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Wu Y, Ridwan AR, Niaz MR, Bennett DA, Arfanakis K. High resolution 0.5mm isotropic T 1-weighted and diffusion tensor templates of the brain of non-demented older adults in a common space for the MIITRA atlas. Neuroimage 2023; 282:120387. [PMID: 37783362 PMCID: PMC10625170 DOI: 10.1016/j.neuroimage.2023.120387] [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: 08/10/2023] [Accepted: 09/22/2023] [Indexed: 10/04/2023] Open
Abstract
High quality, high resolution T1-weighted (T1w) and diffusion tensor imaging (DTI) brain templates located in a common space can enhance the sensitivity and precision of template-based neuroimaging studies. However, such multimodal templates have not been constructed for the older adult brain. The purpose of this work which is part of the MIITRA atlas project was twofold: (A) to develop 0.5 mm isotropic resolution T1w and DTI templates that are representative of the brain of non-demented older adults and are located in the same space, using advanced multimodal template construction techniques and principles of super resolution on data from a large, diverse, community cohort of 400 non-demented older adults, and (B) to systematically compare the new templates to other standardized templates. It was demonstrated that the new MIITRA-0.5mm T1w and DTI templates are well-matched in space, exhibit good definition of brain structures, including fine structures, exhibit higher image sharpness than other standardized templates, and are free of artifacts. The MIITRA-0.5mm T1w and DTI templates allowed higher intra-modality inter-subject spatial normalization precision as well as higher inter-modality intra-subject spatial matching of older adult T1w and DTI data compared to other available templates. Consequently, MIITRA-0.5mm templates allowed detection of smaller inter-group differences for older adult data compared to other templates. The MIITRA-0.5mm templates were also shown to be most representative of the brain of non-demented older adults compared to other templates with submillimeter resolution. The new templates constructed in this work constitute two of the final products of the MIITRA atlas project and are anticipated to have important implications for the sensitivity and precision of studies on older adults.
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Affiliation(s)
- Yingjuan Wu
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - Abdur Raquib Ridwan
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - Mohammad Rakeen Niaz
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States.
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Niaz MR, Ridwan AR, Wu Y, Bennett DA, Arfanakis K. Development and evaluation of a high resolution 0.5mm isotropic T1-weighted template of the older adult brain. Neuroimage 2022; 248:118869. [PMID: 34986396 PMCID: PMC8855670 DOI: 10.1016/j.neuroimage.2021.118869] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 12/08/2021] [Accepted: 12/29/2021] [Indexed: 10/28/2022] Open
Abstract
Investigating the structure of the older adult brain at high spatial resolution is of high significance, and a dedicated older adult structural brain template with sub-millimeter resolution is currently lacking. Therefore, the purpose of this work was twofold: (A) to develop a 0.5mm isotropic resolution standardized T1-weighted template of the older adult brain by applying principles of super resolution to high quality MRI data from 222 older adults (65-95 years of age), and (B) to systematically compare the new template to other standardized and study-specific templates in terms of image quality and performance when used as a reference for alignment of older adult data. The new template exhibited higher spatial resolution and improved visualization of fine structural details of the older adult brain compared to a template constructed using a conventional template building approach and the same data. In addition, the new template exhibited higher image sharpness and did not contain image artifacts observed in some of the other templates considered in this work. Due to the above enhancements, the new template provided higher inter-subject spatial normalization precision for older adult data compared to the other templates, and consequently enabled detection of smaller inter-group morphometric differences in older adult data. Finally, the new template was among those that were most representative of older adult brain data. Overall, the new template constructed here is an important resource for studies of aging, and the findings of the present work have important implications in template selection for investigations on older adults.
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Affiliation(s)
- Mohammad Rakeen Niaz
- Department of Biomedical Engineering, Illinois Institute of Technology, 3440 S Dearborn St, M-100, Chicago, IL 60616, United States
| | - Abdur Raquib Ridwan
- Department of Biomedical Engineering, Illinois Institute of Technology, 3440 S Dearborn St, M-100, Chicago, IL 60616, United States
| | - Yingjuan Wu
- Department of Biomedical Engineering, Illinois Institute of Technology, 3440 S Dearborn St, M-100, Chicago, IL 60616, United States
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, 3440 S Dearborn St, M-100, Chicago, IL 60616, United States; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States; Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL, United States.
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Tubi MA, Kothapalli D, Hapenney M, Feingold FW, Mack WJ, King KS, Thompson PM, Braskie MN. Regional relationships between CSF VEGF levels and Alzheimer's disease brain biomarkers and cognition. Neurobiol Aging 2021; 105:241-251. [PMID: 34126466 DOI: 10.1016/j.neurobiolaging.2021.04.025] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 04/22/2021] [Accepted: 04/24/2021] [Indexed: 01/17/2023]
Abstract
Vascular endothelial growth factor (VEGF) is a complex signaling protein that supports vascular and neuronal function. Alzheimer's disease (AD) -neuropathological hallmarks interfere with VEGF signaling and modify previously detected positive associations between cerebral spinal fluid (CSF) VEGF and cognition and hippocampal volume. However, it remains unknown 1) whether regional relationships between VEGF and glucose metabolism and cortical thinning exist, and 2) whether AD-neuropathological hallmarks (CSF Aβ, t-tau, p-tau) also modify these relationships. We addressed this in 310 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants (92 cognitively normal, 149 mild cognitive impairment, 69 AD; 215 CSF Aβ+, 95 CSF Aβ-) with regional cortical thickness and cognition measurements and 158 participants with FDG-PET. In Aβ + participants (CSF Aβ42 ≤ 192 pg/mL), higher CSF VEGF levels were associated with greater FDG-PET signal in the inferior parietal, and middle and inferior temporal cortices. Abnormal CSF amyloid and tau levels strengthened the positive association between VEGF and regional FDG-PET indices. VEGF also had both direct associations with semantic memory, as well as indirect associations mediated by regional FDG-PET signal to cognition.
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Affiliation(s)
- Meral A Tubi
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Deydeep Kothapalli
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Matthew Hapenney
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Franklin W Feingold
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA; Stanford University, Stanford, CA 94305
| | - Wendy J Mack
- Division of Biostatistics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kevin S King
- Huntington Medical Research Institutes, Imaging Division, Pasadena, CA, 91105 USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Meredith N Braskie
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
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Ridwan AR, Niaz MR, Wu Y, Qi X, Zhang S, Kontzialis M, Javierre-Petit C, Tazwar M, Bennett DA, Yang Y, Arfanakis K. Development and evaluation of a high performance T1-weighted brain template for use in studies on older adults. Hum Brain Mapp 2021; 42:1758-1776. [PMID: 33449398 PMCID: PMC7978143 DOI: 10.1002/hbm.25327] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 11/16/2020] [Accepted: 12/13/2020] [Indexed: 01/03/2023] Open
Abstract
Τhe accuracy of template-based neuroimaging investigations depends on the template's image quality and representativeness of the individuals under study. Yet a thorough, quantitative investigation of how available standardized and study-specific T1-weighted templates perform in studies on older adults has not been conducted. The purpose of this work was to construct a high-quality standardized T1-weighted template specifically designed for the older adult brain, and systematically compare the new template to several other standardized and study-specific templates in terms of image quality, performance in spatial normalization of older adult data and detection of small inter-group morphometric differences, and representativeness of the older adult brain. The new template was constructed with state-of-the-art spatial normalization of high-quality data from 222 older adults. It was shown that the new template (a) exhibited high image sharpness, (b) provided higher inter-subject spatial normalization accuracy and (c) allowed detection of smaller inter-group morphometric differences compared to other standardized templates, (d) had similar performance to that of study-specific templates constructed with the same methodology, and (e) was highly representative of the older adult brain.
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Affiliation(s)
- Abdur Raquib Ridwan
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Mohammad Rakeen Niaz
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Yingjuan Wu
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Xiaoxiao Qi
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Shengwei Zhang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Marinos Kontzialis
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, Illinois, USA
| | - Carles Javierre-Petit
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Mahir Tazwar
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | | | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Yongyi Yang
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA.,Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA.,Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, Illinois, USA
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Bettcher BM, Gross AL, Gavett BE, Widaman KF, Fletcher E, Dowling NM, Buckley RF, Arenaza-Urquijo EM, Zahodne LB, Hohman TJ, Vonk JMJ, Rentz DM, Mungas D. Dynamic change of cognitive reserve: associations with changes in brain, cognition, and diagnosis. Neurobiol Aging 2019; 83:95-104. [PMID: 31585371 PMCID: PMC6977973 DOI: 10.1016/j.neurobiolaging.2019.08.016] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 08/15/2019] [Accepted: 08/16/2019] [Indexed: 11/29/2022]
Abstract
Cognitive reserve is inherently a dynamic construct; however, traditional methods of estimating reserve have focused on static proxy variables. A recently proposed psychometric approach entails modeling reserve as residual cognition not explained by demographic and brain variables. In this study, we extended this approach to longitudinal measurement and examined how change in reserve relates to clinical outcomes in late life and influences the effect of brain atrophy on cognitive decline. Results indicated that cognitive reserve changes were associated with progression of clinical diagnosis. More rapid depletion of cognitive reserve was associated with faster decline in nonmemory cognitive functions, even after accounting for longitudinal brain atrophy. The effect of longitudinal brain atrophy on cognitive decline differed based on the extent to which an individual's reserve changed. Whereas depletion of reserve appeared to unmask the effects of brain atrophy on cognitive decline, maintenance of reserve buffered against the negative effects of brain atrophy. Study results highlight that changes in reserve may have important implications for individual differences in cognitive aging trajectories.
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Affiliation(s)
- Brianne M Bettcher
- Departments of Neurology and Neurosurgery, Behavioral Neurology Section, Rocky Mountain Alzheimer's Disease Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Alden L Gross
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Brandon E Gavett
- School of Psychological Science, The University of Western Australia, Perth, Australia
| | - Keith F Widaman
- Graduate School of Education, University of California Riverside, Riverside, CA, USA
| | - Evan Fletcher
- Department of Neurology, UC Davis School of Medicine, Sacramento, CA, USA
| | - N Maritza Dowling
- Department of Acute and Chronic Care, Department of Epidemiology and Biostatistics, George Washington School of Nursing and Milken Institute School of Public Health, George Washington University, Washington, DC, USA
| | - Rachel F Buckley
- Departments of Neurology, Brigham and Women's Hospital and Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Florey Institute of Neuroscience and Mental Health, University of Melbourne, Victoria, Australia
| | | | - Laura B Zahodne
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Timothy J Hohman
- Department of Neurology, Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jet M J Vonk
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY, USA
| | - Dorene M Rentz
- Departments of Neurology, Brigham and Women's Hospital and Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Dan Mungas
- Department of Neurology, UC Davis School of Medicine, Sacramento, CA, USA
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Fletcher E, Gavett B, Harvey D, Farias ST, Olichney J, Beckett L, DeCarli C, Mungas D. Brain volume change and cognitive trajectories in aging. Neuropsychology 2018; 32:436-449. [PMID: 29494196 PMCID: PMC6525569 DOI: 10.1037/neu0000447] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE Examine how longitudinal cognitive trajectories relate to brain baseline measures and change in lobar volumes in a racially/ethnically and cognitively diverse sample of older adults. METHOD Participants were 460 older adults enrolled in a longitudinal aging study. Cognitive outcomes were measures of episodic memory, semantic memory, executive function, and spatial ability derived from the Spanish and English Neuropsychological Assessment Scales (SENAS). Latent variable multilevel modeling of the four cognitive outcomes as parallel longitudinal processes identified intercepts for each outcome and a second order global change factor explaining covariance among the highly correlated slopes. We examined how baseline brain volumes (lobar gray matter, hippocampus, and white matter hyperintensity) and change in brain volumes (lobar gray matter) were associated with cognitive intercepts and global cognitive change. Lobar volumes were dissociated into global and specific components using latent variable methods. RESULTS Cognitive change was most strongly associated with brain gray matter volume change, with strong independent effects of global gray matter change and specific temporal lobe gray matter change. Baseline white matter hyperintensity and hippocampal volumes had significant incremental effects on cognitive decline beyond gray matter change. Baseline lobar gray matter was related to cognitive decline, but did not contribute beyond gray matter change. CONCLUSION Cognitive decline was strongly influenced by gray matter volume change and, especially, temporal lobe change. The strong influence of temporal lobe gray matter change on cognitive decline may reflect involvement of temporal lobe structures that are critical for late life cognitive health but also are vulnerable to diseases of aging. (PsycINFO Database Record
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The neuroanatomical delineation of agentic and affiliative extraversion. COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2016; 15:321-34. [PMID: 25712871 DOI: 10.3758/s13415-014-0331-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Extraversion is a fascinating personality dimension that consists of two major components, agentic extraversion and affiliative extraversion. Agentic extraversion involves incentive motivation and is expressed as a tendency toward assertiveness, persistence, and achievement. Affiliative extraversion involves the positive emotion of social warmth and is expressed as a tendency toward amicability, gregariousness, and affection. Here we investigate the neuroanatomical correlates of the personality traits of agentic and affiliative extraversion using the Multidimensional Personality Questionnaire Brief Form, structural magnetic resonance imaging, and voxel-based morphometry in a sample of 83 healthy adult volunteers. We found that trait agentic extraversion and trait affiliative extraversion were each positively associated with the volume of the medial orbitofrontal cortex bilaterally (t's ≥ 2.03, r's ≥ .23, p's < .05). Agentic extraversion was specifically and positively related to the volume of the left parahippocampal gyrus (t = 4.08, r = .21, p < .05), left cingulate gyrus (t = 4.75, r = .28, p < .05), left caudate (t = 4.29, r = .24, p < .05), and left precentral gyrus (t = 4.00, r = .18, p < .05) in males and females, and the volume of the right nucleus accumbens in males (t = 2.92, r = .20, p < .05). Trait affiliative extraversion was not found to be associated with additional regions beyond the medial orbitofrontal cortex. The findings provide the first evidence of a neuroanatomical dissociation between the personality traits of agentic and affiliative extraversion in healthy adults.
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8
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Methodology for Morphometric Analysis of Modern Human Contralateral Premolars. J Comput Assist Tomogr 2016; 40:617-25. [DOI: 10.1097/rct.0000000000000417] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Ahmad S, Khan MF. Topology preserving non-rigid image registration using time-varying elasticity model for MRI brain volumes. Comput Biol Med 2015; 67:21-8. [PMID: 26492319 DOI: 10.1016/j.compbiomed.2015.09.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Accepted: 09/29/2015] [Indexed: 10/22/2022]
Abstract
In this paper, we present a new non-rigid image registration method that imposes a topology preservation constraint on the deformation. We propose to incorporate the time varying elasticity model into the deformable image matching procedure and constrain the Jacobian determinant of the transformation over the entire image domain. The motion of elastic bodies is governed by a hyperbolic partial differential equation, generally termed as elastodynamics wave equation, which we propose to use as a deformation model. We carried out clinical image registration experiments on 3D magnetic resonance brain scans from IBSR database. The results of the proposed registration approach in terms of Kappa index and relative overlap computed over the subcortical structures were compared against the existing topology preserving non-rigid image registration methods and non topology preserving variant of our proposed registration scheme. The Jacobian determinant maps obtained with our proposed registration method were qualitatively and quantitatively analyzed. The results demonstrated that the proposed scheme provides good registration accuracy with smooth transformations, thereby guaranteeing the preservation of topology.
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Affiliation(s)
- Sahar Ahmad
- National University of Sciences and Technology (NUST), Military College of Signals, Islamabad, Pakistan.
| | - Muhammad Faisal Khan
- National University of Sciences and Technology (NUST), Military College of Signals, Islamabad, Pakistan.
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Ou Y, Akbari H, Bilello M, Da X, Davatzikos C. Comparative evaluation of registration algorithms in different brain databases with varying difficulty: results and insights. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:2039-65. [PMID: 24951685 PMCID: PMC4371548 DOI: 10.1109/tmi.2014.2330355] [Citation(s) in RCA: 98] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Evaluating various algorithms for the inter-subject registration of brain magnetic resonance images (MRI) is a necessary topic receiving growing attention. Existing studies evaluated image registration algorithms in specific tasks or using specific databases (e.g., only for skull-stripped images, only for single-site images, etc.). Consequently, the choice of registration algorithms seems task- and usage/parameter-dependent. Nevertheless, recent large-scale, often multi-institutional imaging-related studies create the need and raise the question whether some registration algorithms can 1) generally apply to various tasks/databases posing various challenges; 2) perform consistently well, and while doing so, 3) require minimal or ideally no parameter tuning. In seeking answers to this question, we evaluated 12 general-purpose registration algorithms, for their generality, accuracy and robustness. We fixed their parameters at values suggested by algorithm developers as reported in the literature. We tested them in 7 databases/tasks, which present one or more of 4 commonly-encountered challenges: 1) inter-subject anatomical variability in skull-stripped images; 2) intensity homogeneity, noise and large structural differences in raw images; 3) imaging protocol and field-of-view (FOV) differences in multi-site data; and 4) missing correspondences in pathology-bearing images. Totally 7,562 registrations were performed. Registration accuracies were measured by (multi-)expert-annotated landmarks or regions of interest (ROIs). To ensure reproducibility, we used public software tools, public databases (whenever possible), and we fully disclose the parameter settings. We show evaluation results, and discuss the performances in light of algorithms' similarity metrics, transformation models and optimization strategies. We also discuss future directions for the algorithm development and evaluations.
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Braskie MN, Boyle CP, Rajagopalan P, Gutman BA, Toga AW, Raji CA, Tracy RP, Kuller LH, Becker JT, Lopez OL, Thompson PM. Physical activity, inflammation, and volume of the aging brain. Neuroscience 2014; 273:199-209. [PMID: 24836855 PMCID: PMC4076831 DOI: 10.1016/j.neuroscience.2014.05.005] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2014] [Revised: 04/23/2014] [Accepted: 05/02/2014] [Indexed: 01/06/2023]
Abstract
Physical activity influences inflammation, and both affect brain structure and Alzheimer's disease (AD) risk. We hypothesized that older adults with greater reported physical activity intensity and lower serum levels of the inflammatory marker tumor necrosis factor α (TNFα) would have larger regional brain volumes on subsequent magnetic resonance imaging (MRI) scans. In 43 cognitively intact older adults (79.3±4.8 years) and 39 patients with AD (81.9±5.1 years at the time of MRI) participating in the Cardiovascular Health Study, we examined year-1 reported physical activity intensity, year-5 blood serum TNFα measures, and year-9 volumetric brain MRI scans. We examined how prior physical activity intensity and TNFα related to subsequent total and regional brain volumes. Physical activity intensity was measured using the modified Minnesota Leisure Time Physical Activities questionnaire at year 1 of the study, when all subjects included here were cognitively intact. Stability of measures was established for exercise intensity over 9 years and TNFα over 3 years in a subset of subjects who had these measurements at multiple time points. When considered together, more intense physical activity intensity and lower serum TNFα were both associated with greater total brain volume on follow-up MRI scans. TNFα, but not physical activity, was associated with regional volumes of the inferior parietal lobule, a region previously associated with inflammation in AD patients. Physical activity and TNFα may independently influence brain structure in older adults.
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Affiliation(s)
- M N Braskie
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, Dept. of Neurology, Keck/USC School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - C P Boyle
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, Dept. of Neurology, Keck/USC School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - P Rajagopalan
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, Dept. of Neurology, Keck/USC School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - B A Gutman
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, Dept. of Neurology, Keck/USC School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - A W Toga
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, Dept. of Neurology, Keck/USC School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - C A Raji
- Department of Radiology, University of California Los Angeles School of Medicine, Los Angeles, CA, USA
| | - R P Tracy
- Departments of Pathology, Biochemistry, and Center for Clinical and Translational Science, University of Vermont, Burlington, VT, USA
| | - L H Kuller
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - J T Becker
- Departments of Neurology, Psychiatry, and Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - O L Lopez
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - P M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, Dept. of Neurology, Keck/USC School of Medicine, University of Southern California, Los Angeles, CA, USA; Depts. of Psychiatry, Engineering, Radiology, & Ophthalmology, Keck/USC School of Medicine, University of Southern California, Los Angeles, CA, USA.
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12
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A tensor-based morphometry analysis of regional differences in brain volume in relation to prenatal alcohol exposure. NEUROIMAGE-CLINICAL 2014; 5:152-60. [PMID: 25057467 PMCID: PMC4097000 DOI: 10.1016/j.nicl.2014.04.001] [Citation(s) in RCA: 54] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2014] [Revised: 03/12/2014] [Accepted: 04/02/2014] [Indexed: 11/29/2022]
Abstract
Reductions in brain volumes represent a neurobiological signature of fetal alcohol spectrum disorders (FASD). Less clear is how regional brain tissue reductions differ after normalizing for brain size differences linked with FASD and whether these profiles can predict the degree of prenatal exposure to alcohol. To examine associations of regional brain tissue excesses/deficits with degree of prenatal alcohol exposure and diagnosis with and without correction for overall brain volume, tensor-based morphometry (TBM) methods were applied to structural imaging data from a well-characterized, demographically homogeneous sample of children diagnosed with FASD (n = 39, 9.6–11.0 years) and controls (n = 16, 9.5–11.0 years). Degree of prenatal alcohol exposure was significantly associated with regionally pervasive brain tissue reductions in: (1) the thalamus, midbrain, and ventromedial frontal lobe, (2) the superior cerebellum and inferior occipital lobe, (3) the dorsolateral frontal cortex, and (4) the precuneus and superior parietal lobule. When overall brain size was factored out of the analysis on a subject-by-subject basis, no regions showed significant associations with alcohol exposure. FASD diagnosis was associated with a similar deformation pattern, but few of the regions survived FDR correction. In data-driven independent component analyses (ICA) regional brain tissue deformations successfully distinguished individuals based on extent of prenatal alcohol exposure and to a lesser degree, diagnosis. The greater sensitivity of the continuous measure of alcohol exposure compared with the categorical diagnosis across diverse brain regions underscores the dose dependence of these effects. The ICA results illustrate that profiles of brain tissue alterations may be a useful indicator of prenatal alcohol exposure when reliable historical data are not available and facial features are not apparent. Tensor-based morphometry predicts brain volume reductions in fetal alcohol syndrome. Normalizing for brain size in FASD may mask regional differences in tissue volume. Patterns of volumetric change are pervasive, particularly in midline structures. Degree of prenatal alcohol exposure predicts pervasiveness of volumetric deficits. Pattern of volumetric change may be useful in identifying individuals with FASD.
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Lutkenhoff ES, McArthur DL, Hua X, Thompson PM, Vespa PM, Monti MM. Thalamic atrophy in antero-medial and dorsal nuclei correlates with six-month outcome after severe brain injury. NEUROIMAGE-CLINICAL 2013; 3:396-404. [PMID: 24273723 PMCID: PMC3815017 DOI: 10.1016/j.nicl.2013.09.010] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2013] [Revised: 09/26/2013] [Accepted: 09/27/2013] [Indexed: 11/25/2022]
Abstract
The primary and secondary damage to neural tissue inflicted by traumatic brain injury is a leading cause of death and disability. The secondary processes, in particular, are of great clinical interest because of their potential susceptibility to intervention. We address the dynamics of tissue degeneration in cortico-subcortical circuits after severe brain injury by assessing volume change in individual thalamic nuclei over the first six-months post-injury in a sample of 25 moderate to severe traumatic brain injury patients. Using tensor-based morphometry, we observed significant localized thalamic atrophy over the six-month period in antero-dorsal limbic nuclei as well as in medio-dorsal association nuclei. Importantly, the degree of atrophy in these nuclei was predictive, even after controlling for full-brain volume change, of behavioral outcome at six-months post-injury. Furthermore, employing a data-driven decision tree model, we found that physiological measures, namely the extent of atrophy in the anterior thalamic nucleus, were the most predictive variables of whether patients had regained consciousness by six-months, followed by behavioral measures. Overall, these findings suggest that the secondary non-mechanical degenerative processes triggered by severe brain injury are still ongoing after the first week post-trauma and target specifically antero-medial and dorsal thalamic nuclei. This result therefore offers a potential window of intervention, and a specific target region, in agreement with the view that specific cortico-thalamo-cortical circuits are crucial to the maintenance of large-scale network neural activity and thereby the restoration of cognitive function after severe brain injury. Performed acute and chronic structural MRI in 25 severe TBI patients Tensor brain morphometry (TBM) shows localized thalamic acute-to-chronic atrophy. Anterior, medio- and lateral-dorsal nuclei are the most significant. Atrophy in these nuclei predicts 6-month outcome scores (GOSe).
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Affiliation(s)
- Evan S Lutkenhoff
- Department of Psychology, University of California Los Angeles, Los Angeles, CA 90095, USA
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14
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Shi J, Thompson PM, Gutman B, Wang Y. Surface fluid registration of conformal representation: application to detect disease burden and genetic influence on hippocampus. Neuroimage 2013; 78:111-34. [PMID: 23587689 PMCID: PMC3683848 DOI: 10.1016/j.neuroimage.2013.04.018] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2012] [Revised: 03/06/2013] [Accepted: 04/05/2013] [Indexed: 11/23/2022] Open
Abstract
In this paper, we develop a new automated surface registration system based on surface conformal parameterization by holomorphic 1-forms, inverse consistent surface fluid registration, and multivariate tensor-based morphometry (mTBM). First, we conformally map a surface onto a planar rectangle space with holomorphic 1-forms. Second, we compute surface conformal representation by combining its local conformal factor and mean curvature and linearly scale the dynamic range of the conformal representation to form the feature image of the surface. Third, we align the feature image with a chosen template image via the fluid image registration algorithm, which has been extended into the curvilinear coordinates to adjust for the distortion introduced by surface parameterization. The inverse consistent image registration algorithm is also incorporated in the system to jointly estimate the forward and inverse transformations between the study and template images. This alignment induces a corresponding deformation on the surface. We tested the system on Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset to study AD symptoms on hippocampus. In our system, by modeling a hippocampus as a 3D parametric surface, we nonlinearly registered each surface with a selected template surface. Then we used mTBM to analyze the morphometry difference between diagnostic groups. Experimental results show that the new system has better performance than two publicly available subcortical surface registration tools: FIRST and SPHARM. We also analyzed the genetic influence of the Apolipoprotein E[element of]4 allele (ApoE4), which is considered as the most prevalent risk factor for AD. Our work successfully detected statistically significant difference between ApoE4 carriers and non-carriers in both patients of mild cognitive impairment (MCI) and healthy control subjects. The results show evidence that the ApoE genotype may be associated with accelerated brain atrophy so that our work provides a new MRI analysis tool that may help presymptomatic AD research.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, UCLA Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Boris Gutman
- Laboratory of Neuro Imaging, UCLA Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
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15
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Sharma S, Rousseau F, Heitz F, Rumbach L, Armspach JP. On the estimation and correction of bias in local atrophy estimations using example atrophy simulations. Comput Med Imaging Graph 2013; 37:538-51. [PMID: 23988649 DOI: 10.1016/j.compmedimag.2013.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2012] [Revised: 06/01/2013] [Accepted: 07/25/2013] [Indexed: 10/26/2022]
Abstract
Brain atrophy is considered an important marker of disease progression in many chronic neuro-degenerative diseases such as multiple sclerosis (MS). A great deal of attention is being paid toward developing tools that manipulate magnetic resonance (MR) images for obtaining an accurate estimate of atrophy. Nevertheless, artifacts in MR images, inaccuracies of intermediate steps and inadequacies of the mathematical model representing the physical brain volume change, make it rather difficult to obtain a precise and unbiased estimate. This work revolves around the nature and magnitude of bias in atrophy estimations as well as a potential way of correcting them. First, we demonstrate that for different atrophy estimation methods, bias estimates exhibit varying relations to the expected atrophy and these bias estimates are of the order of the expected atrophies for standard algorithms, stressing the need for bias correction procedures. Next, a framework for estimating uncertainty in longitudinal brain atrophy by means of constructing confidence intervals is developed. Errors arising from MRI artifacts and bias in estimations are learned from example atrophy simulations and anatomies. Results are discussed for three popular non-rigid registration approaches with the help of simulated localized brain atrophy in real MR images.
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Affiliation(s)
- Swati Sharma
- DeVry University, Chicago Campus, 3300 North Campbell Avenue, Chicago 60618, USA.
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16
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van Eede MC, Scholz J, Chakravarty MM, Henkelman RM, Lerch JP. Mapping registration sensitivity in MR mouse brain images. Neuroimage 2013; 82:226-36. [PMID: 23756204 DOI: 10.1016/j.neuroimage.2013.06.004] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2013] [Revised: 05/28/2013] [Accepted: 06/01/2013] [Indexed: 01/15/2023] Open
Abstract
Nonlinear registration algorithms provide a way to estimate structural (brain) differences based on magnetic resonance images. Their ability to align images of different individuals and across modalities has been well-researched, but the bounds of their sensitivity with respect to the recovery of salient morphological differences between groups are unclear. Here we develop a novel approach to simulate deformations on MR brain images to evaluate the ability of two registration algorithms to extract structural differences corresponding to biologically plausible atrophy and expansion. We show that at a neuroanatomical level registration accuracy is influenced by the size and compactness of structures, but do so differently depending on how much change is simulated. The size of structures has a small influence on the recovered accuracy. There is a trend for larger structures to be recovered more accurately, which becomes only significant as the amount of simulated change is large. More compact structures can be recovered more accurately regardless of the amount of simulated change. Both tested algorithms underestimate the full extent of the simulated atrophy and expansion. Finally we show that when multiple comparisons are corrected for at a voxelwise level, a very low rate of false positives is obtained. More interesting is that true positive rates average around 40%, indicating that the simulated changes are not fully recovered. Simulation experiments were run using two fundamentally different registration algorithms and we identified the same results, suggesting that our findings are generalizable across different classes of nonlinear registration algorithms.
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Affiliation(s)
- Matthijs C van Eede
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada.
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17
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Gallagher JJ, Zhang X, Hall FS, Uhl GR, Bearer EL, Jacobs RE. Altered reward circuitry in the norepinephrine transporter knockout mouse. PLoS One 2013; 8:e57597. [PMID: 23469209 PMCID: PMC3587643 DOI: 10.1371/journal.pone.0057597] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2012] [Accepted: 01/22/2013] [Indexed: 01/08/2023] Open
Abstract
Synaptic levels of the monoamine neurotransmitters dopamine, serotonin, and norepinephrine are modulated by their respective plasma membrane transporters, albeit with a few exceptions. Monoamine transporters remove monoamines from the synaptic cleft and thus influence the degree and duration of signaling. Abnormal concentrations of these neuronal transmitters are implicated in a number of neurological and psychiatric disorders, including addiction, depression, and attention deficit/hyperactivity disorder. This work concentrates on the norepinephrine transporter (NET), using a battery of in vivo magnetic resonance imaging techniques and histological correlates to probe the effects of genetic deletion of the norepinephrine transporter on brain metabolism, anatomy and functional connectivity. MRS recorded in the striatum of NET knockout mice indicated a lower concentration of NAA that correlates with histological observations of subtle dysmorphisms in the striatum and internal capsule. As with DAT and SERT knockout mice, we detected minimal structural alterations in NET knockout mice by tensor-based morphometric analysis. In contrast, longitudinal imaging after stereotaxic prefrontal cortical injection of manganese, an established neuronal circuitry tracer, revealed that the reward circuit in the NET knockout mouse is biased toward anterior portions of the brain. This is similar to previous results observed for the dopamine transporter (DAT) knockout mouse, but dissimilar from work with serotonin transporter (SERT) knockout mice where Mn2+ tracings extended to more posterior structures than in wildtype animals. These observations correlate with behavioral studies indicating that SERT knockout mice display anxiety-like phenotypes, while NET knockouts and to a lesser extent DAT knockout mice display antidepressant-like phenotypic features. Thus, the mainly anterior activity detected with manganese-enhanced MRI in the DAT and NET knockout mice is likely indicative of more robust connectivity in the frontal portion of the reward circuit of the DAT and NET knockout mice compared to the SERT knockout mice.
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Affiliation(s)
- Joseph J. Gallagher
- Biological Imaging Center, Beckman Institute, California Institute of Technology, Pasadena, California, United States of America
| | - Xiaowei Zhang
- Biological Imaging Center, Beckman Institute, California Institute of Technology, Pasadena, California, United States of America
| | - F. Scott Hall
- Molecular Neurobiology Branch, National Institute on Drug Abuse, Intramural Research Program, Baltimore, Maryland, United States of America
| | - George R. Uhl
- Molecular Neurobiology Branch, National Institute on Drug Abuse, Intramural Research Program, Baltimore, Maryland, United States of America
| | - Elaine L. Bearer
- Biological Imaging Center, Beckman Institute, California Institute of Technology, Pasadena, California, United States of America
- Department of Pathology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, United States of America
| | - Russell E. Jacobs
- Biological Imaging Center, Beckman Institute, California Institute of Technology, Pasadena, California, United States of America
- * E-mail:
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18
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Fletcher E, Knaack A, Singh B, Lloyd E, Wu E, Carmichael O, DeCarli C. Combining boundary-based methods with tensor-based morphometry in the measurement of longitudinal brain change. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:223-236. [PMID: 23014714 PMCID: PMC3775845 DOI: 10.1109/tmi.2012.2220153] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Tensor-based morphometry is a powerful tool for automatically computing longitudinal change in brain structure. Because of bias in images and in the algorithm itself, however, a penalty term and inverse consistency are needed to control the over-reporting of nonbiological change. These may force a tradeoff between the intrinsic sensitivity and specificity, potentially leading to an under-reporting of authentic biological change with time. We propose a new method incorporating prior information about tissue boundaries (where biological change is likely to exist) that aims to keep the robustness and specificity contributed by the penalty term and inverse consistency while maintaining localization and sensitivity. Results indicate that this method has improved sensitivity without increased noise. Thus it will have enhanced power to detect differences within normal aging and along the spectrum of cognitive impairment.
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Affiliation(s)
- Evan Fletcher
- IDeA Laboratory, Department of Neurology, University of California-Davis, Davis, CA 95618, USA.
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19
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Hyperbolic harmonic brain surface registration with curvature-based landmark matching. INFORMATION PROCESSING IN MEDICAL IMAGING : PROCEEDINGS OF THE ... CONFERENCE 2013. [PMID: 24683966 DOI: 10.1007/978-3-642-38868-2_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register]
Abstract
Brain Cortical surface registration is required for inter-subject studies of functional and anatomical data. Harmonic mapping has been applied for brain mapping, due to its existence, uniqueness, regularity and numerical stability. In order to improve the registration accuracy, sculcal landmarks are usually used as constraints for brain registration. Unfortunately, constrained harmonic mappings may not be diffeomorphic and produces invalid registration. This work conquer this problem by changing the Riemannian metric on the target cortical surface o a hyperbolic metric, so that the harmonic mapping is guaranteed to be a diffeomorphism while the landmark constraints are enforced as boundary matching condition. The computational algorithms are based on the Ricci flow method and yperbolic heat diffusion. Experimental results demonstrate that, by changing the Riemannian metric, the registrations are always diffeomorphic, with higher qualities in terms of landmark alignment, curvature matching, area distortion and overlapping of region of interests.
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20
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Tustison NJ, Avants BB, Cook PA, Kim J, Whyte J, Gee JC, Stone JR. Logical circularity in voxel-based analysis: normalization strategy may induce statistical bias. Hum Brain Mapp 2012; 35:745-59. [PMID: 23151955 DOI: 10.1002/hbm.22211] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2012] [Revised: 08/26/2012] [Accepted: 09/19/2012] [Indexed: 11/08/2022] Open
Abstract
Recent discussions within the neuroimaging community have highlighted the problematic presence of selection bias in experimental design. Although initially centering on the selection of voxels during the course of fMRI studies, we demonstrate how this bias can potentially corrupt voxel-based analyses. For such studies, template-based registration plays a critical role in which a representative template serves as the normalized space for group alignment. A standard approach maps each subject's image to a representative template before performing statistical comparisons between different groups. We analytically demonstrate that in these scenarios the popular sum of squared difference (SSD) intensity metric, implicitly surrogating as a quantification of anatomical alignment, instead explicitly maximizes effect size--an experimental design flaw referred to as "circularity bias." We illustrate how this selection bias varies in strength with the similarity metric used during registration under the hypothesis that while SSD-related metrics, such as Demons, will manifest similar effects, other metrics which are not formulated based on absolute intensity differences will produce less of an effect. Consequently, given the variability in voxel-based analysis outcomes with similarity metric choice, we caution researchers specifically in the use of SSD and SSD-related measures where normalization and statistical analysis involve the same image set. Instead, we advocate a more cautious approach where normalization of the individual subject images to the reference space occurs through corresponding image sets which are independent of statistical testing. Alternatively, one can use similarity terms that are less sensitive to this bias.
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Affiliation(s)
- Nicholas J Tustison
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia
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21
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Hua X, Hibar DP, Ching CRK, Boyle CP, Rajagopalan P, Gutman BA, Leow AD, Toga AW, Jack CR, Harvey D, Weiner MW, Thompson PM. Unbiased tensor-based morphometry: improved robustness and sample size estimates for Alzheimer's disease clinical trials. Neuroimage 2012; 66:648-61. [PMID: 23153970 DOI: 10.1016/j.neuroimage.2012.10.086] [Citation(s) in RCA: 88] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2012] [Revised: 10/29/2012] [Accepted: 10/30/2012] [Indexed: 01/11/2023] Open
Abstract
Various neuroimaging measures are being evaluated for tracking Alzheimer's disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI.
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Affiliation(s)
- Xue Hua
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-1769, USA
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22
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Lutkenhoff E, Karlsgodt KH, Gutman B, Stein JL, Thompson PM, Cannon TD, Jentsch JD. Structural and functional neuroimaging phenotypes in dysbindin mutant mice. Neuroimage 2012; 62:120-9. [PMID: 22584233 DOI: 10.1016/j.neuroimage.2012.05.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2011] [Revised: 05/02/2012] [Accepted: 05/05/2012] [Indexed: 12/19/2022] Open
Abstract
Schizophrenia is a highly heritable psychiatric disorder that is associated with a number of structural and functional neurophenotypes. DTNBP1, the gene encoding dysbindin-1, is a promising candidate gene for schizophrenia. Use of a mouse model carrying a large genomic deletion exclusively within the dysbindin gene permits a direct investigation of the gene in isolation. Here, we use manganese-enhanced magnetic resonance imaging (MEMRI) to explore the regional alterations in brain structure and function caused by loss of the gene encoding dysbindin-1. We report novel findings that uniquely inform our understanding of the relationship of dysbindin-1 to known schizophrenia phenotypes. First, in mutant mice, analysis of the rate of manganese uptake into the brain over a 24-hour period, putatively indexing basal cellular activity, revealed differences in dopamine rich brain regions, as well as in CA1 and dentate subregions of the hippocampus formation. Finally, novel tensor-based morphometry techniques were applied to the mouse MRI data, providing evidence for structural volume deficits in cortical regions, subiculum and dentate gyrus, and the striatum of dysbindin mutant mice. The affected cortical regions were primarily localized to the sensory cortices in particular the auditory cortex. This work represents the first application of manganese-enhanced small animal imaging to a mouse model of schizophrenia endophenotypes, and a novel combination of functional and structural measures. It revealed both hypothesized and novel structural and functional neural alterations related to dysbindin-1.
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Affiliation(s)
- Evan Lutkenhoff
- Interdisciplinary Neuroscience Program, University of California, Los Angeles, CA 90095, USA
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23
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Han Z, Thornton-Wells TA, Dykens EM, Gore JC, Dawant BM. Effect of nonrigid registration algorithms on deformation-based morphometry: a comparative study with control and Williams syndrome subjects. Magn Reson Imaging 2012; 30:774-88. [PMID: 22459439 DOI: 10.1016/j.mri.2012.02.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2011] [Revised: 12/09/2011] [Accepted: 02/14/2012] [Indexed: 11/16/2022]
Abstract
Deformation-based morphometry (DBM) is a widely used method for characterizing anatomical differences across groups. DBM is based on the analysis of the deformation fields generated by nonrigid registration algorithms, which warp the individual volumes to a DBM atlas. Although several studies have compared nonrigid registration algorithms for segmentation tasks, few studies have compared the effect of the registration algorithms on group differences that may be uncovered through DBM. In this study, we compared group atlas creation and DBM results obtained with five well-established nonrigid registration algorithms using 13 subjects with Williams syndrome and 13 normal control subjects. The five nonrigid registration algorithms include the following: (1) the adaptive bases algorithm, (2) the image registration toolkit, (3) The FSL nonlinear image registration tool, (4) the automatic registration tool, and (5) the normalization algorithm available in Statistical Parametric Mapping (SPM8). Results indicate that the choice of algorithm has little effect on the creation of group atlases. However, regions of differences between groups detected with DBM vary from algorithm to algorithm both qualitatively and quantitatively. Some regions are detected by several algorithms, but their extent varies. Others are detected only by a subset of the algorithms. Based on these results, we recommend using more than one algorithm when performing DBM studies.
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Affiliation(s)
- Zhaoying Han
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA.
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24
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Simões R, Slump C. Change detection and classification in brain MR images using change vector analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:7803-7. [PMID: 22256148 DOI: 10.1109/iembs.2011.6091923] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The automatic detection of longitudinal changes in brain images is valuable in the assessment of disease evolution and treatment efficacy. Most existing change detection methods that are currently used in clinical research to monitor patients suffering from neurodegenerative diseases--such as Alzheimer's--focus on large-scale brain deformations. However, such patients often have other brain impairments, such as infarcts, white matter lesions and hemorrhages, which are typically overlooked by the deformation-based methods. Other unsupervised change detection algorithms have been proposed to detect tissue intensity changes. The outcome of these methods is typically a binary change map, which identifies changed brain regions. However, understanding what types of changes these regions underwent is likely to provide equally important information about lesion evolution. In this paper, we present an unsupervised 3D change detection method based on Change Vector Analysis. We compute and automatically threshold the Generalized Likelihood Ratio map to obtain a binary change map. Subsequently, we perform histogram-based clustering to classify the change vectors. We obtain a Kappa Index of 0.82 using various types of simulated lesions. The classification error is 2%. Finally, we are able to detect and discriminate both small changes and ventricle expansions in datasets from Mild Cognitive Impairment patients.
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Affiliation(s)
- Rita Simões
- Signals and Systems Group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7500 AE Enschede, The Netherlands.
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25
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Lu PH, Thompson PM, Leow A, Lee GJ, Lee A, Yanovsky I, Parikshak N, Khoo T, Wu S, Geschwind D, Bartzokis G. Apolipoprotein E genotype is associated with temporal and hippocampal atrophy rates in healthy elderly adults: a tensor-based morphometry study. J Alzheimers Dis 2011; 23:433-42. [PMID: 21098974 DOI: 10.3233/jad-2010-101398] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Apolipoprotein E (ApoE) ε4 genotype is a strong risk factor for developing Alzheimer's disease (AD). Conversely, the presence of the ε2 allele has been shown to mitigate cognitive decline. Tensor-based morphometry (TBM), a novel computational approach for visualizing longitudinal progression of brain atrophy, was used to determine whether cognitively intact elderly participants with the ε4 allele demonstrate greater volume reduction than those with the ε2 allele. Healthy "younger elderly" volunteers, aged 55-75, were recruited from the community and hospital staff. They were evaluated with a baseline and follow-up MRI scan (mean scan interval = 4.72 years, s.d. = 0.55) and completed ApoE genotyping. Twenty-seven participants were included in the study of which 16 had the ε4 allele (all heterozygous ε3ε4 genotype) and 11 had the ε2ε3 genotype. The two groups did not differ significantly on any demographic characteristics and all subjects were cognitively "normal" at both baseline and follow-up time points. TBM was used to create 3D maps of local brain tissue atrophy rates for individual participants; these spatially detailed 3D maps were compared between the two ApoE groups. Regional analyses were performed and the ε4 group demonstrated significantly greater annual atrophy rates in the temporal lobes (p = 0.048) and hippocampus (p = 0.016); greater volume loss was observed in the right hippocampus than the left. TBM appears to be useful in tracking longitudinal progression of brain atrophy in cognitively asymptomatic adults. Possession of the ε4 allele is associated with greater temporal and hippocampal volume reduction well before the onset of cognitive deficits.
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Affiliation(s)
- Po H Lu
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-7226, USA.
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26
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Toga AW, Dinov ID, Thompson PM, Woods RP, Van Horn JD, Shattuck DW, Parker DS. The Center for Computational Biology: resources, achievements, and challenges. J Am Med Inform Assoc 2011; 19:202-6. [PMID: 22081221 DOI: 10.1136/amiajnl-2011-000525] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
The Center for Computational Biology (CCB) is a multidisciplinary program where biomedical scientists, engineers, and clinicians work jointly to combine modern mathematical and computational techniques, to perform phenotypic and genotypic studies of biological structure, function, and physiology in health and disease. CCB has developed a computational framework built around the Manifold Atlas, an integrated biomedical computing environment that enables statistical inference on biological manifolds. These manifolds model biological structures, features, shapes, and flows, and support sophisticated morphometric and statistical analyses. The Manifold Atlas includes tools, workflows, and services for multimodal population-based modeling and analysis of biological manifolds. The broad spectrum of biomedical topics explored by CCB investigators include the study of normal and pathological brain development, maturation and aging, discovery of associations between neuroimaging and genetic biomarkers, and the modeling, analysis, and visualization of biological shape, form, and size. CCB supports a wide range of short-term and long-term collaborations with outside investigators, which drive the center's computational developments and focus the validation and dissemination of CCB resources to new areas and scientific domains.
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Affiliation(s)
- Arthur W Toga
- Center for Computational Biology, Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, California 90095-7334, USA.
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27
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Holland D, McEvoy LK, Dale AM. Unbiased comparison of sample size estimates from longitudinal structural measures in ADNI. Hum Brain Mapp 2011; 33:2586-602. [PMID: 21830259 DOI: 10.1002/hbm.21386] [Citation(s) in RCA: 73] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2011] [Revised: 05/07/2011] [Accepted: 05/23/2011] [Indexed: 01/23/2023] Open
Abstract
Structural changes in neuroanatomical subregions can be measured using serial magnetic resonance imaging scans, and provide powerful biomarkers for detecting and monitoring Alzheimer's disease. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has made a large database of longitudinal scans available, with one of its primary goals being to explore the utility of structural change measures for assessing treatment effects in clinical trials of putative disease-modifying therapies. Several ADNI-funded research laboratories have calculated such measures from the ADNI database and made their results publicly available. Here, using sample size estimates, we present a comparative analysis of the overall results that come from the application of each laboratory's extensive processing stream to the ADNI database. Obtaining accurate measures of change requires correcting for potential bias due to the measurement methods themselves; and obtaining realistic sample size estimates for treatment response, based on longitudinal imaging measures from natural history studies such as ADNI, requires calibrating measured change in patient cohorts with respect to longitudinal anatomical changes inherent to normal aging. We present results showing that significant longitudinal change is present in healthy control subjects who test negative for amyloid-β pathology. Therefore, sample size estimates as commonly reported from power calculations based on total structural change in patients, rather than change in patients relative to change in healthy controls, are likely to be unrealistically low for treatments targeting amyloid-related pathology. Of all the measures publicly available in ADNI, thinning of the entorhinal cortex quantified with the Quarc methodology provides the most powerful change biomarker.
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Affiliation(s)
- Dominic Holland
- Department of Neurosciences, University of California, San Diego, La Jolla, California 92037, USA.
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Hua X, Hibar DP, Lee S, Toga AW, Jack CR, Weiner MW, Thompson PM. Sex and age differences in atrophic rates: an ADNI study with n=1368 MRI scans. Neurobiol Aging 2011; 31:1463-80. [PMID: 20620666 DOI: 10.1016/j.neurobiolaging.2010.04.033] [Citation(s) in RCA: 157] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2010] [Revised: 04/26/2010] [Accepted: 04/27/2010] [Indexed: 01/20/2023]
Abstract
We set out to determine factors that influence the rate of brain atrophy in 1-year longitudinal magnetic resonance imaging (MRI) data. With tensor-based morphometry (TBM), we mapped the 3-dimensional profile of progressive atrophy in 144 subjects with probable Alzheimer's disease (AD) (age: 76.5 +/- 7.4 years), 338 with amnestic mild cognitive impairment (MCI; 76.0 +/- 7.2), and 202 healthy controls (77.0 +/- 5.1), scanned twice, 1 year apart. Statistical maps revealed significant age and sex differences in atrophic rates. Brain atrophic rates were about 1%-1.5% faster in women than men. Atrophy was faster in younger than older subjects, most prominently in mild cognitive impairment, with a 1% increase in the rates of atrophy and 2% in ventricular expansion, for every 10-year decrease in age. TBM-derived atrophic rates correlated with reduced beta-amyloid and elevated tau levels (n = 363) at baseline, baseline and progressive deterioration in clinical measures, and increasing numbers of risk alleles for the ApoE4 gene. TBM is a sensitive, high-throughput biomarker for tracking disease progression in large imaging studies; sub-analyses focusing on women or younger subjects gave improved sample size requirements for clinical trials.
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Affiliation(s)
- Xue Hua
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-1769, USA
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29
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Holland D, Dale AM. Nonlinear registration of longitudinal images and measurement of change in regions of interest. Med Image Anal 2011; 15:489-97. [PMID: 21388857 DOI: 10.1016/j.media.2011.02.005] [Citation(s) in RCA: 93] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2010] [Revised: 02/08/2011] [Accepted: 02/14/2011] [Indexed: 01/18/2023]
Abstract
We describe here a method, Quarc, for accurately quantifying structural changes in organs, based on serial MRI scans. The procedure can be used to measure deformations globally or in regions of interest (ROIs), including large-scale changes in the whole organ, and subtle changes in small-scale structures. We validate the method with model studies, and provide an illustrative analysis using the brain. We apply the method to the large, publicly available ADNI database of serial brain scans, and calculate Cohen's d effect sizes for several ROIs. Using publicly available derived-data, we directly compare effect sizes from Quarc with those from four existing methods that quantify cerebral structural change. Quarc produced a slightly improved, though not significantly different, whole brain effect size compared with the standard KN-BSI method, but in all other cases it produced significantly larger effect sizes.
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Affiliation(s)
- Dominic Holland
- Multimodal Imaging Laboratory, The University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92037, USA.
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Accurate measurement of brain changes in longitudinal MRI scans using tensor-based morphometry. Neuroimage 2011; 57:5-14. [PMID: 21320612 DOI: 10.1016/j.neuroimage.2011.01.079] [Citation(s) in RCA: 69] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2010] [Revised: 01/16/2011] [Accepted: 01/28/2011] [Indexed: 11/22/2022] Open
Abstract
This paper responds to Thompson and Holland (2011), who challenged our tensor-based morphometry (TBM) method for estimating rates of brain changes in serial MRI from 431 subjects scanned every 6 months, for 2 years. Thompson and Holland noted an unexplained jump in our atrophy rate estimates: an offset between 0 and 6 months that may bias clinical trial power calculations. We identified why this jump occurs and propose a solution. By enforcing inverse-consistency in our TBM method, the offset dropped from 1.4% to 0.28%, giving plausible anatomical trajectories. Transitivity error accounted for the minimal remaining offset. Drug trial sample size estimates with the revised TBM-derived metrics are highly competitive with other methods, though higher than previously reported sample size estimates by a factor of 1.6 to 2.4. Importantly, estimates are far below those given in the critique. To demonstrate a 25% slowing of atrophic rates with 80% power, 62 AD and 129 MCI subjects would be required for a 2-year trial, and 91 AD and 192 MCI subjects for a 1-year trial.
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Thompson WK, Holland D. Bias in tensor based morphometry Stat-ROI measures may result in unrealistic power estimates. Neuroimage 2011; 57:1-4. [PMID: 21349340 DOI: 10.1016/j.neuroimage.2010.11.092] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2010] [Revised: 10/29/2010] [Accepted: 11/02/2010] [Indexed: 11/17/2022] Open
Abstract
A series of reports have recently appeared using tensor based morphometry statistically-defined regions of interest, Stat-ROIs, to quantify longitudinal atrophy in structural MRIs from the Alzheimer's Disease Neuroimaging Initiative (ADNI). This commentary focuses on one of these reports, Hua et al. (2010), but the issues raised here are relevant to the others as well. Specifically, we point out a temporal pattern of atrophy in subjects with Alzheimer's disease and mild cognitive impairment whereby the majority of atrophy in two years occurs within the first 6 months, resulting in overall elevated estimated rates of change. Using publicly-available ADNI data, this temporal pattern is also found in a group of identically-processed healthy controls, strongly suggesting that methodological bias is corrupting the measures. The resulting bias seriously impacts the validity of conclusions reached using these measures; for example, sample size estimates reported by Hua et al. (2010) may be underestimated by a factor of five to sixteen.
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Affiliation(s)
- Wesley K Thompson
- Department of Psychiatry, The University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92037, USA; Stein Institute for Research on Aging, The University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92037, USA.
| | - Dominic Holland
- Department of Neurosciences, The University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92037, USA; Multimodal Imaging Laboratory, The University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92037, USA
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Brun CC, Lepore N, Pennec X, Chou YY, Lee AD, de Zubicaray G, McMahon KL, Wright MJ, Gee JC, Thompson PM. A nonconservative Lagrangian framework for statistical fluid registration-SAFIRA. IEEE TRANSACTIONS ON MEDICAL IMAGING 2011; 30:184-202. [PMID: 20813636 DOI: 10.1109/tmi.2010.2067451] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3-D brain images. This algorithm is named SAFIRA, acronym for statistically-assisted fluid image registration algorithm. A nonstatistical version of this algorithm was implemented , where the deformation was regularized by penalizing deviations from a zero rate of strain. In , the terms regularizing the deformation included the covariance of the deformation matrices (Σ) and the vector fields (q) . Here, we used a Lagrangian framework to reformulate this algorithm, showing that the regularizing terms essentially allow nonconservative work to occur during the flow. Given 3-D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the nonstatistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the nonconservative terms, creating four versions of SAFIRA. We evaluated and compared our algorithms' performance on 92 3-D brain scans from healthy monozygotic and dizygotic twins; 2-D validations are also shown for corpus callosum shapes delineated at midline in the same subjects. After preliminary tests to demonstrate each method, we compared their detection power using tensor-based morphometry (TBM), a technique to analyze local volumetric differences in brain structure. We compared the accuracy of each algorithm variant using various statistical metrics derived from the images and deformation fields. All these tests were also run with a traditional fluid method, which has been quite widely used in TBM studies. The versions incorporating vector-based empirical statistics on brain variation were consistently more accurate than their counterparts, when used for automated volumetric quantification in new brain images. This suggests the advantages of this approach for large-scale neuroimaging studies.
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Affiliation(s)
- Caroline C Brun
- Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA 90095, USA
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Anderson VM, Schott JM, Bartlett JW, Leung KK, Miller DH, Fox NC. Gray matter atrophy rate as a marker of disease progression in AD. Neurobiol Aging 2010; 33:1194-202. [PMID: 21163551 PMCID: PMC3657171 DOI: 10.1016/j.neurobiolaging.2010.11.001] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2009] [Revised: 11/01/2010] [Accepted: 11/02/2010] [Indexed: 11/26/2022]
Abstract
Global gray matter (GM) atrophy rates were quantified from magnetic resonance imaging (MRI) over 6- and 12-month intervals in 37 patients with Alzheimer's disease (AD) and 19 controls using: (1) nonlinear registration and integration of Jacobian values, and (2) segmentation and subtraction of serial GM volumes. Sample sizes required to power treatment trials using global GM atrophy rate as an outcome measure were estimated and compared between the 2 techniques, and to global brain atrophy measures quantified using the boundary shift integral (brain boundary shift integral; BBSI) and structural image evaluation, using normalization, of atrophy (SIENA). Increased GM atrophy rates (approximately 2% per year) were observed in patients compared with controls. Although mean atrophy rates provided by Jacobian integration were smaller than those from segmentation and subtraction of GM volumes, measurement variance was reduced. The number of patients required per treatment arm to detect a 20% reduction in GM atrophy rate over a 12-month follow-up (90% power) was 202 (95% confidence interval [CI], 118–423) using Jacobian integration and 2047 (95% CI 271 to > 10 000) using segmentation and subtraction. Comparable sample sizes for whole brain atrophy were 240 (95% CI, 142–469) using the BBSI and 196 (95% CI, 110–425) using SIENA. Jacobian integration could be useful for measuring GM atrophy rate in Alzheimer's disease as a marker of disease progression and treatment efficacy.
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Affiliation(s)
- Valerie M Anderson
- Department of Neuroinflammation, UCL Institute of Neurology, Queen Square, London, UK.
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Differentiating prenatal exposure to methamphetamine and alcohol versus alcohol and not methamphetamine using tensor-based brain morphometry and discriminant analysis. J Neurosci 2010; 30:3876-85. [PMID: 20237258 DOI: 10.1523/jneurosci.4967-09.2010] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Here we investigate the effects of prenatal exposure to methamphetamine (MA) on local brain volume using magnetic resonance imaging. Because many who use MA during pregnancy also use alcohol, a known teratogen, we examined whether local brain volumes differed among 61 children (ages 5-15 years), 21 with prenatal MA exposure, 18 with concomitant prenatal alcohol exposure (the MAA group), 13 with heavy prenatal alcohol but not MA exposure (ALC group), and 27 unexposed controls. Volume reductions were observed in both exposure groups relative to controls in striatal and thalamic regions bilaterally and in right prefrontal and left occipitoparietal cortices. Striatal volume reductions were more severe in the MAA group than in the ALC group, and, within the MAA group, a negative correlation between full-scale intelligence quotient (FSIQ) scores and caudate volume was observed. Limbic structures, including the anterior and posterior cingulate, the inferior frontal gyrus (IFG), and ventral and lateral temporal lobes bilaterally, were increased in volume in both exposure groups. Furthermore, cingulate and right IFG volume increases were more pronounced in the MAA than ALC group. Discriminant function analyses using local volume measurements and FSIQ were used to predict group membership, yielding factor scores that correctly classified 72% of participants in jackknife analyses. These findings suggest that striatal and limbic structures, known to be sites of neurotoxicity in adult MA abusers, may be more vulnerable to prenatal MA exposure than alcohol exposure and that more severe striatal damage is associated with more severe cognitive deficit.
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Hua X, Lee S, Hibar DP, Yanovsky I, Leow AD, Toga AW, Jack CR, Bernstein MA, Reiman EM, Harvey DJ, Kornak J, Schuff N, Alexander GE, Weiner MW, Thompson PM. Mapping Alzheimer's disease progression in 1309 MRI scans: power estimates for different inter-scan intervals. Neuroimage 2010; 51:63-75. [PMID: 20139010 PMCID: PMC2846999 DOI: 10.1016/j.neuroimage.2010.01.104] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2009] [Revised: 01/26/2010] [Accepted: 01/29/2010] [Indexed: 12/31/2022] Open
Abstract
Neuroimaging centers and pharmaceutical companies are working together to evaluate treatments that might slow the progression of Alzheimer's disease (AD), a common but devastating late-life neuropathology. Recently, automated brain mapping methods, such as tensor-based morphometry (TBM) of structural MRI, have outperformed cognitive measures in their precision and power to track disease progression, greatly reducing sample size estimates for drug trials. In the largest TBM study to date, we studied how sample size estimates for tracking structural brain changes depend on the time interval between the scans (6-24 months). We analyzed 1309 brain scans from 91 probable AD patients (age at baseline: 75.4+/-7.5 years) and 189 individuals with mild cognitive impairment (MCI; 74.6+/-7.1 years), scanned at baseline, 6, 12, 18, and 24 months. Statistical maps revealed 3D patterns of brain atrophy at each follow-up scan relative to the baseline; numerical summaries were used to quantify temporal lobe atrophy within a statistically-defined region-of-interest. Power analyses revealed superior sample size estimates over traditional clinical measures. Only 80, 46, and 39 AD patients were required for a hypothetical clinical trial, at 6, 12, and 24 months respectively, to detect a 25% reduction in average change using a two-sided test (alpha=0.05, power=80%). Correspondingly, 106, 79, and 67 subjects were needed for an equivalent MCI trial aiming for earlier intervention. A 24-month trial provides most power, except when patient attrition exceeds 15-16%/year, in which case a 12-month trial is optimal. These statistics may facilitate clinical trial design using voxel-based brain mapping methods such as TBM.
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Affiliation(s)
- Xue Hua
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Suh Lee
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Derrek P. Hibar
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Igor Yanovsky
- Department of Mathematics, UCLA, Los Angeles, CA, USA
| | - Alex D. Leow
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
- Resnick Neuropsychiatric Hospital at UCLA, Los Angeles, CA, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | | | | | - Eric M. Reiman
- Banner Alzheimer’s Institute, Department Psychiatry, University of Arizona, Phoenix, AZ, USA
| | - Danielle J. Harvey
- Department of Public Health Sciences, UCD School of Medicine, Davis, CA, USA
| | - John Kornak
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, USA
| | - Norbert Schuff
- Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - Gene E. Alexander
- Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Michael W. Weiner
- Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
- Department of Medicine, UCSF, San Francisco, CA, USA
- Department of Psychiatry, UCSF, San Francisco, CA, USA
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
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36
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Stein JL, Hua X, Lee S, Ho AJ, Leow AD, Toga AW, Saykin AJ, Shen L, Foroud T, Pankratz N, Huentelman MJ, Craig DW, Gerber JD, Allen AN, Corneveaux JJ, Dechairo BM, Potkin SG, Weiner MW, Thompson P. Voxelwise genome-wide association study (vGWAS). Neuroimage 2010; 53:1160-74. [PMID: 20171287 DOI: 10.1016/j.neuroimage.2010.02.032] [Citation(s) in RCA: 183] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2009] [Revised: 01/21/2010] [Accepted: 02/11/2010] [Indexed: 01/23/2023] Open
Abstract
The structure of the human brain is highly heritable, and is thought to be influenced by many common genetic variants, many of which are currently unknown. Recent advances in neuroimaging and genetics have allowed collection of both highly detailed structural brain scans and genome-wide genotype information. This wealth of information presents a new opportunity to find the genes influencing brain structure. Here we explore the relation between 448,293 single nucleotide polymorphisms in each of 31,622 voxels of the entire brain across 740 elderly subjects (mean age+/-s.d.: 75.52+/-6.82 years; 438 male) including subjects with Alzheimer's disease, Mild Cognitive Impairment, and healthy elderly controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used tensor-based morphometry to measure individual differences in brain structure at the voxel level relative to a study-specific template based on healthy elderly subjects. We then conducted a genome-wide association at each voxel to identify genetic variants of interest. By studying only the most associated variant at each voxel, we developed a novel method to address the multiple comparisons problem and computational burden associated with the unprecedented amount of data. No variant survived the strict significance criterion, but several genes worthy of further exploration were identified, including CSMD2 and CADPS2. These genes have high relevance to brain structure. This is the first voxelwise genome wide association study to our knowledge, and offers a novel method to discover genetic influences on brain structure.
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Affiliation(s)
- Jason L Stein
- Laboratory of Neuro Imaging, Department of Neurology, University of California, Los Angeles School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
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38
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Yushkevich PA, Avants BB, Das SR, Pluta J, Altinay M, Craige C. Bias in estimation of hippocampal atrophy using deformation-based morphometry arises from asymmetric global normalization: an illustration in ADNI 3 T MRI data. Neuroimage 2009; 50:434-45. [PMID: 20005963 DOI: 10.1016/j.neuroimage.2009.12.007] [Citation(s) in RCA: 107] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2009] [Revised: 11/26/2009] [Accepted: 12/01/2009] [Indexed: 11/28/2022] Open
Abstract
Measurement of brain change due to neurodegenerative disease and treatment is one of the fundamental tasks of neuroimaging. Deformation-based morphometry (DBM) has been long recognized as an effective and sensitive tool for estimating the change in the volume of brain regions over time. This paper demonstrates that a straightforward application of DBM to estimate the change in the volume of the hippocampus can result in substantial bias, i.e., an overestimation of the rate of change in hippocampal volume. In ADNI data, this bias is manifested as a non-zero intercept of the regression line fitted to the 6 and 12 month rates of hippocampal atrophy. The bias is further confirmed by applying DBM to repeat scans of subjects acquired on the same day. This bias appears to be the result of asymmetry in the interpolation of baseline and followup images during longitudinal image registration. Correcting this asymmetry leads to bias-free atrophy estimation.
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Affiliation(s)
- Paul A Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Hua X, Lee S, Yanovsky I, Leow AD, Chou YY, Ho AJ, Gutman B, Toga AW, Jack CR, Bernstein MA, Reiman EM, Harvey DJ, Kornak J, Schuff N, Alexander GE, Weiner MW, Thompson PM. Optimizing power to track brain degeneration in Alzheimer's disease and mild cognitive impairment with tensor-based morphometry: an ADNI study of 515 subjects. Neuroimage 2009; 48:668-81. [PMID: 19615450 PMCID: PMC2971697 DOI: 10.1016/j.neuroimage.2009.07.011] [Citation(s) in RCA: 95] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2009] [Revised: 07/01/2009] [Accepted: 07/03/2009] [Indexed: 10/20/2022] Open
Abstract
Tensor-based morphometry (TBM) is a powerful method to map the 3D profile of brain degeneration in Alzheimer's disease (AD) and mild cognitive impairment (MCI). We optimized a TBM-based image analysis method to determine what methodological factors, and which image-derived measures, maximize statistical power to track brain change. 3D maps, tracking rates of structural atrophy over time, were created from 1030 longitudinal brain MRI scans (1-year follow-up) of 104 AD patients (age: 75.7+/-7.2 years; MMSE: 23.3+/-1.8, at baseline), 254 amnestic MCI subjects (75.0+/-7.2 years; 27.0+/-1.8), and 157 healthy elderly subjects (75.9+/-5.1 years; 29.1+/-1.0), as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). To determine which TBM designs gave greatest statistical power, we compared different linear and nonlinear registration parameters (including different regularization functions), and different numerical summary measures derived from the maps. Detection power was greatly enhanced by summarizing changes in a statistically-defined region-of-interest (ROI) derived from an independent training sample of 22 AD patients. Effect sizes were compared using cumulative distribution function (CDF) plots and false discovery rate methods. In power analyses, the best method required only 48 AD and 88 MCI subjects to give 80% power to detect a 25% reduction in the mean annual change using a two-sided test (at alpha=0.05). This is a drastic sample size reduction relative to using clinical scores as outcome measures (619 AD/6797 MCI for the ADAS-Cog, and 408 AD/796 MCI for the Clinical Dementia Rating sum-of-boxes scores). TBM offers high statistical power to track brain changes in large, multi-site neuroimaging studies and clinical trials of AD.
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Affiliation(s)
- Xue Hua
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Suh Lee
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Igor Yanovsky
- Department of Mathematics, UCLA, Los Angeles, CA, USA
| | - Alex D. Leow
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
- Resnick Neuropsychiatric Hospital at UCLA, Los Angeles, CA, USA
| | - Yi-Yu Chou
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - April J. Ho
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Boris Gutman
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
| | | | | | - Eric M. Reiman
- Banner Alzheimer’s Institute, Department of Psychiatry, University of Arizona, Phoenix, AZ, USA
| | - Danielle J. Harvey
- Department of Public Health Sciences, UCD School of Medicine, Davis, CA, USA
| | - John Kornak
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, USA
| | - Norbert Schuff
- Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | | | - Michael W. Weiner
- Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
- Department of Medicine, UCSF, San Francisco, CA, USA
- Department of Psychiatry, UCSF, San Francisco, CA, USA
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA
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