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Lin Z, Sur S, Soldan A, Pettigrew C, Miller M, Oishi K, Bilgel M, Moghekar A, Pillai JJ, Albert M, Lu H. Brain Oxygen Extraction by Using MRI in Older Individuals: Relationship to Apolipoprotein E Genotype and Amyloid Burden. Radiology 2019; 292:140-148. [PMID: 31012816 DOI: 10.1148/radiol.2019182726] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Background Apolipoprotein E4 (APOE4) is a major genetic risk factor for late-onset Alzheimer disease. However, the mechanisms by which APOE4 affects the brain, underpinning this risk, have not been fully elucidated. Purpose To investigate the influence of APOE4 on global cerebral oxygen extraction fraction (OEF) and possible mediation through amyloid burden by using MRI-based brain oxygen extraction technique. Materials and Methods Participants were enrolled from a longitudinal prospective study, the Biomarkers for Older Controls at Risk for Dementia study (data collected from January 2015 to December 2017), of whom 35% (50 of 143 participants) were APOE4 carriers. OEF was measured by using a T2-relaxation-under-spin-tagging MRI technique with a 3.0-T MRI system. PET acquired with carbon 11-labeled Pittsburgh compound B tracer was available in 119 participants to measure amyloid burden. Cognitive performance was assessed by using domain-specific composite scores including executive function, episodic memory, visual-spatial processing, and language. Linear regression analysis was performed to investigate the relationship between APOE4, OEF, and amyloid burden. The association between OEF and cognitive function was studied for the entire study cohort and separately for the APOE4 carriers and noncarriers. Results A total of 143 cognitively healthy individuals (mean age 6 standard deviation, 69.1 years 6 8.2; 57 men and 86 women) were studied. APOE4 genetic status was associated with lower OEF (noncarriers, 41.1% 6 5.8; one E4 allele, 40.1% 6 4.9; two E4 alleles, 36.7% 6 4.5; P = .03). Furthermore, among APOE4 carriers, lower OEF correlated with lower executive function scores (b = 0.079 z score for each percent change in OEF; P = .03). Amyloid burden and OEF were independently associated with APOE4 but were not associated with one another, suggesting that the effect of APOE4 on OEF is not mediated by amyloid. Conclusion MRI-based brain oxygen extraction shows that cognitively healthy carriers of the apolipoprotein E4 gene manifest diminished brain oxygen extraction capacity independent of amyloid burden. ©RSNA, 2019 Online supplemental material is available for this article.
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Affiliation(s)
- Zixuan Lin
- From the Department of Biomedical Engineering (Z.L., M.M., H.L.), The Russell H. Morgan Department of Radiology and Radiological Science (Z.L., S.S., K.O., J.J.P., H.L.), and Department of Neurology (A.S., C.P., A.M., M.A.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Park 322, Baltimore, Md; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Md (M.B.); and F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Md (H.L.)
| | - Sandeepa Sur
- From the Department of Biomedical Engineering (Z.L., M.M., H.L.), The Russell H. Morgan Department of Radiology and Radiological Science (Z.L., S.S., K.O., J.J.P., H.L.), and Department of Neurology (A.S., C.P., A.M., M.A.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Park 322, Baltimore, Md; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Md (M.B.); and F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Md (H.L.)
| | - Anja Soldan
- From the Department of Biomedical Engineering (Z.L., M.M., H.L.), The Russell H. Morgan Department of Radiology and Radiological Science (Z.L., S.S., K.O., J.J.P., H.L.), and Department of Neurology (A.S., C.P., A.M., M.A.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Park 322, Baltimore, Md; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Md (M.B.); and F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Md (H.L.)
| | - Corinne Pettigrew
- From the Department of Biomedical Engineering (Z.L., M.M., H.L.), The Russell H. Morgan Department of Radiology and Radiological Science (Z.L., S.S., K.O., J.J.P., H.L.), and Department of Neurology (A.S., C.P., A.M., M.A.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Park 322, Baltimore, Md; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Md (M.B.); and F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Md (H.L.)
| | - Michael Miller
- From the Department of Biomedical Engineering (Z.L., M.M., H.L.), The Russell H. Morgan Department of Radiology and Radiological Science (Z.L., S.S., K.O., J.J.P., H.L.), and Department of Neurology (A.S., C.P., A.M., M.A.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Park 322, Baltimore, Md; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Md (M.B.); and F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Md (H.L.)
| | - Kenichi Oishi
- From the Department of Biomedical Engineering (Z.L., M.M., H.L.), The Russell H. Morgan Department of Radiology and Radiological Science (Z.L., S.S., K.O., J.J.P., H.L.), and Department of Neurology (A.S., C.P., A.M., M.A.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Park 322, Baltimore, Md; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Md (M.B.); and F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Md (H.L.)
| | - Murat Bilgel
- From the Department of Biomedical Engineering (Z.L., M.M., H.L.), The Russell H. Morgan Department of Radiology and Radiological Science (Z.L., S.S., K.O., J.J.P., H.L.), and Department of Neurology (A.S., C.P., A.M., M.A.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Park 322, Baltimore, Md; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Md (M.B.); and F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Md (H.L.)
| | - Abhay Moghekar
- From the Department of Biomedical Engineering (Z.L., M.M., H.L.), The Russell H. Morgan Department of Radiology and Radiological Science (Z.L., S.S., K.O., J.J.P., H.L.), and Department of Neurology (A.S., C.P., A.M., M.A.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Park 322, Baltimore, Md; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Md (M.B.); and F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Md (H.L.)
| | - Jay J Pillai
- From the Department of Biomedical Engineering (Z.L., M.M., H.L.), The Russell H. Morgan Department of Radiology and Radiological Science (Z.L., S.S., K.O., J.J.P., H.L.), and Department of Neurology (A.S., C.P., A.M., M.A.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Park 322, Baltimore, Md; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Md (M.B.); and F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Md (H.L.)
| | - Marilyn Albert
- From the Department of Biomedical Engineering (Z.L., M.M., H.L.), The Russell H. Morgan Department of Radiology and Radiological Science (Z.L., S.S., K.O., J.J.P., H.L.), and Department of Neurology (A.S., C.P., A.M., M.A.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Park 322, Baltimore, Md; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Md (M.B.); and F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Md (H.L.)
| | - Hanzhang Lu
- From the Department of Biomedical Engineering (Z.L., M.M., H.L.), The Russell H. Morgan Department of Radiology and Radiological Science (Z.L., S.S., K.O., J.J.P., H.L.), and Department of Neurology (A.S., C.P., A.M., M.A.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Park 322, Baltimore, Md; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Md (M.B.); and F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Md (H.L.)
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102
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Arenaza-Urquijo EM, Przybelski SA, Lesnick TL, Graff-Radford J, Machulda MM, Knopman DS, Schwarz CG, Lowe VJ, Mielke MM, Petersen RC, Jack CR, Vemuri P. The metabolic brain signature of cognitive resilience in the 80+: beyond Alzheimer pathologies. Brain 2019; 142:1134-1147. [PMID: 30851100 PMCID: PMC6439329 DOI: 10.1093/brain/awz037] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 12/04/2018] [Accepted: 12/21/2018] [Indexed: 11/14/2022] Open
Abstract
Research into cognitive resilience imaging markers may help determine the clinical significance of Alzheimer's disease pathology among older adults over 80 years (80+). In this study, we aimed to identify a fluorodeoxyglucose (FDG)-PET based imaging marker of cognitive resilience. We identified 457 participants ≥ 80 years old (357 cognitively unimpaired, 118 cognitively impaired at baseline, mean age of 83.5 ± 3.2 years) from the population-based Mayo Clinic Study of Aging (MCSA) with baseline MRI, Pittsburgh compound B-PET and FDG-PET scans and neuropsychological evaluation. We identified a subset of 'resilient' participants (cognitively stable 80+, n = 192) who maintained normal cognition for an average of 5 years (2-10 years). Global PIB ratio, FDG-PET ratio and cortical thickness from Alzheimer's disease signature regions were used as Alzheimer's disease imaging biomarker outcomes and global cognitive z-score was used as a cognitive outcome. First, using voxel-wise multiple regression analysis, we identified the metabolic areas underlying cognitive resilience in cognitively stable 80+ participants, which we call the 'resilience signature'. Second, using multivariate linear regression models, we evaluated the association of risk and protective factors with the resilience signature and its added value for predicting global cognition beyond established Alzheimer's disease imaging biomarkers in the full 80+ sample. Third, we evaluated the utility of the resilience signature in conjunction with amyloidosis in predicting longitudinal cognition using linear mixed effect models. Lastly, we assessed the utility of the resilience signature in an independent cohort using ADNI (n = 358, baseline mean age of 80 ± 3.8). Our main findings were: (i) FDG-PET uptake in the bilateral anterior cingulate cortex and anterior temporal pole was associated with baseline global cognition in cognitively stable 80+ (the resilience signature); (ii) established Alzheimer's disease imaging biomarkers did not predict baseline global cognition in this subset of participants; (iii) in the full MCSA 80+ and ADNI cohorts, amyloid burden and FDG-PET in the resilience signature were the stronger predictors of baseline global cognition; (iv) sex and systemic vascular health predicted FDG-PET in the resilience signature, suggesting vascular health maintenance as a potential pathway to preserve the metabolism of these areas; and (v) the resilience signature provided significant information about global longitudinal cognitive change even when considering amyloid status in both the MCSA and ADNI cohorts. The FDG-PET resilience signature may be able to provide important information in conjunction with other Alzheimer's disease biomarkers for the determination of clinical prognosis. It may also facilitate identification of disease targeting modifiable risk factors such as vascular health maintenance.
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Affiliation(s)
| | | | | | | | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Michelle M Mielke
- Health Science Research, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
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104
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Li X, Chen L, Kutten K, Ceritoglu C, Li Y, Kang N, Hsu JT, Qiao Y, Wei H, Liu C, Miller MI, Mori S, Yousem DM, van Zijl PCM, Faria AV. Multi-atlas tool for automated segmentation of brain gray matter nuclei and quantification of their magnetic susceptibility. Neuroimage 2019; 191:337-349. [PMID: 30738207 DOI: 10.1016/j.neuroimage.2019.02.016] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 02/03/2019] [Accepted: 02/06/2019] [Indexed: 01/09/2023] Open
Abstract
Quantification of tissue magnetic susceptibility using MRI offers a non-invasive measure of important tissue components in the brain, such as iron and myelin, potentially providing valuable information about normal and pathological conditions during aging. Despite many advances made in recent years on imaging techniques of quantitative susceptibility mapping (QSM), accurate and robust automated segmentation tools for QSM images that can help generate universal and sharable susceptibility measures in a biologically meaningful set of structures are still not widely available. In the present study, we developed an automated process to segment brain nuclei and quantify tissue susceptibility in these regions based on a susceptibility multi-atlas library, consisting of 10 atlases with T1-weighted images, gradient echo (GRE) magnitude images and QSM images of brains with different anatomic patterns. For each atlas in this library, 10 regions of interest in iron-rich deep gray matter structures that are better defined by QSM contrast were manually labeled, including caudate, putamen, globus pallidus internal/external, thalamus, pulvinar, subthalamic nucleus, substantia nigra, red nucleus and dentate nucleus in both left and right hemispheres. We then tested different pipelines using different combinations of contrast channels to bring the set of labels from the multi-atlases to each target brain and compared them with the gold standard manual delineation. The results showed that the segmentation accuracy using dual contrasts QSM/T1 pipeline outperformed other dual-contrast or single-contrast pipelines. The dice values of 0.77 ± 0.09 using the QSM/T1 multi-atlas pipeline rivaled with the segmentation reliability obtained from multiple evaluators with dice values of 0.79 ± 0.07 and gave comparable or superior performance in segmenting subcortical nuclei in comparison with standard FSL FIRST or recent multi-atlas package of volBrain. The segmentation performance of the QSM/T1 multi-atlas was further tested on QSM images acquired using different acquisition protocols and platforms and showed good reliability and reproducibility with average dice of 0.79 ± 0.08 to manual labels and 0.89 ± 0.04 in an inter-protocol manner. The extracted quantitative magnetic susceptibility values in the deep gray matter nuclei also correlated well between different protocols with inter-protocol correlation constants all larger than 0.97. Such reliability and performance was ultimately validated in an external dataset acquired at another study site with consistent susceptibility measures obtained using the QSM/T1 multi-atlas approach in comparison to those using manual delineation. In summary, we designed a susceptibility multi-atlas tool for automated and reliable segmentation of QSM images and for quantification of magnetic susceptibilities. It is publicly available through our cloud-based platform (www.mricloud.org). Further improvement on the performance of this multi-atlas tool is expected by increasing the number of atlases in the future.
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Affiliation(s)
- Xu Li
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
| | - Lin Chen
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Kwame Kutten
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
| | - Can Ceritoglu
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Yue Li
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ningdong Kang
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - John T Hsu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ye Qiao
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hongjiang Wei
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Susumu Mori
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David M Yousem
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Peter C M van Zijl
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Andreia V Faria
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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109
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Cohen-Zimerman S, Salvi C, Krueger F, Gordon B, Grafman J. Intelligence across the seventh decade in patients with brain injuries acquired in young adulthood. Trends Neurosci Educ 2018; 13:1-7. [PMID: 30613804 DOI: 10.1016/j.tine.2018.08.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
In this longitudinal study, we examined intelligence in a group of Vietnam veterans in their 60 s who suffered combat-related penetrating traumatic brain injuries (pTBI) in their 20 s (n = 120), as well as matched veterans with no brain damage (n = 33). Intelligence was evaluated using the Armed Forces Qualification Test (AFQT) administered before the injury occurred and then again at three points in time over the following 45 years. We tested for potential predictors and correlates of late midlife intelligence score, as well as the recent change in score over the seventh decade. The pTBI group had lower intelligence scores than the control group when currently evaluated. Pre-injury intelligence and the presence of a pTBI were the most consistent predictors of current intelligence scores. While exacerbated intellectual decline occurs following a young-adulthood pTBI and affects everyday life, no evidence for late midlife accelerated cognitive decline or dementia was found.
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Affiliation(s)
- Shira Cohen-Zimerman
- Cognitive Neuroscience Laboratory, Think+Speak Lab, Shirley Ryan AbilityLab, 355 E Erie St., Chicago, IL 60611, USA
| | - Carola Salvi
- Cognitive Neuroscience Laboratory, Think+Speak Lab, Shirley Ryan AbilityLab, 355 E Erie St., Chicago, IL 60611, USA.,Department of Psychology, Northwestern University, Chicago, IL, USA
| | - Frank Krueger
- School of Systems Biology, George Mason University, Fairfax, VA, USA.,Department of Psychology, University of Mannheim, Mannheim, Germany
| | - Barry Gordon
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Cognitive Science Department, Johns Hopkins University, Baltimore, MD, USA
| | - Jordan Grafman
- Cognitive Neuroscience Laboratory, Think+Speak Lab, Shirley Ryan AbilityLab, 355 E Erie St., Chicago, IL 60611, USA.,Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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