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Zhang Q, Li F, Wei M, Wang M, Wang L, Han Y, Jiang J. Prediction of Cognitive Progression Due to Alzheimer's Disease in Normal Subjects Based on Individual Default Mode Network Metabolic Connectivity Strength. Biol Psychiatry Cogn Neurosci Neuroimaging 2024:S2451-9022(24)00104-6. [PMID: 38631552 DOI: 10.1016/j.bpsc.2024.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/11/2024] [Accepted: 04/05/2024] [Indexed: 04/19/2024]
Abstract
BACKGROUND Predicting cognitive decline in those already Aβ positive or Tau positive among the aging population poses clinical challenges. In Alzheimer's disease (AD) research, intra-default mode network (DMN) connections play a pivotal role in diagnosis. This paper proposes metabolic connectivity within the DMN as a supplementary biomarker to the AT(N) framework. METHODS Extracting data from 1292 subjects in the Alzheimer's Disease Neuroimaging Initiative, we collected paired T1-weighted structural MRI and 18F-labeled-fluorodeoxyglucose positron emission computed tomography (PET) scans. Individual metabolic DMN networks were constructed, and metabolic connectivity (MC) strength in DMN was assessed. In the cognitively unimpaired (CU) group, the Cox model identified CU(MC+), high-risk subjects, with Kaplan-Meier survival analyses and hazard ratio (HR) revealing MC strength's predictive performance. Spearman correlation analyses explored relationships between MC strength, AT(N) biomarkers, and clinical scales. DMN standard uptake value ratio (SUVR) provided comparative insights in the analyses. RESULTS Both MC strength and SUVR exhibit gradual declines with cognitive deterioration, displaying significant intergroup differences. Survival analyses indicate enhanced Aβ and Tau prediction with both metrics, with MC strength outperforming SUVR. Combined MC strength and Aβ yield optimal predictive performance (HR = 9.29), followed by MC strength and Tau (HR = 8.92). In CU(MC+), MC strength correlates significantly with CSF Aβ42 and AV45 PET SUVR (r = 0.22, -0.19). Generally, MC strength's correlation with AT(N) biomarkers exceeded SUVR. CONCLUSIONS Individuals with normal cognition and disrupted DMN metabolic connectivity face an elevated cognitive decline risk linked to Aβ, preceding metabolic issues.
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Affiliation(s)
- Qi Zhang
- School of Communication & Information Engineering, Shanghai University, Shanghai, China, 200444; School of Life Sciences, Shanghai University, Shanghai, China, 200444
| | - Fangjie Li
- Shanghai University of Traditional Chinese Medicine, Shanghai, China, 201203
| | - Min Wei
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China, 100053
| | - Min Wang
- School of Life Sciences, Shanghai University, Shanghai, China, 200444
| | - Luyao Wang
- School of Life Sciences, Shanghai University, Shanghai, China, 200444
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China, 100053; School of Biomedical Engineering, Hainan University, Haikou, China, 570228; Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China, 100053; National Clinical Research Center for Geriatric Diseases, Beijing, China, 100053.
| | - Jiehui Jiang
- School of Life Sciences, Shanghai University, Shanghai, China, 200444.
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Li YL, Wu JJ, Li WK, Gao X, Wei D, Xue X, Hua XY, Zheng MX, Xu JG. Effects of individual metabolic brain network changes co-affected by T2DM and aging on the probabilities of T2DM: protective and risk factors. Cereb Cortex 2024; 34:bhad439. [PMID: 37991271 DOI: 10.1093/cercor/bhad439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/23/2023] Open
Abstract
Neuroimaging markers for risk and protective factors related to type 2 diabetes mellitus are critical for clinical prevention and intervention. In this work, the individual metabolic brain networks were constructed with Jensen-Shannon divergence for 4 groups (elderly type 2 diabetes mellitus and healthy controls, and middle-aged type 2 diabetes mellitus and healthy controls). Regional network properties were used to identify hub regions. Rich-club, feeder, and local connections were subsequently obtained, intergroup differences in connections and correlations between them and age (or fasting plasma glucose) were analyzed. Multinomial logistic regression was performed to explore effects of network changes on the probability of type 2 diabetes mellitus. The elderly had increased rich-club and feeder connections, and decreased local connection than the middle-aged among type 2 diabetes mellitus; type 2 diabetes mellitus had decreased rich-club and feeder connections than healthy controls. Protective factors including glucose metabolism in triangle part of inferior frontal gyrus, metabolic connectivity between triangle of the inferior frontal gyrus and anterior cingulate cortex, degree centrality of putamen, and risk factors including metabolic connectivities between triangle of the inferior frontal gyrus and Heschl's gyri were identified for the probability of type 2 diabetes mellitus. Metabolic interactions among critical brain regions increased in type 2 diabetes mellitus with aging. Individual metabolic network changes co-affected by type 2 diabetes mellitus and aging were identified as protective and risk factors for the likelihood of type 2 diabetes mellitus, providing guiding evidence for clinical interventions.
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Affiliation(s)
- Yu-Lin Li
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Wei-Kai Li
- School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai 200233, China
| | - Dong Wei
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Xin Xue
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Jian-Guang Xu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
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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 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Van Laere K, Ceccarini J, Gebruers J, Goffin K, Boon E. Simultaneous 18F-FDG PET/MR metabolic and structural changes in visual snow syndrome and diagnostic use. EJNMMI Res 2022; 12:77. [PMID: 36583806 PMCID: PMC9803799 DOI: 10.1186/s13550-022-00949-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 12/21/2022] [Indexed: 12/31/2022] Open
Abstract
PURPOSE Visual snow syndrome (VSS) is a recently recognized chronic neurologic condition characterized by the constant perceiving of tiny flickering dots throughout the entire visual field. Metabolic overactivity and grey matter volume increase in the lingual gyrus has been reported. We investigated this by 18F-FDG PET/MR in comparison to healthy controls. Aside from voxel-based characterization, the classification accuracy of volume-of-interest (VOI)-based multimodal assessment was evaluated, also in comparison with visual analysis. METHODS Simultaneous 18F-FDG PET and MR imaging was performed in 7 patients with VSS (24.6 ± 5.7 years; 5 M/2F) and 15 age-matched healthy controls (CON) (28.0 ± 5.3 years; 8 M/7F). SPM12 and voxel-based morphometric analysis was performed. A VOI-based discriminant analysis was performed with relative 18F-FDG uptake, MR grey matter (GM) volumes and their combination. A visual analysis was done by two blinded experienced readers. RESULTS Relative increased hypermetabolism was found in VSS patients in the lingual gyrus and cuneus (pFWE < 0.05, peak change + 24%), and hypometabolism in the mesiotemporal cortex (pheight,uncorr < 0.001, peak change - 14%). VSS patients also had increased GM volume in the limbic system and frontotemporal cortex bilaterally (pFWE < 0.05), and in the left secondary and associative visual cortex and in the left lingual gyrus (pheight,uncorr < 0.001). Discriminant analysis resulted in 100% correct classification accuracy for 18F-FDG with lingual gyrus, cuneus and lateral occipital lobe (BA 17 and BA 18) as main discriminators. Unimodal MR- and combined 18F-FDG + MR classification resulted in an accuracy of 91% and 95%, respectively. Visual analysis of 18F-FDG was highly observer dependent. CONCLUSION Patients with VSS have highly significant structural and metabolic abnormalities in the visual and limbic system. VOI-based discriminant analysis of 18F-FDG PET allows reliable individual classification versus controls, whereas visual analysis of experienced observers was highly variable. Further investigation in larger series, also in comparison to VSS mimicking disorders such as migraine, is warranted. TRAIL REGISTRATION Retrospectively registered at clinicaltrials.gov under NCT05569733 on Oct 5, 2022.
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Affiliation(s)
- Koen Van Laere
- grid.410569.f0000 0004 0626 3338Nuclear Medicine, University Hospitals Leuven, UZ Leuven, Campus Gasthuisberg, Nucleaire Geneeskunde, E901, Herestraat 49, 3000 Leuven, Belgium ,grid.5596.f0000 0001 0668 7884Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium ,grid.5596.f0000 0001 0668 7884Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Jenny Ceccarini
- grid.5596.f0000 0001 0668 7884Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium ,grid.5596.f0000 0001 0668 7884Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium
| | - Juanito Gebruers
- grid.410569.f0000 0004 0626 3338Nuclear Medicine, University Hospitals Leuven, UZ Leuven, Campus Gasthuisberg, Nucleaire Geneeskunde, E901, Herestraat 49, 3000 Leuven, Belgium
| | - Karolien Goffin
- grid.410569.f0000 0004 0626 3338Nuclear Medicine, University Hospitals Leuven, UZ Leuven, Campus Gasthuisberg, Nucleaire Geneeskunde, E901, Herestraat 49, 3000 Leuven, Belgium ,grid.5596.f0000 0001 0668 7884Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Elizabet Boon
- grid.410569.f0000 0004 0626 3338Division of Neurology and Psychiatry, University Hospitals Leuven and UPC Kortenberg, Leuven, Belgium
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Seiffert AP, Gómez-Grande A, Alonso-Gómez L, Méndez-Guerrero A, Villarejo-Galende A, Gómez EJ, Sánchez-González P. Differences in Striatal Metabolism in [ 18F]FDG PET in Parkinson's Disease and Atypical Parkinsonism. Diagnostics (Basel) 2022; 13:diagnostics13010006. [PMID: 36611298 PMCID: PMC9818161 DOI: 10.3390/diagnostics13010006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/12/2022] [Accepted: 12/18/2022] [Indexed: 12/24/2022] Open
Abstract
Neurodegenerative parkinsonisms affect mainly cognitive and motor functions and are syndromes of overlapping symptoms and clinical manifestations such as tremor, rigidness, and bradykinesia. These include idiopathic Parkinson's disease (PD) and the atypical parkinsonisms, namely progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), multiple system atrophy (MSA) and dementia with Lewy body (DLB). Differences in the striatal metabolism among these syndromes are evaluated using [18F]FDG PET, caused by alterations to the dopaminergic activity and neuronal loss. A study cohort of three patients with PD, 29 with atypical parkinsonism (10 PSP, 6 CBD, 2 MSA, 7 DLB, and 4 non-classifiable), and a control group of 25 patients with normal striatal metabolism is available. Standardized uptake value ratios (SUVR) are extracted from the striatum, and the caudate and the putamen separately. SUVRs are compared among the study groups. In addition, hemispherical and caudate-putamen differences are evaluated in atypical parkinsonisms. Striatal hypermetabolism is detected in patients with PD, while atypical parkinsonisms show hypometabolism, compared to the control group. Hemispherical differences are observed in CBD, MSA and DLB, with the latter also showing statistically significant caudate-putamen asymmetry (p = 0.018). These results indicate disease-specific metabolic uptake patterns in the striatum that can support the differential diagnosis.
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Affiliation(s)
- Alexander P. Seiffert
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain
- Correspondence: (A.P.S.); (P.S.-G.)
| | - Adolfo Gómez-Grande
- Department of Nuclear Medicine, Hospital Universitario 12 de Octubre, 28041 Madrid, Spain
- Facultad de Medicina, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Laura Alonso-Gómez
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | | | - Alberto Villarejo-Galende
- Facultad de Medicina, Universidad Complutense de Madrid, 28040 Madrid, Spain
- Department of Neurology, Hospital Universitario 12 de Octubre, 28041 Madrid, Spain
- Group of Neurodegenerative Diseases, Hospital 12 de Octubre Research Institute (imas12), 28041 Madrid, Spain
- Biomedical Research Networking Center in Neurodegenerative Diseases (CIBERNED), 28029 Madrid, Spain
| | - Enrique J. Gómez
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Patricia Sánchez-González
- Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Correspondence: (A.P.S.); (P.S.-G.)
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Andersen KB, Hansen AK, Knudsen K, Schacht AC, Damholdt MF, Brooks DJ, Borghammer P. Healthy brain aging assessed with [ 18F]FDG and [ 11C]UCB-J PET. Nucl Med Biol 2022; 112-113:52-8. [PMID: 35820300 DOI: 10.1016/j.nucmedbio.2022.06.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 06/21/2022] [Accepted: 06/28/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND The average human lifespan has increased dramatically over the past century. However, molecular and physiological alterations of the healthy brain during aging remain incompletely understood. Generalized synaptic restructuring may contribute to healthy aging and the reduced metabolism observed in the aged brain. The aim of this study was to assess healthy brain aging using [18F]FDG as a measure of cerebral glucose consumption and [11C]UCB-J PET as an indicator of synaptic density. METHOD Using in vivo PET imaging and the novel synaptic-vesicle-glycoprotein 2A (SV2A) radioligand [11C]UCB-J alongside with the fluorodeoxyglucose radioligand [18F]FDG, we obtained SUVR-1 values for 14 pre-defined volume-of-interest brain regions defined on MRI T1 scans. Regional differences in relative [18F]FDG and [11C]UCB-J uptake were investigated using a voxel-wise approach. Finally, correlations between [11C]UCB-J, [18F]FDG PET, and age were examined. RESULTS We found widespread cortical reduction of synaptic density in a cohort of older HC subjects (N = 15) compared with young HC subjects (N = 11). However, no reduction persisted after partial volume correction and corrections for multiple comparison. Our study confirms previously reported synaptic stability during aging. Regional differences in relative [18F]FDG and [11C]UCB-J uptake were observed with up to 20 % higher [11C]UCB-J uptake in the amygdala and temporal lobe and up to 34 % higher glucose metabolism in thalamus, striatum, occipital, parietal and frontal cortex. CONCLUSION In vivo PET using [11C]UCB-J does not support declining synaptic density levels during aging. Thus, loss of synaptic density may be unrelated to aging and does not seem to be a sufficient explanation for the recognized reduction in brain metabolism during aging. Our study also demonstrates that the relationship between glucose consumption and synaptic density is not uniform throughout the human brain with implications for our understanding of neuroenergetics.
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Li YL, Wu JJ, Ma J, Li SS, Xue X, Wei D, Shan CL, Hua XY, Zheng MX, Xu JG. Alteration of the Individual Metabolic Network of the Brain Based on Jensen-Shannon Divergence Similarity Estimation in Elderly Patients With Type 2 Diabetes Mellitus. Diabetes 2022; 71:894-905. [PMID: 35133397 DOI: 10.2337/db21-0600] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 02/03/2022] [Indexed: 11/13/2022]
Abstract
The aim of this study was to investigate the interactive effect between aging and type 2 diabetes mellitus (T2DM) on brain glucose metabolism, individual metabolic connectivity, and network properties. Using a 2 × 2 factorial design, 83 patients with T2DM (40 elderly and 43 middle-aged) and 69 sex-matched healthy control subjects (HCs) (34 elderly and 35 middle-aged) underwent 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance scanning. Jensen-Shannon divergence was applied to construct individual metabolic connectivity and networks. The topological properties of the networks were quantified using graph theoretical analysis. The general linear model was used to mainly estimate the interaction effect between aging and T2DM on glucose metabolism, metabolic connectivity, and network. There was an interaction effect between aging and T2DM on glucose metabolism, metabolic connectivity, and regional metabolic network properties (all P < 0.05). The post hoc analyses showed that compared with elderly HCs and middle-aged patients with T2DM, elderly patients with T2DM had decreased glucose metabolism, increased metabolic connectivity, and regional metabolic network properties in cognition-related brain regions (all P < 0.05). Age and fasting plasma glucose had negative correlations with glucose metabolism and positive correlations with metabolic connectivity. Elderly patients with T2DM had glucose hypometabolism, strengthened functional integration, and increased efficiency of information communication mainly located in cognition-related brain regions. Metabolic connectivity pattern changes might be compensatory changes for glucose hypometabolism.
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Affiliation(s)
- Yu-Lin Li
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jie Ma
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Si-Si Li
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Xue
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dong Wei
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chun-Lei Shan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Mertens N, Sunaert S, Van Laere K, Koole M. The Effect of Aging on Brain Glucose Metabolic Connectivity Revealed by [18F]FDG PET-MR and Individual Brain Networks. Front Aging Neurosci 2022; 13:798410. [PMID: 35221983 PMCID: PMC8865456 DOI: 10.3389/fnagi.2021.798410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 12/27/2021] [Indexed: 11/13/2022] Open
Abstract
Contrary to group-based brain connectivity analyses, the aim of this study was to construct individual brain metabolic networks to determine age-related effects on brain metabolic connectivity. Static 40–60 min [18F]FDG positron emission tomography (PET) images of 67 healthy subjects between 20 and 82 years were acquired with an integrated PET-MR system. Network nodes were defined by brain parcellation using the Schaefer atlas, while connectivity strength between two nodes was determined by comparing the distribution of PET uptake values within each node using a Kullback–Leibler divergence similarity estimation (KLSE). After constructing individual brain networks, a linear and quadratic regression analysis of metabolic connectivity strengths within- and between-networks was performed to model age-dependency. In addition, the age dependency of metrics for network integration (characteristic path length), segregation (clustering coefficient and local efficiency), and centrality (number of hubs) was assessed within the whole brain and within predefined functional subnetworks. Overall, a decrease of metabolic connectivity strength with healthy aging was found within the whole-brain network and several subnetworks except within the somatomotor, limbic, and visual network. The same decrease of metabolic connectivity was found between several networks across the whole-brain network and the functional subnetworks. In terms of network topology, a less integrated and less segregated network was observed with aging, while the distribution and the number of hubs did not change with aging, suggesting that brain metabolic networks are not reorganized during the adult lifespan. In conclusion, using an individual brain metabolic network approach, a decrease in metabolic connectivity strength was observed with healthy aging, both within the whole brain and within several predefined networks. These findings can be used in a diagnostic setting to differentiate between age-related changes in brain metabolic connectivity strength and changes caused by early development of neurodegeneration.
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Affiliation(s)
- Nathalie Mertens
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- *Correspondence: Nathalie Mertens,
| | - Stefan Sunaert
- Translational MRI, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - Koen Van Laere
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Division of Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
| | - Michel Koole
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
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