper we discuss the most important challenges currently hampering the measurement and interpretation of GM atrophy in MS. The focus is on measurements that are obtained in individual patients rather than on group analysis methods, because of their importance in clinical trials and ultimately in clinical care. We discuss the sources and possible solutions of the current challenges, and provide recommendations to achieve reliable measurement and interpretation of brain GM atrophy in MS.
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Key Words
- BET, brain extraction tool
- Brain atrophy
- CNS, central nervous system
- CTh, cortical thickness
- DGM, deep grey matter
- DTI, diffusion tensor imaging
- FA, fractional anisotropy
- GM, grey matter
- Grey matter
- MRI, magnetic resonance imaging
- MS, multiple sclerosis
- Magnetic resonance imaging
- Multiple sclerosis
- TE, echo time
- TI, inversion time
- TR, repetition time
- VBM, voxel-based morphometry
- WM, white matter
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Affiliation(s)
- Houshang Amiri
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Alexandra de Sitter
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands.
| | | | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | | | - Massimiliano Calabrese
- Multiple Sclerosis Centre, Neurology Section, Department of Neurosciences, Biomedicine and Movements, University of Verona, Italy
| | - Jeroen J G Geurts
- Anatomy & Neurosciences, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Jaume Sastre-Garriga
- Servei de Neurologia/Neuroimmunologia, Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Christian Enzinger
- Department of Neurology & Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology, Medical University of Graz, Austria
| | - Nicola de Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Álex Rovira
- Unitat de Ressonància Magnètica (Servei de Radiologia), Hospital universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands; Institutes of Neurology and Healthcare Engineering, UCL, London, UK
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
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46
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Fiford CM, Ridgway GR, Cash DM, Modat M, Nicholas J, Manning EN, Malone IB, Biessels GJ, Ourselin S, Carmichael OT, Cardoso MJ, Barnes J. Patterns of progressive atrophy vary with age in Alzheimer's disease patients. Neurobiol Aging 2018; 63:22-32. [PMID: 29220823 PMCID: PMC5805840 DOI: 10.1016/j.neurobiolaging.2017.11.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Revised: 10/14/2017] [Accepted: 11/06/2017] [Indexed: 01/18/2023]
Abstract
Age is not only the greatest risk factor for Alzheimer's disease (AD) but also a key modifier of disease presentation and progression. Here, we investigate how longitudinal atrophy patterns vary with age in mild cognitive impairment (MCI) and AD. Data comprised serial longitudinal 1.5-T magnetic resonance imaging scans from 153 AD, 339 MCI, and 191 control subjects. Voxel-wise maps of longitudinal volume change were obtained and aligned across subjects. Local volume change was then modeled in terms of diagnostic group and an interaction between group and age, adjusted for total intracranial volume, white-matter hyperintensity volume, and apolipoprotein E genotype. Results were significant at p < 0.05 with family-wise error correction for multiple comparisons. An age-by-group interaction revealed that younger AD patients had significantly faster atrophy rates in the bilateral precuneus, parietal, and superior temporal lobes. These results suggest younger AD patients have predominantly posterior progressive atrophy, unexplained by white-matter hyperintensity, apolipoprotein E, or total intracranial volume. Clinical trials may benefit from adapting outcome measures for patient groups with lower average ages, to capture progressive atrophy in posterior cortices.
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Affiliation(s)
- Cassidy M Fiford
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.
| | - Gerard R Ridgway
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK
| | - David M Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Marc Modat
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | | | - Emily N Manning
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Ian B Malone
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Sebastien Ourselin
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | | | - M Jorge Cardoso
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Josephine Barnes
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
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47
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Kinnunen KM, Cash DM, Poole T, Frost C, Benzinger TLS, Ahsan RL, Leung KK, Cardoso MJ, Modat M, Malone IB, Morris JC, Bateman RJ, Marcus DS, Goate A, Salloway SP, Correia S, Sperling RA, Chhatwal JP, Mayeux RP, Brickman AM, Martins RN, Farlow MR, Ghetti B, Saykin AJ, Jack CR, Schofield PR, McDade E, Weiner MW, Ringman JM, Thompson PM, Masters CL, Rowe CC, Rossor MN, Ourselin S, Fox NC. Presymptomatic atrophy in autosomal dominant Alzheimer's disease: A serial magnetic resonance imaging study. Alzheimers Dement 2018; 14:43-53. [PMID: 28738187 PMCID: PMC5751893 DOI: 10.1016/j.jalz.2017.06.2268] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Revised: 06/10/2017] [Accepted: 06/12/2017] [Indexed: 01/01/2023]
Abstract
INTRODUCTION Identifying at what point atrophy rates first change in Alzheimer's disease is important for informing design of presymptomatic trials. METHODS Serial T1-weighted magnetic resonance imaging scans of 94 participants (28 noncarriers, 66 carriers) from the Dominantly Inherited Alzheimer Network were used to measure brain, ventricular, and hippocampal atrophy rates. For each structure, nonlinear mixed-effects models estimated the change-points when atrophy rates deviate from normal and the rates of change before and after this point. RESULTS Atrophy increased after the change-point, which occurred 1-1.5 years (assuming a single step change in atrophy rate) or 3-8 years (assuming gradual acceleration of atrophy) before expected symptom onset. At expected symptom onset, estimated atrophy rates were at least 3.6 times than those before the change-point. DISCUSSION Atrophy rates are pathologically increased up to seven years before "expected onset". During this period, atrophy rates may be useful for inclusion and tracking of disease progression.
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Affiliation(s)
- Kirsi M. Kinnunen
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - David M. Cash
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK,Department of Medical Physics and Bioengineering, Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK,Corresponding author. Tel.: +44 203 448 3054; Fax: +44 (0)20 3448 3104.,
| | - Teresa Poole
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK,Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Chris Frost
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK,Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | | | - R. Laila Ahsan
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Kelvin K. Leung
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - M. Jorge Cardoso
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK,Department of Medical Physics and Bioengineering, Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Marc Modat
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK,Department of Medical Physics and Bioengineering, Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Ian B. Malone
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J. Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Alison Goate
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stephen P. Salloway
- Department of Neurology, Butler Hospital, Warren Alpert Medical School, Brown University, Providence, RI, USA
| | - Stephen Correia
- Department of Neurology, Butler Hospital, Warren Alpert Medical School, Brown University, Providence, RI, USA
| | - Reisa A. Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jasmeer P. Chhatwal
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Richard P. Mayeux
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Adam M. Brickman
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Ralph N. Martins
- Centre of Excellence for Alzheimer’s Disease Research and Care, School of Exercise, Biomedical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Martin R. Farlow
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Centre for Neuroimaging, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Peter R. Schofield
- Neuroscience Research Australia, Randwick, NSW, Australia,School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Eric McDade
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Michael W. Weiner
- Department of Radiology, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - John M. Ringman
- Department of Neurology, Keck USC School of Medicine, Los Angeles, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Colin L. Masters
- The Florey Institute, University of Melbourne, Parkville, VIC, Australia
| | - Christopher C. Rowe
- Department of Nuclear Medicine and Centre for PET, Austin Health, Heidelberg, VIC, Australia,Department of Medicine, University of Melbourne, Austin Health, Heidelberg, VIC, Australia
| | - Martin N. Rossor
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Sebastien Ourselin
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK,Department of Medical Physics and Bioengineering, Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Nick C. Fox
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
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48
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Kehoe PG, Blair PS, Howden B, Thomas DL, Malone IB, Horwood J, Clement C, Selman LE, Baber H, Lane A, Coulthard E, Passmore AP, Fox NC, Wilkinson IB, Ben-Shlomo Y. The Rationale and Design of the Reducing Pathology in Alzheimer's Disease through Angiotensin TaRgeting (RADAR) Trial. J Alzheimers Dis 2018; 61:803-814. [PMID: 29226862 DOI: 10.3233/jad-170101] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
BACKGROUND Anti-hypertensives that modify the renin angiotensin system may reduce Alzheimer's disease (AD) pathology and reduce the rate of disease progression. OBJECTIVE To conduct a phase II, two arm, double-blind, placebo-controlled, randomized trial of losartan to test the efficacy of Reducing pathology in Alzheimer's Disease through Angiotensin TaRgeting (RADAR). METHODS Men and women aged at least 55 years with mild-to-moderate AD will be randomly allocated 100 mg encapsulated generic losartan or placebo once daily for 12 months after successful completion of a 2-week open-label phase and 2-week placebo washout to establish drug tolerability. 228 participants will provide at least 182 subjects with final assessments to provide 84% power to detect a 25% difference in atrophy rate (therapeutic benefit) change over 12 months at an alpha level of 0.05. We will use intention-to-treat analysis, estimating between-group differences in outcomes derived from appropriate (linear or logistic) multivariable regression models adjusting for minimization variables. RESULTS The primary outcome will be rate of whole brain atrophy as a surrogate measure of disease progression. Secondary outcomes will include changes to 1) white matter hyperintensity volume and cerebral blood flow; 2) performance on a standard series of assessments of memory, cognitive function, activities of daily living, and quality of life. Major assessments (for all outcomes) and relevant safety monitoring of blood pressure and bloods will be at baseline and 12 months. Additional cognitive assessment will also be conducted at 6 months along with safety blood pressure and blood monitoring. Monitoring of blood pressure, bloods, and self-reported side effects will occur during the open-label phase and during the majority of the post-randomization dispensing visits. CONCLUSION This study will identify whether losartan is efficacious in the treatment of AD and whether definitive Phase III trials are warranted.
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Affiliation(s)
- Patrick G Kehoe
- Dementia Research Group, Translational Health Sciences, Bristol Medical School, University of Bristol, Faculty of Health Sciences, Level 1 Learning and Research>, Southmead Hospital, Bristol, UK
| | - Peter S Blair
- Bristol Randomised Trials Collaboration (BRTC), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Beth Howden
- Bristol Randomised Trials Collaboration (BRTC), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - David L Thomas
- Leonard Wolfson Experimental Neurology Centre, UCL Institute of Neurology, Queen Square, London, UK
- Dementia Research Centre (DRC), Institute of Neurology, University College London, Queen Square, London, UK
| | - Ian B Malone
- Dementia Research Centre (DRC), Institute of Neurology, University College London, Queen Square, London, UK
| | - Jeremy Horwood
- Bristol Randomised Trials Collaboration (BRTC), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Clare Clement
- Bristol Randomised Trials Collaboration (BRTC), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Lucy E Selman
- Bristol Randomised Trials Collaboration (BRTC), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Hannah Baber
- Bristol Randomised Trials Collaboration (BRTC), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Athene Lane
- Bristol Randomised Trials Collaboration (BRTC), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Elizabeth Coulthard
- ReMemBr Group, Translational Health Sciences, Bristol Medical School, University of Bristol, Faculty of Health Sciences, Brain Centre, Southmead Hospital, Bristol, UK
| | - Anthony Peter Passmore
- Institute of Clinical Sciences, Queens University Belfast, Royal Victoria Hospital, Belfast, UK
| | - Nick C Fox
- Dementia Research Centre (DRC), Institute of Neurology, University College London, Queen Square, London, UK
| | - Ian B Wilkinson
- Division of Experimental Medicine and Immunotherapeutics, School of Clinical Medicine, University of Cambridge, and Clinical Trials Unit, Addenbrookes Hospital, Cambridge, UK
| | - Yoav Ben-Shlomo
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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49
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Dillen KN, Jacobs HI, Kukolja J, Richter N, von Reutern B, Onur ÖA, Langen KJ, Fink GR. Functional Disintegration of the Default Mode Network in Prodromal Alzheimer’s Disease. J Alzheimers Dis 2017; 59:169-187. [DOI: 10.3233/jad-161120] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- Kim N.H. Dillen
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, Jülich, Germany
| | - Heidi I.L. Jacobs
- Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, The Netherlands
| | - Juraj Kukolja
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, Jülich, Germany
- Department of Neurology, University Hospital Cologne, Cologne, Germany
| | - Nils Richter
- Department of Neurology, University Hospital Cologne, Cologne, Germany
| | - Boris von Reutern
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, Jülich, Germany
- Department of Neurology, University Hospital Cologne, Cologne, Germany
| | - Özgür A. Onur
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, Jülich, Germany
- Department of Neurology, University Hospital Cologne, Cologne, Germany
| | - Karl-Josef Langen
- Institute of Neuroscience and Medicine (INM-4), Research Centre Jülich, Jülich, Germany
- Department of Nuclear Medicine, University of Aachen, Aachen, Germany
| | - Gereon R. Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, Jülich, Germany
- Department of Neurology, University Hospital Cologne, Cologne, Germany
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50
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Xiao Z, Ding Y, Lan T, Zhang C, Luo C, Qin Z. Brain MR Image Classification for Alzheimer's Disease Diagnosis Based on Multifeature Fusion. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:1952373. [PMID: 28611848 PMCID: PMC5458434 DOI: 10.1155/2017/1952373] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Revised: 03/15/2017] [Accepted: 04/02/2017] [Indexed: 01/02/2023]
Abstract
We propose a novel classification framework to precisely identify individuals with Alzheimer's disease (AD) or mild cognitive impairment (MCI) from normal controls (NC). The proposed method combines three different features from structural MR images: gray-matter volume, gray-level cooccurrence matrix, and Gabor feature. These features can obtain both the 2D and 3D information of brains, and the experimental results show that a better performance can be achieved through the multifeature fusion. We also analyze the multifeatures combination correlation technologies and improve the SVM-RFE algorithm through the covariance method. The results of comparison experiments on public Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrate the effectiveness of the proposed method. Besides, it also indicates that multifeatures combination is better than the single-feature method. The proposed features selection algorithm could effectively extract the optimal features subset in order to improve the classification performance.
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Affiliation(s)
- Zhe Xiao
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan Province 610054, China
| | - Yi Ding
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan Province 610054, China
| | - Tian Lan
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan Province 610054, China
| | - Cong Zhang
- China Gas Turbine Establishment, Mianyang, Sichuan 621000, China
| | - Chuanji Luo
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan Province 610054, China
| | - Zhiguang Qin
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan Province 610054, China
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