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Guo K, Zhang Q, Quan Z, Wang Y, Ma T, Jiang J, Kang F, Wang J. Whole-brain glucose metabolic pattern differentiates minimally conscious state from unresponsive wakefulness syndrome. CNS Neurosci Ther 2024; 30:e14787. [PMID: 38894559 PMCID: PMC11187933 DOI: 10.1111/cns.14787] [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: 03/12/2024] [Revised: 05/05/2024] [Accepted: 05/14/2024] [Indexed: 06/21/2024] Open
Abstract
AIMS The patient being minimally conscious state (MCS) may benefit from wake-up interventions aimed at improving quality of life and have a higher probability of recovering higher level of consciousness compared to patients with the unresponsive wakefulness syndrome (UWS). However, differentiation of the MCS and UWS poses challenge in clinical practice. This study aimed to explore glucose metabolic pattern (GMP) obtained from 18F-labeled-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) in distinguishing between UWS and MCS. METHODS Fifty-seven patients with disorders of consciousness (21 cases of UWS and 36 cases of MCS) who had undergone repeated standardized Coma Recovery Scale-Revised (CRS-R) evaluations were enrolled in this prospective study. 18F-FDG-PET was carried out in all patients and healthy controls (HCs). Voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was used to generate GMPs. The expression score of whole-brain GMP was obtained, and its diagnostic accuracy was compared with the standardized uptake value ratio (SUVR). The diagnostic efficiency was validated by one-year later clinical outcomes. RESULTS UWS-MCS GMP exhibited hypometabolism in the frontal-parietal cortex, along with hypermetabolism in the unilateral lentiform nucleus, putamen, and anterior cingulate gyrus. The UWS-MCS-GMP expression score was significantly higher in UWS compared to MCS patients (0.90 ± 0.85 vs. 0 ± 0.93, p < 0.001). UWS-MCS-GMP expression score achieved an area under the curve (AUC) of 0.77 to distinguish MCS from UWS, surpassing that of SUVR based on the frontoparietal cortex (AUC = 0.623). UWS-MCS-GMP expression score was significantly correlated with the CRS-R score (r = -0.45, p = 0.004) and accurately predicted the one-year outcome in 73.7% of patients. CONCLUSION UWS and MCS exhibit specific glucose metabolism patterns, the UWS-MCS-GMP expression score significantly distinguishes MCS from UWS, making SSM/PCA a potential diagnostic methods in clinical practice for individual patients.
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Affiliation(s)
- Kun Guo
- Department of Nuclear Medicine, Xijing HospitalFourth Military Medical UniversityXi'anChina
| | - Qi Zhang
- School of Communication & Information EngineeringShanghai UniversityShanghaiChina
| | - Zhiyong Quan
- Department of Nuclear Medicine, Xijing HospitalFourth Military Medical UniversityXi'anChina
| | - Yirong Wang
- Department of Nuclear Medicine, Xijing HospitalFourth Military Medical UniversityXi'anChina
| | - Taoqi Ma
- Department of Nuclear Medicine, Xijing HospitalFourth Military Medical UniversityXi'anChina
| | - Jiehui Jiang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Fei Kang
- Department of Nuclear Medicine, Xijing HospitalFourth Military Medical UniversityXi'anChina
| | - Jing Wang
- Department of Nuclear Medicine, Xijing HospitalFourth Military Medical UniversityXi'anChina
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2
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Wheeler KV, Irimia A, Braskie MN. Using Neuroimaging to Study Cerebral Amyloid Angiopathy and Its Relationship to Alzheimer's Disease. J Alzheimers Dis 2024; 97:1479-1502. [PMID: 38306032 DOI: 10.3233/jad-230553] [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] [Indexed: 02/03/2024]
Abstract
Cerebral amyloid angiopathy (CAA) is characterized by amyloid-β aggregation in the media and adventitia of the leptomeningeal and cortical blood vessels. CAA is one of the strongest vascular contributors to Alzheimer's disease (AD). It frequently co-occurs in AD patients, but the relationship between CAA and AD is incompletely understood. CAA may drive AD risk through damage to the neurovascular unit and accelerate parenchymal amyloid and tau deposition. Conversely, early AD may also drive CAA through cerebrovascular remodeling that impairs blood vessels from clearing amyloid-β. Sole reliance on autopsy examination to study CAA limits researchers' ability to investigate CAA's natural disease course and the effect of CAA on cognitive decline. Neuroimaging allows for in vivo assessment of brain function and structure and can be leveraged to investigate CAA staging and explore its associations with AD. In this review, we will discuss neuroimaging modalities that can be used to investigate markers associated with CAA that may impact AD vulnerability including hemorrhages and microbleeds, blood-brain barrier permeability disruption, reduced cerebral blood flow, amyloid and tau accumulation, white matter tract disruption, reduced cerebrovascular reactivity, and lowered brain glucose metabolism. We present possible areas for research inquiry to advance biomarker discovery and improve diagnostics.
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Affiliation(s)
- Koral V Wheeler
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina Del Rey, CA, USA
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, USC Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
- Department of Biomedical Engineering, Corwin D. Denney Research Center, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Meredith N Braskie
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina Del Rey, CA, USA
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3
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Liu J, Tang M, Zhu D, Ruan G, Zou S, Cheng Z, Zhu X, Zhu Y. The remodeling of metabolic brain pattern in patients with extracranial diffuse large B-cell lymphoma. EJNMMI Res 2023; 13:94. [PMID: 37902852 PMCID: PMC10616001 DOI: 10.1186/s13550-023-01046-6] [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: 06/28/2023] [Accepted: 10/22/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND Owing to the advances in diagnosis and therapy, survival or remission rates for lymphoma have improved prominently. Apart from the lymphoma- and chemotherapy-related somatic symptom burden, increasing attention has been drawn to the health-related quality of life. The application of 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) has been routinely recommended for the staging and response assessment of FDG-avid lymphoma. However, up till now, only a few researches have investigated the brain metabolic impairments in patients with pre-treatment lymphoma. The determination of the lymphoma-related metabolic brain pattern would facilitate exploring the tailored therapeutic regimen to alleviate not only the physiological, but also the psychological symptoms. In this retrospective study, we aimed to establish the diffuse large B-cell lymphoma-related pattern (DLBCLRP) of metabolic brain network and investigate the correlations between DLBCLRP and several indexes of the staging and response assessment. RESULTS The established DLBCLRP was characterized by the increased metabolic activity in bilateral cerebellum, brainstem, thalamus, striatum, hippocampus, amygdala, parahippocampal gyrus and right middle temporal gyrus and by the decreased metabolic activity in bilateral occipital lobe, parietal lobe, anterior cingulate gyrus, midcingulate cortex and medial frontal gyrus. Significant difference in the baseline expression of DLBCLRP was found among complete metabolic response (CMR), partial metabolic response (PMR) and progressive metabolic disease (PMD) groups (P < 0.01). DLBCLRP expressions were also significantly or tended to be positively correlated with international prognostic index (IPI) (rs = 0.306, P < 0.05), lg(total metabolic tumor volume, TMTV) (r = 0.298, P < 0.05) and lg(total lesion glycolysis, TLG) (r = 0.233, P = 0.064). Though no significant correlation of DLBCLRP expression was found with Ann Arbor staging or tumor SUVmax (P > 0.05), the post-treatment declines of DLBCLRP expression were significantly positively correlated with Ann Arbor staging (rs = 0.284, P < 0.05) and IPI (rs = 0.297, P < 0.05). CONCLUSIONS The proposed DLBCLRP would lay the foundation for further investigating the cerebral dysfunction related to DLBCL itself and/or treatments. Besides, the expression of DLBCLRP was associated with the tumor burden of lymphoma, implying a potential biomarker for prognosis.
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Affiliation(s)
- Junyi Liu
- Department of Nuclear Medicine and PET Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Ave, Wuhan, 430030, China
| | - Ming Tang
- Department of Nuclear Medicine and PET Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Ave, Wuhan, 430030, China
| | - Dongling Zhu
- Department of Nuclear Medicine and PET Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Ave, Wuhan, 430030, China
| | - Ge Ruan
- Department of Radiology, Hospital, Hubei University, Wuhan, 430062, China
| | - Sijuan Zou
- Department of Nuclear Medicine and PET Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Ave, Wuhan, 430030, China
| | - Zhaoting Cheng
- Department of Nuclear Medicine and PET Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Ave, Wuhan, 430030, China
| | - Xiaohua Zhu
- Department of Nuclear Medicine and PET Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Ave, Wuhan, 430030, China.
| | - Yuankai Zhu
- Department of Nuclear Medicine and PET Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Ave, Wuhan, 430030, China.
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4
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Lu J, Wang M, Wu P, Yakushev I, Zhang H, Ziegler S, Jiang J, Förster S, Wang J, Schwaiger M, Rominger A, Huang SC, Liu F, Zuo C, Shi K. Adjustment for the Age- and Gender-Related Metabolic Changes Improves the Differential Diagnosis of Parkinsonism. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:50-63. [PMID: 36939769 PMCID: PMC9883378 DOI: 10.1007/s43657-022-00079-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 06/18/2023]
Abstract
Age and gender are the important factors for brain metabolic declines in both normal aging and neurodegeneration, and the confounding effects may influence early and differential diagnosis of neurodegenerative diseases based on the [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET). We aimed to explore the potential of the adjustment of age- and gender-related confounding factors on [18F]FDG PET images in differentiation of Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supra-nuclear palsy (PSP). Eight hundred and seventy-seven clinically definitely diagnosed Parkinsonian patients from a benchmark Huashan Parkinsonian PET imaging database were included. An age- and gender-adjusted Z (AGAZ) score was established based on the gender-specific longitudinal metabolic changes on healthy subjects. AGAZ scores and standardized uptake value ratio (SUVR) values were quantified at regional-level and support vector machine-based error-correcting output codes method was applied for classification. Additional references of the classifications based on metabolic pattern scores were included. The feature-based AGAZ score showed the best performance in classification (accuracy for PD, MSA, PSP: 93.1%, 96.3%, 94.8%). In both genders, the AGAZ score consistently achieved the best efficiency, and the improvements compared to the conventional SUVR value for PD, MSA, and PSP mainly laid in specificity (Male: 5.7%; Female: 11.1%), sensitivity (Male: 7.2%; Female: 7.3%), and sensitivity (Male: 7.3%; Female: 17.2%). Female patients benefited more from the adjustment on [18F]FDG PET in MSA and PSP groups (absolute net reclassification index, p < 0.001). Collectively, the adjustment of age- and gender-related confounding factors may improve the differential diagnosis of Parkinsonism. Particularly, the diagnosis of female Parkinsonian population has the best improvement from this correction. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-022-00079-6.
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Affiliation(s)
- Jiaying Lu
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235 China
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Min Wang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444 China
- Department of Informatics, Technische Universität München, 80333 Munich, Germany
| | - Ping Wu
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235 China
- National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Igor Yakushev
- Department of Nuclear Medicine, Technische Universität München, 80333 Munich, Germany
| | - Huiwei Zhang
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235 China
| | - Sibylle Ziegler
- Department of Nuclear Medicine, University Hospital LMU Munich, 80539 Munich, Germany
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444 China
| | - Stefan Förster
- Department of Nuclear Medicine, Technische Universität München, 80333 Munich, Germany
- Department of Nuclear Medicine, Klinikum Bayreuth, 95445, Bayreuth, Germany
| | - Jian Wang
- National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 200040 China
- Department of Neurology, Huashan Hospital, Fudan University, 12 Middle Wulumuqi Road, Shanghai, 200040 China
| | - Markus Schwaiger
- Klinikum r. d. Isar, Technische Universität München, 95445 Munich, Germany
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Sung-Cheng Huang
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, 90095 USA
| | - Fengtao Liu
- National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 200040 China
- Department of Neurology, Huashan Hospital, Fudan University, 12 Middle Wulumuqi Road, Shanghai, 200040 China
| | - Chuantao Zuo
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235 China
- National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 200040 China
- Human Phenome Institute, Fudan University, Shanghai, 200433 China
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
- Department of Informatics, Technische Universität München, 80333 Munich, Germany
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5
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Perovnik M, Rus T, Schindlbeck KA, Eidelberg D. Functional brain networks in the evaluation of patients with neurodegenerative disorders. Nat Rev Neurol 2023; 19:73-90. [PMID: 36539533 DOI: 10.1038/s41582-022-00753-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/21/2022] [Indexed: 12/24/2022]
Abstract
Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Tomaž Rus
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | | | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA.
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Jiang J, Sheng C, Chen G, Liu C, Jin S, Li L, Jiang X, Han Y. Glucose metabolism patterns: A potential index to characterize brain ageing and predict high conversion risk into cognitive impairment. GeroScience 2022; 44:2319-2336. [PMID: 35581512 PMCID: PMC9616982 DOI: 10.1007/s11357-022-00588-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 05/07/2022] [Indexed: 12/28/2022] Open
Abstract
Exploring individual hallmarks of brain ageing is important. Here, we propose the age-related glucose metabolism pattern (ARGMP) as a potential index to characterize brain ageing in cognitively normal (CN) elderly people. We collected 18F-fluorodeoxyglucose (18F-FDG) PET brain images from two independent cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 127) and the Xuanwu Hospital of Capital Medical University, Beijing, China (N = 84). During follow-up (mean 80.60 months), 23 participants in the ADNI cohort converted to cognitive impairment. ARGMPs were identified using the scaled subprofile model/principal component analysis method, and cross-validations were conducted in both independent cohorts. A survival analysis was further conducted to calculate the predictive effect of conversion risk by using ARGMPs. The results showed that ARGMPs were characterized by hypometabolism with increasing age primarily in the bilateral medial superior frontal gyrus, anterior cingulate and paracingulate gyri, caudate nucleus, and left supplementary motor area and hypermetabolism in part of the left inferior cerebellum. The expression network scores of ARGMPs were significantly associated with chronological age (R = 0.808, p < 0.001), which was validated in both the ADNI and Xuanwu cohorts. Individuals with higher network scores exhibited a better predictive effect (HR: 0.30, 95% CI: 0.1340 ~ 0.6904, p = 0.0068). These findings indicate that ARGMPs derived from CN participants may represent a novel index for characterizing brain ageing and predicting high conversion risk into cognitive impairment.
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Affiliation(s)
- Jiehui Jiang
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China.
| | - Can Sheng
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Guanqun Chen
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Chunhua Liu
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China
| | - Shichen Jin
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China
| | - Lanlan Li
- Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China
| | - Xueyan Jiang
- School of Biomedical Engineering, Hainan University, Haikou, 570228, China
- German Centre for Neurodegenerative Disease, Clinical Research Group, Venusberg Campus 1, 53121, Bonn, Germany
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
- School of Biomedical Engineering, Hainan University, Haikou, 570228, China.
- Centre of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China.
- National Clinical Research Centre for Geriatric Disorders, Beijing, 100053, China.
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Li TR, Yao YX, Jiang XY, Dong QY, Yu XF, Wang T, Cai YN, Han Y. β-Amyloid in blood neuronal-derived extracellular vesicles is elevated in cognitively normal adults at risk of Alzheimer's disease and predicts cerebral amyloidosis. Alzheimers Res Ther 2022; 14:66. [PMID: 35550625 PMCID: PMC9097146 DOI: 10.1186/s13195-022-01010-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 04/27/2022] [Indexed: 02/08/2023]
Abstract
Background Blood biomarkers that can be used for preclinical Alzheimer’s disease (AD) diagnosis would enable trial enrollment at a time when the disease is potentially reversible. Here, we investigated plasma neuronal-derived extracellular vesicle (nEV) cargo in patients along the Alzheimer’s continuum, focusing on cognitively normal controls (NCs) with high brain β-amyloid (Aβ) loads (Aβ+). Methods The study was based on the Sino Longitudinal Study on Cognitive Decline project. We enrolled 246 participants, including 156 NCs, 45 amnestic mild cognitive impairment (aMCI) patients, and 45 AD dementia (ADD) patients. Brain Aβ loads were determined using positron emission tomography. NCs were classified into 84 Aβ− NCs and 72 Aβ+ NCs. Baseline plasma nEVs were isolated by immunoprecipitation with an anti-CD171 antibody. After verification, their cargos, including Aβ, tau phosphorylated at threonine 181, and neurofilament light, were quantified using a single-molecule array. Concentrations of these cargos were compared among the groups, and their receiver operating characteristic (ROC) curves were constructed. A subset of participants underwent follow-up cognitive assessment and magnetic resonance imaging. The relationships of nEV cargo levels with amyloid deposition, longitudinal changes in cognition, and brain regional volume were explored using correlation analysis. Additionally, 458 subjects in the project had previously undergone plasma Aβ quantification. Results Only nEV Aβ was included in the subsequent analysis. We focused on Aβ42 in the current study. After normalization of nEVs, the levels of Aβ42 were found to increase gradually across the cognitive continuum, with the lowest in the Aβ− NC group, an increase in the Aβ+ NC group, a further increase in the aMCI group, and the highest in the ADD group, contributing to their diagnoses (Aβ− NCs vs. Aβ+ NCs, area under the ROC curve values of 0.663; vs. aMCI, 0.857; vs. ADD, 0.957). Furthermore, nEV Aβ42 was significantly correlated with amyloid deposition, as well as longitudinal changes in cognition and entorhinal volume. There were no differences in plasma Aβ levels among NCs, aMCI, and ADD individuals. Conclusions Our findings suggest the potential use of plasma nEV Aβ42 levels in diagnosing AD-induced cognitive impairment and Aβ+ NCs. This biomarker reflects cortical amyloid deposition and predicts cognitive decline and entorhinal atrophy. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01010-x.
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Affiliation(s)
- Tao-Ran Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Yun-Xia Yao
- Department of Neurobiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Xue-Yan Jiang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.,School of Biomedical Engineering, Hainan University, Haikou, 570228, China
| | - Qiu-Yue Dong
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Information and Communication Engineering, Shanghai University, Shanghai, 200444, China
| | - Xian-Feng Yu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Ting Wang
- Department of Neurobiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Yan-Ning Cai
- Department of Neurobiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China. .,School of Biomedical Engineering, Hainan University, Haikou, 570228, China. .,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China. .,National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China.
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Jiang J, Zhang J, Li Z, Li L, Huang B. Using Deep Learning Radiomics to Distinguish Cognitively Normal Adults at Risk of Alzheimer's Disease From Normal Control: An Exploratory Study Based on Structural MRI. Front Med (Lausanne) 2022; 9:894726. [PMID: 35530047 PMCID: PMC9070098 DOI: 10.3389/fmed.2022.894726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 03/28/2022] [Indexed: 11/24/2022] Open
Abstract
Objectives We proposed a novel deep learning radiomics (DLR) method to distinguish cognitively normal adults at risk of Alzheimer's disease (AD) from normal control based on T1-weighted structural MRI images. Methods In this study, we selected MRI data from the Alzheimer's Disease Neuroimaging Initiative Database (ADNI), which included 417 cognitively normal adults. These subjects were divided into 181 individuals at risk of Alzheimer's disease (preAD group) and 236 normal control individuals (NC group) according to standard uptake ratio >1.18 calculated by amyloid Positron Emission Tomography (PET). We further divided the preaAD group into APOE+ and APOE- subgroups according to whether APOE ε4 was positive or not. All data sets were divided into one training/validation group and one independent test group. The proposed DLR method included three steps: (1) the pre-training of basic deep learning (DL) models, (2) the extraction, selection and fusion of DLR features, and (3) classification. The support vector machine (SVM) was used as the classifier. In the comparative experiments, we compared our proposed DLR method with three existing models: hippocampal model, clinical model, and traditional radiomics model. Ten-fold cross-validation was performed with 100 time repetitions. Results The DLR method achieved the best classification performance between preAD and NC than other models with an accuracy of 89.85% ± 1.12%. In comparison, the accuracies of the other three models were 72.44% ± 1.37%, 82.00% ± 4.09% and 79.65% ± 2.21%. In addition, the DLR model also showed the best classification performance (85.45% ± 9.04% and 92.80% ± 2.61%) in the subgroup experiment. Conclusion The results showed that the DLR method provided a potentially clinical value to distinguish preAD from NC.
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Affiliation(s)
- Jiehui Jiang
- Department of Radiology, Gongli Hospital, School of Medicine, Shanghai University, Shanghai, China
- School of Life Sciences, Institute of Biomedical Engineering, Shanghai University, Shanghai, China
| | - Jieming Zhang
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Zhuoyuan Li
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Lanlan Li
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Bingcang Huang
- Department of Radiology, Gongli Hospital, School of Medicine, Shanghai University, Shanghai, China
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