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Koops EA, Jacobs HIL. Untangling white matter fibre changes in Alzheimer's disease and small vessel disease. Brain 2023; 146:413-415. [PMID: 36567494 DOI: 10.1093/brain/awac493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 12/21/2022] [Indexed: 12/27/2022] Open
Abstract
This scientific commentary refers to ‘Disentangling the effects of Alzheimer’s and small vessel disease on white matter fibre tracts’ by Dewenter et al. (https://doi.org/10.1093/brain/awac265).
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Affiliation(s)
- Elouise A Koops
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Heidi I L Jacobs
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
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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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Wen Q, Risacher SL, Xie L, Li J, Harezlak J, Farlow MR, Unverzagt FW, Gao S, Apostolova LG, Saykin AJ, Wu YC. Tau-related white-matter alterations along spatially selective pathways. Neuroimage 2020; 226:117560. [PMID: 33189932 PMCID: PMC8364310 DOI: 10.1016/j.neuroimage.2020.117560] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 11/08/2020] [Indexed: 01/07/2023] Open
Abstract
Progressive accumulation of tau neurofibrillary tangles in the brain is a defining pathologic feature of Alzheimer’s disease (AD). Tau pathology exhibits a predictable spatiotemporal spreading pattern, but the underlying mechanisms of this spread are poorly understood. Although AD is conventionally considered a disease of the gray matter, it is also associated with pronounced and progressive deterioration of the white matter (WM). A link between abnormal tau and WM degeneration is suggested by findings from both animal and postmortem studies, but few studies demonstrated their interplay in vivo. Recent advances in diffusion magnetic resonance imaging and the availability of tau positron emission tomography (PET) have made it possible to evaluate the association of tau and WM degeneration (tau-WM) in vivo. In this study, we explored the spatial pattern of tau-WM associations across the whole brain to evaluate the hypothesis that tau deposition is associated with WM microstructural alterations not only in isolated tracts, but in continuous structural connections in a stereotypic pattern. Sixty-two participants, including 22 cognitively normal subjects, 22 individuals with subjective cognitive decline, and 18 with mild cognitive impairment were included in the study. WM characteristics were inferred by classic diffusion tensor imaging (DTI) and a complementary diffusion compartment model – neurite orientation dispersion and density imaging (NODDI) that provides a proxy for axonal density. A data-driven iterative searching (DDIS) approach, coupled with whole-brain graph theory analyses, was developed to continuously track tau-WM association patterns. Without applying prior knowledge of the tau spread, we observed a distinct spatial pattern that resembled the typical propagation of tau pathology in AD. Such association pattern was not observed between diffusion and amyloid-β PET signal. Tau-related WM degeneration is characterized by an increase in the mean diffusivity (with a dominant change in the radial direction) and a decrease in the intra-axonal volume fraction. These findings suggest that cortical tau deposition (as measured in tau PET) is associated with a lower axonal packing density and greater diffusion freedom. In conclusion, our in vivo findings using a data-driven method on cross-sectional data underline the important role of WM alterations in the AD pathological cascade with an association pattern similar to the postmortem Braak staging of AD. Future studies will focus on longitudinal analyses to provide in vivo evidence of tau pathology spreads along neuroanatomically connected brain areas.
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Affiliation(s)
- Qiuting Wen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine,, Indianapolis, IN 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine,, Indianapolis, IN 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Linhui Xie
- Department of Electrical and Computer Engineering, Indiana University Purdue University Indianapolis, IN, USA
| | - Junjie Li
- University Information Technology Service - Research Technology, Indiana University, Indianapolis, IN, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, Bloomington, IN, USA
| | - Martin R Farlow
- Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Frederick W Unverzagt
- Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Clinical Psychology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sujuan Gao
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Liana G Apostolova
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine,, Indianapolis, IN 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine,, Indianapolis, IN 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Clinical Psychology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine,, Indianapolis, IN 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA.
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Fu X, Shrestha S, Sun M, Wu Q, Luo Y, Zhang X, Yin J, Ni H. Microstructural White Matter Alterations in Mild Cognitive Impairment and Alzheimer's Disease : Study Based on Neurite Orientation Dispersion and Density Imaging (NODDI). Clin Neuroradiol 2019; 30:569-579. [PMID: 31175374 DOI: 10.1007/s00062-019-00805-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Accepted: 05/21/2019] [Indexed: 12/28/2022]
Abstract
PURPOSE To investigate microstructural alterations in white matter in mild cognitive impairment (MCI) and Alzheimer's disease (AD) using neurite orientation dispersion and density imaging (NODDI) and to assess the potential diagnostic performance of NODDI-derived parameters. METHODS In this study 14 MCI patients, 14 AD patients, and 14 healthy controls (HC) were recruited. The diffusion tensor imaging(DTI)-derived fractional anisotropy (FA) and NODDI-derived neurite density index (NDI), orientation dispersion index (ODI), and volume fraction of isotropic water molecules (Viso) were calculated from the diffusion data. The tract-based spatial statistics (TBSS) method was used for statistical analysis with one-way ANOVA. The correlations between the parameter values and mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) scores were examined. A receiver operating characteristic (ROC) curve was conducted to assess the diagnostic performance of different parameters. RESULTS Compared with the HC group, the NDI and ODI values decreased significantly and the Viso values were significantly increased in the MCI and AD groups (p < 0.01, threshold-free cluster enhancement (TFCE)-corrected); however, there were no significant differences in FA values in the MCI group. The NDI, ODI, and Viso values of multiple fibers were significantly correlated with MMSE and MoCA scores. For the diagnosis of AD, the area under the ROC curve (AUC) for the NDI value of the splenium of corpus callosum was larger than the FA value (AUC = 0.885, 0.714, p = 0.042). The AUC of the Viso value of the right cerebral peduncle was larger than FA value (AUC = 0.934, 0.531, p = 0.004). CONCLUSION The NDI is more sensitive to white matter microstructural changes than FA and NODDI could be superior to DTI in the diagnosis of AD.
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Affiliation(s)
- Xiuwei Fu
- Department of Radiology, First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Susan Shrestha
- Department of Radiology, First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Man Sun
- Department of Radiology, Tianjin Hospital of Tianjin, Tianjin, China
| | - Qiaoling Wu
- Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Yuan Luo
- Department of Radiology, First Central Clinical College, Tianjin Medical University, Tianjin, China
| | | | - Jianzhong Yin
- Department of Radiology, Tianjin First Central Hospital, 24 Fukang Road, Nankai District, 300192, Tianjin, China
| | - Hongyan Ni
- Department of Radiology, Tianjin First Central Hospital, 24 Fukang Road, Nankai District, 300192, Tianjin, China.
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