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Ciceri T, De Luca A, Agarwal N, Arrigoni F, Peruzzo D. A framework for optimizing the acquisition protocol multishell diffusion-weighted imaging for multimodel assessment. NMR IN BIOMEDICINE 2024; 37:e5141. [PMID: 38520215 DOI: 10.1002/nbm.5141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 11/22/2023] [Accepted: 02/15/2024] [Indexed: 03/25/2024]
Abstract
Complementary aspects of tissue microstructure can be studied with diffusion-weighted imaging (DWI). However, there is no consensus on how to design a diffusion acquisition protocol for multiple models within a clinically feasible time. The purpose of this study is to provide a flexible framework that is able to optimize the shell acquisition protocol given a set of DWI models. Eleven healthy subjects underwent an extensive DWI acquisition protocol, including 15 candidate shells, ranging from 10 to 3500 s/mm2. The proposed framework aims to determine the optimized acquisition scheme (OAS) with a data-driven procedure minimizing the squared error of model-estimated parameters. We tested the proposed method over five heterogeneous DWI models exploiting both low and high b-values (i.e., diffusion tensor imaging [DTI], free water, intra-voxel incoherent motion [IVIM], diffusion kurtosis imaging [DKI], and neurite orientation dispersion and density imaging [NODDI]). A voxel-level and region of interest (ROI)-level analysis was conducted over the white matter and in 48 fiber bundles, respectively. Results showed that acquiring data for the five abovementioned models via OAS requires 14 min, compared with 35 min for the joint recommended acquisition protocol. The parameters derived from the reference acquisition scheme and the OAS are comparable in terms of estimated values, noise, and tissue contrast. Furthermore, the power analysis showed that the OAS retains the potential sensitivity to group-level differences in the parameters of interest, with the exception of the free water model. Overall, there is a linear correspondence (R2 = 0.91) between OAS and reference-derived parameters. In conclusion, the proposed framework optimizes the shell acquisition scheme for a given set of DWI models (i.e., DTI, free water, IVIM, DKI, and NODDI), combining low and high b-values while saving acquisition time.
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Affiliation(s)
- Tommaso Ciceri
- Neuroimaging Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
- Department of Information Engineering, University of Padua, Padua, Italy
| | - Alberto De Luca
- Image Sciences Institute, Division Imaging and Oncology, UMC Utrecht, Utrecht, The Netherlands
- Neurology Department, UMC Utrecht Brain Center, UMC Utrecht, Utrecht, The Netherlands
| | - Nivedita Agarwal
- Diagnostic Imaging and Neuroradiology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Filippo Arrigoni
- Pediatric Radiology and Neuroradiology Department, V. Buzzi Children's Hospital, Milan, Italy
| | - Denis Peruzzo
- Neuroimaging Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
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Lv S, Tai H, Sun J, Zhuo Z, Duan Y, Liu S, Wang A, Zhang Z, Liu Y. Mapping macrostructural and microstructural brain alterations in patients with neuronal intranuclear inclusion disease. Neuroradiology 2024; 66:1373-1382. [PMID: 38866958 DOI: 10.1007/s00234-024-03406-y] [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: 03/14/2024] [Accepted: 06/09/2024] [Indexed: 06/14/2024]
Abstract
BACKGROUND AND PURPOSE Neuronal intranuclear inclusion disease (NIID) is a rare complex neurodegenerative disorder presents with various radiological features. The study aimed to investigate the structural abnormalities in NIID using multi-shell diffusion MR. MATERIALS AND METHODS Twenty-eight patients with adult-onset NIID and 32 healthy controls were included. Volumetric and diffusion MRI measures, including volume, fractional anisotropy (FA), mean diffusivity (MD), intracellular volume fraction (ICVF), orientation dispersion index (ODI), and isotropic volume fraction (ISOVF) of six brain structures, including cortex, subcortical GM, cerebral WM, cerebellar GM and WM, and brainstem, were obtained and compared between NIID and healthy controls. Associations between MRI measures and clinical variables were investigated. RESULTS Brain lesions of NIID included corticomedullary junction lesions on DWI, confluent leukoencephalopathy, lesions on callosum, cerebellar middle peduncle, cerebellar paravermal area and brainstem, and brain atrophy. Compared to healthy controls, NIID showed extensive volume loss of all the six brain regions (all p < 0.001); lower FA in cerebral WM (p < 0.001); higher MD in all WM regions; lower ODI in cortex (p < 0.001); higher ODI in subcortical GM (p < 0.001) and brainstem (p = 0.016); lower ICVF in brainstem (p = 0.001), and cerebral WM (p < 0.001); higher ISOVF in all the brain regions (p < 0.001). Higher MD of cerebellar WM was associated with worse cognitive level as evaluated by MoCA scores (p = 0.011). CONCLUSIONS NIID patients demonstrated widespread brain atrophy but heterogeneous diffusion alterations. Cerebellar WM integrity impairment was correlated with the cognitive decline. The findings of the current study offer a sophisticated picture of brain structural alterations in NIID.
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Affiliation(s)
- Shan Lv
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
- Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Hongfei Tai
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jun Sun
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
- Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
- Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
- Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Shaocheng Liu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - An Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Zaiqiang Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
- China National Clinical Research Center for Neurological Diseases, Beijing, China.
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
- Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China.
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Foesleitner O, Sulaj A, Sturm V, Kronlage M, Preisner F, Kender Z, Bendszus M, Szendroedi J, Heiland S, Schwarz D. Diffusion tensor imaging in anisotropic tissues: application of reduced gradient vector schemes in peripheral nerves. Eur Radiol Exp 2024; 8:37. [PMID: 38561526 PMCID: PMC10984907 DOI: 10.1186/s41747-024-00444-2] [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: 11/23/2023] [Accepted: 01/23/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND In contrast to the brain, fibers within peripheral nerves have distinct monodirectional structure questioning the necessity of complex multidirectional gradient vector schemes for DTI. This proof-of-concept study investigated the diagnostic utility of reduced gradient vector schemes in peripheral nerve DTI. METHODS Three-Tesla magnetic resonance neurography of the tibial nerve using 20-vector DTI (DTI20) was performed in 10 healthy volunteers, 12 patients with type 2 diabetes, and 12 age-matched healthy controls. From the full DTI20 dataset, three reduced datasets including only two or three vectors along the x- and/or y- and z-axes were built to calculate major parameters. The influence of nerve angulation and intraneural connective tissue was assessed. The area under the receiver operating characteristics curve (ROC-AUC) was used for analysis. RESULTS Simplified datasets achieved excellent diagnostic accuracy equal to DTI20 (ROC-AUC 0.847-0.868, p ≤ 0.005), but compared to DTI20, the reduced models yielded mostly lower absolute values of DTI scalars: median fractional anisotropy (FA) ≤ 0.12; apparent diffusion coefficient (ADC) ≤ 0.25; axial diffusivity ≤ 0.96, radial diffusivity ≤ 0.07). The precision of FA and ADC with the three-vector model was closest to DTI20. Intraneural connective tissue was negatively correlated with FA and ADC (r ≥ -0.49, p < 0.001). Small deviations of nerve angulation had little effect on FA accuracy. CONCLUSIONS In peripheral nerves, bulk tissue DTI metrics can be approximated with only three predefined gradient vectors along the scanner's main axes, yielding similar diagnostic accuracy as a 20-vector DTI, resulting in substantial scan time reduction. RELEVANCE STATEMENT DTI bulk tissue parameters of peripheral nerves can be calculated with only three predefined gradient vectors at similar diagnostic performance as a standard DTI but providing a substantial scan time reduction. KEY POINTS • In peripheral nerves, DTI parameters can be approximated using only three gradient vectors. • The simplified model achieves a similar diagnostic performance as a standard DTI. • The simplified model allows for a significant acceleration of image acquisition. • This can help to introduce multi-b-value DTI techniques into clinical practice.
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Affiliation(s)
- Olivia Foesleitner
- Department of Neuroradiology, Heidelberg University Hospital, INF 400, 69120, Heidelberg, Germany
| | - Alba Sulaj
- Department of Internal Medicine I and Clinical Chemistry, Heidelberg University Hospital, INF 410, Heidelberg, Germany
- German Center of Diabetes Research (DZD), Neuherberg, Germany
| | - Volker Sturm
- Department of Neuroradiology, Heidelberg University Hospital, INF 400, 69120, Heidelberg, Germany
| | - Moritz Kronlage
- Department of Neuroradiology, Heidelberg University Hospital, INF 400, 69120, Heidelberg, Germany
| | - Fabian Preisner
- Department of Neuroradiology, Heidelberg University Hospital, INF 400, 69120, Heidelberg, Germany
| | - Zoltan Kender
- Department of Internal Medicine I and Clinical Chemistry, Heidelberg University Hospital, INF 410, Heidelberg, Germany
- German Center of Diabetes Research (DZD), Neuherberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, INF 400, 69120, Heidelberg, Germany
| | - Julia Szendroedi
- Department of Internal Medicine I and Clinical Chemistry, Heidelberg University Hospital, INF 410, Heidelberg, Germany
- German Center of Diabetes Research (DZD), Neuherberg, Germany
- Joint Heidelberg-IDC Translational Diabetes Program, Helmholtz Center Munich, Neuherberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, INF 400, 69120, Heidelberg, Germany
| | - Daniel Schwarz
- Department of Neuroradiology, Heidelberg University Hospital, INF 400, 69120, Heidelberg, Germany.
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Clancy U, Kancheva AK, Valdés Hernández MDC, Jochems ACC, Muñoz Maniega S, Quinn TJ, Wardlaw JM. Imaging Biomarkers of VCI: A Focused Update. Stroke 2024; 55:791-800. [PMID: 38445496 DOI: 10.1161/strokeaha.123.044171] [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: 03/07/2024]
Abstract
Vascular cognitive impairment is common after stroke, in memory clinics, medicine for the elderly services, and undiagnosed in the community. Vascular disease is said to be the second most common cause of dementia after Alzheimer disease, yet vascular dysfunction is now known to predate cognitive decline in Alzheimer disease, and most dementias at older ages are mixed. Neuroimaging has a major role in identifying the proportion of vascular versus other likely pathologies in patients with cognitive impairment. Here, we aim to provide a pragmatic but evidence-based summary of the current state of potential imaging biomarkers, focusing on magnetic resonance imaging and computed tomography, which are relevant to diagnosing, estimating prognosis, monitoring vascular cognitive impairment, and incorporating our own experiences. We focus on markers that are well-established, with a known profile of association with cognitive measures, but also consider more recently described, including quantitative tissue markers of vascular injury. We highlight the gaps in accessibility and translation to more routine clinical practice.
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Affiliation(s)
- Una Clancy
- Centre for Clinical Brain Sciences and UK Dementia Research Institute, The University of Edinburgh, United Kingdom (U.C., M.d.C.V.H. A.C.C.J., S.M.M., J.M.W.)
| | - Angelina K Kancheva
- School of Cardiovascular and Metabolic Health, University of Glasgow, United Kingdom (A.K.K., T.J.Q.)
| | - Maria Del C Valdés Hernández
- Centre for Clinical Brain Sciences and UK Dementia Research Institute, The University of Edinburgh, United Kingdom (U.C., M.d.C.V.H. A.C.C.J., S.M.M., J.M.W.)
| | - Angela C C Jochems
- Centre for Clinical Brain Sciences and UK Dementia Research Institute, The University of Edinburgh, United Kingdom (U.C., M.d.C.V.H. A.C.C.J., S.M.M., J.M.W.)
| | - Susana Muñoz Maniega
- Centre for Clinical Brain Sciences and UK Dementia Research Institute, The University of Edinburgh, United Kingdom (U.C., M.d.C.V.H. A.C.C.J., S.M.M., J.M.W.)
| | - Terence J Quinn
- School of Cardiovascular and Metabolic Health, University of Glasgow, United Kingdom (A.K.K., T.J.Q.)
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences and UK Dementia Research Institute, The University of Edinburgh, United Kingdom (U.C., M.d.C.V.H. A.C.C.J., S.M.M., J.M.W.)
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Wei HL, Wei C, Yu YS, Yu X, Chen Y, Li J, Zhang H, Chen X. Dysfunction of the triple-network model is associated with cognitive impairment in patients with cerebral small vessel disease. Heliyon 2024; 10:e24701. [PMID: 38298689 PMCID: PMC10828708 DOI: 10.1016/j.heliyon.2024.e24701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 11/29/2023] [Accepted: 01/12/2024] [Indexed: 02/02/2024] Open
Abstract
Purpose This study aimed to demonstrate the correlations between the altered functional connectivity patterns in the triple-network model and cognitive impairment in patients with cerebral small vascular disease (CSVD). Methods Resting-state functional magnetic resonance imaging data were obtained from 22 patients with CSVD and 20 healthy controls. The resting-state data were analyzed using independent component analysis and functional network connectivity (FNC) analysis to explore the functional alterations in the intrinsic triple-network model including the salience network (SN), default mode network (DMN), and central executive network (CEN), and their correlations with the cognitive deficits and clinical observations in the patients with CSVD. Results Compared to the healthy controls, the patients with CSVD exhibited increased connectivity patterns in the CEN-DMN and decreased connectivity patterns in the DMN-SN, CEN-SN, intra-SN, and intra-DMN. Significant negative correlations were detected between the intra-DMN connectivity pattern and the Montreal Cognitive Assessment (MoCA) total scores (r = -0.460, p = 0.048) and MoCA abstraction scores (r = -0.565, p = 0.012), and a positive correlation was determined between the intra-SN connectivity pattern and the MoCA abstraction scores (r = 0.491, p = 0.033). Conclusions Our study findings suggest that the functional alterations in the triple-network model are associated with the cognitive deficits in patients with CSVD and shed light on the importance of the triple-network model in the pathogenesis of CSVD.
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Affiliation(s)
- Heng-Le Wei
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Cunsheng Wei
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Yu-Sheng Yu
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Xiaorong Yu
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Yuan Chen
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Junrong Li
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Hong Zhang
- Department of Radiology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
| | - Xuemei Chen
- Department of Neurology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing 211100, Jiangsu, PR China
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Huang P, Chen K, Liu C, Zhen Z, Zhang R. Visualizing Cerebral Small Vessel Degeneration During Aging and Diseases Using Magnetic Resonance Imaging. J Magn Reson Imaging 2023; 58:1323-1337. [PMID: 37052571 DOI: 10.1002/jmri.28736] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 03/28/2023] [Accepted: 03/30/2023] [Indexed: 04/14/2023] Open
Abstract
Cerebral small vessel disease is a major contributor to brain disorders in older adults. It is associated with a much higher risk of stroke and dementia. Due to a lack of clinical and fluid biomarkers, diagnosing and grading small vessel disease are highly dependent on magnetic resonance imaging. In the past, researchers mostly used brain parenchymal imaging markers to represent small vessel damage, but the relationships between these surrogate markers and small vessel pathologies are complex. Recent progress in high-resolution magnetic resonance imaging methods, including time-of-flight MR angiography, phase-contrast MR angiography, black blood vessel wall imaging, susceptibility-weighted imaging, and contrast-enhanced methods, allow for direct visualization of cerebral small vessel structures. They could be powerful tools for understanding aging-related small vessel degeneration and improving disease diagnosis and treatment. This article will review progress in these imaging techniques and their application in aging and disease studies. Some challenges and future directions are also discussed. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: 3.
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Affiliation(s)
- Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Kang Chen
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Zhiming Zhen
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Ruiting Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Wang X, Wang Y, Gao D, Zhao Z, Wang H, Wang S, Liu S. Characterizing the penumbras of white matter hyperintensities in patients with cerebral small vessel disease. Jpn J Radiol 2023; 41:928-937. [PMID: 37160589 PMCID: PMC10468925 DOI: 10.1007/s11604-023-01419-w] [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: 11/10/2022] [Accepted: 03/24/2023] [Indexed: 05/11/2023]
Abstract
PURPOSE The white matter hyperintensity penumbra (WMH-P) is the subtly changed normal-appearing white matter (NAWM) that surrounds white matter hyperintensities (WMHs). The goal of this study was to define WMH-P in cerebral small vessel disease (CSVD) by arterial spin labeling (ASL) and diffusion tensor imaging (DTI)/diffusion kurtosis imaging (DKI). MATERIALS AND METHODS We prospectively analyzed 42 patients with CSVD. To determine the range of cerebral blood flow (CBF) and DTI/DKI penumbras around white matter hyperintensities, we generated NAWM layer masks from periventricular WMHs (PVWMHs) and deep WMHs (DWMHs). Mean values of CBF, fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity, mean kurtosis, axial kurtosis, and radial kurtosis within the WMHs and their corresponding NAWM layer masks were analyzed. Paired sample t tests were used for analysis, and differences were considered statistically significant if the associated p value was ≤ 0.05. RESULTS For DWMHs, the CBF penumbras were 13 mm, and the DTI/DKI penumbras were 8 mm. For PVWMHs, the CBF penumbras were 14 mm, and the DTI/DKI penumbras were 14 mm. CONCLUSIONS Our findings revealed that DTI/DKI and ASL can show structural and blood flow changes in brain tissue surrounding WMHs. In DWMHs, the blood flow penumbra was larger than the structural penumbra, while in PVWMHs, the blood flow penumbra was almost the same as the structural penumbra.
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Affiliation(s)
- Xin Wang
- Department of Radiology, Tangshan Gongren Hospital, 27 Wenhua Road, Tangshan City, 063000, Hebei Province, China.
| | - Yu Wang
- Department of Radiology, Tangshan Gongren Hospital, 27 Wenhua Road, Tangshan City, 063000, Hebei Province, China
| | - Deyu Gao
- North China University of Technology, Tangshan City, 063000, Hebei Province, China
| | - Zhichao Zhao
- Department of Radiology, Tangshan Gongren Hospital, 27 Wenhua Road, Tangshan City, 063000, Hebei Province, China
| | - Haiping Wang
- Department of Radiology, Tangshan Gongren Hospital, 27 Wenhua Road, Tangshan City, 063000, Hebei Province, China
| | - Sujie Wang
- Department of Neurology, Tangshan Gongren Hospital, 27 Wenhua Road, Tangshan City, 063000, Hebei Province, China
| | - Shiguang Liu
- Department of Radiology, Tangshan Gongren Hospital, 27 Wenhua Road, Tangshan City, 063000, Hebei Province, China
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Wu B, Guo S, Jia X, Geng Z, Yang Q. White Matter Microstructural Alterations over the Year after Acute Ischemic Stroke in Patients with Baseline Impaired Cognitive Functions. Neural Plast 2023; 2023:6762225. [PMID: 37456365 PMCID: PMC10348854 DOI: 10.1155/2023/6762225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 05/14/2023] [Accepted: 06/13/2023] [Indexed: 07/18/2023] Open
Abstract
Background The disruption of white matter (WM) integrity is related to poststroke cognitive impairment (PSCI). The exploration of WM integrity alterations in the chronic stage of acute ischemic stroke (AIS) may help to improve the long-term outcomes of PSCI. Methods Sixty patients showing impaired cognitive functions within 3 days after AIS (baseline) and 25 healthy controls underwent diffusion kurtosis imaging scan and cognitive assessment at baseline and 1 year. Based on the tract-based spatial statistics (TBSS), kurtosis fractional anisotropy (KFA) and mean kurtosis (MK) were compared in WM tracts between the groups. Results One year after AIS, 25 patients were diagnosed with PSCI and 35 patients with non-cognitive impairment (NCI). Compared with baseline, cognitive performance improved in 54 patients and remained unchanged in 6 patients at 1 year. TBSS analysis showed that there were no significant differences in WM tract integrity between the AIS and control groups at baseline (P > 0.05). Compared with the control group, the KFA and MK in multiple WM tracts in the AIS group decreased significantly at 1 year (P < 0.05). Longitudinal analysis showed that the KFA and MK of multiple WM tracts recorded at 1 year were significantly lower than those recorded at baseline in the AIS, PSCI, and NCI groups (P < 0.05), and PSCI group had a faster degeneration than NCI group (P < 0.05). Conclusion The finding suggests that the patients with baseline impaired cognitive functions still have WM microstructural damages at 1 year poststroke, even if their cognitive function has improved or returned to normal. Cautions should be taken against the possible negative impact of these changes on long-term cognition.
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Affiliation(s)
- Bingyuan Wu
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China
| | - Shida Guo
- Department of Radiology, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, Hebei, China
| | - Xiuqin Jia
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China
| | - Zuojun Geng
- Department of Radiology, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, Hebei, China
| | - Qi Yang
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China
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Salvalaggio S, Turolla A, Andò M, Barresi R, Burgio F, Busan P, Cortese AM, D’Imperio D, Danesin L, Ferrazzi G, Maistrello L, Mascotto E, Parrotta I, Pezzetta R, Rigon E, Vedovato A, Zago S, Zorzi M, Arcara G, Mantini D, Filippini N. Prediction of rehabilitation induced motor recovery after stroke using a multi-dimensional and multi-modal approach. Front Aging Neurosci 2023; 15:1205063. [PMID: 37469951 PMCID: PMC10352609 DOI: 10.3389/fnagi.2023.1205063] [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: 04/13/2023] [Accepted: 06/19/2023] [Indexed: 07/21/2023] Open
Abstract
Background Stroke is a debilitating disease affecting millions of people worldwide. Despite the survival rate has significantly increased over the years, many stroke survivors are left with severe impairments impacting their quality of life. Rehabilitation programs have proved to be successful in improving the recovery process. However, a reliable model of sensorimotor recovery and a clear identification of predictive markers of rehabilitation-induced recovery are still needed. This article introduces the cross-modality protocols designed to investigate the rehabilitation treatment's effect in a group of stroke survivors. Methods/design A total of 75 stroke patients, admitted at the IRCCS San Camillo rehabilitation Hospital in Venice (Italy), will be included in this study. Here, we describe the rehabilitation programs, clinical, neuropsychological, and physiological/imaging [including electroencephalography (EEG), transcranial magnetic stimulation (TMS), and magnetic resonance imaging (MRI) techniques] protocols set up for this study. Blood collection for the characterization of predictive biological biomarkers will also be taken. Measures derived from data acquired will be used as candidate predictors of motor recovery. Discussion/summary The integration of cutting-edge physiological and imaging techniques, with clinical and cognitive assessment, dose of rehabilitation and biological variables will provide a unique opportunity to define a predictive model of recovery in stroke patients. Taken together, the data acquired in this project will help to define a model of rehabilitation induced sensorimotor recovery, with the final aim of developing personalized treatments promoting the greatest chance of recovery of the compromised functions.
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Affiliation(s)
- Silvia Salvalaggio
- IRCCS San Camillo Hospital, Venice, Italy
- Padova Neuroscience Center, Università degli Studi di Padova, Padua, Italy
| | - Andrea Turolla
- Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater Studiorum – Università di Bologna, Bologna, Italy
- Unit of Occupational Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | | | | | | | | | - Anna Maria Cortese
- Department of Rehabilitation Medicine, AULSS 3 Serenissima, Venice, Italy
| | | | | | | | | | - Eleonora Mascotto
- Department of Physical Medicine and Rehabilitation, Venice Hospital, Venice, Italy
| | | | | | | | - Anna Vedovato
- General Hospital San Camillo of Treviso, Treviso, Italy
| | - Sara Zago
- IRCCS San Camillo Hospital, Venice, Italy
| | - Marco Zorzi
- IRCCS San Camillo Hospital, Venice, Italy
- Padova Neuroscience Center, Università degli Studi di Padova, Padua, Italy
- Department of General Psychology, University of Padova, Padua, Italy
| | | | - Dante Mantini
- IRCCS San Camillo Hospital, Venice, Italy
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
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Dietrich O, Cai M, Tuladhar AM, Jacob MA, Drenthen GS, Jansen JFA, Marques JP, Topalis J, Ingrisch M, Ricke J, de Leeuw FE, Duering M, Backes WH. Integrated intravoxel incoherent motion tensor and diffusion tensor brain MRI in a single fast acquisition. NMR IN BIOMEDICINE 2023; 36:e4905. [PMID: 36637237 DOI: 10.1002/nbm.4905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 12/21/2022] [Accepted: 01/11/2023] [Indexed: 06/15/2023]
Abstract
The acquisition of intravoxel incoherent motion (IVIM) data and diffusion tensor imaging (DTI) data from the brain can be integrated into a single measurement, which offers the possibility to determine orientation-dependent (tensorial) perfusion parameters in addition to established IVIM and DTI parameters. The purpose of this study was to evaluate the feasibility of such a protocol with a clinically feasible scan time below 6 min and to use a model-selection approach to find a set of DTI and IVIM tensor parameters that most adequately describes the acquired data. Diffusion-weighted images of the brain were acquired at 3 T in 20 elderly participants with cerebral small vessel disease using a multiband echoplanar imaging sequence with 15 b-values between 0 and 1000 s/mm2 and six non-collinear diffusion gradient directions for each b-value. Seven different IVIM-diffusion models with 4 to 14 parameters were implemented, which modeled diffusion and pseudo-diffusion as scalar or tensor quantities. The models were compared with respect to their fitting performance based on the goodness of fit (sum of squared fit residuals, chi2 ) and their Akaike weights (calculated from the corrected Akaike information criterion). Lowest chi2 values were found using the model with the largest number of model parameters. However, significantly highest Akaike weights indicating the most appropriate models for the acquired data were found with a nine-parameter IVIM-DTI model (with isotropic perfusion modeling) in normal-appearing white matter (NAWM), and with an 11-parameter model (IVIM-DTI with additional pseudo-diffusion anisotropy) in white matter with hyperintensities (WMH) and in gray matter (GM). The latter model allowed for the additional calculation of the fractional anisotropy of the pseudo-diffusion tensor (with a median value of 0.45 in NAWM, 0.23 in WMH, and 0.36 in GM), which is not accessible with the usually performed IVIM acquisitions based on three orthogonal diffusion-gradient directions.
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Affiliation(s)
- Olaf Dietrich
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Mengfei Cai
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Anil Man Tuladhar
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mina A Jacob
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Gerald S Drenthen
- Schools for Mental Health and Neuroscience (MHeNs) and Cardiovascular Diseases (CARIM), Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Jacobus F A Jansen
- Schools for Mental Health and Neuroscience (MHeNs) and Cardiovascular Diseases (CARIM), Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - José P Marques
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Johanna Topalis
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Michael Ingrisch
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Center for Medical Neurosciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marco Duering
- Medical Image Analysis Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Walter H Backes
- Schools for Mental Health and Neuroscience (MHeNs) and Cardiovascular Diseases (CARIM), Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
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11
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Schwarz G, Kanber B, Prados F, Browning S, Simister R, Jäger HR, Ambler G, Gandini Wheeler-Kingshott CAM, Werring DJ. Whole-brain diffusion tensor imaging predicts 6-month functional outcome in acute intracerebral haemorrhage. J Neurol 2023; 270:2640-2648. [PMID: 36806785 PMCID: PMC10129992 DOI: 10.1007/s00415-023-11592-7] [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: 10/22/2022] [Revised: 01/25/2023] [Accepted: 01/27/2023] [Indexed: 02/23/2023]
Abstract
INTRODUCTION Small vessel disease (SVD) causes most spontaneous intracerebral haemorrhage (ICH) and is associated with widespread microstructural brain tissue disruption, which can be quantified via diffusion tensor imaging (DTI) metrics: mean diffusivity (MD) and fractional anisotropy (FA). Little is known about the impact of whole-brain microstructural alterations after SVD-related ICH. We aimed to investigate: (1) association between whole-brain DTI metrics and functional outcome after ICH; and (2) predictive ability of these metrics compared to the pre-existing ICH score. METHODS Sixty-eight patients (38.2% lobar) were retrospectively included. We assessed whole-brain DTI metrics (obtained within 5 days after ICH) in cortical and deep grey matter and white matter. We used univariable logistic regression to assess the associations between DTI and clinical-radiological variables and poor outcome (modified Rankin Scale > 2). We determined the optimal predictive variables (via LASSO estimation) in: model 1 (DTI variables only), model 2 (DTI plus non-DTI variables), model 3 (DTI plus ICH score). Optimism-adjusted C-statistics were calculated for each model and compared (likelihood ratio test) against the ICH score. RESULTS Deep grey matter MD (OR 1.04 [95% CI 1.01-1.07], p = 0.010) and white matter MD (OR 1.11 [95% CI 1.01-1.23], p = 0.044) were associated (univariate analysis) with poor outcome. Discrimination values for model 1 (0.67 [95% CI 0.52-0.83]), model 2 (0.71 [95% CI 0.57-0.85) and model 3 (0.66 [95% CI 0.52-0.82]) were all significantly higher than the ICH score (0.62 [95% CI 0.49-0.75]). CONCLUSION Our exploratory study suggests that whole-brain microstructural disruption measured by DTI is associated with poor 6-month functional outcome after SVD-related ICH. Whole-brain DTI metrics performed better at predicting recovery than the existing ICH score.
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Affiliation(s)
- G Schwarz
- Neurologia-Stroke Unit ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
- Stroke Research Centre, Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, University College London, and National Hospital for Neurology and Neurosurgery, London, UK
| | - B Kanber
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, UCL, London, UK
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, UCL, London, UK
- National Institute for Health Research, University College London Hospitals, Biomedical Research Centre, London, UK
| | - F Prados
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, UCL, London, UK
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, UCL, London, UK
- National Institute for Health Research, University College London Hospitals, Biomedical Research Centre, London, UK
- E-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - S Browning
- Stroke Research Centre, Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, University College London, and National Hospital for Neurology and Neurosurgery, London, UK
| | - R Simister
- Stroke Research Centre, Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, University College London, and National Hospital for Neurology and Neurosurgery, London, UK
| | - H R Jäger
- Lysholm Department of Neuroradiology and the Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London, UK
| | - G Ambler
- Department of Statistical Science, University College London, Gower Street, London, UK
| | - C A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, UCL, London, UK
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - D J Werring
- Stroke Research Centre, Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, University College London, and National Hospital for Neurology and Neurosurgery, London, UK.
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12
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Christiaanse E, Wyss PO, Scheel‐Sailer A, Frotzler A, Lehnick D, Verma RK, Berger MF, Leemans A, De Luca A. Mean kurtosis-Curve (MK-Curve) correction improves the test-retest reproducibility of diffusion kurtosis imaging at 3 T. NMR IN BIOMEDICINE 2023; 36:e4856. [PMID: 36285630 PMCID: PMC10078439 DOI: 10.1002/nbm.4856] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 09/25/2022] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
Diffusion kurtosis imaging (DKI) is applied to gain insights into the microstructural organization of brain tissues. However, the reproducibility of DKI outside brain white matter, particularly in combination with advanced estimation to remedy its noise sensitivity, remains poorly characterized. Therefore, in this study, we investigated the variability and reliability of DKI metrics while correcting implausible values with a fit method called mean kurtosis (MK)-Curve. A total of 10 volunteers (four women; age: 41.4 ± 9.6 years) were included and underwent two MRI examinations of the brain. The images were acquired on a clinical 3-T scanner and included a T1-weighted image and a diffusion sequence with multiple diffusion weightings suitable for DKI. Region of interest analysis of common kurtosis and tensor metrics derived with the MK-Curve DKI fit was performed, including intraclass correlation (ICC) and Bland-Altman (BA) plot statistics. A p value of less than 0.05 was considered statistically significant. The analyses showed good to excellent agreement of both kurtosis tensor- and diffusion tensor-derived MK-Curve-corrected metrics (ICC values: 0.77-0.98 and 0.87-0.98, respectively), with the exception of two DKI-derived metrics (axial kurtosis in the cortex: ICC = 0.68, and radial kurtosis in deep gray matter: ICC = 0.544). Non-MK-Curve-corrected kurtosis tensor-derived metrics ranged from 0.01 to 0.52 and diffusion tensor-derived metrics from 0.06 to 0.66, indicating poor to moderate reliability. No structural bias was observed in the BA plots for any of the diffusion metrics. In conclusion, MK-Curve-corrected DKI metrics of the human brain can be reliably acquired in white and gray matter at 3 T and DKI metrics have good to excellent agreement in a test-retest setting.
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Affiliation(s)
- Ernst Christiaanse
- Department of RadiologySwiss Paraplegic CentreNottwilSwitzerland
- Image Sciences Institute, Division Imaging & OncologyUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Patrik O. Wyss
- Department of RadiologySwiss Paraplegic CentreNottwilSwitzerland
| | - Anke Scheel‐Sailer
- Rehabilitation and Quality ManagementSwiss Paraplegic CentreNottwilSwitzerland
- Department of Health Sciences and MedicineUniversity of LucerneLucerneSwitzerland
| | - Angela Frotzler
- Clinical Trial UnitSwiss Paraplegic CentreNottwilSwitzerland
| | - Dirk Lehnick
- Department of Health Sciences and Medicine, Biostatistics and MethodologyUniversity LucerneLucerneSwitzerland
| | - Rajeev K. Verma
- Department of RadiologySwiss Paraplegic CentreNottwilSwitzerland
| | - Markus F. Berger
- Department of RadiologySwiss Paraplegic CentreNottwilSwitzerland
| | - Alexander Leemans
- Image Sciences Institute, Division Imaging & OncologyUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Alberto De Luca
- Image Sciences Institute, Division Imaging & OncologyUniversity Medical Center UtrechtUtrechtthe Netherlands
- Neurology Department, UMC Utrecht Brain CenterUniversity Medical Center UtrechtUtrechtthe Netherlands
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13
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Zanon Zotin MC, Yilmaz P, Sveikata L, Schoemaker D, van Veluw SJ, Etherton MR, Charidimou A, Greenberg SM, Duering M, Viswanathan A. Peak Width of Skeletonized Mean Diffusivity: A Neuroimaging Marker for White Matter Injury. Radiology 2023; 306:e212780. [PMID: 36692402 PMCID: PMC9968775 DOI: 10.1148/radiol.212780] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 10/01/2022] [Accepted: 10/14/2022] [Indexed: 01/25/2023]
Abstract
A leading cause of white matter (WM) injury in older individuals is cerebral small vessel disease (SVD). Cerebral SVD is the most prevalent vascular contributor to cognitive impairment and dementia. Therapeutic progress for cerebral SVD and other WM disorders depends on the development and validation of neuroimaging markers suitable as outcome measures in future interventional trials. Diffusion-tensor imaging (DTI) is one of the best-suited MRI techniques for assessing the extent of WM damage in the brain. But the optimal method to analyze individual DTI data remains hindered by labor-intensive and time-consuming processes. Peak width of skeletonized mean diffusivity (PSMD), a recently developed fast, fully automated DTI marker, was designed to quantify the WM damage secondary to cerebral SVD and reflect related cognitive impairment. Despite its promising results, knowledge about PSMD is still limited in the radiologic community. This focused review provides an overview of the technical details of PSMD while synthesizing the available data on its clinical and neuroimaging associations. From a critical expert viewpoint, the authors discuss the limitations of PSMD and its current validation status as a neuroimaging marker for vascular cognitive impairment. Finally, they point out the gaps to be addressed to further advance the field.
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Affiliation(s)
| | | | - Lukas Sveikata
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Dorothee Schoemaker
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Susanne J. van Veluw
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Mark R. Etherton
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Andreas Charidimou
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Steven M. Greenberg
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Marco Duering
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Anand Viswanathan
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
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14
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Dobrynina LA, Kremneva EI, Shamtieva KV, Geints AA, Filatov AS, Trubitsyna VV, Bitsieva ET, Byrochkina AA, Akhmetshina YI, Maksimov II, Krotenkova MV. [Disruption of corpus callosum microstructural integrity by diffusion MRI as a predictor of progression of cerebral microangiopathy]. Zh Nevrol Psikhiatr Im S S Korsakova 2023; 123:95-104. [PMID: 37994894 DOI: 10.17116/jnevro202312311195] [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: 11/24/2023]
Abstract
OBJECTIVE To assess the microstructural integrity of the corpus callosum in patients with cerebral small vessel disease (cSVD) using signal and biophysical diffusion MRI models and to identify the most sensitive markers of disease progression. MATERIAL AND METHODS Diffusion MRI (3 Tesla) was performed in 166 patients (51.8% women; mean age 60.4±7.6) with cSVD and cognitive impairment of varying severity and in 44 healthy volunteers (65.9% women; mean age 59.6±6.8), followed by calculation of signal (diffusion tensor and diffusion kurtosis) and biophysical (WMTI, NODDI, MC-SMT) models, from which profiles of three corpus callosum segments were constructed. RESULTS The best results were obtained for metrics in the forceps minor and body of the corpus callosum. Among the metrics of the signal models in the forceps minor, fraction anisotropy (FA) and mean diffusion (MD), which characterize the overall loss of microstructural integrity and increase in extra-axonal water, as well as indirect markers of demyelination when considering transverse diffusion parameters (radial diffusion and radial kurtosis), had the larger area under the curve according to the ROC analysis. Among the metrics of the biophysical models in the forceps minor, a larger area under the curve was found in the MC-SMT model for extra-axonal transverse diffusion (ETR), mean diffusion (EMD), and intra-axonal water fraction (INTRA), and in the WMTI model for intra-axonal water fraction (AWF). ETR had high inverse correlations with INTRA and AWF, while INTRA and AWF had high direct intercorrelations. CONCLUSION Metrics of signaling (FA, MD, RD, RK) and biophysical patterns (ETR, EMD, INTRA, AWF) in the forceps minor and the corpus callosum body can be considered as indicators of cSVD progression. They indicate disease progression, mainly by an increase in extra-axonal water with the development of demyelination and tissue degeneration in the corpus callosum.
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Affiliation(s)
| | | | | | - A A Geints
- Lomonosov Moscow State University, Moscow, Russia
| | - A S Filatov
- Research Center of Neurology, Moscow, Russia
| | | | | | | | | | - I I Maksimov
- West Norwegian University of Applied Sciences (HVL), Bergen, Norway
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15
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Abstract
Cerebral small vessel disease (cSVD) is a major cause of stroke and dementia. This review summarizes recent developments in advanced neuroimaging of cSVD with a focus on clinical and research applications. In the first section, we highlight how advanced structural imaging techniques, including diffusion magnetic resonance imaging (MRI), enable improved detection of tissue damage, including characterization of tissue appearing normal on conventional MRI. These techniques enable progression to be monitored and may be useful as surrogate endpoint in clinical trials. Quantitative MRI, including iron and myelin imaging, provides insights into tissue composition on the molecular level. In the second section, we cover how advanced MRI techniques can demonstrate functional or dynamic abnormalities of the blood vessels, which could be targeted in mechanistic research and early-stage intervention trials. Such techniques include the use of dynamic contrast enhanced MRI to measure blood-brain barrier permeability, and MRI methods to assess cerebrovascular reactivity. In the third section, we discuss how the increased spatial resolution provided by ultrahigh field MRI at 7 T allows imaging of perforating arteries, and flow velocity and pulsatility within them. The advanced MRI techniques we describe are providing novel pathophysiological insights in cSVD and allow improved quantification of disease burden and progression. They have application in clinical trials, both in assessing novel therapeutic mechanisms, and as a sensitive endpoint to assess efficacy of interventions on parenchymal tissue damage. We also discuss challenges of these advanced techniques and suggest future directions for research.
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Affiliation(s)
- Hilde van den Brink
- Department of Neurology and
Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University,
Utrecht, The Netherlands
| | - Fergus N Doubal
- Centre for Clinical Brain Sciences, UK
Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Marco Duering
- Medical Image Analysis Center (MIAC AG)
and qbig, Department of Biomedical Engineering, University of Basel, Basel,
Switzerland,Marco Duering, Medical Image Analysis
Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of
Basel, Marktgasse 8, Basel, CH-4051, Switzerland.
; @MarcoDuering
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Maillard P, Hillmer LJ, Lu H, Arfanakis K, Gold BT, Bauer CE, Kramer JH, Staffaroni AM, Stables L, Wang DJ, Seshadri S, Satizabal CL, Beiser A, Habes M, Fornage M, Mosley TH, Rosenberg GA, Singh B, Singh H, Schwab K, Helmer KG, Greenberg SM, DeCarli C, Caprihan A. MRI free water as a biomarker for cognitive performance: Validation in the MarkVCID consortium. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12362. [PMID: 36523847 PMCID: PMC9745638 DOI: 10.1002/dad2.12362] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/11/2022] [Accepted: 08/29/2022] [Indexed: 12/15/2022]
Abstract
Introduction To evaluate the clinical validity of free water (FW), a diffusion tensor imaging-based biomarker kit proposed by the MarkVCID consortium, by investigating the association between mean FW (mFW) and executive function. Methods Baseline mFW was related to a baseline composite measure of executive function (EFC), adjusting for relevant covariates, in three MarkVCID sub-cohorts, and replicated in five, large, independent legacy cohorts. In addition, we tested whether baseline mFW predicted accelerated EFC score decline (mean follow-up time: 1.29 years). Results Higher mFW was found to be associated with lower EFC scores in MarkVCID legacy and sub-cohorts (p-values < 0.05). In addition, higher baseline mFW was associated significantly with accelerated decline in EFC scores (p = 0.0026). Discussion mFW is a sensitive biomarker of cognitive decline, providing a strong clinical rational for its use as a marker of white matter (WM) injury in multi-site observational studies and clinical trials of vascular cognitive impairment and dementia (VCID).
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Affiliation(s)
- Pauline Maillard
- Department of NeurologyUniversity of CaliforniaDavisCaliforniaUSA
| | - Laura J. Hillmer
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Hanzhang Lu
- Department of RadiologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Konstantinos Arfanakis
- Department of Biomedical EngineeringIllinois Institute of TechnologyChicagoIllinoisUSA
- Rush Alzheimer's Disease CenterDepartment of Diagnostic Radiology and Nuclear MedicineRush University Medical CenterChicagoIllinoisUSA
| | - Brian T. Gold
- Department of NeuroscienceUniversity of KentuckyLexingtonKentuckyUSA
| | | | - Joel H. Kramer
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Adam M. Staffaroni
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Lara Stables
- Department of NeurologyMemory and Aging CenterWeill Institute for NeurosciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Danny J.J. Wang
- Laboratory of FMRI Technology (LOFT)Stevens Neuroimaging and Informatics InstituteKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Sudha Seshadri
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Claudia L. Satizabal
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
- Department of Population Health SciencesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Alexa Beiser
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- Department of BiostatisticsBoston University School of Public HealthBostonMassachusettsUSA
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular MedicineMcGovern Medical SchoolSchool of Public HealthThe University of Texas Health Science Center at HoustonHoustonTexasUSA
- Human Genetics CenterSchool of Public HealthThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Thomas H. Mosley
- MIND CenterUniversity of Mississippi Medical CenterJacksonMississippiUSA
| | - Gary A. Rosenberg
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Baljeet Singh
- Department of NeurologyUniversity of CaliforniaDavisCaliforniaUSA
| | - Herpreet Singh
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Kristin Schwab
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Karl G. Helmer
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
| | | | - Charles DeCarli
- Department of NeurologyUniversity of CaliforniaDavisCaliforniaUSA
| | - Arvind Caprihan
- The Mind Research NetworkAlbuquerqueNew MexicoAlbuquerqueNew MexicoUSA
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Wang S, Zhang F, Huang P, Hong H, Jiaerken Y, Yu X, Zhang R, Zeng Q, Zhang Y, Kikinis R, Rathi Y, Makris N, Lou M, Pasternak O, Zhang M, O'Donnell LJ. Superficial white matter microstructure affects processing speed in cerebral small vessel disease. Hum Brain Mapp 2022; 43:5310-5325. [PMID: 35822593 PMCID: PMC9812245 DOI: 10.1002/hbm.26004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 06/10/2022] [Accepted: 06/15/2022] [Indexed: 01/15/2023] Open
Abstract
White matter hyperintensities (WMH) are a typical feature of cerebral small vessel disease (CSVD), which contributes to about 50% of dementias worldwide. Microstructural alterations in deep white matter (DWM) have been widely examined in CSVD. However, little is known about abnormalities in superficial white matter (SWM) and their relevance for processing speed, the main cognitive deficit in CSVD. In 141 CSVD patients, processing speed was assessed using Trail Making Test Part A. White matter abnormalities were assessed by WMH burden (volume on T2-FLAIR) and diffusion MRI measures. SWM imaging measures had a large contribution to processing speed, despite a relatively low SWM WMH burden. Across all imaging measures, SWM free water (FW) had the strongest association with processing speed, followed by SWM mean diffusivity (MD). SWM FW was the only marker to significantly increase between two subgroups with the lowest WMH burdens. When comparing two subgroups with the highest WMH burdens, the involvement of WMH in the SWM was accompanied by significant differences in processing speed and white matter microstructure. Mediation analysis revealed that SWM FW fully mediated the association between WMH volume and processing speed, while no mediation effect of MD or DWM FW was observed. Overall, results suggest that the SWM has an important contribution to processing speed, while SWM FW is a sensitive imaging marker associated with cognition in CSVD. This study extends the current understanding of CSVD-related dysfunction and suggests that the SWM, as an understudied region, can be a potential target for monitoring pathophysiological processes.
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Affiliation(s)
- Shuyue Wang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Fan Zhang
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Peiyu Huang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Hui Hong
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Yeerfan Jiaerken
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Xinfeng Yu
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ruiting Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Qingze Zeng
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Yao Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ron Kikinis
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Nikos Makris
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Center for Morphometric AnalysisMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Min Lou
- Department of Neurologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ofer Pasternak
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Minming Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
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Markus HS, van Der Flier WM, Smith EE, Bath P, Biessels GJ, Briceno E, Brodtman A, Chabriat H, Chen C, de Leeuw FE, Egle M, Ganesh A, Georgakis MK, Gottesman RF, Kwon S, Launer L, Mok V, O'Brien J, Ottenhoff L, Pendlebury S, Richard E, Sachdev P, Schmidt R, Springer M, Tiedt S, Wardlaw JM, Verdelho A, Webb A, Werring D, Duering M, Levine D, Dichgans M. Framework for Clinical Trials in Cerebral Small Vessel Disease (FINESSE): A Review. JAMA Neurol 2022; 79:1187-1198. [PMID: 35969390 PMCID: PMC11036410 DOI: 10.1001/jamaneurol.2022.2262] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance Cerebral small vessel disease (SVD) causes a quarter of strokes and is the most common pathology underlying vascular cognitive impairment and dementia. An important step to developing new treatments is better trial methodology. Disease mechanisms in SVD differ from other stroke etiologies; therefore, treatments need to be evaluated in cohorts in which SVD has been well characterized. Furthermore, SVD itself can be caused by a number of different pathologies, the most common of which are arteriosclerosis and cerebral amyloid angiopathy. To date, there have been few sufficiently powered high-quality randomized clinical trials in SVD, and inconsistent trial methodology has made interpretation of some findings difficult. Observations To address these issues and develop guidelines for optimizing design of clinical trials in SVD, the Framework for Clinical Trials in Cerebral Small Vessel Disease (FINESSE) was created under the auspices of the International Society of Vascular Behavioral and Cognitive Disorders. Experts in relevant aspects of SVD trial methodology were convened, and a structured Delphi consensus process was used to develop recommendations. Areas in which recommendations were developed included optimal choice of study populations, choice of clinical end points, use of brain imaging as a surrogate outcome measure, use of circulating biomarkers for participant selection and as surrogate markers, novel trial designs, and prioritization of therapeutic agents using genetic data via Mendelian randomization. Conclusions and Relevance The FINESSE provides recommendations for trial design in SVD for which there are currently few effective treatments. However, new insights into understanding disease pathogenesis, particularly from recent genetic studies, provide novel pathways that could be therapeutically targeted. In addition, whether other currently available cardiovascular interventions are specifically effective in SVD, as opposed to other subtypes of stroke, remains uncertain. FINESSE provides a framework for design of trials examining such therapeutic approaches.
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Affiliation(s)
- Hugh S Markus
- Alzheimer Center Amsterdam, Department of Neurology, Epidemiology and Data Science, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Wiesje M van Der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Epidemiology and Data Science, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Eric E Smith
- Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Philip Bath
- Stroke Trials Unit, Mental Health & Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom
| | - Geert Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Emily Briceno
- Department of Physical Medicine & Rehabilitation, University of Michigan Medical School, Ann Arbor
| | - Amy Brodtman
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- University of Melbourne, Melbourne, Victoria, Australia
- Monash University, Melbourne, Victoria, Australia
| | - Hugues Chabriat
- Department of Neurology, FHU NeuroVasc, APHP, University of Paris, Paris, France
| | - Christopher Chen
- Memory Aging and Cognition Centre, Departments of Pharmacology and Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijimegen, the Netherlands
| | - Marco Egle
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Aravind Ganesh
- Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Marios K Georgakis
- Institute for Stroke and Dementia Research (ISD), LMU University Hospital, Munich, Germany
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Rebecca F Gottesman
- Now with National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, Maryland
- Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Sun Kwon
- University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Lenore Launer
- Intramural Research Program, National Institute on Aging, Baltimore, Maryland
| | - Vincent Mok
- Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, Margaret K.L. Cheung Research Centre for Management of Parkinsonism, Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - John O'Brien
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Lois Ottenhoff
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam and the Netherlands and Brain Research Center Amsterdam, the Netherlands
| | - Sarah Pendlebury
- Wolfson Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, NIHR Oxford Biomedical Research Centre, Departments of General (internal) Medicine and Geratology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Edo Richard
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijimegen, the Netherlands
| | - Perminder Sachdev
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, New South Wales, Australia
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | | | - Stefan Tiedt
- Institute for Stroke and Dementia Research (ISD), LMU University Hospital, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, United Kingdom
| | - Ana Verdelho
- Faculdade de Medicina, Department of Neurosciences and Mental Health, CHULN-Hospital de Santa Maria Instituto de Medicina Molecular (IMM) e Instituto de Saúde Ambiental (ISAMB), University of Lisbon, Lisbon, Portugal
| | - Alastair Webb
- Wolfson Centre for Prevention of Stroke and Dementia, Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - David Werring
- Stroke Research Centre, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology and the National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), LMU University Hospital, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical Imaging Group, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Deborah Levine
- Departments of Internal Medicine and Neurology, University of Michigan, Ann Arbor
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), LMU University Hospital, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
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Fouto AR, Nunes RG, Pinto J, Alves L, Calado S, Gonçalves C, Rebolo M, Viana-Baptista M, Vilela P, Figueiredo P. Impact of white-matter mask selection on DTI histogram-based metrics as potential biomarkers in cerebral small vessel disease. MAGMA (NEW YORK, N.Y.) 2022; 35:779-790. [PMID: 34997895 DOI: 10.1007/s10334-021-00991-4] [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: 07/09/2021] [Revised: 12/09/2021] [Accepted: 12/11/2021] [Indexed: 06/14/2023]
Abstract
OBJECTIVE Histogram-based metrics extracted from diffusion-tensor imaging (DTI) have been suggested as potential biomarkers for cerebral small vessel disease (SVD), but methods and results have varied across studies. This work aims to assess the impact of mask selection for extracting histogram-based metrics of fractional anisotropy (FA) and mean diffusivity (MD) on their sensitivity as SVD biomarkers. METHODS DTI data were collected from 17 SVD patients and 12 healthy controls. FA and MD maps were estimated; from these, histograms were computed on two whole-brain white-matter masks: normal-appearing white-matter (NAWM) and mean FA tract skeleton (TBSS). Histogram-based metrics (median, peak height, peak width, peak value) were extracted from the FA and MD maps. These were compared between groups and correlated with the patients' cognitive scores (executive function and processing speed). RESULTS White-matter mask selection significantly impacted FA and MD histogram metrics. In particular, significant interactions were found between Mask and Group for FA peak height (p = 0.027), MD Median (p = 0.035) and MD peak width (p = 0.047); indicating that the mask used affected their ability to discriminate between groups. In fact, MD peak width showed a significant 8.8% increase in patients when using TBSS (p = 0.037), but not when using NAWM (p = 0.69). Moreover, the mask may have an effect on the correlations with cognitive measures. Nevertheless, MD peak width (TBSS: r = - 0.75, NAWM: r = - 0.71) and MD peak height (TBSS: r = 0.65, NAWM: r = 0.62) remained significantly correlated with executive function, regardless of the mask. CONCLUSION The impact of the processing methodology, in particular the choice of white-matter mask, highlights the need for standardized MRI data-processing pipelines.
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Affiliation(s)
- Ana R Fouto
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal.
| | - Rita G Nunes
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal
| | - Joana Pinto
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Luísa Alves
- Neurology Department, Hospital Egas Moniz, Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal
- CEDOC - NOVA Medical School, NOVA University of Lisbon, Lisbon, Portugal
| | - Sofia Calado
- Neurology Department, Hospital Egas Moniz, Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal
- CEDOC - NOVA Medical School, NOVA University of Lisbon, Lisbon, Portugal
| | - Carina Gonçalves
- Neurology Department, Hospital Egas Moniz, Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal
- CEDOC - NOVA Medical School, NOVA University of Lisbon, Lisbon, Portugal
| | | | - Miguel Viana-Baptista
- Neurology Department, Hospital Egas Moniz, Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal
- CEDOC - NOVA Medical School, NOVA University of Lisbon, Lisbon, Portugal
| | - Pedro Vilela
- Imaging Department, Hospital da Luz, Lisbon, Portugal
| | - Patrícia Figueiredo
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal
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Sun Y, Hu Y, Qiu Y, Zhang Y, Jiang C, Lu P, Xu Q, Shi Y, Wei H, Zhou Y. Characterization of white matter over 1–2 years in small vessel disease using MR-based quantitative susceptibility mapping and free-water mapping. Front Aging Neurosci 2022; 14:998051. [PMID: 36247993 PMCID: PMC9562046 DOI: 10.3389/fnagi.2022.998051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/07/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeThe aim of this study was to investigate alterations in white matter lesions (WMLs) and normal-appearing white matter (NAWM) with small vessel disease (SVD) over 1–2 years using quantitative susceptibility mapping (QSM) and free-water (FW) mapping.MethodsFifty-one SVD patients underwent MRI brain scans and neuropsychological testing both at baseline and follow-up. The main approach for treating these patients is the management of risk factors. Quantitative susceptibility (QS), fractional anisotropy (FA), mean diffusivity (MD), FW, FW-corrected FA (FAT), and FW-corrected MD (MDT) maps within WMLs and NAWM were generated. Furthermore, the JHU-ICBM-DTI label atlas was used as an anatomic guide, and the measurements of the segmented NAWMs were calculated. The average regional values were extracted, and a paired t-test was used to analyze the longitudinal change. Partial correlations were used to assess the relationship between the MRI indices changes (e.g., ΔQSfollowup − baseline/QSbaseline) and the cognitive function changes (e.g., ΔMoCAfollowup − baseline/MoCAbaseline).ResultsAfter SVD risk factor control, no gradual cognitive decline occurred during 1–2 years. However, we still found that the QS values (index of demyelination) increased in the NAWM at follow-up, especially in the NAWM part of the left superior frontal blade (SF), left occipital blade, right uncinate fasciculus, and right corticospinal tract (CST). FW (index of neuroinflammation/edema) analysis revealed that the follow-up group differed from the baseline group in the NAWM part of the right CST and inferior frontal blade (IF). Decreased FAT (index of axonal loss) was observed in the NAWM part of the right SF and IF at follow-up. In addition, the FAT changes in the NAWM part of the right IF were associated with overall cognitive performance changes. In contrast, no significant differences were found in the WMLs.ConclusionThe NAWM was still in the progressive injury process over time, while WMLs remained relatively stable, which supports the notion that SVD is a chronic progressive disease. The process of axonal loss in the NAWM part of the prefrontal lobe might be a biomarker of cognitive changes in the evolution of SVD.
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Affiliation(s)
- Yawen Sun
- Department of Radiology, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Ying Hu
- Department of Radiology, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yage Qiu
- Department of Radiology, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yuyao Zhang
- School of Information and Science and Technology, ShanghaiTech University, Shanghai, China
| | - Changhao Jiang
- School of Information and Science and Technology, ShanghaiTech University, Shanghai, China
| | - Peiwen Lu
- Department of Neurology, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
- Ren Ji-UNSW CHeBA Neurocognitive Center, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Qun Xu
- Department of Neurology, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
- Ren Ji-UNSW CHeBA Neurocognitive Center, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
- Department of Health Manage Center, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yuting Shi
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Yan Zhou
| | - Yan Zhou
- Department of Radiology, School of Medicine, Ren Ji Hospital, Shanghai Jiao Tong University, Shanghai, China
- Hongjiang Wei
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Multimodal tract-based MRI metrics outperform whole brain markers in determining cognitive impact of small vessel disease-related brain injury. Brain Struct Funct 2022; 227:2553-2567. [PMID: 35994115 PMCID: PMC9418106 DOI: 10.1007/s00429-022-02546-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/27/2022] [Indexed: 01/04/2023]
Abstract
In cerebral small vessel disease (cSVD), whole brain MRI markers of cSVD-related brain injury explain limited variance to support individualized prediction. Here, we investigate whether considering abnormalities in brain tracts by integrating multimodal metrics from diffusion MRI (dMRI) and structural MRI (sMRI), can better capture cognitive performance in cSVD patients than established approaches based on whole brain markers. We selected 102 patients (73.7 ± 10.2 years old, 59 males) with MRI-visible SVD lesions and both sMRI and dMRI. Conventional linear models using demographics and established whole brain markers were used as benchmark of predicting individual cognitive scores. Multi-modal metrics of 73 major brain tracts were derived from dMRI and sMRI, and used together with established markers as input of a feed-forward artificial neural network (ANN) to predict individual cognitive scores. A feature selection strategy was implemented to reduce the risk of overfitting. Prediction was performed with leave-one-out cross-validation and evaluated with the R2 of the correlation between measured and predicted cognitive scores. Linear models predicted memory and processing speed with R2 = 0.26 and R2 = 0.38, respectively. With ANN, feature selection resulted in 13 tract-specific metrics and 5 whole brain markers for predicting processing speed, and 28 tract-specific metrics and 4 whole brain markers for predicting memory. Leave-one-out ANN prediction with the selected features achieved R2 = 0.49 and R2 = 0.40 for processing speed and memory, respectively. Our results show proof-of-concept that combining tract-specific multimodal MRI metrics can improve the prediction of cognitive performance in cSVD by leveraging tract-specific multi-modal metrics.
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22
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Braun EJ, Billot A, Meier EL, Pan Y, Parrish TB, Kurani AS, Kiran S. White matter microstructural integrity pre- and post-treatment in individuals with chronic post-stroke aphasia. BRAIN AND LANGUAGE 2022; 232:105163. [PMID: 35921727 PMCID: PMC9641951 DOI: 10.1016/j.bandl.2022.105163] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 07/21/2022] [Accepted: 07/23/2022] [Indexed: 06/15/2023]
Abstract
While previous studies have found that white matter damage relates to impairment severity in individuals with aphasia, further study is required to understand the relationship between white matter integrity and treatment response. In this study, 34 individuals with chronic post-stroke aphasia underwent behavioral testing and structural magnetic resonance imaging at two timepoints. Thirty participants within this sample completed typicality-based semantic feature treatment for anomia. Tractography of bi-hemispheric white matter tracts was completed via Automated Fiber Quantification. Associations between microstructural integrity metrics and behavioral measures were evaluated at the tract level and in nodes along the tract. Diffusion measures of the left inferior longitudinal, superior longitudinal, and arcuate fasciculi were related to aphasia severity and diffusion measures of the left inferior longitudinal fasciculus were related to naming and treatment response. This study also found preliminary evidence of left inferior longitudinal fasciculus microstructural changes following treatment.
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Affiliation(s)
- Emily J Braun
- Aphasia Research Laboratory, Department of Speech, Language & Hearing Sciences, College of Health & Rehabilitation Sciences, Sargent College, Boston University, 635 Commonwealth Avenue, Room 326, Boston, MA 02115, USA.
| | - Anne Billot
- Aphasia Research Laboratory, Department of Speech, Language & Hearing Sciences, College of Health & Rehabilitation Sciences, Sargent College, Boston University, 635 Commonwealth Avenue, Room 326, Boston, MA 02115, USA; School of Medicine, Boston University, Boston, MA, USA
| | - Erin L Meier
- Aphasia Research Laboratory, Department of Speech, Language & Hearing Sciences, College of Health & Rehabilitation Sciences, Sargent College, Boston University, 635 Commonwealth Avenue, Room 326, Boston, MA 02115, USA
| | - Yue Pan
- Aphasia Research Laboratory, Department of Speech, Language & Hearing Sciences, College of Health & Rehabilitation Sciences, Sargent College, Boston University, 635 Commonwealth Avenue, Room 326, Boston, MA 02115, USA
| | - Todd B Parrish
- Department of Radiology, Feinberg School of Medicine, Northwestern University, 737 N. Michigan Avenue, Suite 1600, Chicago, IL 60611, USA
| | - Ajay S Kurani
- Department of Neurology, Feinberg School of Medicine, Northwestern University, 625 N. Michigan Avenue, Suite 1150, Chicago, IL 60611, USA
| | - Swathi Kiran
- Aphasia Research Laboratory, Department of Speech, Language & Hearing Sciences, College of Health & Rehabilitation Sciences, Sargent College, Boston University, 635 Commonwealth Avenue, Room 326, Boston, MA 02115, USA
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da Silva PHR, Paschoal AM, Secchinatto KF, Zotin MCZ, Dos Santos AC, Viswanathan A, Pontes-Neto OM, Leoni RF. Contrast agent-free state-of-the-art magnetic resonance imaging on cerebral small vessel disease - Part 2: Diffusion tensor imaging and functional magnetic resonance imaging. NMR IN BIOMEDICINE 2022; 35:e4743. [PMID: 35429070 DOI: 10.1002/nbm.4743] [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: 03/17/2022] [Accepted: 04/04/2022] [Indexed: 06/14/2023]
Abstract
Cerebral small vessel disease (cSVD) has been widely studied using conventional magnetic resonance imaging (MRI) methods, although the association between MRI findings and clinical features of cSVD is not always concordant. We assessed the additional contribution of contrast agent-free, state-of-the-art MRI techniques, particularly diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), to understand brain damage and structural and functional connectivity impairment related to cSVD. We performed a review following the PICOS worksheet and Search Strategy, including 152 original papers in English, published from 2000 to 2022. For each MRI method, we extracted information about their contributions regarding the origins, pathology, markers, and clinical outcomes in cSVD. In general, DTI studies have shown that changes in mean, radial, and axial diffusivity measures are related to the presence of cSVD. In addition to the classical deficit in executive functions and processing speed, fMRI studies indicate connectivity dysfunctions in other domains, such as sensorimotor, memory, and attention. Neuroimaging metrics have been correlated with the diagnosis, prognosis, and rehabilitation of patients with cSVD. In short, the application of contrast agent-free, state-of-the-art MRI techniques has provided a complete picture of cSVD markers and tools to explore questions that have not yet been clarified about this clinical condition. Longitudinal studies are desirable to look for causal relationships between image biomarkers and clinical outcomes.
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Affiliation(s)
| | - André Monteiro Paschoal
- Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | | | - Maria Clara Zanon Zotin
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
- J Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Antônio Carlos Dos Santos
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Anand Viswanathan
- J Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Octavio M Pontes-Neto
- Department of Neurosciences and Behavioral Science, Ribeirão Preto Medical School, University of Sao Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Renata Ferranti Leoni
- Department of Physics, FFCLRP, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
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24
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Dewenter A, Jacob MA, Cai M, Gesierich B, Hager P, Kopczak A, Biel D, Ewers M, Tuladhar AM, de Leeuw FE, Dichgans M, Franzmeier N, Duering M. Disentangling the effects of Alzheimer's and small vessel disease on white matter fibre tracts. Brain 2022; 146:678-689. [PMID: 35859352 PMCID: PMC9924910 DOI: 10.1093/brain/awac265] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/30/2022] [Accepted: 06/25/2022] [Indexed: 12/27/2022] Open
Abstract
Alzheimer's disease and cerebral small vessel disease are the two leading causes of cognitive decline and dementia and coexist in most memory clinic patients. White matter damage as assessed by diffusion MRI is a key feature in both Alzheimer's and cerebral small vessel disease. However, disease-specific biomarkers of white matter alterations are missing. Recent advances in diffusion MRI operating on the fixel level (fibre population within a voxel) promise to advance our understanding of disease-related white matter alterations. Fixel-based analysis allows derivation of measures of both white matter microstructure, measured by fibre density, and macrostructure, measured by fibre-bundle cross-section. Here, we evaluated the capacity of these state-of-the-art fixel metrics to disentangle the effects of cerebral small vessel disease and Alzheimer's disease on white matter integrity. We included three independent samples (total n = 387) covering genetically defined cerebral small vessel disease and age-matched controls, the full spectrum of biomarker-confirmed Alzheimer's disease including amyloid- and tau-PET negative controls and a validation sample with presumed mixed pathology. In this cross-sectional analysis, we performed group comparisons between patients and controls and assessed associations between fixel metrics within main white matter tracts and imaging hallmarks of cerebral small vessel disease (white matter hyperintensity volume, lacune and cerebral microbleed count) and Alzheimer's disease (amyloid- and tau-PET), age and a measure of neurodegeneration (brain volume). Our results showed that (i) fibre density was reduced in genetically defined cerebral small vessel disease and strongly associated with cerebral small vessel disease imaging hallmarks; (ii) fibre-bundle cross-section was mainly associated with brain volume; and (iii) both fibre density and fibre-bundle cross-section were reduced in the presence of amyloid, but not further exacerbated by abnormal tau deposition. Fixel metrics were only weakly associated with amyloid- and tau-PET. Taken together, our results in three independent samples suggest that fibre density captures the effect of cerebral small vessel disease, while fibre-bundle cross-section is largely determined by neurodegeneration. The ability of fixel-based imaging markers to capture distinct effects on white matter integrity can propel future applications in the context of precision medicine.
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Affiliation(s)
- Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
| | - Mina A Jacob
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mengfei Cai
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Benno Gesierich
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
- Medical Image Analysis Center (MIAC) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Paul Hager
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
- Institute for AI and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Anna Kopczak
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
| | - Davina Biel
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
- German Center for Neurodegenerative Disease (DZNE), Munich, Germany
| | - Anil M Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
- German Center for Neurodegenerative Disease (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | | | - Marco Duering
- Correspondence to: Marco Duering Medical Image Analysis Center (MIAC AG) Marktgasse 8 CH-4051 Basel Switzerland E-mail:
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25
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De Luca A, Karayumak SC, Leemans A, Rathi Y, Swinnen S, Gooijers J, Clauwaert A, Bahr R, Sandmo SB, Sochen N, Kaufmann D, Muehlmann M, Biessels GJ, Koerte I, Pasternak O. Cross-site harmonization of multi-shell diffusion MRI measures based on rotational invariant spherical harmonics (RISH). Neuroimage 2022; 259:119439. [PMID: 35788044 DOI: 10.1016/j.neuroimage.2022.119439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 06/23/2022] [Accepted: 06/30/2022] [Indexed: 11/25/2022] Open
Abstract
Quantification methods based on the acquisition of diffusion magnetic resonance imaging (dMRI) with multiple diffusion weightings (e.g., multi-shell) are becoming increasingly applied to study the in-vivo brain. Compared to single-shell data for diffusion tensor imaging (DTI), multi-shell data allows to apply more complex models such as diffusion kurtosis imaging (DKI), which attempts to capture both diffusion hindrance and restriction effects, or biophysical models such as NODDI, which attempt to increase specificity by separating biophysical components. Because of the strong dependence of the dMRI signal on the measurement hardware, DKI and NODDI metrics show scanner and site differences, much like other dMRI metrics. These effects limit the implementation of multi-shell approaches in multicenter studies, which are needed to collect large sample sizes for robust analyses. Recently, a post-processing technique based on rotation invariant spherical harmonics (RISH) features was introduced to mitigate cross-scanner differences in DTI metrics. Unlike statistical harmonization methods, which require repeated application to every dMRI metric of choice, RISH harmonization is applied once on the raw data, and can be followed by any analysis. RISH features harmonization has been tested on DTI features but not its generalizability to harmonize multi-shell dMRI. In this work, we investigated whether performing the RISH features harmonization of multi-shell dMRI data removes cross-site differences in DKI and NODDI metrics while retaining longitudinal effects. To this end, 46 subjects underwent a longitudinal (up to 3 time points) two-shell dMRI protocol at 3 imaging sites. DKI and NODDI metrics were derived before and after harmonization and compared both at the whole brain level and at the voxel level. Then, the harmonization effects on cross-sectional and on longitudinal group differences were evaluated. RISH features averaged for each of the 3 sites exhibited prominent between-site differences in the frontal and posterior part of the brain. Statistically significant differences in fractional anisotropy, mean diffusivity and mean kurtosis were observed both at the whole brain and voxel level between all the acquisition sites before harmonization, but not after. The RISH method also proved effective to harmonize NODDI metrics, particularly in white matter. The RISH based harmonization maintained the magnitude and variance of longitudinal changes as compared to the non-harmonized data of all considered metrics. In conclusion, the application of RISH feature based harmonization to multi-shell dMRI data can be used to remove cross-site differences in DKI metrics and NODDI analyses, while retaining inherent relations between longitudinal acquisitions.
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Affiliation(s)
- Alberto De Luca
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands; Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia.
| | | | - Alexander Leemans
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Stephan Swinnen
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium; KU Leuven Brain Institute (LBI), Leuven, Belgium
| | - Jolien Gooijers
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium; KU Leuven Brain Institute (LBI), Leuven, Belgium
| | - Amanda Clauwaert
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium; KU Leuven Brain Institute (LBI), Leuven, Belgium
| | - Roald Bahr
- Oslo Sports Trauma Research Center, Norwegian School of Sport Sciences, Oslo, Norway
| | - Stian Bahr Sandmo
- Oslo Sports Trauma Research Center, Norwegian School of Sport Sciences, Oslo, Norway
| | - Nir Sochen
- Department of Applied Mathematics, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - David Kaufmann
- Radiology Department, Charite University Hospital, Berlin, Germany
| | - Marc Muehlmann
- Department of Radiology, Ludwig-Maximilians-Universität, Munich, Germany
| | - Geert-Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Inga Koerte
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; cBRAIN, Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany
| | - Ofer Pasternak
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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26
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Dewenter A, Gesierich B, Ter Telgte A, Wiegertjes K, Cai M, Jacob MA, Marques JP, Norris DG, Franzmeier N, de Leeuw FE, Tuladhar AM, Duering M. Systematic validation of structural brain networks in cerebral small vessel disease. J Cereb Blood Flow Metab 2022; 42:1020-1032. [PMID: 34929104 PMCID: PMC9125482 DOI: 10.1177/0271678x211069228] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Cerebral small vessel disease (SVD) is considered a disconnection syndrome, which can be quantified using structural brain network analysis obtained from diffusion MRI. Network analysis is a demanding analysis approach and the added benefit over simpler diffusion MRI analysis is largely unexplored in SVD. In this pre-registered study, we assessed the clinical and technical validity of network analysis in two non-overlapping samples of SVD patients from the RUN DMC study (n = 52 for exploration and longitudinal analysis and n = 105 for validation). We compared two connectome pipelines utilizing single-shell or multi-shell diffusion MRI, while also systematically comparing different node and edge definitions. For clinical validation, we assessed the added benefit of network analysis in explaining processing speed and in detecting short-term disease progression. For technical validation, we determined test-retest repeatability.Our findings in clinical validation show that structural brain networks provide only a small added benefit over simpler global white matter diffusion metrics and do not capture short-term disease progression. Test-retest reliability was excellent for most brain networks. Our findings question the added value of brain network analysis in clinical applications in SVD and highlight the utility of simpler diffusion MRI based markers.
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Affiliation(s)
- Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Benno Gesierich
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Annemieke Ter Telgte
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.,VASCage - Research Centre on Vascular Ageing and Stroke, Innsbruck, Austria
| | - Kim Wiegertjes
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mengfei Cai
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mina A Jacob
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - David G Norris
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Anil M Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.,Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.,Medical Image Analysis Center (MIAC) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
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27
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Janssen E, ter Telgte A, Verburgt E, de Jong JJA, Marques JP, Kessels RPC, Backes WH, Maas MC, Meijer FJA, Deinum J, Riksen NP, Tuladhar AM, de Leeuw FE. The Hyperintense study: Assessing the effects of induced blood pressure increase and decrease on MRI markers of cerebral small vessel disease: Study rationale and protocol. Eur Stroke J 2022; 7:331-338. [PMID: 36082259 PMCID: PMC9446329 DOI: 10.1177/23969873221100331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 04/24/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Neuroimaging markers of cerebral small vessel disease (SVD) are common in
older individuals, but the pathophysiological mechanisms causing these
lesions remain poorly understood. Although hypertension is a major risk
factor for SVD, the direct causal effects of increased blood pressure are
unknown. The Hyperintense study is designed to examine cerebrovascular and
structural abnormalities, possibly preceding SVD, in young adults with
hypertension. These patients undergo a diagnostic work-up that requires
patients to temporarily discontinue their antihypertensive agents, often
leading to an increase in blood pressure followed by a decrease once
effective medication is restarted. This allows examination of the effects of
blood pressure increase and decrease on the cerebral small vessels. Methods: Hyperintense is a prospective observational cohort study in 50 hypertensive
adults (18–55 years) who will temporarily discontinue antihypertensive
medication for diagnostic purposes. MRI and clinical data is collected at
four timepoints: before medication withdrawal (baseline), once
antihypertensives are largely or completely withdrawn
(T = 1), when patients have restarted medication
(T = 2) and reached target blood pressure and 1 year
later (T = 3). The 3T MRI protocol includes conventional
structural sequences and advanced techniques to assess various aspects of
microvascular integrity, including blood-brain barrier function using
Dynamic Contrast Enhanced MRI, white matter integrity, and microperfusion.
Clinical assessments include motor and cognitive examinations and blood
sampling. Discussion: The Hyperintense study will improve the understanding of the
pathophysiological mechanisms following hypertension that may cause SVD.
This knowledge can ultimately help to identify new targets for treatment of
SVD, aimed at prevention or limiting disease progression.
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Affiliation(s)
- Esther Janssen
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | | | - Esmée Verburgt
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Joost JA de Jong
- School for Mental Health & Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Roy PC Kessels
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
- Vincent van Gogh Institute for Psychiatry, Venray, The Netherlands
- Department of Medical Psychology and Radboudumc Alzheimer Center, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Walter H Backes
- School for Mental Health & Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Marnix C Maas
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Frederick JA Meijer
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jaap Deinum
- Department of Internal Medicine and Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Niels P Riksen
- Department of Internal Medicine and Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Anil M Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
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28
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Raja R, Na X, Moore A, Otoo R, Glasier CM, Badger TM, Ou X. Associations Between White Matter Microstructures and Cognitive Functioning in 8-Year-Old Children: A Track-Weighted Imaging Study. J Child Neurol 2022; 37:471-490. [PMID: 35254148 PMCID: PMC9149064 DOI: 10.1177/08830738221083487] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
PURPOSE Quantitative tractography using diffusion-weighted magnetic resonance imaging data is widely used in characterizing white matter microstructure throughout childhood, but more studies are still needed to investigate comprehensive brain-behavior relationships between tract-specific white matter measures and multiple cognitive functions in children. METHODS In this study, we analyzed diffusion-weighted MRI data of 71 healthy 8-year-old children utilizing white matter tract-specific quantitative measures derived from diffusion-weighted MRI tractography based on a novel track-weighted imaging approach. Track density imaging, average path length map and 4 track-weighted diffusion tensor imaging measures including: mean diffusivity, fractional anisotropy, axial diffusivity, and radial diffusivity were computed for 63 white matter tracts. The track-weighted imaging measures were then correlated with a comprehensive set of neuropsychological test scores in different cognitive domains including intelligence, language, memory, academic skills, and executive functions to identify tract-specific brain-behavior relationships. RESULTS Significant correlations (P < .05, false discovery rate corrected; r = 0.27-0.57) were found in multiple white matter tracts, with a total of 40 correlations identified between various track-weighted imaging measures including average path length map, track-weighted imaging-fractional anisotropy, and neuropsychological test scores and subscales. Specifically, track-weighted imaging measures indicative of better white matter connectivity and/or microstructural development significantly correlated with higher IQ and better language abilities. CONCLUSION Our findings demonstrate the ability of track-weighted imaging measures in establishing associations between white matter and cognitive functioning in healthy children and can serve as a reference for normal brain/cognition relationships in young school-age children and further aid in identifying imaging biomarkers predictive of adverse neurodevelopmental outcomes.
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Affiliation(s)
- Rajikha Raja
- Department of Radiology, University of Arkansas for Medical Sciences
| | - Xiaoxu Na
- Department of Radiology, University of Arkansas for Medical Sciences
| | - Alexandra Moore
- College of Medicine, University of Arkansas for Medical Sciences
| | - Raymond Otoo
- College of Medicine, University of Arkansas for Medical Sciences
| | - Charles M. Glasier
- Department of Radiology, University of Arkansas for Medical Sciences,Department of Pediatrics, University of Arkansas for Medical Sciences
| | - Thomas M. Badger
- Department of Pediatrics, University of Arkansas for Medical Sciences,Arkansas Children’s Nutrition Center
| | - Xiawei Ou
- Department of Radiology, University of Arkansas for Medical Sciences,Department of Pediatrics, University of Arkansas for Medical Sciences
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29
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Rost NS, Brodtmann A, Pase MP, van Veluw SJ, Biffi A, Duering M, Hinman JD, Dichgans M. Post-Stroke Cognitive Impairment and Dementia. Circ Res 2022; 130:1252-1271. [PMID: 35420911 DOI: 10.1161/circresaha.122.319951] [Citation(s) in RCA: 216] [Impact Index Per Article: 108.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Poststroke cognitive impairment and dementia (PSCID) is a major source of morbidity and mortality after stroke worldwide. PSCID occurs as a consequence of ischemic stroke, intracerebral hemorrhage, or subarachnoid hemorrhage. Cognitive impairment and dementia manifesting after a clinical stroke is categorized as vascular even in people with comorbid neurodegenerative pathology, which is common in elderly individuals and can contribute to the clinical expression of PSCID. Manifestations of cerebral small vessel disease, such as covert brain infarcts, white matter lesions, microbleeds, and cortical microinfarcts, are also common in patients with stroke and likewise contribute to cognitive outcomes. Although studies of PSCID historically varied in the approach to timing and methods of diagnosis, most of them demonstrate that older age, lower educational status, socioeconomic disparities, premorbid cognitive or functional decline, life-course exposure to vascular risk factors, and a history of prior stroke increase risk of PSCID. Stroke characteristics, in particular stroke severity, lesion volume, lesion location, multiplicity and recurrence, also influence PSCID risk. Understanding the complex interaction between an acute stroke event and preexisting brain pathology remains a priority and will be critical for developing strategies for personalized prediction, prevention, targeted interventions, and rehabilitation. Current challenges in the field relate to a lack of harmonization of definition and classification of PSCID, timing of diagnosis, approaches to neurocognitive assessment, and duration of follow-up after stroke. However, evolving knowledge on pathophysiology, neuroimaging, and biomarkers offers potential for clinical applications and may inform clinical trials. Preventing stroke and PSCID remains a cornerstone of any strategy to achieve optimal brain health. We summarize recent developments in the field and discuss future directions closing with a call for action to systematically include cognitive outcome assessment into any clinical studies of poststroke outcome.
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Affiliation(s)
- Natalia S Rost
- J. Philip Kistler Stroke Research Center (N.S.R., S.J.v.V., A. Biffi), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Amy Brodtmann
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Australia (A. Brodtmann).,Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia (A. Brodtmann. M.P.P.)
| | - Matthew P Pase
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia (A. Brodtmann. M.P.P.).,Harvard T.H. Chan School of Public Health, Boston (M.P.P.)
| | - Susanne J van Veluw
- MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital, Charlestown (S.J.v.V.)
| | - Alessandro Biffi
- J. Philip Kistler Stroke Research Center (N.S.R., S.J.v.V., A. Biffi), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston.,Divisions of Memory Disorders and Behavioral Neurology (A. Biffi), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston
| | - Marco Duering
- J. Philip Kistler Stroke Research Center (N.S.R., S.J.v.V., A. Biffi), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston.,Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany (M. Duering, M. Dichgans).,Medical Image Analysis Center and Department of Biomedical Engineering, University of Basel, Switzerland (M. Duering)
| | - Jason D Hinman
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles (J.D.H.).,Department of Neurology, West Los Angeles VA Medical Center, CA (J.D.H.)
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany (M. Duering, M. Dichgans).,German Center for Neurodegenerative Diseases (DZNE), Munich, Germany (M. Dichgans).,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany (M. Dichgans)
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30
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Maillard P, Lu H, Arfanakis K, Gold BT, Bauer CE, Zachariou V, Stables L, Wang DJ, Jann K, Seshadri S, Duering M, Hillmer LJ, Rosenberg GA, Snoussi H, Sepehrband F, Habes M, Singh B, Kramer JH, Corriveau RA, Singh H, Schwab K, Helmer KG, Greenberg SM, Caprihan A, DeCarli C, Satizabal CL. Instrumental validation of free water, peak-width of skeletonized mean diffusivity, and white matter hyperintensities: MarkVCID neuroimaging kits. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12261. [PMID: 35382232 PMCID: PMC8959640 DOI: 10.1002/dad2.12261] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Indexed: 11/11/2022]
Abstract
Introduction To describe the protocol and findings of the instrumental validation of three imaging-based biomarker kits selected by the MarkVCID consortium: free water (FW) and peak width of skeletonized mean diffusivity (PSMD), both derived from diffusion tensor imaging (DTI), and white matter hyperintensity (WMH) volume derived from fluid attenuation inversion recovery and T1-weighted imaging. Methods The instrumental validation of imaging-based biomarker kits included inter-rater reliability among participating sites, test-retest repeatability, and inter-scanner reproducibility across three types of magnetic resonance imaging (MRI) scanners using intra-class correlation coefficients (ICC). Results The three biomarkers demonstrated excellent inter-rater reliability (ICC >0.94, P-values < .001), very high agreement between test and retest sessions (ICC >0.98, P-values < .001), and were extremely consistent across the three scanners (ICC >0.98, P-values < .001). Discussion The three biomarker kits demonstrated very high inter-rater reliability, test-retest repeatability, and inter-scanner reproducibility, offering robust biomarkers suitable for future multi-site observational studies and clinical trials in the context of vascular cognitive impairment and dementia (VCID).
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Affiliation(s)
- Pauline Maillard
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Hanzhang Lu
- Department of RadiologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Konstantinos Arfanakis
- Department of Biomedical EngineeringIllinois Institute of TechnologyChicagoIllinoisUSA
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
| | - Brian T. Gold
- Department of NeuroscienceUniversity of KentuckyLexingtonKentuckyUSA
| | | | | | - Lara Stables
- Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Danny J.J. Wang
- Laboratory of FMRI Technology (LOFT)Stevens Neuroimaging and Informatics InstituteKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Kay Jann
- Laboratory of FMRI Technology (LOFT)Stevens Neuroimaging and Informatics InstituteKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Sudha Seshadri
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Marco Duering
- Department of Biomedical EngineeringMedical Image Analysis Center (MIAC AG)University of BaselBaselSwitzerland
| | - Laura J. Hillmer
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Gary A. Rosenberg
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Haykel Snoussi
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Farshid Sepehrband
- Laboratory of FMRI Technology (LOFT)Stevens Neuroimaging and Informatics InstituteKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Baljeet Singh
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Joel H. Kramer
- Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | | | - Herpreet Singh
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Kristin Schwab
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Karl G. Helmer
- Department of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
| | | | | | - Charles DeCarli
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Claudia L. Satizabal
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
- Department of Population Health SciencesUniversity of Texas Health San AntonioSan AntonioTexasUSA
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Jacobs HI, Schoemaker D, Torrico-Teave H, Zuluaga Y, Velilla-Jimenez L, Ospina-Villegas C, Lopera F, Arboleda-Velasquez JF, Quiroz YT. Specific Abnormalities in White Matter Pathways as Interface to Small Vessels Disease and Cognition in Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy Individuals. Brain Connect 2022; 12:52-60. [PMID: 33980027 PMCID: PMC8867102 DOI: 10.1089/brain.2020.0980] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Background: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is characterized by leukoencephalopathy leading to cognitive impairment. Subtle cognitive deficits can be observed early in the course of the disease, before the occurrence of the first stroke. Therefore, markers that can predict disease progression at this early stage, when interventions are likely to alter disease course, are needed. We aimed to examine the biological cascade of microstructural and macrostructural white matter (WM) abnormalities underlying cognitive deficits in CADASIL. Methods: We examined 20 nondemented CADASIL mutation carriers and 23 noncarriers who underwent neuropsychological evaluation and magnetic resonance imaging. Using probabilistic tractography of key WM tracts, we examined group differences in diffusivity measures and WM hyperintensity volume. Successive mediation models examined whether tract-specific WM abnormalities mediated subtle cognitive differences between CADASIL mutation carriers and noncarriers. Results: The largest effect size differentiating the two groups was observed for left superior longitudinal fasciculus-temporal (SLFt) diffusivity (Cohen's f = 0.49). No group differences were observed with a global diffusion measure. These specific microstructural differences in the SLFt were associated with higher WM hyperintensities burden, and subtle executive deficits in CADASIL mutation carriers. Discussion: Worse diffusivity in the left SLFt is related to greater severity of small vessel disease and worse executive functioning in the asymptomatic stage of the disease. Worse diffusivity of the left SLFt may potentially hold promise as an indicator of disease progression. Impact statement Diffusion tensor imaging outperforms conventional imaging of subcortical small vessel disease as a potential marker of future disease progression. Here we identified the left superior longitudinal temporal fasciculus as a critical white matter fiber bundle, of which worse diffusivity can link presence of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy mutations to greater severity of small vessel disease and worse executive functioning in asymptomatic stages of the disease. This tract may hold promise and deserves further examination as an early indicator of disease progression.
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Affiliation(s)
- Heidi I.L. Jacobs
- Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Gordon Center for Medical Imaging, Boston, Massachusetts, USA.,Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, The Netherlands
| | - Dorothee Schoemaker
- Department of Ophthalmology, Harvard Medical School, Schepens Eye Research Institute of Massachusetts Eye and Ear, Boston, Massachusetts, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Hei Torrico-Teave
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yesica Zuluaga
- Grupo Neurociencias de Antioquia, Universidad de Antioquia, Medellín, Colombia
| | | | | | - Francisco Lopera
- Grupo Neurociencias de Antioquia, Universidad de Antioquia, Medellín, Colombia
| | - Joseph F. Arboleda-Velasquez
- Department of Ophthalmology, Harvard Medical School, Schepens Eye Research Institute of Massachusetts Eye and Ear, Boston, Massachusetts, USA
| | - Yakeel T. Quiroz
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Address correspondence to: Yakeel T. Quiroz, Department of Psychiatry and Neurology, Harvard Medical School, Massachusetts General Hospital, 100 1st Avenue, Building 39, Suite 101, Charlestown, MA 02129, USA
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32
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Vemuri P, Decarli CS, Duering M. Imaging Markers of Vascular Brain Health: Quantification, Clinical Implications, and Future Directions. Stroke 2022; 53:416-426. [PMID: 35000423 PMCID: PMC8830603 DOI: 10.1161/strokeaha.120.032611] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Cerebrovascular disease (CVD) manifests through a broad spectrum of mechanisms that negatively impact brain and cognitive health. Oftentimes, CVD changes (excluding acute stroke) are insufficiently considered in aging and dementia studies which can lead to an incomplete picture of the etiologies contributing to the burden of cognitive impairment. Our goal with this focused review is 3-fold. First, we provide a research update on the current magnetic resonance imaging methods that can measure CVD lesions as well as early CVD-related brain injury specifically related to small vessel disease. Second, we discuss the clinical implications and relevance of these CVD imaging markers for cognitive decline, incident dementia, and disease progression in Alzheimer disease, and Alzheimer-related dementias. Finally, we present our perspective on the outlook and challenges that remain in the field. With the increased research interest in this area, we believe that reliable CVD imaging biomarkers for aging and dementia studies are on the horizon.
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Affiliation(s)
| | - Charles S. Decarli
- Departments of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, California, USA
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
- Medical Image Analysis Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of Basel, Switzerland
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33
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Petersen M, Frey BM, Mayer C, Kühn S, Gallinat J, Hanning U, Fiehler J, Borof K, Jagodzinski A, Gerloff C, Thomalla G, Cheng B. Fixel based analysis of white matter alterations in early stage cerebral small vessel disease. Sci Rep 2022; 12:1581. [PMID: 35091684 PMCID: PMC8799636 DOI: 10.1038/s41598-022-05665-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 01/11/2022] [Indexed: 12/13/2022] Open
Abstract
Cerebral small vessel disease (CSVD) is a common cause of morbidity and cognitive decline in the elderly population. However, characterizing the disease pathophysiology and its association with potential clinical sequelae in early stages is less well explored. We applied fixel-based analysis (FBA), a novel framework of investigating microstructural white matter integrity by diffusion-weighted imaging, to data of 921 participants of the Hamburg City Health Study, comprising middle-aged individuals with increased cerebrovascular risk in early stages of CSVD. In individuals in the highest quartile of white matter hyperintensity loads (n = 232, median age 63 years; IQR 15.3 years), FBA detected significantly reduced axonal density and increased atrophy of transcallosal fiber tracts, the bilateral superior longitudinal fasciculus, and corticospinal tracts compared to participants in the lowest quartile of white matter hyperintensities (n = 228, mean age 55 years; IQR 10 years). Analysis of all participants (N = 921) demonstrated a significant association between reduced fiber density and worse executive functions operationalized by the Trail Making Test. Findings were confirmed by complementary analysis of diffusion tensor metrics.
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Affiliation(s)
- Marvin Petersen
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany.
| | - Benedikt M Frey
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - Carola Mayer
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - Simone Kühn
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jürgen Gallinat
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Uta Hanning
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Katrin Borof
- Epidemiological Study Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Annika Jagodzinski
- Epidemiological Study Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of General and Interventional Cardiology, University Heart and Vascular Center, Hamburg, Germany
| | - Christian Gerloff
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - Bastian Cheng
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
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34
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de Brito Robalo BM, Biessels GJ, Chen C, Dewenter A, Duering M, Hilal S, Koek HL, Kopczak A, Yin Ka Lam B, Leemans A, Mok V, Onkenhout LP, van den Brink H, de Luca A. Diffusion MRI harmonization enables joint-analysis of multicentre data of patients with cerebral small vessel disease. Neuroimage Clin 2021; 32:102886. [PMID: 34911192 PMCID: PMC8609094 DOI: 10.1016/j.nicl.2021.102886] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 11/16/2021] [Indexed: 01/10/2023]
Abstract
OBJECTIVES Acquisition-related differences in diffusion magnetic resonance imaging (dMRI) hamper pooling of multicentre data to achieve large sample sizes. A promising solution is to harmonize the raw diffusion signal using rotation invariant spherical harmonic (RISH) features, but this has not been tested in elderly subjects. Here we aimed to establish if RISH harmonization effectively removes acquisition-related differences in multicentre dMRI of elderly subjects with cerebral small vessel disease (SVD), while preserving sensitivity to disease effects. METHODS Five cohorts of patients with SVD (N = 397) and elderly controls (N = 175) with 3 Tesla MRI on different systems were included. First, to establish effectiveness of harmonization, the RISH method was trained with data of 13 to 15 age and sex-matched controls from each site. Fractional anisotropy (FA) and mean diffusivity (MD) were compared in matched controls between sites using tract-based spatial statistics (TBSS) and voxel-wise analysis, before and after harmonization. Second, to assess sensitivity to disease effects, we examined whether the contrast (effect sizes of FA, MD and peak width of skeletonized MD - PSMD) between patients and controls within each site remained unaffected by harmonization. Finally, we evaluated the association between white matter hyperintensity (WMH) burden, FA, MD and PSMD using linear regression analyses both within individual cohorts as well as with pooled scans from multiple sites, before and after harmonization. RESULTS Before harmonization, significant differences in FA and MD were observed between matched controls of different sites (p < 0.05). After harmonization these site-differences were removed. Within each site, RISH harmonization did not alter the effect sizes of FA, MD and PSMD between patients and controls (relative change in Cohen's d = 4 %) nor the strength of association with WMH volume (relative change in R2 = 2.8 %). After harmonization, patient data of all sites could be aggregated in a single analysis to infer the association between WMH volume and FA (R2 = 0.62), MD (R2 = 0.64), and PSMD (R2 = 0.60). CONCLUSIONS We showed that RISH harmonization effectively removes acquisition-related differences in dMRI of elderly subjects while preserving sensitivity to SVD-related effects. This study provides proof of concept for future multicentre SVD studies with pooled datasets.
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Affiliation(s)
- Bruno M de Brito Robalo
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Christopher Chen
- Memory, Aging and Cognition Center, Department of Pharmacology, National University of Singapore, Singapore.
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany.
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany; Medical Image Analysis Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
| | - Saima Hilal
- Memory, Aging and Cognition Center, Department of Pharmacology, National University of Singapore, Singapore.
| | - Huiberdina L Koek
- Department of Geriatric Medicine, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Anna Kopczak
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany.
| | - Bonnie Yin Ka Lam
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region.
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Vincent Mok
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region.
| | - Laurien P Onkenhout
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Hilde van den Brink
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Alberto de Luca
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
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35
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Raghavan S, Reid RI, Przybelski SA, Lesnick TG, Graff-Radford J, Schwarz CG, Knopman DS, Mielke MM, Machulda MM, Petersen RC, Jack CR, Vemuri P. Diffusion models reveal white matter microstructural changes with ageing, pathology and cognition. Brain Commun 2021; 3:fcab106. [PMID: 34136811 PMCID: PMC8202149 DOI: 10.1093/braincomms/fcab106] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/24/2021] [Accepted: 04/12/2021] [Indexed: 01/20/2023] Open
Abstract
White matter microstructure undergoes progressive changes during the lifespan, but the neurobiological underpinnings related to ageing and disease remains unclear. We used an advanced diffusion MRI, Neurite Orientation Dispersion and Density Imaging, to investigate the microstructural alterations due to demographics, common age-related pathological processes (amyloid, tau and white matter hyperintensities) and cognition. We also compared Neurite Orientation Dispersion and Density Imaging findings to the older Diffusion Tensor Imaging model-based findings. Three hundred and twenty-eight participants (264 cognitively unimpaired, 57 mild cognitive impairment and 7 dementia with a mean age of 68.3 ± 13.1 years) from the Mayo Clinic Study of Aging with multi-shell diffusion imaging, fluid attenuated inversion recovery MRI as well as amyloid and tau PET scans were included in this study. White matter tract level diffusion measures were calculated from Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging. Pearson correlation and multiple linear regression analyses were performed with diffusion measures as the outcome and age, sex, education/occupation, white matter hyperintensities, amyloid and tau as predictors. Analyses were also performed with each diffusion MRI measure as a predictor of cognitive outcomes. Age and white matter hyperintensities were the strongest predictors of all white matter diffusion measures with low associations with amyloid and tau. However, neurite density decrease from Neurite Orientation Dispersion and Density Imaging was observed with amyloidosis specifically in the temporal lobes. White matter integrity (mean diffusivity and free water) in the corpus callosum showed the greatest associations with cognitive measures. All diffusion measures provided information about white matter ageing and white matter changes due to age-related pathological processes and were associated with cognition. Neurite orientation dispersion and density imaging and diffusion tensor imaging are two different diffusion models that provide distinct information about variation in white matter microstructural integrity. Neurite Orientation Dispersion and Density Imaging provides additional information about synaptic density, organization and free water content which may aid in providing mechanistic insights into disease progression.
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Affiliation(s)
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic, Rochester, MN 55905, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Timothy G Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Michelle M Mielke
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA.,Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Mary M Machulda
- Department of Psychology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
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