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Lin Z, Jiang D, Hong Y, Zhang Y, Hsu YC, Lu H, Wu D. Vessel-specific quantification of cerebral venous oxygenation with velocity-encoding preparation and rapid acquisition. Magn Reson Med 2024; 92:782-791. [PMID: 38523598 DOI: 10.1002/mrm.30092] [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: 01/29/2024] [Revised: 03/03/2024] [Accepted: 03/07/2024] [Indexed: 03/26/2024]
Abstract
PURPOSE Non-invasive measurement of cerebral venous oxygenation (Yv) is of critical importance in brain diseases. The present work proposed a fast method to quantify regional Yv map for both large and small veins. METHODS A new sequence was developed, referred to as TRU-VERA (T2 relaxation under velocity encoding and rapid acquisition, which isolates blood spins from static tissue with velocity-encoding preparation, modulates the T2 weighting of venous signal with T2-preparation and utilizes a bSSFP readout to achieve fast acquisition with high resolution. The sequence was first optimized to achieve best sensitivity for both large and small veins, and then validated with TRUST (T2 relaxation under spin tagging), TRUPC (T2 relaxation under phase contrast), and accelerated TRUPC MRI. Regional difference of Yv was evaluated, and test-retest reproducibility was examined. RESULTS Optimal Venc was determined to be 3 cm/s, while recovery time and balanced SSFP flip angle within reasonable range had minimal effect on SNR efficiency. Venous T2 measured with TRU-VERA was highly correlated with T2 from TRUST (R2 = 0.90), and a conversion equation was established for further calibration to Yv. TRU-VERA sequences showed consistent Yv estimation with TRUPC (R2 = 0.64) and accelerated TRUPC (R2 = 0.79). Coefficient of variation was 0.84% for large veins and 2.49% for small veins, suggesting an excellent test-retest reproducibility. CONCLUSION The proposed TRU-VERA sequence is a promising method for vessel-specific oxygenation assessment.
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Affiliation(s)
- Zixuan Lin
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Dengrong Jiang
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yiwen Hong
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Yi-Cheng Hsu
- MR Collaboration, Siemens Healthineers Ltd, Shanghai, China
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
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2
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Engle J, Saberi P, Bain P, Ikram A, Selim M, Soman S. Oxygen extraction fraction (OEF) values and applications in neurological diseases. Neurol Sci 2024; 45:3007-3020. [PMID: 38367153 DOI: 10.1007/s10072-024-07362-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 01/22/2024] [Indexed: 02/19/2024]
Abstract
One of the goals of this systematic review is to provide a meta-analysis-derived mean OEF of healthy volunteers. Another aim of this study is to indicate the OEF ranges of various neurological pathologies. Potential clinical applications of OEF metrics are presented. Peer-reviewed studies reporting OEF metrics derived from computed tomography (CT)/positron emission tomography (PET) and/or magnetic resonance imaging (MRI) were considered. Databases utilized included MEDLINE, PubMed, EMBASE, Web of Science, and Google Scholar. The Newcastle-Ottawa scoring system was used for evaluating studies. R Studio was utilized for the meta-analysis calculations when appropriate. The GRADE framework was utilized to assess additional findings. Of 2267 potential studies, 165 met the inclusion criteria. The healthy volunteer meta-analysis included 339 subjects and found a mean OEF value of 38.87 (37.38, 40.36), with a prediction interval of 32.40-45.34. There were no statistical differences in OEF values derived from PET versus MRI. We provided a GRADE A certainty rating for the use of OEF metrics to predict stroke occurrence in patients with symptomatic carotid or cerebral vessel disease. We provided a GRADE B certainty rating for monitoring treatment response in Moyamoya disease. Use of OEF metrics in diagnosing and/or monitoring other conditions had a GRADE C certainty rating or less. OEF might have a role in diagnosing and monitoring patients with symptomatic carotid or cerebral vessel disease and Moyamoya disease. While we found insufficient evidence to support measuring OEF metrics in other patient populations, in many cases, further studies are warranted.
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Affiliation(s)
- Joshua Engle
- Beth Israel Deaconess Medical Center (Radiology), Boston, MA, USA.
| | - Parastoo Saberi
- Beth Israel Deaconess Medical Center (Radiology), Boston, MA, USA
| | - Paul Bain
- Harvard Medical School, Boston, MA, USA
| | - Asad Ikram
- Beth Israel Deaconess Medical Center (Radiology), Boston, MA, USA
| | - Magdy Selim
- Beth Israel Deaconess Medical Center (Radiology), Boston, MA, USA
| | - Salil Soman
- Beth Israel Deaconess Medical Center (Radiology), Boston, MA, USA
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3
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Fellah S, Ying C, Wang Y, Guilliams KP, Fields ME, Chen Y, Lewis J, Mirro A, Cohen R, Igwe N, Eldeniz C, Jiang D, Lu H, Powers WJ, Lee JM, Ford AL, An H. Comparison of cerebral oxygen extraction fraction using ASE and TRUST methods in patients with sickle cell disease and healthy controls. J Cereb Blood Flow Metab 2024:271678X241237072. [PMID: 38436254 DOI: 10.1177/0271678x241237072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
Abnormal oxygen extraction fraction (OEF), a putative biomarker of cerebral metabolic stress, may indicate compromised oxygen delivery and ischemic vulnerability in patients with sickle cell disease (SCD). Elevated OEF was observed at the tissue level across the brain using an asymmetric spin echo (ASE) MR method, while variable global OEFs were found from the superior sagittal sinus (SSS) using a T2-relaxation-under-spin-tagging (TRUST) MRI method with different calibration models. In this study, we aimed to compare the average ASE-OEF in the SSS drainage territory and TRUST-OEF in the SSS from the same SCD patients and healthy controls. 74 participants (SCD: N = 49; controls: N = 25) underwent brain MRI. TRUST-OEF was quantified using the Lu-bovine, Bush-HbA and Li-Bush-HbS models. ASE-OEF and TRUST-OEF were significantly associated in healthy controls after controlling for hematocrit using the Lu-bovine or the Bush-HbA model. However, no association was found between ASE-OEF and TRUST-OEF in patients with SCD using either the Bush-HbA or the Li-Bush-HbS model. Plausible explanations include a discordance between spatially volume-averaged oxygenation brain tissue and flow-weighted volume-averaged oxygenation in SSS or sub-optimal calibration in SCD. Further work is needed to refine and validate non-invasive MR OEF measurements in SCD.
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Affiliation(s)
- Slim Fellah
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Chunwei Ying
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Yan Wang
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Kristin P Guilliams
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Melanie E Fields
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Yasheng Chen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Josiah Lewis
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Amy Mirro
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Rachel Cohen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Nkemdilim Igwe
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Cihat Eldeniz
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Dengrong Jiang
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hanzhang Lu
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - William J Powers
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
| | - Jin-Moo Lee
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Andria L Ford
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Hongyu An
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
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4
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Bao H, Cao J, Chen M, Chen M, Chen W, Chen X, Chen Y, Chen Y, Chen Y, Chen Z, Chhetri JK, Ding Y, Feng J, Guo J, Guo M, He C, Jia Y, Jiang H, Jing Y, Li D, Li J, Li J, Liang Q, Liang R, Liu F, Liu X, Liu Z, Luo OJ, Lv J, Ma J, Mao K, Nie J, Qiao X, Sun X, Tang X, Wang J, Wang Q, Wang S, Wang X, Wang Y, Wang Y, Wu R, Xia K, Xiao FH, Xu L, Xu Y, Yan H, Yang L, Yang R, Yang Y, Ying Y, Zhang L, Zhang W, Zhang W, Zhang X, Zhang Z, Zhou M, Zhou R, Zhu Q, Zhu Z, Cao F, Cao Z, Chan P, Chen C, Chen G, Chen HZ, Chen J, Ci W, Ding BS, Ding Q, Gao F, Han JDJ, Huang K, Ju Z, Kong QP, Li J, Li J, Li X, Liu B, Liu F, Liu L, Liu Q, Liu Q, Liu X, Liu Y, Luo X, Ma S, Ma X, Mao Z, Nie J, Peng Y, Qu J, Ren J, Ren R, Song M, Songyang Z, Sun YE, Sun Y, Tian M, Wang S, Wang S, Wang X, Wang X, Wang YJ, Wang Y, Wong CCL, Xiang AP, Xiao Y, Xie Z, Xu D, Ye J, Yue R, Zhang C, Zhang H, Zhang L, Zhang W, Zhang Y, Zhang YW, Zhang Z, Zhao T, Zhao Y, Zhu D, Zou W, Pei G, Liu GH. Biomarkers of aging. SCIENCE CHINA. LIFE SCIENCES 2023; 66:893-1066. [PMID: 37076725 PMCID: PMC10115486 DOI: 10.1007/s11427-023-2305-0] [Citation(s) in RCA: 77] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/27/2023] [Indexed: 04/21/2023]
Abstract
Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.
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Affiliation(s)
- Hainan Bao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Jiani Cao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Mengting Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Min Chen
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Wei Chen
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Third Military Medical University, Chongqing, 400042, China
| | - Yanhao Chen
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yu Chen
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Yutian Chen
- The Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Zhiyang Chen
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China
| | - Jagadish K Chhetri
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Yingjie Ding
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Junlin Feng
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jun Guo
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China
| | - Mengmeng Guo
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Chuting He
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Yujuan Jia
- Department of Neurology, First Affiliated Hospital, Shanxi Medical University, Taiyuan, 030001, China
| | - Haiping Jiang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Ying Jing
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Dingfeng Li
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China
| | - Jiaming Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyi Li
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Qinhao Liang
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China
| | - Rui Liang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China
| | - Feng Liu
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China
| | - Xiaoqian Liu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Zuojun Liu
- School of Life Sciences, Hainan University, Haikou, 570228, China
| | - Oscar Junhong Luo
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, 510632, China
| | - Jianwei Lv
- School of Life Sciences, Xiamen University, Xiamen, 361102, China
| | - Jingyi Ma
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Kehang Mao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Jiawei Nie
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xinhua Qiao
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xinpei Sun
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China
| | - Xiaoqiang Tang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Jianfang Wang
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Qiaoran Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Siyuan Wang
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China
| | - Xuan Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China
| | - Yaning Wang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yuhan Wang
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Rimo Wu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China
| | - Kai Xia
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Fu-Hui Xiao
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Lingyan Xu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Yingying Xu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Haoteng Yan
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Liang Yang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China
| | - Ruici Yang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yuanxin Yang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China
| | - Yilin Ying
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China
| | - Le Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Weiwei Zhang
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China
| | - Wenwan Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xing Zhang
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Zhuo Zhang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Min Zhou
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Qingchen Zhu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Zhengmao Zhu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Feng Cao
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China.
| | - Zhongwei Cao
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Piu Chan
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
| | - Chang Chen
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Guobing Chen
- Department of Microbiology and Immunology, School of Medicine, Jinan University, Guangzhou, 510632, China.
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, Guangzhou, 510000, China.
| | - Hou-Zao Chen
- Department of Biochemistryand Molecular Biology, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
| | - Jun Chen
- Peking University Research Center on Aging, Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function, Department of Biochemistry and Molecular Biology, Department of Integration of Chinese and Western Medicine, School of Basic Medical Science, Peking University, Beijing, 100191, China.
| | - Weimin Ci
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Bi-Sen Ding
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Qiurong Ding
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Feng Gao
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Kai Huang
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Zhenyu Ju
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China.
| | - Qing-Peng Kong
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
| | - Ji Li
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Jian Li
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China.
| | - Xin Li
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Baohua Liu
- School of Basic Medical Sciences, Shenzhen University Medical School, Shenzhen, 518060, China.
| | - Feng Liu
- Metabolic Syndrome Research Center, The Second Xiangya Hospital, Central South Unversity, Changsha, 410011, China.
| | - Lin Liu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China.
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China.
- Institute of Translational Medicine, Tianjin Union Medical Center, Nankai University, Tianjin, 300000, China.
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300350, China.
| | - Qiang Liu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China.
| | - Qiang Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China.
- Tianjin Institute of Immunology, Tianjin Medical University, Tianjin, 300070, China.
| | - Xingguo Liu
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China.
| | - Yong Liu
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China.
| | - Xianghang Luo
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China.
| | - Shuai Ma
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Xinran Ma
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
| | - Zhiyong Mao
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Jing Nie
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - Yaojin Peng
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jie Ren
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ruibao Ren
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Center for Aging and Cancer, Hainan Medical University, Haikou, 571199, China.
| | - Moshi Song
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Zhou Songyang
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China.
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
| | - Yi Eve Sun
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China.
| | - Yu Sun
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Department of Medicine and VAPSHCS, University of Washington, Seattle, WA, 98195, USA.
| | - Mei Tian
- Human Phenome Institute, Fudan University, Shanghai, 201203, China.
| | - Shusen Wang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China.
| | - Si Wang
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
| | - Xia Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China.
| | - Xiaoning Wang
- Institute of Geriatrics, The second Medical Center, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China.
| | - Yan-Jiang Wang
- Department of Neurology and Center for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, 400042, China.
| | - Yunfang Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China.
| | - Catherine C L Wong
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China.
| | - Andy Peng Xiang
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China.
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Yichuan Xiao
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Zhengwei Xie
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China.
- Beijing & Qingdao Langu Pharmaceutical R&D Platform, Beijing Gigaceuticals Tech. Co. Ltd., Beijing, 100101, China.
| | - Daichao Xu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China.
| | - Jing Ye
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China.
| | - Rui Yue
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Cuntai Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China.
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Hongbo Zhang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Liang Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yong Zhang
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Yun-Wu Zhang
- Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, 361102, China.
| | - Zhuohua Zhang
- Key Laboratory of Molecular Precision Medicine of Hunan Province and Center for Medical Genetics, Institute of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, 410078, China.
- Department of Neurosciences, Hengyang Medical School, University of South China, Hengyang, 421001, China.
| | - Tongbiao Zhao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Yuzheng Zhao
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China.
| | - Dahai Zhu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Weiguo Zou
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Gang Pei
- Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Biomedicine, The Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200070, China.
| | - Guang-Hui Liu
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
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Uchida Y, Kan H, Sakurai K, Oishi K, Matsukawa N. Contributions of blood-brain barrier imaging to neurovascular unit pathophysiology of Alzheimer's disease and related dementias. Front Aging Neurosci 2023; 15:1111448. [PMID: 36861122 PMCID: PMC9969807 DOI: 10.3389/fnagi.2023.1111448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 01/26/2023] [Indexed: 02/11/2023] Open
Abstract
The blood-brain barrier (BBB) plays important roles in the maintenance of brain homeostasis. Its main role includes three kinds of functions: (1) to protect the central nervous system from blood-borne toxins and pathogens; (2) to regulate the exchange of substances between the brain parenchyma and capillaries; and (3) to clear metabolic waste and other neurotoxic compounds from the central nervous system into meningeal lymphatics and systemic circulation. Physiologically, the BBB belongs to the glymphatic system and the intramural periarterial drainage pathway, both of which are involved in clearing interstitial solutes such as β-amyloid proteins. Thus, the BBB is believed to contribute to preventing the onset and progression for Alzheimer's disease. Measurements of BBB function are essential toward a better understanding of Alzheimer's pathophysiology to establish novel imaging biomarkers and open new avenues of interventions for Alzheimer's disease and related dementias. The visualization techniques for capillary, cerebrospinal, and interstitial fluid dynamics around the neurovascular unit in living human brains have been enthusiastically developed. The purpose of this review is to summarize recent BBB imaging developments using advanced magnetic resonance imaging technologies in relation to Alzheimer's disease and related dementias. First, we give an overview of the relationship between Alzheimer's pathophysiology and BBB dysfunction. Second, we provide a brief description about the principles of non-contrast agent-based and contrast agent-based BBB imaging methodologies. Third, we summarize previous studies that have reported the findings of each BBB imaging method in individuals with the Alzheimer's disease continuum. Fourth, we introduce a wide range of Alzheimer's pathophysiology in relation to BBB imaging technologies to advance our understanding of the fluid dynamics around the BBB in both clinical and preclinical settings. Finally, we discuss the challenges of BBB imaging techniques and suggest future directions toward clinically useful imaging biomarkers for Alzheimer's disease and related dementias.
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Affiliation(s)
- Yuto Uchida
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States,*Correspondence: Yuto Uchida, ; Noriyuki Matsukawa,
| | - Hirohito Kan
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Keita Sakurai
- Department of Radiology, National Center for Geriatrics and Gerontology, Ōbu, Aichi, Japan
| | - Kenichi Oishi
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Noriyuki Matsukawa
- Department of Neurology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan,*Correspondence: Yuto Uchida, ; Noriyuki Matsukawa,
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Jiang D, Liu P, Lin Z, Hazel K, Pottanat G, Lucke E, Moghekar A, Pillai JJ, Lu H. MRI assessment of cerebral oxygen extraction fraction in the medial temporal lobe. Neuroimage 2023; 266:119829. [PMID: 36565971 PMCID: PMC9878351 DOI: 10.1016/j.neuroimage.2022.119829] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/29/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
The medial temporal lobe (MTL) is a key area implicated in many brain diseases, such as Alzheimer's disease. As a functional biomarker, the oxygen extraction fraction (OEF) of MTL may be more sensitive than structural atrophy of MTL, especially at the early stages of diseases. However, there is a lack of non-invasive techniques to measure MTL-OEF in humans. The goal of this work is to develop an MRI technique to assess MTL-OEF in a clinically practical time without using contrast agents. The proposed method measures venous oxygenation (Yv) in the basal veins of Rosenthal (BVs), which are the major draining veins of the MTL. MTL-OEF can then be estimated as the arterio-venous difference in oxygenation. We developed an MRI sequence, dubbed arterial-suppressed accelerated T2-relaxation-under-phase-contrast (AS-aTRUPC), to quantify the blood T2 of the BVs, which was then converted to Yv through a well-established calibration model. MTL-OEF was calculated as (Ya-Yv)/Ya × 100%, where Ya was the arterial oxygenation. The feasibility of AS-aTRUPC to quantify MTL-OEF was evaluated in 16 healthy adults. The sensitivity of AS-aTRUPC in detecting OEF changes was assessed by a caffeine ingestion (200 mg) challenge. For comparison, T2-relaxation-under-spin-tagging (TRUST) MRI, which is a widely used global OEF technique, was also acquired. The dependence of MTL-OEF on age was examined by including another seven healthy elderly subjects. The results showed that in healthy adults, MTL-OEF of the left and right hemispheres were correlated (P=0.005). MTL-OEF was measured to be 23.9±3.6% (mean±standard deviation) and was significantly lower (P<0.0001) than the OEF of 33.3±2.9% measured in superior sagittal sinus (SSS). After caffeine ingestion, there was an absolute percentage increase of 9.1±4.0% in MTL-OEF. Additionally, OEF in SSS measured with AS-aTRUPC showed a strong correlation with TRUST OEF (intra-class correlation coefficient=0.94 with 95% confidence interval [0.91, 0.96]), with no significant bias (P=0.12). MTL-OEF was found to increase with age (MTL-OEF=20.997+0.100 × age; P=0.02). In conclusion, AS-aTRUPC MRI provides non-invasive assessments of MTL-OEF and may facilitate future clinical applications of MTL-OEF as a disease biomarker.
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Affiliation(s)
- Dengrong Jiang
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
| | - Peiying Liu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Zixuan Lin
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Kaisha Hazel
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - George Pottanat
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Emma Lucke
- Department of Biology, Johns Hopkins University School of Arts & Sciences, Baltimore, MD, United States
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jay J Pillai
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States; Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, United States
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Lin Z, Lim C, Jiang D, Soldan A, Pettigrew C, Oishi K, Zhu Y, Moghekar A, Liu P, Albert M, Lu H. Longitudinal changes in brain oxygen extraction fraction (OEF) in older adults: Relationship to markers of vascular and Alzheimer's pathology. Alzheimers Dement 2023; 19:569-577. [PMID: 35791732 PMCID: PMC10838398 DOI: 10.1002/alz.12727] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 05/18/2022] [Accepted: 05/31/2022] [Indexed: 11/07/2022]
Abstract
INTRODUCTION Oxygen extraction fraction (OEF) reflects the balance between oxygen delivery and consumption. We longitudinally measured OEF in older adults to examine the relationship with markers of Alzheimer's disease (AD) and vascular pathology. METHODS One hundred thirty-seven participants were studied at two time-points at an interval of 2.16 years. OEF was measured using T2 -relaxation-under-spin-tagging (TRUST) magnetic resonance imaging (MRI). The association between OEF and vascular risks, white matter hyperintensities (WMH), cerebrospinal fluid (CSF) measures of amyloid beta (Aβ), total tau (t-tau), and phosphorylated tau 181 (p-tau181) was examined. RESULTS OEF increased from baseline to follow-up. The increase in OEF was more prominent in individuals with high vascular risks compared to those with low vascular risks, and was associated with progression of vascular risks and the growth in WMH volume. OEF change was not related to CSF markers of AD pathology or their progression. DISCUSSION Longitudinal OEF change in older adults is primarily related to vascular pathology.
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Affiliation(s)
- Zixuan Lin
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Chantelle Lim
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Dengrong Jiang
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Anja Soldan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Corinne Pettigrew
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kumiko Oishi
- Center for Imaging Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yuxin Zhu
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Peiying Liu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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Hanon O, Vidal JS, Lehmann S, Bombois S, Allinquant B, Baret-Rose C, Tréluyer JM, Abdoul H, Gelé P, Delmaire C, Blanc F, Mangin JF, Buée L, Touchon J, Hugon J, Vellas B, Galbrun E, Benetos A, Berrut G, Paillaud E, Wallon D, Castelnovo G, Volpe-Gillot L, Paccalin M, Robert P, Godefroy O, Camus V, Belmin J, Vandel P, Novella JL, Duron E, Rigaud AS, Schraen-Maschke S, Gabelle A. Plasma amyloid beta predicts conversion to dementia in subjects with mild cognitive impairment: The BALTAZAR study. Alzheimers Dement 2022; 18:2537-2550. [PMID: 35187794 DOI: 10.1002/alz.12613] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 10/20/2021] [Accepted: 12/10/2021] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Blood-based biomarkers are the next challenge for Alzheimer's disease (AD) diagnosis and prognosis. METHODS Mild cognitive impairment (MCI) participants (N = 485) of the BALTAZAR study, a large-scale longitudinal multicenter cohort, were followed-up for 3 years. A total of 165 of them converted to dementia (95% AD). Associations of conversion and plasma amyloid beta (Aβ)1-42 , Aβ1-40 , Aβ1-42 /Aβ1-40 ratio were analyzed with logistic and Cox models. RESULTS Converters to dementia had lower level of plasma Aβ1-42 (37.1 pg/mL [12.5] vs. 39.2 [11.1] , P value = .03) and lower Aβ1-42 /Aβ1-40 ratio than non-converters (0.148 [0.125] vs. 0.154 [0.076], P value = .02). MCI participants in the highest quartile of Aβ1-42 /Aβ1-40 ratio (>0.169) had a significant lower risk of conversion (hazard ratio adjusted for age, sex, education, apolipoprotein E ε4, hippocampus atrophy = 0.52 (95% confidence interval [0.31-0.86], P value = .01). DISCUSSION In this large cohort of MCI subjects we identified a threshold for plasma Aβ1-42 /Aβ1-40 ratio that may detect patients with a low risk of conversion to dementia within 3 years.
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Affiliation(s)
- Olivier Hanon
- Memory Resource and Research Centre of de Paris-Broca-Ile de France, Université de Paris, EA 4468, APHP, Hopital Broca, Paris, France
| | - Jean-Sébastien Vidal
- Memory Resource and Research Centre of de Paris-Broca-Ile de France, Université de Paris, EA 4468, APHP, Hopital Broca, Paris, France
| | - Sylvain Lehmann
- CHU Montpellier, LBPC, Inserm, Université de Montpellier, Montpellier, France
| | - Stéphanie Bombois
- CHU Lille, U1172-LilNCog, LiCEND, LabEx DISTALZ, Université de Lille, Inserm, Lille, France
| | - Bernadette Allinquant
- UMR-S 1266, Université de Paris, Institute of Psychiatric and Neurosciences, Inserm, Paris, France
| | - Christiane Baret-Rose
- UMR-S 1266, Université de Paris, Institute of Psychiatric and Neurosciences, Inserm, Paris, France
| | - Jean-Marc Tréluyer
- Clinical Research Unit, Université de Paris, APHP, Hôpital Necker, Paris, France
| | - Hendy Abdoul
- Clinical Research Unit, Université de Paris, APHP, Hôpital Necker, Paris, France
| | - Patrick Gelé
- CHU Lille, CRB/CIC1403, Université de Lille, Inserm, Lille, France
| | - Christine Delmaire
- CHU Lille, U1172-LilNCog, LiCEND, LabEx DISTALZ, Université de Lille, Inserm, Lille, France
| | - Fredéric Blanc
- CM2R, pôle de Gériatrie, Laboratoire ICube, FMTS, CNRS, équipe IMIS, Université de Strasbourg, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Jean-François Mangin
- Neurospin, CEA, CNRS, cati-neuroimaging.com, CATI Multicenter Neuroimaging Platform, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Luc Buée
- CHU Lille, U1172-LilNCog, LiCEND, LabEx DISTALZ, Université de Lille, Inserm, Lille, France
| | - Jacques Touchon
- Department of Neurology, Memory Research and Resources Center of Montpellier, Inserm INM NeuroPEPs Team, Excellence Center of Neurodegenerative Disorders, Université de Montpellier, CHU Montpellier, Montpellier, France
| | - Jacques Hugon
- APHP, Groupe Hospitalier Saint Louis-Lariboisière Fernand Widal, Center of Cognitive Neurology, Université de Paris, Paris, France
| | - Bruno Vellas
- Memory Resource and Research Centre of Midi-Pyrénées, Université de Toulouse III, CHU La Grave-Casselardit, Toulouse, France
| | - Evelyne Galbrun
- Department of Gérontology 2, Sorbonne Université, APHP, Centre Hospitalier Dupuytren, Draveil, France
| | - Athanase Benetos
- Memory Resource and Research Centre of Lorraine, Université de Lorraine, CHRU de Nancy, Vandoeuvre-lès-Nancy, France
| | - Gilles Berrut
- Department of Clinical Gerontology, Memory Research Resource Center of Nantes, Université de Nantes, EA 4334 Movement-Interactions-Performance, CHU Nantes, Nantes, France
| | - Elena Paillaud
- Service de Gériatrie, Université de Paris, APHP, Hôpital Europeen Georges Pompidou, Paris, France
| | - David Wallon
- CHU de Rouen, Department of Neurology and CNR-MAJ, Normandy Center for Genomic and Personalized Medicine, CIC-CRB1404, Normandie Univ, UNIROUEN, Inserm U1245, Rouen, France
| | | | - Lisette Volpe-Gillot
- Service de Neuro-Psycho-Gériatrie, Memory Clinic, Hôpital Léopold Bellan, Paris, France
| | - Marc Paccalin
- Memory Resource and Research Centre of Poitiers, CHU de Poitiers, Poitiers, France
| | - Philippe Robert
- Memory Research Resource Center of Nice, CoBTek lab, Université Côte d'Azur, CHU de Nice, Nice, France
| | - Olivier Godefroy
- Memory Resource and Research Centre of Amiens Picardie, CHU d'Amiens-Picardie, Amiens, France
| | - Vincent Camus
- CHRU de Tours, UMR Inserm U1253, Université François-Rabelais de Tours, Tours, France
| | - Joël Belmin
- Service de Gériatrie Ambulatoire, Sorbonne Université, APHP, Hôpitaux Universitaires Pitie-Salpêtrière-Charles Foix, Paris, France
| | - Pierre Vandel
- Laboratoire de Recherches Intégratives en Neurosciences et Psychologie Cognitive, CHU de Besançon, Memory Resource and Research Centre of Besançon Franche-Comté, Université Bourgogne Franche-Comté, Besançon, France
| | - Jean-Luc Novella
- Memory Resource and Research Centre of Champagne-Ardenne, Université de Reims Champagne-Ardenne, EA 3797, CHU de Reims, Reims, France
| | - Emmanuelle Duron
- Département de gériatrie, Équipe MOODS, Inserm 1178, Université Paris-Saclay, APHP, Hôpital Paul Brousse, Villejuif, France
| | - Anne-Sophie Rigaud
- Memory Resource and Research Centre of de Paris-Broca-Ile de France, Université de Paris, EA 4468, APHP, Hopital Broca, Paris, France
| | | | - Audrey Gabelle
- Department of Neurology, Memory Research and Resources Center of Montpellier, Inserm INM NeuroPEPs Team, Excellence Center of Neurodegenerative Disorders, Université de Montpellier, CHU Montpellier, Montpellier, France
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9
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Han H, Lin Z, Soldan A, Pettigrew C, Betz JF, Oishi K, Li Y, Liu P, Albert M, Lu H. Longitudinal Changes in Global Cerebral Blood Flow in Cognitively Normal Older Adults: A Phase-Contrast MRI Study. J Magn Reson Imaging 2022; 56:1538-1545. [PMID: 35218111 PMCID: PMC9411265 DOI: 10.1002/jmri.28133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/14/2022] [Accepted: 02/14/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Characterization of blood supply changes in older individuals is important in understanding brain aging and diseases. However, prior studies largely focused on cross-sectional design, thus change in cerebral blood flow (CBF) could not be assessed on an individual level. PURPOSE To evaluate longitudinal short-term changes in global CBF in cognitively normal older adults. STUDY TYPE Prospective, longitudinal, and cohort. POPULATION One-hundred twenty-seven cognitive-normal participants (mean age 69 ± 7 years, 47 males) underwent serial MRI with an average follow-up time of 2.1 years. FIELD STRENGTH/SEQUENCE 3 T phase-contrast (PC), three-dimensional magnetization-prepared-rapid-acquisition-of-gradient-echo (MPRAGE) and fluid-attenuated inversion recovery (FLAIR) MRI. ASSESSMENT Total CBF was measured with PC MRI allowing assessment of quantitative flow in four major feeding arteries by a trained radiologist with >3 years' experience (O.K.). Brain volume was obtained from MPRAGE MRI and measured by T1-MultiAtlas MRICloud tool. The ratio between total CBF and brain volume yielded global CBF in mL/100 g/min. White matter hyperintensity (WMH) was measured automatically using a Bayesian probability approach on FLAIR. STATISTICAL TESTS Linear mixed effect model was used to simultaneously assess cross-sectional age-differences and longitudinal age-changes in CBF. Spearman rank correlation was used to evaluate the relationship between CBF change and WMH progression. A P-value of <0.05 (two-tailed) was considered significant. RESULTS Global CBF decreased with age at a longitudinal rate of -0.56 mL/100 g/min/year (95% confidence interval [CI]: -1.09, -0.03), compared to a cross-sectional rate of -0.26 mL/100 g/min/year (95% CI: -0.41, -0.11). Changes in CBF were significantly associated with progression of WMH (Spearman rank correlation r = -0.25), as those participants who had a more rapid CBF reduction had greater increases in WMH volumes and the relationship remained significant when adjusting for baseline vascular risk scores. Additionally, age-related changes in whole-brain volume were found to be -0.151%/year (95% CI: -0.186, -0.116). DATA CONCLUSION These findings suggest that brain aging in older adults is accompanied by a rapid longitudinal reduction in CBF, the rate of which is associated with white matter damage. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Hualu Han
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Zixuan Lin
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Anja Soldan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Corinne Pettigrew
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Joshua F. Betz
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Kumiko Oishi
- Center for Imaging Science, Johns Hopkins University, Whiting School of Engineering, Baltimore, MD, United States
| | - Yang Li
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Peiying Liu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
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10
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Jiang D, Lu H. Cerebral oxygen extraction fraction MRI: Techniques and applications. Magn Reson Med 2022; 88:575-600. [PMID: 35510696 PMCID: PMC9233013 DOI: 10.1002/mrm.29272] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/20/2022] [Accepted: 03/29/2022] [Indexed: 12/20/2022]
Abstract
The human brain constitutes 2% of the body's total mass but uses 20% of the oxygen. The rate of the brain's oxygen utilization can be derived from a knowledge of cerebral blood flow and the oxygen extraction fraction (OEF). Therefore, OEF is a key physiological parameter of the brain's function and metabolism. OEF has been suggested to be a useful biomarker in a number of brain diseases. With recent advances in MRI techniques, several MRI-based methods have been developed to measure OEF in the human brain. These MRI OEF techniques are based on the T2 of blood, the blood signal phase, the magnetic susceptibility of blood-containing voxels, the effect of deoxyhemoglobin on signal behavior in extravascular tissue, and the calibration of the BOLD signal using gas inhalation. Compared to 15 O PET, which is considered the "gold standard" for OEF measurement, MRI-based techniques are non-invasive, radiation-free, and are more widely available. This article provides a review of these emerging MRI-based OEF techniques. We first briefly introduce the role of OEF in brain oxygen homeostasis. We then review the methodological aspects of different categories of MRI OEF techniques, including their signal mechanisms, acquisition methods, and data analyses. The strengths and limitations of the techniques are discussed. Finally, we review key applications of these techniques in physiological and pathological conditions.
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Affiliation(s)
- Dengrong Jiang
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA
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11
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Jang J, Kang J, Nam Y. [Brain Iron Imaging in Aging and Cognitive Disorders: MRI Approaches]. TAEHAN YONGSANG UIHAKHOE CHI 2022; 83:527-537. [PMID: 36238502 PMCID: PMC9514519 DOI: 10.3348/jksr.2022.0038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/09/2022] [Accepted: 05/16/2022] [Indexed: 11/23/2022]
Abstract
Iron has a vital role in the human body, including the central nervous system. Increased deposition of iron in the brain has been reported in aging and important neurodegenerative diseases. Owing to the unique magnetic resonance properties of iron, MRI has great potential for in vivo assessment of iron deposition, distribution, and non-invasive quantification. In this paper, we will review the MRI methods for iron assessment and their changes in aging and neurodegenerative diseases, focusing on Alzheimer's disease. In addition, we will summarize the limitations of current approaches and introduce new areas and MRI methods for iron imaging that are expected in the future.
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12
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Robb WH, Khan OA, Ahmed HA, Li J, Moore EE, Cambronero FE, Pechman KR, Liu D, Gifford KA, Landman BA, Donahue MJ, Hohman TJ, Jefferson AL. Lower cerebral oxygen utilization is associated with Alzheimer's disease-related neurodegeneration and poorer cognitive performance among apolipoprotein E ε4 carriers. J Cereb Blood Flow Metab 2022; 42:642-655. [PMID: 34743630 PMCID: PMC9051148 DOI: 10.1177/0271678x211056393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2) are markers of cerebral oxygen homeostasis and metabolism that may offer insights into abnormal changes in brain aging. The present study cross-sectionally related OEF and CMRO2 to cognitive performance and structural neuroimaging variables among older adults (n = 246, 74 ± 7 years, 37% female) and tested whether apolipoprotein E (APOE)-ε4 status modified these associations. Main effects of OEF and CMRO2 were null (p-values >0.06), and OEF interactions with APOE-ε4 status on cognitive and structural imaging outcomes were null (p-values >0.06). However, CMRO2 interacted with APOE-ε4 status on language (p = 0.002), executive function (p = 0.03), visuospatial (p = 0.005), and episodic memory performances (p = 0.03), and on hippocampal (p = 0.006) and inferior lateral ventricle volumes (p = 0.02). In stratified analyses, lower oxygen metabolism related to worse language (p = 0.02) and episodic memory performance (p = 0.03) among APOE-ε4 carriers only. Associations between CMRO2 and cognitive performance were primarily driven by APOE-ε4 carriers with existing cognitive impairment. Congruence across language and episodic memory results as well as hippocampal and inferior lateral ventricle volume findings suggest that APOE-ε4 may interact with cerebral oxygen metabolism in the pathogenesis of Alzheimer's disease and related neurodegeneration.
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Affiliation(s)
- W Hudson Robb
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Omair A Khan
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Biostatistics, 12328Vanderbilt University Medical Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Humza A Ahmed
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Judy Li
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Elizabeth E Moore
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Francis E Cambronero
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kimberly R Pechman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dandan Liu
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Biostatistics, 12328Vanderbilt University Medical Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Katherine A Gifford
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Neurology, 12328Vanderbilt University Medical Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A Landman
- Department of Neurology, 12328Vanderbilt University Medical Center, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Biomedical Engineering, 5718Vanderbilt University, Vanderbilt University, Nashville, TN, USA.,Department of Electrical Engineering and Computer Science, 5718Vanderbilt University, Vanderbilt University, Nashville, TN, USA.,Department of Radiology and Radiological Sciences, 12328Vanderbilt University Medical Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Manus J Donahue
- Department of Neurology, 12328Vanderbilt University Medical Center, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Radiology and Radiological Sciences, 12328Vanderbilt University Medical Center, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Neurology, 12328Vanderbilt University Medical Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Angela L Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Neurology, 12328Vanderbilt University Medical Center, Vanderbilt University Medical Center, Nashville, TN, USA.,Department of Medicine, 12328Vanderbilt University Medical Center, Vanderbilt University Medical Center, Nashville, TN, USA
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13
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Wu D, Zhou Y, Cho J, Shen N, Li S, Qin Y, Zhang G, Yan S, Xie Y, Zhang S, Zhu W, Wang Y. The Spatiotemporal Evolution of MRI-Derived Oxygen Extraction Fraction and Perfusion in Ischemic Stroke. Front Neurosci 2021; 15:716031. [PMID: 34483830 PMCID: PMC8415351 DOI: 10.3389/fnins.2021.716031] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 07/12/2021] [Indexed: 12/13/2022] Open
Abstract
Purpose This study aimed to assess the spatiotemporal evolution of oxygen extraction fraction (OEF) in ischemic stroke with a newly developed cluster analysis of time evolution (CAT) for a combined quantitative susceptibility mapping and quantitative blood oxygen level-dependent model (QSM + qBOLD, QQ). Method One hundred and fifteen patients in different ischemic stroke phases were retrospectively collected for measurement of OEF of the infarcted area defined on diffusion-weighted imaging (DWI). Clinical severity was assessed using the National Institutes of Health Stroke Scale (NIHSS). Of the 115 patients, 11 underwent two longitudinal MRI scans, namely, three-dimensional (3D) multi-echo gradient recalled echo (mGRE) and 3D pseudo-continuous arterial spin labeling (pCASL), to evaluate the reversal region (RR) of the initial diffusion lesion (IDL) that did not overlap with the final infarct (FI). The temporal evolution of OEF and the cerebral blood flow (CBF) in the IDL, the RR, and the FI were assessed. Results Compared to the contralateral mirror area, the OEF of the infarcted region was decreased regardless of stroke phases (p < 0.05) and showed a declining tendency from the acute to the chronic phase (p = 0.022). Five of the 11 patients with longitudinal scans showed reversal of the IDL. Relative oxygen extraction fraction (rOEF, compared to the contralateral mirror area) of the RR increased from the first to the second MRI (p = 0.044). CBF was about 1.5-fold higher in the IDL than in the contralateral mirror area in the first MRI. Two patients showed penumbra according to the enlarged FI volume. The rOEF of the penumbra fluctuated around 1.0 at earlier scan times and then decreased, while the CBF decreased continuously. Conclusion The spatiotemporal evolution of OEF and perfusion in ischemic lesions is heterogeneous, and the CAT-based QQ method is feasible to capture cerebral oxygen metabolic information.
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Affiliation(s)
- Di Wu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yiran Zhou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Junghun Cho
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States.,Department of Biomedical Engineering, Cornell University, Ithaca, NY, United States
| | - Nanxi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shihui Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guiling Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Su Yan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shun Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States.,Department of Biomedical Engineering, Cornell University, Ithaca, NY, United States
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14
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Wei Z, Xu J, Chen L, Hirschler L, Barbier EL, Li T, Wong PC, Lu H. Brain metabolism in tau and amyloid mouse models of Alzheimer's disease: An MRI study. NMR IN BIOMEDICINE 2021; 34:e4568. [PMID: 34050996 PMCID: PMC9574887 DOI: 10.1002/nbm.4568] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/17/2021] [Accepted: 05/20/2021] [Indexed: 06/12/2023]
Abstract
Alzheimer's disease (AD) is the leading cause of cognitive impairment and dementia in elderly individuals. According to the current biomarker framework for "unbiased descriptive classification", biomarkers of neurodegeneration, "N", constitute a critical component in the tri-category "A/T/N" system. Current biomarkers of neurodegeneration suffer from potential drawbacks such as requiring invasive lumbar puncture, involving ionizing radiation, or representing a late, irreversible marker. Recent human studies have suggested that reduced brain oxygen metabolism may be a new functional marker of neurodegeneration in AD, but the heterogeneity and the presence of mixed pathology in human patients did not allow a full understanding of the role of oxygen extraction and metabolism in AD. In this report, global brain oxygen metabolism and related physiological parameters were studied in two AD mouse models with relatively pure pathology, using advanced MRI techniques including T2 -relaxation-under-spin-tagging (TRUST) and phase contrast (PC) MRI. Additionally, regional cerebral blood flow (CBF) was determined with pseudocontinuous arterial spin labeling. Reduced global oxygen extraction fraction (by -18.7%, p = 0.008), unit-mass cerebral metabolic rate of oxygen (CMRO2 ) (by -17.4%, p = 0.04) and total CMRO2 (by -30.8%, p < 0.001) were observed in Tau4RΔK mice-referred to as the tau AD model-which manifested pronounced neurodegeneration, as measured by diminished brain volume (by -15.2%, p < 0.001). Global and regional CBF in these mice were not different from those of wild-type mice (p > 0.05), suggesting normal vascular function. By contrast, in B6;SJL-Tg [APPSWE]2576Kha (APP) mice-referred to as the amyloid AD model-no brain volume reduction, as well as relatively intact brain oxygen extraction and metabolism, were found (p > 0.05). Consistent with the imaging data, behavioral measures of walking distance were impaired in Tau4RΔK mice (p = 0.004), but not in APP mice (p = 0.88). Collectively, these findings support the hypothesis that noninvasive MRI measurement of brain oxygen metabolism may be a promising biomarker of neurodegeneration in AD.
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Affiliation(s)
- Zhiliang Wei
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA
| | - Jiadi Xu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA
| | - Lin Chen
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, Fujian, China
| | - Lydiane Hirschler
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble, France
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Emmanuel L. Barbier
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, Grenoble, France
| | - Tong Li
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Philip C. Wong
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanzhang Lu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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15
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van Zijl PCM, Brindle K, Lu H, Barker PB, Edden R, Yadav N, Knutsson L. Hyperpolarized MRI, functional MRI, MR spectroscopy and CEST to provide metabolic information in vivo. Curr Opin Chem Biol 2021; 63:209-218. [PMID: 34298353 PMCID: PMC8384704 DOI: 10.1016/j.cbpa.2021.06.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 06/12/2021] [Accepted: 06/15/2021] [Indexed: 12/13/2022]
Abstract
Access to metabolic information in vivo using magnetic resonance (MR) technologies has generally been the niche of MR spectroscopy (MRS) and spectroscopic imaging (MRSI). Metabolic fluxes can be studied using the infusion of substrates labeled with magnetic isotopes, with the use of hyperpolarization especially powerful. Unfortunately, these promising methods are not yet accepted clinically, where fast, simple, and reliable measurement and diagnosis are key. Recent advances in functional MRI and chemical exchange saturation transfer (CEST) MRI allow the use of water imaging to study oxygen metabolism and tissue metabolite levels. These, together with the use of novel data analysis approaches such as machine learning for all of these metabolic MR approaches, are increasing the likelihood of their clinical translation.
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Affiliation(s)
- Peter C M van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA.
| | - Kevin Brindle
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Hanzhang Lu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Peter B Barker
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Richard Edden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Nirbhay Yadav
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Linda Knutsson
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Medical Radiation Physics, Lund University, Lund, Sweden
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16
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Cho J, Lee J, An H, Goyal MS, Su Y, Wang Y. Cerebral oxygen extraction fraction (OEF): Comparison of challenge-free gradient echo QSM+qBOLD (QQ) with 15O PET in healthy adults. J Cereb Blood Flow Metab 2021; 41:1658-1668. [PMID: 33243071 PMCID: PMC8221765 DOI: 10.1177/0271678x20973951] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
We aimed to validate oxygen extraction fraction (OEF) estimations by quantitative susceptibility mapping plus quantitative blood oxygen-level dependence (QSM+qBOLD, or QQ) using 15O-PET. In ten healthy adult brains, PET and MRI were acquired simultaneously on a PET/MR scanner. PET was acquired using C[15O], O[15O], and H2[15O]. Image-derived arterial input functions and standard models of oxygen metabolism provided quantification of PET. MRI included T1-weighted imaging, time-of-flight angiography, and multi-echo gradient-echo imaging that was processed for QQ. Region of interest (ROI) analyses compared PET OEF and QQ OEF. In ROI analyses, the averaged OEF differences between PET and QQ were generally small and statistically insignificant. For whole brains, the average and standard deviation of OEF was 32.8 ± 6.7% for PET; OEF was 34.2 ± 2.6% for QQ. Bland-Altman plots quantified agreement between PET OEF and QQ OEF. The interval between the 95% limits of agreement was 16.9 ± 4.0% for whole brains. Our validation study suggests that respiratory challenge-free QQ-OEF mapping may be useful for non-invasive clinical assessment of regional OEF impairment.
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Affiliation(s)
- Junghun Cho
- Department of Radiology, Weill Cornell Medical College, New York, USA
| | - John Lee
- Mallinkckrodt Institute of Radiology, Washington University School of Medicine, St Louis, USA
| | - Hongyu An
- Mallinkckrodt Institute of Radiology, Washington University School of Medicine, St Louis, USA
| | - Manu S Goyal
- Mallinkckrodt Institute of Radiology, Washington University School of Medicine, St Louis, USA
| | - Yi Su
- Computational Image Analysis, Banner Alzheimer's Institute, Phoenix, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, USA.,Department of Biomedical Engineering, Cornell University, Ithaca, USA
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17
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Jiang D, Deng S, Franklin CG, O’Boyle M, Zhang W, Heyl BL, Pan L, Jerabek PA, Fox PT, Lu H. Validation of T 2 -based oxygen extraction fraction measurement with 15 O positron emission tomography. Magn Reson Med 2021; 85:290-297. [PMID: 32643207 PMCID: PMC9973312 DOI: 10.1002/mrm.28410] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 05/19/2020] [Accepted: 06/11/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE To evaluate the accuracy of T2 -based whole-brain oxygen extraction fraction (OEF) estimation by comparing it with gold standard 15 O-PET measurements. METHODS Sixteen healthy adult subjects underwent MRI and 15 O-PET OEF measurements on the same day. On MRI, whole-brain OEF was quantified by T2 -relaxation-under-spin-tagging (TRUST) MRI, based on subject-specific hematocrit. The TRUST OEF was compared to the whole-brain averaged OEF produced by 15 O-PET. Agreement between TRUST and 15 O-PET whole-brain OEF measurements was examined in terms of intraclass correlation coefficient (ICC) and in absolute OEF values. In a subset of 10 subjects, test-retest reproducibility of whole-brain OEF was also evaluated and compared between the two modalities. RESULTS Across the 16 subjects, the mean whole-brain OEF of TRUST and 15 O-PET were 36.44 ± 4.07% and 36.45 ± 3.65%, respectively, showing no difference between the two modalities (P = .99). TRUST whole-brain OEF strongly correlated with that of 15 O-PET (N = 16, ICC = 0.90, P = 4 × 10-7 ). The coefficient-of-variation of TRUST and 15 O-PET whole-brain OEF measurements were 1.79 ± 0.67% and 2.06 ± 1.55%, respectively, showing no difference between the two modalities (N = 10, P = .64). Further analyses on the effect of hematocrit revealed that correlation between PET OEF and TRUST OEF with assumed hematocrit remained significant (ICC = 0.8, P < 2 × 10-5 ). CONCLUSION Whole-brain OEF measured by TRUST was in excellent agreement with gold standard 15 O-PET, with highly comparable accuracy and reproducibility. These findings suggest that TRUST MRI can provide accurate quantification of whole-brain OEF noninvasively.
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Affiliation(s)
- Dengrong Jiang
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Shengwen Deng
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Crystal G. Franklin
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Michael O’Boyle
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Wei Zhang
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Betty L. Heyl
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Li Pan
- Siemens Healthineers, Baltimore, Maryland, USA
| | - Paul A. Jerabek
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Peter T. Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.,Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA,South Texas Veterans Health Care System, San Antonio, Texas, USA
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA
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18
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Jiang D, Lin Z, Liu P, Sur S, Xu C, Hazel K, Pottanat G, Darrow J, Pillai JJ, Yasar S, Rosenberg P, Moghekar A, Albert M, Lu H. Brain Oxygen Extraction Is Differentially Altered by Alzheimer's and Vascular Diseases. J Magn Reson Imaging 2020; 52:1829-1837. [PMID: 32567195 PMCID: PMC9973301 DOI: 10.1002/jmri.27264] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 06/02/2020] [Accepted: 06/03/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Alzheimer's disease and vascular cognitive impairment (VCI), as well as their concurrence, represent the most common types of cognitive dysfunction. Treatment strategies for these two conditions are quite different; however, there exists a considerable overlap in their clinical manifestations, and most biomarkers reveal similar abnormalities between these two conditions. PURPOSE To evaluate the potential of cerebral oxygen extraction fraction (OEF) as a biomarker for differential diagnosis of Alzheimer's disease and VCI. We hypothesized that in Alzheimer's disease OEF will be reduced (decreased oxygen consumption due to decreased neural activity), while in vascular diseases OEF will be elevated (increased oxygen extraction due to abnormally decreased blood flow). STUDY TYPE Prospective cross-sectional. POPULATION Sixty-five subjects aged 52-89 years, including 33 mild cognitive impairment (MCI), 7 dementia, and 25 cognitively normal subjects. FIELD STRENGTH/SEQUENCE 3T T2 -relaxation-under-spin-tagging (TRUST) and fluid-attenuated inversion recovery imaging (FLAIR). ASSESSMENT OEF, consensus diagnoses of cognitive impairment, vascular risk factors (such as hypertension, hypercholesterolemia, diabetes, smoking, and obesity), cognitive assessments, and cerebrospinal fluid concentration of amyloid and tau were assessed. STATISTICAL TESTS Multiple linear regression analyses of OEF with diagnostic category (normal, MCI, or dementia), vascular risks, cognitive performance, amyloid and tau pathology. RESULTS When evaluating the entire group, OEF was found to be lower with more severe cognitive impairment (β = -2.70 ± 1.15, T = -2.34, P = 0.02), but was higher with greater vascular risk factors (β = 1.36 ± 0.55, T = 2.48, P = 0.02). Further investigation of the subgroup of participants with low vascular risks (N = 44) revealed that lower OEF was associated with worse cognitive performance (β = 0.04 ± 0.01, T = 3.27, P = 0.002) and greater amyloid burden (β = 92.12 ± 41.23, T = 2.23, P = 0.03). Among cognitively impaired individuals (N = 40), higher OEF was associated with greater vascular risk factors (β = 2.19 ± 0.71, T = 3.08, P = 0.004). DATA CONCLUSION These findings suggest that OEF is differentially affected by Alzheimer's disease and VCI pathology and may be useful in etiology-based diagnosis of cognitive impairment. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 3 J. MAGN. RESON. IMAGING 2020;52:1829-1837.
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Affiliation(s)
- Dengrong Jiang
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Zixuan Lin
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Peiying Liu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Sandeepa Sur
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Cuimei Xu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kaisha Hazel
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - George Pottanat
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jacqueline Darrow
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jay J. Pillai
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Sevil Yasar
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Paul Rosenberg
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA
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19
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Chen L, Soldan A, Oishi K, Faria A, Zhu Y, Albert M, van Zijl PCM, Li X. Quantitative Susceptibility Mapping of Brain Iron and β-Amyloid in MRI and PET Relating to Cognitive Performance in Cognitively Normal Older Adults. Radiology 2020; 298:353-362. [PMID: 33231528 DOI: 10.1148/radiol.2020201603] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Background For individuals with mild cognitive impairment (MCI) or dementia, elevated brain iron together with β-amyloid is associated with lower cognitive functioning. But this needs further investigation among cognitively normal older adults. Purpose To investigate via quantitative susceptibility mapping (QSM) in MRI and PET how cerebral iron together with β-amyloid affects cognition among cognitively normal older adults. Materials and Methods In this secondary analysis of a prospective study, cognitively normal older adults underwent QSM MRI to measure brain iron. A majority underwent PET to measure cerebral β-amyloid within 30 days of MRI. Multiple linear regression analyses were performed for 12 cortical and subcortical gray matter regions to assess the effect of brain iron on cognitive functions. Voxel-based analyses investigated the associations between tissue iron and β-amyloid load and their relationship to cognitive performance. Results Evaluated were 150 cognitively normal older adults (mean age, 69 years ± 8 [standard deviation]; 93 women). Of 150, 97 underwent PET; 22 of the 97 (mean age, 71 years ± 6; 13 women) were positive for β-amyloid. In all participants, brain iron content in the hippocampus negatively correlated with global cognitive composite score (standardized β = -0.24; 95% CI: -0.40, -0.07; P = .005). In the PET subgroup, brain iron in the hippocampus negatively correlated with episodic memory (β = -0.24; 95% CI: -0.40, -0.08; P = .004) and visuospatial score (β = -0.34; 95% CI: -0.56, -0.12; P = .003) independent of β-amyloid burden. Both negative and positive correlations between brain iron and β-amyloid were observed in the PET subgroup, revealing clusters where brain iron content negatively correlated with β-amyloid and global cognitive scores (eg, in the frontal cortex: β = -0.13; 95% CI: -0.23, -0.02; P = .02). No clusters showed associations between β-amyloid and global cognition. Conclusion Among cognitively normal older adults, quantitative susceptibility mapping in MRI and PET indicated that elevated cerebral iron load was related to lower cognitive performance independent of β-amyloid. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Chiang in this issue.
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Affiliation(s)
- Lin Chen
- From the F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 N Broadway, Room G-25, Baltimore, MD 21205 (L.C., P.C.M.v.Z., X.L.); Department of Radiology and Radiological Sciences (L.C., K.O., A.F., P.C.M.v.Z., X.L.) and Department of Neurology (A.S., M.A.), Johns Hopkins University School of Medicine, Baltimore, Md; and Department of Biostatistics, Johns Hopkins University School of Public Health, Baltimore, Md (Y.Z.)
| | - Anja Soldan
- From the F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 N Broadway, Room G-25, Baltimore, MD 21205 (L.C., P.C.M.v.Z., X.L.); Department of Radiology and Radiological Sciences (L.C., K.O., A.F., P.C.M.v.Z., X.L.) and Department of Neurology (A.S., M.A.), Johns Hopkins University School of Medicine, Baltimore, Md; and Department of Biostatistics, Johns Hopkins University School of Public Health, Baltimore, Md (Y.Z.)
| | - Kenichi Oishi
- From the F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 N Broadway, Room G-25, Baltimore, MD 21205 (L.C., P.C.M.v.Z., X.L.); Department of Radiology and Radiological Sciences (L.C., K.O., A.F., P.C.M.v.Z., X.L.) and Department of Neurology (A.S., M.A.), Johns Hopkins University School of Medicine, Baltimore, Md; and Department of Biostatistics, Johns Hopkins University School of Public Health, Baltimore, Md (Y.Z.)
| | - Andreia Faria
- From the F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 N Broadway, Room G-25, Baltimore, MD 21205 (L.C., P.C.M.v.Z., X.L.); Department of Radiology and Radiological Sciences (L.C., K.O., A.F., P.C.M.v.Z., X.L.) and Department of Neurology (A.S., M.A.), Johns Hopkins University School of Medicine, Baltimore, Md; and Department of Biostatistics, Johns Hopkins University School of Public Health, Baltimore, Md (Y.Z.)
| | - Yuxin Zhu
- From the F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 N Broadway, Room G-25, Baltimore, MD 21205 (L.C., P.C.M.v.Z., X.L.); Department of Radiology and Radiological Sciences (L.C., K.O., A.F., P.C.M.v.Z., X.L.) and Department of Neurology (A.S., M.A.), Johns Hopkins University School of Medicine, Baltimore, Md; and Department of Biostatistics, Johns Hopkins University School of Public Health, Baltimore, Md (Y.Z.)
| | - Marilyn Albert
- From the F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 N Broadway, Room G-25, Baltimore, MD 21205 (L.C., P.C.M.v.Z., X.L.); Department of Radiology and Radiological Sciences (L.C., K.O., A.F., P.C.M.v.Z., X.L.) and Department of Neurology (A.S., M.A.), Johns Hopkins University School of Medicine, Baltimore, Md; and Department of Biostatistics, Johns Hopkins University School of Public Health, Baltimore, Md (Y.Z.)
| | - Peter C M van Zijl
- From the F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 N Broadway, Room G-25, Baltimore, MD 21205 (L.C., P.C.M.v.Z., X.L.); Department of Radiology and Radiological Sciences (L.C., K.O., A.F., P.C.M.v.Z., X.L.) and Department of Neurology (A.S., M.A.), Johns Hopkins University School of Medicine, Baltimore, Md; and Department of Biostatistics, Johns Hopkins University School of Public Health, Baltimore, Md (Y.Z.)
| | - Xu Li
- From the F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 N Broadway, Room G-25, Baltimore, MD 21205 (L.C., P.C.M.v.Z., X.L.); Department of Radiology and Radiological Sciences (L.C., K.O., A.F., P.C.M.v.Z., X.L.) and Department of Neurology (A.S., M.A.), Johns Hopkins University School of Medicine, Baltimore, Md; and Department of Biostatistics, Johns Hopkins University School of Public Health, Baltimore, Md (Y.Z.)
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