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Luo JS, Zhai WH, Ding LL, Zhang XJ, Han J, Ning JQ, Chen XM, Jiang WC, Yan RY, Chen MJ. MAMs and Mitochondrial Quality Control: Overview and Their Role in Alzheimer's Disease. Neurochem Res 2024:10.1007/s11064-024-04205-w. [PMID: 39002091 DOI: 10.1007/s11064-024-04205-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/20/2024] [Accepted: 07/02/2024] [Indexed: 07/15/2024]
Abstract
Alzheimer's disease (AD) represents the most widespread neurodegenerative disorder, distinguished by a gradual onset and slow progression, presenting a substantial challenge to global public health. The mitochondrial-associated membrane (MAMs) functions as a crucial center for signal transduction and material transport between mitochondria and the endoplasmic reticulum, playing a pivotal role in various pathological mechanisms of AD. The dysregulation of mitochondrial quality control systems is considered a fundamental factor in the development of AD, leading to mitochondrial dysfunction and subsequent neurodegenerative events. Recent studies have emphasized the role of MAMs in regulating mitochondrial quality control. This review will delve into the molecular mechanisms underlying the imbalance in mitochondrial quality control in AD and provide a comprehensive overview of the role of MAMs in regulating mitochondrial quality control.
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Affiliation(s)
- Jian-Sheng Luo
- Department of Anesthesiology, Deyang People's Hospital, Deyang, 618000, China
| | - Wen-Hu Zhai
- Department of Anesthesiology, Deyang People's Hospital, Deyang, 618000, China
| | - Ling-Ling Ding
- Department of Anesthesiology, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, China.
| | - Xian-Jie Zhang
- Department of Anesthesiology, Deyang People's Hospital, Deyang, 618000, China
| | - Jia Han
- Department of Anesthesiology, Deyang People's Hospital, Deyang, 618000, China
| | - Jia-Qi Ning
- Department of Anesthesiology, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, China
| | - Xue-Meng Chen
- Department of Anesthesiology, Deyang People's Hospital, Deyang, 618000, China
| | - Wen-Cai Jiang
- Department of Anesthesiology, Deyang People's Hospital, Deyang, 618000, China
| | - Ru-Yu Yan
- Department of Anesthesiology, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, China
| | - Meng-Jie Chen
- Department of Anesthesiology, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, 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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DiNuzzo M, Dienel GA, Behar KL, Petroff OA, Benveniste H, Hyder F, Giove F, Michaeli S, Mangia S, Herculano-Houzel S, Rothman DL. Neurovascular coupling is optimized to compensate for the increase in proton production from nonoxidative glycolysis and glycogenolysis during brain activation and maintain homeostasis of pH, pCO 2, and pO 2. J Neurochem 2024; 168:632-662. [PMID: 37150946 PMCID: PMC10628336 DOI: 10.1111/jnc.15839] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 04/22/2023] [Accepted: 05/02/2023] [Indexed: 05/09/2023]
Abstract
During transient brain activation cerebral blood flow (CBF) increases substantially more than cerebral metabolic rate of oxygen consumption (CMRO2) resulting in blood hyperoxygenation, the basis of BOLD-fMRI contrast. Explanations for the high CBF versus CMRO2 slope, termed neurovascular coupling (NVC) constant, focused on maintenance of tissue oxygenation to support mitochondrial ATP production. However, paradoxically the brain has a 3-fold lower oxygen extraction fraction (OEF) than other organs with high energy requirements, like heart and muscle during exercise. Here, we hypothesize that the NVC constant and the capillary oxygen mass transfer coefficient (which in combination determine OEF) are co-regulated during activation to maintain simultaneous homeostasis of pH and partial pressure of CO2 and O2 (pCO2 and pO2). To test our hypothesis, we developed an arteriovenous flux balance model for calculating blood and brain pH, pCO2, and pO2 as a function of baseline OEF (OEF0), CBF, CMRO2, and proton production by nonoxidative metabolism coupled to ATP hydrolysis. Our model was validated against published brain arteriovenous difference studies and then used to calculate pH, pCO2, and pO2 in activated human cortex from published calibrated fMRI and PET measurements. In agreement with our hypothesis, calculated pH, pCO2, and pO2 remained close to constant independently of CMRO2 in correspondence to experimental measurements of NVC and OEF0. We also found that the optimum values of the NVC constant and OEF0 that ensure simultaneous homeostasis of pH, pCO2, and pO2 were remarkably similar to their experimental values. Thus, the high NVC constant is overall determined by proton removal by CBF due to increases in nonoxidative glycolysis and glycogenolysis. These findings resolve the paradox of the brain's high CBF yet low OEF during activation, and may contribute to explaining the vulnerability of brain function to reductions in blood flow and capillary density with aging and neurovascular disease.
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Affiliation(s)
| | - Gerald A Dienel
- Department of Neurology, University of Arkansas for Medical Sciences, Little Rock, AR, 72205 USA
- Department of Cell Biology and Physiology, University of New Mexico School of Medicine, Albuquerque, NM, 87131 USA
| | - Kevin L Behar
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511 USA
| | - Ognen A Petroff
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511 USA
| | - Helene Benveniste
- Department of Anesthesiology, Yale University, New Haven, CT, 06520 USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520 USA
| | - Fahmeed Hyder
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520 USA
- Department of Radiology, Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, 06520 USA
| | - Federico Giove
- Centro Ricerche Enrico Fermi, Rome, RM, 00184 Italy
- Fondazione Santa Lucia IRCCS, Rome, RM, 00179 Italy
| | - Shalom Michaeli
- Department of Radiology, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, 55455 USA
| | - Silvia Mangia
- Department of Radiology, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, 55455 USA
| | - Suzana Herculano-Houzel
- Department of Psychology, Vanderbilt University, Nashville, TN
- Department of Biological Sciences, Vanderbilt University, Nashville, TN
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN
| | - Douglas L Rothman
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520 USA
- Department of Radiology, Magnetic Resonance Research Center (MRRC), Yale University, New Haven, CT, 06520 USA
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Gou Y, Golden WC, Lin Z, Shepard J, Tekes A, Hu Z, Li X, Oishi K, Albert M, Lu H, Liu P, Jiang D. Automatic Rejection based on Tissue Signal (ARTS) for motion-corrected quantification of cerebral venous oxygenation in neonates and older adults. Magn Reson Imaging 2024; 105:92-99. [PMID: 37939974 PMCID: PMC10841989 DOI: 10.1016/j.mri.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/04/2023] [Indexed: 11/10/2023]
Abstract
OBJECTIVE Cerebral venous oxygenation (Yv) is a key parameter for the brain's oxygen utilization and has been suggested to be a valuable biomarker in various brain diseases including hypoxic ischemic encephalopathy in neonates and Alzheimer's disease in older adults. T2-Relaxation-Under-Spin-Tagging (TRUST) MRI is a widely used technique to measure global Yv level and has been validated against gold-standard PET. However, subject motion during TRUST MRI scan can introduce considerable errors in Yv quantification, especially for noncompliant subjects. The aim of this study was to develop an Automatic Rejection based on Tissue Signal (ARTS) algorithm for automatic detection and exclusion of motion-contaminated images to improve the precision of Yv quantification. METHODS TRUST MRI data were collected from a neonatal cohort (N = 37, 16 females, gestational age = 39.12 ± 1.11 weeks, postnatal age = 1.89 ± 0.74 days) and an older adult cohort (N = 223, 134 females, age = 68.02 ± 9.01 years). Manual identification of motion-corrupted images was conducted for both cohorts to serve as a gold-standard. 9.3% of the images in the neonatal datasets and 0.4% of the images in the older adult datasets were manually identified as motion-contaminated. The ARTS algorithm was trained using the neonatal datasets. TRUST Yv values, as well as the estimation uncertainty (ΔR2) and test-retest coefficient-of-variation (CoV) of Yv, were calculated with and without ARTS motion exclusion. The ARTS algorithm was tested on datasets of older adults: first on the original adult datasets with little motion, and then on simulated adult datasets where the percentage of motion-corrupted images matched that of the neonatal datasets. RESULTS In the neonatal datasets, the ARTS algorithm exhibited a sensitivity of 0.95 and a specificity of 0.97 in detecting motion-contaminated images. Compared to no motion exclusion, ARTS significantly reduced the ΔR2 (median = 3.68 Hz vs. 4.89 Hz, P = 0.0002) and CoV (median = 2.57% vs. 6.87%, P = 0.0005) of Yv measurements. In the original older adult datasets, the sensitivity and specificity of ARTS were 0.70 and 1.00, respectively. In the simulated adult datasets, ARTS demonstrated a sensitivity of 0.91 and a specificity of 1.00. Additionally, ARTS significantly reduced the ΔR2 compared to no motion exclusion (median = 2.15 Hz vs. 3.54 Hz, P < 0.0001). CONCLUSION ARTS can improve the reliability of Yv estimation in noncompliant subjects, which may enhance the utility of Yv as a biomarker for brain diseases.
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Affiliation(s)
- Yifan Gou
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - W Christopher Golden
- Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Zixuan Lin
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jennifer Shepard
- Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Aylin Tekes
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Zhiyi Hu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Xin Li
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kumiko Oishi
- Center for Imaging Science, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hanzhang Lu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; The Russell H. Morgan Department of Radiology & 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
| | - Peiying Liu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Dengrong Jiang
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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Yang A, Zhuang H, Du L, Liu B, Lv K, Luan J, Hu P, Chen F, Wu K, Shu N, Shmuel A, Ma G, Wang Y. Evaluation of whole-brain oxygen metabolism in Alzheimer's disease using QSM and quantitative BOLD. Neuroimage 2023; 282:120381. [PMID: 37734476 DOI: 10.1016/j.neuroimage.2023.120381] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 08/14/2023] [Accepted: 09/18/2023] [Indexed: 09/23/2023] Open
Abstract
OBJECTIVE The objective of this study was to evaluate the whole-brain pattern of oxygen extraction fraction (OEF), cerebral blood flow (CBF), and cerebral metabolic rate of oxygen consumption (CMRO2) perturbation in Alzheimer's disease (AD) and investigate the relationship between regional cerebral oxygen metabolism and global cognition. METHODS Twenty-six AD patients and 25 age-matched healthy controls (HC) were prospectively recruited in this study. Mini-Mental State Examination (MMSE) was used to evaluate cognitive status. We applied the QQ-CCTV algorithm which combines quantitative susceptibility mapping and quantitative blood oxygen level-dependent models (QQ) for OEF calculation. CBF map was computed from arterial spin labeling and CMRO2 was generated based on Fick's principle. Whole-brain and regional OEF, CBF, and CMRO2 analyses were performed. The associations between these measures in substructures of deep brain gray matter and MMSE scores were assessed. RESULTS Whole brain voxel-wise analysis showed that CBF and CMRO2 values significantly decreased in AD predominantly in the bilateral angular gyrus, precuneus gyrus and parieto-temporal regions. Regional analysis showed that CBF value decreased in the bilateral caudal hippocampus and left rostral hippocampus and CMRO2 value decreased in left caudal and rostral hippocampus in AD patients. Considering all subjects in the AD and HC groups combined, the mean CBF and CMRO2 values in the bilateral hippocampus positively correlated with the MMSE score. CONCLUSION CMRO2 mapping with the QQ-CCTV method - which is readily available in MR systems for clinical practice - can be a potential biomarker for AD. In addition, CMRO2 in the hippocampus may be a useful tool for monitoring cognitive impairment.
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Affiliation(s)
- Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China
| | - Hangwei Zhuang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York 14853, USA; Department of Radiology, Weill Cornell Medical College, New York, New York 10065, USA
| | - Lei Du
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing 100142, PR China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China
| | - Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Peking University China-Japan Friendship School of Clinical Medicine, Beijing 100029, PR China
| | - Jixin Luan
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China
| | - Pianpian Hu
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Peking University China-Japan Friendship School of Clinical Medicine, Beijing 100029, PR China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou 570311, Hainan, PR China
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangdong 510006, Guangzhou, PR China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Amir Shmuel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Departments of Neurology and Neurosurgery, Physiology, and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, PR China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, PR China.
| | - Yi Wang
- Department of Biomedical Engineering, Cornell University, Ithaca, New York 14853, USA; Department of Radiology, Weill Cornell Medical College, New York, New York 10065, USA
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Biondetti E, Cho J, Lee H. Cerebral oxygen metabolism from MRI susceptibility. Neuroimage 2023; 276:120189. [PMID: 37230206 PMCID: PMC10335841 DOI: 10.1016/j.neuroimage.2023.120189] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 04/26/2023] [Accepted: 05/23/2023] [Indexed: 05/27/2023] Open
Abstract
This article provides an overview of MRI methods exploiting magnetic susceptibility properties of blood to assess cerebral oxygen metabolism, including the tissue oxygen extraction fraction (OEF) and the cerebral metabolic rate of oxygen (CMRO2). The first section is devoted to describing blood magnetic susceptibility and its effect on the MRI signal. Blood circulating in the vasculature can have diamagnetic (oxyhemoglobin) or paramagnetic properties (deoxyhemoglobin). The overall balance between oxygenated and deoxygenated hemoglobin determines the induced magnetic field which, in turn, modulates the transverse relaxation decay of the MRI signal via additional phase accumulation. The following sections of this review then illustrate the principles underpinning susceptibility-based techniques for quantifying OEF and CMRO2. Here, it is detailed whether these techniques provide global (OxFlow) or local (Quantitative Susceptibility Mapping - QSM, calibrated BOLD - cBOLD, quantitative BOLD - qBOLD, QSM+qBOLD) measurements of OEF or CMRO2, and what signal components (magnitude or phase) and tissue pools they consider (intravascular or extravascular). Validations studies and potential limitations of each method are also described. The latter include (but are not limited to) challenges in the experimental setup, the accuracy of signal modeling, and assumptions on the measured signal. The last section outlines the clinical uses of these techniques in healthy aging and neurodegenerative diseases and contextualizes these reports relative to results from gold-standard PET.
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Affiliation(s)
- Emma Biondetti
- Department of Neuroscience, Imaging and Clinical Sciences, "D'Annunzio University" of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, "D'Annunzio University" of Chieti-Pescara, Chieti, Italy
| | - Junghun Cho
- Department of Biomedical Engineering, University at Buffalo, The State University of New York, New York, USA
| | - Hyunyeol Lee
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Republic of Korea; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
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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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8
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Swinford CG, Risacher SL, Wu YC, Apostolova LG, Gao S, Bice PJ, Saykin AJ. Altered cerebral blood flow in older adults with Alzheimer's disease: a systematic review. Brain Imaging Behav 2023; 17:223-256. [PMID: 36484922 PMCID: PMC10117447 DOI: 10.1007/s11682-022-00750-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 09/26/2022] [Accepted: 11/20/2022] [Indexed: 12/13/2022]
Abstract
The prevalence of Alzheimer's disease is projected to reach 13 million in the U.S. by 2050. Although major efforts have been made to avoid this outcome, so far there are no treatments that can stop or reverse the progressive cognitive decline that defines Alzheimer's disease. The utilization of preventative treatment before significant cognitive decline has occurred may ultimately be the solution, necessitating a reliable biomarker of preclinical/prodromal disease stages to determine which older adults are most at risk. Quantitative cerebral blood flow is a promising potential early biomarker for Alzheimer's disease, but the spatiotemporal patterns of altered cerebral blood flow in Alzheimer's disease are not fully understood. The current systematic review compiles the findings of 81 original studies that compared resting gray matter cerebral blood flow in older adults with mild cognitive impairment or Alzheimer's disease and that of cognitively normal older adults and/or assessed the relationship between cerebral blood flow and objective cognitive function. Individuals with Alzheimer's disease had relatively decreased cerebral blood flow in all brain regions investigated, especially the temporoparietal and posterior cingulate, while individuals with mild cognitive impairment had consistent results of decreased cerebral blood flow in the posterior cingulate but more mixed results in other regions, especially the frontal lobe. Most papers reported a positive correlation between regional cerebral blood flow and cognitive function. This review highlights the need for more studies assessing cerebral blood flow changes both spatially and temporally over the course of Alzheimer's disease, as well as the importance of including potential confounding factors in these analyses.
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Affiliation(s)
- Cecily G Swinford
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St. IU Neuroscience Center, GH 4101, 46202, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St. IU Neuroscience Center, GH 4101, 46202, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St. IU Neuroscience Center, GH 4101, 46202, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Liana G Apostolova
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St. IU Neuroscience Center, GH 4101, 46202, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sujuan Gao
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Paula J Bice
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St. IU Neuroscience Center, GH 4101, 46202, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St. IU Neuroscience Center, GH 4101, 46202, Indianapolis, IN, USA.
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA.
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA.
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9
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Astrocyte strategies in the energy-efficient brain. Essays Biochem 2023; 67:3-16. [PMID: 36350053 DOI: 10.1042/ebc20220077] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/11/2022] [Accepted: 10/13/2022] [Indexed: 11/10/2022]
Abstract
Astrocytes generate ATP through glycolysis and mitochondrion respiration, using glucose, lactate, fatty acids, amino acids, and ketone bodies as metabolic fuels. Astrocytic mitochondria also participate in neuronal redox homeostasis and neurotransmitter recycling. In this essay, we aim to integrate the multifaceted evidence about astrocyte bioenergetics at the cellular and systems levels, with a focus on mitochondrial oxidation. At the cellular level, the use of fatty acid β-oxidation and the existence of molecular switches for the selection of metabolic mode and fuels are examined. At the systems level, we discuss energy audits of astrocytes and how astrocytic Ca2+ signaling might contribute to the higher performance and lower energy consumption of the brain as compared to engineered circuits. We finish by examining the neural-circuit dysregulation and behavior impairment associated with alterations of astrocytic mitochondria. We conclude that astrocytes may contribute to brain energy efficiency by coupling energy, redox, and computational homeostasis in neural circuits.
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10
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Driesen NR, Herman P, Rowland MA, Thompson G, Qiu M, He G, Fineberg S, Barron DS, Helgeson L, Lacadie C, Chow R, Gueorguieva R, Straun TC, Krystal JH, Hyder F. Ketamine Effects on Energy Metabolism, Functional Connectivity and Working Memory in Healthy Humans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.21.529425. [PMID: 36865249 PMCID: PMC9980048 DOI: 10.1101/2023.02.21.529425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Working memory (WM) is a crucial resource for temporary memory storage and the guiding of ongoing behavior. N-methyl-D-aspartate glutamate receptors (NMDARs) are thought to support the neural underpinnings of WM. Ketamine is an NMDAR antagonist that has cognitive and behavioral effects at subanesthetic doses. To shed light on subanesthetic ketamine effects on brain function, we employed a multimodal imaging design, combining gas-free calibrated functional magnetic resonance imaging (fMRI) measurement of oxidative metabolism (CMRO 2 ), resting-state cortical functional connectivity assessed with fMRI, and WM-related fMRI. Healthy subjects participated in two scan sessions in a randomized, double-blind, placebo-controlled design. Ketamine increased CMRO 2 and cerebral blood flow (CBF) in prefrontal cortex (PFC) and other cortical regions. However, resting-state cortical functional connectivity was not affected. Ketamine did not alter CBF-CMRO 2 coupling brain-wide. Higher levels of basal CMRO 2 were associated with lower task-related PFC activation and WM accuracy impairment under both saline and ketamine conditions. These observations suggest that CMRO 2 and resting-state functional connectivity index distinct dimensions of neural activity. Ketamine’s impairment of WM-related neural activity and performance appears to be related to its ability to produce cortical metabolic activation. This work illustrates the utility of direct measurement of CMRO 2 via calibrated fMRI in studies of drugs that potentially affect neurovascular and neurometabolic coupling.
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11
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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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12
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Chiarelli AM, Germuska M, Chandler H, Stickland R, Patitucci E, Biondetti E, Mascali D, Saxena N, Khot S, Steventon J, Foster C, Rodríguez-Soto AE, Englund E, Murphy K, Tomassini V, Wehrli FW, Wise RG. A flow-diffusion model of oxygen transport for quantitative mapping of cerebral metabolic rate of oxygen (CMRO 2) with single gas calibrated fMRI. J Cereb Blood Flow Metab 2022; 42:1192-1209. [PMID: 35107026 PMCID: PMC9207485 DOI: 10.1177/0271678x221077332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
One promising approach for mapping CMRO2 is dual-calibrated functional MRI (dc-fMRI). This method exploits the Fick Principle to combine estimates of CBF from ASL, and OEF derived from BOLD-ASL measurements during arterial O2 and CO2 modulations. Multiple gas modulations are required to decouple OEF and deoxyhemoglobin-sensitive blood volume. We propose an alternative single gas calibrated fMRI framework, integrating a model of oxygen transport, that links blood volume and CBF to OEF and creates a mapping between the maximum BOLD signal, CBF and OEF (and CMRO2). Simulations demonstrated the method's viability within physiological ranges of mitochondrial oxygen pressure, PmO2, and mean capillary transit time. A dc-fMRI experiment, performed on 20 healthy subjects using O2 and CO2 challenges, was used to validate the approach. The validation conveyed expected estimates of model parameters (e.g., low PmO2), with spatially uniform OEF maps (grey matter, GM, OEF spatial standard deviation ≈ 0.13). GM OEF estimates obtained with hypercapnia calibrated fMRI correlated with dc-fMRI (r = 0.65, p = 2·10-3). For 12 subjects, OEF measured with dc-fMRI and the single gas calibration method were correlated with whole-brain OEF derived from phase measures in the superior sagittal sinus (r = 0.58, p = 0.048; r = 0.64, p = 0.025 respectively). Simplified calibrated fMRI using hypercapnia holds promise for clinical application.
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Affiliation(s)
- Antonio M Chiarelli
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. D'Annunzio of Chieti-Pescara, Chieti, Italy.,Institute for Advanced Biomedical Technologies, University G. D'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Michael Germuska
- Department of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Hannah Chandler
- Department of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Rachael Stickland
- Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Eleonora Patitucci
- Department of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Emma Biondetti
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. D'Annunzio of Chieti-Pescara, Chieti, Italy.,Institute for Advanced Biomedical Technologies, University G. D'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Daniele Mascali
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. D'Annunzio of Chieti-Pescara, Chieti, Italy.,Institute for Advanced Biomedical Technologies, University G. D'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Neeraj Saxena
- Department of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Sharmila Khot
- Department of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Jessica Steventon
- Department of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Catherine Foster
- Wales Institute of Social and Economic Research and Data (WISERD), School of Social Sciences, Cardiff University, Cardiff, UK
| | - Ana E Rodríguez-Soto
- Department of Radiology, University of California, San Diego, La Jolla, California, USA
| | - Erin Englund
- Department of Radiology, University of Colorado, Aurora, Colorado, USA
| | - Kevin Murphy
- Department of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Valentina Tomassini
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. D'Annunzio of Chieti-Pescara, Chieti, Italy.,Institute for Advanced Biomedical Technologies, University G. D'Annunzio of Chieti-Pescara, Chieti, Italy.,Department of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK.,MS Centre, Dept of Clinical Neurology, SS. Annunziata University Hospital, Chieti, Italy.,Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK.,Helen Durham Centre for Neuroinflammation, University Hospital of Wales, Cardiff, UK
| | - Felix W Wehrli
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Richard G Wise
- Department of Neuroscience, Imaging, and Clinical Sciences, University G. D'Annunzio of Chieti-Pescara, Chieti, Italy.,Institute for Advanced Biomedical Technologies, University G. D'Annunzio of Chieti-Pescara, Chieti, Italy.,Department of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
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13
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Chen JJ, Uthayakumar B, Hyder F. Mapping oxidative metabolism in the human brain with calibrated fMRI in health and disease. J Cereb Blood Flow Metab 2022; 42:1139-1162. [PMID: 35296177 PMCID: PMC9207484 DOI: 10.1177/0271678x221077338] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Conventional functional MRI (fMRI) with blood-oxygenation level dependent (BOLD) contrast is an important tool for mapping human brain activity non-invasively. Recent interest in quantitative fMRI has renewed the importance of oxidative neuroenergetics as reflected by cerebral metabolic rate of oxygen consumption (CMRO2) to support brain function. Dynamic CMRO2 mapping by calibrated fMRI require multi-modal measurements of BOLD signal along with cerebral blood flow (CBF) and/or volume (CBV). In human subjects this "calibration" is typically performed using a gas mixture containing small amounts of carbon dioxide and/or oxygen-enriched medical air, which are thought to produce changes in CBF (and CBV) and BOLD signal with minimal or no CMRO2 changes. However non-human studies have demonstrated that the "calibration" can also be achieved without gases, revealing good agreement between CMRO2 changes and underlying neuronal activity (e.g., multi-unit activity and local field potential). Given the simpler set-up of gas-free calibrated fMRI, there is evidence of recent clinical applications for this less intrusive direction. This up-to-date review emphasizes technological advances for such translational gas-free calibrated fMRI experiments, also covering historical progression of the calibrated fMRI field that is impacting neurological and neurodegenerative investigations of the human brain.
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Affiliation(s)
- J Jean Chen
- Medical Biophysics, University of Toronto, Toronto, Canada.,Rotman Research Institute, Baycrest, Toronto, Canada
| | - Biranavan Uthayakumar
- Medical Biophysics, University of Toronto, Toronto, Canada.,Sunnybrook Research Institute, Toronto, Canada
| | - Fahmeed Hyder
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, Connecticut, USA.,Department of Radiology, Yale University, New Haven, Connecticut, USA.,Quantitative Neuroscience with Magnetic Resonance (QNMR) Research Program, Yale University, New Haven, Connecticut, USA.,Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
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14
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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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15
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Zhang M, Qin Q, Zhang S, Liu W, Meng H, Xu M, Huang X, Lin X, Lin M, Herman P, Hyder F, Stevens RC, Wang Z, Li B, Thompson GJ. Aerobic glycolysis imaging of epileptic foci during the inter-ictal period. EBioMedicine 2022; 79:104004. [PMID: 35436726 PMCID: PMC9035653 DOI: 10.1016/j.ebiom.2022.104004] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/29/2022] [Accepted: 03/29/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND In drug-resistant epilepsy, surgical resection of the epileptic focus can end seizures. However, success is dependent on the ability to identify foci locations and, unfortunately, current methods like electrophysiology and positron emission tomography can give contradictory results. During seizures, glucose is metabolized at epileptic foci through aerobic glycolysis, which can be imaged through the oxygen-glucose index (OGI) biomarker. However, inter-ictal (between seizures) OGI changes have not been studied, which has limited its application. METHODS 18 healthy controls and 24 inter-ictal, temporal lobe epilepsy patients underwent simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI) scans. We used [18F]fluorodeoxyglucose-PET (FDG-PET) to detect cerebral glucose metabolism, and calibrated functional MRI to acquire relative oxygen consumption. With these data, we calculated relative OGI maps. FINDINGS While bilaterally symmetrical in healthy controls, we observed, in patients during the inter-ictal period, higher OGI ipsilateral to the epileptic focus than contralateral. While traditional FDG-PET results and temporal lobe OGI results usually both agreed with invasive electrophysiology, in cases where FDG-PET disagreed with electrophysiology, temporal lobe OGI agreed with electrophysiology, and vice-versa. INTERPRETATION As either our novel epilepsy biomarker or traditional approaches located foci in every case, our work provides promising insights into metabolic changes in epilepsy. Our method allows single-session OGI measurement which can be useful in other diseases. FUNDING This work was supported by ShanghaiTech University, the Shanghai Municipal Government, the National Natural Science Foundation of China Grant (No. 81950410637) and Shanghai Municipal Key Clinical Specialty (No. shslczdzk03403). F. H. and P. H. were supported by USA National Institute of Health grants (R01 NS-100106, R01 MH-067528).Z. W. was supported by the Key-Area Research and Development Program of Guangdong Province (2019B030335001), National Natural Science Foundation of China (No. 82151303), and National Key R&D Program of China (No. 2021ZD0204002).
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Affiliation(s)
- Miao Zhang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Qikai Qin
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuning Zhang
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Liu
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hongping Meng
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Mengyang Xu
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; University of Chinese Academy of Sciences, Beijing 100049, China; Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xinyun Huang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xiaozhu Lin
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Mu Lin
- MR Collaboration, Siemens Healthineers Ltd., Shanghai 201318, China
| | - Peter Herman
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven 06520, USA; Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven 06520, USA; Radiology and Biomedical Imaging, Yale University, New Haven 06520, USA
| | - Fahmeed Hyder
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven 06520, USA; Quantitative Neuroscience with Magnetic Resonance (QNMR) Core Center, Yale University, New Haven 06520, USA; Radiology and Biomedical Imaging, Yale University, New Haven 06520, USA; Biomedical Engineering, Yale University, New Haven 06520, USA
| | - Raymond C Stevens
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Zheng Wang
- School of Psychological and Cognitive Sciences; Beijing Key Laboratory of Behavior and Mental Health; IDG/McGovern Institute for Brain Research; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai 200025, China.
| | - Garth J Thompson
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China.
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16
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Burtscher J, Romani M, Bernardo G, Popa T, Ziviani E, Hummel FC, Sorrentino V, Millet GP. Boosting mitochondrial health to counteract neurodegeneration. Prog Neurobiol 2022; 215:102289. [DOI: 10.1016/j.pneurobio.2022.102289] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/23/2022] [Accepted: 05/25/2022] [Indexed: 12/22/2022]
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17
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Beaudin AE, McCreary CR, Mazerolle EL, Gee M, Sharma B, Subotic A, Zwiers AM, Cox E, Nelles K, Charlton A, Frayne R, Ismail Z, Beaulieu C, Jickling G, Camicioli RM, Pike GB, Smith E. Cerebrovascular Reactivity Across the Entire Brain in Cerebral Amyloid Angiopathy. Neurology 2022; 98:e1716-e1728. [PMID: 35210294 PMCID: PMC9071369 DOI: 10.1212/wnl.0000000000200136] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 01/18/2022] [Indexed: 11/25/2022] Open
Abstract
Background and Objectives Reduced cerebrovascular reactivity is proposed to be a feature of cerebral amyloid angiopathy (CAA) but has not been measured directly. Employing a global vasodilatory stimulus (hypercapnia), this study assessed the relationships between cerebrovascular reactivity and MRI markers of CAA and cognitive function. Methods In a cross-sectional study, individuals with probable CAA, mild cognitive impairment, or dementia due to Alzheimer disease and healthy controls underwent neuropsychological testing and an MRI that included a 5% carbon dioxide challenge. Cerebrovascular reactivity was compared across groups controlling for age, sex, and the presence of hypertension, and its associations with MRI markers of CAA in participants with CAA and with cognition across all participants were determined using multivariable linear regression adjusting for group, age, sex, education, and the presence of hypertension. Results Cerebrovascular reactivity data (mean ± SD) were available for 26 participants with CAA (9 female; 74.4 ± 7.7 years), 19 participants with mild cognitive impairment (5 female; 72.1 ± 8.5 years), 12 participants with dementia due to Alzheimer disease (4 female; 69.4 ± 6.6 years), and 39 healthy controls (30 female; 68.8 ± 5.4 years). Gray and whiter matter reactivity averaged across the entire brain was lower in participants with CAA and Alzheimer disease dementia compared to healthy controls, with a predominantly posterior distribution of lower reactivity in both groups. Higher white matter hyperintensity volume was associated with lower white matter reactivity (standardized coefficient [β], 95% CI −0.48, −0.90 to −0.01). Higher gray matter reactivity was associated with better global cognitive function (β 0.19, 0.03–0.36), memory (β 0.21, 0.07–0.36), executive function (β 0.20, 0.02–0.39), and processing speed (β 0.27, 0.10–0.45) and higher white matter reactivity was associated with higher memory (β 0.22, 0.08–0.36) and processing speed (β 0.23, 0.06–0.40). Conclusions Reduced cerebrovascular reactivity is a core feature of CAA and its assessment may provide an additional biomarker for disease severity and cognitive impairment.
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Affiliation(s)
- Andrew E Beaudin
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada .,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Cheryl R McCreary
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Radiology, University of Calgary, Calgary, AB, Canada.,Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health Services, Calgary, AB, Canada
| | - Erin L Mazerolle
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Radiology, University of Calgary, Calgary, AB, Canada.,Department of Psychology, St. Francis Xavier University, Antigonish, NS, Canada
| | - Myrlene Gee
- Division of Neurology and Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Breni Sharma
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Arsenije Subotic
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Angela M Zwiers
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Emily Cox
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Krista Nelles
- Division of Neurology and Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Anna Charlton
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Richard Frayne
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Radiology, University of Calgary, Calgary, AB, Canada.,Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health Services, Calgary, AB, Canada
| | - Zahinoor Ismail
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Psychiatry, University of Calgary, Calgary, AB, Canada.,Mathison Centre for Mental Health Research & Education, University of Calgary, Calgary, AB, Canada
| | - Christian Beaulieu
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | - Glen Jickling
- Division of Neurology and Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Richard M Camicioli
- Division of Neurology and Department of Medicine, University of Alberta, Edmonton, AB, Canada.,Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| | - G Bruce Pike
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Radiology, University of Calgary, Calgary, AB, Canada.,Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health Services, Calgary, AB, Canada
| | - Eric Smith
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Radiology, University of Calgary, Calgary, AB, Canada.,Seaman Family MR Research Centre, Foothills Medical Centre, Alberta Health Services, Calgary, AB, Canada
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18
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Polansky H, Goral B. How an increase in the copy number of HSV-1 during latency can cause Alzheimer's disease: the viral and cellular dynamics according to the microcompetition model. J Neurovirol 2021; 27:895-916. [PMID: 34635992 DOI: 10.1007/s13365-021-01012-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 04/28/2021] [Accepted: 08/16/2021] [Indexed: 12/11/2022]
Abstract
Numerous studies observed a link between the herpes smplex virus-1 (HSV-1) and Alzheimer's disease. However, the exact viral and cellular dynamics that lead from an HSV-1 infection to Alzheimer's disease are unknown. In this paper, we use the microcompetition model to formulate these dynamics by connecting seemingly unconnected observations reported in the literature. We concentrate on four pathologies characteristic of Alzheimer's disease. First, we explain how an increase in the copy number of HSV-1 during latency can decrease the expression of BECN1/Beclin1, the degradative trafficking protein, which, in turn, can cause a dysregulation of autophagy and Alzheimer's disease. Second, we show how an increase in the copy number of the latent HSV-1 can decrease the expression of many genes important for mitochondrial genome metabolism, respiratory chain, and homeostasis, which can lead to oxidative stress and neuronal damage, resulting in Alzheimer's disease. Third, we describe how an increase in this copy number can reduce the concentration of the NMDA receptor subunits NR1 and NR2b (Grin1 and Grin2b genes), and brain derived neurotrophic factor (BDNF), which can cause an impaired synaptic plasticity, Aβ accumulation and eventually Alzheimer's disease. Finally, we show how an increase in the copy number of HSV-1 in neural stem/progenitor cells in the hippocampus during the latent phase can lead to an abnormal quantity and quality of neurogenesis, and the clinical presentation of Alzheimer's disease. Since the current understanding of the dynamics and homeostasis of the HSV-1 reservoir during latency is limited, the proposed model represents only a first step towards a complete understanding of the relationship between the copy number of HSV-1 during latency and Alzheimer's disease.
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Affiliation(s)
- Hanan Polansky
- The Center for the Biology of Chronic Disease (CBCD), 3 Germay Dr, Wilmington, DE, 19804, USA.
| | - Benjamin Goral
- The Center for the Biology of Chronic Disease (CBCD), 3 Germay Dr, Wilmington, DE, 19804, USA
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19
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Mani S, Swargiary G, Singh M, Agarwal S, Dey A, Ojha S, Jha NK. Mitochondrial defects: An emerging theranostic avenue towards Alzheimer's associated dysregulations. Life Sci 2021; 285:119985. [PMID: 34592237 DOI: 10.1016/j.lfs.2021.119985] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/10/2021] [Accepted: 09/18/2021] [Indexed: 01/02/2023]
Abstract
Mitochondria play a crucial role in expediting the energy homeostasis under varying environmental conditions. As mitochondria are controllers of both energy production and apoptotic pathways, they are also distinctively involved in controlling the neuronal cell survival and/or death. Numerous factors are responsible for mitochondria to get degraded with aging and huge functional failures in mitochondria are also found to be associated with the commencement of numerous neurodegenerative conditions, including Alzheimer's disease (AD). A large number of existing literatures promote the pivotal role of mitochondrial damage and oxidative impairment in the pathogenesis of AD. Numerous mitochondria associated processes such as mitochondrial biogenesis, fission, fusion, mitophagy, transportation and bioenergetics are crucial for proper functioning of mitochondria but are reported to be defective in AD patients. Though, the knowledge on the precise and in-depth mechanisms of these actions is still in infancy. Based upon the outcome of various significant studies, mitochondria are also being considered as therapeutic targets for AD. Here, we review the current status of mitochondrial defects in AD and also summarize the possible role of these defects in the pathogenesis of AD. The various approaches for developing the mitochondria-targeted therapies are also discussed here in detail. Consequently, it is suggested that improving mitochondrial activity via pharmacological and/or non-pharmacological interventions could postpone the onset and slow the development of AD. Further research and consequences of ongoing clinical trials should extend our understanding and help to validate conclusions regarding the causation of AD.
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Affiliation(s)
- Shalini Mani
- Centre for Emerging Diseases, Department of Biotechnology, Jaypee Institute of Information Technology, A-10, Sector 62, Noida, UP 201307, India.
| | - Geeta Swargiary
- Centre for Emerging Diseases, Department of Biotechnology, Jaypee Institute of Information Technology, A-10, Sector 62, Noida, UP 201307, India
| | - Manisha Singh
- Centre for Emerging Diseases, Department of Biotechnology, Jaypee Institute of Information Technology, A-10, Sector 62, Noida, UP 201307, India
| | | | - Abhijit Dey
- Department of Life Sciences, Presidency University, College Street, Kolkata 700073, India
| | - Shreesh Ojha
- Department of Internal Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain 17666, United Arab Emirates
| | - Niraj Kumar Jha
- Department of Biotechnology, School of Engineering & Technology (SET), Sharda University, Greater Noida, Uttar Pradesh 201310, India
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20
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Mofrad SA, Lundervold A, Lundervold AS. A predictive framework based on brain volume trajectories enabling early detection of Alzheimer's disease. Comput Med Imaging Graph 2021; 90:101910. [PMID: 33862355 DOI: 10.1016/j.compmedimag.2021.101910] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 02/12/2021] [Accepted: 03/26/2021] [Indexed: 10/21/2022]
Abstract
We present a framework for constructing predictive models of cognitive decline from longitudinal MRI examinations, based on mixed effects models and machine learning. We apply the framework to detect conversion from cognitively normal (CN) to mild cognitive impairment (MCI) and from MCI to Alzheimer's disease (AD), using a large collection of subjects sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Aging (AIBL). We extract subcortical segmentation and cortical parcellation from corresponding T1-weighted images using FreeSurfer v.6.0, select bilateral 3D regions of interest relevant to neurodegeneration/dementia, and fit their longitudinal volume trajectories using linear mixed effects models. Features describing these model-based trajectories are then used to train an ensemble of machine learning classifiers to distinguish stable CN from converters to MCI, and stable MCI from converters to AD. On separate test sets the models achieved an average of accuracy/precision/recall score of 69/73/60% for converted to MCI and 75/74/77% for converted to AD, illustrating the framework's ability to extract predictive imaging-based biomarkers from routine T1-weighted MRI acquisitions.
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Affiliation(s)
- Samaneh Abolpour Mofrad
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Postbox 7030, 5020 Bergen, Norway; The Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway.
| | - Arvid Lundervold
- The Neural Networks and Microcircuits Research Group, Department of Biomedicine, University of Bergen, Bergen, Norway; The Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Alexander Selvikvåg Lundervold
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Postbox 7030, 5020 Bergen, Norway; The Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
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- Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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- Data used in the preparation of this article was obtained from the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing (AIBL) funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database. The AIBL researchers contributed data but did not participate in analysis or writing of this report. AIBL researchers are listed at www.aibl.csiro.au
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21
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Chen JJ, Gauthier CJ. The Role of Cerebrovascular-Reactivity Mapping in Functional MRI: Calibrated fMRI and Resting-State fMRI. Front Physiol 2021; 12:657362. [PMID: 33841190 PMCID: PMC8027080 DOI: 10.3389/fphys.2021.657362] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 03/02/2021] [Indexed: 12/14/2022] Open
Abstract
Task and resting-state functional MRI (fMRI) is primarily based on the same blood-oxygenation level-dependent (BOLD) phenomenon that MRI-based cerebrovascular reactivity (CVR) mapping has most commonly relied upon. This technique is finding an ever-increasing role in neuroscience and clinical research as well as treatment planning. The estimation of CVR has unique applications in and associations with fMRI. In particular, CVR estimation is part of a family of techniques called calibrated BOLD fMRI, the purpose of which is to allow the mapping of cerebral oxidative metabolism (CMRO2) using a combination of BOLD and cerebral-blood flow (CBF) measurements. Moreover, CVR has recently been shown to be a major source of vascular bias in computing resting-state functional connectivity, in much the same way that it is used to neutralize the vascular contribution in calibrated fMRI. Furthermore, due to the obvious challenges in estimating CVR using gas challenges, a rapidly growing field of study is the estimation of CVR without any form of challenge, including the use of resting-state fMRI for that purpose. This review addresses all of these aspects in which CVR interacts with fMRI and the role of CVR in calibrated fMRI, provides an overview of the physiological biases and assumptions underlying hypercapnia-based CVR and calibrated fMRI, and provides a view into the future of non-invasive CVR measurement.
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Affiliation(s)
- J Jean Chen
- Baycrest Centre for Geriatric Care, Rotman Research Institute, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Claudine J Gauthier
- Department of Physics, Concordia University, Montreal, QC, Canada.,Montreal Heart Institute, Montreal, QC, Canada
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22
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Park MW, Cha HW, Kim J, Kim JH, Yang H, Yoon S, Boonpraman N, Yi SS, Yoo ID, Moon JS. NOX4 promotes ferroptosis of astrocytes by oxidative stress-induced lipid peroxidation via the impairment of mitochondrial metabolism in Alzheimer's diseases. Redox Biol 2021; 41:101947. [PMID: 33774476 PMCID: PMC8027773 DOI: 10.1016/j.redox.2021.101947] [Citation(s) in RCA: 258] [Impact Index Per Article: 86.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 03/10/2021] [Accepted: 03/12/2021] [Indexed: 12/16/2022] Open
Abstract
Oxidative stress has been implicated in the pathogenesis of Alzheimer's disease (AD). Mitochondrial dysfunction is linked to oxidative stress and reactive oxygen species (ROS) in neurotoxicity during AD. Impaired mitochondrial metabolism has been associated with mitochondrial dysfunction in brain damage of AD. While the role of NADPH oxidase 4 (NOX4), a major source of ROS, has been identified in brain damage, the mechanism by which NOX4 regulates ferroptosis of astrocytes in AD remains unclear. Here, we show that the protein levels of NOX4 were significantly elevated in impaired astrocytes of cerebral cortex from patients with AD and APP/PS1 double-transgenic mouse model of AD. The levels of 4-hydroxynonenal (4-HNE) and malondialdehyde (MDA), a marker of oxidative stress-induced lipid peroxidation, were significantly also elevated in impaired astrocytes of patients with AD and mouse AD. We demonstrate that the over-expression of NOX4 significantly increases the impairment of mitochondrial metabolism by inhibition of mitochondrial respiration and ATP production via the reduction of five protein complexes in the mitochondrial ETC in human astrocytes. Moreover, the elevation of NOX4 induces oxidative stress by mitochondrial ROS (mtROS) production, mitochondrial fragmentation, and inhibition of cellular antioxidant process in human astrocytes. Furthermore, the elevation of NOX4 increased ferroptosis-dependent cytotoxicity by the activation of oxidative stress-induced lipid peroxidation in human astrocytes. These results suggest that NOX4 promotes ferroptosis of astrocytes by oxidative stress-induced lipid peroxidation via the impairment of mitochondrial metabolism in AD.
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Affiliation(s)
- Min Woo Park
- Department of Integrated Biomedical Science, Soonchunhyang Institute of Medi-bio Science (SIMS), Soonchunhyang University, Cheonan, 31151, Chungcheongnam-do, Republic of Korea
| | - Hyeon Woo Cha
- Department of Integrated Biomedical Science, Soonchunhyang Institute of Medi-bio Science (SIMS), Soonchunhyang University, Cheonan, 31151, Chungcheongnam-do, Republic of Korea
| | - Junhyung Kim
- Department of Integrated Biomedical Science, Soonchunhyang Institute of Medi-bio Science (SIMS), Soonchunhyang University, Cheonan, 31151, Chungcheongnam-do, Republic of Korea
| | - Jung Han Kim
- Department of Integrated Biomedical Science, Soonchunhyang Institute of Medi-bio Science (SIMS), Soonchunhyang University, Cheonan, 31151, Chungcheongnam-do, Republic of Korea
| | - Haesung Yang
- Department of Biomedical Laboratory Science, College of Medical Sciences, Soonchunhyang University, Asan, 31538, Chungcheongnam-do, Republic of Korea; BK21 Four Project, Department of Biomedical Laboratory Science, General Graduate School, College of Medical Sciences, Soonchunhyang University, Asan, 31538, Chungcheongnam-do, Republic of Korea
| | - Sunmi Yoon
- Department of Biomedical Laboratory Science, College of Medical Sciences, Soonchunhyang University, Asan, 31538, Chungcheongnam-do, Republic of Korea; BK21 Four Project, Department of Biomedical Laboratory Science, General Graduate School, College of Medical Sciences, Soonchunhyang University, Asan, 31538, Chungcheongnam-do, Republic of Korea
| | - Napissara Boonpraman
- Department of Biomedical Laboratory Science, College of Medical Sciences, Soonchunhyang University, Asan, 31538, Chungcheongnam-do, Republic of Korea; BK21 Four Project, Department of Biomedical Laboratory Science, General Graduate School, College of Medical Sciences, Soonchunhyang University, Asan, 31538, Chungcheongnam-do, Republic of Korea
| | - Sun Shin Yi
- Department of Biomedical Laboratory Science, College of Medical Sciences, Soonchunhyang University, Asan, 31538, Chungcheongnam-do, Republic of Korea; BK21 Four Project, Department of Biomedical Laboratory Science, General Graduate School, College of Medical Sciences, Soonchunhyang University, Asan, 31538, Chungcheongnam-do, Republic of Korea
| | - Ik Dong Yoo
- Department of Nuclear Medicine, Soonchunhyang University Hospital Cheonan, Cheonan, 31151, Chungcheongnam-do, Republic of Korea.
| | - Jong-Seok Moon
- Department of Integrated Biomedical Science, Soonchunhyang Institute of Medi-bio Science (SIMS), Soonchunhyang University, Cheonan, 31151, Chungcheongnam-do, Republic of Korea.
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23
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Rizzi L, Aventurato ÍK, Balthazar MLF. Neuroimaging Research on Dementia in Brazil in the Last Decade: Scientometric Analysis, Challenges, and Peculiarities. Front Neurol 2021; 12:640525. [PMID: 33790850 PMCID: PMC8005640 DOI: 10.3389/fneur.2021.640525] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 02/18/2021] [Indexed: 12/12/2022] Open
Abstract
The last years have evinced a remarkable growth in neuroimaging studies around the world. All these studies have contributed to a better understanding of the cerebral outcomes of dementia, even in the earliest phases. In low- and middle-income countries, studies involving structural and functional neuroimaging are challenging due to low investments and heterogeneous populations. Outstanding the importance of diagnosing mild cognitive impairment and dementia, the purpose of this paper is to offer an overview of neuroimaging dementia research in Brazil. The review includes a brief scientometric analysis of quantitative information about the development of this field over the past 10 years. Besides, discusses some peculiarities and challenges that have limited neuroimaging dementia research in this big and heterogeneous country of Latin America. We systematically reviewed existing neuroimaging literature with Brazilian authors that presented outcomes related to a dementia syndrome, published from 2010 to 2020. Briefly, the main neuroimaging methods used were morphometrics, followed by fMRI, and DTI. The major diseases analyzed were Alzheimer's disease, mild cognitive impairment, and vascular dementia, respectively. Moreover, research activity in Brazil has been restricted almost entirely to a few centers in the Southeast region, and funding could be the main driver for publications. There was relative stability concerning the number of publications per year, the citation impact has historically been below the world average, and the author's gender inequalities are not relevant in this specific field. Neuroimaging research in Brazil is far from being developed and widespread across the country. Fortunately, increasingly collaborations with foreign partnerships contribute to the impact of Brazil's domestic research. Although the challenges, neuroimaging researches performed in the native population regarding regional peculiarities and adversities are of pivotal importance.
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Affiliation(s)
- Liara Rizzi
- Department of Neurology, University of Campinas (UNICAMP), Campinas, Brazil
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24
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Hawkins PCT, Zelaya FO, O'Daly O, Holiga S, Dukart J, Umbricht D, Mehta MA. The effect of risperidone on reward-related brain activity is robust to drug-induced vascular changes. Hum Brain Mapp 2021; 42:2766-2777. [PMID: 33666305 PMCID: PMC8127149 DOI: 10.1002/hbm.25400] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 01/22/2021] [Accepted: 02/16/2021] [Indexed: 12/20/2022] Open
Abstract
Dopamine (DA) mediated brain activity is intimately linked to reward‐driven cerebral responses, while aberrant reward processing has been implicated in several psychiatric disorders. fMRI has been a valuable tool in understanding the mechanism by which DA modulators alter reward‐driven responses and how they may exert their therapeutic effect. However, the potential effects of a pharmacological compound on aspects of neurovascular coupling may cloud the interpretability of the BOLD contrast. Here, we assess the effects of risperidone on reward driven BOLD signals produced by reward anticipation and outcome, while attempting to control for potential drug effects on regional cerebral blood flow (CBF) and cerebrovascular reactivity (CVR). Healthy male volunteers (n = 21) each received a single oral dose of either 0.5 mg, 2 mg of risperidone or placebo in a double‐blind, placebo‐controlled, randomised, three‐period cross‐over study design. Participants underwent fMRI scanning while performing the widely used Monetary Incentive Delay (MID) task to assess drug impact on reward function. Measures of CBF (Arterial Spin Labelling) and breath‐hold challenge induced BOLD signal changes (as a proxy for CVR) were also acquired and included as covariates. Risperidone produced divergent, dose‐dependent effects on separate phases of reward processing, even after controlling for potential nonneuronal influences on the BOLD signal. These data suggest the D2 antagonist risperidone has a wide‐ranging influence on DA‐mediated reward function independent of nonneuronal factors. We also illustrate that assessment of potential vascular confounds on the BOLD signal may be advantageous when investigating CNS drug action and advocate for the inclusion of these additional measures into future study designs.
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Affiliation(s)
- Peter C T Hawkins
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Fernando O Zelaya
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Owen O'Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Stefan Holiga
- Roche Pharma Research and Early Development, Roche Innovation Centre Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Juergen Dukart
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Daniel Umbricht
- Roche Pharma Research and Early Development, Roche Innovation Centre Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Mitul A Mehta
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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25
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Sleight E, Stringer MS, Marshall I, Wardlaw JM, Thrippleton MJ. Cerebrovascular Reactivity Measurement Using Magnetic Resonance Imaging: A Systematic Review. Front Physiol 2021; 12:643468. [PMID: 33716793 PMCID: PMC7947694 DOI: 10.3389/fphys.2021.643468] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 02/01/2021] [Indexed: 12/27/2022] Open
Abstract
Cerebrovascular reactivity (CVR) magnetic resonance imaging (MRI) probes cerebral haemodynamic changes in response to a vasodilatory stimulus. CVR closely relates to the health of the vasculature and is therefore a key parameter for studying cerebrovascular diseases such as stroke, small vessel disease and dementias. MRI allows in vivo measurement of CVR but several different methods have been presented in the literature, differing in pulse sequence, hardware requirements, stimulus and image processing technique. We systematically reviewed publications measuring CVR using MRI up to June 2020, identifying 235 relevant papers. We summarised the acquisition methods, experimental parameters, hardware and CVR quantification approaches used, clinical populations investigated, and corresponding summary CVR measures. CVR was investigated in many pathologies such as steno-occlusive diseases, dementia and small vessel disease and is generally lower in patients than in healthy controls. Blood oxygen level dependent (BOLD) acquisitions with fixed inspired CO2 gas or end-tidal CO2 forcing stimulus are the most commonly used methods. General linear modelling of the MRI signal with end-tidal CO2 as the regressor is the most frequently used method to compute CVR. Our survey of CVR measurement approaches and applications will help researchers to identify good practice and provide objective information to inform the development of future consensus recommendations.
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Affiliation(s)
- Emilie Sleight
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom,UK Dementia Research Institute, Edinburgh, United Kingdom
| | - Michael S. Stringer
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom,UK Dementia Research Institute, Edinburgh, United Kingdom,*Correspondence: Michael S. Stringer
| | - Ian Marshall
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom,UK Dementia Research Institute, Edinburgh, United Kingdom
| | - Joanna M. Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom,UK Dementia Research Institute, Edinburgh, United Kingdom
| | - Michael J. Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom,UK Dementia Research Institute, Edinburgh, United Kingdom
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26
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F. Leoni R. Editorial for “Brain Oxygen Extraction Is Differentially Altered by Alzheimer's and Vascular Diseases”. J Magn Reson Imaging 2020; 52:1838-1839. [DOI: 10.1002/jmri.27281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 06/16/2020] [Accepted: 06/16/2020] [Indexed: 11/08/2022] Open
Affiliation(s)
- Renata F. Leoni
- Department of Physics University of Sao Paulo Ribeirao Preto Brazil
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27
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Switzer AR, Cheema I, McCreary CR, Zwiers A, Charlton A, Alvarez-Veronesi A, Sekhon R, Zerna C, Stafford RB, Frayne R, Goodyear BG, Smith EE. Cerebrovascular reactivity in cerebral amyloid angiopathy, Alzheimer disease, and mild cognitive impairment. Neurology 2020; 95:e1333-e1340. [PMID: 32641520 DOI: 10.1212/wnl.0000000000010201] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 03/16/2020] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVE To assess cerebrovascular reactivity in response to a visual task in participants with cerebral amyloid angiopathy (CAA), Alzheimer disease (AD), and mild cognitive impairment (MCI) using fMRI. METHODS This prospective cohort study included 40 patients with CAA, 22 with AD, 27 with MCI, and 25 healthy controls. Each participant underwent a visual fMRI task using a contrast-reversing checkerboard stimulus. Visual evoked potentials (VEPs) were used to compare visual cortex neuronal activity in 83 participants. General linear models using least-squares means, adjusted for multiple comparisons with the Tukey test, were used to estimate mean blood oxygen level-dependent (BOLD) signal change during the task and VEP differences between groups. RESULTS After adjustment for age and hypertension, estimated mean BOLD response amplitude was as follows: CAA 1.88% (95% confidence interval [CI] 1.60%-2.15%), AD 2.26% (1.91%-2.61%), MCI 2.15% (1.84%-2.46%), and control 2.65% (2.29%-3.00%). Only patients with CAA differed from controls (p = 0.01). In the subset with VEPs, group was not associated with prolonged latencies or lower amplitudes. Lower BOLD amplitude response was associated with higher white matter hyperintensity (WMH) volumes in CAA (for each 0.1% lower BOLD response amplitude, the WMH volume was 9.2% higher, 95% CI 6.0%-12.4%) but not other groups (p = 0.002 for interaction) when controlling for age and hypertension. CONCLUSIONS Mean visual BOLD response amplitude was lowest in participants with CAA compared to controls, without differences in VEP latencies and amplitudes. This suggests that the impaired visual BOLD response is due to reduced vascular reactivity in CAA. In contrast to participants with CAA, the visual BOLD response amplitude did not differ between those with AD or MCI and controls.
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Affiliation(s)
- Aaron R Switzer
- From the Department of Clinical Neurosciences (A.R.S., C.R.M., A.Z., A.C., A.A.-V., R.S., C.Z., R.B.S., R.F., B.G.G., E.E.S), Hotchkiss Brain Institute (R.F., B.G.G., E.E.S), Department of Community Health Sciences (C.Z., E.E.S), and Department of Radiology (R.F., B.G.G., E.E.S), University of Calgary, Alberta; Faculty of Medicine (I.C.), University of Toronto, Ontario; and Seaman Family MR Research Centre (C.R.M., R.F., B.G.G.), Foothills Medical Centre, Calgary, Alberta, Canada
| | - Ikreet Cheema
- From the Department of Clinical Neurosciences (A.R.S., C.R.M., A.Z., A.C., A.A.-V., R.S., C.Z., R.B.S., R.F., B.G.G., E.E.S), Hotchkiss Brain Institute (R.F., B.G.G., E.E.S), Department of Community Health Sciences (C.Z., E.E.S), and Department of Radiology (R.F., B.G.G., E.E.S), University of Calgary, Alberta; Faculty of Medicine (I.C.), University of Toronto, Ontario; and Seaman Family MR Research Centre (C.R.M., R.F., B.G.G.), Foothills Medical Centre, Calgary, Alberta, Canada
| | - Cheryl R McCreary
- From the Department of Clinical Neurosciences (A.R.S., C.R.M., A.Z., A.C., A.A.-V., R.S., C.Z., R.B.S., R.F., B.G.G., E.E.S), Hotchkiss Brain Institute (R.F., B.G.G., E.E.S), Department of Community Health Sciences (C.Z., E.E.S), and Department of Radiology (R.F., B.G.G., E.E.S), University of Calgary, Alberta; Faculty of Medicine (I.C.), University of Toronto, Ontario; and Seaman Family MR Research Centre (C.R.M., R.F., B.G.G.), Foothills Medical Centre, Calgary, Alberta, Canada
| | - Angela Zwiers
- From the Department of Clinical Neurosciences (A.R.S., C.R.M., A.Z., A.C., A.A.-V., R.S., C.Z., R.B.S., R.F., B.G.G., E.E.S), Hotchkiss Brain Institute (R.F., B.G.G., E.E.S), Department of Community Health Sciences (C.Z., E.E.S), and Department of Radiology (R.F., B.G.G., E.E.S), University of Calgary, Alberta; Faculty of Medicine (I.C.), University of Toronto, Ontario; and Seaman Family MR Research Centre (C.R.M., R.F., B.G.G.), Foothills Medical Centre, Calgary, Alberta, Canada
| | - Anna Charlton
- From the Department of Clinical Neurosciences (A.R.S., C.R.M., A.Z., A.C., A.A.-V., R.S., C.Z., R.B.S., R.F., B.G.G., E.E.S), Hotchkiss Brain Institute (R.F., B.G.G., E.E.S), Department of Community Health Sciences (C.Z., E.E.S), and Department of Radiology (R.F., B.G.G., E.E.S), University of Calgary, Alberta; Faculty of Medicine (I.C.), University of Toronto, Ontario; and Seaman Family MR Research Centre (C.R.M., R.F., B.G.G.), Foothills Medical Centre, Calgary, Alberta, Canada
| | - Ana Alvarez-Veronesi
- From the Department of Clinical Neurosciences (A.R.S., C.R.M., A.Z., A.C., A.A.-V., R.S., C.Z., R.B.S., R.F., B.G.G., E.E.S), Hotchkiss Brain Institute (R.F., B.G.G., E.E.S), Department of Community Health Sciences (C.Z., E.E.S), and Department of Radiology (R.F., B.G.G., E.E.S), University of Calgary, Alberta; Faculty of Medicine (I.C.), University of Toronto, Ontario; and Seaman Family MR Research Centre (C.R.M., R.F., B.G.G.), Foothills Medical Centre, Calgary, Alberta, Canada
| | - Ramnik Sekhon
- From the Department of Clinical Neurosciences (A.R.S., C.R.M., A.Z., A.C., A.A.-V., R.S., C.Z., R.B.S., R.F., B.G.G., E.E.S), Hotchkiss Brain Institute (R.F., B.G.G., E.E.S), Department of Community Health Sciences (C.Z., E.E.S), and Department of Radiology (R.F., B.G.G., E.E.S), University of Calgary, Alberta; Faculty of Medicine (I.C.), University of Toronto, Ontario; and Seaman Family MR Research Centre (C.R.M., R.F., B.G.G.), Foothills Medical Centre, Calgary, Alberta, Canada
| | - Charlotte Zerna
- From the Department of Clinical Neurosciences (A.R.S., C.R.M., A.Z., A.C., A.A.-V., R.S., C.Z., R.B.S., R.F., B.G.G., E.E.S), Hotchkiss Brain Institute (R.F., B.G.G., E.E.S), Department of Community Health Sciences (C.Z., E.E.S), and Department of Radiology (R.F., B.G.G., E.E.S), University of Calgary, Alberta; Faculty of Medicine (I.C.), University of Toronto, Ontario; and Seaman Family MR Research Centre (C.R.M., R.F., B.G.G.), Foothills Medical Centre, Calgary, Alberta, Canada
| | - Randall B Stafford
- From the Department of Clinical Neurosciences (A.R.S., C.R.M., A.Z., A.C., A.A.-V., R.S., C.Z., R.B.S., R.F., B.G.G., E.E.S), Hotchkiss Brain Institute (R.F., B.G.G., E.E.S), Department of Community Health Sciences (C.Z., E.E.S), and Department of Radiology (R.F., B.G.G., E.E.S), University of Calgary, Alberta; Faculty of Medicine (I.C.), University of Toronto, Ontario; and Seaman Family MR Research Centre (C.R.M., R.F., B.G.G.), Foothills Medical Centre, Calgary, Alberta, Canada
| | - Richard Frayne
- From the Department of Clinical Neurosciences (A.R.S., C.R.M., A.Z., A.C., A.A.-V., R.S., C.Z., R.B.S., R.F., B.G.G., E.E.S), Hotchkiss Brain Institute (R.F., B.G.G., E.E.S), Department of Community Health Sciences (C.Z., E.E.S), and Department of Radiology (R.F., B.G.G., E.E.S), University of Calgary, Alberta; Faculty of Medicine (I.C.), University of Toronto, Ontario; and Seaman Family MR Research Centre (C.R.M., R.F., B.G.G.), Foothills Medical Centre, Calgary, Alberta, Canada
| | - Bradley G Goodyear
- From the Department of Clinical Neurosciences (A.R.S., C.R.M., A.Z., A.C., A.A.-V., R.S., C.Z., R.B.S., R.F., B.G.G., E.E.S), Hotchkiss Brain Institute (R.F., B.G.G., E.E.S), Department of Community Health Sciences (C.Z., E.E.S), and Department of Radiology (R.F., B.G.G., E.E.S), University of Calgary, Alberta; Faculty of Medicine (I.C.), University of Toronto, Ontario; and Seaman Family MR Research Centre (C.R.M., R.F., B.G.G.), Foothills Medical Centre, Calgary, Alberta, Canada
| | - Eric E Smith
- From the Department of Clinical Neurosciences (A.R.S., C.R.M., A.Z., A.C., A.A.-V., R.S., C.Z., R.B.S., R.F., B.G.G., E.E.S), Hotchkiss Brain Institute (R.F., B.G.G., E.E.S), Department of Community Health Sciences (C.Z., E.E.S), and Department of Radiology (R.F., B.G.G., E.E.S), University of Calgary, Alberta; Faculty of Medicine (I.C.), University of Toronto, Ontario; and Seaman Family MR Research Centre (C.R.M., R.F., B.G.G.), Foothills Medical Centre, Calgary, Alberta, Canada
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Wang W, Zhao F, Ma X, Perry G, Zhu X. Mitochondria dysfunction in the pathogenesis of Alzheimer's disease: recent advances. Mol Neurodegener 2020; 15:30. [PMID: 32471464 PMCID: PMC7257174 DOI: 10.1186/s13024-020-00376-6] [Citation(s) in RCA: 521] [Impact Index Per Article: 130.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 04/24/2020] [Indexed: 12/22/2022] Open
Abstract
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases, characterized by impaired cognitive function due to progressive loss of neurons in the brain. Under the microscope, neuronal accumulation of abnormal tau proteins and amyloid plaques are two pathological hallmarks in affected brain regions. Although the detailed mechanism of the pathogenesis of AD is still elusive, a large body of evidence suggests that damaged mitochondria likely play fundamental roles in the pathogenesis of AD. It is believed that a healthy pool of mitochondria not only supports neuronal activity by providing enough energy supply and other related mitochondrial functions to neurons, but also guards neurons by minimizing mitochondrial related oxidative damage. In this regard, exploration of the multitude of mitochondrial mechanisms altered in the pathogenesis of AD constitutes novel promising therapeutic targets for the disease. In this review, we will summarize recent progress that underscores the essential role of mitochondria dysfunction in the pathogenesis of AD and discuss mechanisms underlying mitochondrial dysfunction with a focus on the loss of mitochondrial structural and functional integrity in AD including mitochondrial biogenesis and dynamics, axonal transport, ER-mitochondria interaction, mitophagy and mitochondrial proteostasis.
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Affiliation(s)
- Wenzhang Wang
- Department of Pathology, Case Western Reserve University, 2103 Cornell Road, Cleveland, OH, 44106, USA.
| | - Fanpeng Zhao
- Department of Pathology, Case Western Reserve University, 2103 Cornell Road, Cleveland, OH, 44106, USA
| | - Xiaopin Ma
- Department of Pathology, Case Western Reserve University, 2103 Cornell Road, Cleveland, OH, 44106, USA
| | - George Perry
- College of Sciences, University of Texas at San Antonio, San Antonio, TX, USA.
| | - Xiongwei Zhu
- Department of Pathology, Case Western Reserve University, 2103 Cornell Road, Cleveland, OH, 44106, USA.
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29
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Germuska M, Chandler H, Okell T, Fasano F, Tomassini V, Murphy K, Wise R. A frequency-domain machine learning method for dual-calibrated fMRI mapping of oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen consumption (CMRO 2). Front Artif Intell 2020; 3. [PMID: 32885165 PMCID: PMC7116003 DOI: 10.3389/frai.2020.00012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Magnetic resonance imaging (MRI) offers the possibility to non-invasively map the brain's metabolic oxygen consumption (CMRO2), which is essential for understanding and monitoring neural function in both health and disease. However, in depth study of oxygen metabolism with MRI has so far been hindered by the lack of robust methods. One MRI method of mapping CMRO2 is based on the simultaneous acquisition of cerebral blood flow (CBF) and blood oxygen level dependent (BOLD) weighted images during respiratory modulation of both oxygen and carbon dioxide. Although this dual-calibrated methodology has shown promise in the research setting, current analysis methods are unstable in the presence of noise and/or are computationally demanding. In this paper, we present a machine learning implementation for the multi-parametric assessment of dual-calibrated fMRI data. The proposed method aims to address the issues of stability, accuracy, and computational overhead, removing significant barriers to the investigation of oxygen metabolism with MRI. The method utilizes a time-frequency transformation of the acquired perfusion and BOLD-weighted data, from which appropriate feature vectors are selected for training of machine learning regressors. The implemented machine learning methods are chosen for their robustness to noise and their ability to map complex non-linear relationships (such as those that exist between BOLD signal weighting and blood oxygenation). An extremely randomized trees (ET) regressor is used to estimate resting blood flow and a multi-layer perceptron (MLP) is used to estimate CMRO2 and the oxygen extraction fraction (OEF). Synthetic data with additive noise are used to train the regressors, with data simulated to cover a wide range of physiologically plausible parameters. The performance of the implemented analysis method is compared to published methods both in simulation and with in-vivo data (n = 30). The proposed method is demonstrated to significantly reduce computation time, error, and proportional bias in both CMRO2 and OEF estimates. The introduction of the proposed analysis pipeline has the potential to not only increase the detectability of metabolic difference between groups of subjects, but may also allow for single subject examinations within a clinical context.
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Affiliation(s)
- Michael Germuska
- CUBRIC, Department of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Hannah Chandler
- CUBRIC, Department of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Thomas Okell
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | | | - Valentina Tomassini
- CUBRIC, Department of Psychology, Cardiff University, Cardiff, United Kingdom.,Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom.,Department of Neuroscience, Imaging and Clinical Sciences, "G. D'Annunzio University" of Chieti-Pescara, 66100, Chieti, Italy
| | - Kevin Murphy
- CUBRIC, Department of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Richard Wise
- CUBRIC, Department of Psychology, Cardiff University, Cardiff, United Kingdom.,Department of Neuroscience, Imaging and Clinical Sciences, "G. D'Annunzio University" of Chieti-Pescara, 66100, Chieti, Italy.,Institute for Advanced Biomedical Technologies, "G. D'Annunzio University" of Chieti-Pescara, 66100, Chieti, Italy
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30
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Champagne AA, Coverdale NS, Germuska M, Cook DJ. Multi-parametric analysis reveals metabolic and vascular effects driving differences in BOLD-based cerebrovascular reactivity associated with a history of sport concussion. Brain Inj 2019; 33:1479-1489. [PMID: 31354054 PMCID: PMC7115911 DOI: 10.1080/02699052.2019.1644375] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 07/12/2019] [Indexed: 12/19/2022]
Abstract
Objective: Identify alterations in cerebrovascular reactivity (CVR) based on the history of sport-related concussion (SRC). Further explore possible mechanisms underlying differences in vascular physiology using hemodynamic parameters modeled using calibrated magnetic resonance imaging (MRI). Method: End-tidal targeting and dual-echo MRI were combined to probe hypercapnic and hyperoxic challenges in athletes with (n = 32) and without (n = 31) a history of SRC. Concurrent blood oxygenation level dependent (BOLD) and arterial spin labeling (ASL) data were used to compute BOLD-CVR, ASL-CVR, and other physiological parameters including resting oxygen extraction fraction (OEF0) and cerebral blood volume (CBV0). Multiple linear and logistic regressions were then used to identify dominant parameters driving group-differences in BOLD-CVR. Results: Robust evidence for elevated BOLD-CVR were found in athletes with SRC history spreading over parts of the cortical hemispheres. Follow-up analyses showed co-localized differences in ASL-CVR (representing modulation of cerebral blood flow) and hemodynamic factors representing static vascular (i.e., CBV0) and metabolic (i.e., OEF0) effects suggesting that group-based differences in BOLD-CVR may be driven by a mixed effect from factors with vascular and metabolic origins. Conclusion: These results emphasize that while BOLD-CVR offers promises as a surrogate non-specific biomarker for cerebrovascular health following SRC, multiple hemodynamic parameters can affect its relative measurements. Abbreviations: [dHb]: concentration of deoxyhemoglobin; AFNI: Analysis of Functional NeuroImages ( https://afni.nimh.nih.gov ); ASL: arterial spin labeling; BIG: position group: defensive and offensive linemen; BIG-SKILL: position group: full backs, linebackers, running backs, tight-ends; BOLD: blood oxygen level dependent; CBF: cerebral blood flow; CMRO2: cerebral metabolic rate of oxygen consumption; CTL: group of control subjects; CVR: cerebrovascular reactivity; fMRI: functional magnetic resonance imaging; FSL: FMRIB software library ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/ ); HC: hypercapnia; HO: hyperoxia; HX: group with history of concussion; M: maximal theoretical BOLD signal upon complete removal of venous dHb; pCASL: pseudo-continuous arterial spin labeling; PETCO2: end-tidal carbon dioxide; PETO2: end-tidal oxygen; SCAT: sport-concussion assessment tool; SKILL: position group: defensive backs, kickers, quarterbacks, safeties, wide-receivers; SRC: sport-related concussion.
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Affiliation(s)
- Allen A. Champagne
- Centre for Neuroscience Studies, Queen’s University, Kingston, ON, Canada
| | | | - Michael Germuska
- Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, United Kingdom
| | - Douglas J. Cook
- Centre for Neuroscience Studies, Queen’s University, Kingston, ON, Canada
- Department of Surgery, Queen’s University, Kingston, ON, Canada
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31
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Su J, Wang M, Ban S, Wang L, Cheng X, Hua F, Tang Y, Zhou H, Zhai Y, Du X, Liu J. Relationship between changes in resting-state spontaneous brain activity and cognitive impairment in patients with CADASIL. J Headache Pain 2019; 20:36. [PMID: 30995925 PMCID: PMC6734224 DOI: 10.1186/s10194-019-0982-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Accepted: 03/14/2019] [Indexed: 01/15/2023] Open
Abstract
Background Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) mainly manifests with cognitive impairment. Cognitive deficits in patients with CADASIL are correlated with structural brain changes such as lacunar lesion burden, normalized brain volume, and anterior thalamic radiation lesions, but changes in resting-state functional brain activity in patients with CADASIL have not been reported. Methods This study used resting-state functional magnetic resonance imaging (fMRI) to measure the amplitude of low-frequency fluctuation (ALFF) in 22 patients with CADASIL and 44 healthy matched controls. A seed-based functional connectivity (FC) analysis was used to investigate whether the dysfunctional areas identified by ALFF analysis exhibited abnormal FC with other brain areas. Pearson’s correlation analysis was used to detect correlations between the ALFF z-score of abnormal brain areas and clinical scores in patients with CADASIL. Results Patients with CADASIL exhibited significantly lower ALFF values in the right precuneus and cuneus (Pcu/CU) and higher ALFF values in the bilateral superior frontal gyrus (SFG) and left cerebellar anterior and posterior lobes compared with controls. Patients with CADASIL showed weaker FC between the areas with abnormal ALFF (using peaks in the left and right SFG and the right Pcu/CU) and other brain areas. Importantly, the ALFF z-scores for the left and right SFG were negatively associated with cognitive performance, including Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment scores (MoCA), respectively, whereas those of the right Pcu/CU were positively correlated with the MMSE score. Conclusions This preliminary study provides evidence for changes in ALFF of the right Pcu/CU, bilateral SFG and left cerebellar anterior and posterior lobes, and associations between ALFF values for abnormal brain areas and cognitive performance in patients with CADASIL. Therefore, spontaneous brain activity may be a novel imaging biomarker of cognitive impairment in this population.
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Affiliation(s)
- Jingjing Su
- Department of Neurology and Jiuyuan Municipal Stroke Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai, 200011, People's Republic of China
| | - Mengxing Wang
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, School of Physics and Materials Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, 200062, People's Republic of China.,College of Medical Imaging, Shanghai University of Medicine & Health Sciences, 279 Zhouzhu Highway, Shanghai, 201318, People's Republic of China
| | - Shiyu Ban
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, School of Physics and Materials Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, 200062, People's Republic of China
| | - Liang Wang
- Department of Neurology, Huashan Hospital, Fudan University, 12 Middle Wulumuqi Road, Shanghai, 200040, People's Republic of China
| | - Xin Cheng
- Department of Neurology, Huashan Hospital, Fudan University, 12 Middle Wulumuqi Road, Shanghai, 200040, People's Republic of China
| | - Fengchun Hua
- PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235, People's Republic of China
| | - Yuping Tang
- Department of Neurology, Huashan Hospital, Fudan University, 12 Middle Wulumuqi Road, Shanghai, 200040, People's Republic of China
| | - Houguang Zhou
- Department of Geriatrics Neurology, Huashan Hospital, Fudan University, 12 Middle Wulumuqi Road, Shanghai, 200040, People's Republic of China
| | - Yu Zhai
- Department of Neurology and Jiuyuan Municipal Stroke Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai, 200011, People's Republic of China.
| | - Xiaoxia Du
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, School of Physics and Materials Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, 200062, People's Republic of China.
| | - Jianren Liu
- Department of Neurology and Jiuyuan Municipal Stroke Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai, 200011, People's Republic of China.
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32
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Ning Y, Yin D, Jia W, Zhu H, Xue S, Liu J, Jia H. Cognitive impairment in patients with kidney deficiency syndrome: A resting-state fMRI study. Eur J Integr Med 2018. [DOI: 10.1016/j.eujim.2018.10.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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33
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Dienel GA. Metabolomic Assays of Postmortem Brain Extracts: Pitfalls in Extrapolation of Concentrations of Glucose and Amino Acids to Metabolic Dysregulation In Vivo in Neurological Diseases. Neurochem Res 2018; 44:2239-2260. [DOI: 10.1007/s11064-018-2611-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 08/05/2018] [Accepted: 08/06/2018] [Indexed: 01/03/2023]
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34
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Chen JJ. Cerebrovascular-Reactivity Mapping Using MRI: Considerations for Alzheimer's Disease. Front Aging Neurosci 2018; 10:170. [PMID: 29922153 PMCID: PMC5996106 DOI: 10.3389/fnagi.2018.00170] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 05/18/2018] [Indexed: 01/14/2023] Open
Abstract
Alzheimer’s disease (AD) is associated with well-established macrostructural and cellular markers, including localized brain atrophy and deposition of amyloid. However, there is growing recognition of the link between cerebrovascular dysfunction and AD, supported by continuous experimental evidence in the animal and human literature. As a result, neuroimaging studies of AD are increasingly aiming to incorporate vascular measures, exemplified by measures of cerebrovascular reactivity (CVR). CVR is a measure that is rooted in clinical practice, and as non-invasive CVR-mapping techniques become more widely available, routine CVR mapping may open up new avenues of investigation into the development of AD. This review focuses on the use of MRI to map CVR, paying specific attention to recent developments in MRI methodology and on the emerging stimulus-free approaches to CVR mapping. It also summarizes the biological basis for the vascular contribution to AD, and provides critical perspective on the choice of CVR-mapping techniques amongst frail populations.
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Affiliation(s)
- J J Chen
- Rotman Research Institute, Baycrest, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
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35
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Chen JJ. Functional MRI of brain physiology in aging and neurodegenerative diseases. Neuroimage 2018; 187:209-225. [PMID: 29793062 DOI: 10.1016/j.neuroimage.2018.05.050] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 05/16/2018] [Accepted: 05/20/2018] [Indexed: 12/14/2022] Open
Abstract
Brain aging and associated neurodegeneration constitute a major societal challenge as well as one for the neuroimaging community. A full understanding of the physiological mechanisms underlying neurodegeneration still eludes medical researchers, fuelling the development of in vivo neuroimaging markers. Hence it is increasingly recognized that our understanding of neurodegenerative processes likely will depend upon the available information provided by imaging techniques. At the same time, the imaging techniques are often developed in response to the desire to observe certain physiological processes. In this context, functional MRI (fMRI), which has for decades provided information on neuronal activity, has evolved into a large family of techniques well suited for in vivo observations of brain physiology. Given the rapid technical advances in fMRI in recent years, this review aims to summarize the physiological basis of fMRI observations in healthy aging as well as in age-related neurodegeneration. This review focuses on in-vivo human brain imaging studies in this review and on disease features that can be imaged using fMRI methods. In addition to providing detailed literature summaries, this review also discusses future directions in the study of brain physiology using fMRI in the clinical setting.
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Affiliation(s)
- J Jean Chen
- Rotman Research Institute at Baycrest Centre, Canada; Department of Medical Biophysics, University of Toronto, Canada.
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36
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Germuska M, Wise RG. Calibrated fMRI for mapping absolute CMRO 2: Practicalities and prospects. Neuroimage 2018; 187:145-153. [PMID: 29605580 DOI: 10.1016/j.neuroimage.2018.03.068] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 03/14/2018] [Accepted: 03/28/2018] [Indexed: 01/21/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) is an essential workhorse of modern neuroscience, providing valuable insight into the functional organisation of the brain. The physiological mechanisms underlying the blood oxygenation level dependent (BOLD) effect are complex and preclude a straightforward interpretation of the signal. However, by employing appropriate calibration of the BOLD signal, quantitative measurements can be made of important physiological parameters including the absolute rate of cerebral metabolic oxygen consumption or oxygen metabolism (CMRO2) and oxygen extraction (OEF). The ability to map such fundamental parameters has the potential to greatly expand the utility of fMRI and to broaden its scope of application in clinical research and clinical practice. In this review article we discuss some of the practical issues related to the calibrated-fMRI approach to the measurement of CMRO2. We give an overview of the necessary precautions to ensure high quality data acquisition, and explore some of the pitfalls and challenges that must be considered as it is applied and interpreted in a widening array of diseases and research questions.
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Affiliation(s)
- M Germuska
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, CF24 4HQ, Cardiff, UK
| | - R G Wise
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, CF24 4HQ, Cardiff, UK.
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37
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De Vis JB, Peng SL, Chen X, Li Y, Liu P, Sur S, Rodrigue KM, Park DC, Lu H. Arterial-spin-labeling (ASL) perfusion MRI predicts cognitive function in elderly individuals: A 4-year longitudinal study. J Magn Reson Imaging 2018; 48:449-458. [PMID: 29292540 DOI: 10.1002/jmri.25938] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Accepted: 12/12/2017] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND With the disappointing outcomes of clinical trials on patients with Alzheimer's disease or mild cognitive impairment (MCI), there is increasing attention to understanding cognitive decline in normal elderly individuals, with the goal of identifying subjects who are most susceptible to imminent cognitive impairment. PURPOSE/HYPOTHESIS To evaluate the potential of cerebral blood flow (CBF) as a biomarker by investigating the relationship between CBF at baseline and cognition at follow-up. STUDY TYPE Prospective longitudinal study with a 4-year time interval. POPULATION 309 healthy subjects aged 20-89 years old. FIELD STRENGTH/SEQUENCE 3T pseudo-continuous-arterial-spin-labeling MRI. ASSESSMENT CBF at baseline and cognitive assessment at both baseline and follow-up. STATISTICAL TESTS Linear regression analyses with age, systolic blood pressure, physical activity, and baseline cognition as covariates. RESULTS Linear regression analyses revealed that whole-brain CBF at baseline was predictive of general fluid cognition at follow-up. This effect was observed in the older group (age ≥54 years, β = 0.221, P = 0.004), but not in younger or entire sample (β = 0.018, P = 0.867 and β = 0.089, P = 0.098, respectively). Among major brain lobes, frontal CBF had the highest sensitivity in predicting future cognition, with a significant effect observed for fluid cognition (β = 0.244 P = 0.001), episodic memory (β = 0.294, P = 0.001), and reasoning (β = 0.186, P = 0.027). These associations remained significant after accounting for baseline cognition. Voxelwise analysis revealed that medial frontal cortex and anterior cingulate cortex, part of the default mode network (DMN), are among the most important regions in predicting fluid cognition. DATA CONCLUSION In a healthy aging cohort, CBF can predict general cognitive ability as well as specific domains of cognitive function. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2018;48:449-458.
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Affiliation(s)
- Jill B De Vis
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Shin-Lei Peng
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan
| | - Xi Chen
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Texas, USA
| | - Yang Li
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Peiying Liu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Sandeepa Sur
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Karen M Rodrigue
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Texas, USA
| | - Denise C Park
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Texas, USA
| | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Bright MG, Croal PL, Blockley NP, Bulte DP. Multiparametric measurement of cerebral physiology using calibrated fMRI. Neuroimage 2017; 187:128-144. [PMID: 29277404 DOI: 10.1016/j.neuroimage.2017.12.049] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 12/14/2017] [Accepted: 12/15/2017] [Indexed: 02/07/2023] Open
Abstract
The ultimate goal of calibrated fMRI is the quantitative imaging of oxygen metabolism (CMRO2), and this has been the focus of numerous methods and approaches. However, one underappreciated aspect of this quest is that in the drive to measure CMRO2, many other physiological parameters of interest are often acquired along the way. This can significantly increase the value of the dataset, providing greater information that is clinically relevant, or detail that can disambiguate the cause of signal variations. This can also be somewhat of a double-edged sword: calibrated fMRI experiments combine multiple parameters into a physiological model that requires multiple steps, thereby providing more opportunity for error propagation and increasing the noise and error of the final derived values. As with all measurements, there is a trade-off between imaging time, spatial resolution, coverage, and accuracy. In this review, we provide a brief overview of the benefits and pitfalls of extracting multiparametric measurements of cerebral physiology through calibrated fMRI experiments.
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Affiliation(s)
- Molly G Bright
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK; Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Paula L Croal
- IBME, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Nicholas P Blockley
- FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Daniel P Bulte
- IBME, Department of Engineering Science, University of Oxford, Oxford, UK; FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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