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Li W, Sun L, Yue L, Xiao S. Diagnostic and predictive power of plasma proteins in Alzheimer's disease: a cross-sectional and longitudinal study in China. Sci Rep 2024; 14:17557. [PMID: 39080359 PMCID: PMC11289122 DOI: 10.1038/s41598-024-66195-7] [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: 09/06/2023] [Accepted: 06/28/2024] [Indexed: 08/02/2024] Open
Abstract
Convenient and effective biomarkers are essential for the early diagnosis and treatment of Alzheimer's disease (AD). In the cross-sectional study, 103 patients with AD, 82 patients with aMCI and 508 normal controls (NC) were enrolled. The single-molecule array (Simoa) technique was used to assess the levels of plasma proteins, including NfL, T-tau, P-tau-181, Aβ40, Aβ42. Montreal Cognitive Assessment (MoCA) was used to assess the overall cognitive function of all subjects. Moreover, Amyloid PET and structural head MRI were also performed in a subset of the population. In the follow-up, the previous 508 normal older adults were followed up for two years, then COX regression analysis was used to investigate the association between baseline plasma proteins and future cognitive outcomes. NfL, T-tau, P-tau-181, Aβ40, Aβ42 and Aβ42/40 were altered in AD dementia, and NfL, Aβ42 and Aβ42/40 significantly outperformed all plasma proteins in differentiating AD dementia from NC, while NfL and Aβ42/40 could effectively distinguish between aMCI and NC. However, only plasma NfL was associated with future cognitive decline, and it was negatively correlated with MoCA (r = - 0.298, p < 0.001) and the volume of the left globus pallidus (r = - 0.278, p = 0.033). Plasma NfL can help distinguish between cognitively normal and cognitively impaired individuals (MCI/dementia) at the syndrome level. However, since we have not introduced other biomarkers for AD, such as PET CT or cerebrospinal fluid, and have not verified in other neurodegenerative diseases, whether plasma NFL can be used as a biomarker for AD needs to be further studied and explored.
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Affiliation(s)
- Wei Li
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Lin Sun
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Ling Yue
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China.
| | - Shifu Xiao
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China.
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2
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Langley J, Bennett IJ, Hu XP. Examining iron-related off-target binding effects of 18F-AV1451 PET in the cortex of Aβ+ individuals. Eur J Neurosci 2024; 60:3614-3628. [PMID: 38722153 DOI: 10.1111/ejn.16362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 12/22/2023] [Accepted: 04/01/2024] [Indexed: 07/06/2024]
Abstract
The presence of neurofibrillary tangles containing hyper-phosphorylated tau is a characteristic of Alzheimer's disease (AD) pathology. The positron emission tomography (PET) radioligand sensitive to tau neurofibrillary tangles (18F-AV1451) also binds with iron. This off-target binding effect may be enhanced in older adults on the AD spectrum, particularly those with amyloid-positive biomarkers. Here, we examined group differences in 18F-AV1451 PET after controlling for iron-sensitive measures from magnetic resonance imaging (MRI) and its relationships to tissue microstructure and cognition in 40 amyloid beta positive (Aβ+) individuals, 20 amyloid beta negative (Aβ-) with MCI and 31 Aβ- control participants. After controlling for iron, increased 18F-AV1451 PET uptake was found in the temporal lobe and hippocampus of Aβ+ participants compared to Aβ- MCI and control participants. Within the Aβ+ group, significant correlations were seen between 18F-AV1451 PET uptake and tissue microstructure and these correlations remained significant after controlling for iron. These findings indicate that off-target binding of iron to the 18F-AV1451 ligand may not affect its sensitivity to Aβ status or cognition in early-stage AD.
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Affiliation(s)
- Jason Langley
- Center for Advanced Neuroimaging, University of California Riverside, Riverside, California, USA
| | - Ilana J Bennett
- Department of Psychology, University of California Riverside, Riverside, California, USA
| | - Xiaoping P Hu
- Center for Advanced Neuroimaging, University of California Riverside, Riverside, California, USA
- Department of Bioengineering, University of California Riverside, Riverside, California, USA
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3
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Cohen Z, Lau L, Ahmed M, Jack CR, Liu C. Quantitative susceptibility mapping in the brain reflects spatial expression of genes involved in iron homeostasis and myelination. Hum Brain Mapp 2024; 45:e26688. [PMID: 38896001 PMCID: PMC11187871 DOI: 10.1002/hbm.26688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 04/02/2024] [Accepted: 04/05/2024] [Indexed: 06/21/2024] Open
Abstract
Quantitative susceptibility mapping (QSM) is an MRI modality used to non-invasively measure iron content in the brain. Iron exhibits a specific anatomically varying pattern of accumulation in the brain across individuals. The highest regions of accumulation are the deep grey nuclei, where iron is stored in paramagnetic molecule ferritin. This form of iron is considered to be what largely contributes to the signal measured by QSM in the deep grey nuclei. It is also known that QSM is affected by diamagnetic myelin contents. Here, we investigate spatial gene expression of iron and myelin related genes, as measured by the Allen Human Brain Atlas, in relation to QSM images of age-matched subjects. We performed multiple linear regressions between gene expression and the average QSM signal within 34 distinct deep grey nuclei regions. Our results show a positive correlation (p < .05, corrected) between expression of ferritin and the QSM signal in deep grey nuclei regions. We repeated the analysis for other genes that encode proteins thought to be involved in the transport and storage of iron in the brain, as well as myelination. In addition to ferritin, our findings demonstrate a positive correlation (p < .05, corrected) between the expression of ferroportin, transferrin, divalent metal transporter 1, several gene markers of myelinating oligodendrocytes, and the QSM signal in deep grey nuclei regions. Our results suggest that the QSM signal reflects both the storage and active transport of iron in the deep grey nuclei regions of the brain.
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Affiliation(s)
- Zoe Cohen
- Department of Electrical Engineering and Computer SciencesUniversity of California, BerkeleyBerkeleyCaliforniaUSA
| | - Laurance Lau
- Department of Electrical Engineering and Computer SciencesUniversity of California, BerkeleyBerkeleyCaliforniaUSA
| | - Maruf Ahmed
- Department of Electrical Engineering and Computer SciencesUniversity of California, BerkeleyBerkeleyCaliforniaUSA
| | - Clifford R. Jack
- Mayo Foundation for Medical Education and ResearchRochesterMinnesotaUSA
| | - Chunlei Liu
- Department of Electrical Engineering and Computer SciencesUniversity of California, BerkeleyBerkeleyCaliforniaUSA
- Helen Wills Neuroscience InstituteUniversity of California, BerkeleyBerkeleyCaliforniaUSA
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4
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Zhou J, Wearn A, Huck J, Hughes C, Baracchini G, Tremblay-Mercier J, Poirier J, Villeneuve S, Tardif CL, Chakravarty MM, Daugherty AM, Gauthier CJ, Turner GR, Spreng RN. Iron Deposition and Distribution Across the Hippocampus Is Associated with Pattern Separation and Pattern Completion in Older Adults at Risk for Alzheimer's Disease. J Neurosci 2024; 44:e1973232024. [PMID: 38388425 PMCID: PMC11079967 DOI: 10.1523/jneurosci.1973-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/16/2023] [Accepted: 01/03/2024] [Indexed: 02/24/2024] Open
Abstract
Elevated iron deposition in the brain has been observed in older adult humans and persons with Alzheimer's disease (AD), and has been associated with lower cognitive performance. We investigated the impact of iron deposition, and its topographical distribution across hippocampal subfields and segments (anterior, posterior) measured along its longitudinal axis, on episodic memory in a sample of cognitively unimpaired older adults at elevated familial risk for AD (N = 172, 120 females, 52 males; mean age = 68.8 ± 5.4 years). MRI-based quantitative susceptibility maps were acquired to derive estimates of hippocampal iron deposition. The Mnemonic Similarity Task was used to measure pattern separation and pattern completion, two hippocampally mediated episodic memory processes. Greater hippocampal iron load was associated with lower pattern separation and higher pattern completion scores, both indicators of poorer episodic memory. Examination of iron levels within hippocampal subfields across its long axis revealed topographic specificity. Among the subfields and segments investigated here, iron deposition in the posterior hippocampal CA1 was the most robustly and negatively associated with the fidelity memory representations. This association remained after controlling for hippocampal volume and was observed in the context of normal performance on standard neuropsychological memory measures. These findings reveal that the impact of iron load on episodic memory performance is not uniform across the hippocampus. Both iron deposition levels as well as its spatial distribution, must be taken into account when examining the relationship between hippocampal iron and episodic memory in older adults at elevated risk for AD.
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Affiliation(s)
- Jing Zhou
- Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Alfie Wearn
- Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Julia Huck
- Physics Department, Concordia University, Montreal, Quebec H4B 1R6, Canada
- Department of Radiology, Université de Sherbrooke, Sherbrooke, Quebec J1G 1E4, Canada
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Faculty of Science, University of Sherbrooke, Quebec J1K 0A5, Canada
| | - Colleen Hughes
- Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Giulia Baracchini
- Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4, Canada
| | | | - Judes Poirier
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montreal, Quebec H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Sylvia Villeneuve
- Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4, Canada
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montreal, Quebec H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Christine Lucas Tardif
- Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - M Mallar Chakravarty
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Quebec H3A 2B4, Canada
- Cerebral Imaging Centre, Douglas Mental Health Institute Research Centre, Montreal, Quebec H4H 1R3, Canada
| | - Ana M Daugherty
- Department of Psychology and Institute of Gerontology, Wayne State University, Detroit, Michigan 48202
| | - Claudine J Gauthier
- Physics Department, Concordia University, Montreal, Quebec H4B 1R6, Canada
- Montreal Heart Institute, Montreal, Quebec H1T 1C8, Canada
| | - Gary R Turner
- Department of Psychology, York University, Toronto, ON M3J 1P3, Canada
| | - R Nathan Spreng
- Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4, Canada
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montreal, Quebec H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
- Department of Neurology and Neurosurgery, Montréal Neurological Institute, Montréal, Quebec H3A 1A1, Canada
- Departments of Psychiatry and Psychology, McGill University, Montréal, Quebec H3A 1G1, Canada
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Joshi J, Yao M, Kakazu A, Ouyang Y, Duan W, Aggarwal M. Distinguishing microgliosis and tau deposition in the mouse brain using paramagnetic and diamagnetic susceptibility source separation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.11.588962. [PMID: 38659855 PMCID: PMC11042227 DOI: 10.1101/2024.04.11.588962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Tauopathies, including Alzheimer's disease (AD), are neurodegenerative disorders characterized by hyperphosphorylated tau protein aggregates in the brain. In addition to protein aggregates, microglia-mediated inflammation and iron dyshomeostasis are other pathological features observed in AD and other tauopathies. It is known that these alterations at the subcellular level occur much before the onset of macroscopic tissue atrophy or cognitive deficits. The ability to detect these microstructural changes with MRI therefore has substantive importance for improved characterization of disease pathogenesis. In this study, we demonstrate that quantitative susceptibility mapping (QSM) with paramagnetic and diamagnetic susceptibility source separation has the potential to distinguish neuropathological alterations in a transgenic mouse model of tauopathy. 3D multi-echo gradient echo data were acquired from fixed brains of PS19 (Tau) transgenic mice and age-matched wild-type (WT) mice (n = 5 each) at 11.7 T. The multi-echo data were fit to a 3-pool complex signal model to derive maps of paramagnetic component susceptibility (PCS) and diamagnetic component susceptibility (DCS). Group-averaged signal fraction and composite susceptibility maps showed significant region-specific differences between the WT and Tau mouse brains. Significant bilateral increases in PCS and |DCS| were observed in specific hippocampal and cortical sub-regions of the Tau mice relative to WT controls. Comparison with immunohistological staining for microglia (Iba1) and phosphorylated-tau (AT8) further indicated that the PCS and DCS differences corresponded to regional microgliosis and tau deposition in the PS19 mouse brains, respectively. The results demonstrate that quantitative susceptibility source separation may provide sensitive imaging markers to detect distinct pathological alterations in tauopathies.
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Affiliation(s)
- Jayvik Joshi
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Minmin Yao
- Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Aaron Kakazu
- Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yuxiao Ouyang
- Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Wenzhen Duan
- Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Manisha Aggarwal
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Chen H, Yang A, Huang W, Du L, Liu B, Lv K, Luan J, Hu P, Shmuel A, Shu N, Ma G. Associations of quantitative susceptibility mapping with cortical atrophy and brain connectome in Alzheimer's disease: A multi-parametric study. Neuroimage 2024; 290:120555. [PMID: 38447683 DOI: 10.1016/j.neuroimage.2024.120555] [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: 11/11/2023] [Revised: 01/07/2024] [Accepted: 02/24/2024] [Indexed: 03/08/2024] Open
Abstract
Aberrant susceptibility due to iron level abnormality and brain network disconnections are observed in Alzheimer's disease (AD), with disrupted iron homeostasis hypothesized to be linked to AD pathology and neuronal loss. However, whether associations exist between abnormal quantitative susceptibility mapping (QSM), brain atrophy, and altered brain connectome in AD remains unclear. Based on multi-parametric brain imaging data from 30 AD patients and 26 healthy controls enrolled at the China-Japan Friendship Hospital, we investigated the abnormality of the QSM signal and volumetric measure across 246 brain regions in AD patients. The structural and functional connectomes were constructed based on diffusion MRI tractography and functional connectivity, respectively. The network topology was quantified using graph theory analyses. We identified seven brain regions with both reduced cortical thickness and abnormal QSM (p < 0.05) in AD, including the right superior frontal gyrus, left superior temporal gyrus, right fusiform gyrus, left superior parietal lobule, right superior parietal lobule, left inferior parietal lobule, and left precuneus. Correlations between cortical thickness and network topology computed across patients in the AD group resulted in statistically significant correlations in five of these regions, with higher correlations in functional compared to structural topology. We computed the correlation between network topological metrics, QSM value and cortical thickness across regions at both individual and group-averaged levels, resulting in a measure we call spatial correlations. We found a decrease in the spatial correlation of QSM and the global efficiency of the structural network in AD patients at the individual level. These findings may provide insights into the complex relationships among QSM, brain atrophy, and brain connectome in AD.
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Affiliation(s)
- Haojie Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; BABRI Centre, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China; China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; BABRI Centre, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Lei Du
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China; China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Kuan Lv
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China
| | - Jixin Luan
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China; China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Pianpian Hu
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China
| | - Amir Shmuel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Departments of Neurology and Neurosurgery, Physiology, and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; BABRI Centre, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China; China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
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7
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Lee S, Kovacs GG. The Irony of Iron: The Element with Diverse Influence on Neurodegenerative Diseases. Int J Mol Sci 2024; 25:4269. [PMID: 38673855 PMCID: PMC11049980 DOI: 10.3390/ijms25084269] [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: 02/29/2024] [Revised: 04/07/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Iron accumulation in the brain is a common feature of many neurodegenerative diseases. Its involvement spans across the main proteinopathies involving tau, amyloid-beta, alpha-synuclein, and TDP-43. Accumulating evidence supports the contribution of iron in disease pathologies, but the delineation of its pathogenic role is yet challenged by the complex involvement of iron in multiple neurotoxicity mechanisms and evidence supporting a reciprocal influence between accumulation of iron and protein pathology. Here, we review the major proteinopathy-specific observations supporting four distinct hypotheses: (1) iron deposition is a consequence of protein pathology; (2) iron promotes protein pathology; (3) iron protects from or hinders protein pathology; and (4) deposition of iron and protein pathology contribute parallelly to pathogenesis. Iron is an essential element for physiological brain function, requiring a fine balance of its levels. Understanding of disease-related iron accumulation at a more intricate and systemic level is critical for advancements in iron chelation therapies.
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Affiliation(s)
- Seojin Lee
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON M5T 0S8, Canada;
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Gabor G. Kovacs
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON M5T 0S8, Canada;
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
- Edmond J. Safra Program in Parkinson’s Disease, Rossy Program for PSP Research and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, Toronto, ON M5T 2S8, Canada
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8
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Chen L, Shin HG, van Zijl PC, Li X. Exploiting gradient-echo frequency evolution: Probing white matter microstructure and extracting bulk susceptibility-induced frequency for quantitative susceptibility mapping. Magn Reson Med 2024; 91:1676-1693. [PMID: 38102838 PMCID: PMC10880384 DOI: 10.1002/mrm.29958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 10/08/2023] [Accepted: 11/17/2023] [Indexed: 12/17/2023]
Abstract
PURPOSE This work is to investigate the microstructure-induced frequency shift in white matter (WM) with crossing fibers and to separate the microstructure-related frequency shift from the bulk susceptibility-induced frequency shift by model fitting the gradient-echo (GRE) frequency evolution for potentially more accurate quantitative susceptibility mapping (QSM). METHODS A hollow-cylinder fiber model (HCFM) with two fiber populations was developed to investigate GRE frequency evolutions in WM voxels with microstructural orientation dispersion. The simulated and experimentally measured TE-dependent local frequency shift was then fitted to a simplified frequency evolution model to obtain a microstructure-related frequency difference parameter (∆ f $$ \Delta f $$ ) and a TE-independent bulk susceptibility-induced frequency shift (C f $$ {C}_f $$ ). The obtainedC f $$ {C}_f $$ was then used for QSM reconstruction. Reconstruction performances were evaluated using a numerical head phantom and in vivo data and then compared to other multi-echo combination methods. RESULTS GRE frequency evolutions and∆ f $$ \Delta f $$ -based tissue parameters in both parallel and crossing fibers determined from our simulations were comparable to those observed in vivo. The TE-dependent frequency fitting method outperformed other multi-echo combination methods in estimatingC f $$ {C}_f $$ in simulations. The fitted∆ f $$ \Delta f $$ ,C f $$ {C}_f $$ , and QSM could be improved further by navigator-based B0 fluctuation correction. CONCLUSION A HCFM with two fiber populations can be used to characterize microstructure-induced frequency shifts in WM regions with crossing fibers. HCFM-based TE-dependent frequency fitting provides tissue contrast related to microstructure (∆ f $$ \Delta f $$ ) and in addition may help improve the quantification accuracy ofC f $$ {C}_f $$ and the corresponding QSM.
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Affiliation(s)
- Lin Chen
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
| | - Hyeong-Geol Shin
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
| | - Peter C.M. van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
| | - Xu Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
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9
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Du L, Roy S, Wang P, Li Z, Qiu X, Zhang Y, Yuan J, Guo B. Unveiling the future: Advancements in MRI imaging for neurodegenerative disorders. Ageing Res Rev 2024; 95:102230. [PMID: 38364912 DOI: 10.1016/j.arr.2024.102230] [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: 01/11/2024] [Revised: 02/11/2024] [Accepted: 02/11/2024] [Indexed: 02/18/2024]
Abstract
Neurodegenerative disorders represent a significant and growing global health challenge, necessitating continuous advancements in diagnostic tools for accurate and early detection. This work explores the recent progress in Magnetic Resonance Imaging (MRI) techniques and their application in the realm of neurodegenerative disorders. The introductory section provides a comprehensive overview of the study's background, significance, and objectives. Recognizing the current challenges associated with conventional MRI, the manuscript delves into advanced imaging techniques such as high-resolution structural imaging (HR-MRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and positron emission tomography-MRI (PET-MRI) fusion. Each technique is critically examined regarding its potential to address theranostic limitations and contribute to a more nuanced understanding of the underlying pathology. A substantial portion of the work is dedicated to exploring the applications of advanced MRI in specific neurodegenerative disorders, including Parkinson's disease, Alzheimer's disease, Huntington's disease, and Amyotrophic Lateral Sclerosis (ALS). In addressing the future landscape, the manuscript examines technological advances, including the integration of machine learning and artificial intelligence in neuroimaging. The conclusion summarizes key findings, outlines implications for future research, and underscores the importance of these advancements in reshaping our understanding and approach to neurodegenerative disorders.
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Affiliation(s)
- Lixin Du
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen 518110, China.
| | - Shubham Roy
- School of Science, Shenzhen Key Laboratory of Flexible Printed Electronics Technology, Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application, Harbin Institute of Technology, Shenzhen 518055, China
| | - Pan Wang
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen 518110, China
| | - Zhigang Li
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen 518110, China
| | - Xiaoting Qiu
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen 518110, China
| | - Yinghe Zhang
- School of Science, Shenzhen Key Laboratory of Flexible Printed Electronics Technology, Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application, Harbin Institute of Technology, Shenzhen 518055, China
| | - Jianpeng Yuan
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518107, China.
| | - Bing Guo
- School of Science, Shenzhen Key Laboratory of Flexible Printed Electronics Technology, Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application, Harbin Institute of Technology, Shenzhen 518055, China.
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10
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Ghaderi S, Mohammadi S, Nezhad NJ, Karami S, Sayehmiri F. Iron quantification in basal ganglia: quantitative susceptibility mapping as a potential biomarker for Alzheimer's disease - a systematic review and meta-analysis. Front Neurosci 2024; 18:1338891. [PMID: 38469572 PMCID: PMC10925682 DOI: 10.3389/fnins.2024.1338891] [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: 11/15/2023] [Accepted: 02/13/2024] [Indexed: 03/13/2024] Open
Abstract
Introduction Alzheimer's disease (AD), characterized by distinctive pathologies such as amyloid-β plaques and tau tangles, also involves deregulation of iron homeostasis, which may accelerate neurodegeneration. This meta-analysis evaluated the use of quantitative susceptibility mapping (QSM) to detect iron accumulation in the deep gray matter (DGM) of the basal ganglia in AD, contributing to a better understanding of AD progression, and potentially leading to new diagnostic and therapeutic approaches. Methods Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched the PubMed, Scopus, Web of Sciences, and Google Scholar databases up to October 2023 for studies employing QSM in AD research. Eligibility criteria were based on the PECO framework, and we included studies assessing alterations in magnetic susceptibility indicative of iron accumulation in the DGM of patients with AD. After initial screening and quality assessment using the Newcastle-Ottawa Scale, a meta-analysis was conducted to compare iron levels between patients with AD and healthy controls (HCs) using a random-effects model. Results The meta-analysis included nine studies comprising 267 patients with AD and 272 HCs. There were significantly higher QSM values, indicating greater iron deposition, in the putamen (standardized mean difference (SMD) = 1.23; 95% CI: 0.62 to 1.84; p = 0.00), globus pallidus (SMD = 0.79; 95% CI: 0.07 to 1.52; p = 0.03), and caudate nucleus (SMD = 0.72; 95% CI: 0.39 to 1.06; p = 0.00) of AD patients compared to HCs. However, no significant differences were found in the thalamus (SMD = 1.00; 95% CI: -0.42 to 2.43; p = 0.17). The sensitivity analysis indicated that no single study impacted the overall results. Age was identified as a major contributor to heterogeneity across all basal ganglia nuclei in subgroup analysis. Older age (>69 years) and lower male percentage (≤30%) were associated with greater putamen iron increase in patients with AD. Conclusion The study suggests that excessive iron deposition is linked to the basal ganglia in AD, especially the putamen. The study underscores the complex nature of AD pathology and the accumulation of iron, influenced by age, sex, and regional differences, necessitating further research for a comprehensive understanding.
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Affiliation(s)
- Sadegh Ghaderi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Sana Mohammadi
- Department of Medical Sciences, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Nahid Jashire Nezhad
- The Persian Gulf Tropical Medicine Research Center, The Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Shaghayegh Karami
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Fatemeh Sayehmiri
- Skull Base Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Science, Tehran, Iran
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11
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Satoh R, Ali F, Botha H, Lowe VJ, Josephs KA, Whitwell JL. Direct comparison between 18F-Flortaucipir tau PET and quantitative susceptibility mapping in progressive supranuclear palsy. Neuroimage 2024; 286:120509. [PMID: 38184157 PMCID: PMC10868646 DOI: 10.1016/j.neuroimage.2024.120509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/22/2023] [Accepted: 01/03/2024] [Indexed: 01/08/2024] Open
Abstract
PURPOSE The pattern of flortaucipir tau PET uptake is topographically similar to the pattern of magnetic susceptibility in progressive supranuclear palsy (PSP); both with increased signal in subcortical structures such as the basal ganglia and midbrain, suggesting that they may be closely related. However, their relationship remains unknown since no studies have directly compared these two modalities in the same PSP cohort. We hypothesized that some flortaucipir uptake in PSP is associated with magnetic susceptibility, and hence iron deposition. The aim of this study was to evaluate the regional relationship between flortaucipir uptake and magnetic susceptibility and to examine the effects of susceptibility on flortaucipir uptake in PSP. METHODS Fifty PSP patients and 67 cognitively normal controls were prospectively recruited and underwent three Tesla MRI and flortaucipir tau PET scans. Quantitative susceptibility maps were reconstructed from multi-echo gradient-echo MRI images. Region of interest (ROI) analysis was performed to obtain flortaucipir and susceptibility values in the subcortical regions. Relationships between flortaucipir and susceptibility signals were evaluated using partial correlation analysis in the subcortical ROIs and voxel-based analysis in the whole brain. The effects of susceptibility on flortaucipir uptake were examined by using the framework of mediation analysis. RESULTS Both flortaucipir and susceptibility were greater in PSP compared to controls in the putamen, pallidum, subthalamic nucleus, red nucleus, and cerebellar dentate (p<0.05). The ROI-based and voxel-based analyses showed that these two signals were positively correlated in these five regions (r = 0.36-0.59, p<0.05). Mediation analysis showed that greater flortaucipir uptake was partially explained by susceptibility in the putamen, pallidum, subthalamic nucleus, and red nucleus, and fully explained in the cerebellar dentate. CONCLUSIONS These results suggest that some of the flortaucipir uptake in subcortical regions in PSP is related to iron deposition. These findings will contribute to our understanding of the mechanisms underlying flortaucipir tau PET findings in PSP and other neurodegenerative diseases.
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Affiliation(s)
- Ryota Satoh
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Farwa Ali
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, 200 1st St SW, 55905, Rochester, MN, USA
| | | | - Jennifer L Whitwell
- Department of Radiology, Mayo Clinic, 200 1st St SW, 55905, Rochester, MN, USA.
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12
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Sun Y, Yang Z, Deng K, Geng Y, Hu X, Song Y, Jiang R. Histogram analysis of quantitative susceptibility mapping and apparent diffusion coefficient for identifying isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas. Quant Imaging Med Surg 2023; 13:8681-8693. [PMID: 38106258 PMCID: PMC10722066 DOI: 10.21037/qims-23-832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 10/19/2023] [Indexed: 12/19/2023]
Abstract
Background Accurate preoperative identification of isocitrate dehydrogenase (IDH) genotypes and tumor subtypes is highly important for proper treatment planning and prognosis evaluation in patients with glioma. This study aimed to differentiate IDH genotypes and tumor subtypes of adult-type diffuse gliomas using histogram features of quantitative susceptibility mapping (QSM) and apparent diffusion coefficient (ADC). Methods This prospective study enrolled patients with suspected gliomas between March 2019 and January 2022 in a random series. Histogram features of QSM and ADC were extracted from the tumor parenchyma. The Mann-Whitney U test was used to compare the difference in histogram features between different IDH genotypes and among tumor subtypes. Receiver operating characteristic (ROC) curves were constructed to assess the corresponding diagnostic performance. Results This study included 47 patients with histopathologically confirmed adult-type diffuse gliomas. Totals of seven QSM features including 10th percentile (P10), 90th percentile (P90), interquartile range (IQR), maximum, mean absolute deviation (MAD), root mean squared (RMS), and variance, and five ADC features including P10, mean, median, RMS, and skewness exhibited significant differences between different IDH genotypes (P<0.05 for all), with the IQR of QSM demonstrating the highest area under curve (AUC) of 0.774 [95% confidence interval (CI): 0.635-0.913]. For separating tumor subtypes, the IQR of QSM also showed the highest AUC of 0.745 (95% CI: 0.566-0.924) for glioblastoma (GBM) versus astrocytoma and 0.848 (95% CI: 0.706-0.989) for GBM versus oligodendroglioma, but none of the features could discriminate astrocytoma from oligodendroglioma. The combination of the IQR of QSM, P10 of ADC, and age achieved the highest AUC of 0.910 (95% CI: 0.826-0.994) for IDH genotypes, and 0.939 (95% CI: 0.859-1.000) and 0.967 (95% CI: 0.904-1.000) for GBM versus astrocytoma and GBM versus oligodendroglioma, respectively. Conclusions QSM and ADC histogram features may serve as potential imaging markers for noninvasively assessing IDH genotypes and tumor subtypes of adult-type diffuse gliomas. Combining significant features may enhance the diagnostic performance substantially.
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Affiliation(s)
- Yifan Sun
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
- School of Medical Imaging, Fujian Medical University, Fuzhou, China
| | - Zheting Yang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
- School of Medical Imaging, Fujian Medical University, Fuzhou, China
| | - Kaiji Deng
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
- School of Medical Imaging, Fujian Medical University, Fuzhou, China
| | - Yingqian Geng
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
- School of Medical Imaging, Fujian Medical University, Fuzhou, China
| | - Xiaomei Hu
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthcare, Shanghai, China
| | - Rifeng Jiang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
- School of Medical Imaging, Fujian Medical University, Fuzhou, China
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13
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Satoh R, Weigand SD, Pham T, Ali F, Arani A, Senjem ML, Jack CR, Whitwell JL, Josephs KA. Magnetic Susceptibility in Progressive Supranuclear Palsy Variants, Parkinson's Disease, and Corticobasal Syndrome. Mov Disord 2023; 38:2282-2290. [PMID: 37772771 PMCID: PMC10840892 DOI: 10.1002/mds.29613] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 09/11/2023] [Indexed: 09/30/2023] Open
Abstract
BACKGROUND Previous studies have shown that magnetic susceptibility is increased in several subcortical regions in progressive supranuclear palsy (PSP). However, it is still unclear how subcortical and cortical susceptibilities vary across different PSP variants, Parkinson's disease (PD), and corticobasal syndrome (CBS). OBJECTIVE This study aims to clarify the susceptibility profiles in the subcortical and cortical regions in different PSP variants, PD, and CBS. METHODS Sixty-four patients, 20 PSP-Richardson syndrome (PSP-RS), 9 PSP-parkinsonism (PSP-P), 7 PSP-progressive gait freezing, 4 PSP-postural instability, 11 PD, and 13 CBS, and 20 cognitively normal control subjects underwent a 3-Tesla magnetic resonance imaging scan to reconstruct quantitative susceptibility maps. Region-of-interest analysis was performed to obtain susceptibility in several subcortical and cortical regions. Bayesian linear mixed effect models were used to estimate susceptibility within group and differences between groups. RESULTS In the subcortical regions, patients with PSP-RS and PSP-P showed greater susceptibility than control subjects in the pallidum, substantia nigra, red nucleus, and cerebellar dentate (P < 0.05). Patients with PSP-RS also showed greater susceptibility than patients with PSP-progressive gait freezing, PD, and CBS in the red nucleus and cerebellar dentate, and patients with PSP-P showed greater susceptibility than PD in the red nucleus. Patients with PSP-postural instability and CBS showed greater susceptibility than control subjects in the pallidum and substantia nigra. No significant differences were observed in any cortical region. CONCLUSIONS The PSP variants and CBS had different patterns of magnetic susceptibility in the subcortical regions. The findings will contribute to our understanding about iron profiles and pathophysiology of PSP and may provide a potential biomarker to differentiate PSP variants, PD, and CBS. © 2023 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Ryota Satoh
- Department of Neurology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Stephen D Weigand
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA
| | - Thu Pham
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Farwa Ali
- Department of Neurology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Arvin Arani
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Matthew L. Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Information Technology, Mayo Clinic, Rochester, MN, 55905, USA
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14
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Burnham SC, Iaccarino L, Pontecorvo MJ, Fleisher AS, Lu M, Collins EC, Devous MD. A review of the flortaucipir literature for positron emission tomography imaging of tau neurofibrillary tangles. Brain Commun 2023; 6:fcad305. [PMID: 38187878 PMCID: PMC10768888 DOI: 10.1093/braincomms/fcad305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/13/2023] [Accepted: 11/14/2023] [Indexed: 01/09/2024] Open
Abstract
Alzheimer's disease is defined by the presence of β-amyloid plaques and neurofibrillary tau tangles potentially preceding clinical symptoms by many years. Previously only detectable post-mortem, these pathological hallmarks are now identifiable using biomarkers, permitting an in vivo definitive diagnosis of Alzheimer's disease. 18F-flortaucipir (previously known as 18F-T807; 18F-AV-1451) was the first tau positron emission tomography tracer to be introduced and is the only Food and Drug Administration-approved tau positron emission tomography tracer (Tauvid™). It has been widely adopted and validated in a number of independent research and clinical settings. In this review, we present an overview of the published literature on flortaucipir for positron emission tomography imaging of neurofibrillary tau tangles. We considered all accessible peer-reviewed literature pertaining to flortaucipir through 30 April 2022. We found 474 relevant peer-reviewed publications, which were organized into the following categories based on their primary focus: typical Alzheimer's disease, mild cognitive impairment and pre-symptomatic populations; atypical Alzheimer's disease; non-Alzheimer's disease neurodegenerative conditions; head-to-head comparisons with other Tau positron emission tomography tracers; and technical considerations. The available flortaucipir literature provides substantial evidence for the use of this positron emission tomography tracer in assessing neurofibrillary tau tangles in Alzheimer's disease and limited support for its use in other neurodegenerative disorders. Visual interpretation and quantitation approaches, although heterogeneous, mostly converge and demonstrate the high diagnostic and prognostic value of flortaucipir in Alzheimer's disease.
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Affiliation(s)
| | | | | | | | - Ming Lu
- Avid, Eli Lilly and Company, Philadelphia, PA 19104, USA
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15
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Kim HW, Lee S, Yang JH, Moon Y, Lee J, Moon WJ. Cortical Iron Accumulation as an Imaging Marker for Neurodegeneration in Clinical Cognitive Impairment Spectrum: A Quantitative Susceptibility Mapping Study. Korean J Radiol 2023; 24:1131-1141. [PMID: 37899522 PMCID: PMC10613848 DOI: 10.3348/kjr.2023.0490] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 07/19/2023] [Accepted: 08/22/2023] [Indexed: 10/31/2023] Open
Abstract
OBJECTIVE Cortical iron deposition has recently been shown to occur in Alzheimer's disease (AD). In this study, we aimed to evaluate how cortical gray matter iron, measured using quantitative susceptibility mapping (QSM), differs in the clinical cognitive impairment spectrum. MATERIALS AND METHODS This retrospective study evaluated 73 participants (mean age ± standard deviation, 66.7 ± 7.6 years; 52 females and 21 males) with normal cognition (NC), 158 patients with mild cognitive impairment (MCI), and 48 patients with AD dementia. The participants underwent brain magnetic resonance imaging using a three-dimensional multi-dynamic multi-echo sequence on a 3-T scanner. We employed a deep neural network (QSMnet+) and used automatic segmentation software based on FreeSurfer v6.0 to extract anatomical labels and volumes of interest in the cortex. We used analysis of covariance to investigate the differences in susceptibility among the clinical diagnostic groups in each brain region. Multivariable linear regression analysis was performed to study the association between susceptibility values and cognitive scores including the Mini-Mental State Examination (MMSE). RESULTS Among the three groups, the frontal (P < 0.001), temporal (P = 0.004), parietal (P = 0.001), occipital (P < 0.001), and cingulate cortices (P < 0.001) showed a higher mean susceptibility in patients with MCI and AD than in NC subjects. In the combined MCI and AD group, the mean susceptibility in the cingulate cortex (β = -216.21, P = 0.019) and insular cortex (β = -276.65, P = 0.001) were significant independent predictors of MMSE scores after correcting for age, sex, education, regional volume, and APOE4 carrier status. CONCLUSION Iron deposition in the cortex, as measured by QSMnet+, was higher in patients with AD and MCI than in NC participants. Iron deposition in the cingulate and insular cortices may be an early imaging marker of cognitive impairment related neurodegeneration.
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Affiliation(s)
- Hyeong Woo Kim
- Department of Radiology, Konkuk University Medical Center, Seoul, Republic of Korea
| | - Subin Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Jin Ho Yang
- Department of Radiology, Konkuk University Medical Center, Seoul, Republic of Korea
| | - Yeonsil Moon
- Department of Neurology, Konkuk University Medical Center, Seoul, Republic of Korea
- Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Jongho Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Won-Jin Moon
- Department of Radiology, Konkuk University Medical Center, Seoul, Republic of Korea
- Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, Republic of Korea.
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16
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Ikebe Y, Sato R, Amemiya T, Udo N, Matsushima M, Yabe I, Yamaguchi A, Sasaki M, Harada M, Matsukawa N, Kawata Y, Bito Y, Shirai T, Ochi H, Kudo K. Prediction of amyloid positron emission tomography positivity using multiple regression analysis of quantitative susceptibility mapping. Magn Reson Imaging 2023; 103:192-197. [PMID: 37558171 DOI: 10.1016/j.mri.2023.08.002] [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: 03/20/2023] [Revised: 08/04/2023] [Accepted: 08/06/2023] [Indexed: 08/11/2023]
Abstract
PURPOSE To develop a method for predicting amyloid positron emission tomography (PET) positivity based on multiple regression analysis of quantitative susceptibility mapping (QSM). MATERIALS AND METHODS This prospective study included 39 patients with suspected dementia from four centers. QSM images were obtained through a 3-T, three-dimensional radiofrequency-spoiled gradient-echo sequence with multiple echoes. The cortical standard uptake value ratio (SUVR) was obtained using amyloid PET with 18F-flutemetamol, and susceptibility in the brain regions was obtained using QSM. A multiple regression model to predict cortical SUVR was constructed based on susceptibilities in multiple brain regions, with the constraint that cortical SUVR and susceptibility were positively correlated. The discrimination performance of the Aβ-positive and Aβ-negative cohorts was evaluated based on the predicted SUVR using the area under the receiver operating characteristic curve (AUC) and Mann-Whitney U test. RESULTS The correlation coefficients between true and predicted SUVR were increased by incorporating the constraint, and the AUC to discriminate between the Aβ-positive and Aβ-negative cohorts reached to 0.79 (p < 0.01). CONCLUSION These preliminary results suggest that a QSM-based multiple regression model can predict amyloid PET positivity with fair accuracy.
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Affiliation(s)
- Yohei Ikebe
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Hokkaido, Japan; Center for Cause of Death Investigation, Faculty of Medicine, Hokkaido University, Sapporo, Japan
| | - Ryota Sato
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Hokkaido, Japan
| | - Tomoki Amemiya
- Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan
| | - Niki Udo
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Hokkaido, Japan
| | - Masaaki Matsushima
- Department of Neurology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Hokkaido, Japan
| | - Ichiro Yabe
- Department of Neurology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Hokkaido, Japan
| | - Akinori Yamaguchi
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Hokkaido, Japan
| | - Makoto Sasaki
- Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, Iwate Medical University, Iwate, Japan
| | - Masafumi Harada
- Department of Radiology, Tokushima University, Tokushima, Japan
| | | | - Yasuo Kawata
- Radiation Diagnostic Systems Division, FUJIFILM Healthcare Corporation, Tokyo, Japan
| | - Yoshitaka Bito
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Hokkaido, Japan; Radiation Diagnostic Systems Division, FUJIFILM Healthcare Corporation, Tokyo, Japan
| | - Toru Shirai
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Hokkaido, Japan; Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan
| | - Hisaaki Ochi
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Hokkaido, Japan; Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Hokkaido, Japan.
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Kim TJ, Kim MH, Kim JH, Jun JS, Byun JI, Sunwoo JS, Shin JW, Gho SM, Sohn CH, Jung KY. Change of iron content in brain regions after intravenous iron therapy in restless legs syndrome: quantitative susceptibility mapping study. Sleep 2023; 46:zsad154. [PMID: 37257418 DOI: 10.1093/sleep/zsad154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/22/2023] [Indexed: 06/02/2023] Open
Abstract
STUDY OBJECTIVES The pathomechanism of restless legs syndrome (RLS) is related to brain iron deficiency and iron therapy is effective for RLS; however, the effect of iron therapy on human brain iron state has never been studied with magnetic resonance imaging. This study aimed to investigate the change of brain iron concentrations in patients with RLS after intravenous iron therapy using quantitative susceptibility mapping (QSM). METHODS We enrolled 31 RLS patients and 20 healthy controls. All participants underwent initial baseline (t0) assessment using brain magnetic resonance imaging, serum iron status, and sleep questionnaires including international RLS Study Group rating scale (IRLS). RLS patients underwent follow-up tests at 6 and 24 weeks (t1 and t2) after receiving 1000 mg ferric carboxymaltose. Iron content of region-of-interest on QSM images was measured for 13 neural substrates using the fixed-shaped method. RESULTS RLS symptoms evaluated using IRLS were significantly improved after iron treatment (t0: 29.7 ± 6.5, t1: 19.5 ± 8.5, t2: 21.3 ± 10.1; p < .001). There was no significant difference in susceptibility values between the controls and RLS patients at t0. In the caudate nucleus, putamen, and pulvinar thalamus of RLS patients, the QSM values differed significantly for three timepoints (p = .035, .048, and .032, respectively). The post-hoc analysis revealed that the QSM values increased at t1 in the caudate nucleus (66.8 ± 18.0 vs 76.4 ± 16.6, p = .037) and decreased from t1 to t2 in the putamen (69.4 ± 16.3 vs 62.5 ± 13.6, p = .025). Changes in the QSM values for the pulvinar and caudate nuclei at t1 were positively and negatively correlated with symptomatic improvement, respectively (r = 0.361 and -0.466, respectively). CONCLUSIONS Intravenous iron treatment results in changes in brain iron content which correlate to reductions in RLS severity. This suggests a connection between symptom improvement and the associated specific brain regions constituting the sensorimotor network.
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Affiliation(s)
- Tae-Joon Kim
- Department of Neurology, Ajou University School of Medicine, Suwon, Republic of Korea
- Department of Neurology, Ajou University Hospital, Suwon, Republic of Korea
| | - Min Hye Kim
- Department of Neurology, Ajou University Hospital, Suwon, Republic of Korea
| | - Jung Hwan Kim
- Department of Neurology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Jin-Sun Jun
- Department of Neurology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea
| | - Jung-Ick Byun
- Department of Neurology, Kyung Hee University Hospital at Gangdong, Seoul, Republic of Korea
| | - Jun-Sang Sunwoo
- Department of Neurology, Kangbuk Samsung Hospital, Seoul, Republic of Korea
| | - Jung-Won Shin
- Department of Neurology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea
| | - Sung-Min Gho
- MR Clinical Solutions & Research Collaborations, GE Healthcare, Seoul, Republic of Korea
| | - Chul-Ho Sohn
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Ki-Young Jung
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
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Ali S, Zhou J. Highlights on U.S. FDA-approved fluorinated drugs over the past five years (2018-2022). Eur J Med Chem 2023; 256:115476. [PMID: 37207534 PMCID: PMC10247436 DOI: 10.1016/j.ejmech.2023.115476] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/02/2023] [Accepted: 05/08/2023] [Indexed: 05/21/2023]
Abstract
The objective of this review is to provide an update on the fluorine-containing drugs approved by U.S. Food and Drug Administration in the span of past five years (2018-2022). The agency accepted a total of fifty-eight fluorinated entities to diagnose, mitigate and treat a plethora of diseases. Among them, thirty drugs are for therapy of various types of cancers, twelve for infectious diseases, eleven for CNS disorders, and six for some other diseases. These are categorized and briefly discussed based on their therapeutic areas. In addition, this review gives a glimpse about their trade name, date of approval, active ingredients, company developers, indications, and drug mechanisms. We anticipate that this review may inspire the drug discovery and medicinal chemistry community in both industrial and academic settings to explore the fluorinated molecules leading to the discovery of new drugs in the near future.
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Affiliation(s)
- Saghir Ali
- Chemical Biology Program, Department of Pharmacology and Toxicology, University of Texas Medical Branch (UTMB), Galveston, TX, 77555, United States
| | - Jia Zhou
- Chemical Biology Program, Department of Pharmacology and Toxicology, University of Texas Medical Branch (UTMB), Galveston, TX, 77555, United States.
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Schwarz CG, Kremers WK, Arani A, Savvides M, Reid RI, Gunter JL, Senjem ML, Cogswell PM, Vemuri P, Kantarci K, Knopman DS, Petersen RC, Jack CR. A face-off of MRI research sequences by their need for de-facing. Neuroimage 2023; 276:120199. [PMID: 37269958 PMCID: PMC10389782 DOI: 10.1016/j.neuroimage.2023.120199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/19/2023] [Accepted: 05/25/2023] [Indexed: 06/05/2023] Open
Abstract
It is now widely known that research brain MRI, CT, and PET images may potentially be re-identified using face recognition, and this potential can be reduced by applying face-deidentification ("de-facing") software. However, for research MRI sequences beyond T1-weighted (T1-w) and T2-FLAIR structural images, the potential for re-identification and quantitative effects of de-facing are both unknown, and the effects of de-facing T2-FLAIR are also unknown. In this work we examine these questions (where applicable) for T1-w, T2-w, T2*-w, T2-FLAIR, diffusion MRI (dMRI), functional MRI (fMRI), and arterial spin labelling (ASL) sequences. Among current-generation, vendor-product research-grade sequences, we found that 3D T1-w, T2-w, and T2-FLAIR were highly re-identifiable (96-98%). 2D T2-FLAIR and 3D multi-echo GRE (ME-GRE) were also moderately re-identifiable (44-45%), and our derived T2* from ME-GRE (comparable to a typical 2D T2*) matched at only 10%. Finally, diffusion, functional and ASL images were each minimally re-identifiable (0-8%). Applying de-facing with mri_reface version 0.3 reduced successful re-identification to ≤8%, while differential effects on popular quantitative pipelines for cortical volumes and thickness, white matter hyperintensities (WMH), and quantitative susceptibility mapping (QSM) measurements were all either comparable with or smaller than scan-rescan estimates. Consequently, high-quality de-facing software can greatly reduce the risk of re-identification for identifiable MRI sequences with only negligible effects on automated intracranial measurements. The current-generation echo-planar and spiral sequences (dMRI, fMRI, and ASL) each had minimal match rates, suggesting that they have a low risk of re-identification and can be shared without de-facing, but this conclusion should be re-evaluated if they are acquired without fat suppression, with a full-face scan coverage, or if newer developments reduce the current levels of artifacts and distortion around the face.
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Affiliation(s)
- Christopher G Schwarz
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
| | - Walter K Kremers
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Arvin Arani
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Marios Savvides
- CyLab Biometrics Center and Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Robert I Reid
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Jeffrey L Gunter
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Matthew L Senjem
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Petrice M Cogswell
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | | | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
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20
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Si W, Guo Y, Zhang Q, Zhang J, Wang Y, Feng Y. Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs. Front Neurosci 2023; 17:1165446. [PMID: 37383103 PMCID: PMC10293650 DOI: 10.3389/fnins.2023.1165446] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 05/17/2023] [Indexed: 06/30/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) quantifies the distribution of magnetic susceptibility and shows great potential in assessing tissue contents such as iron, myelin, and calcium in numerous brain diseases. The accuracy of QSM reconstruction was challenged by an ill-posed field-to-susceptibility inversion problem, which is related to the impaired information near the zero-frequency response of the dipole kernel. Recently, deep learning methods demonstrated great capability in improving the accuracy and efficiency of QSM reconstruction. However, the construction of neural networks in most deep learning-based QSM methods did not take the intrinsic nature of the dipole kernel into account. In this study, we propose a dipole kernel-adaptive multi-channel convolutional neural network (DIAM-CNN) method for the dipole inversion problem in QSM. DIAM-CNN first divided the original tissue field into high-fidelity and low-fidelity components by thresholding the dipole kernel in the frequency domain, and it then inputs the two components as additional channels into a multichannel 3D Unet. QSM maps from the calculation of susceptibility through multiple orientation sampling (COSMOS) were used as training labels and evaluation reference. DIAM-CNN was compared with two conventional model-based methods [morphology enabled dipole inversion (MEDI) and improved sparse linear equation and least squares (iLSQR) and one deep learning method (QSMnet)]. High-frequency error norm (HFEN), peak signal-to-noise-ratio (PSNR), normalized root mean squared error (NRMSE), and the structural similarity index (SSIM) were reported for quantitative comparisons. Experiments on healthy volunteers demonstrated that the DIAM-CNN results had superior image quality to those of the MEDI, iLSQR, or QSMnet results. Experiments on data with simulated hemorrhagic lesions demonstrated that DIAM-CNN produced fewer shadow artifacts around the bleeding lesion than the compared methods. This study demonstrates that the incorporation of dipole-related knowledge into the network construction has a potential to improve deep learning-based QSM reconstruction.
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Affiliation(s)
- Wenbin Si
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Yihao Guo
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, China
| | - Qianqian Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Jinwei Zhang
- Department of Biomedical Engineering, College of Engineering, Cornell University, Ithaca, NY, United States
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY, United States
| | - Yi Wang
- Department of Biomedical Engineering, College of Engineering, Cornell University, Ithaca, NY, United States
- Department of Radiology, Weill Cornell Medicine, Cornell University, New York, NY, United States
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence and Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
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21
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He XY, Wu BS, Kuo K, Zhang W, Ma Q, Xiang ST, Li YZ, Wang ZY, Dong Q, Feng JF, Cheng W, Yu JT. Association between polygenic risk for Alzheimer's disease and brain structure in children and adults. Alzheimers Res Ther 2023; 15:109. [PMID: 37312172 DOI: 10.1186/s13195-023-01256-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 06/01/2023] [Indexed: 06/15/2023]
Abstract
BACKGROUND The correlations between genetic risk for Alzheimer's disease (AD) with comprehensive brain regions at a regional scale are still not well understood. We aim to explore whether these associations vary across different age stages. METHODS This study used large existing genome-wide association datasets to calculate polygenic risk score (PRS) for AD in two populations from the UK Biobank (N ~ 23 000) and Adolescent Brain Cognitive Development Study (N ~ 4660) who had multimodal macrostructural and microstructural magnetic resonance imaging (MRI) metrics. We used linear mixed-effect models to assess the strength of the association between AD PRS and multiple MRI metrics of regional brain structures at different stages of life. RESULTS Compared to those with lower PRSs, adolescents with higher PRSs had thinner cortex in the caudal anterior cingulate and supramarginal. In the middle-aged and elderly population, AD PRS had correlations with regional structure shrink primarily located in the cingulate, prefrontal cortex, hippocampus, thalamus, amygdala, and striatum, whereas the brain expansion was concentrated near the occipital lobe. Furthermore, both adults and adolescents with higher PRSs exhibited widespread white matter microstructural changes, indicated by decreased fractional anisotropy (FA) or increased mean diffusivity (MD). CONCLUSIONS In conclusion, our results suggest genetic loading for AD may influence brain structures in a highly dynamic manner, with dramatically different patterns at different ages. This age-specific change is consistent with the classical pattern of brain impairment observed in AD patients.
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Affiliation(s)
- Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Kevin Kuo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Qing Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Shi-Tong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yu-Zhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Zi-Yi Wang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China
- ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China.
- ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China.
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22
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Cogswell PM, Fan AP. Multimodal comparisons of QSM and PET in neurodegeneration and aging. Neuroimage 2023; 273:120068. [PMID: 37003447 PMCID: PMC10947478 DOI: 10.1016/j.neuroimage.2023.120068] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/17/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) has been used to study susceptibility changes that may occur based on tissue composition and mineral deposition. Iron is a primary contributor to changes in magnetic susceptibility and of particular interest in applications of QSM to neurodegeneration and aging. Iron can contribute to neurodegeneration through inflammatory processes and via interaction with aggregation of disease-related proteins. To better understand the local susceptibility changes observed on QSM, its signal has been studied in association with other imaging metrics such as positron emission tomography (PET). The associations of QSM and PET may provide insight into the pathophysiology of disease processes, such as the role of iron in aging and neurodegeneration, and help to determine the diagnostic utility of QSM as an indirect indicator of disease processes typically evaluated with PET. In this review we discuss the proposed mechanisms and summarize prior studies of the associations of QSM and amyloid PET, tau PET, TSPO PET, FDG-PET, 15O-PET, and F-DOPA PET in evaluation of neurologic diseases with a focus on aging and neurodegeneration.
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Affiliation(s)
- Petrice M Cogswell
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
| | - Audrey P Fan
- Department of Biomedical Engineering and Department of Neurology, University of California, Davis, 1590 Drew Avenue, Davis, CA 95618, USA
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23
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He J, Peng Y, Fu B, Zhu Y, Wang L, Wang R. msQSM: Morphology-based Self-supervised Deep Learning for Quantitative Susceptibility Mapping. Neuroimage 2023; 275:120181. [PMID: 37220799 DOI: 10.1016/j.neuroimage.2023.120181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 04/20/2023] [Accepted: 05/19/2023] [Indexed: 05/25/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) has been applied to the measurement of iron deposition and the auxiliary diagnosis of neurodegenerative disease. There still exists a dipole inversion problem in QSM reconstruction. Recently, deep learning approaches have been proposed to resolve this problem. However, most of these approaches are supervised methods that need pairs of the input phase and ground-truth. It remains a challenge to train a model for all resolutions without using the ground-truth and only using one resolution data. To address this, we proposed a self-supervised QSM deep learning method based on morphology. It consists of a morphological QSM builder to decouple the dependency of the QSM on acquisition resolution, and a morphological loss to reduce artifacts effectively and save training time efficiently. The proposed method can reconstruct arbitrary resolution QSM on both human data and animal data, regardless of whether the resolution is higher or lower than that of the training set. Our method outperforms the previous best unsupervised method with a 3.6% higher peak signal-to-noise ratio, 16.2% lower normalized root mean square error, and 22.1% lower high-frequency error norm. The morphological loss reduces training time by 22.1% with respect to the cycle gradient loss used in the previous unsupervised methods. Experimental results show that the proposed method accurately measures QSM with arbitrary resolutions, and achieves state-of-the-art results among unsupervised deep learning methods. Research on applications in neurodegenerative diseases found that our method is robust enough to measure significant increase in striatal magnetic susceptibility in patients during Alzheimer's disease progression, as well as significant increase in substantia nigra susceptibility in Parkinson's disease patients, and can be used as an auxiliary differential diagnosis tool for Alzheimer's disease and Parkinson's disease.
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Affiliation(s)
- Junjie He
- Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, No. 2288, Huaxi Avenue, Guiyang, 550002, Guizhou, China; Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, No. 83, Zhongshan Dong Road, Guiyang, 550002, Guizhou, China
| | - Yunsong Peng
- Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, No. 83, Zhongshan Dong Road, Guiyang, 550002, Guizhou, China
| | - Bangkang Fu
- Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, No. 83, Zhongshan Dong Road, Guiyang, 550002, Guizhou, China
| | - Yuemin Zhu
- CREATIS, IRP Metislab, University of Lyon, INSA Lyon, CNRS UMR 5220, Inserm U1294, Lyon, France
| | - Lihui Wang
- Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, No. 2288, Huaxi Avenue, Guiyang, 550002, Guizhou, China
| | - Rongpin Wang
- Department of Radiology, International Exemplary Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, No. 83, Zhongshan Dong Road, Guiyang, 550002, Guizhou, China.
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24
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Jin B, Fei G, Sang S, Zhong C. Identification of biomarkers differentiating Alzheimer's disease from other neurodegenerative diseases by integrated bioinformatic analysis and machine-learning strategies. Front Mol Neurosci 2023; 16:1152279. [PMID: 37234685 PMCID: PMC10205980 DOI: 10.3389/fnmol.2023.1152279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023] Open
Abstract
Background Alzheimer's disease (AD) is the most common neurodegenerative disease, imposing huge mental and economic burdens on patients and society. The specific molecular pathway(s) and biomarker(s) that distinguish AD from other neurodegenerative diseases and reflect the disease progression are still not well studied. Methods Four frontal cortical datasets of AD were integrated to conduct differentially expressed genes (DEGs) and functional gene enrichment analyses. The transcriptional changes after the integrated frontal cortical datasets subtracting the cerebellar dataset of AD were further compared with frontal cortical datasets of frontotemporal dementia and Huntingdon's disease to identify AD-frontal-associated gene expression. Integrated bioinformatic analysis and machine-learning strategies were applied for screening and determining diagnostic biomarkers, which were further validated in another two frontal cortical datasets of AD by receiver operating characteristic (ROC) curves. Results Six hundred and twenty-six DEGs were identified as AD frontal associated, including 580 downregulated genes and 46 upregulated genes. The functional enrichment analysis revealed that immune response and oxidative stress were enriched in AD patients. Decorin (DCN) and regulator of G protein signaling 1 (RGS1) were screened as diagnostic biomarkers in distinguishing AD from frontotemporal dementia and Huntingdon's disease of AD. The diagnostic effects of DCN and RGS1 for AD were further validated in another two datasets of AD: the areas under the curve (AUCs) reached 0.8148 and 0.8262 in GSE33000, and 0.8595 and 0.8675 in GSE44770. There was a better value for AD diagnosis when combining performances of DCN and RGS1 with the AUCs of 0.863 and 0.869. Further, DCN mRNA level was correlated to CDR (Clinical Dementia Rating scale) score (r = 0.5066, p = 0.0058) and Braak staging (r = 0.3348, p = 0.0549). Conclusion DCN and RGS1 associated with the immune response may be useful biomarkers for diagnosing AD and distinguishing the disease from frontotemporal dementia and Huntingdon's disease. DCN mRNA level reflects the development of the disease.
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Affiliation(s)
- Boru Jin
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology, Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
| | - Guoqiang Fei
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology, Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
| | - Shaoming Sang
- Shanghai Raising Pharmaceutical Technology Co., Ltd., Shanghai, China
| | - Chunjiu Zhong
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology, Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
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25
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Sacchi L, Contarino VE, Siggillino S, Carandini T, Fumagalli GG, Pietroboni AM, Arcaro M, Fenoglio C, Orunesu E, Castellani M, Casale S, Conte G, Liu C, Triulzi F, Galimberti D, Scarpini E, Arighi A. Banks of the Superior Temporal Sulcus in Alzheimer's Disease: A Pilot Quantitative Susceptibility Mapping Study. J Alzheimers Dis 2023:JAD230095. [PMID: 37182885 DOI: 10.3233/jad-230095] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND Brain iron homeostasis is disrupted in neurodegeneration and areas of iron overload partially overlap with regions of amyloid and tau burden in Alzheimer's disease (AD). Previous studies demonstrated alterations in brain iron accumulation in AD using quantitative susceptibility mapping (QSM). OBJECTIVE Here, we investigate brain alterations of QSM values in AD and non-AD patients as compared to healthy controls (HC) in the superior temporal sulcus and its banks (BANKSSTS), one of the top AD-affected regions. METHODS Thirty-four patients who underwent brain MRI including a multi-echo gradient-echo sequence were subdivided into AD (n = 19) and non-AD (n = 15) groups according to their clinical profile, CSF (Aβ 42/40) and/or amyloid-PET status. Ten HC were also included. QSM values were extracted from left and right BANKSSTS and compared among groups. Correlation and binomial regression analyses between QSM values and CSF-AD biomarkers were conducted. RESULTS QSM in left BANKSSTS was significantly different among groups (p = 0.003, H = 11.40), being higher in AD. QSM values in left BANKSSTS were correlated with Aβ 42 (rho -0.55, p = 0.005), Aβ 42/40 (rho -0.66, p < 0.001), pTau (rho 0.63, p < 0.001), tTau (rho 0.56, p = 0.005), tTau/Aβ 42 (rho 0.68, p < 0.001) and pTau/Aβ 42 (rho 0.71, p < 0.001). No correlations between QSM values and amyloid-PET SUVR in the left BANKSSTS were found. QSM values in left BANKSSTS showed good accuracy in discriminating AD (AUC = 0.80, CI95 % [0.66-0.93]). Higher QSM values were independent predictors of Aβ 42 (B = 0.63, p = 0.032), Aβ 42/40 (B = 0.81, p = 0.028), pTau (B = 0.96, p = 0.046), tTau (B = 0.55, p = 0.027), and tTau/Aβ 42 (B = 1.13, p = 0.042) positivity. CONCLUSION Our preliminary data support the potential role of increased QSM values in the left BANKSSTS as an auxiliary imaging biomarker in AD diagnosis.
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Affiliation(s)
- Luca Sacchi
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - Valeria Elisa Contarino
- Department of Neuroradiology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Silvia Siggillino
- Department of Neuroradiology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Tiziana Carandini
- Neurodegenerative Diseases Unit, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | | | - Anna Margherita Pietroboni
- Neurodegenerative Diseases Unit, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Marina Arcaro
- Neurodegenerative Diseases Unit, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Chiara Fenoglio
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Eva Orunesu
- Nuclear Medicine Unit, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Massimo Castellani
- Nuclear Medicine Unit, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Silvia Casale
- Department of Neuroradiology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giorgio Conte
- Department of Neuroradiology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Fabio Triulzi
- Department of Neuroradiology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Daniela Galimberti
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Neurodegenerative Diseases Unit, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Elio Scarpini
- Neurodegenerative Diseases Unit, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Andrea Arighi
- Neurodegenerative Diseases Unit, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
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26
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Li Z, Feng R, Liu Q, Feng J, Lao G, Zhang M, Li J, Zhang Y, Wei H. APART-QSM: an improved sub-voxel quantitative susceptibility mapping for susceptibility source separation using an iterative data fitting method. Neuroimage 2023; 274:120148. [PMID: 37127191 DOI: 10.1016/j.neuroimage.2023.120148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 02/06/2023] [Accepted: 04/28/2023] [Indexed: 05/03/2023] Open
Abstract
The brain tissue phase contrast in MRI sequences reflects the spatial distributions of multiple substances, such as iron, myelin, calcium, and proteins. These substances with paramagnetic and diamagnetic susceptibilities often colocalize in one voxel in brain regions. Both opposing susceptibilities play vital roles in brain development and neurodegenerative diseases. Conventional QSM methods only provide voxel-averaged susceptibility value and cannot disentangle intravoxel susceptibilities with opposite signs. Advanced susceptibility imaging methods have been recently developed to distinguish the contributions of opposing susceptibility sources for QSM. The basic concept of separating paramagnetic and diamagnetic susceptibility proportions is to include the relaxation rate R2* with R2' in QSM. The magnitude decay kernel, describing the proportionality coefficient between R2' and susceptibility, is an essential reconstruction coefficient for QSM separation methods. In this study, we proposed a more comprehensive complex signal model that describes the relationship between 3D GRE signal and the contributions of paramagnetic and diamagnetic susceptibility to the frequency shift and R2* relaxation. The algorithm is implemented as a constrained minimization problem in which the voxel-wise magnitude decay kernel and sub-voxel susceptibilities are determined alternately in each iteration until convergence. The calculated voxel-wise magnitude decay kernel could realistically model the relationship between the R2' relaxation and the volume susceptibility. Thus, the proposed method effectively prevents the errors of the magnitude decay kernel from propagating to the final susceptibility separation reconstruction. Phantom studies, ex vivo macaque brain experiments, and in vivo human brain imaging studies were conducted to evaluate the ability of the proposed method to distinguish paramagnetic and diamagnetic susceptibility sources. The results demonstrate that the proposed method provides state-of-the-art performances for quantifying brain iron and myelin compared to previous QSM separation methods. Our results show that the proposed method has the potential to simultaneously quantify whole brain iron and myelin during brain development and aging. The proposed model was also deployed with multiple-orientation complex GRE data input measurements, resulting in high-quality QSM separation maps with more faithful tissue delineation between brain structures compared to those reconstructed by single-orientation QSM separation methods.
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Affiliation(s)
- Zhenghao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ruimin Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Qiangqiang Liu
- Department of Neurosurgery, Clinical Neuroscience Center Comprehensive Epilepsy Unit, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Guoyan Lao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Li
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Yuyao Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
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Xu X, Zhou M, Wu X, Zhao F, Luo X, Li K, Zeng Q, He J, Cheng H, Guan X, Huang P, Zhang M, Liu K. Increased iron deposition in nucleus accumbens associated with disease progression and chronicity in migraine. BMC Med 2023; 21:136. [PMID: 37024948 PMCID: PMC10080952 DOI: 10.1186/s12916-023-02855-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 03/29/2023] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND Migraine is one of the world's most prevalent and disabling diseases. Despite huge advances in neuroimaging research, more valuable neuroimaging markers are still urgently needed to provide important insights into the brain mechanisms that underlie migraine symptoms. We therefore aim to investigate the regional iron deposition in subcortical nuclei of migraineurs as compared to controls and its association with migraine-related pathophysiological assessments. METHODS A total of 200 migraineurs (56 chronic migraine [CM], 144 episodic migraine [EM]) and 41 matched controls were recruited. All subjects underwent MRI and clinical variables including frequency/duration of migraine, intensity of migraine, 6-item Headache Impact Test (HIT-6), Migraine Disability Assessment (MIDAS), and Pittsburgh Sleep Quality Index (PSQI) were recorded. Quantitative susceptibility mapping was employed to quantify the regional iron content in subcortical regions. Associations between clinical variables and regional iron deposition were studied as well. RESULTS Increased iron deposition in the putamen, caudate, and nucleus accumbens (NAC) was observed in migraineurs more than controls. Meanwhile, patients with CM had a significantly higher volume of iron deposits compared to EM in multiple subcortical nuclei, especially in NAC. Volume of iron in NAC can be used to distinguish patients with CM from EM with a sensitivity of 85.45% and specificity of 71.53%. As the most valuable neuroimaging markers in all of the subcortical nuclei, higher iron deposition in NAC was significantly associated with disease progression, and higher HIT-6, MIDAS, and PSQI. CONCLUSIONS These findings provide evidence that iron deposition in NAC may be a biomarker for migraine chronicity and migraine-related dysfunctions, thus may help to understand the underlying vascular and neural mechanisms of migraine. TRIAL REGISTRATION ClinicalTrials.gov, number NCT04939922.
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Affiliation(s)
- Xiaopei Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No 88 Jiefang Road, Hangzhou, Zhejiang, China
| | - Mengting Zhou
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No 88 Jiefang Road, Hangzhou, Zhejiang, China
| | - Xiao Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No 88 Jiefang Road, Hangzhou, Zhejiang, China
| | - Fangling Zhao
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No 88 Jiefang Road, Hangzhou, Zhejiang, China
| | - Xiao Luo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No 88 Jiefang Road, Hangzhou, Zhejiang, China
| | - Kaicheng Li
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No 88 Jiefang Road, Hangzhou, Zhejiang, China
| | - Qingze Zeng
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No 88 Jiefang Road, Hangzhou, Zhejiang, China
| | - Jiahui He
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No 88 Jiefang Road, Hangzhou, Zhejiang, China
| | - Hongrong Cheng
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No 88 Jiefang Road, Hangzhou, Zhejiang, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No 88 Jiefang Road, Hangzhou, Zhejiang, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No 88 Jiefang Road, Hangzhou, Zhejiang, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No 88 Jiefang Road, Hangzhou, Zhejiang, China.
| | - Kaiming Liu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No 88 Jiefang Road, Hangzhou, Zhejiang, China.
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Li G, Tong R, Zhang M, Gillen KM, Jiang W, Du Y, Wang Y, Li J. Age-dependent changes in brain iron deposition and volume in deep gray matter nuclei using quantitative susceptibility mapping. Neuroimage 2023; 269:119923. [PMID: 36739101 DOI: 10.1016/j.neuroimage.2023.119923] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/10/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Microstructural changes in deep gray matter (DGM) nuclei are related to physiological behavior, cognition, and memory. Therefore, it is critical to study age-dependent trajectories of biomarkers in DGM nuclei for understanding brain development and aging, as well as predicting cognitive or neurodegenerative diseases. OBJECTIVES We aimed to (1) characterize age-dependent trajectories of mean susceptibility, adjusted volume, and total iron content simultaneously in DGM nuclei using quantitative susceptibility mapping (QSM); (2) examine potential contributions of sex related effects to the different age-dependence trajectories of volume and iron deposition; and (3) evaluate the ability of brain age prediction by combining mean magnetic susceptibility and volume of DGM nuclei. METHODS Magnetic susceptibilities and volumetric values of DGM nuclei were obtained from 220 healthy participants (aged 10-70 years) scanned on a 3T MRI system. Regions of interest (ROIs) were drawn manually on the QSM images. Univariate regression analysis between age and each of the MRI measurements in a single ROI was performed. Pearson correlation coefficients were calculated between magnetic susceptibility and adjusted volume in a single ROI. The statistical significance of sex differences in age-dependent trajectories of magnetic susceptibilities and adjusted volumes were determined using one-way ANCOVA. Multiple regression analysis was used to evaluate the ability to estimate brain age using a combination of the mean susceptibilities and adjusted volumes in multiple DGM nuclei. RESULTS Mean susceptibility and total iron content increased linearly, quadratically, or exponentially with age in all six DGM nuclei. Negative linear correlation was observed between adjusted volume and age in the head of the caudate nucleus (CN; R2 = 0.196, p < 0.001). Quadratic relationships were found between adjusted volume and age in the putamen (PUT; R2 = 0.335, p < 0.001), globus pallidus (GP; R2 = 0.062, p = 0.001), and dentate nucleus (DN; R2 = 0.077, p < 0.001). Males had higher mean magnetic susceptibility than females in the PUT (p = 0.001), red nucleus (RN; p = 0.002), and substantia nigra (SN; p < 0.001). Adjusted volumes of the CN (p < 0.001), PUT (p = 0.030), GP (p = 0.007), SN (p = 0.021), and DN (p < 0.001) were higher in females than those in males throughout the entire age range (10-70 years old). The total iron content of females was higher than that of males in the CN (p < 0.001), but lower than that of males in the PUT (p = 0.014) and RN (p = 0.043) throughout the entire age range (10-70 years old). Multiple regression analyses revealed that the combination of the mean susceptibility value of the PUT, and the volumes of the CN and PUT had the strongest associations with brain age (R2 = 0.586). CONCLUSIONS QSM can be used to simultaneously investigate age- and sex- dependent changes in magnetic susceptibility and volume of DGM nuclei, thus enabling a comprehensive understanding of the developmental trajectories of iron accumulation and volume in DGM nuclei during brain development and aging.
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Affiliation(s)
- Gaiying Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, China 200062
| | - Rui Tong
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, China 200062
| | - Miao Zhang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, China 200062
| | - Kelly M Gillen
- Department of Radiology, Weill Medical College of Cornell University, 407 East 61st St., New York, New York, United States 10065
| | - Wenqing Jiang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 South Wanping Road, Shanghai, China 200030
| | - Yasong Du
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 South Wanping Road, Shanghai, China 200030
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, 407 East 61st St., New York, New York, United States 10065
| | - Jianqi Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, China 200062; Institute of Brain and Education Innovation, East China Normal University, 3663 North Zhongshan Road, Shanghai, China 200062.
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Satoh R, Arani A, Senjem ML, Duffy JR, Clark HM, Utianski RL, Botha H, Machulda MM, Jack CR, Whitwell JL, Josephs KA. Spatial patterns of elevated magnetic susceptibility in progressive apraxia of speech. Neuroimage Clin 2023; 38:103394. [PMID: 37003130 PMCID: PMC10102559 DOI: 10.1016/j.nicl.2023.103394] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/23/2023] [Accepted: 03/27/2023] [Indexed: 03/30/2023]
Abstract
PURPOSE Progressive apraxia of speech (PAOS) is a neurodegenerative disorder affecting the planning or programming of speech. Little is known about its magnetic susceptibility profiles indicative of biological processes such as iron deposition and demyelination. This study aims to clarify (1) the pattern of susceptibility in PAOS patients, (2) the susceptibility differences between the phonetic (characterized by predominance of distorted sound substitutions and additions) and prosodic (characterized by predominance of slow speech rate and segmentation) subtypes of PAOS, and (3) the relationships between susceptibility and symptom severity. METHODS Twenty patients with PAOS (nine phonetic and eleven prosodic subtypes) were prospectively recruited and underwent a 3 Tesla MRI scan. They also underwent detailed speech, language, and neurological evaluations. Quantitative susceptibility maps (QSM) were reconstructed from multi-echo gradient echo MRI images. Region of interest analysis was conducted to estimate susceptibility coefficients in several subcortical and frontal regions. We compared susceptibility values between PAOS and an age-matched control group and performed a correlation analysis between susceptibilities and an apraxia of speech rating scale (ASRS) phonetic and prosodic feature ratings. RESULTS The magnetic susceptibility of PAOS was statistically greater than that of controls in subcortical regions (left putamen, left red nucleus, and right dentate nucleus) (p < 0.01, also survived FDR correction) and in the left white-matter precentral gyrus (p < 0.05, but not survived FDR correction). The prosodic patients showed greater susceptibilities than controls in these subcortical and precentral regions. The susceptibility in the left red nucleus and in the left precentral gyrus correlated with the prosodic sub-score of the ASRS. CONCLUSION Magnetic susceptibility in PAOS patients was greater than controls mainly in the subcortical regions. While larger samples are needed before QSM is considered ready for clinical differential diagnosis, the present study contributes to our understanding of magnetic susceptibility changes and the pathophysiology of PAOS.
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Affiliation(s)
- Ryota Satoh
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Arvin Arani
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN, USA; Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Joseph R Duffy
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
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Wang EW, Brown GL, Lewis MM, Jellen LC, Pu C, Johnson ML, Chen H, Kong L, Du G, Huang X. Susceptibility Magnetic Resonance Imaging Correlates with Glial Density and Tau in the Substantia Nigra Pars Compacta. Mov Disord 2023; 38:464-473. [PMID: 36598274 PMCID: PMC10445152 DOI: 10.1002/mds.29311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 12/13/2022] [Accepted: 12/16/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Susceptibility magnetic resonance imaging (MRI) is sensitive to iron-related changes in the substantia nigra pars compacta (SNc), the key pathologic locus of parkinsonisms. It is unclear, however, if iron deposition in the SNc is associated with its neurodegeneration. OBJECTIVE The objective of this study was to test whether susceptibility MRI metrics in parkinsonisms are associated with SNc neuropathologic features of dopaminergic neuron loss, gliosis, and α-synuclein and tau burden. METHODS This retrospective study included 27 subjects with both in vivo MRI and postmortem data. Multigradient echo imaging was used to derive the apparent transverse relaxation rate (R2*) and quantitative susceptibility mapping (QSM) in the SNc. Archived midbrain slides that were stained with hematoxylin and eosin, anti-α-synuclein, and anti-tau were digitized to quantify neuromelanin-positive neuron density, glial density, and the percentages of area occupied by positive α-synuclein and tau staining. MRI-histology associations were examined using Pearson correlations and regression. RESULTS Twenty-four subjects had postmortem parkinsonism diagnoses (Lewy body disorder, progressive supranuclear palsy, multiple system atrophy, and corticobasal degeneration), two had only Alzheimer's neuropathology, and one exhibited only mild atrophy. Among all subjects, both R2* and QSM were associated with glial density (r ≥ 0.67; P < 0.001) and log-transformed tau burden (r ≥ 0.53; P ≤ 0.007). Multiple linear regression identified glial density and log-transformed tau as determinants for both MRI metrics (R2 ≥ 0.580; P < 0.0001). Neither MRI metric was associated with neuron density or α-synuclein burden. CONCLUSIONS R2* and QSM are associated with both glial density and tau burden, key neuropathologic features in the parkinsonism SNc. © 2023 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Ernest W. Wang
- Department of Neurology, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Gregory L. Brown
- Department of Neurology, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Mechelle M. Lewis
- Department of Neurology, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Department of Pharmacology, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Leslie C. Jellen
- Department of Neurology, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Cunfeng Pu
- Department of Pathology, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Melinda L. Johnson
- Department of Neurology, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Hairong Chen
- Department of Neurology, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Lan Kong
- Department of Public Health Sciences, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Guangwei Du
- Department of Neurology, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Xuemei Huang
- Department of Neurology, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Department of Pharmacology, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Departments of Neurosurgery and Radiology, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
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Adams AR, Li X, Byanyima JI, Vesslee SA, Nguyen TD, Wang Y, Moon B, Pond T, Kranzler HR, Witschey WR, Shi Z, Wiers CE. Peripheral and Central Iron Measures in Alcohol Use Disorder and Aging: A Quantitative Susceptibility Mapping Pilot Study. Int J Mol Sci 2023; 24:4461. [PMID: 36901892 PMCID: PMC10002495 DOI: 10.3390/ijms24054461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 02/18/2023] [Accepted: 02/21/2023] [Indexed: 02/26/2023] Open
Abstract
Chronic excessive alcohol use has neurotoxic effects, which may contribute to cognitive decline and the risk of early-onset dementia. Elevated peripheral iron levels have been reported in individuals with alcohol use disorder (AUD), but its association with brain iron loading has not been explored. We evaluated whether (1) serum and brain iron loading are higher in individuals with AUD than non-dependent healthy controls and (2) serum and brain iron loading increase with age. A fasting serum iron panel was obtained and a magnetic resonance imaging scan with quantitative susceptibility mapping (QSM) was used to quantify brain iron concentrations. Although serum ferritin levels were higher in the AUD group than in controls, whole-brain iron susceptibility did not differ between groups. Voxel-wise QSM analyses revealed higher susceptibility in a cluster in the left globus pallidus in individuals with AUD than controls. Whole-brain iron increased with age and voxel-wise QSM indicated higher susceptibility with age in various brain areas including the basal ganglia. This is the first study to analyze both serum and brain iron loading in individuals with AUD. Larger studies are needed to examine the effects of alcohol use on iron loading and its associations with alcohol use severity, structural and functional brain changes, and alcohol-induced cognitive impairments.
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Affiliation(s)
- Aiden R. Adams
- Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Xinyi Li
- Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Juliana I. Byanyima
- Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Sianneh A. Vesslee
- Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medicine, 525 E 68th St, New York, NY 10065, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, 525 E 68th St, New York, NY 10065, USA
| | - Brianna Moon
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104, USA
| | - Timothy Pond
- Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Henry R. Kranzler
- Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Walter R. Witschey
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104, USA
| | - Zhenhao Shi
- Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
| | - Corinde E. Wiers
- Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3535 Market St Ste 500, Philadelphia, PA 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104, USA
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Uchida Y, Kan H, Sakurai K, Oishi K, Matsukawa N. Contributions of blood-brain barrier imaging to neurovascular unit pathophysiology of Alzheimer's disease and related dementias. Front Aging Neurosci 2023; 15:1111448. [PMID: 36861122 PMCID: PMC9969807 DOI: 10.3389/fnagi.2023.1111448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 01/26/2023] [Indexed: 02/11/2023] Open
Abstract
The blood-brain barrier (BBB) plays important roles in the maintenance of brain homeostasis. Its main role includes three kinds of functions: (1) to protect the central nervous system from blood-borne toxins and pathogens; (2) to regulate the exchange of substances between the brain parenchyma and capillaries; and (3) to clear metabolic waste and other neurotoxic compounds from the central nervous system into meningeal lymphatics and systemic circulation. Physiologically, the BBB belongs to the glymphatic system and the intramural periarterial drainage pathway, both of which are involved in clearing interstitial solutes such as β-amyloid proteins. Thus, the BBB is believed to contribute to preventing the onset and progression for Alzheimer's disease. Measurements of BBB function are essential toward a better understanding of Alzheimer's pathophysiology to establish novel imaging biomarkers and open new avenues of interventions for Alzheimer's disease and related dementias. The visualization techniques for capillary, cerebrospinal, and interstitial fluid dynamics around the neurovascular unit in living human brains have been enthusiastically developed. The purpose of this review is to summarize recent BBB imaging developments using advanced magnetic resonance imaging technologies in relation to Alzheimer's disease and related dementias. First, we give an overview of the relationship between Alzheimer's pathophysiology and BBB dysfunction. Second, we provide a brief description about the principles of non-contrast agent-based and contrast agent-based BBB imaging methodologies. Third, we summarize previous studies that have reported the findings of each BBB imaging method in individuals with the Alzheimer's disease continuum. Fourth, we introduce a wide range of Alzheimer's pathophysiology in relation to BBB imaging technologies to advance our understanding of the fluid dynamics around the BBB in both clinical and preclinical settings. Finally, we discuss the challenges of BBB imaging techniques and suggest future directions toward clinically useful imaging biomarkers for Alzheimer's disease and related dementias.
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Affiliation(s)
- Yuto Uchida
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States,*Correspondence: Yuto Uchida, ; Noriyuki Matsukawa,
| | - Hirohito Kan
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Keita Sakurai
- Department of Radiology, National Center for Geriatrics and Gerontology, Ōbu, Aichi, Japan
| | - Kenichi Oishi
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Noriyuki Matsukawa
- Department of Neurology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan,*Correspondence: Yuto Uchida, ; Noriyuki Matsukawa,
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Li K, Rashid T, Li J, Honnorat N, Nirmala AB, Fadaee E, Wang D, Charisis S, Liu H, Franklin C, Maybrier M, Katragadda H, Abazid L, Ganapathy V, Valaparla VL, Badugu P, Vasquez E, Solano L, Clarke G, Maestre G, Richardson T, Walker J, Fox PT, Bieniek K, Seshadri S, Habes M. Postmortem Brain Imaging in Alzheimer's Disease and Related Dementias: The South Texas Alzheimer's Disease Research Center Repository. J Alzheimers Dis 2023; 96:1267-1283. [PMID: 37955086 PMCID: PMC10693476 DOI: 10.3233/jad-230389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/24/2023] [Indexed: 11/14/2023]
Abstract
BACKGROUND Neuroimaging bears the promise of providing new biomarkers that could refine the diagnosis of dementia. Still, obtaining the pathology data required to validate the relationship between neuroimaging markers and neurological changes is challenging. Existing data repositories are focused on a single pathology, are too small, or do not precisely match neuroimaging and pathology findings. OBJECTIVE The new data repository introduced in this work, the South Texas Alzheimer's Disease research center repository, was designed to address these limitations. Our repository covers a broad diversity of dementias, spans a wide age range, and was specifically designed to draw exact correspondences between neuroimaging and pathology data. METHODS Using four different MRI sequences, we are reaching a sample size that allows for validating multimodal neuroimaging biomarkers and studying comorbid conditions. Our imaging protocol was designed to capture markers of cerebrovascular disease and related lesions. Quantification of these lesions is currently underway with MRI-guided histopathological examination. RESULTS A total of 139 postmortem brains (70 females) with mean age of 77.9 years were collected, with 71 brains fully analyzed. Of these, only 3% showed evidence of AD-only pathology and 76% had high prevalence of multiple pathologies contributing to clinical diagnosis. CONCLUSION This repository has a significant (and increasing) sample size consisting of a wide range of neurodegenerative disorders and employs advanced imaging protocols and MRI-guided histopathological analysis to help disentangle the effects of comorbid disorders to refine diagnosis, prognosis and better understand neurodegenerative disorders.
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Affiliation(s)
- Karl Li
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Tanweer Rashid
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Jinqi Li
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Nicolas Honnorat
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Anoop Benet Nirmala
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Elyas Fadaee
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Di Wang
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Sokratis Charisis
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Hangfan Liu
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Crystal Franklin
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Mallory Maybrier
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Haritha Katragadda
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Leen Abazid
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Vinutha Ganapathy
- Department of Neurology, University of Texas Health Science Center, San Antonio, TX, USA
| | | | - Pradeepthi Badugu
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Eliana Vasquez
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Leigh Solano
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Geoffrey Clarke
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Gladys Maestre
- Department of Neuroscience, School of Medicine, University of Texas Rio Grande Valley, Harlingen, TX, USA
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Tim Richardson
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jamie Walker
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Peter T. Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Kevin Bieniek
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Pathology, University of Texas Health Science Center, San Antonio, TX, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Mohamad Habes
- Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
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Bazala R, Zoppellaro G, Kletetschka G. Iron Level Changes in the Brain with Neurodegenerative Disease. BRAIN MULTIPHYSICS 2023. [DOI: 10.1016/j.brain.2023.100063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
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Quantitative Susceptibility Mapping in Cognitive Decline: A Review of Technical Aspects and Applications. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10095-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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36
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Guan X, Guo T, Zhou C, Wu J, Zeng Q, Li K, Luo X, Bai X, Wu H, Gao T, Gu L, Liu X, Cao Z, Wen J, Chen J, Wei H, Zhang Y, Liu C, Song Z, Yan Y, Pu J, Zhang B, Xu X, Zhang M. Altered brain iron depositions from aging to Parkinson's disease and Alzheimer's disease: A quantitative susceptibility mapping study. Neuroimage 2022; 264:119683. [PMID: 36243270 DOI: 10.1016/j.neuroimage.2022.119683] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 10/09/2022] [Accepted: 10/11/2022] [Indexed: 11/07/2022] Open
Abstract
Brain iron deposition is a promising marker for human brain health, providing insightful information for understanding aging as well as neurodegenerations, e.g., Parkinson's disease (PD) and Alzheimer's disease (AD). To comprehensively evaluate brain iron deposition along with aging, PD-related neurodegeneration, from prodromal PD (pPD) to clinical PD (cPD), and AD-related neurodegeneration, from mild cognitive impairment (MCI) to AD, a total of 726 participants from July 2013 to December 2020, including 100 young adults, 189 old adults, 184 pPD, 171 cPD, 31 MCI and 51 AD patients, were included. Quantitative susceptibility mapping data were acquired and used to quantify regional magnetic susceptibility, and the resulting spatial standard deviations were recorded. A general linear model was applied to perform the inter-group comparison. As a result, relative to young adults, old adults showed significantly higher iron deposition with higher spatial variation in all of the subcortical nuclei (p < 0.01). pPD showed a high spatial variation of iron distribution in the subcortical nuclei except for substantia nigra (SN); and iron deposition in SN and red nucleus (RN) were progressively increased from pPD to cPD (p < 0.01). AD showed significantly higher iron deposition in caudate and putamen with higher spatial variation compared with old adults, pPD and cPD (p < 0.01), and significant iron deposition in SN compared with old adults (p < 0.01). Also, linear regression models had significances in predicting motor score in pPD and cPD (Rmean = 0.443, Ppermutation = 0.001) and cognition score in MCI and AD (Rmean = 0.243, Ppermutation = 0.037). In conclusion, progressive iron deposition in the SN and RN may characterize PD-related neurodegeneration, namely aging to cPD through pPD. On the other hand, extreme iron deposition in the caudate and putamen may characterize AD-related neurodegeneration.
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Affiliation(s)
- Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Tao Guo
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Cheng Zhou
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Jingjing Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Qingze Zeng
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Kaicheng Li
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Xiao Luo
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Xueqin Bai
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Haoting Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Ting Gao
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Luyan Gu
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Zhengye Cao
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Jiaqi Wen
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Jingwen Chen
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yuyao Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, United States; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, United States
| | - Zhe Song
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yaping Yan
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Pu
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China.
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China.
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Lancione M, Bosco P, Costagli M, Nigri A, Aquino D, Carne I, Ferraro S, Giulietti G, Napolitano A, Palesi F, Pavone L, Pirastru A, Savini G, Tagliavini F, Bruzzone MG, Gandini Wheeler-Kingshott CA, Tosetti M, Biagi L. Multi-centre and multi-vendor reproducibility of a standardized protocol for quantitative susceptibility Mapping of the human brain at 3T. Phys Med 2022; 103:37-45. [DOI: 10.1016/j.ejmp.2022.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/12/2022] [Accepted: 09/27/2022] [Indexed: 11/16/2022] Open
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Distinct brain iron profiles associated with logopenic progressive aphasia and posterior cortical atrophy. Neuroimage Clin 2022; 36:103161. [PMID: 36029670 PMCID: PMC9428862 DOI: 10.1016/j.nicl.2022.103161] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 08/05/2022] [Accepted: 08/18/2022] [Indexed: 12/14/2022]
Abstract
Quantitative susceptibility mapping (QSM) can detect iron distribution in the brain by estimating local tissue magnetic susceptibility properties at every voxel. Iron deposition patterns are well studied in typical Alzheimer's disease (tAD), but little is known about these patterns in atypical clinical presentations of AD such as logopenic progressive aphasia (LPA) and posterior cortical atrophy (PCA). Seventeen PCA patients and eight LPA patients were recruited by the Neurodegenerative Research Group at Mayo Clinic, Rochester, MN, and underwent MRI that included a five-echo gradient echo sequence for calculation of QSM. Mean QSM signal was extracted from gray and white matter for regions-of-interest across the brain using the Mayo Clinic Adult Lifespan Template. Bayesian hierarchical models were fit per-region and per-hemisphere to compare PCA, LPA, 63 healthy controls, and 20 tAD patients. Strong evidence (posterior probability > 0.99) was observed for greater susceptibility in the middle occipital gyrus and amygdala in both LPA and PCA, and in the right inferior parietal, inferior temporal, and angular gyri in PCA and the caudate and substantia nigra in LPA compared to controls. Moderate evidence for greater susceptibility (posterior probability > 0.90) was also observed in the inferior occipital gyrus, precuneus, putamen and entorhinal cortex in both LPA and PCA, along with superior frontal gyrus in PCA and inferior temporal gyri, insula and basal ganglia in LPA, when compared to controls. Between phenotypic comparisons, LPA had greater susceptibility in the caudate, hippocampus, and posterior cingulate compared to PCA, while PCA showed greater susceptibility in the right superior frontal and middle temporal gyri compared to LPA. Both LPA and PCA showed moderate and strong evidence for greater susceptibility than tAD, particularly in medial and lateral parietal regions, while tAD showed greater susceptibility in the hippocampus and basal ganglia. This study proposes the possibility of unique iron profiles existing between LPA and PCA within cortical and subcortical structures. These changes match well with the disease-related changes of the clinical phenotypes, suggesting that QSM could be an informative candidate marker to study iron deposition in these patients.
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Fei HX, Qian CF, Wu XM, Wei YH, Huang JY, Wei LH. Role of micronutrients in Alzheimer's disease: Review of available evidence. World J Clin Cases 2022; 10:7631-7641. [PMID: 36158513 PMCID: PMC9372870 DOI: 10.12998/wjcc.v10.i22.7631] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 04/29/2022] [Accepted: 06/26/2022] [Indexed: 02/06/2023] Open
Abstract
Alzheimer's disease (AD) is one of the most common age-related neurodegenerative disorders that have been studied for more than 100 years. Although an increased level of amyloid precursor protein is considered a key contributor to the development of AD, the exact pathogenic mechanism remains known. Multiple factors are related to AD, such as genetic factors, aging, lifestyle, and nutrients. Both epidemiological and clinical evidence has shown that the levels of micronutrients, such as copper, zinc, and iron, are closely related to the development of AD. In this review, we summarize the roles of eight micronutrients, including copper, zinc, iron, selenium, silicon, manganese, arsenic, and vitamin D in AD based on recently published studies.
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Affiliation(s)
- Hong-Xin Fei
- Department of Pathology, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi Zhuang Autonomous Region, China
| | - Chao-Fan Qian
- Department of Pathology, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi Zhuang Autonomous Region, China
| | - Xiang-Mei Wu
- Department of Pathology, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi Zhuang Autonomous Region, China
| | - Yu-Hua Wei
- Department of Pathology, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi Zhuang Autonomous Region, China
| | - Jin-Yu Huang
- Department of Neurology, The First Affiliated Hospital of Guangxi University of Science and Technology, Liuzhou 545000, Guangxi Zhuang Autonomous Region, China
| | - Li-Hua Wei
- Department of Pathology, Guangxi University of Science and Technology, Liuzhou 545000, Guangxi Zhuang Autonomous Region, China
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Uchida Y, Kan H, Sakurai K, Oishi K, Matsukawa N. Quantitative susceptibility mapping as an imaging biomarker for Alzheimer’s disease: The expectations and limitations. Front Neurosci 2022; 16:938092. [PMID: 35992906 PMCID: PMC9389285 DOI: 10.3389/fnins.2022.938092] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/14/2022] [Indexed: 11/25/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common type of dementia and a distressing diagnosis for individuals and caregivers. Researchers and clinical trials have mainly focused on β-amyloid plaques, which are hypothesized to be one of the most important factors for neurodegeneration in AD. Meanwhile, recent clinicopathological and radiological studies have shown closer associations of tau pathology rather than β-amyloid pathology with the onset and progression of Alzheimer’s symptoms. Toward a biological definition of biomarker-based research framework for AD, the 2018 National Institute on Aging–Alzheimer’s Association working group has updated the ATN classification system for stratifying disease status in accordance with relevant pathological biomarker profiles, such as cerebral β-amyloid deposition, hyperphosphorylated tau, and neurodegeneration. In addition, altered iron metabolism has been considered to interact with abnormal proteins related to AD pathology thorough generating oxidative stress, as some prior histochemical and histopathological studies supported this iron-mediated pathomechanism. Quantitative susceptibility mapping (QSM) has recently become more popular as a non-invasive magnetic resonance technique to quantify local tissue susceptibility with high spatial resolution, which is sensitive to the presence of iron. The association of cerebral susceptibility values with other pathological biomarkers for AD has been investigated using various QSM techniques; however, direct evidence of these associations remains elusive. In this review, we first briefly describe the principles of QSM. Second, we focus on a large variety of QSM applications, ranging from common applications, such as cerebral iron deposition, to more recent applications, such as the assessment of impaired myelination, quantification of venous oxygen saturation, and measurement of blood– brain barrier function in clinical settings for AD. Third, we mention the relationships among QSM, established biomarkers, and cognitive performance in AD. Finally, we discuss the role of QSM as an imaging biomarker as well as the expectations and limitations of clinically useful diagnostic and therapeutic implications for AD.
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Affiliation(s)
- Yuto Uchida
- Department of Neurology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- *Correspondence: Yuto Uchida,
| | - Hirohito Kan
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Keita Sakurai
- Department of Radiology, National Center for Geriatrics and Gerontology, Ōbu, Japan
| | - Kenichi Oishi
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Noriyuki Matsukawa
- Department of Neurology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
- Noriyuki Matsukawa,
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Chen CY, Lin YS, Lee WJ, Liao YC, Kuo YS, Yang AC, Fuh JL. Endophenotypic effects of the SORL1 variant rs2298813 on regional brain volume in patients with late-onset Alzheimer’s disease. Front Aging Neurosci 2022; 14:885090. [PMID: 35992588 PMCID: PMC9389408 DOI: 10.3389/fnagi.2022.885090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 07/12/2022] [Indexed: 12/02/2022] Open
Abstract
Introduction: Two common variants of sortilin-related receptor 1 gene (SORL1), rs2298813 and rs1784933, have been associated with late-onset Alzheimer’s disease (AD) in the Han Chinese population in Taiwan. However, neuroimaging correlates of these two SORL1 variants remain unknown. We aimed to determine whether the two SORL1 polymorphisms were associated with any volumetric differences in brain regions in late-onset AD patients. Methods: We recruited 200 patients with late-onset AD from Taipei Veterans General Hospital. All patients received a structural magnetic resonance (MR) imaging brain scan and completed a battery of neurocognitive tests at enrollment. We followed up to assess changes in Mini-Mental State Examination (MMSE) scores in 155 patients (77.5%) at an interval of 2 years. Volumetric measures and cortical thickness of various brain regions were performed using FreeSurfer. Regression analysis controlled for apolipoprotein E status. Multiple comparisons were corrected for using the false discovery rate. Results: The homozygous major allele of rs2298813 was associated with larger volumes in the right putamen (p = 0.0442) and right pallidum (p = 0.0346). There was no link between the rs1784933 genotypes with any regional volume or thickness of the brain. In the rs2298813 homozygous major allele carriers, the right putaminal volume was associated with verbal fluency (p = 0.008), and both the right putaminal and pallidal volumes were predictive of clinical progression at follow-up (p = 0.020). In the minor allele carriers, neither of the nuclei was related to cognitive test performance or clinical progression. Conclusion: The major and minor alleles of rs2298813 had differential effects on the right lentiform nucleus volume and distinctively modulated the association between the regional volume and cognitive function in patients with AD.
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Affiliation(s)
- Chun-Yu Chen
- Department of Medicine, Taipei Veterans General Hospital Yuli Branch, Hualien, Taiwan
- Division of General Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yung-Shuan Lin
- Division of General Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wei-Ju Lee
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Neurological Institute, Taichung Veterans General Hospital, Taichung, Taiwan
- Dementia Center and Center for Geriatrics and Gerontology, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Yi-Chu Liao
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Division of Peripheral Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yu-Shan Kuo
- Division of General Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Albert C. Yang
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jong-Ling Fuh
- Division of General Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- *Correspondence: Jong-Ling Fuh
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Nikparast F, Ganji Z, Zare H. Early differentiation of neurodegenerative diseases using the novel QSM technique: what is the biomarker of each disorder? BMC Neurosci 2022; 23:48. [PMID: 35902793 PMCID: PMC9336059 DOI: 10.1186/s12868-022-00725-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 06/17/2022] [Indexed: 11/10/2022] Open
Abstract
During neurodegenerative diseases, the brain undergoes morphological and pathological changes; Iron deposits are one of the causes of pathological changes in the brain. The Quantitative susceptibility mapping (QSM) technique, a type of magnetic resonance (MR) image reconstruction, is one of the newest diagnostic methods for iron deposits to detect changes in magnetic susceptibility. Numerous research projects have been conducted in this field. The purpose of writing this review article is to identify the first deep brain nuclei that undergo magnetic susceptibility changes during neurodegenerative diseases such as Alzheimer's or Parkinson's disease. The purpose of this article is to identify the brain nuclei that are prone to iron deposition in any specific disorder. In addition to the mentioned purpose, this paper proposes the optimal scan parameters and appropriate algorithms of each QSM reconstruction step by reviewing the results of different articles. As a result, The QSM technique can identify nuclei exposed to iron deposition in various neurodegenerative diseases. Also, the selection of scan parameters is different based on the sequence and purpose; an example of the parameters is placed in the tables. The BET toolbox in FSL, Laplacian-based phase-unwrapping process, the V_SHARP algorithm, and morphology-enabled dipole inversion (MEDI) method are the most widely used algorithms in various stages of QSM reconstruction. In this article, A review of the results of articles on the use of QSM technique to identify nuclei exposed to iron deposition in various neurodegenerative diseases was performed. Brain nuclei with the highest changes in iron deposition were identified as a biomarker for the identification of specific neurological diseases By studying recent articles, The best toolbox for each step of the QSM processing algorithm was introduced.
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Affiliation(s)
- Farzaneh Nikparast
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zohreh Ganji
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hoda Zare
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. .,Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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Kuchcinski G, Patin L, Lopes R, Leroy M, Delbeuck X, Rollin-Sillaire A, Lebouvier T, Wang Y, Spincemaille P, Tourdias T, Hacein-Bey L, Devos D, Pasquier F, Leclerc X, Pruvo JP, Verclytte S. Quantitative susceptibility mapping demonstrates different patterns of iron overload in subtypes of early-onset Alzheimer's disease. Eur Radiol 2022; 33:184-195. [PMID: 35881183 DOI: 10.1007/s00330-022-09014-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/27/2022] [Accepted: 06/30/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES We aimed to define brain iron distribution patterns in subtypes of early-onset Alzheimer's disease (EOAD) by the use of quantitative susceptibility mapping (QSM). METHODS EOAD patients prospectively underwent MRI on a 3-T scanner and concomitant clinical and neuropsychological evaluation, between 2016 and 2019. An age-matched control group was constituted of cognitively healthy participants at risk of developing AD. Volumetry of the hippocampus and cerebral cortex was performed on 3DT1 images. EOAD subtypes were defined according to the hippocampal to cortical volume ratio (HV:CTV). Limbic-predominant atrophy (LPMRI) is referred to HV:CTV ratios below the 25th percentile, hippocampal-sparing (HpSpMRI) above the 75th percentile, and typical-AD between the 25th and 75th percentile. Brain iron was estimated using QSM. QSM analyses were made voxel-wise and in 7 regions of interest within deep gray nuclei and limbic structures. Iron distribution in EOAD subtypes and controls was compared using an ANOVA. RESULTS Sixty-eight EOAD patients and 43 controls were evaluated. QSM values were significantly higher in deep gray nuclei (p < 0.001) and limbic structures (p = 0.04) of EOAD patients compared to controls. Among EOAD subtypes, HpSpMRI had the highest QSM values in deep gray nuclei (p < 0.001) whereas the highest QSM values in limbic structures were observed in LPMRI (p = 0.005). QSM in deep gray nuclei had an AUC = 0.92 in discriminating HpSpMRI and controls. CONCLUSIONS In early-onset Alzheimer's disease patients, we observed significant variations of iron distribution reflecting the pattern of brain atrophy. Iron overload in deep gray nuclei could help to identify patients with atypical presentation of Alzheimer's disease. KEY POINTS • In early-onset AD patients, QSM indicated a significant brain iron overload in comparison with age-matched controls. • Iron load in limbic structures was higher in participants with limbic-predominant subtype. • Iron load in deep nuclei was more important in participants with hippocampal-sparing subtype.
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Affiliation(s)
- Grégory Kuchcinski
- Inserm, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ Lille, F-59000, Lille, France. .,UMS 2014 - US 41 - PLBS - Plateformes Lilloises en Biologie & Santé, Univ Lille, F-59000, Lille, France. .,Department of Neuroradiology, CHU Lille, Rue Emile Laine, F-59000, Lille, France.
| | - Lucas Patin
- Department of Neuroradiology, CHU Lille, Rue Emile Laine, F-59000, Lille, France
| | - Renaud Lopes
- Inserm, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ Lille, F-59000, Lille, France.,UMS 2014 - US 41 - PLBS - Plateformes Lilloises en Biologie & Santé, Univ Lille, F-59000, Lille, France
| | - Mélanie Leroy
- Memory Center - CNR MAJ, DISTALZ-LICEND, F-59000, Lille, France
| | - Xavier Delbeuck
- Memory Center - CNR MAJ, DISTALZ-LICEND, F-59000, Lille, France
| | - Adeline Rollin-Sillaire
- Memory Center - CNR MAJ, DISTALZ-LICEND, F-59000, Lille, France.,Department of Neurology, CHU Lille, F-59000, Lille, France
| | - Thibaud Lebouvier
- Inserm, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ Lille, F-59000, Lille, France.,Memory Center - CNR MAJ, DISTALZ-LICEND, F-59000, Lille, France.,Department of Neurology, CHU Lille, F-59000, Lille, France
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | | | - Thomas Tourdias
- Neuroimagerie diagnostique et thérapeutique, CHU de Bordeaux, F-33000, Bordeaux, France.,Neurocentre Magendie, Inserm, U1215, Université de Bordeaux, F-33000, Bordeaux, France
| | - Lotfi Hacein-Bey
- Radiology Department, University of California Davis School of Medicine, Sacramento, CA, USA
| | - David Devos
- Inserm, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ Lille, F-59000, Lille, France.,Department of Pharmacology, CHU Lille, F-59000, Lille, France
| | - Florence Pasquier
- Inserm, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ Lille, F-59000, Lille, France.,Memory Center - CNR MAJ, DISTALZ-LICEND, F-59000, Lille, France.,Department of Neurology, CHU Lille, F-59000, Lille, France
| | - Xavier Leclerc
- Inserm, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ Lille, F-59000, Lille, France.,UMS 2014 - US 41 - PLBS - Plateformes Lilloises en Biologie & Santé, Univ Lille, F-59000, Lille, France.,Department of Neuroradiology, CHU Lille, Rue Emile Laine, F-59000, Lille, France
| | - Jean-Pierre Pruvo
- Inserm, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ Lille, F-59000, Lille, France.,UMS 2014 - US 41 - PLBS - Plateformes Lilloises en Biologie & Santé, Univ Lille, F-59000, Lille, France.,Department of Neuroradiology, CHU Lille, Rue Emile Laine, F-59000, Lille, France
| | - Sébastien Verclytte
- Department of Imaging, Lille Catholic Hospitals, Lille Catholic University, F-59000, Lille, France
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Chai C, Wu M, Wang H, Cheng Y, Zhang S, Zhang K, Shen W, Liu Z, Xia S. CAU-Net: A Deep Learning Method for Deep Gray Matter Nuclei Segmentation. Front Neurosci 2022; 16:918623. [PMID: 35720705 PMCID: PMC9204516 DOI: 10.3389/fnins.2022.918623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 05/03/2022] [Indexed: 12/04/2022] Open
Abstract
The abnormal iron deposition of the deep gray matter nuclei is related to many neurological diseases. With the quantitative susceptibility mapping (QSM) technique, it is possible to quantitatively measure the brain iron content in vivo. To assess the magnetic susceptibility of the deep gray matter nuclei in the QSM, it is mandatory to segment the nuclei of interest first, and many automatic methods have been proposed in the literature. This study proposed a contrast attention U-Net for nuclei segmentation and evaluated its performance on two datasets acquired using different sequences with different parameters from different MRI devices. Experimental results revealed that our proposed method was superior on both datasets over other commonly adopted network structures. The impacts of training and inference strategies were also discussed, which showed that adopting test time augmentation during the inference stage can impose an obvious improvement. At the training stage, our results indicated that sufficient data augmentation, deep supervision, and nonuniform patch sampling contributed significantly to improving the segmentation accuracy, which indicated that appropriate choices of training and inference strategies were at least as important as designing more advanced network structures.
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Affiliation(s)
- Chao Chai
- Department of Radiology, Tianjin Institute of Imaging Medicine, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Mengran Wu
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, China
| | - Huiying Wang
- School of Medicine, Nankai University, Tianjin, China
| | - Yue Cheng
- Department of Radiology, Tianjin Institute of Imaging Medicine, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | | | - Kun Zhang
- Department of Radiology, Tianjin Institute of Imaging Medicine, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Wen Shen
- Department of Radiology, Tianjin Institute of Imaging Medicine, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Zhiyang Liu
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin, China
- Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Tianjin, China
- *Correspondence: Zhiyang Liu,
| | - Shuang Xia
- Department of Radiology, Tianjin Institute of Imaging Medicine, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
- Shuang Xia,
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45
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Cerebral Iron Deposition in Neurodegeneration. Biomolecules 2022; 12:biom12050714. [PMID: 35625641 PMCID: PMC9138489 DOI: 10.3390/biom12050714] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/12/2022] [Accepted: 05/13/2022] [Indexed: 02/04/2023] Open
Abstract
Disruption of cerebral iron regulation appears to have a role in aging and in the pathogenesis of various neurodegenerative disorders. Possible unfavorable impacts of iron accumulation include reactive oxygen species generation, induction of ferroptosis, and acceleration of inflammatory changes. Whole-brain iron-sensitive magnetic resonance imaging (MRI) techniques allow the examination of macroscopic patterns of brain iron deposits in vivo, while modern analytical methods ex vivo enable the determination of metal-specific content inside individual cell-types, sometimes also within specific cellular compartments. The present review summarizes the whole brain, cellular, and subcellular patterns of iron accumulation in neurodegenerative diseases of genetic and sporadic origin. We also provide an update on mechanisms, biomarkers, and effects of brain iron accumulation in these disorders, focusing on recent publications. In Parkinson’s disease, Friedreich’s disease, and several disorders within the neurodegeneration with brain iron accumulation group, there is a focal siderosis, typically in regions with the most pronounced neuropathological changes. The second group of disorders including multiple sclerosis, Alzheimer’s disease, and amyotrophic lateral sclerosis shows iron accumulation in the globus pallidus, caudate, and putamen, and in specific cortical regions. Yet, other disorders such as aceruloplasminemia, neuroferritinopathy, or Wilson disease manifest with diffuse iron accumulation in the deep gray matter in a pattern comparable to or even more extensive than that observed during normal aging. On the microscopic level, brain iron deposits are present mostly in dystrophic microglia variably accompanied by iron-laden macrophages and in astrocytes, implicating a role of inflammatory changes and blood–brain barrier disturbance in iron accumulation. Options and potential benefits of iron reducing strategies in neurodegeneration are discussed. Future research investigating whether genetic predispositions play a role in brain Fe accumulation is necessary. If confirmed, the prevention of further brain Fe uptake in individuals at risk may be key for preventing neurodegenerative disorders.
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46
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Meng X, Liu J, Fan X, Bian C, Wei Q, Wang Z, Liu W, Jiao Z. Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer’s Disease. Front Aging Neurosci 2022; 14:911220. [PMID: 35651528 PMCID: PMC9149574 DOI: 10.3389/fnagi.2022.911220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 04/19/2022] [Indexed: 11/29/2022] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative brain disease, and it is challenging to mine features that distinguish AD and healthy control (HC) from multiple datasets. Brain network modeling technology in AD using single-modal images often lacks supplementary information regarding multi-source resolution and has poor spatiotemporal sensitivity. In this study, we proposed a novel multi-modal LassoNet framework with a neural network for AD-related feature detection and classification. Specifically, data including two modalities of resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were adopted for predicting pathological brain areas related to AD. The results of 10 repeated experiments and validation experiments in three groups prove that our proposed framework outperforms well in classification performance, generalization, and reproducibility. Also, we found discriminative brain regions, such as Hippocampus, Frontal_Inf_Orb_L, Parietal_Sup_L, Putamen_L, Fusiform_R, etc. These discoveries provide a novel method for AD research, and the experimental study demonstrates that the framework will further improve our understanding of the mechanisms underlying the development of AD.
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Affiliation(s)
- Xianglian Meng
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Junlong Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Xiang Fan
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Chenyuan Bian
- Shandong Provincial Key Laboratory of Digital Medicine and Computer-Assisted Surgery, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qingpeng Wei
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Ziwei Wang
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
| | - Wenjie Liu
- School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou, China
- *Correspondence: Wenjie Liu,
| | - Zhuqing Jiao
- School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
- Zhuqing Jiao,
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47
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Nikparast F, Ganji Z, Danesh Doust M, Faraji R, Zare H. Brain pathological changes during neurodegenerative diseases and their identification methods: How does QSM perform in detecting this process? Insights Imaging 2022; 13:74. [PMID: 35416533 PMCID: PMC9008086 DOI: 10.1186/s13244-022-01207-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 03/13/2022] [Indexed: 12/14/2022] Open
Abstract
The presence of iron is essential for many biological processes in the body. But sometimes, for various reasons, the amount of iron deposition in different areas of the brain increases, which leads to problems related to the nervous system. Quantitative susceptibility mapping (QSM) is one of the newest magnetic resonance imaging (MRI)-based methods for assessing iron accumulation in target areas. This Narrative Review article aims to evaluate the performance of QSM compared to other methods of assessing iron deposition in the clinical field. Based on the results, we introduced related basic definitions, some neurodegenerative diseases, methods of examining iron deposition in these diseases, and their advantages and disadvantages. This article states that the QSM method can be introduced as a new, reliable, and non-invasive technique for clinical evaluations.
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Affiliation(s)
- Farzaneh Nikparast
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zohreh Ganji
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammad Danesh Doust
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Reyhane Faraji
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hoda Zare
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. .,Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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48
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Cogswell PM, Wiste HJ, Weigand SD, Therneau TM, Jack CR. Response to "On the reproducibility of Quantitative Susceptibility Mapping and its potential as a clinical biomarker: a comment on Cogswell et al. 2021". Neuroimage 2022; 251:118992. [PMID: 35181550 DOI: 10.1016/j.neuroimage.2022.118992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 10/19/2022] Open
Affiliation(s)
- Petrice M Cogswell
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
| | - Heather J Wiste
- Department of Health Sciences Research, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Stephen D Weigand
- Department of Health Sciences Research, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Terry M Therneau
- Department of Health Sciences Research, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
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Takita H, Doishita S, Yoneda T, Tatekawa H, Abe T, Itoh Y, Horiuchi D, Tsukamoto T, Shimono T, Miki Y. Correlation between Phase-difference-enhanced MR Imaging and Amyloid Positron Emission Tomography: A Study on Alzheimer's Disease Patients and Normal Controls. Magn Reson Med Sci 2022; 22:67-78. [PMID: 35082221 PMCID: PMC9849423 DOI: 10.2463/mrms.mp.2021-0123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
PURPOSE While amyloid-β deposition in the cerebral cortex for Alzheimer's disease (AD) is often evaluated by amyloid positron emission tomography (PET), amyloid-β-related iron can be detected using phase difference enhanced (PADRE) imaging; however, no study has validated the association between PADRE imaging and amyloid PET. This study investigated whether the degree of hypointense areas on PADRE imaging correlated with the uptake of amyloid PET. METHODS PADRE imaging and amyloid PET were performed in 8 patients with AD and 10 age-matched normal controls. ROIs in the cuneus, precuneus, superior frontal gyrus (SFG), and superior temporal gyrus (STG) were automatically segmented. The degree of hypointense areas on PADRE imaging in each ROI was evaluated using 4-point scaling of visual assessment or volumetric semiquantitative assessment (the percentage of hypointense volume within each ROI). The mean standardized uptake value ratio (SUVR) of amyloid PET in each ROI was also calculated. The Spearman's correlation coefficient between the 4-point scale of PADRE imaging and SUVR of amyloid PET or between the semiquantitative hypointense volume percentage and SUVR in each ROI was evaluated. RESULTS In the precuneus, a significant positive correlation was identified between the 4-point scale of PADRE imaging and SUVR of amyloid PET (Rs = 0.5; P = 0.034) in all subjects. In the cuneus, a significant positive correlation was identified between the semiquantitative volume percentage of PADRE imaging and SUVR of amyloid PET (Rs = 0.55; P = 0.02) in all subjects. CONCLUSION Amyloid-β-enhancing PADRE imaging can be used to predict the SUVR of amyloid PET, especially in the cuneus and precuneus, and may have the potential to be used for diagnosing AD by detecting amyloid deposition.
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Affiliation(s)
- Hirotaka Takita
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Osaka, Japan
| | - Satoshi Doishita
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Osaka, Japan,Department of Radiology, Saitama Red Cross Hospital, Saitama, Saitama, Japan
| | - Tetsuya Yoneda
- Department of Medical Physics in Advanced Biomedical Sciences, Faculty of Life Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
| | - Hiroyuki Tatekawa
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Osaka, Japan,Corresponding Author: Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, 1-4-3, Asahi-machi, Abeno-ku, Osaka, Osaka 545-8585, Japan. Phone: +81-6-6645-3831, Fax: +81-6-6646-6655,
| | - Takato Abe
- Department of Neurology, Tokai University School of Medicine, Isehara, Kanagawa, Japan,Department of Neurology, Graduate School of Medicine, Osaka City University, Osaka, Osaka, Japan
| | - Yoshiaki Itoh
- Department of Neurology, Graduate School of Medicine, Osaka City University, Osaka, Osaka, Japan
| | - Daisuke Horiuchi
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Osaka, Japan
| | - Taro Tsukamoto
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Osaka, Japan
| | - Taro Shimono
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Osaka, Japan
| | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka City University, Osaka, Osaka, Japan
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50
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Mao H, Dou W, Wang X, Chen K, Wang X, Guo Y, Zhang C. Iron Deposition in Gray Matter Nuclei of Patients With Intracranial Artery Stenosis: A Quantitative Susceptibility Mapping Study. Front Neurol 2022; 12:785822. [PMID: 35069414 PMCID: PMC8766754 DOI: 10.3389/fneur.2021.785822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 12/14/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: This study aimed to use quantitative susceptibility mapping (QSM) to systematically investigate the changes of iron content in gray matter (GM) nuclei in patients with long-term anterior circulation artery stenosis (ACAS) and posterior circulation artery stenosis (PCAS). Methods: Twenty-five ACAS patients, 25 PCAS patients, and 25 age- and sex-matched healthy controls underwent QSM examination. Patients were scored using the National Institutes of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS) to assess the degree of neural function deficiency. On QSM images, iron related susceptibility of GM nuclei, including bilateral caudate nucleus, putamen (PU), globus pallidus (GP), thalamus (TH), substantia nigra (SN), red nucleus, and dentate nucleus (DN), were assessed. Susceptibility was compared between bilateral GM nuclei in healthy controls, ACAS patients, and PCAS patients. Partial correlation analysis, with age as a covariate, was separately performed to assess the relationships of susceptibility with NIHSS and mRS scores. Results: There were no significant differences between the susceptibilities for left and right hemispheres in all seven GM nucleus subregions for healthy controls, ACAS patients, and PCAS patients. Compared with healthy controls, mean susceptibility of bilateral PU, GP, and SN in ACAS patients and of bilateral PU, GP, SN, and DN in PCAS patients were significantly increased (all P < 0.05). In addition, mean susceptibility of bilateral TH and SN in PCAS patients was significantly higher than in ACAS patients (both P < 0.05). With partial correlation analysis, mean susceptibility at bilateral PU of ACAS patients was significantly correlated with mRS score (r = 0.415, P < 0.05), and at bilateral PU in PCAS patients was correlated with NIHSS score (r = 0.424, P < 0.05). Conclusion: Our findings indicated that abnormal iron metabolism may present in different subregions of GM nuclei after long-term ACAS and PCAS. In addition, iron content of PU in patients with ACAS and PCAS was correlated with neurological deficit scores. Therefore, iron quantification measured by QSM susceptibility may provide a new insight to understand the pathological mechanism of ischemic stroke caused by ACAS and PCAS.
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Affiliation(s)
- Huimin Mao
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.,Postgraduate Department, Shandong First Medical University, Jinan, China
| | | | - Xinyi Wang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Kunjian Chen
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.,Postgraduate Department, Shandong First Medical University, Jinan, China
| | - Xinyu Wang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.,Postgraduate Department, Shandong First Medical University, Jinan, China
| | - Yu Guo
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.,Postgraduate Department, Shandong First Medical University, Jinan, China
| | - Chao Zhang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
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