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Schlaeger S, Mühlau M, Gilbert G, Vavasour I, Amthor T, Doneva M, Menegaux A, Mora M, Lauerer M, Pongratz V, Zimmer C, Wiestler B, Kirschke JS, Preibisch C, Berg RC. Sensitivity of multi-parametric quantitative magnetic resonance imaging for multiple sclerosis pathology. PLoS One 2025; 20:e0318415. [PMID: 40238815 PMCID: PMC12002544 DOI: 10.1371/journal.pone.0318415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 01/15/2025] [Indexed: 04/18/2025] Open
Abstract
BACKGROUND In recent years, quantitative magnetic resonance imaging (MRI) made progress towards clinical applicability mainly through advances in acceleration techniques. In patients with multiple sclerosis (MS), objective quantitative MRI-based characterization of subtle pathological alterations in lesions, perilesion (PL), as well as normal-appearing (NA) white matter (NAWM) and grey matter (NAGM) would revolutionize clinical assessment. While numerous quantitative techniques have been applied in studies of MS patients, their diagnostic significance especially for individual patients with relatively short disease duration is unclear. Therefore, we investigated the sensitivity of several quantitative MRI parameters to focal and diffuse MS pathology in a clinical feasibility study with a small sample size. METHODS In 13 MS patients with a mean disease duration of 8 years and a mean EDSS of 1.1 as well as 14 healthy age-matched controls (HC), we acquired nine (semi-)quantitative magnetic resonance (MR) biomarkers, namely myelin water fraction (MWF), magnetization transfer (MT) saturation (MTsat), inhomogeneous MT ratio (ihMTR), quantitative longitudinal relaxation time (qT1), intrinsic (qT2) and effective (qT2*) quantitative transverse relaxation times, proton density (PD), quantitative susceptibility mapping (QSM), and the ratio between T1-weighted and T2-weighted images (T1w/T2w). Four volumes of interest were automatically defined (NA/HC grey matter (GM), NA/HC white matter (WM), lesion, and PL), and biomarker values were analyzed between groups and tissue types. RESULTS For all nine assessed biomarkers, mean values per patient were significantly different between lesion, PL, and NAWM (p < 0.05, FDR corrected). The lesion values of qT1, qT2, qT2 * , PD, and QSM were rather inhomogeneous. Furthermore, MWF, MTsat, and ihMTR were sensitive to diffuse WM pathology in MS with the largest absolute differences between NAWM and HCWM medians, albeit not statistically significant after correction for multiple testing. DISCUSSION In our study, we successfully compared nine different quantitative MR parameters within the same subjects for tissue characterization of MS. Our study adds relevant aspects to the current debate on different sensitivities of various quantitative MR biomarkers to MS pathology. While all investigated MR biomarkers allowed characterizing lesions in individual patients, a separation of NAWM and HCWM could be most promising with the myelin-sensitive measures MWF, MTsat, and ihMTR.
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Affiliation(s)
- Sarah Schlaeger
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Mark Mühlau
- Department of Neurology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | | | - Irene Vavasour
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | | | | | - Aurore Menegaux
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Maria Mora
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Markus Lauerer
- Department of Neurology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Viola Pongratz
- Department of Neurology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Christine Preibisch
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Neurology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Ronja C. Berg
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Neurology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
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Wang H, Qi Y, Liu X, Song LJ, Yang WB, Li MA, Bai XY, Xu MS, Zhu HN, Cai SQ, Wang Y, Yang ZH, Li YZ, Wang ZC, Guo YF. Habitat analysis of iron deposition in the basal ganglia for diagnosing cognitive impairment in chronic kidney disease: evidence from a case-control study. BMC Med Imaging 2025; 25:113. [PMID: 40200199 PMCID: PMC11980340 DOI: 10.1186/s12880-025-01656-7] [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: 11/21/2024] [Accepted: 04/01/2025] [Indexed: 04/10/2025] Open
Abstract
BACKGROUND Chronic kidney disease induces alterations in the heterogeneity of iron deposition within the basal ganglia. Quantitative analysis of the heterogeneity of iron deposition within the basal ganglia may be valuable for diagnosing chronic kidney disease-related cognitive impairment. METHODS In this prospective observational cohort study, quantitative susceptibility mapping (QSM) was performed in chronic kidney disease patients. Susceptibility values of each nucleus within the basal ganglia were measured. Radiomic features were extracted from habitats of the basal ganglia on QSM images. Habitat-based models for diagnosing cognitive impairment were constructed using the random forest algorithm. Logistic regression was employed to build the clinical model and the combined model. The performance of each model was evaluated by the receiver operating characteristic (ROC) analysis. RESULTS A total of 146 patients (mean age, 51 ± 13 years; 92 male) were included, of which 79 had cognitive impairment. The two habitats-based model achieved an area under the curve of 0.926 (95% CI 0.842-1.000) on the test set, the highest among all prediction models. The two-habitat maps indicated that chronic kidney disease had two distinct patterns of impact on iron deposition in the basal ganglia region. The capability of the two habitats-based model to identify chronic kidney disease-related cognitive impairment was significantly superior to that of the susceptibility values measured in various nuclei (all p < 0.05). CONCLUSIONS This study innovatively applied a habitat-based quantitative analysis technique to QSM, successfully constructing a model that accurately diagnoses chronic kidney disease-related cognitive impairment. TRIAL REGISTRATION This study was approved by the Beijing Friendship Hospital Ethics Board (ClinicalTrials.gov Identifier: NCTO5137470) and conducted in accordance with the Declaration of Helsinki ethical standards.
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Affiliation(s)
- Hao Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, Beijing, 100050, China
| | - Yu Qi
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Xu Liu
- Department of Nephrology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Li-Jun Song
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, Beijing, 100050, China
| | - Wen-Bo Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, Beijing, 100050, China
| | - Ming-An Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, Beijing, 100050, China
| | - Xiao-Yan Bai
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, Beijing, 100050, China
| | - Mao-Sheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Hao-Nan Zhu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang, China
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Si-Qing Cai
- Center of Radiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
- School of Medical Imaging, Fujian Medical University, Fuzhou, Fujian, China
| | - Yi Wang
- Center of Radiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China
| | - Zheng-Han Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, Beijing, 100050, China
| | - Yuan-Zhe Li
- Center of Radiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
- School of Medical Imaging, Fujian Medical University, Fuzhou, Fujian, China.
| | - Zhen-Chang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 Yong An Road, Xicheng District, Beijing, 100050, China.
| | - Yi-Fan Guo
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang, China.
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
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Li Y, Jiang Y, Gao B, Liu N, Zhang Y, Zhou H, Song Q, Wang N, Miao Y. Regional high iron deposition on brain quantitative susceptibility mapping correlates with cognitive decline in chronic kidney disease patients. Brain Imaging Behav 2025; 19:395-406. [PMID: 39930019 PMCID: PMC11978685 DOI: 10.1007/s11682-025-00976-0] [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] [Accepted: 01/25/2025] [Indexed: 04/09/2025]
Abstract
This study aimed to evaluate changes in gray matter nuclei iron deposition in chronic kidney disease (CKD) patients using the quantitative susceptibility mapping (QSM) threshold method, and analyze the relationship between brain iron levels and cognitive function. A total of fifty-three CKD patients were prospectively recruited, comprising 35 hemodialysis (HD, 57.54 ± 10.42 years, 21 males) and 18 non-hemodialysis (NHD, 55.06 ± 11.47 years, 10 males ), and were compared to 43 healthy controls (HC, 55.67 ± 7.79 years, 18 males). All participants underwent clinical assessments, neuropsychological tests, and QSM scans. The mean magnetic susceptibility value (MSV) and volume of the whole nuclei (MSVM, VM) and high iron region (MSVRII, VRII) were measured. Correlations between QSM data, neuropsychological scores, and clinical variables in HD group were analyzed. Linear regression analysis was performed to explore the effect of iron deposition on cognition and emotional well-being in HD group. A statistically significant P-value was set at 0.05. HD patients exhibited higher MSVM in the right red nucleus (RN) compared to HCs (P = 0.006). Additionally, significant differences in the MSVRII were observed in the left caudate nucleus (CN), bilateral putamen (Put), and right RN among the three groups (all P = 0.027, FDR-corrected). MSVRII of the left Put was positively correlated with creatinine and uric acid levels, while the MSVRII of the right Put was negatively correlated with mean corpuscular hemoglobin and mean corpuscular hemoglobin concentration. Regression analysis revealed that iron deposition in left CN was independently associated with depression, while iron deposition in left Put and right RN were independently positively associated with delayed recall performance. Conversely, iron deposition in bilateral Put and right RN were negatively associated with orientation ability, after controlling for age, sex, years of education and duration of dialysis. Brain iron deposition is often excessive and uneven in CKD patients, particularly those undergoing hemodialysis. Assessing regional high-iron deposition can provide valuable insights into the distribution of iron, which is associated with cognitive dysfunction and emotional disorders.
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Affiliation(s)
- Yuan Li
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, China
| | - Yuhan Jiang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, China
- Department of Radiology, Zhongshan Hospital Affiliated to Dalian University, No.6 Jiefang Street, Zhongshan District, Liaoning, 116001, Dalian, China
| | - Bingbing Gao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, China
| | - Na Liu
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, China
| | - Yukun Zhang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, China
| | - Huiling Zhou
- Department of Neurology, The First Affiliated Hospital of Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, China
| | - Qingwei Song
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, China
| | - Nan Wang
- Department of Nephrology, The First Affiliated Hospital of Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, China
| | - Yanwei Miao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, 116011, China.
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Shen T, Sheriff S, Qu Y, Gupta VK, Graham SL, Klistorner A, Jia H, Sun X, You Y. Correlations between postmortem quantitative MRI parameters and demyelination, axonal loss and gliosis in multiple sclerosis: A systematic review and meta-analysis. Brain Imaging Behav 2025; 19:323-335. [PMID: 39871045 DOI: 10.1007/s11682-025-00971-5] [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] [Accepted: 01/17/2025] [Indexed: 01/29/2025]
Abstract
Magnetic resonance imaging (MRI) is frequently used to monitor disease progression in multiple sclerosis (MS). This study aims to systematically evaluate the correlation between MRI measures and histopathological changes, including demyelination, axonal loss, and gliosis, in the central nervous system of MS patients. We systematically reviewed post-mortem histological studies evaluating myelin density, axonal loss, and gliosis using quantitative imaging in MS. Relevant studies were identified through searches in PubMed, EMBASE, and Web of Science. A total of 38 studies involving 1782 regions of interest from 229 subjects were included. Pooled random-effects models were used to calculate the correlation between demyelination, axonal loss, gliosis, and various MRI parameters, including magnetization transfer ratio (MTR), T1 and T2 relaxation times, myelin water fraction (MWF), proton density (PD), and diffusivities. Pair-wise analyses compared results between lesioned and non-lesioned tissues. Our results demonstrated moderate to strong correlations between MRI parameters and myelin density in MS, with correlation coefficients: T1 (0.72), T2 (0.72), MTR (-0.73), FA (-0.73), RD (0.70), MD (0.70), MWF (-0.82), and PD (0.73). Interestingly, stronger correlations were found in lesioned tissues compared to non-lesioned tissues (P < 0.001). Moderate correlations were found between MRI parameters and axonal loss and gliosis. Our study reveals significant correlations between MRI techniques and histological assessments of myelin, axonal damage, and gliosis in MS. MRI metrics exhibited a more robust association with demyelination in lesioned areas than in non-lesioned brain tissue, highlighting the pronounced degree of myelin degradation in MS lesions. Further investigation is warranted to corroborate these results and refine MRI-based monitoring of MS pathology.
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Affiliation(s)
- Ting Shen
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University, School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
- Macquarie Medical School, Macquarie University, Sydney, NSW, Australia.
- Save Sight Institute, The University of Sydney, Sydney, NSW, Australia.
| | - Samran Sheriff
- Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Yanlin Qu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University, School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Vivek K Gupta
- Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Stuart L Graham
- Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
- Save Sight Institute, The University of Sydney, Sydney, NSW, Australia
| | - Alexander Klistorner
- Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
- Save Sight Institute, The University of Sydney, Sydney, NSW, Australia
| | - Huixun Jia
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University, School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
- School of Public Health, Key Laboratory of Public Health Safety and Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China.
| | - Xiaodong Sun
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University, School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
| | - Yuyi You
- Macquarie Medical School, Macquarie University, Sydney, NSW, Australia.
- Save Sight Institute, The University of Sydney, Sydney, NSW, Australia.
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Xie Y, Zhang Y, Wu S, Zhang S, Zhu H, Zhu W, Wang Y. Atrophy-Independent and Dependent Iron and Myelin Changes in Deep Gray Matter of Multiple Sclerosis: A Longitudinal Study Using χ-Separation Imaging. Acad Radiol 2025; 32:988-999. [PMID: 39084936 DOI: 10.1016/j.acra.2024.07.031] [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: 06/10/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 08/02/2024]
Abstract
RATIONALE AND OBJECTIVES To investigate iron and myelin changes in deep gray matter (DGM) of relapsing-remitting multiple sclerosis (RRMS) patients and their relationship to atrophy by χ-separation imaging. MATERIALS AND METHODS 33 RRMS patients and 34 healthy controls (HC) were included in this study. The χ-separation map reconstructed from a 3D multi-echo gradient echo scan was used to measure the positive susceptibility (χpos) and negative susceptibility (χneg) of DGM. To take into account the effect of atrophy, susceptibility mass of DGM was calculated by multiplying volume by the mean bulk susceptibility. Differences in MRI metrics between baseline patients, follow-up patients, and HC were compared respectively. RESULTS Compared to HC, χpos of basal ganglia were significantly increased in follow-up patients (P < 0.05). The χpos of pallidum was significantly higher in follow-up patients than that in baseline patients (P = 0.006). The χneg of caudate, pallidum and hippocampus in baseline and follow-up patients was significantly higher than that in HC (P < 0.05). When taking into account the effect of atrophy, there was a significant decrease in χpos mass and a significant increase in χneg mass of thalamus, accumbens and amygdala in follow-up patients compared to HC (P < 0.05). The χpos mass of the thalamus was further decreased in follow-up patients compared to baseline patients (P = 0.006). CONCLUSION χ-separation imaging could generate independent information on iron and myelin changes in RRMS patients, showing atrophy-dependent iron increase in basal ganglia and atrophy-independent iron and myelin decrease in thalamus.
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Affiliation(s)
- Yan Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shaolong Wu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shun Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongquan Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA; Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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Tiet MY, Scoffings D, Blanchard C, Dineen RA, Horvath R, Hensiek A. Novel observation for adult ataxia-telangiectasia: evaluating the lack of hypointensity of the dentate nuclei. J Neurol Neurosurg Psychiatry 2025; 96:202-204. [PMID: 39358010 DOI: 10.1136/jnnp-2024-334398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Accepted: 08/21/2024] [Indexed: 10/04/2024]
Affiliation(s)
- May Yung Tiet
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - Caroline Blanchard
- Mental Health and Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - Robert A Dineen
- Radiological Sciences, Division of Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - Rita Horvath
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Anke Hensiek
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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Zachariou V, Pappas C, Bauer CE, Seago ER, Gold BT. Exploring the links among brain iron accumulation, cognitive performance, and dietary intake in older adults: A longitudinal MRI study. Neurobiol Aging 2025; 145:1-12. [PMID: 39447489 PMCID: PMC11578767 DOI: 10.1016/j.neurobiolaging.2024.10.006] [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: 04/26/2024] [Revised: 10/09/2024] [Accepted: 10/17/2024] [Indexed: 10/26/2024]
Abstract
This study evaluated longitudinal brain iron accumulation in older adults, its association with cognition, and the role of specific nutrients in mitigating iron accumulation. MRI-based, quantitative susceptibility mapping estimates of brain iron concentration were acquired from seventy-two healthy older adults (47 women, ages 60-86) at a baseline timepoint (TP1) and a follow-up timepoint (TP2) 2.5-3.0 years later. Dietary intake was evaluated at baseline using a validated questionnaire. Cognitive performance was assessed at TP2 using the uniform data set (Version 3) neuropsychological tests of episodic memory (MEM) and executive function (EF). Voxel-wise, linear mixed-effects models, adjusted for longitudinal gray matter volume alterations, age, and several non-dietary lifestyle factors revealed brain iron accumulation in multiple subcortical and cortical brain regions, which was negatively associated with both MEM and EF performance at T2. However, consumption of specific dietary nutrients at TP1 was associated with reduced brain iron accumulation. Our study provides a map of brain regions showing iron accumulation in older adults over a short 2.5-year follow-up and indicates that certain dietary nutrients may slow brain iron accumulation.
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Affiliation(s)
- Valentinos Zachariou
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY, USA.
| | - Colleen Pappas
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Christopher E Bauer
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Elayna R Seago
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA
| | - Brian T Gold
- Department of Neuroscience, College of Medicine, University of Kentucky, Lexington, KY, USA; Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA; Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, KY, USA.
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Thompson GJ, Wang Z, Kim JY, Li H, Kim DH, Ye Q, Su MY. Histological Validation of Multi-Echo Gradient Echo (MGRE)-Derived Myelin Water Fraction (MWF) at 9.4 T and the Influence of Orientation on Quantification. NMR IN BIOMEDICINE 2025; 38:e5303. [PMID: 39701559 DOI: 10.1002/nbm.5303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2024] [Revised: 11/18/2024] [Accepted: 11/19/2024] [Indexed: 12/21/2024]
Abstract
Myelin is essential in the nervous system of mammals. As the location and degree of myelin loss can reflect varied pathophysiological status, noninvasive measurement of myelin is of high importance. The magnetic resonance imaging (MRI) technique of myelin water fraction (MWF) derived from multi-echo gradient echo (MGRE) sequence is a promising tool for the quantification of myelin content due to the low specific absorption rate (SAR) compared with the spin-echo sequence, time efficiency, and wide availability. Yet to our knowledge, MGRE-derived MWF has never been quantitatively validated with histology. The main objective of this study was to quantitatively validate the MRI findings by referencing the myelin histology using a rat model. As a second objective, we investigated how the orientation of white matter fibers with respect to the static B0 field impacted both the apparent transverse relaxation rate (R2* = 1/T2*) and the derived MWF. Moreover, MWF is known to change with age; thus, we compared rat brains of different ages. The orientation effect of MWF in a clinical setting was studied using 3 T human data. Twenty ex vivo rat brains with different ages and three healthy volunteers were scanned on a 9.4 T Bruker and 3.0 T Siemens systems, respectively. The 3D MGRE and diffusion tensor imaging (DTI) data were acquired. Our results showed a highly significant correlation between MGRE-derived MWF and histological stain of myelin, and susceptibility and diffusivity also demonstrated a significant association with myelin. Both MWF and R2* (R2* = 1/T2*) values changed as a function of orientation, and the function varied with age. Furthermore, MWF and R2* were more sensitive to age than DTI. In vivo 3 T human MWF also changed substantially with the orientation as well. Our results support that MGRE-derived MWF can be used to assess the myelin content quantitatively.
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Affiliation(s)
| | - Ziyi Wang
- Human Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Jae-Yoon Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Hui Li
- Human Institute, ShanghaiTech University, Shanghai, China
| | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Qiong Ye
- High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, California, USA
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Wang N, Liao C, Cao X, Nishimura M, Brackenier YWE, Yurt M, Gao M, Abraham D, Alkan C, Iyer SS, Zhou Z, Jeong H, Kerr A, Haldar JP, Setsompop K. Spherical echo-planar time-resolved imaging (sEPTI) for rapid 3D quantitative T 2 * and susceptibility imaging. Magn Reson Med 2025; 93:121-137. [PMID: 39250435 DOI: 10.1002/mrm.30255] [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: 03/28/2024] [Revised: 07/28/2024] [Accepted: 07/31/2024] [Indexed: 09/11/2024]
Abstract
PURPOSE To develop a 3D spherical EPTI (sEPTI) acquisition and a comprehensive reconstruction pipeline for rapid high-quality whole-brain submillimeterT 2 * $$ {\mathrm{T}}_2^{\ast } $$ and QSM quantification. METHODS For the sEPTI acquisition, spherical k-space coverage is utilized with variable echo-spacing and maximum kx ramp-sampling to improve efficiency and signal incoherency compared to existing EPTI approaches. For reconstruction, an iterative rank-shrinking B0 estimation and odd-even high-order phase correction algorithms were incorporated into the reconstruction to better mitigate artifacts from field imperfections. A physics-informed unrolled network was utilized to boost the SNR, where 1-mm and 0.75-mm isotropic whole-brain imaging were performed in 45 and 90 s at 3 T, respectively. These protocols were validated through simulations, phantom, and in vivo experiments. Ten healthy subjects were recruited to provide sufficient data for the unrolled network. The entire pipeline was validated on additional five healthy subjects where different EPTI sampling approaches were compared. Two additional pediatric patients with epilepsy were recruited to demonstrate the generalizability of the unrolled reconstruction. RESULTS sEPTI achieved 1.4× $$ \times $$ faster imaging with improved image quality and quantitative map precision compared to existing EPTI approaches. The B0 update and the phase correction provide improved reconstruction performance with lower artifacts. The unrolled network boosted the SNR, achieving high-qualityT 2 * $$ {\mathrm{T}}_2^{\ast } $$ and QSM quantification with single average data. High-quality reconstruction was also obtained in the pediatric patients using this network. CONCLUSION sEPTI achieved whole-brain distortion-free multi-echo imaging andT 2 * $$ {\mathrm{T}}_2^{\ast } $$ and QSM quantification at 0.75 mm in 90 s which has the potential to be useful for wide clinical applications.
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Affiliation(s)
- Nan Wang
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Mark Nishimura
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | | | - Mahmut Yurt
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Mengze Gao
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Daniel Abraham
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Cagan Alkan
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Siddharth Srinivasan Iyer
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA
| | - Zihan Zhou
- Department of Radiology, Stanford University, Stanford, California, USA
| | | | - Adam Kerr
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Cognitive and Neurobiological Imaging Center, Stanford University, Stanford, California, USA
| | - Justin P Haldar
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
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10
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Stirnberg R, Deistung A, Reichenbach JR, Breteler MMB, Stöcker T. Rapid submillimeter QSM and R 2* mapping using interleaved multishot 3D-EPI at 7 and 3 Tesla. Magn Reson Med 2024; 92:2294-2311. [PMID: 38988040 DOI: 10.1002/mrm.30216] [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: 12/28/2023] [Revised: 06/05/2024] [Accepted: 06/24/2024] [Indexed: 07/12/2024]
Abstract
PURPOSE To explore the high signal-to-noise ratio (SNR) efficiency of interleaved multishot 3D-EPI with standard image reconstruction for fast and robust high-resolution whole-brain quantitative susceptibility (QSM) andR 2 ∗ $$ {R}_2^{\ast } $$ mapping at 7 and 3T. METHODS Single- and multi-TE segmented 3D-EPI is combined with conventional CAIPIRINHA undersampling for up to 72-fold effective gradient echo (GRE) imaging acceleration. Across multiple averages, scan parameters are varied (e.g., dual-polarity frequency-encoding) to additionally correct forB 0 $$ {\mathrm{B}}_0 $$ -induced artifacts, geometric distortions and motion retrospectively. A comparison to established GRE protocols is made. Resolutions range from 1.4 mm isotropic (1 multi-TE average in 36 s) up to 0.4 mm isotropic (2 single-TE averages in approximately 6 min) with whole-head coverage. RESULTS Only 1-4 averages are needed for sufficient SNR with 3D-EPI, depending on resolution and field strength. Fast scanning and small voxels together with retrospective corrections result in substantially reduced image artifacts, which improves susceptibility andR 2 ∗ $$ {R}_2^{\ast } $$ mapping. Additionally, much finer details are obtained in susceptibility-weighted image projections through significantly reduced partial voluming. CONCLUSION Using interleaved multishot 3D-EPI, single-TE and multi-TE data can readily be acquired 10 times faster than with conventional, accelerated GRE imaging. Even 0.4 mm isotropic whole-head QSM within 6 min becomes feasible at 7T. At 3T, motion-robust 0.8 mm isotropic whole-brain QSM andR 2 ∗ $$ {R}_2^{\ast } $$ mapping with no apparent distortion in less than 7 min becomes clinically feasible. Stronger gradient systems may allow for even higher effective acceleration rates through larger EPI factors while maintaining optimal contrast.
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Affiliation(s)
- Rüdiger Stirnberg
- MR Physics, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Andreas Deistung
- Clinic and Outpatient Clinic for Radiology, University Hospital Halle (Saale), University Medicine Halle, Halle (Saale), Germany
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Monique M B Breteler
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Faculty of Medicine, Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), University of Bonn, Bonn, Germany
| | - Tony Stöcker
- MR Physics, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Physics and Astronomy, University of Bonn, Bonn, Germany
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11
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Otsuka FS, Otaduy MCG, Rodriguez RD, Langkammer C, Barbosa JHO, Salmon CEG. Biophysical contrast sources for magnetic susceptibility and R2* mapping: A combined 7 Tesla, mass spectrometry and electron paramagnetic resonance study. Neuroimage 2024; 302:120892. [PMID: 39433113 DOI: 10.1016/j.neuroimage.2024.120892] [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: 08/28/2024] [Accepted: 10/16/2024] [Indexed: 10/23/2024] Open
Abstract
Iron is the most abundant trace metal in the human brain and consistently shown elevated in prevalent neurological disorders. Because of its paramagnetism, brain iron can be assessed in vivo by quantitative MRI techniques such as R2* mapping and Quantitative Susceptibility Mapping (QSM). While Inductively Coupled Plasma Mass Spectrometry (ICP-MS) has demonstrated good correlations of the total iron content to MRI parameters in gray matter, the relationship to ferritin levels as assessed by Electron Paramagnetic Resonance (EPR) has not been systematically analyzed. Therefore, we included 15 postmortem subjects (age: 26-91 years) which underwent quantitative in-situ MRI at 7 Tesla within a post-mortem interval of 24 h after death. ICP-MS and EPR were used to measure the total iron and ferritin content in 8 selected gray matter (GM) structures and the correlations to R2* and QSM were calculated. We found that R2* and QSM in the iron rich basal ganglia and the red nucleus were highly correlated with iron (R² > 0.7) and ferritin (R² > 0.6), whereas those correlations were lost in cortical regions and the hippocampus. The neuromelanin-rich substantia nigra showed a different behavior with a correlation with total iron only (R² > 0.5) but not with ferritin. Although qualitative results were similar for both qMRI techniques the observed correlation was always stronger for QSM than R2*. This study demonstrated the quantitative correlations between R2*, QSM, total iron and ferritin levels in an in-situ MRI setup and therefore aids to understand how molecular forms of iron are responsible for MRI contrast generation.
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Affiliation(s)
- Fábio Seiji Otsuka
- InBrain, Departamento de Física, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), Universidade de São Paulo USP, Avenida Bandeirantes 3900, Vila Monte Alegre, Ribeirão Preto, São Paulo CEP 14040-901, Brazil.
| | - Maria Concepción Garcia Otaduy
- LIM44, Instituto de Radiologia (InRad), Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo, São Paulo, Brazil
| | - Roberta Diehl Rodriguez
- LIM44, Instituto de Radiologia (InRad), Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo, São Paulo, Brazil
| | | | - Jeam Haroldo Oliveira Barbosa
- InBrain, Departamento de Física, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), Universidade de São Paulo USP, Avenida Bandeirantes 3900, Vila Monte Alegre, Ribeirão Preto, São Paulo CEP 14040-901, Brazil; Setor de Radioterapia, Santa Casa de Misericórdia de Lavras, Minas Gerais, Brazil
| | - Carlos Ernesto Garrido Salmon
- InBrain, Departamento de Física, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), Universidade de São Paulo USP, Avenida Bandeirantes 3900, Vila Monte Alegre, Ribeirão Preto, São Paulo CEP 14040-901, Brazil; Departamento de Imagens Médicas, Hematologia e Oncologia Clínica, Faculdade de Medicina de Ribeirão Preto (FMRP), Universidade de Sãoo Paulo, Ribeirão Preto, Brazil.
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12
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Calabro FJ, Parr AC, Sydnor VJ, Hetherington H, Prasad KM, Ibrahim TS, Sarpal DK, Famalette A, Verma P, Luna B. Leveraging ultra-high field (7T) MRI in psychiatric research. Neuropsychopharmacology 2024; 50:85-102. [PMID: 39251774 PMCID: PMC11525672 DOI: 10.1038/s41386-024-01980-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/21/2024] [Accepted: 07/23/2024] [Indexed: 09/11/2024]
Abstract
Non-invasive brain imaging has played a critical role in establishing our understanding of the neural properties that contribute to the emergence of psychiatric disorders. However, characterizing core neurobiological mechanisms of psychiatric symptomatology requires greater structural, functional, and neurochemical specificity than is typically obtainable with standard field strength MRI acquisitions (e.g., 3T). Ultra-high field (UHF) imaging at 7 Tesla (7T) provides the opportunity to identify neurobiological systems that confer risk, determine etiology, and characterize disease progression and treatment outcomes of major mental illnesses. Increases in scanner availability, regulatory approval, and sequence availability have made the application of UHF to clinical cohorts more feasible than ever before, yet the application of UHF approaches to the study of mental health remains nascent. In this technical review, we describe core neuroimaging methodologies which benefit from UHF acquisition, including high resolution structural and functional imaging, single (1H) and multi-nuclear (e.g., 31P) MR spectroscopy, and quantitative MR techniques for assessing brain tissue iron and myelin. We discuss advantages provided by 7T MRI, including higher signal- and contrast-to-noise ratio, enhanced spatial resolution, increased test-retest reliability, and molecular and neurochemical specificity, and how these have begun to uncover mechanisms of psychiatric disorders. Finally, we consider current limitations of UHF in its application to clinical cohorts, and point to ongoing work that aims to overcome technical hurdles through the continued development of UHF hardware, software, and protocols.
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Affiliation(s)
- Finnegan J Calabro
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Ashley C Parr
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Valerie J Sydnor
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Konasale M Prasad
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - Tamer S Ibrahim
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Deepak K Sarpal
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alyssa Famalette
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Piya Verma
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
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13
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Zhu Z, Naji N, Esfahani JH, Snyder J, Seres P, Emery DJ, Noga M, Blevins G, Smyth P, Wilman AH. MR Susceptibility Separation for Quantifying Lesion Paramagnetic and Diamagnetic Evolution in Relapsing-Remitting Multiple Sclerosis. J Magn Reson Imaging 2024; 60:1867-1879. [PMID: 38308397 DOI: 10.1002/jmri.29266] [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: 10/04/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Multiple sclerosis (MS) lesion evolution may involve changes in diamagnetic myelin and paramagnetic iron. Conventional quantitative susceptibility mapping (QSM) can provide net susceptibility distribution, but not the discrete paramagnetic and diamagnetic components. PURPOSE To apply susceptibility separation (χ separation) to follow lesion evolution in MS with comparison to R2*/R2 '/QSM. STUDY TYPE Longitudinal, prospective. SUBJECTS Twenty relapsing-remitting MS subjects (mean age: 42.5 ± 9.4 years, 13 females; mean years of symptoms: 4.3 ± 1.4 years). FIELD STRENGTH/SEQUENCE Three-dimensional multiple echo gradient echo (QSM and R2* mapping), two-dimensional dual echo fast spin echo (R2 mapping), T2-weighted fluid attenuated inversion recovery, and T1-weighted magnetization prepared gradient echo sequences at 3 T. ASSESSMENT Data were analyzed from two scans separated by a mean interval of 14.4 ± 2.0 months. White matter lesions on fluid-attenuated inversion recovery were defined by an automatic pipeline, then manually refined (by ZZ/AHW, 3/25 years' experience in MRI), and verified by a radiologist (MN, 25 years' experience in MS). Susceptibility separation yielded the paramagnetic and diamagnetic susceptibility content of each voxel. Lesions were classified into four groups based on the variation of QSM/R2* or separated into positive/negative components from χ separation. STATISTICAL TESTS Two-sample paired t tests for assessment of longitudinal differences. Spearman correlation coefficients to assess associations between χ separation and R2*/R2 '/QSM. Significant level: P < 0.005. RESULTS A total of 183 lesions were quantified. Categorizing lesions into groups based on χ separation demonstrated significant annual changes in QSM//R2*/R2 '. When lesions were grouped based on changes in QSM and R2*, both changing in unison yielded a significant dominant paramagnetic variation and both opposing yielded a dominant diamagnetic variation. Significant Spearman correlation coefficients were found between susceptibility-sensitive MRI indices and χ separation. DATA CONCLUSION Susceptibility separation changes in MS lesions may distinguish and quantify paramagnetic and diamagnetic evolution, potentially providing additional insight compared to R2* and QSM alone. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ziyan Zhu
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Nashwan Naji
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Javad Hamidi Esfahani
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Jeff Snyder
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Peter Seres
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Derek J Emery
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Michelle Noga
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Gregg Blevins
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Penelope Smyth
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
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14
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Fan SP, Chen YF, Li CH, Kuo YC, Lee NC, Chien YH, Hwu WL, Tseng TC, Su TH, Hsu CT, Chen HL, Lin CH, Ni YH. Topographical metal burden correlates with brain atrophy and clinical severity in Wilson's disease. Neuroimage 2024; 299:120829. [PMID: 39233127 DOI: 10.1016/j.neuroimage.2024.120829] [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: 05/22/2024] [Revised: 08/30/2024] [Accepted: 09/01/2024] [Indexed: 09/06/2024] Open
Abstract
BACKGROUND Quantitative susceptibility mapping (QSM) is a post-processing technique that creates brain susceptibility maps reflecting metal burden through tissue magnetic susceptibility. We assessed topographic differences in magnetic susceptibility between participants with and without Wilson's disease (WD), correlating these findings with clinical severity, brain volume, and biofluid copper and iron indices. METHODS A total of 43 patients with WD and 20 unaffected controls, were recruited. QSM images were derived from a 3T MRI scanner. Clinical severity was defined using the minimal Unified Wilson's Disease Rating Scale (M-UWDRS) and Montreal Cognitive Assessment scoring. Differences in magnetic susceptibilities between groups were evaluated using general linear regression models, adjusting for age and sex. Correlations between the susceptibilities and clinical scores were analyzed using Spearman's method. RESULTS In age- and sex-adjusted analyses, magnetic susceptibility values were increased in WD patients compared with controls, including caudate nucleus, putamen, globus pallidus, and substantia nigra (all p < 0.01). Putaminal susceptibility was greater with an initial neuropsychiatric presentation (n = 25) than with initial hepatic dysfunction (n = 18; p = 0.04). Susceptibility changes correlated negatively with regional brain volume in almost all topographic regions. Serum ferritin, but not serum copper or ceruloplasmin, correlated positively with magnetic susceptibility level in the caudate nucleus (p = 0.04), putamen (p = 0.04) and the hippocampus (p = 0.03). The dominance of magnetic susceptibility in cortical over subcortical regions correlated with M-UWDRS scores (p < 0.01). CONCLUSION The magnetic susceptibility changes could serve as a surrogate marker for patients with WD.
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Affiliation(s)
- Sung-Pin Fan
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan
| | - Ya-Fang Chen
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan
| | - Cheng-Hsuan Li
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan; Department of Neurology, National Taiwan University Hospital Hsinchu Branch, Hsinchu, Taiwan
| | - Yih-Chih Kuo
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan; Department of Neurology, National Taiwan University Hospital Hsinchu Branch, Hsinchu, Taiwan
| | - Ni-Chung Lee
- Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan; Department of Pediatrics, National Taiwan University Hospital, No. 7, Chung-Shan South Road, Taipei, 100, Taiwan
| | - Yin-Hsiu Chien
- Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan; Department of Pediatrics, National Taiwan University Hospital, No. 7, Chung-Shan South Road, Taipei, 100, Taiwan
| | - Wuh-Liang Hwu
- Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan; Department of Pediatrics, National Taiwan University Hospital, No. 7, Chung-Shan South Road, Taipei, 100, Taiwan
| | - Tai-Chung Tseng
- Department of Medical Research, National Taiwan University Hospital, Taipei, Taiwan; Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Tung-Hung Su
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chien-Ting Hsu
- Department of Pediatrics, National Taiwan University Hospital, No. 7, Chung-Shan South Road, Taipei, 100, Taiwan; Department of Pediatrics, National Taiwan University Hospital Hsinchu Branch, Hsinchu, Taiwan
| | - Huey-Ling Chen
- Department of Pediatrics, National Taiwan University Hospital, No. 7, Chung-Shan South Road, Taipei, 100, Taiwan
| | - Chin-Hsien Lin
- Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan; Institute of Biochemistry and Molecular Biology, College of Medicine, National Taiwan University, Taipei, Taiwan; Institute of Molecular Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.
| | - Yen-Hsuan Ni
- Department of Pediatrics, National Taiwan University Hospital, No. 7, Chung-Shan South Road, Taipei, 100, Taiwan.
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15
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Knoll C, Doehler J, Northall A, Schreiber S, Rotta J, Mattern H, Kuehn E. Age-related differences in human cortical microstructure depend on the distance to the nearest vein. Brain Commun 2024; 6:fcae321. [PMID: 39355004 PMCID: PMC11443451 DOI: 10.1093/braincomms/fcae321] [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: 12/21/2023] [Revised: 08/13/2024] [Accepted: 09/17/2024] [Indexed: 10/03/2024] Open
Abstract
Age-related differences in cortical microstructure are used to understand the neuronal mechanisms that underlie human brain ageing. The cerebral vasculature contributes to cortical ageing, but its precise interaction with cortical microstructure is poorly understood. In a cross-sectional study, we combine venous imaging with vessel distance mapping to investigate the interaction between venous distances and age-related differences in the microstructural architecture of the primary somatosensory cortex, the primary motor cortex and additional areas in the frontal cortex as non-sensorimotor control regions. We scanned 18 younger adults and 17 older adults using 7 Tesla MRI to measure age-related changes in longitudinal relaxation time (T1) and quantitative susceptibility mapping (QSM) values at 0.5 mm isotropic resolution. We modelled different cortical depths using an equi-volume approach and assessed the distance of each voxel to its nearest vein using vessel distance mapping. Our data reveal a dependence of cortical quantitative T1 values and positive QSM values on venous distance. In addition, there is an interaction between venous distance and age on quantitative T1 values, driven by lower quantitative T1 values in older compared to younger adults in voxels that are closer to a vein. Together, our data show that the local venous architecture explains a significant amount of variance in standard measures of cortical microstructure and should be considered in neurobiological models of human brain organisation and cortical ageing.
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Affiliation(s)
- Christoph Knoll
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto von Guericke University Magdeburg, Magdeburg 39120, Germany
- German Center for Neurodegenerative Diseases (DZNE) Magdeburg, Magdeburg 39120, Germany
| | - Juliane Doehler
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto von Guericke University Magdeburg, Magdeburg 39120, Germany
- German Center for Neurodegenerative Diseases (DZNE) Magdeburg, Magdeburg 39120, Germany
| | - Alicia Northall
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto von Guericke University Magdeburg, Magdeburg 39120, Germany
- German Center for Neurodegenerative Diseases (DZNE) Magdeburg, Magdeburg 39120, Germany
| | - Stefanie Schreiber
- German Center for Neurodegenerative Diseases (DZNE) Magdeburg, Magdeburg 39120, Germany
- Center for Behavioral Brain Sciences (CBBS), Otto von Guericke University Magdeburg, Magdeburg 39106, Germany
- Department of Neurology, Otto von Guericke University of Magdeburg, Magdeburg 39120, Germany
| | - Johanna Rotta
- Department of Neurology, Otto von Guericke University of Magdeburg, Magdeburg 39120, Germany
- Department of Neurology, Katharinenhospital, Klinikum Stuttgart, Stuttgart 70174, Germany
| | - Hendrik Mattern
- German Center for Neurodegenerative Diseases (DZNE) Magdeburg, Magdeburg 39120, Germany
- Center for Behavioral Brain Sciences (CBBS), Otto von Guericke University Magdeburg, Magdeburg 39106, Germany
- Department Biomedical Magnetic Resonance (BMMR), Otto von Guericke University Magdeburg, Magdeburg 39120, Germany
| | - Esther Kuehn
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto von Guericke University Magdeburg, Magdeburg 39120, Germany
- Hertie Institute for Clinical Brain Research (HIH), Tübingen 72076, Germany
- German Center for Neurodegenerative Diseases (DZNE) Tübingen, Tübingen 72076, Germany
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Williams T, John N, Calvi A, Bianchi A, De Angelis F, Doshi A, Wright S, Shatila M, Yiannakas MC, Chowdhury F, Stutters J, Ricciardi A, Prados F, MacManus D, Grussu F, Karsa A, Samson B, Battiston M, Gandini Wheeler-Kingshott CAM, Shmueli K, Ciccarelli O, Barkhof F, Chataway J. Investigating the relationship between thalamic iron concentration and disease severity in secondary progressive multiple sclerosis using quantitative susceptibility mapping: Cross-sectional analysis from the MS-STAT2 randomised controlled trial. NEUROIMAGE. REPORTS 2024; 4:100216. [PMID: 39328985 PMCID: PMC11422291 DOI: 10.1016/j.ynirp.2024.100216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 08/02/2024] [Accepted: 08/22/2024] [Indexed: 09/28/2024]
Abstract
Background Deep grey matter pathology is a key driver of disability worsening in people with multiple sclerosis. Quantitative susceptibility mapping (QSM) is an advanced magnetic resonance imaging (MRI) technique which quantifies local magnetic susceptibility from variations in phase produced by changes in the local magnetic field. In the deep grey matter, susceptibility has previously been validated against tissue iron concentration. However, it currently remains unknown whether susceptibility is abnormal in older progressive MS cohorts, and whether it correlates with disability. Objectives To investigate differences in mean regional susceptibility in deep grey matter between people with secondary progressive multiple sclerosis (SPMS) and healthy controls; to examine in patients the relationships between deep grey matter susceptibility and clinical and imaging measures of disease severity. Methods Baseline data from a subgroup of the MS-STAT2 trial (simvastatin vs. placebo in SPMS, NCT03387670) were included. The subgroup underwent clinical assessments and an advanced MRI protocol at 3T. A cohort of age-matched healthy controls underwent the same MRI protocol. Susceptibility maps were reconstructed using a robust QSM pipeline from multi-echo 3D gradient-echo sequence. Regions of interest (ROIs) in the thalamus, globus pallidus and putamen were segmented from 3D T1-weighted images, and lesions segmented from 3D fluid-attenuated inversion recovery images. Linear regression was used to compare susceptibility from ROIs between patients and controls, adjusting for age and sex. Where significant differences were found, we further examined the associations between ROI susceptibility and clinical and imaging measures of MS severity. Results 149 SPMS (77% female; mean age: 53 yrs; median Expanded Disability Status Scale (EDSS): 6.0 [interquartile range 4.5-6.0]) and 33 controls (52% female, mean age: 57) were included.Thalamic susceptibility was significantly lower in SPMS compared to controls: mean (SD) 28.6 (12.8) parts per billion (ppb) in SPMS vs. 39.2 (12.7) ppb in controls; regression coefficient: -12.0 [95% confidence interval: -17.0 to -7.1], p < 0.001. In contrast, globus pallidus and putamen susceptibility were similar between both groups.In SPMS, a 10 ppb lower thalamic susceptibility was associated with a +0.13 [+0.01 to +0.24] point higher EDSS (p < 0.05), a -2.4 [-3.8 to -1.0] point lower symbol digit modality test (SDMT, p = 0.001), and a -2.4 [-3.7 to -1.1] point lower Sloan low contrast acuity, 2.5% (p < 0.01).Lower thalamic susceptibility was also strongly associated with a higher T2 lesion volume (T2LV, p < 0.001) and lower normalised whole brain, deep grey matter and thalamic volumes (all p < 0.001). Conclusions The reduced thalamic susceptibility found in SPMS compared to controls suggests that thalamic iron concentrations are lower at this advanced stage of the disease. The observed relationships between lower thalamic susceptibility and more severe physical, cognitive and visual disability suggests that reductions in thalamic iron may correlate with important mechanisms of clinical disease progression. Such mechanisms appear to intimately link reductions in thalamic iron with higher T2LV and the development of thalamic atrophy, encouraging further research into QSM-derived thalamic susceptibility as a biomarker of disease severity in SPMS.
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Affiliation(s)
- Thomas Williams
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Nevin John
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- Monash University, Department of Medicine, School of Clinical Sciences, Clayton, Australia
| | - Alberto Calvi
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Alessia Bianchi
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Floriana De Angelis
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Anisha Doshi
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Sarah Wright
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Madiha Shatila
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Marios C Yiannakas
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Fatima Chowdhury
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Jon Stutters
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Antonio Ricciardi
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Ferran Prados
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- University College London, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
- Universitat Oberta de Catalunya, Barcelona, Spain
| | - David MacManus
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Francesco Grussu
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- University College London, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Anita Karsa
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Becky Samson
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- University College London, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Marco Battiston
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- University College London, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Olga Ciccarelli
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- National Institute for Health Research, Biomedical Research Centre, University College London Hospitals, London, United Kingdom
| | - Frederik Barkhof
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- University College London, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
- National Institute for Health Research, Biomedical Research Centre, University College London Hospitals, London, United Kingdom
- Vrije Universiteit Amsterdam, Department of Radiology & Nuclear Medicine, VU University Medical Centre, Amsterdam, Netherlands
| | - Jeremy Chataway
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- Medical Research Council Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, United Kingdom
- National Institute for Health Research, Biomedical Research Centre, University College London Hospitals, London, United Kingdom
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17
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Cagol A, Tsagkas C, Granziera C. Advanced Brain Imaging in Central Nervous System Demyelinating Diseases. Neuroimaging Clin N Am 2024; 34:335-357. [PMID: 38942520 DOI: 10.1016/j.nic.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
In recent decades, advances in neuroimaging have profoundly transformed our comprehension of central nervous system demyelinating diseases. Remarkable technological progress has enabled the integration of cutting-edge acquisition and postprocessing techniques, proving instrumental in characterizing subtle focal changes, diffuse microstructural alterations, and macroscopic pathologic processes. This review delves into state-of-the-art modalities applied to multiple sclerosis, neuromyelitis optica spectrum disorders, and myelin oligodendrocyte glycoprotein antibody-associated disease. Furthermore, it explores how this dynamic landscape holds significant promise for the development of effective and personalized clinical management strategies, encompassing support for differential diagnosis, prognosis, monitoring treatment response, and patient stratification.
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Affiliation(s)
- Alessandro Cagol
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Hegenheimermattweg 167b, 4123 Allschwil, Switzerland; Department of Neurology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Spitalstrasse 2, 4031 Basel, Switzerland; Department of Health Sciences, University of Genova, Via A. Pastore, 1 16132 Genova, Italy. https://twitter.com/CagolAlessandr0
| | - Charidimos Tsagkas
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Hegenheimermattweg 167b, 4123 Allschwil, Switzerland; Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NIH), 10 Center Drive, Bethesda, MD 20892, USA
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Hegenheimermattweg 167b, 4123 Allschwil, Switzerland; Department of Neurology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Spitalstrasse 2, 4031 Basel, Switzerland.
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18
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Baynat L, Yamamoto T, Tourdias T, Zhang B, Prevost V, Infante A, Klein A, Caid J, Cadart O, Dousset V, Gatta Cherifi B. Quantitative MRI Biomarkers Measure Changes in Targeted Brain Areas in Patients With Obesity. J Clin Endocrinol Metab 2024; 109:1850-1857. [PMID: 38195765 DOI: 10.1210/clinem/dgae014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 12/14/2023] [Accepted: 01/08/2024] [Indexed: 01/11/2024]
Abstract
CONTEXT Obesity is accompanied by damages to several tissues, including the brain. Pathological data and animal models have demonstrated an increased inflammatory reaction in hypothalamus and hippocampus. OBJECTIVE We tested whether we could observe such pathological modifications in vivo through quantitative magnetic resonance imaging (MRI) metrics. METHODS This prospective study was conducted between May 2019 and November 2022. The study was conducted in the Specialized Center for the Care of Obesity in a French University Hospital. Twenty-seven patients with obesity and 23 age and gender-paired normal-weight controls were prospectively recruited. All participants were examined using brain MRI. Anthropometric and biological data, eating behavior, anxiety, depression, and memory performance were assessed in both groups. The main outcome measure was brain MRI with the following parametric maps: quantitative susceptibility mapping (QSM), mean diffusivity (MD), fractional anisotropy (FA), magnetization transfer ratio map, and T2 relaxivity map. RESULTS In the hypothalamus, patients with obesity had higher FA and lower QSM than normal-weight controls. In the hippocampus, patients with obesity had higher FA and lower MD. There was no correlation between imaging biomarkers and eating behavior or anxiety. CONCLUSION Our findings are consistent with the presence of neuroinflammation in brain regions involved in food intake. In vivo brain biomarkers from quantitative MRI appear to provide an incremental information for the assessment of brain damages in patients with obesity.
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Affiliation(s)
- Louise Baynat
- University of Bordeaux, INSERM U1215, Neurocentre Magendie, 33000 Bordeaux, France
- CHU Bordeaux, Hôpital Haut Lévêque Service Endocrinologie, Diabétologie, Nutrition, 33600 Pessac, France
| | - Takayuki Yamamoto
- University of Bordeaux, INSERM U1215, Neurocentre Magendie, 33000 Bordeaux, France
| | - Thomas Tourdias
- University of Bordeaux, INSERM U1215, Neurocentre Magendie, 33000 Bordeaux, France
- CHU Bordeaux, Hôpital Pellegrin, Service de Neuroimagerie diagnostique et thérapeutique, 33000 Bordeaux, France
| | - Bei Zhang
- Magnetic Resonance, Canon Medical Systems Europe, 2718 Zoetermeer, Netherlands
| | - Valentin Prevost
- CT-MR Solution Planning Department, Canon Medical Systems Corporation, Tochigi, Japan
| | - Asael Infante
- CHU Bordeaux, Hôpital Haut Lévêque Service Endocrinologie, Diabétologie, Nutrition, 33600 Pessac, France
| | - Achille Klein
- CHU Bordeaux, Hôpital Haut Lévêque Service Endocrinologie, Diabétologie, Nutrition, 33600 Pessac, France
| | - Julien Caid
- CHU Bordeaux, Hôpital Haut Lévêque Service Endocrinologie, Diabétologie, Nutrition, 33600 Pessac, France
| | - Olivier Cadart
- Endocrinology, Centre Hospitalier d'Angoulême, Endocrinolology, Rond point Girac, 16000 Angouleme, France
| | - Vincent Dousset
- University of Bordeaux, INSERM U1215, Neurocentre Magendie, 33000 Bordeaux, France
- CHU Bordeaux, Hôpital Pellegrin, Service de Neuroimagerie diagnostique et thérapeutique, 33000 Bordeaux, France
| | - Blandine Gatta Cherifi
- University of Bordeaux, INSERM U1215, Neurocentre Magendie, 33000 Bordeaux, France
- CHU Bordeaux, Hôpital Haut Lévêque Service Endocrinologie, Diabétologie, Nutrition, 33600 Pessac, France
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19
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Öz G, Cocozza S, Henry PG, Lenglet C, Deistung A, Faber J, Schwarz AJ, Timmann D, Van Dijk KRA, Harding IH. MR Imaging in Ataxias: Consensus Recommendations by the Ataxia Global Initiative Working Group on MRI Biomarkers. CEREBELLUM (LONDON, ENGLAND) 2024; 23:931-945. [PMID: 37280482 PMCID: PMC11102392 DOI: 10.1007/s12311-023-01572-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/18/2023] [Indexed: 06/08/2023]
Abstract
With many viable strategies in the therapeutic pipeline, upcoming clinical trials in hereditary and sporadic degenerative ataxias will benefit from non-invasive MRI biomarkers for patient stratification and the evaluation of therapies. The MRI Biomarkers Working Group of the Ataxia Global Initiative therefore devised guidelines to facilitate harmonized MRI data acquisition in clinical research and trials in ataxias. Recommendations are provided for a basic structural MRI protocol that can be used for clinical care and for an advanced multi-modal MRI protocol relevant for research and trial settings. The advanced protocol consists of modalities with demonstrated utility for tracking brain changes in degenerative ataxias and includes structural MRI, magnetic resonance spectroscopy, diffusion MRI, quantitative susceptibility mapping, and resting-state functional MRI. Acceptable ranges of acquisition parameters are provided to accommodate diverse scanner hardware in research and clinical contexts while maintaining a minimum standard of data quality. Important technical considerations in setting up an advanced multi-modal protocol are outlined, including the order of pulse sequences, and example software packages commonly used for data analysis are provided. Outcome measures most relevant for ataxias are highlighted with use cases from recent ataxia literature. Finally, to facilitate access to the recommendations by the ataxia clinical and research community, examples of datasets collected with the recommended parameters are provided and platform-specific protocols are shared via the Open Science Framework.
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Affiliation(s)
- Gülin Öz
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, 2021 Sixth Street Southeast, Minneapolis, MN, 55455, USA.
| | - Sirio Cocozza
- UNINA Department of Advanced Biomedical Sciences, University of Naples Federico II , Naples, Italy
| | - Pierre-Gilles Henry
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, 2021 Sixth Street Southeast, Minneapolis, MN, 55455, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, 2021 Sixth Street Southeast, Minneapolis, MN, 55455, USA
| | - Andreas Deistung
- Department for Radiation Medicine, University Clinic and Outpatient Clinic for Radiology, University Hospital Halle (Saale), Halle (Saale), Germany
| | - Jennifer Faber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University Hospital Bonn, Bonn, Germany
| | | | - Dagmar Timmann
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen, Germany
| | - Koene R A Van Dijk
- Digital Sciences and Translational Imaging, Early Clinical Development, Pfizer, Inc., Cambridge, MA, USA
| | - Ian H Harding
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
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20
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Schormair B, Zhao C, Bell S, Didriksen M, Nawaz MS, Schandra N, Stefani A, Högl B, Dauvilliers Y, Bachmann CG, Kemlink D, Sonka K, Paulus W, Trenkwalder C, Oertel WH, Hornyak M, Teder-Laving M, Metspalu A, Hadjigeorgiou GM, Polo O, Fietze I, Ross OA, Wszolek ZK, Ibrahim A, Bergmann M, Kittke V, Harrer P, Dowsett J, Chenini S, Ostrowski SR, Sørensen E, Erikstrup C, Pedersen OB, Topholm Bruun M, Nielsen KR, Butterworth AS, Soranzo N, Ouwehand WH, Roberts DJ, Danesh J, Burchell B, Furlotte NA, Nandakumar P, Earley CJ, Ondo WG, Xiong L, Desautels A, Perola M, Vodicka P, Dina C, Stoll M, Franke A, Lieb W, Stewart AFR, Shah SH, Gieger C, Peters A, Rye DB, Rouleau GA, Berger K, Stefansson H, Ullum H, Stefansson K, Hinds DA, Di Angelantonio E, Oexle K, Winkelmann J. Genome-wide meta-analyses of restless legs syndrome yield insights into genetic architecture, disease biology and risk prediction. Nat Genet 2024; 56:1090-1099. [PMID: 38839884 PMCID: PMC11176086 DOI: 10.1038/s41588-024-01763-1] [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: 03/09/2023] [Accepted: 04/19/2024] [Indexed: 06/07/2024]
Abstract
Restless legs syndrome (RLS) affects up to 10% of older adults. Their healthcare is impeded by delayed diagnosis and insufficient treatment. To advance disease prediction and find new entry points for therapy, we performed meta-analyses of genome-wide association studies in 116,647 individuals with RLS (cases) and 1,546,466 controls of European ancestry. The pooled analysis increased the number of risk loci eightfold to 164, including three on chromosome X. Sex-specific meta-analyses revealed largely overlapping genetic predispositions of the sexes (rg = 0.96). Locus annotation prioritized druggable genes such as glutamate receptors 1 and 4, and Mendelian randomization indicated RLS as a causal risk factor for diabetes. Machine learning approaches combining genetic and nongenetic information performed best in risk prediction (area under the curve (AUC) = 0.82-0.91). In summary, we identified targets for drug development and repurposing, prioritized potential causal relationships between RLS and relevant comorbidities and risk factors for follow-up and provided evidence that nonlinear interactions are likely relevant to RLS risk prediction.
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Affiliation(s)
- Barbara Schormair
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
| | - Chen Zhao
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Steven Bell
- Department of Oncology, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
| | - Maria Didriksen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
| | | | - Nathalie Schandra
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Ambra Stefani
- Sleep Disorders Clinic, Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Birgit Högl
- Sleep Disorders Clinic, Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Yves Dauvilliers
- Sleep-Wake Disorders Center, Department of Neurology, Hôpital Gui-de-Chauliac, CHU Montpellier, Institut des Neurosciences de Montpellier, INSERM, Université de Montpellier, Montpellier, France
| | - Cornelius G Bachmann
- SomnoDiagnostics, Osnabrück, Germany
- Department of Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - David Kemlink
- Department of Neurology and Centre of Clinical Neuroscience, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic
| | - Karel Sonka
- Department of Neurology and Centre of Clinical Neuroscience, Charles University, First Faculty of Medicine and General University Hospital, Prague, Czech Republic
| | - Walter Paulus
- Department of Neurology, Ludwig Maximilians University Munich, Munich, Germany
| | - Claudia Trenkwalder
- Paracelsus-Elena-Klinik, Kassel, Germany
- Department of Neurosurgery, University Medical Center Göttingen, Göttingen, Germany
| | - Wolfgang H Oertel
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | | | - Maris Teder-Laving
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Georgios M Hadjigeorgiou
- Department of Neurology, Nicosia General Hospital Medical School, University of Cyprus, Nicosia, Cyprus
| | - Olli Polo
- Bragée ME/CFS Center, Stockholm, Sweden
| | - Ingo Fietze
- Department of Pulmonology, Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic College of Medicine, Jacksonville, FL, USA
| | | | - Abubaker Ibrahim
- Sleep Disorders Clinic, Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Melanie Bergmann
- Sleep Disorders Clinic, Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Volker Kittke
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Philip Harrer
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Joseph Dowsett
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Sofiene Chenini
- Sleep-Wake Disorders Center, Department of Neurology, Hôpital Gui-de-Chauliac, CHU Montpellier, Institut des Neurosciences de Montpellier, INSERM, Université de Montpellier, Montpellier, France
| | - Sisse Rye Ostrowski
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Ole B Pedersen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Mie Topholm Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | - Kaspar R Nielsen
- Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Nicole Soranzo
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
- Department of Human Genetics, the Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Willem H Ouwehand
- Department of Haematology, University of Cambridge, Cambridge, UK
- NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge, UK
- Department of Haematology, University College London Hospitals, London, UK
| | - David J Roberts
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Radcliffe Department of Medicine and National Health Service Blood and Transplant, Oxford, UK
- Department of Haematology and BRC Haematology Theme, Churchill Hospital, Headington, Oxford, UK
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Department of Human Genetics, the Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | | | | | | | - Christopher J Earley
- Center for Restless Legs Syndrome, Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - William G Ondo
- Department of Neurology, Methodist Neurological Institute, Weill Cornell Medical School, Houston, TX, USA
| | - Lan Xiong
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Alex Desautels
- Centre d'Études Avancées en Médecine du Sommeil, Hôpital du Sacré-Cœur de Montréal, Montreal, Quebec, Canada
- Department of Neurosciences, Université de Montréal, Montreal, Quebec, Canada
| | - Markus Perola
- Clinical and Molecular Metabolism Research Program (CAMM), Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health and Welfare, National Institute for Health and Welfare, Helsinki, Finland
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine, Academy of Science of Czech Republic, Prague, Czech Republic
- First Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
- Biomedical Centre, Faculty of Medicine in Pilsen, Charles University in Prague, Pilsen, Czech Republic
| | - Christian Dina
- L'institut du thorax, CNRS, INSERM, Nantes Université, Nantes, France
| | - Monika Stoll
- Department of Genetic Epidemiology, Institute for Human Genetics, University of Münster, Münster, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Wolfgang Lieb
- PopGen Biobank and Institute of Epidemiology, Christian Albrechts University Kiel, Kiel, Germany
| | - Alexandre F R Stewart
- John and Jennifer Ruddy Canadian Cardiovascular Genetics Centre, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Svati H Shah
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Research Center for Cardiovascular Disease (DZHK), partner site Munich Heart Alliance, Hannover, Germany
- Chair of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
| | - David B Rye
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Guy A Rouleau
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | | | | | | | | | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Science Research Centre, Fondazione Human Technopole, Milan, Italy
| | - Konrad Oexle
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Neurogenetic Systems Analysis Group, Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Juliane Winkelmann
- Institute of Neurogenomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- German Center for Mental Health (DZPG), partner site Munich-Augsburg, Munich-Augsburg, Germany
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21
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Guillemin C, Vandeleene N, Charonitis M, Requier F, Delrue G, Lommers E, Maquet P, Phillips C, Collette F. Brain microstructure is linked to cognitive fatigue in early multiple sclerosis. J Neurol 2024; 271:3537-3545. [PMID: 38538776 DOI: 10.1007/s00415-024-12316-1] [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: 12/29/2023] [Revised: 03/08/2024] [Accepted: 03/08/2024] [Indexed: 05/30/2024]
Abstract
Cognitive fatigue is a major symptom of Multiple Sclerosis (MS), from the early stages of the disease. This study aims to detect if brain microstructure is altered early in the disease course and is associated with cognitive fatigue in people with MS (pwMS) compared to matched healthy controls (HC). Recently diagnosed pwMS (N = 18, age < 45 years old) with either a Relapsing-Remitting or a Clinically Isolated Syndrome course of the disease, and HC (N = 19) matched for sex, age and education were analyzed. Quantitative multiparameter maps (MTsat, PD, R1 and R2*) of pwMS and HC were calculated. Parameters were extracted within the normal appearing white matter, cortical grey matter and deep grey matter (NAWM, NACGM and NADGM, respectively). Bayesian T-test for independent samples assessed between-group differences in brain microstructure while associations between score at a cognitive fatigue scale and each parameter in each tissue class were investigated with Generalized Linear Mixed Models. Patients exhibited lower MTsat and R1 values within NAWM and NACGM, and higher R1 values in NADGM compared to HC. Cognitive fatigue was associated with PD measured in every tissue class and to MTsat in NAWM, regardless of group. Disease-specific negative correlations were found in pwMS in NAWM (R1, R2*) and NACGM (R1). These findings suggest that brain microstructure within normal appearing tissues is already altered in the very early stages of the disease. Moreover, additional microstructure alterations (e.g. diffuse and widespread demyelination or axonal degeneration) in pwMS may lead to disease-specific complaint of cognitive fatigue.
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Affiliation(s)
- Camille Guillemin
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Nora Vandeleene
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
| | - Maëlle Charonitis
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Florence Requier
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Gaël Delrue
- Department of Neurology, CHU of Liège Sart Tilman, Liège, Belgium
| | - Emilie Lommers
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
- Department of Neurology, CHU of Liège Sart Tilman, Liège, Belgium
| | - Pierre Maquet
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
- Department of Neurology, CHU of Liège Sart Tilman, Liège, Belgium
| | - Christophe Phillips
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
- GIGA In Silico Medicine, University of Liège, Liège, Belgium
| | - Fabienne Collette
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium.
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium.
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22
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Voon CC, Wiltgen T, Wiestler B, Schlaeger S, Mühlau M. Quantitative susceptibility mapping in multiple sclerosis: A systematic review and meta-analysis. Neuroimage Clin 2024; 42:103598. [PMID: 38582068 PMCID: PMC11002889 DOI: 10.1016/j.nicl.2024.103598] [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: 12/21/2023] [Revised: 03/07/2024] [Accepted: 03/24/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Quantitative susceptibility mapping (QSM) is a quantitative measure based on magnetic resonance imaging sensitive to iron and myelin content. This makes QSM a promising non-invasive tool for multiple sclerosis (MS) in research and clinical practice. OBJECTIVE We performed a systematic review and meta-analysis on the use of QSM in MS. METHODS Our review was prospectively registered on PROSPERO (CRD42022309563). We searched five databases for studies published between inception and 30th April 2023. We identified 83 English peer-reviewed studies that applied QSM images on MS cohorts. Fifty-five included studies had at least one of the following outcome measures: deep grey matter QSM values in MS, either compared to healthy controls (HC) (k = 13) or correlated with the score on the Expanded Disability Status Scale (EDSS) (k = 7), QSM lesion characteristics (k = 22) and their clinical correlates (k = 17), longitudinal correlates (k = 11), histological correlates (k = 7), or correlates with other imaging techniques (k = 12). Two meta-analyses on deep grey matter (DGM) susceptibility data were performed, while the remaining findings could only be analyzed descriptively. RESULTS After outlier removal, meta-analyses demonstrated a significant increase in the basal ganglia susceptibility (QSM values) in MS compared to HC, caudate (k = 9, standardized mean difference (SDM) = 0.54, 95 % CI = 0.39-0.70, I2 = 46 %), putamen (k = 9, SDM = 0.38, 95 % CI = 0.19-0.57, I2 = 59 %), and globus pallidus (k = 9, SDM = 0.48, 95 % CI = 0.28-0.67, I2 = 60 %), whereas thalamic QSM values exhibited a significant reduction (k = 12, SDM = -0.39, 95 % CI = -0.66--0.12, I2 = 84 %); these susceptibility differences in MS were independent of age. Further, putamen QSM values positively correlated with EDSS (k = 4, r = 0.36, 95 % CI = 0.16-0.53, I2 = 0 %). Regarding rim lesions, four out of seven studies, representing 73 % of all patients, reported rim lesions to be associated with more severe disability. Moreover, lesion development from initial detection to the inactive stage is paralleled by increasing, plateauing (after about two years), and gradually decreasing QSM values, respectively. Only one longitudinal study provided clinical outcome measures and found no association. Histological data suggest iron content to be the primary source of QSM values in DGM and at the edges of rim lesions; further, when also considering data from myelin water imaging, the decrease of myelin is likely to drive the increase of QSM values within WM lesions. CONCLUSIONS We could provide meta-analytic evidence for DGM susceptibility changes in MS compared to HC; basal ganglia susceptibility is increased and, in the putamen, associated with disability, while thalamic susceptibility is decreased. Beyond these findings, further investigations are necessary to establish the role of QSM in MS for research or even clinical routine.
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Affiliation(s)
- Cui Ci Voon
- Dept. of Neurology, School of Medicine and Health, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Tun Wiltgen
- Dept. of Neurology, School of Medicine and Health, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- Dept. of Neuroradiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Sarah Schlaeger
- Dept. of Neuroradiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Mark Mühlau
- Dept. of Neurology, School of Medicine and Health, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, School of Medicine and Health, Technical University of Munich, Munich, Germany.
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23
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Thomas GE, Hannaway N, Zarkali A, Shmueli K, Weil RS. Longitudinal Associations of Magnetic Susceptibility with Clinical Severity in Parkinson's Disease. Mov Disord 2024; 39:546-559. [PMID: 38173297 PMCID: PMC11141787 DOI: 10.1002/mds.29702] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/29/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Dementia is common in Parkinson's disease (PD), but there is wide variation in its timing. A critical gap in PD research is the lack of quantifiable markers of progression, and methods to identify early stages of dementia. Atrophy-based magnetic resonance imaging (MRI) has limited sensitivity in detecting or tracking changes relating to PD dementia, but quantitative susceptibility mapping (QSM), sensitive to brain tissue iron, shows potential for these purposes. OBJECTIVE The objective of the paper is to study, for the first time, the longitudinal relationship between cognition and QSM in PD in detail. METHODS We present a longitudinal study of clinical severity in PD using QSM, including 59 PD patients (without dementia at study onset), and 22 controls over 3 years. RESULTS In PD, increased baseline susceptibility in the right temporal cortex, nucleus basalis of Meynert, and putamen was associated with greater cognitive severity after 3 years; and increased baseline susceptibility in basal ganglia, substantia nigra, red nucleus, insular cortex, and dentate nucleus was associated with greater motor severity after 3 years. Increased follow-up susceptibility in these regions was associated with increased follow-up cognitive and motor severity, with further involvement of hippocampus relating to cognitive severity. However, there were no consistent increases in susceptibility over 3 years. CONCLUSIONS Our study suggests that QSM may predict changes in cognitive severity many months prior to overt cognitive involvement in PD. However, we did not find robust longitudinal changes in QSM over the course of the study. Additional tissue metrics may be required together with QSM for it to monitor progression in clinical practice and therapeutic trials. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
| | - Naomi Hannaway
- Dementia Research CentreUCL Institute of NeurologyLondonUK
| | | | - Karin Shmueli
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Rimona S. Weil
- Dementia Research CentreUCL Institute of NeurologyLondonUK
- Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
- Movement Disorders ConsortiumUniversity College LondonLondonUK
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24
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Ananthavarathan P, Sahi N, Chard DT. An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression. Expert Rev Neurother 2024; 24:201-216. [PMID: 38235594 DOI: 10.1080/14737175.2024.2304116] [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: 11/01/2023] [Accepted: 01/08/2024] [Indexed: 01/19/2024]
Abstract
INTRODUCTION While magnetic resonance imaging (MRI) is established in diagnosing and monitoring disease activity in multiple sclerosis (MS), its utility in predicting and monitoring disease progression is less clear. AREAS COVERED The authors consider changing concepts in the phenotypic classification of MS, including progression independent of relapses; pathological processes underpinning progression; advances in MRI measures to assess them; how well MRI features explain and predict clinical outcomes, including models that assess disease effects on neural networks, and the potential role for machine learning. EXPERT OPINION Relapsing-remitting and progressive MS have evolved from being viewed as mutually exclusive to having considerable overlap. Progression is likely the consequence of several pathological elements, each important in building more holistic prognostic models beyond conventional phenotypes. MRI is well placed to assess pathogenic processes underpinning progression, but we need to bridge the gap between MRI measures and clinical outcomes. Mapping pathological effects on specific neural networks may help and machine learning methods may be able to optimize predictive markers while identifying new, or previously overlooked, clinically relevant features. The ever-increasing ability to measure features on MRI raises the dilemma of what to measure and when, and the challenge of translating research methods into clinically useable tools.
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Affiliation(s)
- Piriyankan Ananthavarathan
- Department of Neuroinflammation, University College London Queen Square Multiple Sclerosis Centre, London, UK
| | - Nitin Sahi
- Department of Neuroinflammation, University College London Queen Square Multiple Sclerosis Centre, London, UK
| | - Declan T Chard
- Clinical Research Associate & Consultant Neurologist, Institute of Neurology - Queen Square Multiple Sclerosis Centre, London, UK
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25
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Merenstein JL, Zhao J, Overson DK, Truong TK, Johnson KG, Song AW, Madden DJ. Depth- and curvature-based quantitative susceptibility mapping analyses of cortical iron in Alzheimer's disease. Cereb Cortex 2024; 34:bhad525. [PMID: 38185996 PMCID: PMC10839848 DOI: 10.1093/cercor/bhad525] [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: 09/20/2023] [Revised: 11/21/2023] [Accepted: 12/15/2023] [Indexed: 01/09/2024] Open
Abstract
In addition to amyloid beta plaques and neurofibrillary tangles, Alzheimer's disease (AD) has been associated with elevated iron in deep gray matter nuclei using quantitative susceptibility mapping (QSM). However, only a few studies have examined cortical iron, using more macroscopic approaches that cannot assess layer-specific differences. Here, we conducted column-based QSM analyses to assess whether AD-related increases in cortical iron vary in relation to layer-specific differences in the type and density of neurons. We obtained global and regional measures of positive (iron) and negative (myelin, protein aggregation) susceptibility from 22 adults with AD and 22 demographically matched healthy controls. Depth-wise analyses indicated that global susceptibility increased from the pial surface to the gray/white matter boundary, with a larger slope for positive susceptibility in the left hemisphere for adults with AD than controls. Curvature-based analyses indicated larger global susceptibility for adults with AD versus controls; the right hemisphere versus left; and gyri versus sulci. Region-of-interest analyses identified similar depth- and curvature-specific group differences, especially for temporo-parietal regions. Finding that iron accumulates in a topographically heterogenous manner across the cortical mantle may help explain the profound cognitive deterioration that differentiates AD from the slowing of general motor processes in healthy aging.
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Affiliation(s)
- Jenna L Merenstein
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, United States
| | - Jiayi Zhao
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, United States
| | - Devon K Overson
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, United States
- Medical Physics Graduate Program, Duke University, Durham, NC 27708, United States
| | - Trong-Kha Truong
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, United States
- Medical Physics Graduate Program, Duke University, Durham, NC 27708, United States
| | - Kim G Johnson
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, United States
| | - Allen W Song
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, United States
- Medical Physics Graduate Program, Duke University, Durham, NC 27708, United States
| | - David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, United States
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, United States
- Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States
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26
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Morgan AT, Scerri TS, Vogel AP, Reid CA, Quach M, Jackson VE, McKenzie C, Burrows EL, Bennett MF, Turner SJ, Reilly S, Horton SE, Block S, Kefalianos E, Frigerio-Domingues C, Sainz E, Rigbye KA, Featherby TJ, Richards KL, Kueh A, Herold MJ, Corbett MA, Gecz J, Helbig I, Thompson-Lake DGY, Liégeois FJ, Morell RJ, Hung A, Drayna D, Scheffer IE, Wright DK, Bahlo M, Hildebrand MS. Stuttering associated with a pathogenic variant in the chaperone protein cyclophilin 40. Brain 2023; 146:5086-5097. [PMID: 37977818 PMCID: PMC10689913 DOI: 10.1093/brain/awad314] [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: 05/12/2023] [Revised: 07/26/2023] [Accepted: 08/10/2023] [Indexed: 11/19/2023] Open
Abstract
Stuttering is a common speech disorder that interrupts speech fluency and tends to cluster in families. Typically, stuttering is characterized by speech sounds, words or syllables which may be repeated or prolonged and speech that may be further interrupted by hesitations or 'blocks'. Rare variants in a small number of genes encoding lysosomal pathway proteins have been linked to stuttering. We studied a large four-generation family in which persistent stuttering was inherited in an autosomal dominant manner with disruption of the cortico-basal-ganglia-thalamo-cortical network found on imaging. Exome sequencing of three affected family members revealed the PPID c.808C>T (p.Pro270Ser) variant that segregated with stuttering in the family. We generated a Ppid p.Pro270Ser knock-in mouse model and performed ex vivo imaging to assess for brain changes. Diffusion-weighted MRI in the mouse revealed significant microstructural changes in the left corticospinal tract, as previously implicated in stuttering. Quantitative susceptibility mapping also detected changes in cortico-striatal-thalamo-cortical loop tissue composition, consistent with findings in affected family members. This is the first report to implicate a chaperone protein in the pathogenesis of stuttering. The humanized Ppid murine model recapitulates network findings observed in affected family members.
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Affiliation(s)
- Angela T Morgan
- Murdoch Children’s Research Institute, Parkville 3052, Australia
- Department of Audiology and Speech Pathology, University of Melbourne, Parkville 3052, Australia
| | - Thomas S Scerri
- Murdoch Children’s Research Institute, Parkville 3052, Australia
- The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville 3052, Australia
| | - Adam P Vogel
- Department of Audiology and Speech Pathology, University of Melbourne, Parkville 3052, Australia
- Centre for Neuroscience of Speech, The University of Melbourne, Parkville 3053, Australia
- Clinical Trials, Redenlab Inc., Melbourne 3000, Australia
| | - Christopher A Reid
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, 3052, Parkville 3052, Australia
- Department of Medicine, Epilepsy Research Centre, University of Melbourne, Heidelberg 3084, Australia
| | - Mara Quach
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne 3004, Australia
| | - Victoria E Jackson
- The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville 3052, Australia
| | - Chaseley McKenzie
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, 3052, Parkville 3052, Australia
| | - Emma L Burrows
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, 3052, Parkville 3052, Australia
| | - Mark F Bennett
- The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville 3052, Australia
- Department of Medicine, Epilepsy Research Centre, University of Melbourne, Heidelberg 3084, Australia
| | | | - Sheena Reilly
- Murdoch Children’s Research Institute, Parkville 3052, Australia
- Menzies Health Institute Queensland, Griffith University, 4215 Southport, Australia
| | - Sarah E Horton
- Murdoch Children’s Research Institute, Parkville 3052, Australia
- Department of Audiology and Speech Pathology, University of Melbourne, Parkville 3052, Australia
| | - Susan Block
- Discipline of Speech Pathology, School of Allied Health, La Trobe University, Bundoora 3086, Australia
| | - Elaina Kefalianos
- Murdoch Children’s Research Institute, Parkville 3052, Australia
- Department of Audiology and Speech Pathology, University of Melbourne, Parkville 3052, Australia
| | - Carlos Frigerio-Domingues
- Laboratory of Communication Disorders, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD 20892-2320, USA
| | - Eduardo Sainz
- Laboratory of Communication Disorders, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD 20892-2320, USA
| | - Kristin A Rigbye
- Department of Medicine, Epilepsy Research Centre, University of Melbourne, Heidelberg 3084, Australia
| | - Travis J Featherby
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, 3052, Parkville 3052, Australia
| | - Kay L Richards
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, 3052, Parkville 3052, Australia
| | - Andrew Kueh
- The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville 3052, Australia
| | - Marco J Herold
- The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville 3052, Australia
| | - Mark A Corbett
- Adelaide Medical School, The University of Adelaide, Adelaide 5000, Australia
- Neurogenetics Research Program, South Australian Health and Medical Research Institute, Adelaide 5000, Australia
| | - Jozef Gecz
- Adelaide Medical School, The University of Adelaide, Adelaide 5000, Australia
- Neurogenetics Research Program, South Australian Health and Medical Research Institute, Adelaide 5000, Australia
| | - Ingo Helbig
- Department of Neurology, Children’s Hospital, Philadelphia, PA 19104, USA
| | - Daisy G Y Thompson-Lake
- Developmental Neurosciences Department, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Frédérique J Liégeois
- Developmental Neurosciences Department, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Robert J Morell
- Laboratory of Molecular Genetics, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD 20892, USA
- Genomics and Computational Biology Core, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD 20892, USA
| | - Andrew Hung
- School of Science, STEM College, RMIT University, Melbourne 3001, Australia
| | - Dennis Drayna
- Laboratory of Communication Disorders, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD 20892-2320, USA
| | - Ingrid E Scheffer
- Murdoch Children’s Research Institute, Parkville 3052, Australia
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, 3052, Parkville 3052, Australia
- Department of Medicine, Epilepsy Research Centre, University of Melbourne, Heidelberg 3084, Australia
- Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Parkville 3052, Australia
| | - David K Wright
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne 3004, Australia
| | - Melanie Bahlo
- The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville 3052, Australia
- School of Mathematics and Statistics, University of Melbourne, 3010 Parkville, Australia
| | - Michael S Hildebrand
- Murdoch Children’s Research Institute, Parkville 3052, Australia
- Department of Medicine, Epilepsy Research Centre, University of Melbourne, Heidelberg 3084, Australia
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27
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Ji L, Yoon YB, Hendrix CL, Kennelly EC, Majbri A, Bhatia T, Taylor A, Thomason ME. Developmental coupling of brain iron and intrinsic activity in infants during the first 150 days. Dev Cogn Neurosci 2023; 64:101326. [PMID: 37979299 PMCID: PMC10692666 DOI: 10.1016/j.dcn.2023.101326] [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: 07/25/2023] [Revised: 10/30/2023] [Accepted: 11/15/2023] [Indexed: 11/20/2023] Open
Abstract
Brain iron is vital for core neurodevelopmental processes including myelination and neurotransmitter synthesis and, accordingly, iron accumulates in the brain with age. However, little is known about the association between brain iron and neural functioning and how they evolve with age in early infancy. This study investigated brain iron in 48 healthy infants (22 females) aged 64.00 ± 33.28 days by estimating R2 * relaxometry from multi-echo functional MRI (fMRI). Linked independent component analysis was performed to examine the association between iron deposition and spontaneous neural activity, as measured by the amplitude of low frequency fluctuations (ALFF) by interrogating shared component loadings across modalities. Further, findings were validated in an independent dataset (n = 45, 24 females, 77.93 ± 26.18 days). The analysis revealed developmental coupling between the global R2 * and ALFF within the default mode network (DMN). Furthermore, we observed that this coupling effect significantly increased with age (r = 0.78, p = 9.2e-11). Our results highlight the importance of iron-neural coupling during early development and suggest that the neural maturation of the DMN may correspond to growth in distributed brain iron.
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Affiliation(s)
- Lanxin Ji
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA.
| | - Youngwoo Bryan Yoon
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA
| | - Cassandra L Hendrix
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA
| | | | - Amyn Majbri
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA
| | - Tanya Bhatia
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA
| | - Alexis Taylor
- Department of Psychology, Wayne State University, USA
| | - Moriah E Thomason
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA; Department of Population Health, New York University School of Medicine, New York, NY, USA; Neuroscience Institute, New York University School of Medicine, New York, NY, USA
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Corbin N, Oliveira R, Raynaud Q, Di Domenicantonio G, Draganski B, Kherif F, Callaghan MF, Lutti A. Statistical analyses of motion-corrupted MRI relaxometry data computed from multiple scans. J Neurosci Methods 2023; 398:109950. [PMID: 37598941 DOI: 10.1016/j.jneumeth.2023.109950] [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: 04/12/2023] [Revised: 05/30/2023] [Accepted: 08/12/2023] [Indexed: 08/22/2023]
Abstract
BACKGROUND Consistent noise variance across data points (i.e. homoscedasticity) is required to ensure the validity of statistical analyses of MRI data conducted using linear regression methods. However, head motion leads to degradation of image quality, introducing noise heteroscedasticity into ordinary-least square analyses. NEW METHOD The recently introduced QUIQI method restores noise homoscedasticity by means of weighted least square analyses in which the weights, specific for each dataset of an analysis, are computed from an index of motion-induced image quality degradation. QUIQI was first demonstrated in the context of brain maps of the MRI parameter R2 * , which were computed from a single set of images with variable echo time. Here, we extend this framework to quantitative maps of the MRI parameters R1, R2 * , and MTsat, computed from multiple sets of images. RESULTS QUIQI restores homoscedasticity in analyses of quantitative MRI data computed from multiple scans. QUIQI allows for optimization of the noise model by using metrics quantifying heteroscedasticity and free energy. COMPARISON WITH EXISTING METHODS QUIQI restores homoscedasticity more effectively than insertion of an image quality index in the analysis design and yields higher sensitivity than simply removing the datasets most corrupted by head motion from the analysis. CONCLUSION QUIQI provides an optimal approach to group-wise analyses of a range of quantitative MRI parameter maps that is robust to inherent homoscedasticity.
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Affiliation(s)
- Nadège Corbin
- Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/University Bordeaux, Bordeaux, France; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Rita Oliveira
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Quentin Raynaud
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Giulia Di Domenicantonio
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
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Alushaj E, Hemachandra D, Kuurstra A, Menon RS, Ganjavi H, Sharma M, Kashgari A, Barr J, Reisman W, Khan AR, MacDonald PA. Subregional analysis of striatum iron in Parkinson's disease and rapid eye movement sleep behaviour disorder. Neuroimage Clin 2023; 40:103519. [PMID: 37797434 PMCID: PMC10568416 DOI: 10.1016/j.nicl.2023.103519] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/24/2023] [Accepted: 09/26/2023] [Indexed: 10/07/2023]
Abstract
The loss of dopamine in the striatum underlies motor symptoms of Parkinson's disease (PD). Rapid eye movement sleep behaviour disorder (RBD) is considered prodromal PD and has shown similar neural changes in the striatum. Alterations in brain iron suggest neurodegeneration; however, the literature on striatal iron has been inconsistent in PD and scant in RBD. Toward clarifying pathophysiological changes in PD and RBD, and uncovering possible biomarkers, we imaged 26 early-stage PD patients, 16 RBD patients, and 39 age-matched healthy controls with 3 T MRI. We compared mean susceptibility using quantitative susceptibility mapping (QSM) in the standard striatum (caudate, putamen, and nucleus accumbens) and tractography-parcellated striatum. Diffusion MRI permitted parcellation of the striatum into seven subregions based on the cortical areas of maximal connectivity from the Tziortzi atlas. No significant differences in mean susceptibility were found in the standard striatum anatomy. For the parcellated striatum, the caudal motor subregion, the most affected region in PD, showed lower iron levels compared to healthy controls. Receiver operating characteristic curves using mean susceptibility in the caudal motor striatum showed a good diagnostic accuracy of 0.80 when classifying early-stage PD from healthy controls. This study highlights that tractography-based parcellation of the striatum could enhance sensitivity to changes in iron levels, which have not been consistent in the PD literature. The decreased caudal motor striatum iron was sufficiently sensitive to PD, but not RBD. QSM in the striatum could contribute to development of a multivariate or multimodal biomarker of early-stage PD, but further work in larger datasets is needed to confirm its utility in prodromal groups.
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Affiliation(s)
- Erind Alushaj
- Department of Neuroscience, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada; Western Institute for Neuroscience, Western University, London, Ontario, Canada
| | - Dimuthu Hemachandra
- Robarts Research Institute, Western University, London, Ontario, Canada; School of Biomedical Engineering, Western University, London, Ontario, Canada
| | - Alan Kuurstra
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Ravi S Menon
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Hooman Ganjavi
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - Manas Sharma
- Department of Radiology, Western University, London, Ontario, Canada; Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada
| | - Alia Kashgari
- Department of Medicine, Respirology Division, Western University, London, Ontario, Canada
| | - Jennifer Barr
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - William Reisman
- Department of Medicine, Respirology Division, Western University, London, Ontario, Canada
| | - Ali R Khan
- Robarts Research Institute, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Penny A MacDonald
- Western Institute for Neuroscience, Western University, London, Ontario, Canada; Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada.
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Fiscone C, Rundo L, Lugaresi A, Manners DN, Allinson K, Baldin E, Vornetti G, Lodi R, Tonon C, Testa C, Castelli M, Zaccagna F. Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis. Sci Rep 2023; 13:16239. [PMID: 37758804 PMCID: PMC10533494 DOI: 10.1038/s41598-023-42914-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/16/2023] [Indexed: 09/29/2023] Open
Abstract
Multiple Sclerosis (MS) is an autoimmune demyelinating disease characterised by changes in iron and myelin content. These biomarkers are detectable by Quantitative Susceptibility Mapping (QSM), an advanced Magnetic Resonance Imaging technique detecting magnetic properties. When analysed with radiomic techniques that exploit its intrinsic quantitative nature, QSM may furnish biomarkers to facilitate early diagnosis of MS and timely assessment of progression. In this work, we explore the robustness of QSM radiomic features by varying the number of grey levels (GLs) and echo times (TEs), in a sample of healthy controls and patients with MS. We analysed the white matter in total and within six clinically relevant tracts, including the cortico-spinal tract and the optic radiation. After optimising the number of GLs (n = 64), at least 65% of features were robust for each Volume of Interest (VOI), with no difference (p > .05) between left and right hemispheres. Different outcomes in feature robustness among the VOIs depend on their characteristics, such as volume and variance of susceptibility values. This study validated the processing pipeline for robustness analysis and established the reliability of QSM-based radiomics features against GLs and TEs. Our results provide important insights for future radiomics studies using QSM in clinical applications.
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Affiliation(s)
- Cristiana Fiscone
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Italy
| | - Alessandra Lugaresi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- UOSI Riabilitazione Sclerosi Multipla, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - David Neil Manners
- Department for Life Quality Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Kieren Allinson
- Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Elisa Baldin
- Epidemiology and Statistics Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Gianfranco Vornetti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Caterina Tonon
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Claudia Testa
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy.
| | - Mauro Castelli
- NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312, Lisbon, Portugal
| | - Fulvio Zaccagna
- Department of Imaging, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Investigative Medicine Division, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
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31
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Wang H, Liu X, Song L, Yang W, Li M, Chen Q, Lv H, Zhao P, Yang Z, Liu W, Wang ZC. Dysfunctional Coupling of Cerebral Blood Flow and Susceptibility Value in the Bilateral Hippocampus is Associated with Cognitive Decline in Nondialysis Patients with CKD. J Am Soc Nephrol 2023; 34:1574-1588. [PMID: 37476849 PMCID: PMC10482064 DOI: 10.1681/asn.0000000000000185] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 06/13/2023] [Indexed: 07/22/2023] Open
Abstract
SIGNIFICANCE STATEMENT Patients with end stage CKD often develop cognitive decline, but whether this is related to the underlying disease or to hemodialysis remains unclear. We performed three-dimensional pseudocontinuous arterial spin labeling and quantitative susceptibility mapping prospectively in 40 patients with stage 1-4 CKD, 47 nondialysis patients with stage 5 CKD, and 44 healthy controls. Our magnetic resonance imaging data demonstrate that changes in cerebral blood flow-susceptibility coupling might underlie this cognitive decline, perhaps in the hippocampus and thalamus. These results suggest that magnetic resonance imaging parameters are potential biomarkers of cognitive decline in patients with CKD. Moreover, our findings may lead to discovery of novel therapeutic targets to prevent cognitive decline in patients with CKD. BACKGROUND Cerebral blood flow (CBF) and susceptibility values reflect vascular and iron metabolism, providing mechanistic insights into conditions of health and disease. Nondialysis patients with CKD show a cognitive decline, but the pathophysiological mechanisms underlying this remain unclear. METHODS Three-dimensional pseudocontinuous arterial spin labeling and quantitative susceptibility mapping were prospectively performed in 40 patients with stage 1-4 CKD (CKD 1-4), 47 nondialysis patients with stage 5 CKD (CKD 5ND), and 44 healthy controls (HCs). Voxel-based global and regional analyses of CBF, susceptibility values, and vascular-susceptibility coupling were performed. Furthermore, the association between clinical performance and cerebral perfusion and iron deposition was analyzed. RESULTS For CBF, patients with CKD 5ND had higher normalized CBF in the hippocampus and thalamus than HCs. Patients with CKD 5ND had higher normalized CBF in the hippocampus and thalamus than those with CKD 1-4. The susceptibility values in the hippocampus and thalamus were lower in patients with CKD 5ND than in HCs. Patients with CKD 5ND had higher susceptibility value in the caudate nucleus than those with CKD 1-4. More importantly, patients with CKD 5ND had lower CBF-susceptibility coupling than HCs. In addition, CBF and susceptibility values were significantly associated with clinical performance. CONCLUSIONS Our findings demonstrate a new neuropathological mechanism in patients with CKD, which leads to regional changes in CBF-susceptibility coupling. These changes are related to cognitive decline, providing potential imaging markers for assessing clinical disability and cognitive decline in these patients.
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Affiliation(s)
- Hao Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xu Liu
- Department of Nephrology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Lijun Song
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Wenbo Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Mingan Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Qian Chen
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Pengfei Zhao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Wenhu Liu
- Department of Nephrology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhen-chang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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32
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Ravanfar P, Rushmore RJ, Lyall AE, Cropley V, Makris N, Desmond P, Velakoulis D, Shenton ME, Bush AI, Rossell SL, Pantelis C, Syeda WT, Phillipou A. Investigation of brain iron in anorexia nervosa, a quantitative susceptibility mapping study. J Eat Disord 2023; 11:142. [PMID: 37605216 PMCID: PMC10441741 DOI: 10.1186/s40337-023-00870-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 08/14/2023] [Indexed: 08/23/2023] Open
Abstract
BACKGROUND Anorexia nervosa (AN) is a potentially fatal psychiatric condition, associated with structural brain changes such as gray matter volume loss. The pathophysiological mechanisms for these changes are not yet fully understood. Iron is a crucial element in the development and function of the brain. Considering the systemic alterations in iron homeostasis in AN, we hypothesized that brain iron would be altered as a possible factor associated with structural brain changes in AN. METHODS In this study, we used quantitative susceptibility mapping (QSM) magnetic resonance imaging to investigate brain iron in current AN (c-AN) and weight-restored AN compared with healthy individuals. Whole-brain voxel wise comparison was used to probe areas with possible group differences. Further, the thalamus, caudate nucleus, putamen, nucleus accumbens, hippocampus, and amygdala were selected as the regions of interest (ROIs) for ROI-based comparison of mean QSM values. RESULTS Whole-brain voxel-wise and ROI-based comparison of QSM did not reveal any differences between groups. Exploratory analyses revealed a correlation between higher regional QSM (higher iron) and lower body mass index, higher illness severity, longer illness duration, and younger age at onset in the c-AN group. CONCLUSIONS This study did not find evidence of altered brain iron in AN compared to healthy individuals. However, the correlations between clinical variables and QSM suggest a link between brain iron and weight status or biological processes in AN, which warrants further investigation.
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Affiliation(s)
- Parsa Ravanfar
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Royal Melbourne Hospital, Level 3, Alan Gilbert Building, 161 Barry ST, Carlton South, VIC, 3053, Australia.
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - R Jarrett Rushmore
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Center for Morphometric Analysis (CMA), Massachusetts General Hospital, Charlestown, MA, USA
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | - Amanda E Lyall
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Vanessa Cropley
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Royal Melbourne Hospital, Level 3, Alan Gilbert Building, 161 Barry ST, Carlton South, VIC, 3053, Australia
| | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Center for Morphometric Analysis (CMA), Massachusetts General Hospital, Charlestown, MA, USA
| | - Patricia Desmond
- Department of Radiology, Royal Melbourne Hospital, University of Melbourne, Parkville, VIC, Australia
| | - Dennis Velakoulis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Royal Melbourne Hospital, Level 3, Alan Gilbert Building, 161 Barry ST, Carlton South, VIC, 3053, Australia
- Neuropsychiatry, The Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Martha E Shenton
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Ashley I Bush
- Melbourne Dementia Research Centre, The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Susan L Rossell
- Centre for Mental Health and Brain Sciences, Swinburne University, Hawthorn, VIC, Australia
- Department of Mental Health, St Vincent's Hospital, Melbourne, VIC, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Royal Melbourne Hospital, Level 3, Alan Gilbert Building, 161 Barry ST, Carlton South, VIC, 3053, Australia
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Warda T Syeda
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Royal Melbourne Hospital, Level 3, Alan Gilbert Building, 161 Barry ST, Carlton South, VIC, 3053, Australia
| | - Andrea Phillipou
- Department of Mental Health, St Vincent's Hospital, Melbourne, VIC, Australia
- Orygen, Melbourne, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
- Department of Psychological Sciences, Swinburne University of Technology, Melbourne, Australia
- Department of Mental Health, Austin Health, Melbourne, Australia
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Jia X, Li Y, Ying Y, Jia X, Tang W, Bian Y, Zhang J, Wang DJJ, Cheng X, Yang Q. Effect of corticosubcortical iron deposition on dysfunction in CADASIL is mediated by white matter microstructural damage. Neuroimage Clin 2023; 39:103485. [PMID: 37542975 PMCID: PMC10407949 DOI: 10.1016/j.nicl.2023.103485] [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: 03/30/2023] [Revised: 07/26/2023] [Accepted: 07/26/2023] [Indexed: 08/07/2023]
Abstract
Iron dysregulation may attenuate cognitive performance in patients with CADASIL. However, the underlying pathophysiological mechanisms remain incompletely understood. Whether white matter microstructural changes mediate these processes is largely unclear. In the present study, 30 cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) patients were confirmed via genetic analysis and 30 sex- and age-matched healthy controls underwent multimodal MRI examinations and neuropsychological assessments. Quantitative susceptibility mapping and peak width of skeletonized mean diffusivity (PSMD) were analyzed. Mediation effect analysis was performed to explore the interrelationship between iron deposition, white matter microstructural changes and cognitive deficits in CADASIL. Cognitive deterioration was most affected in memory and executive function, followed by attention and working memory in CADASIL. Excessive iron in the temporal-precuneus pathway and deep gray matter specific to CADASIL were identified. Mediation analysis further revealed that PSMD mediated the relationship between iron concentration and cognitive profile in CADASIL. The present findings provide a new perspective on iron deposition in the corticosubcortical circuit and its contribution to disease-related selective cognitive decline, in which iron concentration may affect cognition by white matter microstructural changes in CADASIL.
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Affiliation(s)
- Xiuqin Jia
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China; Key Lab of Medical Engineering for Cardiovascular Disease, Ministry of Education, Beijing 100020, China
| | - Yingying Li
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China
| | - Yunqing Ying
- Department of Neurology, National Center for Neurological Disorders, National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Xuejia Jia
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China
| | - Weijun Tang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yueyan Bian
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China
| | - Jiajia Zhang
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China
| | - Danny J J Wang
- Laboratory of FMRI Technology (LOFT), USC Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90007, United States
| | - Xin Cheng
- Department of Neurology, National Center for Neurological Disorders, National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China.
| | - Qi Yang
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China; Key Lab of Medical Engineering for Cardiovascular Disease, Ministry of Education, Beijing 100020, China.
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Tro' R, Roascio M, Arnulfo G, Tortora D, Severino M, Rossi A, Napolitano A, Fato MM. Influence of adaptive denoising on Diffusion Kurtosis Imaging at 3T and 7T. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 234:107508. [PMID: 37018885 DOI: 10.1016/j.cmpb.2023.107508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/24/2023] [Accepted: 03/23/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND AND OBJECTIVE Choosing the most appropriate denoising method to improve the quality of diagnostic images maximally is key in pre-processing of diffusion MRI images. Recent advancements in acquisition and reconstruction techniques have questioned traditional noise estimation methods favoring adaptive denoising frameworks, circumventing the need to know a priori information that is hardly available in a clinical setting. In this observational study, we compared two innovative adaptive techniques sharing some features, Patch2Self and Nlsam, through application on reference adult data at 3T and 7T. The primary aim was identifying the most effective method in case of Diffusion Kurtosis Imaging (DKI) data - particularly susceptible to noise and signal fluctuations - at 3T and 7T fields. A side goal consisted of investigating the dependence of kurtosis metrics' variability with respect to the magnetic field on the adopted denoising methodology. METHODS For comparison purposes, we focused on qualitative and quantitative analysis of DKI data and related microstructural maps before and after applying the two denoising approaches. Specifically, we assessed computational efficiency, preservation of anatomical details via perceptual metrics, consistency of microstructure model fitting, alleviation of degeneracies in model estimation, and joint variability with varying field strength and denoising method. RESULTS Accounting for all these factors, Patch2Self framework has turned out to be specifically suitable for DKI data, with improving performance at 7T. Nlsam method is more robust in alleviating degenerate black voxels while introducing some blurring, which in turn is reflected in an overall loss of image sharpness. Regarding the impact of denoising on field-dependent variability, both methods have been shown to make variations from standard to Ultra-High Field more concordant with theoretical evidence, claiming that kurtosis metrics are sensitive to susceptibility-induced background gradients, directly proportional to the magnetic field strength and sensitive to the microscopic distribution of iron and myelin. CONCLUSIONS This study serves as a proof-of-concept stressing the need for an accurate choice of a denoising methodology, specifically tailored for the data under analysis and allowing higher spatial resolution acquisition within clinically compatible timings, with all the potential benefits that improving suboptimal quality of diagnostic images entails.
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Affiliation(s)
- Rosella Tro'
- Department of Informatics, Bioengineering Robotics and System Engineering (DIBRIS), University of Genoa, Via all'Opera Pia, 13, Genoa 16145, Italy; RAISE Ecosystem, Genova, Italy.
| | - Monica Roascio
- Department of Informatics, Bioengineering Robotics and System Engineering (DIBRIS), University of Genoa, Via all'Opera Pia, 13, Genoa 16145, Italy; RAISE Ecosystem, Genova, Italy
| | - Gabriele Arnulfo
- Department of Informatics, Bioengineering Robotics and System Engineering (DIBRIS), University of Genoa, Via all'Opera Pia, 13, Genoa 16145, Italy; Neuroscience Center Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; RAISE Ecosystem, Genova, Italy
| | - Domenico Tortora
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | | | - Andrea Rossi
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy; Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | | | - Marco M Fato
- Department of Informatics, Bioengineering Robotics and System Engineering (DIBRIS), University of Genoa, Via all'Opera Pia, 13, Genoa 16145, Italy; RAISE Ecosystem, Genova, Italy
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Berger C, Bauer M, Scheurer E, Lenz C. Temperature correction of post mortem quantitative magnetic resonance imaging using real-time forehead temperature acquisitions. Forensic Sci Int 2023; 348:111738. [PMID: 37263059 DOI: 10.1016/j.forsciint.2023.111738] [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: 12/22/2022] [Revised: 04/24/2023] [Accepted: 05/22/2023] [Indexed: 06/03/2023]
Abstract
Performing magnetic resonance imaging (MRI) of deceased is challenging due to altered body temperatures compared to in vivo temperatures and, hence, requires a temperature correction. This study investigates the possibility to correct brain MRI parameters real-time and non invasively based on the forehead temperature. 17 post mortem cases were included and their forehead temperatures were measured continuously during the in situ brain MRI protocol consisting of a diffusion tensor imaging, multi-contrast spin echo, multi-echo gradient echo and inversion recovery spin echo sequence. Linear models were fitted to the quantitative MRI parameters in a forensically interesting temperature range for white matter, cerebral cortex and deep gray matter, separately, and the influence of the forehead temperature on the MRI parameters was determined. A statistically significant temperature sensitivity was found for T2 and mean diffusivity in white matter, for T1 in cerebral cortex, as well as for T1 and mean diffusivity in deep gray matter. Linear models were computed to temperature correct these MRI parameters in in situ post mortem scans to allow their comparison regardless of temperature. The here presented real-time and non invasive temperature correction method for the brain presents a crucial precondition for quantitative in situ post mortem MRI.
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Affiliation(s)
- Celine Berger
- Institute of Forensic Medicine, Department of Biomedical Engineering, University of Basel, Basel, Switzerland; Institute of Forensic Medicine, Health Department Basel-Stadt, Basel, Switzerland
| | - Melanie Bauer
- Institute of Forensic Medicine, Department of Biomedical Engineering, University of Basel, Basel, Switzerland; Institute of Forensic Medicine, Health Department Basel-Stadt, Basel, Switzerland
| | - Eva Scheurer
- Institute of Forensic Medicine, Department of Biomedical Engineering, University of Basel, Basel, Switzerland; Institute of Forensic Medicine, Health Department Basel-Stadt, Basel, Switzerland
| | - Claudia Lenz
- Institute of Forensic Medicine, Department of Biomedical Engineering, University of Basel, Basel, Switzerland; Institute of Forensic Medicine, Health Department Basel-Stadt, Basel, Switzerland.
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Doehler J, Northall A, Liu P, Fracasso A, Chrysidou A, Speck O, Lohmann G, Wolbers T, Kuehn E. The 3D Structural Architecture of the Human Hand Area Is Nontopographic. J Neurosci 2023; 43:3456-3476. [PMID: 37001994 PMCID: PMC10184749 DOI: 10.1523/jneurosci.1692-22.2023] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 02/15/2023] [Accepted: 03/15/2023] [Indexed: 04/03/2023] Open
Abstract
The functional topography of the human primary somatosensory cortex hand area is a widely studied model system to understand sensory organization and plasticity. It is so far unclear whether the underlying 3D structural architecture also shows a topographic organization. We used 7 Tesla (7T) magnetic resonance imaging (MRI) data to quantify layer-specific myelin, iron, and mineralization in relation to population receptive field maps of individual finger representations in Brodman area 3b (BA 3b) of human S1 in female and male younger adults. This 3D description allowed us to identify a characteristic profile of layer-specific myelin and iron deposition in the BA 3b hand area, but revealed an absence of structural differences, an absence of low-myelin borders, and high similarity of 3D microstructure profiles between individual fingers. However, structural differences and borders were detected between the hand and face areas. We conclude that the 3D structural architecture of the human hand area is nontopographic, unlike in some monkey species, which suggests a high degree of flexibility for functional finger organization and a new perspective on human topographic plasticity.SIGNIFICANCE STATEMENT Using ultra-high-field MRI, we provide the first comprehensive in vivo description of the 3D structural architecture of the human BA 3b hand area in relation to functional population receptive field maps. High similarity of precise finger-specific 3D profiles, together with an absence of structural differences and an absence of low-myelin borders between individual fingers, reveals the 3D structural architecture of the human hand area to be nontopographic. This suggests reduced structural limitations to cortical plasticity and reorganization and allows for shared representational features across fingers.
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Affiliation(s)
- Juliane Doehler
- Institute for Cognitive Neurology and Dementia Research, Medical Faculty, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
| | - Alicia Northall
- Institute for Cognitive Neurology and Dementia Research, Medical Faculty, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
| | - Peng Liu
- Institute for Cognitive Neurology and Dementia Research, Medical Faculty, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
| | - Alessio Fracasso
- Institute of Neuroscience and Psychology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Anastasia Chrysidou
- Institute for Cognitive Neurology and Dementia Research, Medical Faculty, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
| | - Oliver Speck
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
- Department of Biomedical Magnetic Resonance, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany
- Center for Behavioral Brain Sciences, 39120 Magdeburg, Germany
- Leibniz Institute for Neurobiology, 39120 Magdeburg, Germany
| | - Gabriele Lohmann
- Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany
| | - Thomas Wolbers
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
- Center for Behavioral Brain Sciences, 39120 Magdeburg, Germany
| | - Esther Kuehn
- Hertie Institute for Clinical Brain Research, 72076 Tübingen, Germany
- Institute for Cognitive Neurology and Dementia Research, Medical Faculty, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
- Center for Behavioral Brain Sciences, 39120 Magdeburg, Germany
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Stellingwerff MD, Pouwels PJW, Roosendaal SD, Barkhof F, van der Knaap MS. Quantitative MRI in leukodystrophies. Neuroimage Clin 2023; 38:103427. [PMID: 37150021 PMCID: PMC10193020 DOI: 10.1016/j.nicl.2023.103427] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 05/09/2023]
Abstract
Leukodystrophies constitute a large and heterogeneous group of genetic diseases primarily affecting the white matter of the central nervous system. Different disorders target different white matter structural components. Leukodystrophies are most often progressive and fatal. In recent years, novel therapies are emerging and for an increasing number of leukodystrophies trials are being developed. Objective and quantitative metrics are needed to serve as outcome measures in trials. Quantitative MRI yields information on microstructural properties, such as myelin or axonal content and condition, and on the chemical composition of white matter, in a noninvasive fashion. By providing information on white matter microstructural involvement, quantitative MRI may contribute to the evaluation and monitoring of leukodystrophies. Many distinct MR techniques are available at different stages of development. While some are already clinically applicable, others are less far developed and have only or mainly been applied in healthy subjects. In this review, we explore the background, current status, potential and challenges of available quantitative MR techniques in the context of leukodystrophies.
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Affiliation(s)
- Menno D Stellingwerff
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Child Neurology, Emma Children's Hospital, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Petra J W Pouwels
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Stefan D Roosendaal
- Amsterdam UMC Location University of Amsterdam, Department of Radiology, Meibergdreef 9, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands; University College London, Institutes of Neurology and Healthcare Engineering, London, UK
| | - Marjo S van der Knaap
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Child Neurology, Emma Children's Hospital, and Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands; Vrije Universiteit Amsterdam, Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, De Boelelaan 1105, Amsterdam, the Netherlands.
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Jäschke D, Steiner KM, Chang DI, Claaßen J, Uslar E, Thieme A, Gerwig M, Pfaffenrot V, Hulst T, Gussew A, Maderwald S, Göricke SL, Minnerop M, Ladd ME, Reichenbach JR, Timmann D, Deistung A. Age-related differences of cerebellar cortex and nuclei: MRI findings in healthy controls and its application to spinocerebellar ataxia (SCA6) patients. Neuroimage 2023; 270:119950. [PMID: 36822250 DOI: 10.1016/j.neuroimage.2023.119950] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 02/06/2023] [Accepted: 02/15/2023] [Indexed: 02/24/2023] Open
Abstract
Understanding cerebellar alterations due to healthy aging provides a reference point against which pathological findings in late-onset disease, for example spinocerebellar ataxia type 6 (SCA6), can be contrasted. In the present study, we investigated the impact of aging on the cerebellar nuclei and cerebellar cortex in 109 healthy controls (age range: 16 - 78 years) using 3 Tesla magnetic resonance imaging (MRI). Findings were compared with 25 SCA6 patients (age range: 38 - 78 years). A subset of 16 SCA6 (included: 14) patients and 50 controls (included: 45) received an additional MRI scan at 7 Tesla and were re-scanned after one year. MRI included T1-weighted, T2-weighted FLAIR, and multi-echo T2*-weighted imaging. The T2*-weighted phase images were converted to quantitative susceptibility maps (QSM). Since the cerebellar nuclei are characterized by elevated iron content with respect to their surroundings, two independent raters manually outlined them on the susceptibility maps. T1-weighted images acquired at 3T were utilized to automatically identify the cerebellar gray matter (GM) volume. Linear correlations revealed significant atrophy of the cerebellum due to tissue loss of cerebellar cortical GM in healthy controls with increasing age. Reduction of the cerebellar GM was substantially stronger in SCA6 patients. The volume of the dentate nuclei did not exhibit a significant relationship with age, at least in the age range between 18 and 78 years, whereas mean susceptibilities of the dentate nuclei increased with age. As previously shown, the dentate nuclei volumes were smaller and magnetic susceptibilities were lower in SCA6 patients compared to age- and sex-matched controls. The significant dentate volume loss in SCA6 patients could also be confirmed with 7T MRI. Linear mixed effects models and individual paired t-tests accounting for multiple comparisons revealed no statistical significant change in volume and susceptibility of the dentate nuclei after one year in neither patients nor controls. Importantly, dentate volumes were more sensitive to differentiate between SCA6 (Cohen's d = 3.02) and matched controls than the cerebellar cortex volume (d = 2.04). In addition to age-related decline of the cerebellar cortex and atrophy in SCA6 patients, age-related increase of susceptibility of the dentate nuclei was found in controls, whereas dentate volume and susceptibility was significantly decreased in SCA6 patients. Because no significant changes of any of these parameters was found at follow-up, these measures do not allow to monitor disease progression at short intervals.
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Affiliation(s)
- Dominik Jäschke
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen 45147, Germany; Department of Radiology and Nuclear Medicine, University Hospital Basel, Basel 4031, Switzerland
| | - Katharina M Steiner
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen 45147, Germany; LVR-Hospital Essen, Department of Psychiatry and Psychotherapy, Medical Faculty, University of Duisburg-Essen, Essen 45147, Germany
| | - Dae-In Chang
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen 45147, Germany; Clinic for Psychiatry, Psychotherapy and Preventive Medicine, LWL-University Hospital of the Ruhr-University Bochum, Bochum 44791, Germany
| | - Jens Claaßen
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen 45147, Germany; Fachklinik für Neurologie, MEDICLIN Klinik Reichshof, Reichshof-Eckenhagen 51580, Germany
| | - Ellen Uslar
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen 45147, Germany
| | - Andreas Thieme
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen 45147, Germany
| | - Marcus Gerwig
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen 45147, Germany
| | - Viktor Pfaffenrot
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen 45141, Germany
| | - Thomas Hulst
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen 45147, Germany; Erasmus University College, Rotterdam 3011 HP, the Netherlands
| | - Alexander Gussew
- University Clinic and Outpatient Clinic for Radiology, Department for Radiation Medicine, University Hospital Halle (Saale), Ernst-Grube-Str. 40, Halle (Saale) 06120, Germany
| | - Stefan Maderwald
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen 45141, Germany
| | - Sophia L Göricke
- Institute of Diagnostic and Interventional Neuroradiology, Essen University Hospital, University of Duisburg-Essen, Essen 45141, Germany
| | - Martina Minnerop
- Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, Juelich 52425, Germany; Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf 40225, Germany; Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Mark E Ladd
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen 45141, Germany; Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Faculty of Physics and Astronomy and Faculty of Medicine, Heidelberg University, Heidelberg 69120, Germany
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena 07743, Germany
| | - Dagmar Timmann
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen 45147, Germany; Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen 45141, Germany
| | - Andreas Deistung
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen 45147, Germany; University Clinic and Outpatient Clinic for Radiology, Department for Radiation Medicine, University Hospital Halle (Saale), Ernst-Grube-Str. 40, Halle (Saale) 06120, Germany; Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena 07743, Germany.
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Paquola C, Hong SJ. The Potential of Myelin-Sensitive Imaging: Redefining Spatiotemporal Patterns of Myeloarchitecture. Biol Psychiatry 2023; 93:442-454. [PMID: 36481065 DOI: 10.1016/j.biopsych.2022.08.031] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 08/12/2022] [Accepted: 08/30/2022] [Indexed: 02/07/2023]
Abstract
Recent advances in magnetic resonance imaging (MRI) have paved the way for approximation of myelin content in vivo. In this review, our main goal was to determine how to best capitalize on myelin-sensitive imaging. First, we briefly overview the theoretical and empirical basis for the myelin sensitivity of different MRI markers and, in doing so, highlight how multimodal imaging approaches are important for enhancing specificity to myelin. Then, we discuss recent studies that have probed the nonuniform distribution of myelin across cortical layers and along white matter tracts. These approaches, collectively known as myelin profiling, have provided detailed depictions of myeloarchitecture in both the postmortem and living human brain. Notably, MRI-based profiling studies have recently focused on investigating whether it can capture interindividual variability in myelin characteristics as well as trajectories across the lifespan. Finally, another line of recent evidence emphasizes the contribution of region-specific myelination to large-scale organization, demonstrating the impact of myelination on global brain networks. In conclusion, we suggest that combining well-validated MRI markers with profiling techniques holds strong potential to elucidate individual differences in myeloarchitecture, which has important implications for understanding brain function and disease.
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Affiliation(s)
- Casey Paquola
- Institute of Neuroscience and Medicine, Forschungszentrum Jülich, Jülich, Germany.
| | - Seok-Jun Hong
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea; Center for the Developing Brain, Child Mind Institute, New York, New York; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
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40
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Hu R, Gao B, Tian S, Liu Y, Jiang Y, Li W, Li Y, Song Q, Wang W, Miao Y. Regional high iron deposition on quantitative susceptibility mapping correlates with cognitive decline in type 2 diabetes mellitus. Front Neurosci 2023; 17:1061156. [PMID: 36793541 PMCID: PMC9922715 DOI: 10.3389/fnins.2023.1061156] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 01/10/2023] [Indexed: 01/31/2023] Open
Abstract
Objective To quantitatively evaluate the iron deposition and volume changes in deep gray nuclei according to threshold-method of quantitative susceptibility mapping (QSM) acquired by strategically acquired gradient echo (STAGE) sequence, and to analyze the correlation between the magnetic susceptibility values (MSV) and cognitive scores in type 2 diabetes mellitus (T2DM) patients. Methods Twenty-nine patients with T2DM and 24 healthy controls (HC) matched by age and gender were recruited in this prospective study. QSM images were used to evaluate whole-structural volumes (Vwh), regional magnetic susceptibility values (MSVRII), and volumes (VRII) in high-iron regions in nine gray nuclei. All QSM data were compared between groups. Receiver operating characteristic (ROC) analysis was used to assess the discriminating ability between groups. The predictive model from single and combined QSM parameters was also established using logistic regression analysis. The correlation between MSVRII and cognitive scores was further analyzed. Multiple comparisons of all statistical values were corrected by false discovery rate (FDR). A statistically significant P-value was set at 0.05. Results Compared with HC group, the MSVRII of all gray matter nuclei in T2DM were increased by 5.1-14.8%, with significant differences found in bilateral head of caudate nucleus (HCN), right putamen (PUT), right globus pallidus (GP), and left dentate nucleus (DN) (P < 0.05). The Vwh of most gray nucleus in T2DM group were decreased by 1.5-16.9% except bilateral subthalamic nucleus (STN). Significant differences were found in bilateral HCN, bilateral red nucleus (RN), and bilateral substantia nigra (SN) (P < 0.05). VRII was increased in bilateral GP, bilateral PUT (P < 0.05). VRII/Vwh was also increased in bilateral GP, bilateral PUT, bilateral SN, left HCN and right STN (P < 0.05). Compared with the single QSM parameter, the combined parameter showed the largest area under curve (AUC) of 0.86, with a sensitivity of 87.5% and specificity of 75.9%. The MSVRII in the right GP was strongly associated with List A Long-delay free recall (List A LDFR) scores (r = -0.590, P = 0.009). Conclusion In T2DM patients, excessive and heterogeneous iron deposition as well as volume loss occurs in deep gray nuclei. The MSV in high iron regions can better evaluate the distribution of iron, which is related to the decline of cognitive function.
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Clinical correlates of R1 relaxometry and magnetic susceptibility changes in multiple sclerosis: a multi-parameter quantitative MRI study of brain iron and myelin. Eur Radiol 2023; 33:2185-2194. [PMID: 36241917 PMCID: PMC9935712 DOI: 10.1007/s00330-022-09154-y] [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: 12/04/2021] [Revised: 04/07/2022] [Accepted: 05/13/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES The clinical impact of brain microstructural abnormalities in multiple sclerosis (MS) remains elusive. We aimed to characterize the topography of longitudinal relaxation rate (R1) and quantitative susceptibility (χ) changes, as indices of iron and myelin, together with brain atrophy, and to clarify their contribution to cognitive and motor disability in MS. METHODS In this cross-sectional study, voxel-based morphometry, and voxel-based quantification analyses of R1 and χ maps were conducted in gray matter (GM) and white matter (WM) of 117 MS patients and 53 healthy controls. Voxel-wise between-group differences were assessed with nonparametric permutation tests, while correlations between MRI metrics and clinical variables (global disability, cognitive and motor performance) were assessed both globally and voxel-wise within clusters emerging from the between-group comparisons. RESULTS MS patients showed widespread R1 decrease associated with more limited modifications of χ, with atrophy mainly involving deep GM, posterior and infratentorial regions (p < 0.02). While R1 and χ showed a parallel reduction in several WM tracts (p < 0.001), reduced GM R1 values (p < 0.001) were associated with decreased thalamic χ (p < 0.001) and small clusters of increased χ in the caudate nucleus and prefrontal cortex (p < 0.02). In addition to the atrophy, χ values in the cingulum and corona radiata correlated with global disability and motor performance, while focal demyelination correlated with cognitive performance (p < 0.04). CONCLUSIONS We confirmed the presence of widespread R1 changes, involving both GM and WM, and atrophy in MS, with less extensive modifications of tissue χ. While atrophy and χ changes are related to global and motor disability, R1 changes are meaningful correlates of cognition. KEY POINTS • Compared to healthy controls, multiple sclerosis patients showed R1 and χ changes suggestive of iron increase within the basal ganglia and reduced iron and myelin content within (subnuclei of) the thalamus. • Thalamic volume and χ changes significantly predicted clinical disability, as well as pulvinar R1 and χ changes, independently from atrophy. • Atrophy-independent R1 and χ changes, suggestive of thalamic iron and myelin depletion, may represent a sensitive marker of subclinical inflammation.
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Yao J, Morrison MA, Jakary A, Avadiappan S, Chen Y, Luitjens J, Glueck J, Driscoll T, Geschwind MD, Nelson AB, Villanueva-Meyer JE, Hess CP, Lupo JM. Comparison of quantitative susceptibility mapping methods for iron-sensitive susceptibility imaging at 7T: An evaluation in healthy subjects and patients with Huntington's disease. Neuroimage 2023; 265:119788. [PMID: 36476567 PMCID: PMC11588860 DOI: 10.1016/j.neuroimage.2022.119788] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 10/08/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) is a promising tool for investigating iron dysregulation in neurodegenerative diseases, including Huntington's disease (HD). Many diverse methods have been proposed to generate accurate and robust QSM images. In this study, we evaluated the performance of different dipole inversion algorithms for iron-sensitive susceptibility imaging at 7T on healthy subjects of a large age range and patients with HD. We compared an iterative least-squares-based method (iLSQR), iterative methods that use regularization, single-step approaches, and deep learning-based techniques. Their performance was evaluated by comparing: (1) deviations from a multiple-orientation QSM reference; (2) visual appearance of QSM maps and the presence of artifacts; (3) susceptibility in subcortical brain regions with age; (4) regional brain susceptibility with published postmortem brain iron quantification; and (5) susceptibility in HD-affected basal ganglia regions between HD subjects and healthy controls. We found that single-step QSM methods with either total variation or total generalized variation constraints (SSTV/SSTGV) and the single-step deep learning method iQSM generally provided the best performance in terms of correlation with iron deposition and were better at differentiating between healthy controls and premanifest HD individuals, while deep learning QSM methods trained with multiple-orientation susceptibility data created QSM maps that were most similar to the multiple orientation reference and with the best visual scores.
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Affiliation(s)
- Jingwen Yao
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - Melanie A Morrison
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - Angela Jakary
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - Sivakami Avadiappan
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - Yicheng Chen
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA; UCSF/UC Berkeley Graduate Program in Bioengineering, San Francisco & Berkeley, CA, USA; Meta Platforms, Inc., Mountain View, CA, USA
| | - Johanna Luitjens
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA; Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Julia Glueck
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Theresa Driscoll
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Michael D Geschwind
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Alexandra B Nelson
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | | | - Christopher P Hess
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA; Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Janine M Lupo
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA; UCSF/UC Berkeley Graduate Program in Bioengineering, San Francisco & Berkeley, CA, USA.
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Huang P, Zhang M. Magnetic Resonance Imaging Studies of Neurodegenerative Disease: From Methods to Translational Research. Neurosci Bull 2023; 39:99-112. [PMID: 35771383 PMCID: PMC9849544 DOI: 10.1007/s12264-022-00905-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/07/2022] [Indexed: 01/22/2023] Open
Abstract
Neurodegenerative diseases (NDs) have become a significant threat to an aging human society. Numerous studies have been conducted in the past decades to clarify their pathologic mechanisms and search for reliable biomarkers. Magnetic resonance imaging (MRI) is a powerful tool for investigating structural and functional brain alterations in NDs. With the advantages of being non-invasive and non-radioactive, it has been frequently used in both animal research and large-scale clinical investigations. MRI may serve as a bridge connecting micro- and macro-level analysis and promoting bench-to-bed translational research. Nevertheless, due to the abundance and complexity of MRI techniques, exploiting their potential is not always straightforward. This review aims to briefly introduce research progress in clinical imaging studies and discuss possible strategies for applying MRI in translational ND research.
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Affiliation(s)
- Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009 China
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Association between Beta Oscillations from Subthalamic Nucleus and Quantitative Susceptibility Mapping in Deep Gray Matter Structures in Parkinson's Disease. Brain Sci 2023; 13:brainsci13010081. [PMID: 36672062 PMCID: PMC9857066 DOI: 10.3390/brainsci13010081] [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: 10/18/2022] [Revised: 12/15/2022] [Accepted: 12/27/2022] [Indexed: 01/03/2023] Open
Abstract
This study aimed to investigate the association between beta oscillations and brain iron deposition. Beta oscillations were filtered from the microelectrode recordings of local field potentials (LFP) in the subthalamic nucleus (STN), and the ratio of the power spectral density of beta oscillations (PSDXb) to that of the LFP signals was calculated. Iron deposition in the deep gray matter (DGM) structures was indirectly assessed using quantitative susceptibility mapping (QSM). The Unified Parkinson's Disease Rating Scale (UPDRS), part III, was used to assess the severity of symptoms. Spearman correlation coefficients were applied to assess the associations of PSDXb with QSM values in the DGM structures and the severity of symptoms. PSDXb showed a significant positive correlation with the average QSM values in DGM structures, including caudate and substantia nigra (SN) (p = 0.008 and 0.044). Similarly, the PSDXb showed significant negative correlations with the severity of symptoms, including axial symptoms and the gait in the medicine-off state (p = 0.006 for both). The abnormal iron metabolism in the SN and striatum pathways may be one of the underlying mechanisms for the occurrence of abnormal beta oscillations in the STN, and beta oscillations may serve as important pathophysiological biomarkers of PD.
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Anti-Correlated Myelin-Sensitive MRI Levels in Humans Consistent with a Subcortical to Sensorimotor Regulatory Process-Multi-Cohort Multi-Modal Evidence. Brain Sci 2022; 12:brainsci12121693. [PMID: 36552153 PMCID: PMC9776387 DOI: 10.3390/brainsci12121693] [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: 11/25/2022] [Revised: 12/07/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Differential axonal myelination synchronises signalling over different axon lengths. The consequences of myelination processes described at the cellular level for the regulation of myelination at the macroscopic level are unknown. We analysed multiple cohorts of myelin-sensitive brain MRI. Our aim was to (i) confirm a previous report of anti-correlation between myelination in subcortical and sensorimotor areas in healthy subjects, (ii) and thereby test our hypothesis for a regulatory interaction between them. We analysed nine image-sets across three different human cohorts using six MRI modalities. Each image-set contained healthy controls (HC) and ME/CFS subjects. Subcortical and Sensorimotor regions of interest (ROI) were optimised for the detection of anti-correlations and the same ROIs were used to test the HC in all image-sets. For each cohort, median MRI values were computed in both regions for each subject and their correlation across the cohort was computed. We confirmed negative correlations in healthy controls between subcortical and sensorimotor regions in six image-sets: three T1wSE (p = 5 × 10-8, 5 × 10-7, 0.002), T2wSE (p =2 × 10-6), MTC (p = 0.01), and WM volume (p = 0.02). T1/T2 was the exception with a positive correlation (p = 0.01). This myelin regulation study is novel in several aspects: human subjects, cross-sectional design, ROI optimization, spin-echo MRI and reproducible across multiple independent image-sets. In multiple independent image-sets we confirmed an anti-correlation between subcortical and sensorimotor myelination which supports a previously unreported regulatory interaction. The subcortical region contained the brain's primary regulatory nuclei. We suggest a mechanism has evolved whereby relatively low subcortical myelination in an individual is compensated by upregulated sensorimotor myelination to maintain adequate sensorimotor performance.
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Tranfa M, Pontillo G, Petracca M, Brunetti A, Tedeschi E, Palma G, Cocozza S. Quantitative MRI in Multiple Sclerosis: From Theory to Application. AJNR Am J Neuroradiol 2022; 43:1688-1695. [PMID: 35680161 DOI: 10.3174/ajnr.a7536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 02/22/2022] [Indexed: 02/01/2023]
Abstract
Quantitative MR imaging techniques allow evaluating different aspects of brain microstructure, providing meaningful information about the pathophysiology of damage in CNS disorders. In the study of patients with MS, quantitative MR imaging techniques represent an invaluable tool for studying changes in myelin and iron content occurring in the context of inflammatory and neurodegenerative processes. In the first section of this review, we summarize the physics behind quantitative MR imaging, here defined as relaxometry and quantitative susceptibility mapping, and describe the neurobiological correlates of quantitative MR imaging findings. In the second section, we focus on quantitative MR imaging application in MS, reporting the main findings in both the gray and white matter compartments, separately addressing macroscopically damaged and normal-appearing parenchyma.
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Affiliation(s)
- M Tranfa
- From the Departments of Advanced Biomedical Sciences (M.T., G. Pontillo, A.B., E.T., S.C.)
| | - G Pontillo
- From the Departments of Advanced Biomedical Sciences (M.T., G. Pontillo, A.B., E.T., S.C.) .,Electrical Engineering and Information Technology (G. Pontillo), University of Naples "Federico II," Naples, Italy
| | - M Petracca
- Department of Human Neurosciences (M.P.), Sapienza University of Rome, Rome, Italy
| | - A Brunetti
- From the Departments of Advanced Biomedical Sciences (M.T., G. Pontillo, A.B., E.T., S.C.)
| | - E Tedeschi
- From the Departments of Advanced Biomedical Sciences (M.T., G. Pontillo, A.B., E.T., S.C.)
| | - G Palma
- Institute of Nanotechnology (G. Palma), National Research Council, Lecce, Italy
| | - S Cocozza
- From the Departments of Advanced Biomedical Sciences (M.T., G. Pontillo, A.B., E.T., S.C.)
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Kor DZL, Jbabdi S, Huszar IN, Mollink J, Tendler BC, Foxley S, Wang C, Scott C, Smart A, Ansorge O, Pallebage-Gamarallage M, Miller KL, Howard AFD. An automated pipeline for extracting histological stain area fraction for voxelwise quantitative MRI-histology comparisons. Neuroimage 2022; 264:119726. [PMID: 36368503 PMCID: PMC10933753 DOI: 10.1016/j.neuroimage.2022.119726] [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: 07/06/2022] [Revised: 10/27/2022] [Accepted: 10/31/2022] [Indexed: 11/11/2022] Open
Abstract
The acquisition of MRI and histology in the same post-mortem tissue sample enables direct correlation between MRI and histologically-derived parameters. However, there still lacks a standardised automated pipeline to process histology data, with most studies relying on manual intervention. Here, we introduce an automated pipeline to extract a quantitative histological measure for staining density (stain area fraction, SAF) from multiple immunohistochemical (IHC) stains. The pipeline is designed to directly address key IHC artefacts related to tissue staining and slide digitisation. Here, the pipeline was applied to post-mortem human brain data from multiple subjects, relating MRI parameters (FA, MD, RD, AD, R2*, R1) to IHC slides stained for myelin, neurofilaments, microglia and activated microglia. Utilising high-quality MRI-histology co-registrations, we then performed whole-slide voxelwise comparisons (simple correlations, partial correlations and multiple regression analyses) between multimodal MRI- and IHC-derived parameters. The pipeline was found to be reproducible, robust to artefacts and generalisable across multiple IHC stains. Our partial correlation results suggest that some simple MRI-SAF correlations should be interpreted with caution, due to the co-localisation of other tissue features (e.g., myelin and neurofilaments). Further, we find activated microglia-a generic biomarker of inflammation-to consistently be the strongest predictor of high DTI FA and low RD, which may suggest sensitivity of diffusion MRI to aspects of neuroinflammation related to microglial activation, even after accounting for other microstructural changes (demyelination, axonal loss and general microglia infiltration). Together, these results show the utility of this approach in carefully curating IHC data and performing multimodal analyses to better understand microstructural relationships with MRI.
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Affiliation(s)
- Daniel Z L Kor
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom.
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom
| | - Istvan N Huszar
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom
| | - Jeroen Mollink
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom
| | - Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom
| | - Sean Foxley
- Department of Radiology, University of Chicago, Chicago, IL, United States of America
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom
| | - Connor Scott
- Academic Unit of Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Adele Smart
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom; Academic Unit of Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Olaf Ansorge
- Academic Unit of Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Menuka Pallebage-Gamarallage
- Academic Unit of Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom
| | - Amy F D Howard
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Headington, Oxford OX3 9DU, , United Kingdom
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Kim W, Shin HG, Lee H, Park D, Kang J, Nam Y, Lee J, Jang J. χ-Separation Imaging for Diagnosis of Multiple Sclerosis versus Neuromyelitis Optica Spectrum Disorder. Radiology 2022; 307:e220941. [PMID: 36413128 DOI: 10.1148/radiol.220941] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background Use of χ-separation imaging can provide surrogates for iron and myelin that relate closely to abnormal changes in multiple sclerosis (MS) lesions. Purpose To evaluate the appearances of MS and neuromyelitis optica spectrum disorder (NMOSD) brain lesions on χ-separation maps and explore their diagnostic value in differentiating the two diseases in comparison with previously reported diagnostic criteria. Materials and Methods This prospective study included individuals with MS or NMOSD who underwent χ-separation imaging from October 2017 to October 2020. Positive (χpos) and negative (χneg) susceptibility were estimated separately by using local frequency shifts and calculating R2' (R2' = R2* - R2). R2 mapping was performed with a machine learning approach. For each lesion, presence of the central vein sign (CVS) and paramagnetic rim sign (PRS) and signal characteristics on χneg and χpos maps were assessed and compared. For each participant, the proportion of lesions with CVS, PRS, and hypodiamagnetism was calculated. Diagnostic performances were assessed using receiver operating characteristic (ROC) curve analysis. Results A total of 32 participants with MS (mean age, 34 years ± 10 [SD]; 25 women, seven men) and 15 with NMOSD (mean age, 52 years ± 17; 14 women, one man) were evaluated, with a total of 611 MS and 225 NMOSD brain lesions. On the χneg maps, 80.2% (490 of 611) of MS lesions were categorized as hypodiamagnetic versus 13.8% (31 of 225) of NMOSD lesions (P < .001). Lesion appearances on the χpos maps showed no evidence of a difference between the two diseases. In per-participant analysis, participants with MS showed a higher proportion of hypodiamagnetic lesions (83%; IQR, 72-93) than those with NMOSD (6%; IQR, 0-14; P < .001). The proportion of hypodiamagnetic lesions achieved excellent diagnostic performance (area under the ROC curve, 0.96; 95% CI: 0.91, 1.00). Conclusion On χ-separation maps, multiple sclerosis (MS) lesions tend to be hypodiamagnetic, which can serve as an important hallmark to differentiate MS from neuromyelitis optica spectrum disorder. © RSNA, 2022 Supplemental material is available for this article.
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Affiliation(s)
- Woojun Kim
- From the Departments of Neurology (W.K.) and Radiology (H.L., D.P., J.J.), Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea (H.G.S., J.L.); Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Md (H.G.S.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (H.G.S.); and Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea (J.K., Y.N.)
| | - Hyeong-Geol Shin
- From the Departments of Neurology (W.K.) and Radiology (H.L., D.P., J.J.), Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea (H.G.S., J.L.); Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Md (H.G.S.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (H.G.S.); and Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea (J.K., Y.N.)
| | - Hyebin Lee
- From the Departments of Neurology (W.K.) and Radiology (H.L., D.P., J.J.), Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea (H.G.S., J.L.); Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Md (H.G.S.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (H.G.S.); and Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea (J.K., Y.N.)
| | - Dohoon Park
- From the Departments of Neurology (W.K.) and Radiology (H.L., D.P., J.J.), Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea (H.G.S., J.L.); Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Md (H.G.S.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (H.G.S.); and Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea (J.K., Y.N.)
| | - Junghwa Kang
- From the Departments of Neurology (W.K.) and Radiology (H.L., D.P., J.J.), Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea (H.G.S., J.L.); Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Md (H.G.S.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (H.G.S.); and Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea (J.K., Y.N.)
| | - Yoonho Nam
- From the Departments of Neurology (W.K.) and Radiology (H.L., D.P., J.J.), Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea (H.G.S., J.L.); Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Md (H.G.S.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (H.G.S.); and Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea (J.K., Y.N.)
| | - Jongho Lee
- From the Departments of Neurology (W.K.) and Radiology (H.L., D.P., J.J.), Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea (H.G.S., J.L.); Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Md (H.G.S.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (H.G.S.); and Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea (J.K., Y.N.)
| | - Jinhee Jang
- From the Departments of Neurology (W.K.) and Radiology (H.L., D.P., J.J.), Seoul St Mary's Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea; Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea (H.G.S., J.L.); Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, Md (H.G.S.); F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Md (H.G.S.); and Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea (J.K., Y.N.)
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Reeves JA, Bergsland N, Dwyer MG, Wilding GE, Jakimovski D, Salman F, Sule B, Meineke N, Weinstock-Guttman B, Zivadinov R, Schweser F. Susceptibility networks reveal independent patterns of brain iron abnormalities in multiple sclerosis. Neuroimage 2022; 261:119503. [PMID: 35878723 PMCID: PMC10097440 DOI: 10.1016/j.neuroimage.2022.119503] [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: 04/21/2022] [Revised: 07/06/2022] [Accepted: 07/21/2022] [Indexed: 11/24/2022] Open
Abstract
Brain iron homeostasis is necessary for healthy brain function. MRI and histological studies have shown altered brain iron levels in the brains of patients with multiple sclerosis (MS), particularly in the deep gray matter (DGM). Previous studies were able to only partially separate iron-modifying effects because of incomplete knowledge of iron-modifying processes and influencing factors. It is therefore unclear to what extent and at which stages of the disease different processes contribute to brain iron changes. We postulate that spatially covarying magnetic susceptibility networks determined with Independent Component Analysis (ICA) reflect, and allow for the study of, independent processes regulating iron levels. We applied ICA to quantitative susceptibility maps for 170 individuals aged 9-81 years without neurological disease ("Healthy Aging" (HA) cohort), and for a cohort of 120 patients with MS and 120 age- and sex-matched healthy controls (HC; together the "MS/HC" cohort). Two DGM-associated "susceptibility networks" identified in the HA cohort (the Dorsal Striatum and Globus Pallidus Interna Networks) were highly internally reproducible (i.e. "robust") across multiple ICA repetitions on cohort subsets. DGM areas overlapping both robust networks had higher susceptibility levels than DGM areas overlapping only a single robust network, suggesting that these networks were caused by independent processes of increasing iron concentration. Because MS is thought to accelerate brain aging, we hypothesized that associations between age and the two robust DGM-associated networks would be enhanced in patients with MS. However, only one of these networks was altered in patients with MS, and it had a null age association in patients with MS rather than a stronger association. Further analysis of the MS/HC cohort revealed three additional disease-related networks (the Pulvinar, Mesencephalon, and Caudate Networks) that were differentially altered between patients with MS and HCs and between MS subtypes. Exploratory regression analyses of the disease-related networks revealed differential associations with disease duration and T2 lesion volume. Finally, analysis of ROI-based disease effects in the MS/HC cohort revealed an effect of disease status only in the putamen ROI and exploratory regression analysis did not show associations between the caudate and pulvinar ROIs and disease duration or T2 lesion volume, showing the ICA-based approach was more sensitive to disease effects. These results suggest that the ICA network framework increases sensitivity for studying patterns of brain iron change, opening a new avenue for understanding brain iron physiology under normal and disease conditions.
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Affiliation(s)
- Jack A Reeves
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA; MR Research Laboratory, IRCCS, Don Gnocchi Foundation ONLUS, Milan, Italy
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, Clinical and Translational Research Center, State University of New York at Buffalo, 6045C, 875 Ellicott Street, Buffalo, NY 14203, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Gregory E Wilding
- Department of Biostatistics, School of Public Health and Health Professions, State University of New York at Buffalo, Buffalo, NY, USA
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Fahad Salman
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Balint Sule
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Nicklas Meineke
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Bianca Weinstock-Guttman
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA; Jacobs Neurological Institute, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, Clinical and Translational Research Center, State University of New York at Buffalo, 6045C, 875 Ellicott Street, Buffalo, NY 14203, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, Clinical and Translational Research Center, State University of New York at Buffalo, 6045C, 875 Ellicott Street, Buffalo, NY 14203, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA.
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Tessema AW, Lee H, Gong Y, Cho H, Adem HM, Lyu I, Lee JH, Cho H. Automated volumetric determination of high R 2 * regions in substantia nigra: A feasibility study of quantifying substantia nigra atrophy in progressive supranuclear palsy. NMR IN BIOMEDICINE 2022; 35:e4795. [PMID: 35775868 DOI: 10.1002/nbm.4795] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/26/2022] [Accepted: 06/28/2022] [Indexed: 06/15/2023]
Abstract
The establishment of an unbiased protocol for the automated volumetric measurement of iron-rich regions in the substantia nigra (SN) is clinically important for diagnosing neurodegenerative diseases exhibiting midbrain atrophy, such as progressive supranuclear palsy (PSP). This study aimed to automatically quantify the volume and surface properties of the iron-rich 3D regions in the SN using the quantitative MRI-R2 * map. Three hundred and sixty-seven slices of R2 * map and susceptibility-weighted imaging (SWI) at 3-T MRI from healthy control (HC) individuals and Parkinson's disease (PD) patients were used to train customized U-net++ convolutional neural network based on expert-segmented masks. Age- and sex-matched participants were selected from HC, PD, and PSP groups to automate the volumetric determination of iron-rich areas in the SN. Dice similarity coefficient values between expert-segmented and detected masks from the proposed network were 0.91 ± 0.07 for R2 * maps and 0.89 ± 0.08 for SWI. Reductions in iron-rich SN volume from the R2 * map (SWI) were observed in PSP with area under the receiver operating characteristic curve values of 0.96 (0.89) and 0.98 (0.92) compared with HC and PD, respectively. The mean curvature of the PSP showed SN deformation along the side closer to the red nucleus. We demonstrated the automated volumetric measurement of iron-rich regions in the SN using deep learning can quantify the SN atrophy in PSP compared with PD and HC.
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Affiliation(s)
- Abel Worku Tessema
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
- School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia
| | - Hansol Lee
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Yelim Gong
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Hwapyeong Cho
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Hamdia Murad Adem
- School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia
| | - Ilwoo Lyu
- Department of Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Jae-Hyeok Lee
- Department of Neurology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, South Korea
| | - HyungJoon Cho
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
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