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Wang L, Xu H, Wang M, Brendel M, Rominger A, Shi K, Han Y, Jiang J. A metabolism-functional connectome sparse coupling method to reveal imaging markers for Alzheimer's disease based on simultaneous PET/MRI scans. Hum Brain Mapp 2023; 44:6020-6030. [PMID: 37740923 PMCID: PMC10619407 DOI: 10.1002/hbm.26493] [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: 05/07/2023] [Revised: 09/01/2023] [Accepted: 09/08/2023] [Indexed: 09/25/2023] Open
Abstract
Abnormal glucose metabolism and hemodynamic changes in the brain are closely related to cognitive function, providing complementary information from distinct biochemical and physiological processes. However, it remains unclear how to effectively integrate these two modalities across distinct brain regions. In this study, we developed a connectome-based sparse coupling method for hybrid PET/MRI imaging, which could effectively extract imaging markers of Alzheimer's disease (AD) in the early stage. The FDG-PET and resting-state fMRI data of 56 healthy controls (HC), 54 subjective cognitive decline (SCD), and 27 cognitive impairment (CI) participants due to AD were obtained from SILCODE project (NCT03370744). For each participant, the metabolic connectome (MC) was constructed by Kullback-Leibler divergence similarity estimation, and the functional connectome (FC) was constructed by Pearson correlation. Subsequently, we measured the coupling strength between MC and FC at various sparse levels, assessed its stability, and explored the abnormal coupling strength along the AD continuum. Results showed that the sparse MC-FC coupling index was stable in each brain network and consistent across subjects. It was more normally distributed than other traditional indexes and captured more SCD-related brain areas, especially in the limbic and default mode networks. Compared to other traditional indices, this index demonstrated best classification performance. The AUC values reached 0.748 (SCD/HC) and 0.992 (CI/HC). Notably, we found a significant correlation between abnormal coupling strength and neuropsychological scales (p < .05). This study provides a clinically relevant tool for hybrid PET/MRI imaging, allowing for exploring imaging markers in early stage of AD and better understanding the pathophysiology along the AD continuum.
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Affiliation(s)
- Luyao Wang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Huanyu Xu
- School of Communication and Information EngineeringShanghai UniversityShanghaiChina
| | - Min Wang
- School of Life SciencesShanghai UniversityShanghaiChina
| | - Matthias Brendel
- Department of Nuclear MedicineUniversity Hospital of Munich, Ludwig Maximilian University of MunichMunichGermany
| | - Axel Rominger
- Department of Nuclear MedicineInselspital, University Hospital BernBernSwitzerland
| | - Kuangyu Shi
- Department of Nuclear MedicineInselspital, University Hospital BernBernSwitzerland
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- Center of Alzheimer's DiseaseBeijing Institute for Brain DisordersBeijingChina
- National Clinical Research Center for Geriatric DisordersBeijingChina
- Hainan UniversityHaikouChina
| | - Jiehui Jiang
- School of Life SciencesShanghai UniversityShanghaiChina
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Bhujbal SS, Kad MM, Patole VC. Recent diagnostic techniques for the detection of Alzheimer's disease: a short review. Ir J Med Sci 2023; 192:2417-2426. [PMID: 36525239 DOI: 10.1007/s11845-022-03244-y] [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: 07/28/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022]
Abstract
Alzheimer's disease (AD) is a neurological condition that affects millions of individuals around the world and for which there are few effective therapies. Dementia is characterized by the formation of senile plaques and neurofibrillary tangles, which is followed by neurotoxicity, which results in memory loss and mortality. Pathogenesis occurs several years before the onset of disease. As the disease-modifying drugs are most effective in the early stages of Alzheimer's disease, biomarkers for early detection of disease and their development are crucial. This review discusses the diagnostic utility, benefits, and limitations of traditional techniques such as neuroimaging, cognitive testing, positron emission tomography, and biomarkers, as well as the novel techniques such as artificial intelligence, machine learning, immunotherapy, and blood test approaches for early detection, understanding, and treatment of AD.
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Affiliation(s)
- Santosh S Bhujbal
- Dr. D. Y. Patil Institute of Pharmaceutical Sciences & Research, Pimpri, Pune, India.
| | - Minal M Kad
- Dr. D. Y. Patil Institute of Pharmaceutical Sciences & Research, Pimpri, Pune, India
| | - Vinita C Patole
- Dr. D. Y. Patil Institute of Pharmaceutical Sciences & Research, Pimpri, Pune, India
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Zhang J, He X, Liu Y, Cai Q, Chen H, Qing L. Multi-modal cross-attention network for Alzheimer's disease diagnosis with multi-modality data. Comput Biol Med 2023; 162:107050. [PMID: 37269680 DOI: 10.1016/j.compbiomed.2023.107050] [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: 11/19/2022] [Revised: 04/26/2023] [Accepted: 05/03/2023] [Indexed: 06/05/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder, the most common cause of dementia, so the accurate diagnosis of AD and its prodromal stage mild cognitive impairment (MCI) is significant. Recent studies have demonstrated that multiple neuroimaging and biological measures contain complementary information for diagnosis. Many existing multi-modal models based on deep learning simply concatenate each modality's features despite substantial differences in representation spaces. In this paper, we propose a novel multi-modal cross-attention AD diagnosis (MCAD) framework to learn the interaction between modalities for better playing their complementary roles for AD diagnosis with multi-modal data including structural magnetic resonance imaging (sMRI), fluorodeoxyglucose-positron emission tomography (FDG-PET) and cerebrospinal fluid (CSF) biomarkers. Specifically, the imaging and non-imaging representations are learned by the image encoder based on cascaded dilated convolutions and CSF encoder, respectively. Then, a multi-modal interaction module is introduced, which takes advantage of cross-modal attention to integrate imaging and non-imaging information and reinforce relationships between these modalities. Moreover, an extensive objective function is designed to reduce the discrepancy between modalities for effectively fusing the features of multi-modal data, which could further improve the diagnosis performance. We evaluate the effectiveness of our proposed method on the ADNI dataset, and the extensive experiments demonstrate that our MCAD achieves superior performance for multiple AD-related classification tasks, compared to several competing methods. Also, we investigate the importance of cross-attention and the contribution of each modality to the diagnostics performance. The experimental results demonstrate that combining multi-modality data via cross-attention is helpful for accurate AD diagnosis.
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Affiliation(s)
- Jin Zhang
- College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Xiaohai He
- College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Yan Liu
- Department of Neurology, The Affiliated Hospital of Southwest Jiaotong University, The Third People's Hospital of Chengdu, Chengdu, Sichuan, 610031, China
| | - Qingyan Cai
- Department of Geriatric Medicine, The Fourth People's Hospital of Chengdu, Chengdu, Sichuan, 610036, China
| | - Honggang Chen
- College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Linbo Qing
- College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
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Choi HJ, Seo M, Kim A, Park SH. Generation of Conventional 18F-FDG PET Images from 18F-Florbetaben PET Images Using Generative Adversarial Network: A Preliminary Study Using ADNI Dataset. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1281. [PMID: 37512092 PMCID: PMC10385186 DOI: 10.3390/medicina59071281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/07/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023]
Abstract
Background and Objectives: 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) (PETFDG) image can visualize neuronal injury of the brain in Alzheimer's disease. Early-phase amyloid PET image is reported to be similar to PETFDG image. This study aimed to generate PETFDG images from 18F-florbetaben PET (PETFBB) images using a generative adversarial network (GAN) and compare the generated PETFDG (PETGE-FDG) with real PETFDG (PETRE-FDG) images using the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR). Materials and Methods: Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, 110 participants with both PETFDG and PETFBB images at baseline were included. The paired PETFDG and PETFBB images included six and four subset images, respectively. Each subset image had a 5 min acquisition time. These subsets were randomly sampled and divided into 249 paired PETFDG and PETFBB subset images for the training datasets and 95 paired subset images for the validation datasets during the deep-learning process. The deep learning model used in this study is composed of a GAN with a U-Net. The differences in the SSIM and PSNR values between the PETGE-FDG and PETRE-FDG images in the cycleGAN and pix2pix models were evaluated using the independent Student's t-test. Statistical significance was set at p ≤ 0.05. Results: The participant demographics (age, sex, or diagnosis) showed no statistically significant differences between the training (82 participants) and validation (28 participants) groups. The mean SSIM between the PETGE-FDG and PETRE-FDG images was 0.768 ± 0.135 for the cycleGAN model and 0.745 ± 0.143 for the pix2pix model. The mean PSNR was 32.4 ± 9.5 and 30.7 ± 8.0. The PETGE-FDG images of the cycleGAN model showed statistically higher mean SSIM than those of the pix2pix model (p < 0.001). The mean PSNR was also higher in the PETGE-FDG images of the cycleGAN model than those of pix2pix model (p < 0.001). Conclusions: We generated PETFDG images from PETFBB images using deep learning. The cycleGAN model generated PETGE-FDG images with a higher SSIM and PSNR values than the pix2pix model. Image-to-image translation using deep learning may be useful for generating PETFDG images. These may provide additional information for the management of Alzheimer's disease without extra image acquisition and the consequent increase in radiation exposure, inconvenience, or expenses.
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Affiliation(s)
- Hyung Jin Choi
- Department of Nuclear Medicine, Ulsan University Hospital, Ulsan 44033, Republic of Korea
| | - Minjung Seo
- Department of Nuclear Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 44033, Republic of Korea
| | - Ahro Kim
- Department of Neurology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 44033, Republic of Korea
| | - Seol Hoon Park
- Department of Nuclear Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 44033, Republic of Korea
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Aramadaka S, Mannam R, Sankara Narayanan R, Bansal A, Yanamaladoddi VR, Sarvepalli SS, Vemula SL. Neuroimaging in Alzheimer's Disease for Early Diagnosis: A Comprehensive Review. Cureus 2023; 15:e38544. [PMID: 37273363 PMCID: PMC10239271 DOI: 10.7759/cureus.38544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/03/2023] [Indexed: 06/06/2023] Open
Abstract
Alzheimer's disease (AD) is the most common cause of dementia in the elderly, affecting roughly half of those over the age of 85. We briefly discussed the risk factors, epidemiology, and treatment options for AD. The development of therapeutic therapies operating very early in the disease cascade has been spurred by the realization that the disease process begins at least a decade or more before the manifestation of symptoms. Thus, the clinical significance of early diagnosis was emphasized. Using various keywords, a literature search was carried out using PubMed and other databases. For inclusion, pertinent articles were chosen and reviewed. This article has reviewed different neuroimaging techniques that are considered advanced tools to aid in establishing a diagnosis and highlighted the advantages as well as disadvantages of those techniques. Besides, the prevalence of several in vivo biomarkers aided in discriminating affected individuals from healthy controls in the early stages of the disease. Each imaging method has its advantages and disadvantages, hence no single imaging approach can be the optimum modality for diagnosis. This article also commented on a better approach to using these techniques to increase the likelihood of an early diagnosis.
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Affiliation(s)
| | - Raam Mannam
- Research, Narayana Medical College, Nellore, IND
| | | | - Arpit Bansal
- Research, Narayana Medical College, Nellore, IND
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Mirkin S, Albensi BC. Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease? Front Aging Neurosci 2023; 15:1094233. [PMID: 37187577 PMCID: PMC10177660 DOI: 10.3389/fnagi.2023.1094233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 03/27/2023] [Indexed: 05/17/2023] Open
Abstract
Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.
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Affiliation(s)
- Sophia Mirkin
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States
| | - Benedict C. Albensi
- Barry and Judy Silverman College of Pharmacy, Nova Southeastern University, Fort Lauderdale, FL, United States
- St. Boniface Hospital Research, Winnipeg, MB, Canada
- University of Manitoba, Winnipeg, MB, Canada
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Zhong G, Long H, Zhou T, Liu Y, Zhao J, Han J, Yang X, Yu Y, Chen F, Shi S. Blood-brain barrier Permeable nanoparticles for Alzheimer's disease treatment by selective mitophagy of microglia. Biomaterials 2022; 288:121690. [PMID: 35965114 DOI: 10.1016/j.biomaterials.2022.121690] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 05/30/2022] [Accepted: 07/18/2022] [Indexed: 11/30/2022]
Abstract
Current treatments for Alzheimer's disease (AD) that focus on inhibition of Aβ aggregation failed to show effectiveness in people who already had Alzheimer's symptoms. Strategies that synergistically exert neuroprotection and alleviation of oxidative stress could be a promising approach to correct the pathological brain microenvironment. Based on the key roles of microglia in modulation of AD microenvironment, we describe here the development of Prussian blue/polyamidoamine (PAMAM) dendrimer/Angiopep-2 (PPA) nanoparticles that can regulate the mitophagy of microglia as a potential AD treatment. PPA nanoparticles exhibit superior blood-brain barrier (BBB) permeability and exert synergistic effects of ROS scavenging and restoration of mitochondrial function of microglia. PPA nanoparticles effectively reduce neurotoxic Aβ aggregate and rescue the cognitive functions in APP/PS1 model mice. Together, our data suggest that these multifunctional dendrimer nanoparticles exhibit efficient neuroprotection and microglia modulation and can be exploited as a promising approach for the treatment of AD.
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Affiliation(s)
- Gang Zhong
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, China; Center for Materials Synthetic Biology, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Huiping Long
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, China
| | - Tian Zhou
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yisi Liu
- Center for Materials Synthetic Biology, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jianping Zhao
- Center for Materials Synthetic Biology, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jinyu Han
- Center for Materials Synthetic Biology, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Xiaohu Yang
- Center for Materials Synthetic Biology, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yin Yu
- Center for Materials Synthetic Biology, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Fei Chen
- Center for Materials Synthetic Biology, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Shengliang Shi
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530007, China.
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Kim J, Jeong M, Stiles WR, Choi HS. Neuroimaging Modalities in Alzheimer's Disease: Diagnosis and Clinical Features. Int J Mol Sci 2022; 23:6079. [PMID: 35682758 PMCID: PMC9181385 DOI: 10.3390/ijms23116079] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 11/17/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease causing progressive cognitive decline until eventual death. AD affects millions of individuals worldwide in the absence of effective treatment options, and its clinical causes are still uncertain. The onset of dementia symptoms indicates severe neurodegeneration has already taken place. Therefore, AD diagnosis at an early stage is essential as it results in more effective therapy to slow its progression. The current clinical diagnosis of AD relies on mental examinations and brain imaging to determine whether patients meet diagnostic criteria, and biomedical research focuses on finding associated biomarkers by using neuroimaging techniques. Multiple clinical brain imaging modalities emerged as potential techniques to study AD, showing a range of capacity in their preciseness to identify the disease. This review presents the advantages and limitations of brain imaging modalities for AD diagnosis and discusses their clinical value.
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Affiliation(s)
- JunHyun Kim
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.K.); (M.J.); (W.R.S.)
- Department of Cogno-Mechatronics Engineering, Pusan National University, Busan 46241, Korea
| | - Minhong Jeong
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.K.); (M.J.); (W.R.S.)
| | - Wesley R. Stiles
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.K.); (M.J.); (W.R.S.)
| | - Hak Soo Choi
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.K.); (M.J.); (W.R.S.)
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Senthilkumar T, Kumarganesh S, Sivakumar P, Periyarselvam K. Primitive detection of Alzheimer’s disease using neuroimaging: A progression model for Alzheimer’s disease: Their applications, benefits, and drawbacks. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-220628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Alzheimer’s disease (A.D.) is the most widespread type of Dementia, and it is not a curable neurodegenerative disease that affects millions of older people. Researchers were able to use their understanding of Alzheimer’s disease risk variables to develop enrichment processes for longitudinal imaging studies. Using this method, they reduced their sample size and study time. This paper describes the primitive detective of Alzheimer’s diseases using Neuroimaging techniques. Several preprocessing methods were used to ensure that the dataset was ready for subsequent feature extraction and categorization. The noise was reduced by converting and averaging many scan frames from real to DCT space. Both sides of the averaged image were filtered and combined into a single shot after being converted to real space. InceptionV3 and DenseNet201 are two pre-trained models used in the suggested model. The PCA approach was used to select the traits, and the resulting explained variance ratio was 0.99The Simons Foundation Autism Research Initiative (SFARI)—Simon’s Simplex Collection (SSC)—and UCI machine learning datasets showed that our method is faster and more successful at identifying complete long-risk patterns when compared to existing methods.
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Affiliation(s)
- T. Senthilkumar
- GRT Institute of Engineering and Technology, Tiruttani, Tamilnadu, India
| | | | - P. Sivakumar
- GRT Institute of Engineering and Technology, Tiruttani, Tamilnadu, India
| | - K. Periyarselvam
- GRT Institute of Engineering and Technology, Tiruttani, Tamilnadu, India
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Assessment of the In Vivo Relationship Between Cerebral Hypometabolism, Tau Deposition, TSPO Expression, and Synaptic Density in a Tauopathy Mouse Model: a Multi-tracer PET Study. Mol Neurobiol 2022; 59:3402-3413. [PMID: 35312967 PMCID: PMC9148291 DOI: 10.1007/s12035-022-02793-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 03/05/2022] [Indexed: 11/03/2022]
Abstract
Cerebral glucose hypometabolism is a typical hallmark of Alzheimer’s disease (AD), usually associated with ongoing neurodegeneration and neuronal dysfunction. However, underlying pathological processes are not fully understood and reproducibility in animal models is not well established. The aim of the present study was to investigate the regional interrelation of glucose hypometabolism measured by [18F]FDG positron emission tomography (PET) with various molecular targets of AD pathophysiology using the PET tracers [18F]PI-2620 for tau deposition, [18F]DPA-714 for TSPO expression associated with neuroinflammation, and [18F]UCB-H for synaptic density in a transgenic tauopathy mouse model. Seven-month-old rTg4510 mice (n = 8) and non-transgenic littermates (n = 8) were examined in a small animal PET scanner with the tracers listed above. Hypometabolism was observed throughout the forebrain of rTg4510 mice. Tau pathology, increased TSPO expression, and synaptic loss were co-localized in the cortex and hippocampus and correlated with hypometabolism. In the thalamus, however, hypometabolism occurred in the absence of tau-related pathology. Thus, cerebral hypometabolism was associated with two regionally distinct forms of molecular pathology: (1) characteristic neuropathology of the Alzheimer-type including synaptic degeneration and neuroinflammation co-localized with tau deposition in the cerebral cortex, and (2) pathological changes in the thalamus in the absence of other markers of AD pathophysiology, possibly reflecting downstream or remote adaptive processes which may affect functional connectivity. Our study demonstrates the feasibility of a multitracer approach to explore complex interactions of distinct AD-pathomechanisms in vivo in a small animal model. The observations demonstrate that multiple, spatially heterogeneous pathomechanisms can contribute to hypometabolism observed in AD mouse models and they motivate future longitudinal studies as well as the investigation of possibly comparable pathomechanisms in human patients.
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Zhang SS, Zhu L, Peng Y, Zhang L, Chao FL, Jiang L, Xiao Q, Liang X, Tang J, Yang H, He Q, Guo YJ, Zhou CN, Tang Y. Long-term running exercise improves cognitive function and promotes microglial glucose metabolism and morphological plasticity in the hippocampus of APP/PS1 mice. J Neuroinflammation 2022; 19:34. [PMID: 35123512 PMCID: PMC8817568 DOI: 10.1186/s12974-022-02401-5] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 01/24/2022] [Indexed: 02/06/2023] Open
Abstract
Background The role of physical exercise in the prevention of Alzheimer’s disease (AD) has been widely studied. Microglia play an important role in AD. Triggering receptor expressed in myeloid cells 2 (TREM2) is expressed on microglia and is known to mediate microglial metabolic activity and brain glucose metabolism. However, the relationship between brain glucose metabolism and microglial metabolic activity during running exercise in APP/PS1 mice remains unclear. Methods Ten-month-old male APP/PS1 mice and wild-type mice were randomly divided into sedentary groups or running groups (AD_Sed, WT_Sed, AD_Run and WT_Run, n = 20/group). Running mice had free access to a running wheel for 3 months. Behavioral tests, [18]F-FDG-PET and hippocampal RNA-Seq were performed. The expression levels of microglial glucose transporter (GLUT5), TREM2, soluble TREM2 (sTREM2), TYRO protein tyrosine kinase binding protein (TYROBP), secreted phosphoprotein 1 (SPP1), and phosphorylated spleen tyrosine kinase (p-SYK) were estimated by western blot or ELISA. Immunohistochemistry, stereological methods and immunofluorescence were used to investigate the morphology, proliferation and activity of microglia. Results Long-term voluntary running significantly improved cognitive function in APP/PS1 mice. Although there were few differentially expressed genes (DEGs), gene set enrichment analysis (GSEA) showed enriched glycometabolic pathways in APP/PS1 running mice. Running exercise increased FDG uptake in the hippocampus of APP/PS1 mice, as well as the protein expression of GLUT5, TREM2, SPP1 and p-SYK. The level of sTREM2 decreased in the plasma of APP/PS1 running mice. The number of microglia, the length and endpoints of microglial processes, and the ratio of GLUT5+/IBA1+ microglia were increased in the dentate gyrus (DG) of APP/PS1 running mice. Running exercise did not alter the number of 5-bromo-2′-deoxyuridine (BrdU)+/IBA1+ microglia but reduced the immunoactivity of CD68 in the hippocampus of APP/PS1 mice. Conclusions Running exercise inhibited TREM2 shedding and maintained TREM2 protein levels, which were accompanied by the promotion of brain glucose metabolism, microglial glucose metabolism and morphological plasticity in the hippocampus of AD mice. Microglia might be a structural target responsible for the benefits of running exercise in AD. Promoting microglial glucose metabolism and morphological plasticity modulated by TREM2 might be a novel strategy for AD treatment. Supplementary Information The online version contains supplementary material available at 10.1186/s12974-022-02401-5.
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Chen MK, Mecca AP, Naganawa M, Gallezot JD, Toyonaga T, Mondal J, Finnema SJ, Lin SF, O’Dell RS, McDonald JW, Michalak HR, Vander Wyk B, Nabulsi NB, Huang Y, Arnsten AFT, van Dyck CH, Carson RE. Comparison of [ 11C]UCB-J and [ 18F]FDG PET in Alzheimer's disease: A tracer kinetic modeling study. J Cereb Blood Flow Metab 2021; 41:2395-2409. [PMID: 33757318 PMCID: PMC8393289 DOI: 10.1177/0271678x211004312] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 01/29/2021] [Accepted: 02/21/2021] [Indexed: 11/16/2022]
Abstract
[11C]UCB-J PET for synaptic vesicle glycoprotein 2 A (SV2A) has been proposed as a suitable marker for synaptic density in Alzheimer's disease (AD). We compared [11C]UCB-J binding for synaptic density and [18F]FDG uptake for metabolism (correlated with neuronal activity) in 14 AD and 11 cognitively normal (CN) participants. We assessed both absolute and relative outcome measures in brain regions of interest, i.e., K1 or R1 for [11C]UCB-J perfusion, VT (volume of distribution) or DVR to cerebellum for [11C]UCB-J binding to SV2A; and Ki or KiR to cerebellum for [18F]FDG metabolism. [11C]UCB-J binding and [18F]FDG metabolism showed a similar magnitude of reduction in the medial temporal lobe of AD -compared to CN participants. However, the magnitude of reduction of [11C]UCB-J binding in neocortical regions was less than that observed with [18F]FDG metabolism. Inter-tracer correlations were also higher in the medial temporal regions between synaptic density and metabolism, with lower correlations in neocortical regions. [11C]UCB-J perfusion showed a similar pattern to [18F]FDG metabolism, with high inter-tracer regional correlations. In summary, we conducted the first in vivo PET imaging of synaptic density and metabolism in the same AD participants and reported a concordant reduction in medial temporal regions but a discordant reduction in neocortical regions.
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Affiliation(s)
- Ming-Kai Chen
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Adam P Mecca
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Mika Naganawa
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Jean-Dominique Gallezot
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Jayanta Mondal
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Sjoerd J Finnema
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Shu-fei Lin
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Ryan S O’Dell
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Julia W McDonald
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Hannah R Michalak
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Brent Vander Wyk
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Nabeel B Nabulsi
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Yiyun Huang
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
| | - Amy FT Arnsten
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | | | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA
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Amini M, Pedram MM, Moradi A, Jamshidi M, Ouchani M. Single and Combined Neuroimaging Techniques for Alzheimer's Disease Detection. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:9523039. [PMID: 34335726 PMCID: PMC8292054 DOI: 10.1155/2021/9523039] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 06/04/2021] [Accepted: 06/30/2021] [Indexed: 02/07/2023]
Abstract
Alzheimer's disease (AD) consists of the gradual process of decreasing volume and quality of neuron connection in the brain, which consists of gradual synaptic integrity and loss of cognitive functions. In recent years, there has been significant attention in AD classification and early detection with machine learning algorithms. There are different neuroimaging techniques for capturing data and using it for the classification task. Input data as images will help machine learning models to detect different biomarkers for AD classification. This marker has a more critical role for AD detection than other diseases because beta-amyloid can extract complex structures with some metal ions. Most researchers have focused on using 3D and 4D convolutional neural networks for AD classification due to reasonable amounts of data. Also, combination neuroimaging techniques like functional magnetic resonance imaging and positron emission tomography for AD detection have recently gathered much attention. However, gathering a combination of data can be expensive, complex, and tedious. For time consumption reasons, most patients prefer to throw one of the neuroimaging techniques. So, in this review article, we have surveyed different research studies with various neuroimaging techniques and ML methods to see the effect of using combined data as input. The result has shown that the use of the combination method would increase the accuracy of AD detection. Also, according to the sensitivity metrics from different machine learning methods, MRI and fMRI showed promising results.
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Affiliation(s)
- Morteza Amini
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Shahid Beheshti University, Tehran, Iran
| | - Mir Mohsen Pedram
- Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Tehran, Iran
| | - Alireza Moradi
- Department of Clinical Psychology, Faculty of Psychology and Educational Science, Kharazmi University, Tehran, Iran
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
| | - Mahdieh Jamshidi
- Department of Mathematical Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
| | - Mahshad Ouchani
- Institute for Cognitive and Brain Science, Shahid Beheshti University, Tehran, Iran
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14
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Lin W, Gao Q, Du M, Chen W, Tong T. Multiclass diagnosis of stages of Alzheimer's disease using linear discriminant analysis scoring for multimodal data. Comput Biol Med 2021; 134:104478. [PMID: 34000523 DOI: 10.1016/j.compbiomed.2021.104478] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/05/2021] [Accepted: 05/05/2021] [Indexed: 12/15/2022]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease, and mild cognitive impairment (MCI) is a transitional stage between normal control (NC) and AD. A multiclass classification of AD is a difficult task because there are multiple similarities between neighboring groups. The performance of classification can be improved by using multimodal data, but the improvement could be limited with inefficient fusion of multimodal data. This study aims to develop a framework for AD multiclass diagnosis with a linear discriminant analysis (LDA) scoring method to fuse multimodal data more efficiently. Magnetic resonance imaging, positron emission tomography, cerebrospinal fluid biomarkers, and genetic features were first preprocessed by performing age correction, feature selection, and feature reduction. Then, they were individually scored using LDA, and the scores that represent the AD pathological progress in different modalities were obtained. Finally, an extreme learning machine-based decision tree was established to perform multiclass diagnosis using these scores. The experiments were conducted on the AD Neuroimaging Initiative dataset, and accuracies of 66.7% and 57.3% and F1-scores of 64.9% and 55.7% were achieved in three- and four-way classifications, respectively. The results also showed that the proposed framework achieved a better performance than the method that did not score multimodal data and the methods in previous studies, thereby indicating that the LDA scoring strategy is an efficient way for multimodalities fusion in AD multiclass classification.
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Affiliation(s)
- Weiming Lin
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, 361024, China; Fujian Key Laboratory of Communication Network and Information Processing, Xiamen University of Technology, Xiamen, 361024, China
| | - Qinquan Gao
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350116, China; Imperial Vision Technology, Fuzhou, 350001, China
| | - Min Du
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350116, China; Fujian Provincial Key Laboratory of Eco-industrial Green Technology, Wuyi University, Wuyishan, 354300, China
| | - Weisheng Chen
- Department of Thoracic Surgery, Fujian Cancer Hospital, Fuzhou, 350001, China
| | - Tong Tong
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350116, China; Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou, 350116, China.
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15
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van Oostveen WM, de Lange ECM. Imaging Techniques in Alzheimer's Disease: A Review of Applications in Early Diagnosis and Longitudinal Monitoring. Int J Mol Sci 2021; 22:ijms22042110. [PMID: 33672696 PMCID: PMC7924338 DOI: 10.3390/ijms22042110] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/15/2021] [Accepted: 02/16/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting many individuals worldwide with no effective treatment to date. AD is characterized by the formation of senile plaques and neurofibrillary tangles, followed by neurodegeneration, which leads to cognitive decline and eventually death. INTRODUCTION In AD, pathological changes occur many years before disease onset. Since disease-modifying therapies may be the most beneficial in the early stages of AD, biomarkers for the early diagnosis and longitudinal monitoring of disease progression are essential. Multiple imaging techniques with associated biomarkers are used to identify and monitor AD. AIM In this review, we discuss the contemporary early diagnosis and longitudinal monitoring of AD with imaging techniques regarding their diagnostic utility, benefits and limitations. Additionally, novel techniques, applications and biomarkers for AD research are assessed. FINDINGS Reduced hippocampal volume is a biomarker for neurodegeneration, but atrophy is not an AD-specific measure. Hypometabolism in temporoparietal regions is seen as a biomarker for AD. However, glucose uptake reflects astrocyte function rather than neuronal function. Amyloid-β (Aβ) is the earliest hallmark of AD and can be measured with positron emission tomography (PET), but Aβ accumulation stagnates as disease progresses. Therefore, Aβ may not be a suitable biomarker for monitoring disease progression. The measurement of tau accumulation with PET radiotracers exhibited promising results in both early diagnosis and longitudinal monitoring, but large-scale validation of these radiotracers is required. The implementation of new processing techniques, applications of other imaging techniques and novel biomarkers can contribute to understanding AD and finding a cure. CONCLUSIONS Several biomarkers are proposed for the early diagnosis and longitudinal monitoring of AD with imaging techniques, but all these biomarkers have their limitations regarding specificity, reliability and sensitivity. Future perspectives. Future research should focus on expanding the employment of imaging techniques and identifying novel biomarkers that reflect AD pathology in the earliest stages.
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Affiliation(s)
- Wieke M. van Oostveen
- Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands;
| | - Elizabeth C. M. de Lange
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
- Correspondence: ; Tel.: +31-71-527-6330
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16
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Joosten L, Boss M, Jansen T, Brom M, Buitinga M, Aarntzen E, Eriksson O, Johansson L, de Galan B, Gotthardt M. Molecular Imaging of Diabetes. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00041-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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17
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Drzezga A, Bischof GN, Giehl K, van Eimeren T. PET and SPECT Imaging of Neurodegenerative Diseases. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00085-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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18
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Han X, Wu P, Alberts I, Zhou H, Yu H, Bargiotas P, Yakushev I, Wang J, Höglinger G, Förster S, Bassetti C, Oertel W, Schwaiger M, Huang SC, Cumming P, Rominger A, Jiang J, Zuo C, Shi K. Characterizing the heterogeneous metabolic progression in idiopathic REM sleep behavior disorder. NEUROIMAGE-CLINICAL 2020; 27:102294. [PMID: 32570206 PMCID: PMC7322340 DOI: 10.1016/j.nicl.2020.102294] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Revised: 05/10/2020] [Accepted: 05/11/2020] [Indexed: 11/28/2022]
Abstract
Imaging biomarkers of the metabolic trajectory from HC, iRBD and PD are identified. Frontal, limbic and occipital brain regions as imaging biomarkers in PD. Frontal, limbic and occipital brain regions as imaging biomarkers of the phenoconversion from iRBD to PD.
Objective Idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) is a prodromal stage of synucleinopathies such as Parkinson’s disease (PD). Positron emission tomography (PET) with 18F-FDG reveals metabolic perturbations, which are scored by spatial covariance analysis. However, the resultant pattern scores do not capture the spatially heterogeneous trajectories of metabolic changes between individual brain regions. Assuming metabolic progression occurs as a continuum from the healthy control (HC) condition to iRBD and then PD, we investigated spatial dynamics of progressively perturbed glucose metabolism in a cross-sectional study. Methods 19 iRBD patients, 38 PD patients and 19 HC subjects underwent 18F-FDG PET. The images were spatially normalized, scaled to the global mean uptake, and automatically parcellated. We contrasted regional metabolism by group, and allocated the inferred progression to one of several possible trajectories. We further investigated the correlations between 18F-FDG uptake and the disease duration in the iRBD and PD groups, respectively. We also explored relationships between 18F-FDG uptake and the Unified Parkinson’s Disease Rating Scale motor (UPDRS III) scores in the PD group. Results PD patients exhibited more extensive relative hyper- and hypo-metabolism than iRBD patients. We identified three dynamic metabolic trajectories, cross-sectional hypo- or hypermetabolism, cross-sectionally unchanged hypo- or hypermetabolism, cross-sectionally late hypo- or hypermetabolism, appearing only in the contrast of PD with iRBD. No correlation was found between relative 18F-FDG metabolism and disease duration in the iRBD group. Regional hyper- and hypo-metabolism in the PD patients correlated with disease duration or clinical UPDRS III scores. Conclusion Cerebral metabolism changes heterogeneously in a continuum extending from HC to iRBD and PD groups in this preliminary study. The distinctive metabolic trajectories point towards a potential neuroimaging biomarker for conversion of iRBD to frank PD, which should be amenable to advanced pattern recognition analysis in future longitudinal studies.
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Affiliation(s)
- Xianhua Han
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Ping Wu
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Ian Alberts
- Department of Nuclear Medicine, University of Bern, Switzerland
| | - Hucheng Zhou
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication ,Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Huan Yu
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Panagiotis Bargiotas
- Department of Neurology, University Hospital Bern (Inselspital) and University of Bern, Bern, Switzerland; Department of Neurology, Medical School, University of Cyprus, Nicosia, Cyprus
| | - Igor Yakushev
- Department of Nuclear Medicine, Technische Universität München, Munich, Germany
| | - Jian Wang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | | | - Stefan Förster
- Department of Nuclear Medicine, Technische Universität München, Munich, Germany; Department of Nuclear Medicine, Klinikum Bayreuth, Germany
| | - Claudio Bassetti
- Department of Neurology, University Hospital Bern (Inselspital) and University of Bern, Bern, Switzerland
| | | | - Markus Schwaiger
- Klinikum r. d. Isar, Technische Universität München, Munich, Germany
| | - Sung-Cheng Huang
- Department of Molecular and Medical Pharmacology, UCLA, Los Angeles, USA
| | - Paul Cumming
- Department of Nuclear Medicine, University of Bern, Switzerland; School of Psychology and Counselling and IHBI, Queensland University of Technology, Brisbane, Australia
| | - Axel Rominger
- Department of Nuclear Medicine, University of Bern, Switzerland
| | - Jiehui Jiang
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication ,Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China; Human Phenome Institute, Fudan University, Shanghai, China; Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
| | - Kuangyu Shi
- Department of Nuclear Medicine, University of Bern, Switzerland; Dept. Informatics, Technische Universität München, Munich, Germany
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Ryu JC, Zimmer ER, Rosa-Neto P, Yoon SO. Consequences of Metabolic Disruption in Alzheimer's Disease Pathology. Neurotherapeutics 2019; 16:600-610. [PMID: 31270743 PMCID: PMC6694332 DOI: 10.1007/s13311-019-00755-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Alzheimer's disease (AD) is an irreversible, progressive disease that slowly destroys cognitive function, such as thinking, remembering, and reasoning, to a level that one cannot carry out a daily living. As people live longer, the risk of developing AD has increased to 1 in 10 among people who are older than 65 and to almost 1 in 2 among those who are older than 85 according to a 2019 Alzheimer's Association report. As a most common cause of dementia, AD accounts for 60-80% of all dementia cases. AD is characterized by amyloid plaques and neurofibrillary tangles, composed of extracellular aggregates of amyloid-β peptides and intracellular aggregates of hyperphosphorylated tau, respectively. Besides plaques and tangles, AD pathology includes synaptic dysfunction including loss of synapses, inflammation, brain atrophy, and brain hypometabolism, all of which contribute to progressive cognitive decline. Recent genetic studies of sporadic cases of AD have identified a score of risk factors, as reported by Hollingworth et al. (Nat Genet 43:429-435, 2001) and Lambert et al. (Nat Genet 45:1452-1458, 2013). Of all these genes, apolipoprotein E4 (APOE4) still presents the biggest risk factor for sporadic cases of AD, as stated in Saunders et al. (Neurology 43:1467-1472, 1993): depending on whether you have 1 or 2 copies of APOE4 allele, the risk increases from 3- to 12-fold, respectively, in line with Genin et al. (Mol Psychiatry 16:903-907, 2011). Besides these genetic risk factors, having type 2 diabetes (T2D), a chronic metabolic disease, is known to increase the AD risk by at least 2-fold when these individuals age, conforming to Sims-Robinson et al. (Nat Rev Neurol 6:551-559, 2010). Diabetes is reaching a pandemic scale with over 422 million people diagnosed worldwide in 2014 according to World Health Organization. Although what proportion of these diabetic patients develop AD is not known, even if 10% of diabetic patients develop AD later in their life, it would double the number of AD patients in the world. Better understanding between T2D and AD is of paramount of importance for the future. The goal of this review is to examine our current understanding on metabolic dysfunction in AD, so that a potential target can be identified in the near future.
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Affiliation(s)
- J C Ryu
- Department of Biological Chemistry & Pharmacology, Ohio State University, Columbus, OH, USA
| | - E R Zimmer
- Department of Pharmacology, UFRGS, Porto Alegre, Brazil
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
- Graduate Program in Biological Sciences: Pharmacology and Therapeutics, UFRGS, Porto Alegre, Brazil
- Brain Institute of Rio Grande do Sul (BraIns), Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil
| | - P Rosa-Neto
- Montreal Neurological Institute, Montreal, Canada
| | - S O Yoon
- Department of Biological Chemistry & Pharmacology, Ohio State University, Columbus, OH, USA.
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20
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Dong Y, Wang Q, Yao H, Xiao Y, Wei J, Xie P, Hu J, Chen W, Tang Y, Zhou H, Liu J. A promising structural magnetic resonance imaging assessment in patients with preclinical cognitive decline and diabetes mellitus. J Cell Physiol 2019; 234:16838-16846. [PMID: 30786010 DOI: 10.1002/jcp.28359] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 01/30/2019] [Accepted: 02/01/2019] [Indexed: 01/18/2023]
Abstract
Subjective cognitive decline (SCD) is frequently reported in diabetic patients. Diabetes mellitus (DM) is associated with changes in the microstructure of the brain arise in diabetic patients, including changes in gray matter volume (GMV). However, the underlying mechanisms of changes in GMV in DM patients with cognitive impairment remain uncertain. Here, we present an overview of amyloid-β-dependent cognitive impairment in DM patients with SCD. Moreover, we review the evolving insights from studies on the GMV changes in GMV and cognitive dysfunction to which provide the mechanisms of cognitive impairment in T2DM. Ultimately, the novel structural magnetic resonance imaging (MRI) protocol was used for detecting neuroimaging biomarkers that can predict the clinical outcomes in diabetic patients with SCD. A reliable MRI protocol would be helpful to detect neurobiomarkers, and to understand the pathological mechanisms of preclinical cognitive impairment in diabetic patients.
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Affiliation(s)
- Yulan Dong
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Qi Wang
- Department of Radiology, the Hunan Province Hospital, Changsha, China
| | - Hailun Yao
- Institute of Pharmacy and Medical Technology, Hunan Polytechnic of Environment and Biology, Hengyang, Hunan, China
| | - Yawen Xiao
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Jiaohong Wei
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Peihan Xie
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Jun Hu
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Wen Chen
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Yan Tang
- Department of Ultrasound, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Hong Zhou
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China.,Hengyang Medical College, University of South China, Hengyang, China
| | - Jincai Liu
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China
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Tigano V, Cascini GL, Sanchez-Castañeda C, Péran P, Sabatini U. Neuroimaging and Neurolaw: Drawing the Future of Aging. Front Endocrinol (Lausanne) 2019; 10:217. [PMID: 31024455 PMCID: PMC6463811 DOI: 10.3389/fendo.2019.00217] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 03/18/2019] [Indexed: 11/13/2022] Open
Abstract
Human brain-aging is a complex, multidimensional phenomenon. Knowledge of the numerous aspects that revolve around it is therefore essential if not only the medical issues, but also the social, psychological, and legal issues related to this phenomenon are to be managed correctly. In the coming decades, it will be necessary to find solutions to the management of the progressive aging of the population so as to increase the number of individuals that achieve successful aging. The aim of this article is to provide a current overview of the physiopathology of brain aging and of the role and perspectives of neuroimaging in this context. The progressive development of neuroimaging has opened new perspectives in clinical and basic research and it has modified the concept of brain aging. Neuroimaging will play an increasingly important role in the definition of the individual's brain aging in every phase of the physiological and pathological process. However, when the process involved in age-related brain cognitive diseases is being investigated, factors that might affect this process on a clinical and behavioral level (genetic susceptibility, risks factors, endocrine changes) cannot be ignored but must, on the contrary, be integrated into a neuroimaging evaluation to ensure a correct and global management, and they are therefore discussed in this article. Neuroimaging appears important to the correct management of age-related brain cognitive diseases not only within a medical perspective, but also legal, according to a wider approach based on development of relationship between neuroscience and law. The term neurolaw, the neologism born from the relationship between these two disciplines, is an emerging field of study, that deals with various issues in the impact of neurosciences on individual rights. Neuroimaging, enhancing the detection of physiological and pathological brain aging, could give an important contribution to the field of neurolaw in elderly where the full control of cognitive and volitional functions is necessary to maintain a whole series of rights linked to legal capacity. For this reason, in order to provide the clinician and researcher with a broad view of the brain-aging process, the role of neurolaw will be introduced into the brain-aging context.
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Affiliation(s)
- Vincenzo Tigano
- Department of Juridical, Historical, Economic and Social Sciences, University of Magna Graecia, Catanzaro, Italy
| | - Giuseppe Lucio Cascini
- Department of Experimental and Clinical Medicine, University of Magna Graecia, Catanzaro, Italy
| | | | - Patrice Péran
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France
| | - Umberto Sabatini
- Department of Medical and Surgical Sciences, University of Magna Graecia, Catanzaro, Italy
- *Correspondence: Umberto Sabatini
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Nobili F, Arbizu J, Bouwman F, Drzezga A, Agosta F, Nestor P, Walker Z, Boccardi M. European Association of Nuclear Medicine and European Academy of Neurology recommendations for the use of brain 18 F-fluorodeoxyglucose positron emission tomography in neurodegenerative cognitive impairment and dementia: Delphi consensus. Eur J Neurol 2018; 25:1201-1217. [PMID: 29932266 DOI: 10.1111/ene.13728] [Citation(s) in RCA: 125] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 06/20/2018] [Indexed: 12/17/2022]
Abstract
BACKGROUND AND PURPOSE Recommendations for using fluorodeoxyglucose positron emission tomography (FDG-PET) to support the diagnosis of dementing neurodegenerative disorders are sparse and poorly structured. METHODS Twenty-one questions on diagnostic issues and on semi-automated analysis to assist visual reading were defined. Literature was reviewed to assess study design, risk of bias, inconsistency, imprecision, indirectness and effect size. Critical outcomes were sensitivity, specificity, accuracy, positive/negative predictive value, area under the receiver operating characteristic curve, and positive/negative likelihood ratio of FDG-PET in detecting the target conditions. Using the Delphi method, an expert panel voted for/against the use of FDG-PET based on published evidence and expert opinion. RESULTS Of the 1435 papers, 58 papers provided proper quantitative assessment of test performance. The panel agreed on recommending FDG-PET for 14 questions: diagnosing mild cognitive impairment due to Alzheimer's disease (AD), frontotemporal lobar degeneration (FTLD) or dementia with Lewy bodies (DLB); diagnosing atypical AD and pseudo-dementia; differentiating between AD and DLB, FTLD or vascular dementia, between DLB and FTLD, and between Parkinson's disease and progressive supranuclear palsy; suggesting underlying pathophysiology in corticobasal degeneration and progressive primary aphasia, and cortical dysfunction in Parkinson's disease; using semi-automated assessment to assist visual reading. Panellists did not support FDG-PET use for pre-clinical stages of neurodegenerative disorders, for amyotrophic lateral sclerosis and Huntington disease diagnoses, and for amyotrophic lateral sclerosis or Huntington-disease-related cognitive decline. CONCLUSIONS Despite limited formal evidence, panellists deemed FDG-PET useful in the early and differential diagnosis of the main neurodegenerative disorders, and semi-automated assessment helpful to assist visual reading. These decisions are proposed as interim recommendations.
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Affiliation(s)
- F Nobili
- Department of Neuroscience (DINOGMI), University of Genoa and Polyclinic San Martino Hospital, Genoa, Italy
| | - J Arbizu
- Department of Nuclear Medicine, Clinica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - F Bouwman
- Department of Neurology and Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, The Netherlands
| | - A Drzezga
- Department of Nuclear Medicine, University Hospital of Cologne, University of Cologne and German Center for Neurodegenerative Diseases (DZNE), Cologne, Germany
| | - F Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - P Nestor
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Z Walker
- Division of Psychiatry, Essex Partnership University NHS Foundation Trust, University College London, London, UK
| | - M Boccardi
- Department of Psychiatry, Laboratoire du Neuroimagerie du Vieillissement (LANVIE), University of Geneva, Geneva, Switzerland
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Walker Z, Gandolfo F, Orini S, Garibotto V, Agosta F, Arbizu J, Bouwman F, Drzezga A, Nestor P, Boccardi M, Altomare D, Festari C, Nobili F. Clinical utility of FDG PET in Parkinson's disease and atypical parkinsonism associated with dementia. Eur J Nucl Med Mol Imaging 2018; 45:1534-1545. [PMID: 29779045 PMCID: PMC6061481 DOI: 10.1007/s00259-018-4031-2] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 04/16/2018] [Indexed: 12/11/2022]
Abstract
Purpose There are no comprehensive guidelines for the use of FDG PET in the following three clinical scenarios: (1) diagnostic work-up of patients with idiopathic Parkinson’s disease (PD) at risk of future cognitive decline, (2) discriminating idiopathic PD from progressive supranuclear palsy, and (3) identifying the underlying neuropathology in corticobasal syndrome. Methods We therefore performed three literature searches and evaluated the selected studies for quality of design, risk of bias, inconsistency, imprecision, indirectness and effect size. Critical outcomes were the sensitivity, specificity, accuracy, positive/negative predictive value, area under the receiving operating characteristic curve, and positive/negative likelihood ratio of FDG PET in detecting the target condition. Using the Delphi method, a panel of seven experts voted for or against the use of FDG PET based on published evidence and expert opinion. Results Of 91 studies selected from the three literature searches, only four included an adequate quantitative assessment of the performance of FDG PET. The majority of studies lacked robust methodology due to lack of critical outcomes, inadequate gold standard and no head-to-head comparison with an appropriate reference standard. The panel recommended the use of FDG PET for all three clinical scenarios based on nonquantitative evidence of clinical utility. Conclusion Despite widespread use of FDG PET in clinical practice and extensive research, there is still very limited good quality evidence for the use of FDG PET. However, in the opinion of the majority of the panellists, FDG PET is a clinically useful imaging biomarker for idiopathic PD and atypical parkinsonism associated with dementia.
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Affiliation(s)
- Zuzana Walker
- Division of Psychiatry, University College London, London, UK. .,St Margaret's Hospital, Essex Partnership University NHS Foundation Trust, Epping, CM16 6TN, UK.
| | - Federica Gandolfo
- Alzheimer Operative Unit, IRCCS S. Giovanni di Dio, Fatebenefratelli, Brescia, Italy
| | - Stefania Orini
- Alzheimer Operative Unit, IRCCS S. Giovanni di Dio, Fatebenefratelli, Brescia, Italy
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, University Hospitals of Geneva, Geneva University, Geneva, Switzerland
| | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Javier Arbizu
- Department of Nuclear Medicine, Clinica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Femke Bouwman
- Department of Neurology & Alzheimer Center, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, The Netherlands
| | - Alexander Drzezga
- Department of Nuclear Medicine, University Hospital of Cologne, University of Cologne and German Center for Neurodegenerative Diseases (DZNE), Cologne, Germany
| | - Peter Nestor
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Queensland Brain Institute, University of Queensland and the Mater Hospital, Brisbane, Australia
| | - Marina Boccardi
- LANVIE (Laboratoire de Neuroimagerie du Vieillissement), Department of Psychiatry, University of Geneva, Geneva, Switzerland.,LANE - Laboratory of Alzheimer's Neuroimaging & Epidemiology, IRCCS S. Giovanni di Dio, Fatebenefratelli, Brescia, Italy
| | - Daniele Altomare
- LANE - Laboratory of Alzheimer's Neuroimaging & Epidemiology, IRCCS S. Giovanni di Dio, Fatebenefratelli, Brescia, Italy.,Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Cristina Festari
- LANE - Laboratory of Alzheimer's Neuroimaging & Epidemiology, IRCCS S. Giovanni di Dio, Fatebenefratelli, Brescia, Italy.,Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Flavio Nobili
- Department of Neuroscience (DINOGMI), University of Genoa & Clinical Neurology Polyclinic IRCCS San Martino-IST, Genoa, Italy.
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Diagnostic utility of FDG-PET in the differential diagnosis between different forms of primary progressive aphasia. Eur J Nucl Med Mol Imaging 2018; 45:1526-1533. [PMID: 29744573 PMCID: PMC6061469 DOI: 10.1007/s00259-018-4034-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 04/16/2018] [Indexed: 12/14/2022]
Abstract
Purpose A joint effort of the European Association of Nuclear Medicine (EANM) and the European Academy of Neurology (EAN) aims at clinical guidance for the use of FDG-PET in neurodegenerative diseases. This paper addresses the diagnostic utility of FDG-PET over clinical/neuropsychological assessment in the differentiation of the three forms of primary progressive aphasia (PPA). Methods Seven panelists were appointed by the EANM and EAN and a literature search was performed by using harmonized PICO (Population, Intervention, Comparison, Outcome) question keywords. The studies were screened for eligibility, and data extracted to assess their methodological quality. Critical outcomes were accuracy indices in differentiating different PPA clinical forms. Subsequently Delphi rounds were held with the extracted data and quality assessment to reach a consensus based on both literature and expert opinion. Results Critical outcomes for this PICO were available in four of the examined papers. The level of formal evidence supporting clinical utility of FDG-PET in differentiating among PPA variants was considered as poor. However, the consensual recommendation was defined on Delphi round I, with six out of seven panelists supporting clinical use. Conclusions Quantitative evidence demonstrating utility or lack thereof is still missing. Panelists decided consistently to provide interim support for clinical use based on the fact that a typical atrophy or metabolic pattern is needed for PPA according to the diagnostic criteria, and the synaptic failure detected by FDG-PET is an earlier phenomenon than atrophy. Also, a normal FDG-PET points to a non-neurodegenerative cause.
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Clinical utility of FDG-PET for the differential diagnosis among the main forms of dementia. Eur J Nucl Med Mol Imaging 2018; 45:1509-1525. [PMID: 29736698 DOI: 10.1007/s00259-018-4035-y] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 04/18/2018] [Indexed: 12/14/2022]
Abstract
AIM To assess the clinical utility of FDG-PET as a diagnostic aid for differentiating Alzheimer's disease (AD; both typical and atypical forms), dementia with Lewy bodies (DLB), frontotemporal lobar degeneration (FTLD), vascular dementia (VaD) and non-degenerative pseudodementia. METHODS A comprehensive literature search was conducted using the PICO model to extract evidence from relevant studies. An expert panel then voted on six different diagnostic scenarios using the Delphi method. RESULTS The level of empirical study evidence for the use of FDG-PET was considered good for the discrimination of DLB and AD; fair for discriminating FTLD from AD; poor for atypical AD; and lacking for discriminating DLB from FTLD, AD from VaD, and for pseudodementia. Delphi voting led to consensus in all scenarios within two iterations. Panellists supported the use of FDG-PET for all PICOs-including those where study evidence was poor or lacking-based on its negative predictive value and on the assistance it provides when typical patterns of hypometabolism for a given diagnosis are observed. CONCLUSION Although there is an overall lack of evidence on which to base strong recommendations, it was generally concluded that FDG-PET has a diagnostic role in all scenarios. Prospective studies targeting diagnostically uncertain patients for assessing the added value of FDG-PET would be highly desirable.
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Boccardi M, Festari C, Altomare D, Gandolfo F, Orini S, Nobili F, Frisoni GB. Assessing FDG-PET diagnostic accuracy studies to develop recommendations for clinical use in dementia. Eur J Nucl Med Mol Imaging 2018; 45:1470-1486. [DOI: 10.1007/s00259-018-4024-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 04/13/2018] [Indexed: 12/14/2022]
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