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Kang X, Yoon BC, Grossner E, Adamson MM. Characteristics of the Structural Connectivity in Patients with Brain Injury and Chronic Health Symptoms: A Pilot Study. Neuroinformatics 2024:10.1007/s12021-024-09681-7. [PMID: 38990502 DOI: 10.1007/s12021-024-09681-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/03/2024] [Indexed: 07/12/2024]
Abstract
Diffusion properties from diffusion tensor imaging (DTI) are exquisitely sensitive to white matter abnormalities incurred during traumatic brain injury (TBI), especially for those patients with chronic post-TBI symptoms such as headaches, dizziness, fatigue, etc. The evaluation of structural and functional connectivity using DTI has become a promising method for identifying subtle alterations in brain connectivity associated with TBI that are otherwise not visible with conventional imaging. This study assessed whether TBI patients with (n = 17) or without (n = 16) chronic symptoms (TBIcs/TBIncs) exhibit any changes in structural connectivity (SC) and mean fractional anisotropy (mFA) of intra- and inter-hemispheric connections when compared to a control group (CG) (n = 13). Reductions in SC and mFA were observed for TBIcs compared to CG, but not for TBIncs. More connections were found to have mFA reductions than SC reductions. On the whole, SC is dominated by ipsilateral connections for all the groups after the comparison of contralateral and ipsilateral connections. More contra-ipsi reductions of mFA were found for TBIcs than TBIncs compared to CG. These findings suggest that TBI patients with chronic symptoms not only demonstrate decreased global and regional mFA but also reduced structural network connectivity.
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Affiliation(s)
- Xiaojian Kang
- WRIISC-Women, VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94304, USA.
- Rehabilitation Service, VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94304, USA.
| | - Byung C Yoon
- Department of Radiology, Stanford University School of Medicine, VA Palo Alto Heath Care System, Palo Alto, CA, 94304, USA
| | - Emily Grossner
- Department of Psychology, VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94304, USA
| | - Maheen M Adamson
- WRIISC-Women, VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94304, USA
- Rehabilitation Service, VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA, 94304, USA
- Department of Neurosurgery, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA, 94305, USA
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2
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Gou Y, Liu Y, He F, Hunyadi B, Zhu C. Tensor Completion for Alzheimer's Disease Prediction From Diffusion Tensor Imaging. IEEE Trans Biomed Eng 2024; 71:2211-2223. [PMID: 38349831 DOI: 10.1109/tbme.2024.3365131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
OBJECTIVE Alzheimer's disease (AD) is a slowly progressive neurodegenerative disorder with insidious onset. Accurate prediction of the disease progression has received increasing attention. Cognitive scores that reflect patients' cognitive status have become important criteria for predicting AD. Most existing methods consider the relationship between neuroimages and cognitive scores to improve prediction results. However, the inherent structure information in interrelated cognitive scores is rarely considered. METHOD In this article, we propose a relation-aware tensor completion multitask learning method (RATC-MTL), in which the cognitive scores are represented as a third-order tensor to preserve the global structure information in clinical scores. We combine both tensor completion and linear regression into a unified framework, which allows us to capture both inter and intra modes correlations in cognitive tensor with a low-rank constraint, as well as incorporate the relationship between biological features and cognitive status by imposing a regression model on multiple cognitive scores. RESULT Compared to the single-task and state-of-the-art multi-task algorithms, our proposed method obtains the best results for predicting cognitive scores in terms of four commonly used metrics. Furthermore, the overall performance of our method in classifying AD progress is also the best. CONCLUSION Our results demonstrate the effectiveness of the proposed framework in fully exploring the global structure information in cognitive scores. SIGNIFICANCE This study introduces a novel concept of leveraging tensor completion to assist in disease diagnoses, potentially offering a solution to the issue of data scarcity encountered in prolonged monitoring scenarios.
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Yu X, Przybelski SA, Reid RI, Lesnick TG, Raghavan S, Graff‐Radford J, Lowe VJ, Kantarci K, Knopman DS, Petersen RC, Jack CR, Vemuri P. NODDI in gray matter is a sensitive marker of aging and early AD changes. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12627. [PMID: 39077685 PMCID: PMC11284641 DOI: 10.1002/dad2.12627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 06/17/2024] [Accepted: 07/02/2024] [Indexed: 07/31/2024]
Abstract
INTRODUCTION Age-related and Alzheimer's disease (AD) dementia-related neurodegeneration impact brain health. While morphometric measures from T1-weighted scans are established biomarkers, they may be less sensitive to earlier changes. Neurite orientation dispersion and density imaging (NODDI), offering biologically meaningful interpretation of tissue microstructure, may be an advanced brain health biomarker. METHODS We contrasted regional gray matter NODDI and morphometric evaluations concerning their correlation with (1) age, (2) clinical diagnosis stage, and (3) tau pathology as assessed by AV1451 positron emission tomography. RESULTS Our study hypothesizes that NODDI measures are more sensitive to aging and early AD changes than morphometric measures. One NODDI output, free water fraction (FWF), showed higher sensitivity to age-related changes, generally better effect sizes in separating mild cognitively impaired from cognitively unimpaired participants, and stronger associations with regional tau deposition than morphometric measures. DISCUSSION These findings underscore NODDI's utility in capturing early neurodegenerative changes and enhancing our understanding of aging and AD. Highlights Neurite orientation dispersion and density imaging can serve as an effective brain health biomarker for aging and early Alzheimer's disease (AD).Free water fraction has higher sensitivity to normal brain aging.Free water fraction has stronger associations with early AD and regional tau deposition.
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Affiliation(s)
- Xi Yu
- Department of Physiology and Biomedical EngineeringMayo ClinicRochesterMinnesotaUSA
| | - Scott A. Przybelski
- Department of Health Sciences ResearchDivision of Biomedical Statistics and InformaticsMayo Clinic‐RochesterRochesterMinnesotaUSA
| | - Robert I. Reid
- Department of Health Sciences ResearchDivision of Biomedical Statistics and InformaticsMayo Clinic‐RochesterRochesterMinnesotaUSA
- Department of RadiologyMayo Clinic‐RochesterRochesterMinnesotaUSA
| | - Timothy G. Lesnick
- Department of Health Sciences ResearchDivision of Biomedical Statistics and InformaticsMayo Clinic‐RochesterRochesterMinnesotaUSA
| | | | | | - Val J. Lowe
- Department of RadiologyMayo Clinic‐RochesterRochesterMinnesotaUSA
| | - Kejal Kantarci
- Department of RadiologyMayo Clinic‐RochesterRochesterMinnesotaUSA
| | - David S. Knopman
- Department of NeurologyMayo Clinic‐RochesterRochesterMinnesotaUSA
| | | | - Clifford R. Jack
- Department of RadiologyMayo Clinic‐RochesterRochesterMinnesotaUSA
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Song Z, Li H, Zhang Y, Zhu C, Jiang M, Song L, Wang Y, Ouyang M, Hu F, Zheng Q. s 2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer's disease solely from structural MRI. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01178-3. [PMID: 38869733 DOI: 10.1007/s10334-024-01178-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/19/2024] [Accepted: 06/04/2024] [Indexed: 06/14/2024]
Abstract
OBJECTIVE To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD. METHODS A total of 3377 participants' sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s2MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy. RESULTS The s2MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases (p < 0.05). Significant associations (p < 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation. CONCLUSION The s2MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization.
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Affiliation(s)
- Zhiwei Song
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Honglun Li
- Department of Radiology, Yantai Yuhuangding Hospital Affiliated with Qingdao University Medical College, Yantai, 264099, China
| | - Yiyu Zhang
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Chuanzhen Zhu
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Minbo Jiang
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
| | - Limei Song
- School of Medical Imaging, Weifang Medical University, Weifang, 261000, China
| | - Yi Wang
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China
- Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou, 423000, Hunan, China
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Fang Hu
- Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou, 423000, Hunan, China
| | - Qiang Zheng
- School of Computer and Control Engineering, Yantai University, Yantai, 264005, China.
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Seo H, Brand L, Wang H. Learning semi-supervised enrichment of longitudinal imaging-genetic data for improved prediction of cognitive decline. BMC Med Inform Decis Mak 2024; 24:61. [PMID: 38807132 PMCID: PMC11134626 DOI: 10.1186/s12911-024-02455-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: 10/15/2020] [Accepted: 02/05/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND Alzheimer's Disease (AD) is a progressive memory disorder that causes irreversible cognitive decline. Given that there is currently no cure, it is critical to detect AD in its early stage during the disease progression. Recently, many statistical learning methods have been presented to identify cognitive decline with temporal data, but few of these methods integrate heterogeneous phenotype and genetic information together to improve the accuracy of prediction. In addition, many of these models are often unable to handle incomplete temporal data; this often manifests itself in the removal of records to ensure consistency in the number of records across participants. RESULTS To address these issues, in this work we propose a novel approach to integrate the genetic data and the longitudinal phenotype data to learn a fixed-length "enriched" biomarker representation derived from the temporal heterogeneous neuroimaging records. Armed with this enriched representation, as a fixed-length vector per participant, conventional machine learning models can be used to predict clinical outcomes associated with AD. CONCLUSION The proposed method shows improved prediction performance when applied to data derived from Alzheimer's Disease Neruoimaging Initiative cohort. In addition, our approach can be easily interpreted to allow for the identification and validation of biomarkers associated with cognitive decline.
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Affiliation(s)
- Hoon Seo
- Department of Computer Science, Colorado School of Mines, Golden, Colorado, 80401, USA
| | - Lodewijk Brand
- Department of Computer Science, Colorado School of Mines, Golden, Colorado, 80401, USA
| | - Hua Wang
- Department of Computer Science, Colorado School of Mines, Golden, Colorado, 80401, USA.
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Müller HP, Kassubek J. Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing. Front Hum Neurosci 2024; 18:1378896. [PMID: 38628970 PMCID: PMC11018884 DOI: 10.3389/fnhum.2024.1378896] [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: 01/30/2024] [Accepted: 02/26/2024] [Indexed: 04/19/2024] Open
Abstract
Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.
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Shrestha S, Zhu X, Sullivan KJ, Blackshear C, Deal JA, Sharrett AR, Kamath V, Schneider ALC, Jack CR, Huang J, Palta P, Reid RI, Knopman DS, Gottesman RF, Chen H, Windham BG, Griswold ME, Mosley TH. Association of Olfaction and Microstructural Integrity of Brain Tissue in Community-Dwelling Adults: Atherosclerosis Risk in Communities Neurocognitive Study. Neurology 2023; 101:e1328-e1340. [PMID: 37541841 PMCID: PMC10558165 DOI: 10.1212/wnl.0000000000207636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 05/30/2023] [Indexed: 08/06/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Research on olfaction and brain neuropathology may help understand brain regions associated with normal olfaction and dementia pathophysiology. To identify early regional brain structures affected in poor olfaction, we examined cross-sectional associations of microstructural integrity of the brain with olfaction in the Atherosclerosis Risk in Communities Neurocognitive Study. METHODS Participants were selected from a prospective cohort study of community-dwelling adults; selection criteria included the following: evidence of cognitive impairment, participation in a previous MRI study, and a random sample of cognitively normal participants. Microstructural integrity was measured by 2 diffusion tensor imaging (DTI) measures, fractional anisotropy (FA) and mean diffusivity (MD), and olfaction by a 12-item odor identification test at the same visit. Higher FA and MD values indicate better and worse microstructural integrity, respectively, and higher odor identification scores indicate better olfaction. We used brain region-specific linear regression models to examine associations between DTI measures and olfaction, adjusting for potential confounders. RESULTS Among 1,418 participants (mean age 76 ± 5 years, 41% male, 21% Black race, 59% with normal cognition), the mean olfaction score was 9 ± 2.3. Relevant to olfaction, higher MD in the medial temporal lobe (MTL) regions, namely the hippocampus (β -0.79 [95% CI -0.94 to -0.65] units lower olfaction score per 1 SD higher MD), amygdala, entorhinal area, and some white matter (WM) tracts connecting to these regions, was associated with olfaction. We also observed associations with MD and WM FA in multiple atlas regions that were not previously implicated in olfaction. The associations between MD and olfaction were particularly stronger in the MTL regions among individuals with mild cognitive impairment (MCI) compared with those with normal cognition (e.g., βhippocampus -0.75 [95% CI -1.02 to -0.49] and -0.44 [95% CI -0.63 to -0.26] for MCI and normal cognition, respectively, p interaction = 0.004). DISCUSSION Neuronal microstructural integrity in multiple brain regions, particularly the MTL (the regions known to be affected in early Alzheimer disease), is associated with odor identification ability. Differential associations in the MTL regions among cognitively normal individuals compared with those with MCI may reflect the earlier vs later effects of the dementia pathogenesis. It is likely that some of the associated regions may not have any functional relevance to olfaction.
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Affiliation(s)
- Srishti Shrestha
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing.
| | - Xiaoqian Zhu
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Kevin J Sullivan
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Chad Blackshear
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Jennifer A Deal
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - A Richey Sharrett
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Vidyulata Kamath
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Andrea L C Schneider
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Clifford R Jack
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Juebin Huang
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Priya Palta
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Robert I Reid
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - David S Knopman
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Rebecca F Gottesman
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Honglei Chen
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - B Gwen Windham
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Michael E Griswold
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Thomas H Mosley
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
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8
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Feng T, Zhao C, Rao JS, Guo XJ, Bao SS, He LW, Zhao W, Liu Z, Yang ZY, Li XG. Different macaque brain network remodeling after spinal cord injury and NT3 treatment. iScience 2023; 26:106784. [PMID: 37378337 PMCID: PMC10291247 DOI: 10.1016/j.isci.2023.106784] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 03/08/2023] [Accepted: 04/26/2023] [Indexed: 06/29/2023] Open
Abstract
Graph theory-based analysis describes the brain as a complex network. Only a few studies have examined modular composition and functional connectivity (FC) between modules in patients with spinal cord injury (SCI). Little is known about the longitudinal changes in hubs and topological properties at the modular level after SCI and treatment. We analyzed differences in FC and nodal metrics reflecting modular interaction to investigate brain reorganization after SCI-induced compensation and neurotrophin-3 (NT3)-chitosan-induced regeneration. Mean inter-modular FC and participation coefficient of areas related to motor coordination were significantly higher in the treatment animals than in the SCI-only ones at the late stage. The magnocellular part of the red nucleus may reflect the best difference in brain reorganization after SCI and therapy. Treatment can enhance information flows between regions and promote the integration of motor functions to return to normal. These findings may reveal the information processing of disrupted network modules.
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Affiliation(s)
- Ting Feng
- School of Biological Science and Medical Engineering, Beijing Key Laboratory for Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, PR China
| | - Can Zhao
- Institute of Rehabilitation Engineering, China Rehabilitation Science Institute, Beijing, PR China
| | - Jia-Sheng Rao
- School of Biological Science and Medical Engineering, Beijing Key Laboratory for Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, PR China
| | - Xiao-Jun Guo
- School of Biological Science and Medical Engineering, Beijing Key Laboratory for Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, PR China
| | - Shu-Sheng Bao
- School of Biological Science and Medical Engineering, Beijing Key Laboratory for Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, PR China
| | - Le-Wei He
- School of Biological Science and Medical Engineering, Beijing Key Laboratory for Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, PR China
| | - Wen Zhao
- Department of Neurobiology, School of Basic Medical Sciences, Capital Medical University, Beijing, PR China
| | - Zuxiang Liu
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, PR China
- Department of Biology, College of Life Sciences, University of Chinese Academy of Sciences, Beijing, PR China
| | - Zhao-Yang Yang
- Department of Neurobiology, School of Basic Medical Sciences, Capital Medical University, Beijing, PR China
| | - Xiao-Guang Li
- School of Biological Science and Medical Engineering, Beijing Key Laboratory for Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, PR China
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Bergamino M, Nelson MR, Numani A, Scarpelli M, Healey D, Fuentes A, Turner G, Stokes AM. Assessment of complementary white matter microstructural changes and grey matter atrophy in a preclinical model of Alzheimer's disease. Magn Reson Imaging 2023; 101:57-66. [PMID: 37028608 DOI: 10.1016/j.mri.2023.03.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 03/30/2023] [Accepted: 03/31/2023] [Indexed: 04/08/2023]
Abstract
Alzheimer's disease (AD) has been associated with amyloid and tau pathology, as well as neurodegeneration. Beyond these hallmark features, white matter microstructural abnormalities have been observed using MRI. The objective of this study was to assess grey matter atrophy and white matter microstructural changes in a preclinical mouse model of AD (3xTg-AD) using voxel-based morphometry (VBM) and free-water (FW) diffusion tensor imaging (FW-DTI). Compared to controls, lower grey matter density was observed in the 3xTg-AD model, corresponding to the small clusters in the caudate-putamen, hypothalamus, and cortex. DTI-based fractional anisotropy (FA) was decreased in the 3xTg model, while the FW index was increased. Notably, the largest clusters for both FW-FA and FW index were in the fimbria, with other regions including the anterior commissure, corpus callosum, forebrain septum, and internal capsule. Additionally, the presence of amyloid and tau in the 3xTg model was confirmed with histopathology, with significantly higher levels observed across many regions of the brain. Taken together, these results are consistent with subtle neurodegenerative and white matter microstructural changes in the 3xTg-AD model that manifest as increased FW, decreased FW-FA, and decreased grey matter density.
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Affiliation(s)
- Maurizio Bergamino
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Megan R Nelson
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Asfia Numani
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Matthew Scarpelli
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Deborah Healey
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Alberto Fuentes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Gregory Turner
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Ashley M Stokes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA.
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10
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Uchida Y, Onda K, Hou Z, Troncoso JC, Mori S, Oishi K. Microstructural Neurodegeneration of the Entorhinal-Hippocampus Pathway along the Alzheimer's Disease Continuum. J Alzheimers Dis 2023; 95:1107-1117. [PMID: 37638442 PMCID: PMC10578220 DOI: 10.3233/jad-230452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/15/2023] [Indexed: 08/29/2023]
Abstract
BACKGROUND Conventional neuroimaging biomarkers for the neurodegeneration of Alzheimer's disease (AD) are not sensitive enough to detect neurodegenerative alterations during the preclinical stage of AD individuals. OBJECTIVE We examined whether neurodegeneration of the entorhinal-hippocampal pathway could be detected along the AD continuum using ultra-high-field diffusion tensor imaging and tractography for ex vivo brain tissues. METHODS Postmortem brain specimens from a cognitively unimpaired individual without AD pathological changes (non-AD), a cognitively unimpaired individual with AD pathological changes (preclinical AD), and a demented individual with AD pathological changes (AD dementia) were scanned with an 11.7T diffusion magnetic resonance imaging. Fractional anisotropy (FA) values of the entorhinal layer II and number of perforant path fibers counted by tractography were compared among the AD continuum. Following the imaging analyses, the status of myelinated fibers and neuronal cells were verified by subsequent serial histological examinations. RESULTS At 250μm (zipped to 125μm) isotropic resolution, the entorhinal layer II islands and the perforant path fibers could be identified in non-AD and preclinical AD, but not in AD dementia, followed by histological verification. The FA value of the entorhinal layer II was the highest among the entorhinal laminae in non-AD and preclinical AD, whereas the FA values in the entorhinal laminae were homogeneously low in AD dementia. The FA values and number of perforant path fibers decreased along the AD continuum (non-AD>preclinical AD > AD dementia). CONCLUSION We successfully detected neurodegenerative alterations of the entorhinal-hippocampal pathway at the preclinical stage of the AD continuum.
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Affiliation(s)
- Yuto Uchida
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kengo Onda
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Zhipeng Hou
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Juan C. Troncoso
- Department of Pathology, Division of Neuropathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Susumu Mori
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kenichi Oishi
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Richman Family Precision Medicine Center of Excellence in Alzheimer’s Disease, Baltimore, MD, USA
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11
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Planchuelo-Gómez Á, García-Azorín D, Guerrero ÁL, Rodríguez M, Aja-Fernández S, de Luis-García R. Structural brain changes in patients with persistent headache after COVID-19 resolution. J Neurol 2023; 270:13-31. [PMID: 36178541 PMCID: PMC9522538 DOI: 10.1007/s00415-022-11398-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/21/2022] [Accepted: 09/22/2022] [Indexed: 01/09/2023]
Abstract
Headache is among the most frequently reported symptoms after resolution of COVID-19. We assessed structural brain changes using T1- and diffusion-weighted MRI processed data from 167 subjects: 40 patients who recovered from COVID-19 but suffered from persistent headache without prior history of headache (COV), 41 healthy controls, 43 patients with episodic migraine and 43 patients with chronic migraine. To evaluate gray matter and white matter changes, morphometry parameters and diffusion tensor imaging-based measures were employed, respectively. COV patients showed significant lower cortical gray matter volume and cortical thickness than healthy subjects (p < 0.05, false discovery rate corrected) in the inferior frontal and the fusiform cortex. Lower fractional anisotropy and higher radial diffusivity (p < 0.05, family-wise error corrected) were observed in COV patients compared to controls, mainly in the corpus callosum and left hemisphere. COV patients showed higher cortical volume and thickness than migraine patients in the cingulate and frontal gyri, paracentral lobule and superior temporal sulcus, lower volume in subcortical regions and lower curvature in the precuneus and cuneus. Lower diffusion metric values in COV patients compared to migraine were identified prominently in the right hemisphere. COV patients present diverse changes in the white matter and gray matter structure. White matter changes seem to be associated with impairment of fiber bundles. Besides, the gray matter changes and other white matter modifications such as axonal integrity loss seemed subtle and less pronounced than those detected in migraine, showing that persistent headache after COVID-19 resolution could be an intermediate state between normality and migraine.
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Affiliation(s)
- Álvaro Planchuelo-Gómez
- Laboratorio de Procesado de Imagen (LPI), Universidad de Valladolid, 47011, Valladolid, Spain
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, CF24 4HQ, UK
| | - David García-Azorín
- Department of Neurology, Headache Unit, Hospital Clínico Universitario de Valladolid, Avenida Ramón y Cajal, 3, 47003, Valladolid, Spain.
- Department of Medicine, Universidad de Valladolid, 47005, Valladolid, Spain.
| | - Ángel L Guerrero
- Department of Neurology, Headache Unit, Hospital Clínico Universitario de Valladolid, Avenida Ramón y Cajal, 3, 47003, Valladolid, Spain
- Department of Medicine, Universidad de Valladolid, 47005, Valladolid, Spain
| | - Margarita Rodríguez
- Department of Radiology, Hospital Clínico Universitario de Valladolid, 47003, Valladolid, Spain
| | - Santiago Aja-Fernández
- Laboratorio de Procesado de Imagen (LPI), Universidad de Valladolid, 47011, Valladolid, Spain
| | - Rodrigo de Luis-García
- Laboratorio de Procesado de Imagen (LPI), Universidad de Valladolid, 47011, Valladolid, Spain
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Módis LV, Aradi Z, Horváth IF, Bencze J, Papp T, Emri M, Berényi E, Bugán A, Szántó A. Central Nervous System Involvement in Primary Sjögren's Syndrome: Narrative Review of MRI Findings. Diagnostics (Basel) 2022; 13:diagnostics13010014. [PMID: 36611306 PMCID: PMC9818673 DOI: 10.3390/diagnostics13010014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/16/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
Central nervous system (CNS) involvement is one of the numerous extraglandular manifestations of primary Sjögren's syndrome (pSS). Moreover, neurological complaints precede the sicca symptoms in 25-60% of the cases. We review the magnetic resonance imaging (MRI) lesions typical for pSS, involving the conventional examination, volumetric and morphometric studies, diffusion tensor imaging (DTI) and resting-state fMRI. The most common radiological lesions in pSS are white matter hyperintensities (WMH), scattered alterations hyperlucent on T2 and FLAIR sequences, typically located periventricularly and subcortically. Cortical atrophy and ventricular dilatation can also occur in pSS. Whilst these conditions are thought to be more common in pSS than healthy controls, DTI and resting-state fMRI alterations demonstrate evident microstructural changes in pSS. As pSS is often accompanied by cognitive symptoms, these MRI alterations are expectedly related to them. This relationship is not clearly delineated in conventional MRI studies, but DTI and resting-state fMRI examinations show more convincing correlations. In conclusion, the CNS manifestations of pSS do not follow a certain pattern. As the link between the MRI lesions and clinical manifestations is not well established, more studies involving larger populations should be performed to elucidate the correlations.
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Affiliation(s)
- László V. Módis
- Department of Behavioural Sciences, Faculty of General Medicine, University of Debrecen, Móricz Zsigmond krt. 22, HU-4032 Debrecen, Hungary
- Correspondence: ; Tel.: +36-52-411-600 (ext. 55252)
| | - Zsófia Aradi
- Division of Clinical Immunology, Department of Internal Medicine, Faculty of General Medicine, University of Debrecen, Móricz Zsigmond krt. 22, HU-4032 Debrecen, Hungary
| | - Ildikó Fanny Horváth
- Division of Clinical Immunology, Department of Internal Medicine, Faculty of General Medicine, University of Debrecen, Móricz Zsigmond krt. 22, HU-4032 Debrecen, Hungary
| | - János Bencze
- Division of Radiology and Imaging Science, Department of Medical Imaging, Faculty of General Medicine, University of Debrecen, Nagyerdei körút 98, HU-4032 Debrecen, Hungary
| | - Tamás Papp
- Division of Radiology and Imaging Science, Department of Medical Imaging, Faculty of General Medicine, University of Debrecen, Nagyerdei körút 98, HU-4032 Debrecen, Hungary
| | - Miklós Emri
- Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of General Medicine, University of Debrecen, Nagyerdei körút 98, HU-4032 Debrecen, Hungary
| | - Ervin Berényi
- Division of Radiology and Imaging Science, Department of Medical Imaging, Faculty of General Medicine, University of Debrecen, Nagyerdei körút 98, HU-4032 Debrecen, Hungary
| | - Antal Bugán
- Department of Behavioural Sciences, Faculty of General Medicine, University of Debrecen, Móricz Zsigmond krt. 22, HU-4032 Debrecen, Hungary
| | - Antónia Szántó
- Division of Clinical Immunology, Department of Internal Medicine, Faculty of General Medicine, University of Debrecen, Móricz Zsigmond krt. 22, HU-4032 Debrecen, Hungary
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13
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Bahsoun MA, Khan MU, Mitha S, Ghazvanchahi A, Khosravani H, Jabehdar Maralani P, Tardif JC, Moody AR, Tyrrell PN, Khademi A. FLAIR MRI biomarkers of the normal appearing brain matter are related to cognition. Neuroimage Clin 2022; 34:102955. [PMID: 35180579 PMCID: PMC8857609 DOI: 10.1016/j.nicl.2022.102955] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 01/28/2022] [Accepted: 02/01/2022] [Indexed: 01/04/2023]
Abstract
Normal appearing brain matter (NABM) biomarkers in FLAIR MRI are related to cognition. NABM texture in FLAIR MRI is correlated to mean diffusivity (MD) in dMRI. Analysis conducted on large multicentre FLAIR MRI dataset: 1400 subjects, 87 centers. NABM biomarkers vary differently across age and MoCA categories. Biomarkers showed differences in patients with AD dementia and vascular disease.
A novel biomarker panel was proposed to quantify macro and microstructural biomarkers from the normal-appearing brain matter (NABM) in multicentre fluid-attenuation inversion recovery (FLAIR) MRI. The NABM is composed of the white and gray matter regions of the brain, with the lesions and cerebrospinal fluid removed. The primary hypothesis was that NABM biomarkers from FLAIR MRI are related to cognitive outcome as determined by MoCA score. There were three groups of features designed for this task based on 1) texture: microstructural integrity (MII), macrostructural damage (MAD), microstructural damage (MID), 2) intensity: median, skewness, kurtosis and 3) volume: NABM to ICV volume ratio. Biomarkers were extracted from over 1400 imaging volumes from more than 87 centres and unadjusted ANOVA analysis revealed significant differences in means of the MII, MAD, and NABM volume biomarkers across all cognitive groups. In an adjusted ANCOVA model, a significant relationship between MoCA categories was found that was dependent on subject age for MII, MAD, intensity, kurtosis and NABM volume biomarkers. These results demonstrate that structural brain changes in the NABM are related to cognitive outcome (with different relationships depending on the age of the subjects). Therefore these biomarkers have high potential for clinical translation. As a secondary hypothesis, we investigated whether texture features from FLAIR MRI can quantify microstructural changes related to how “structured” or “damaged” the tissue is. Based on correlation analysis with diffusion weighted MRI (dMRI), it was shown that FLAIR MRI texture biomarkers (MII and MAD) had strong correlations to mean diffusivity (MD) which is related to tissue degeneration in the GM and WM regions. As FLAIR MRI is routinely collected for clinical neurological examinations, novel biomarkers from FLAIR MRI could be used to supplement current clinical biomarkers and for monitoring disease progression. Biomarkers could also be used to stratify patients into homogeneous disease subgroups for clinical trials, or to learn more about mechanistic development of dementia disease.
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Affiliation(s)
- M-A Bahsoun
- Electrical, Computer and Biomedical Engineering Dept., Ryerson University, Toronto, ON, Canada
| | - M U Khan
- Electrical, Computer and Biomedical Engineering Dept., Ryerson University, Toronto, ON, Canada
| | - S Mitha
- Electrical, Computer and Biomedical Engineering Dept., Ryerson University, Toronto, ON, Canada
| | - A Ghazvanchahi
- Electrical, Computer and Biomedical Engineering Dept., Ryerson University, Toronto, ON, Canada
| | - H Khosravani
- Hurvitz Brain Sciences Program Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada
| | | | - J-C Tardif
- Montreal Heart Institute, Montreal, QU, Canada; Department of Medicine, Université de Montréal, QU, Canada
| | - A R Moody
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - P N Tyrrell
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada; Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - A Khademi
- Electrical, Computer and Biomedical Engineering Dept., Ryerson University, Toronto, ON, Canada; Keenan Research Center for Biomedical Science, St. Michael's Hospital, Unity Health Network, Toronto, ON, Canada; Institute for Biomedical Engineering, Science and Technology (iBEST), a partnership between St. Michael's Hospital and Ryerson University, Toronto, ON, Canada
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Bukhari SNA. Dietary Polyphenols as Therapeutic Intervention for Alzheimer’s Disease: A Mechanistic Insight. Antioxidants (Basel) 2022; 11:antiox11030554. [PMID: 35326204 PMCID: PMC8945272 DOI: 10.3390/antiox11030554] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/14/2022] [Accepted: 03/14/2022] [Indexed: 02/06/2023] Open
Abstract
Dietary polyphenols encompass a diverse range of secondary metabolites found in nature, such as fruits, vegetables, herbal teas, wine, and cocoa products, etc. Structurally, they are either derivatives or isomers of phenol acid, isoflavonoids and possess hidden health promoting characteristics, such as antioxidative, anti-aging, anti-cancerous and many more. The use of such polyphenols in combating the neuropathological war raging in this generation is currently a hotly debated topic. Lately, Alzheimer’s disease (AD) is emerging as the most common neuropathological disease, destroying the livelihoods of millions in one way or another. Any therapeutic intervention to curtail its advancement in the generation to come has been in vain to date. Using dietary polyphenols to construct the barricade around it is going to be an effective strategy, taking into account their hidden potential to counter multifactorial events taking place under such pathology. Besides their strong antioxidant properties, naturally occurring polyphenols are reported to have neuroprotective effects by modulating the Aβ biogenesis pathway in Alzheimer’s disease. Thus, in this review, I am focusing on unlocking the hidden secrets of dietary polyphenols and their mechanistic advantages to fight the war with AD and related pathology.
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Affiliation(s)
- Syed Nasir Abbas Bukhari
- Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, Aljouf 2014, Saudi Arabia
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15
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Plasma neurofilament light levels correlate with white matter damage prior to Alzheimer's disease: results from ADNI. Aging Clin Exp Res 2022; 34:2363-2372. [PMID: 35226303 DOI: 10.1007/s40520-022-02095-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 02/10/2022] [Indexed: 11/01/2022]
Abstract
BACKGROUND The blood biomarker neurofilament light (NFL) is one of the most widely used for monitoring Alzheimer's disease (AD). According to recent research, a higher NFL plasma level has a substantial predictive value for cognitive deterioration in AD patients. Diffusion tensor imaging (DTI) is an MRI-based approach for detecting neurodegeneration, white matter (WM) disruption, and synaptic damage. There have been few studies on the relationship between plasma NFL and WM microstructure integrity. AIMS The goal of the current study is to assess the associations between plasma levels of NFL, CSF total tau, phosphorylated tau181 (P-tau181), and amyloid-β (Aβ) with WM microstructural alterations. METHODS We herein have investigated the cross-sectional association between plasma levels of NFL and WM microstructural alterations as evaluated by DTI in 92 patients with mild cognitive impairment (MCI) provided by Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. We analyzed the potential association between plasma NFL levels and radial diffusivity (RD), axial diffusivity (AxD), mean diffusivity (MD), and fractional anisotropy (FA) in each region of the Montreal Neurological Institute and Hospital (MNI) atlas, using simple linear regression models stratified by age, sex, and APOE ε4 genotype. RESULTS Our findings demonstrated a significant association between plasma NFL levels and disrupted WM microstructure across the brain. In distinct areas, plasma NFL has a negative association with FA in the fornix, fronto-occipital fasciculus, corpus callosum, uncinate fasciculus, internal capsule, and corona radiata and a positive association with RD, AxD, and MD values in sagittal stratum, corpus callosum, fronto-occipital fasciculus, corona radiata, internal capsule, thalamic radiation, hippocampal cingulum, fornix, and cingulum. Lower FA and higher RD, AxD, and MD values are related to demyelination and degeneration in WM. CONCLUSION Our findings revealed that the level of NFL in the blood is linked to WM alterations in MCI patients. Plasma NFL has the potential to be a biomarker for microstructural alterations. However, further longitudinal studies are necessary to validate the predictive role of plasma NFL in cognitive decline.
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16
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Kiely M, Triebswetter C, Cortina LE, Gong Z, Alsameen MH, Spencer RG, Bouhrara M. Insights into human cerebral white matter maturation and degeneration across the adult lifespan. Neuroimage 2022; 247:118727. [PMID: 34813969 PMCID: PMC8792239 DOI: 10.1016/j.neuroimage.2021.118727] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 10/15/2021] [Accepted: 11/12/2021] [Indexed: 01/01/2023] Open
Abstract
White matter (WM) microstructural properties change across the adult lifespan and with neuronal diseases. Understanding microstructural changes due to aging is paramount to distinguish them from neuropathological changes. Conducted on a large cohort of 147 cognitively unimpaired subjects, spanning a wide age range of 21 to 94 years, our study evaluated sex- and age-related differences in WM microstructure. Specifically, we used diffusion tensor imaging (DTI) magnetic resonance imaging (MRI) indices, sensitive measures of myelin and axonal density in WM, and myelin water fraction (MWF), a measure of the fraction of the signal of water trapped within the myelin sheets, to probe these differences. Furthermore, we examined regional correlations between MWF and DTI indices to evaluate whether the DTI metrics provide information complementary to MWF. While sexual dimorphism was, overall, nonsignificant, we observed region-dependent differences in MWF, that is, myelin content, and axonal density with age and found that both exhibit nonlinear, but distinct, associations with age. Furthermore, DTI indices were moderately correlated with MWF, indicating their good sensitivity to myelin content as well as to other constituents of WM tissue such as axonal density. The microstructural differences captured by our MRI metrics, along with their weak to moderate associations with MWF, strongly indicate the potential value of combining these outcome measures in a multiparametric approach. Furthermore, our results support the last-in-first-out and the gain-predicts-loss hypotheses of WM maturation and degeneration. Indeed, our results indicate that the posterior WM regions are spared from neurodegeneration as compared to anterior regions, while WM myelination follows a temporally symmetric time course across the adult life span.
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Affiliation(s)
- Matthew Kiely
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, 21224 MD, USA
| | - Curtis Triebswetter
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, 21224 MD, USA
| | - Luis E Cortina
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, 21224 MD, USA
| | - Zhaoyuan Gong
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, 21224 MD, USA
| | - Maryam H Alsameen
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, 21224 MD, USA
| | - Richard G Spencer
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, 21224 MD, USA
| | - Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, 21224 MD, USA.
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17
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Zhang L, Wang L, Xia H, Tan Y, Li C, Fang C. Connectomic mapping of brain-spinal cord neural networks: future directions in assessing spinal cord injury at rest. Neurosci Res 2021; 176:9-17. [PMID: 34699861 DOI: 10.1016/j.neures.2021.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 10/20/2021] [Accepted: 10/20/2021] [Indexed: 12/01/2022]
Abstract
Following spinal cord injury (SCI), the central nervous system undergoes significant reconstruction. The dynamic change in the interaction of the brain-spinal cord axis as well as in structure-function relations plays a vital role in the determination of neurological functions, which might have important clinical implications for the treatment and its efficacy evaluation of patients with SCI. Brain connectomes based on neuroimaging data is a relatively new field of research that maps the brain's large-scale structural and functional networks at rest. Importantly, increasing evidence shows that such resting-state signals can also be seen in the spinal cord. In the present review, we focus on the reconstruction of multi-level neural circuits after SCI. We also describe how the connectome concept could further our understanding of neuroplasticity after SCI. We propose that mapping the cortical-subcortical-spinal cord networks can provide novel insights into the pathologies of SCI.
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Affiliation(s)
- Lijian Zhang
- Postdoctoral Research Station of Neurosurgery, Affiliated Hospital of Hebei University, Hebei University, China; Department of Neurosurgery, Affiliated Hospital of Hebei University, Hebei University, China; Key Laboratory of Precise Diagnosis and Treatment of Glioma in Hebei Province, Affiliated Hospital of Hebei University, Hebei University, China
| | - Luxuan Wang
- Department of Neurology, Affiliated Hospital of Hebei University, Hebei University, China
| | - Hechun Xia
- Department of Neurosurgery, General Hospital of Ningxia Medical University, Ningxia Medical University, China
| | - Yanli Tan
- Key Laboratory of Precise Diagnosis and Treatment of Glioma in Hebei Province, Affiliated Hospital of Hebei University, Hebei University, China; Department of Pathology, Affiliated Hospital of Hebei University, Hebei University, China.
| | - Chunhui Li
- Department of Neurosurgery, Affiliated Hospital of Hebei University, Hebei University, China.
| | - Chuan Fang
- Postdoctoral Research Station of Neurosurgery, Affiliated Hospital of Hebei University, Hebei University, China; Department of Neurosurgery, Affiliated Hospital of Hebei University, Hebei University, China; Key Laboratory of Precise Diagnosis and Treatment of Glioma in Hebei Province, Affiliated Hospital of Hebei University, Hebei University, China.
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18
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Piersson AD, Ibrahim B, Suppiah S, Mohamad M, Hassan HA, Omar NF, Ibrahim MI, Yusoff AN, Ibrahim N, Saripan MI, Razali RM. Multiparametric MRI for the improved diagnostic accuracy of Alzheimer's disease and mild cognitive impairment: Research protocol of a case-control study design. PLoS One 2021; 16:e0252883. [PMID: 34547018 PMCID: PMC8454976 DOI: 10.1371/journal.pone.0252883] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 05/18/2021] [Indexed: 11/19/2022] Open
Abstract
Background Alzheimer’s disease (AD) is a major neurocognitive disorder identified by memory loss and a significant cognitive decline based on previous level of performance in one or more cognitive domains that interferes in the independence of everyday activities. The accuracy of imaging helps to identify the neuropathological features that differentiate AD from its common precursor, mild cognitive impairment (MCI). Identification of early signs will aid in risk stratification of disease and ensures proper management is instituted to reduce the morbidity and mortality associated with AD. Magnetic resonance imaging (MRI) using structural MRI (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and magnetic resonance spectroscopy (1H-MRS) performed alone is inadequate. Thus, the combination of multiparametric MRI is proposed to increase the accuracy of diagnosing MCI and AD when compared to elderly healthy controls. Methods This protocol describes a non-interventional case control study. The AD and MCI patients and the healthy elderly controls will undergo multi-parametric MRI. The protocol consists of sMRI, fMRI, DTI, and single-voxel proton MRS sequences. An eco-planar imaging (EPI) will be used to perform resting-state fMRI sequence. The structural images will be analysed using Computational Anatomy Toolbox-12, functional images will be analysed using Statistical Parametric Mapping-12, DPABI (Data Processing & Analysis for Brain Imaging), and Conn software, while DTI and 1H-MRS will be analysed using the FSL (FMRIB’s Software Library) and Tarquin respectively. Correlation of the MRI results and the data acquired from the APOE genotyping, neuropsychological evaluations (i.e. Montreal Cognitive Assessment [MoCA], and Mini–Mental State Examination [MMSE] scores) will be performed. The imaging results will also be correlated with the sociodemographic factors. The diagnosis of AD and MCI will be standardized and based on the DSM-5 criteria and the neuropsychological scores. Discussion The combination of sMRI, fMRI, DTI, and MRS sequences can provide information on the anatomical and functional changes in the brain such as regional grey matter volume atrophy, impaired functional connectivity among brain regions, and decreased metabolite levels specifically at the posterior cingulate cortex/precuneus. The combination of multiparametric MRI sequences can be used to stratify the management of MCI and AD patients. Accurate imaging can decide on the frequency of follow-up at memory clinics and select classifiers for machine learning that may aid in the disease identification and prognostication. Reliable and consistent quantification, using standardised protocols, are crucial to establish an optimal diagnostic capability in the early detection of Alzheimer’s disease.
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Affiliation(s)
- Albert Dayor Piersson
- Diagnostic Imaging and Radiotherapy Programme, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
- Department of Imaging Technology & Sonography, School of Allied Health Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Buhari Ibrahim
- Faculty of Medicine and Health Sciences, Department of Radiology, Universiti Putra Malaysia, Seri Kembangan, Malaysia
- Faculty of Medicine and Health Sciences, Neuroscience Laboratory for Cognitive Function and Behavioural Imaging (NeuroCoB), Universiti Putra Malaysia, Seri Kembangan, Malaysia
- Faculty of Basic Medical Sciences, Department of Physiology, Bauchi State University PMB 65, Gadau, Nigeria
| | - Subapriya Suppiah
- Faculty of Medicine and Health Sciences, Department of Radiology, Universiti Putra Malaysia, Seri Kembangan, Malaysia
- Faculty of Medicine and Health Sciences, Neuroscience Laboratory for Cognitive Function and Behavioural Imaging (NeuroCoB), Universiti Putra Malaysia, Seri Kembangan, Malaysia
- * E-mail:
| | - Mazlyfarina Mohamad
- Diagnostic Imaging and Radiotherapy Programme, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Hasyma Abu Hassan
- Faculty of Medicine and Health Sciences, Department of Radiology, Universiti Putra Malaysia, Seri Kembangan, Malaysia
| | - Nur Farhayu Omar
- Faculty of Medicine and Health Sciences, Department of Radiology, Universiti Putra Malaysia, Seri Kembangan, Malaysia
| | - Mohd Izuan Ibrahim
- Diagnostic Imaging and Radiotherapy Programme, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Ahmad Nazlim Yusoff
- Diagnostic Imaging and Radiotherapy Programme, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Normala Ibrahim
- Faculty of Medicine and Health Sciences, Department of Psychiatry, Universiti Putra Malaysia, Seri Kembangan, Malaysia
| | - M. Iqbal Saripan
- Faculty of Engineering, Department of Computer & Communication Systems, University Putra Malaysia, Seri Kembangan, Malaysia
| | - Rizah Mazzuin Razali
- Gerontology Unit, Department of Medicine, Hospital Kuala Lumpur, Kuala Lumpur, Malaysia
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19
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Rasmussen ID, Boayue NM, Mittner M, Bystad M, Grnli OK, Vangberg TR, Csifcsák G, Aslaksen PM. High-Definition Transcranial Direct Current Stimulation Improves Delayed Memory in Alzheimer's Disease Patients: A Pilot Study Using Computational Modeling to Optimize Electrode Position. J Alzheimers Dis 2021; 83:753-769. [PMID: 34366347 DOI: 10.3233/jad-210378] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND The optimal stimulation parameters when using transcranial direct current stimulation (tDCS) to improve memory performance in patients with Alzheimer's disease (AD) are lacking. In healthy individuals, inter-individual differences in brain anatomy significantly influence current distribution during tDCS, an effect that might be aggravated by variations in cortical atrophy in AD patients. OBJECTIVE To measure the effect of individualized HD-tDCS in AD patients. METHODS Nineteen AD patients were randomly assigned to receive active or sham high-definition tDCS (HD-tDCS). Computational modeling of the HD-tDCS-induced electric field in each patient's brain was analyzed based on magnetic resonance imaging (MRI) scans. The chosen montage provided the highest net anodal electric field in the left dorsolateral prefrontal cortex (DLPFC). An accelerated HD-tDCS design was conducted (2 mA for 3×20 min) on two separate days. Pre- and post-intervention cognitive tests and T1 and T2-weighted MRI and diffusion tensor imaging data at baseline were analyzed. RESULTS Different montages were optimal for individual patients. The active HD-tDCS group improved significantly in delayed memory and MMSE performance compared to the sham group. Five participants in the active group had higher scores on delayed memory post HD-tDCS, four remained stable and one declined. The active HD-tDCS group had a significant positive correlation between fractional anisotropy in the anterior thalamic radiation and delayed memory score. CONCLUSION HD-tDCS significantly improved delayed memory in AD. Our study can be regarded as a proof-of-concept attempt to increase tDCS efficacy. The present findings should be confirmed in larger samples.
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Affiliation(s)
- Ingrid Daae Rasmussen
- Department of Psychology, Research Group for Cognitive Neuroscience, Faculty of Health Sciences, UiT The Artic University of Norway, Tromsø, Norway.,Department of Geropsychiatry, University Hospital of North Norway, Norway
| | - Nya Mehnwolo Boayue
- Department of Psychology, Research Group for Cognitive Neuroscience, Faculty of Health Sciences, UiT The Artic University of Norway, Tromsø, Norway
| | - Matthias Mittner
- Department of Psychology, Research Group for Cognitive Neuroscience, Faculty of Health Sciences, UiT The Artic University of Norway, Tromsø, Norway
| | - Martin Bystad
- Department of Psychology, Research Group for Cognitive Neuroscience, Faculty of Health Sciences, UiT The Artic University of Norway, Tromsø, Norway.,Department of Geropsychiatry, University Hospital of North Norway, Norway
| | - Ole K Grnli
- Department of Geropsychiatry, University Hospital of North Norway, Norway
| | - Torgil Riise Vangberg
- Department of Clinical Medicine, University hospital of North Norway, Norway.,PET Center, University hospital of North Norway, Tromsø, Norway
| | - Gábor Csifcsák
- Department of Psychology, Research Group for Cognitive Neuroscience, Faculty of Health Sciences, UiT The Artic University of Norway, Tromsø, Norway
| | - Per M Aslaksen
- Department of Psychology, Research Group for Cognitive Neuroscience, Faculty of Health Sciences, UiT The Artic University of Norway, Tromsø, Norway.,Department of Child and Adolescent Psychiatry, University Hospital of North Norway, Tromsø, Norway
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20
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Kelley ME, Urban JE, Jones DA, Davenport EM, Miller LE, Snively BM, Powers AK, Whitlow CT, Maldjian JA, Stitzel JD. Analysis of longitudinal head impact exposure and white matter integrity in returning youth football players. J Neurosurg Pediatr 2021; 28:196-205. [PMID: 34130257 PMCID: PMC10193468 DOI: 10.3171/2021.1.peds20586] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 01/11/2021] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The objective of this study was to characterize changes in head impact exposure (HIE) across multiple football seasons and to determine whether changes in HIE correlate with changes in imaging metrics in youth football players. METHODS On-field head impact data and pre- and postseason imaging data, including those produced by diffusion tensor imaging (DTI), were collected from youth football athletes with at least two consecutive seasons of data. ANCOVA was used to evaluate HIE variations (number of impacts, peak linear and rotational accelerations, and risk-weighted cumulative exposure) by season number. DTI scalar metrics, including fractional anisotropy, mean diffusivity, and linear, planar, and spherical anisotropy coefficients, were evaluated. A control group was used to determine the number of abnormal white matter voxels, which were defined as 2 standard deviations above or below the control group mean. The difference in the number of abnormal voxels between consecutive seasons was computed for each scalar metric and athlete. Linear regression analyses were performed to evaluate relationships between changes in HIE metrics and changes in DTI scalar metrics. RESULTS There were 47 athletes with multiple consecutive seasons of HIE, and corresponding imaging data were available in a subsample (n = 19) of these. Increases and decreases in HIE metrics were observed among individual athletes from one season to the next, and no significant differences (all p > 0.05) in HIE metrics were observed by season number. Changes in the number of practice impacts, 50th percentile impacts per practice session, and 50th percentile impacts per session were significantly positively correlated with changes in abnormal voxels for all DTI metrics. CONCLUSIONS These results demonstrate a significant positive association between changes in HIE metrics and changes in the numbers of abnormal voxels between consecutive seasons of youth football. Reducing the number and frequency of head impacts, especially during practice sessions, may decrease the number of abnormal imaging findings from one season to the next in youth football.
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Affiliation(s)
- Mireille E. Kelley
- Departments of Biomedical Engineering
- Virginia Tech–Wake Forest School of Biomedical Engineering and Sciences, Winston-Salem, North Carolina; and
| | - Jillian E. Urban
- Departments of Biomedical Engineering
- Virginia Tech–Wake Forest School of Biomedical Engineering and Sciences, Winston-Salem, North Carolina; and
| | - Derek A. Jones
- Departments of Biomedical Engineering
- Virginia Tech–Wake Forest School of Biomedical Engineering and Sciences, Winston-Salem, North Carolina; and
| | | | - Logan E. Miller
- Departments of Biomedical Engineering
- Virginia Tech–Wake Forest School of Biomedical Engineering and Sciences, Winston-Salem, North Carolina; and
| | | | | | - Christopher T. Whitlow
- Departments of Biomedical Engineering
- Radiology (Neuroradiology), and
- Clinical and Translational Sciences Institute, Wake Forest School of Medicine, Winston-Salem
| | - Joseph A. Maldjian
- Department of Radiology, University of Texas Southwestern, Dallas, Texas
| | - Joel D. Stitzel
- Departments of Biomedical Engineering
- Virginia Tech–Wake Forest School of Biomedical Engineering and Sciences, Winston-Salem, North Carolina; and
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21
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Pourzinal D, Yang JHJ, Bakker A, McMahon KL, Byrne GJ, Pontone GM, Mari Z, Dissanayaka NN. Hippocampal correlates of episodic memory in Parkinson's disease: A systematic review of magnetic resonance imaging studies. J Neurosci Res 2021; 99:2097-2116. [PMID: 34075634 DOI: 10.1002/jnr.24863] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/09/2021] [Accepted: 05/11/2021] [Indexed: 12/15/2022]
Abstract
The present review asks whether magnetic resonance imaging (MRI) studies are able to define neural correlates of episodic memory within the hippocampus in Parkinson's disease (PD). Systematic searches were performed in PubMed, Web of Science, Medline, CINAHL, and EMBASE using search terms related to structural and functional MRI (fMRI), the hippocampus, episodic memory, and PD. Risk of bias was assessed for each study using the Newtown-Ottawa Scale. Thirty-nine studies met inclusion criteria; eight fMRI, seven diffusion MRI (dMRI), and 24 structural MRI (14 exploring whole hippocampus and 10 exploring hippocampal subfields). Critical analysis of the literature revealed mixed evidence from functional and dMRI, but stronger evidence from sMRI of the hippocampus as a biomarker for episodic memory impairment in PD. Hippocampal subfield studies most often implicated CA1, CA3/4, and subiculum volume in episodic memory and cognitive decline in PD. Despite differences in imaging methodology, study design, and sample characteristics, MRI studies have helped elucidate an important neural correlate of episodic memory impairment in PD with both clinical and theoretical implications. Natural progression of this work encourages future research on hippocampal subfield function as a potential biomarker of, or therapeutic target for, episodic memory dysfunction in PD.
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Affiliation(s)
- Dana Pourzinal
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Royal Brisbane & Women's Hospital, Brisbane, QLD, Australia
| | - Ji Hyun J Yang
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Royal Brisbane & Women's Hospital, Brisbane, QLD, Australia
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA.,Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Katie L McMahon
- School of Clinical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Gerard J Byrne
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Royal Brisbane & Women's Hospital, Brisbane, QLD, Australia.,Mental Health Service, Royal Brisbane & Women's Hospital, Brisbane, QLD, Australia
| | - Gregory M Pontone
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA.,Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Zoltan Mari
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | - Nadeeka N Dissanayaka
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Royal Brisbane & Women's Hospital, Brisbane, QLD, Australia.,Department of Neurology, Royal Brisbane & Women's Hospital, Brisbane, QLD, Australia.,School of Psychology, The University of Queensland, Brisbane, QLD, Australia
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22
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Turner S, Lazarus R, Marion D, Main KL. Molecular and Diffusion Tensor Imaging Biomarkers of Traumatic Brain Injury: Principles for Investigation and Integration. J Neurotrauma 2021; 38:1762-1782. [PMID: 33446015 DOI: 10.1089/neu.2020.7259] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The last 20 years have seen the advent of new technologies that enhance the diagnosis and prognosis of traumatic brain injury (TBI). There is recognition that TBI affects the brain beyond initial injury, in some cases inciting a progressive neuropathology that leads to chronic impairments. Medical researchers are now searching for biomarkers to detect and monitor this condition. Perhaps the most promising developments are in the biomolecular and neuroimaging domains. Molecular assays can identify proteins indicative of neuronal injury and/or degeneration. Diffusion imaging now allows sensitive evaluations of the brain's cellular microstructure. As the pace of discovery accelerates, it is important to survey the research landscape and identify promising avenues of investigation. In this review, we discuss the potential of molecular and diffusion tensor imaging (DTI) biomarkers in TBI research. Integration of these technologies could advance models of disease prognosis, ultimately improving care. To date, however, few studies have explored relationships between molecular and DTI variables in patients with TBI. Here, we provide a short primer on each technology, review the latest research, and discuss how these biomarkers may be incorporated in future studies.
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Affiliation(s)
- Stephanie Turner
- Defense and Veterans Brain Injury Center, Silver Spring, Maryland, USA.,General Dynamics Information Technology, Falls Church, Virginia, USA
| | - Rachel Lazarus
- Defense and Veterans Brain Injury Center, Silver Spring, Maryland, USA.,General Dynamics Information Technology, Falls Church, Virginia, USA
| | - Donald Marion
- Defense and Veterans Brain Injury Center, Silver Spring, Maryland, USA.,General Dynamics Information Technology, Falls Church, Virginia, USA
| | - Keith L Main
- Defense and Veterans Brain Injury Center, Silver Spring, Maryland, USA.,General Dynamics Information Technology, Falls Church, Virginia, USA
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23
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Tu MC, Huang SM, Hsu YH, Yang JJ, Lin CY, Kuo LW. Discriminating subcortical ischemic vascular disease and Alzheimer's disease by diffusion kurtosis imaging in segregated thalamic regions. Hum Brain Mapp 2021; 42:2018-2031. [PMID: 33416206 PMCID: PMC8046043 DOI: 10.1002/hbm.25342] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 12/02/2020] [Accepted: 12/29/2020] [Indexed: 12/14/2022] Open
Abstract
Differentiating between subcortical ischemic vascular disease (SIVD), Alzheimer's disease (AD), and normal cognition (NC) remains a challenge, and reliable neuroimaging biomarkers are needed. The current study, therefore, investigated the discriminative ability of diffusion kurtosis imaging (DKI) metrics in segregated thalamic regions and compare with diffusion tensor imaging (DTI) metrics. Twenty‐three SIVD patients, 30 AD patients, and 24 NC participants underwent brain magnetic resonance imaging. The DKI metrics including mean kurtosis (MK), axial kurtosis (Kaxial) and radial kurtosis (Kradial) and the DTI metrics including diffusivity and fractional anisotropy (FA) were measured within the whole thalamus and segregated thalamic subregions. Strategic correlations by group, thalamo‐frontal connectivity, and canonical discriminant analysis (CDA) were used to demonstrate the discriminative ability of DKI for SIVD, AD, and NC. Whole and segregated thalamus analysis suggested that DKI metrics are less affected by white matter hyperintensities compared to DTI metrics. Segregated thalamic analysis showed that MK and Kradial were notably different between SIVD and AD/NC. The correlation analysis between Kaxial and MK showed a nonsignificant relationship in SIVD group, a trend of negative relationship in AD group, and a significant positive relationship in NC group. A wider spatial distribution of thalamo‐frontal connectivity differences across groups was shown by MK compared to FA. CDA showed a discriminant power of 97.4% correct classification using all DKI metrics. Our findings support that DKI metrics could be more sensitive than DTI metrics to reflect microstructural changes within the gray matter, hence providing complementary information for currently outlined pathogenesis of SIVD and AD.
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Affiliation(s)
- Min-Chien Tu
- Department of Neurology, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan.,Department of Neurology, School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Sheng-Min Huang
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
| | - Yen-Hsuan Hsu
- Department of Psychology, National Chung Cheng University, Chiayi, Taiwan.,Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, Chiayi, Taiwan
| | - Jir-Jei Yang
- Department of Radiology, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan
| | | | - Li-Wei Kuo
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan.,Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan
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24
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Teipel SJ, Kuper-Smith JO, Bartels C, Brosseron F, Buchmann M, Buerger K, Catak C, Janowitz D, Dechent P, Dobisch L, Ertl-Wagner B, Fließbach K, Haynes JD, Heneka MT, Kilimann I, Laske C, Li S, Menne F, Metzger CD, Priller J, Pross V, Ramirez A, Scheffler K, Schneider A, Spottke A, Spruth EJ, Wagner M, Wiltfang J, Wolfsgruber S, Düzel E, Jessen F, Dyrba M. Multicenter Tract-Based Analysis of Microstructural Lesions within the Alzheimer's Disease Spectrum: Association with Amyloid Pathology and Diagnostic Usefulness. J Alzheimers Dis 2020; 72:455-465. [PMID: 31594223 PMCID: PMC6918918 DOI: 10.3233/jad-190446] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Diffusion changes as determined by diffusion tensor imaging are potential indicators of microstructural lesions in people with mild cognitive impairment (MCI), prodromal Alzheimer’s disease (AD), and AD dementia. Here we extended the scope of analysis toward subjective cognitive complaints as a pre-MCI at risk stage of AD. In a cohort of 271 participants of the prospective DELCODE study, including 93 healthy controls and 98 subjective cognitive decline (SCD), 45 MCI, and 35 AD dementia cases, we found reductions of fiber tract integrity in limbic and association fiber tracts in MCI and AD dementia compared with controls in a tract-based analysis (p < 0.05, family wise error corrected). In contrast, people with SCD showed spatially restricted white matter alterations only for the mode of anisotropy and only at an uncorrected level of significance. DTI parameters yielded a high cross-validated diagnostic accuracy of almost 80% for the clinical diagnosis of MCI and the discrimination of Aβ positive MCI cases from Aβ negative controls. In contrast, DTI parameters reached only random level accuracy for the discrimination between Aβ positive SCD and control cases from Aβ negative controls. These findings suggest that in prodromal stages of AD, such as in Aβ positive MCI, multicenter DTI with prospectively harmonized acquisition parameters yields diagnostic accuracy meeting the criteria for a useful biomarker. In contrast, automated tract-based analysis of DTI parameters is not useful for the identification of preclinical AD, including Aβ positive SCD and control cases.
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Affiliation(s)
- Stefan J Teipel
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany.,German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Jan O Kuper-Smith
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany
| | - Claudia Bartels
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Goettingen, Germany
| | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Martina Buchmann
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.,Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Munich, Germany.,Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Cihan Catak
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Peter Dechent
- MR-Research in Neurology and Psychiatry, Georg-August-University Göttingen, Göttingen, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Birgit Ertl-Wagner
- Institute for Clinical Radiology, Ludwig-Maximilians-University, Munich, Germany.,Division of Neuroradiology, Department of Medical Imaging, The Hospital for Sick Children, Toronto, Canada
| | - Klaus Fließbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - John-Dylan Haynes
- Bernstein Center for Computational Neuroscience, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Michael T Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Ingo Kilimann
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany.,German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.,Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Siyao Li
- Institute of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Felix Menne
- Institute of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Coraline D Metzger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany.,Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany
| | - Josef Priller
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany.,German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Verena Pross
- Study Center Bonn, Medical Faculty, Bonn, Germany
| | - Alfredo Ramirez
- Department of Psychiatry, University of Cologne, Cologne, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurology, University of Bonn, Bonn, Germany
| | - Eike J Spruth
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany.,German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Goettingen, Germany
| | | | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Psychiatry, University of Cologne, Cologne, Germany
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
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25
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Wegmayr V, Buhmann JM. Entrack: Probabilistic Spherical Regression with Entropy Regularization for Fiber Tractography. Int J Comput Vis 2020. [DOI: 10.1007/s11263-020-01384-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
AbstractWhite matter tractography, based on diffusion-weighted magnetic resonance images, is currently the only available in vivo method to gather information on the structural brain connectivity. The low resolution of diffusion MRI data suggests to employ probabilistic methods for streamline reconstruction, i.e., for fiber crossings. We propose a general probabilistic model for spherical regression based on the Fisher-von-Mises distribution, which efficiently estimates maximum entropy posteriors of local streamline directions with machine learning methods. The optimal precision of posteriors for streamlines is determined by an information-theoretic technique, the expected log-posterior agreement concept. It relies on the requirement that the posterior distributions of streamlines, inferred on retest measurements of the same subject, should yield stable results within the precision determined by the noise level of the data source.
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26
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Li M, Li Y, Jin J, Yang Z, Zhang B, Liu Y, Song M, Freakly C, Weber E, Liu F, Jiang T, Crozier S. A dedicated eight-channel receive RF coil array for monkey brain MRI at 9.4 T. NMR IN BIOMEDICINE 2020; 33:e4369. [PMID: 32729642 DOI: 10.1002/nbm.4369] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 06/09/2020] [Accepted: 06/15/2020] [Indexed: 06/11/2023]
Abstract
The neuroimaging of nonhuman primates (NHPs) realised with magnetic resonance imaging (MRI) plays an important role in understanding brain structures and functions, as well as neurodegenerative diseases and pathological disorders. Theoretically, an ultrahigh field MRI (≥7 T) is capable of providing a higher signal-to-noise ratio (SNR) for better resolution; however, the lack of appropriate radiofrequency (RF) coils for 9.4 T monkey MRI undermines the benefits provided by a higher field strength. In particular, the standard volume birdcage coil at 9.4 T generates typical destructive interferences in the periphery of the brain, which reduces the SNR in the neuroscience-focused cortex region. Also, the standard birdcage coil is not capable of performing parallel imaging. Consequently, extended scan durations may cause unnecessary damage due to overlong anaesthesia. In this work, assisted by numerical simulations, an eight-channel receive RF coil array was specially designed and manufactured for imaging NHPs at 9.4 T. The structure and geometry of the proposed receive array was optimised with numerical simulations, so that the SNR enhancement region was particularly focused on monkey brain. Validated with rhesus monkey and cynomolgus monkey brain images acquired from a 9.4 T MRI scanner, the proposed receive array outperformed standard birdcage coil with higher SNR, mean diffusivity and fractional anisotropy values, as well as providing better capability for parallel imaging.
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Affiliation(s)
- Mingyan Li
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Yu Li
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Jin Jin
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
- Siemens Healthcare Pty. Ltd., Bowen Hills QLD, 4006, Australia
| | - Zhengyi Yang
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yanyan Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Ming Song
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Craig Freakly
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Ewald Weber
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Feng Liu
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
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27
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Tülay EE, Güntekin B, Yener G, Bayram A, Başar-Eroğlu C, Demiralp T. Evoked and induced EEG oscillations to visual targets reveal a differential pattern of change along the spectrum of cognitive decline in Alzheimer's Disease. Int J Psychophysiol 2020; 155:41-48. [DOI: 10.1016/j.ijpsycho.2020.06.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 05/31/2020] [Accepted: 06/02/2020] [Indexed: 11/15/2022]
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28
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Serum neurofilament light chain levels are associated with white matter integrity in autosomal dominant Alzheimer's disease. Neurobiol Dis 2020; 142:104960. [PMID: 32522711 PMCID: PMC7363568 DOI: 10.1016/j.nbd.2020.104960] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/03/2020] [Accepted: 06/04/2020] [Indexed: 12/12/2022] Open
Abstract
Neurofilament light chain (NfL) is a protein that is selectively expressed in neurons. Increased levels of NfL measured in either cerebrospinal fluid or blood is thought to be a biomarker of neuronal damage in neurodegenerative diseases. However, there have been limited investigations relating NfL to the concurrent measures of white matter (WM) decline that it should reflect. White matter damage is a common feature of Alzheimer's disease. We hypothesized that serum levels of NfL would associate with WM lesion volume and diffusion tensor imaging (DTI) metrics cross-sectionally in 117 autosomal dominant mutation carriers (MC) compared to 84 non-carrier (NC) familial controls as well as in a subset (N = 41) of MC with longitudinal NfL and MRI data. In MC, elevated cross-sectional NfL was positively associated with WM hyperintensity lesion volume, mean diffusivity, radial diffusivity, and axial diffusivity and negatively with fractional anisotropy. Greater change in NfL levels in MC was associated with larger changes in fractional anisotropy, mean diffusivity, and radial diffusivity, all indicative of reduced WM integrity. There were no relationships with NfL in NC. Our results demonstrate that blood-based NfL levels reflect WM integrity and supports the view that blood levels of NfL are predictive of WM damage in the brain. This is a critical result in improving the interpretability of NfL as a marker of brain integrity, and for validating this emerging biomarker for future use in clinical and research settings across multiple neurodegenerative diseases. Serum NfL levels reflect white matter integrity in autosomal dominant Alzheimer disease. Associations between NfL and white matter imaging are present throughout all brain regions. Longitudinal white matter alterations are associated with changes in blood NfL. Results improve interpretability of NfL as a marker of brain integrity.
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29
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Ye Z, George A, Wu AT, Niu X, Lin J, Adusumilli G, Naismith RT, Cross AH, Sun P, Song SK. Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions. Ann Clin Transl Neurol 2020; 7:695-706. [PMID: 32304291 PMCID: PMC7261762 DOI: 10.1002/acn3.51037] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 02/24/2020] [Accepted: 03/13/2020] [Indexed: 11/08/2022] Open
Abstract
OBJECTIVE Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterogeneity. We hypothesized that the profiles of multiple DBSI metrics can identify lesion-defining patterns. Here we test this hypothesis by combining a deep learning algorithm using deep neural network (DNN) with DBSI and other imaging methods. METHODS Thirty-eight MS patients were scanned with diffusion-weighted imaging, magnetization transfer imaging, and standard conventional MRI sequences (cMRI). A total of 499 regions of interest were identified on standard MRI and labeled as persistent black holes (PBH), persistent gray holes (PGH), acute black holes (ABH), acute gray holes (AGH), nonblack or gray holes (NBH), and normal appearing white matter (NAWM). DBSI, diffusion tensor imaging (DTI), and magnetization transfer ratio (MTR) were applied to the 43,261 imaging voxels extracted from these ROIs. The optimized DNN with 10 fully connected hidden layers was trained using the imaging metrics of the lesion subtypes and NAWM. RESULTS Concordance, sensitivity, specificity, and accuracy were determined for the different imaging methods. DBSI-DNN derived lesion classification achieved 93.4% overall concordance with predetermined lesion types, compared with 80.2% for DTI-DNN model, 78.3% for MTR-DNN model, and 74.2% for cMRI-DNN model. DBSI-DNN also produced the highest specificity, sensitivity, and accuracy. CONCLUSIONS DBSI-DNN improves the classification of different MS lesion subtypes, which could aid clinical decision making. The efficacy and efficiency of DBSI-DNN shows great promise for clinical applications in automatic MS lesion detection and classification.
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Affiliation(s)
- Zezhong Ye
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, 63110
| | - Ajit George
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, 63110
| | - Anthony T Wu
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri, 63130
| | - Xuan Niu
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, 63110
| | - Joshua Lin
- Keck School of Medicine, University of Southern California, Los Angeles, California, 90033
| | - Gautam Adusumilli
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, 63110
| | - Robert T Naismith
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, 63110
| | - Anne H Cross
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, 63110
| | - Peng Sun
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, 63110
| | - Sheng-Kwei Song
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, 63110
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30
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Marzban EN, Eldeib AM, Yassine IA, Kadah YM. Alzheimer's disease diagnosis from diffusion tensor images using convolutional neural networks. PLoS One 2020; 15:e0230409. [PMID: 32208428 PMCID: PMC7092978 DOI: 10.1371/journal.pone.0230409] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Accepted: 03/01/2020] [Indexed: 12/21/2022] Open
Abstract
Machine learning algorithms are currently being implemented in an escalating manner to classify and/or predict the onset of some neurodegenerative diseases; including Alzheimer's Disease (AD); this could be attributed to the fact of the abundance of data and powerful computers. The objective of this work was to deliver a robust classification system for AD and Mild Cognitive Impairment (MCI) against healthy controls (HC) in a low-cost network in terms of shallow architecture and processing. In this study, the dataset included was downloaded from the Alzheimer's disease neuroimaging initiative (ADNI). The classification methodology implemented was the convolutional neural network (CNN), where the diffusion maps, and gray-matter (GM) volumes were the input images. The number of scans included was 185, 106, and 115 for HC, MCI and AD respectively. Ten-fold cross-validation scheme was adopted and the stacked mean diffusivity (MD) and GM volume produced an AUC of 0.94 and 0.84, an accuracy of 93.5% and 79.6%, a sensitivity of 92.5% and 62.7%, and a specificity of 93.9% and 89% for AD/HC and MCI/HC classification respectively. This work elucidates the impact of incorporating data from different imaging modalities; i.e. structural Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI), where deep learning was employed for the aim of classification. To the best of our knowledge, this is the first study assessing the impact of having more than one scan per subject and propose the proper maneuver to confirm the robustness of the system. The results were competitive among the existing literature, which paves the way for improving medications that could slow down the progress of the AD or prevent it.
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Affiliation(s)
- Eman N. Marzban
- Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza, Egypt
| | - Ayman M. Eldeib
- Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza, Egypt
| | - Inas A. Yassine
- Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza, Egypt
| | - Yasser M. Kadah
- Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza, Egypt
- Biomedical Engineering Program, Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah, Saudi Arabia
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31
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Alm KH, Faria AV, Moghekar A, Pettigrew C, Soldan A, Mori S, Albert M, Bakker A. Medial temporal lobe white matter pathway variability is associated with individual differences in episodic memory in cognitively normal older adults. Neurobiol Aging 2020; 87:78-88. [PMID: 31874745 PMCID: PMC7064393 DOI: 10.1016/j.neurobiolaging.2019.11.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 11/01/2019] [Accepted: 11/16/2019] [Indexed: 11/29/2022]
Abstract
Significant evidence demonstrates that aging is associated with variability in cognitive performance, even among individuals who are cognitively normal. In this study, we examined measures from magnetic resonance imaging and cerebrospinal fluid (CSF) to investigate which measures, alone or in combination, were associated with individual differences in episodic memory performance. Using hierarchical linear regressions, we compared the ability of diffusion tensor imaging (DTI) metrics, CSF measures of amyloid and tau, and gray matter volumes to explain variability in memory performance in a cohort of cognitively normal older adults. Measures of DTI microstructure were significantly associated with variance in memory performance, even after accounting for the contribution of the CSF and magnetic resonance imaging gray matter volume measures. Significant associations were found between DTI measures of the hippocampal cingulum and fornix with individual differences in memory. No such relationships were found between memory performance and CSF markers or gray matter volumes. These findings suggest that DTI metrics may be useful in identifying changes associated with aging or age-related diseases.
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Affiliation(s)
- Kylie H Alm
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Andreia V Faria
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Corinne Pettigrew
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Anja Soldan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Susumu Mori
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD.
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32
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Power MC, Su D, Wu A, Reid RI, Jack CR, Knopman DS, Coresh J, Huang J, Kantarci K, Sharrett AR, Gottesman RG, Griswold ME, Mosley TH. Association of white matter microstructural integrity with cognition and dementia. Neurobiol Aging 2019; 83:63-72. [PMID: 31585368 PMCID: PMC6914220 DOI: 10.1016/j.neurobiolaging.2019.08.021] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 08/07/2019] [Accepted: 08/21/2019] [Indexed: 10/26/2022]
Abstract
Late-life measures of white matter (WM) microstructural integrity may predict cognitive status, cognitive decline, and incident mild cognitive impairment (MCI) or dementia. We considered participants of the Atherosclerosis Risk in Communities study who underwent cognitive assessment and neuroimaging in 2011-2013 and were followed through 2016-2017 (n = 1775 for analyses of prevalent MCI and dementia, baseline cognitive performance, and longitudinal cognitive change and n = 889 for analyses of incident MCI, dementia, or death). Cross-sectionally, both overall WM fractional anisotropy and overall WM mean diffusivity were strongly associated with baseline cognitive performance and risk of prevalent MCI or dementia. Longitudinally, greater overall WM mean diffusivity was associated with accelerated cognitive decline, as well as incident MCI, incident dementia, and mortality, but WM fractional anisotropy was not robustly associated with cognitive change or incident cognitive impairment. Both cross-sectional and longitudinal associations were attenuated after additionally adjusting for likely downstream pathologic changes. Increased WM mean diffusivity may provide an early indication of dementia pathogenesis.
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Affiliation(s)
- Melinda C Power
- Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, George Washington University, Washington, DC, USA.
| | - Dan Su
- Department of Data Science, JD Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA
| | - Aozhou Wu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Joe Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Juebin Huang
- Department of Neurology, University of Mississippi Medical Center, Jackson, MS, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - A Richey Sharrett
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Rebecca G Gottesman
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Mike E Griswold
- Department of Data Science, JD Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA
| | - Thomas H Mosley
- Department of Neurology, University of Mississippi Medical Center, Jackson, MS, USA; Department of Geriatrics, University of Mississippi Medical Center, Jackson, MS, USA
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33
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Manno FAM, Isla AG, Manno SHC, Ahmed I, Cheng SH, Barrios FA, Lau C. Early Stage Alterations in White Matter and Decreased Functional Interhemispheric Hippocampal Connectivity in the 3xTg Mouse Model of Alzheimer's Disease. Front Aging Neurosci 2019; 11:39. [PMID: 30967770 PMCID: PMC6440287 DOI: 10.3389/fnagi.2019.00039] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Accepted: 02/08/2019] [Indexed: 12/21/2022] Open
Abstract
Alzheimer’s disease (AD) is characterized in the late stages by amyloid-β (Aβ) plaques and neurofibrillary tangles. Nevertheless, recent evidence has indicated that early changes in cerebral connectivity could compromise cognitive functions even before the appearance of the classical neuropathological features. Diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI) and volumetry were performed in the triple transgenic mouse model of AD (3xTg-AD) at 2 months of age, prior to the development of intraneuronal plaque accumulation. We found the 3xTg-AD had significant fractional anisotropy (FA) increase and radial diffusivity (RD) decrease in the cortex compared with wild-type controls, while axial diffusivity (AD) and mean diffusivity (MD) were similar. Interhemispheric hippocampal connectivity was decreased in the 3xTg-AD while connectivity in the caudate putamen (CPu) was similar to controls. Most surprising, ventricular volume in the 3xTg-AD was four times larger than controls. The results obtained in this study characterize the early stage changes in interhemispheric hippocampal connectivity in the 3xTg-AD mouse that could represent a translational biomarker to human models in preclinical stages of the AD.
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Affiliation(s)
- Francis A M Manno
- Department of Physics, City University of Hong Kong, Kowloon, Hong Kong.,Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla, Mexico
| | - Arturo G Isla
- Neuronal Oscillations Laboratory, Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden
| | - Sinai H C Manno
- Department of Physics, City University of Hong Kong, Kowloon, Hong Kong.,State Key Laboratory of Marine Pollution (SKLMP), City University of Hong Kong, Kowloon, Hong Kong.,Department of Biomedical Sciences, College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong
| | - Irfan Ahmed
- Department of Physics, City University of Hong Kong, Kowloon, Hong Kong.,Electrical Engineering Department, Sukkur IBA University, Sukkur, Pakistan
| | - Shuk Han Cheng
- State Key Laboratory of Marine Pollution (SKLMP), City University of Hong Kong, Kowloon, Hong Kong.,Department of Biomedical Sciences, College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong.,Department of Materials Science and Engineering, City University of Hong Kong, Kowloon, Hong Kong
| | - Fernando A Barrios
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla, Mexico
| | - Condon Lau
- Department of Physics, City University of Hong Kong, Kowloon, Hong Kong
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Pietzuch M, King AE, Ward DD, Vickers JC. The Influence of Genetic Factors and Cognitive Reserve on Structural and Functional Resting-State Brain Networks in Aging and Alzheimer's Disease. Front Aging Neurosci 2019; 11:30. [PMID: 30894813 PMCID: PMC6414800 DOI: 10.3389/fnagi.2019.00030] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 02/01/2019] [Indexed: 01/22/2023] Open
Abstract
Magnetic resonance imaging (MRI) offers significant insight into the complex organization of neural networks within the human brain. Using resting-state functional MRI data, topological maps can be created to visualize changes in brain activity, as well as to represent and assess the structural and functional connections between different brain regions. Crucially, Alzheimer's disease (AD) is associated with progressive loss in this connectivity, which is particularly evident within the default mode network. In this paper, we review the recent literature on how factors that are associated with risk of dementia may influence the organization of the brain network structures. In particular, we focus on cognitive reserve and the common genetic polymorphisms of APOE and BDNF Val66Met.
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Affiliation(s)
- Manuela Pietzuch
- Wicking Dementia Research and Education Centre, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia
| | - Anna E. King
- Wicking Dementia Research and Education Centre, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia
| | - David D. Ward
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - James C. Vickers
- Wicking Dementia Research and Education Centre, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia
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Zavaliangos-Petropulu A, Nir TM, Thomopoulos SI, Reid RI, Bernstein MA, Borowski B, Jack CR, Weiner MW, Jahanshad N, Thompson PM. Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3. Front Neuroinform 2019; 13:2. [PMID: 30837858 PMCID: PMC6390411 DOI: 10.3389/fninf.2019.00002] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 01/21/2019] [Indexed: 12/14/2022] Open
Abstract
Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter (WM) changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer's Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4 ± 7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FADTI) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FATDF), in 24 bilaterally averaged WM regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FADTI. We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and AD: the AD Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects regression model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix (crus)/stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum (CGH) and uncinate fasciculus (UNC) for associations between axial or mean diffusivity and CDR-sob. FATDF detected robust widespread associations with clinical measures, while FADTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.
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Affiliation(s)
- Artemis Zavaliangos-Petropulu
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Talia M Nir
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, United States
| | - Matt A Bernstein
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Bret Borowski
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Michael W Weiner
- Department of Radiology, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
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Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening. JOURNAL OF HEALTHCARE ENGINEERING 2019; 2019:9507193. [PMID: 30838124 PMCID: PMC6374863 DOI: 10.1155/2019/9507193] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 10/24/2018] [Accepted: 11/05/2018] [Indexed: 01/13/2023]
Abstract
For patients with cognitive disorders and dementia, accurate prognosis of cognitive worsening is critical to their ability to prepare for the future, in collaboration with health-care providers. Despite multiple efforts to apply computational brain magnetic resonance image (MRI) analysis in predicting cognitive worsening, with several successes, brain MRI is not routinely quantified in clinical settings to guide prognosis and clinical decision-making. To encourage the clinical use of a cutting-edge image segmentation method, we developed a prediction model as part of an established web-based cloud platform, MRICloud. The model was built in a training dataset from Alzheimer's Disease Neuroimaging Initiative (ADNI) where baseline MRI scans were combined with clinical data over time. Each MRI was parcellated into 265 anatomical units based on the MRICloud fully automated image segmentation function, to measure the volume of each parcel. The Mini Mental State Examination (MMSE) was used as a measure of cognitive function. The normalized volume of 265 parcels, combined with baseline MMSE score, age, and sex were input variables for a Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, with MMSE change in the subsequent two years as the target for prediction. A leave-one-out analysis performed on the training dataset estimated a correlation coefficient of 0.64 between true and predicted MMSE change. A receiver operating characteristic (ROC) analysis estimated a sensitivity of 0.88 and a specificity of 0.76 in predicting substantial cognitive worsening after two years, defined as MMSE decline of ≥4 points. This MRICloud prediction model was then applied to a test dataset of clinically acquired MRIs from the Johns Hopkins Memory and Alzheimer's Treatment Center (MATC), a clinical care setting. In the latter setting, the model had both sensitivity and specificity of 1.0 in predicting substantial cognitive worsening. While the MRICloud prediction model demonstrated promise as a platform on which computational MRI findings can easily be extended to clinical use, further study with a larger number of patients is needed for validation.
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Alm KH, Bakker A. Relationships Between Diffusion Tensor Imaging and Cerebrospinal Fluid Metrics in Early Stages of the Alzheimer's Disease Continuum. J Alzheimers Dis 2019; 70:965-981. [PMID: 31306117 PMCID: PMC6860011 DOI: 10.3233/jad-181210] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Recently, the field of Alzheimer's disease (AD) research has adopted a new framework that places the progression of AD along a continuum consisting of a preclinical stage, followed by conversion to mild cognitive impairment, and ultimately dementia. Important neuropathological changes occur in the preclinical phase, necessitating the identification of metrics that can detect such early changes. While cerebrospinal fluid (CSF) measures of amyloid and tau are generally accepted as biomarkers of AD pathology, neuroimaging measures used to index white matter alterations throughout the brain remain less widely endorsed as candidate biomarkers. To explore the relationship between white matter alterations and AD pathology, we review the literature on multimodal studies that assessed both CSF markers and white matter indices, derived from diffusion tensor imaging (DTI) methods, across cohorts primarily in the early phases of AD. Our review indicates that abnormal CSF measures of Aβ42 and tau are associated with widespread alterations in white matter microstructure throughout the brain. Furthermore, white matter variability is related to individual differences in behavior and can aid in tracking longitudinal changes in cognition. Our review advocates for the utilization of DTI metrics in investigations of early AD and suggests that the combined use of DTI and CSF markers may better explain individual differences in cognition and disease progression. However, further research is needed to resolve certain mixed findings.
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Affiliation(s)
- Kylie H. Alm
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Tsai CY, Poon YY, Chan JYH, Chan SHH. Baroreflex functionality in the eye of diffusion tensor imaging. J Physiol 2018; 597:41-55. [PMID: 30325020 DOI: 10.1113/jp277008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 10/02/2018] [Indexed: 01/14/2023] Open
Abstract
By applying diffusion tensor imaging (DTI) as a physiological tool to evaluate changes in functional connectivity between key brainstem nuclei in the baroreflex neural circuits of mice and rats, recent work has revealed several hitherto unidentified phenomena regarding baroreflex functionality. (1) The presence of robust functional connectivity between nucleus tractus solitarii (NTS) and nucleus ambiguus (NA) or rostral ventrolateral medulla (RVLM) offers a holistic view on the moment-to-moment modus operandi of the cardiac vagal baroreflex or baroreflex-mediated sympathetic vasomotor tone. (2) Under pathophysiological conditions (e.g. neurogenic hypertension), the disruption of functional connectivity between key nuclei in the baroreflex circuits is reversible. However, fatality ensues on progression from pathophysiological to pathological conditions (e.g. hepatic encephalopathy) when the functional connectivity between NTS and NA or RVLM is irreversibly severed. (3) The absence of functional connectivity between the NTS and caudal ventrolateral medulla (CVLM) necessitates partial rewiring of the classical neural circuit that includes CVLM as an inhibitory intermediate between the NTS and RVLM. (4) Sustained functional connectivity between the NTS and NA is responsible for the vital period between brain death and the inevitable cardiac death. (5) Reduced functional connectivity between the NTS and RVLM or NA points to inherent anomalous baroreflex functionality in floxed and Cre-Lox mice. (6) Disrupted NTS-NA functional connectivity in Flk-1 (VEGFR2) deficient mice offers an explanation for the hypertensive side-effect of anti-vascular endothelial growth factor therapy (anti-VEGF) therapy. These newly identified baroreflex functionalities revealed by DTI bear clinical and therapeutic implications.
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Affiliation(s)
- Ching-Yi Tsai
- Institute for Translational Research in Biomedicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan, Republic of China
| | - Yan-Yuen Poon
- Institute for Translational Research in Biomedicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan, Republic of China.,Department of Anesthesiology, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan, Republic of China
| | - Julie Y H Chan
- Institute for Translational Research in Biomedicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan, Republic of China
| | - Samuel H H Chan
- Institute for Translational Research in Biomedicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan, Republic of China
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Kozora E, Filley CM, Erkan D, Uluğ AM, Vo A, Ramon G, Burleson A, Zimmerman RD, Lockshin MD. Longitudinal evaluation of diffusion tensor imaging and cognition in systemic lupus erythematosus. Lupus 2018; 27:1810-1818. [DOI: 10.1177/0961203318793215] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Objective This pilot study aimed to examine longitudinal changes in brain structure and function in patients with systemic lupus erythematosus (SLE) using diffusion tensor imaging (DTI) and neuropsychological testing. Methods Fifteen female SLE patients with no history of major neuropsychiatric (NP) manifestations had brain magnetic resonance imaging (MRI) with DTI at baseline and approximately 1.5 years later. At the same time points, a standardized battery of cognitive tests yielding a global cognitive impairment index (CII) was administered. At baseline, the SLE patients had mean age of 34.0 years (SD = 11.4), mean education of 14.9 years (SD = 2.1), and mean disease duration of 121.5 months (SD = 106.5). The MRI images were acquired with a 3T GE MRI scanner. A DTI sequence with 33 diffusion directions and b-value of 800 s/mm2 was used. Image acquisition time was about 10 minutes. Results No significant change in cognitive dysfunction (from the CII) was detected. Clinically evaluated MRI scans remained essentially unchanged, with 62% considered normal at both times, and the remainder showing white matter (WM) hyperintensities that remained stable or resolved. DTI showed decreased fractional anisotropy (FA) and increased mean diffusivity (MD) in bilateral cerebral WM and gray matter (GM) with no major change in NP status, medical symptoms, or medications over time. Lower FA was found in the following regions: left and right cerebral WM, and in GM areas including the parahippocampal gyrus, thalamus, precentral gyrus, postcentral gyrus, angular gyrus, parietal lobe, and cerebellum. Greater MD was found in the following regions: left and right cerebral WM, frontal cortex, left cerebral cortex, and the putamen. Conclusions This is the first longitudinal study of DTI and cognition in SLE, and results disclosed changes in both WM and GM without cognitive decline over an 18-month period. DTI abnormalities in our participants were not associated with emergent NP activity, medical decline, or medication changes, and the microstructural changes developed in the absence of macrostructural abnormalities on standard MRI. Microstructural changes may relate to ongoing inflammation, and the stability of cognitive function may be explained by medical treatment, the variability of NP progression in SLE, or the impact of cognitive reserve.
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Affiliation(s)
- E Kozora
- Department of Medicine, National Jewish Health, Denver, CO, USA
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO, USA
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, USA
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - C M Filley
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO, USA
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, USA
- Marcus Institute for Brain Health, University of Colorado, Aurora, CO, USA
| | - D Erkan
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - A M Uluğ
- CorTechs Labs, San Diego, CA, USA
- Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Turkey
| | - A Vo
- The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - G Ramon
- Hospital for Special Surgery, New York, NY, USA
| | - A Burleson
- Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA
| | | | - M D Lockshin
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
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40
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Snow WM, Dale R, O'Brien-Moran Z, Buist R, Peirson D, Martin M, Albensi BC. In Vivo Detection of Gray Matter Neuropathology in the 3xTg Mouse Model of Alzheimer's Disease with Diffusion Tensor Imaging. J Alzheimers Dis 2018; 58:841-853. [PMID: 28505976 DOI: 10.3233/jad-170136] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
A diagnosis of Alzheimer's disease (AD), a neurodegenerative disorder accompanied by severe functional and cognitive decline, is based on clinical findings, with final confirmation of the disease at autopsy by the presence of amyloid-β (Aβ) plaques and neurofibrillary tangles. Given that microstructural brain alterations occur years prior to clinical symptoms, efforts to detect brain changes early could significantly enhance our ability to diagnose AD sooner. Diffusion tensor imaging (DTI), a type of MRI that characterizes the magnitude, orientation, and anisotropy of the diffusion of water in tissues, has been used to infer neuropathological changes in vivo. Its utility in AD, however, is still under investigation. The current study used DTI to examine brain regions susceptible to AD-related pathology; the cerebral cortex, entorhinal cortex, and hippocampus, in 12-14-month-old 3xTg AD mice that possess both Aβ plaques and neurofibrillary tangles. Mean diffusivity did not differ between 3xTg and control mice in any region. Decreased fractional anisotropy (p < 0.01) and axial diffusivity (p < 0.05) were detected only in the hippocampus, in which both congophilic Aβ plaques and hyperphosphorylated tau accumulation, consistent with neurofibrillary tangle formation, were detected. Pathological tau accumulation was seen in the cortex. The entorhinal cortex was largely spared from AD-related neuropathology. This is the first study to demonstrate DTI abnormalities in gray matter in a mouse model of AD in which both pathological hallmarks are present, suggesting the feasibility of DTI as a non-invasive means of detecting brain pathology in vivo in early-stage AD.
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Affiliation(s)
- Wanda M Snow
- Division of Neurodegenerative Disorders, St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, MB, Canada.,Department of Pharmacology & Therapeutics, University of Manitoba, Winnipeg, MB, Canada
| | - Ryan Dale
- Division of Neurodegenerative Disorders, St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, MB, Canada.,Department of Pharmacology & Therapeutics, University of Manitoba, Winnipeg, MB, Canada
| | | | - Richard Buist
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
| | - Danial Peirson
- Division of Neurodegenerative Disorders, St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, MB, Canada.,Department of Pharmacology & Therapeutics, University of Manitoba, Winnipeg, MB, Canada
| | - Melanie Martin
- Department of Pharmacology & Therapeutics, University of Manitoba, Winnipeg, MB, Canada.,Department of Physics, University of Winnipeg, Winnipeg, MB, Canada.,Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
| | - Benedict C Albensi
- Division of Neurodegenerative Disorders, St. Boniface Hospital Albrechtsen Research Centre, Winnipeg, MB, Canada.,Department of Pharmacology & Therapeutics, University of Manitoba, Winnipeg, MB, Canada
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41
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Neher PF, Reicht I, van Bruggen T, Goch C, Reisert M, Nolden M, Zelzer S, Meinzer HP, Stieltjes B, Fritzsche KH. MITK Diffusion Imaging. Methods Inf Med 2018; 51:441-8. [DOI: 10.3414/me11-02-0031] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2011] [Accepted: 03/23/2012] [Indexed: 11/09/2022]
Abstract
SummaryBackground: Diffusion-MRI provides a unique window on brain anatomy and insights into aspects of tissue structure in living humans that could not be studied previously. There is a major effort in this rapidly evolving field of research to develop the algorithmic tools necessary to cope with the complexity of the datasets.Objectives: This work illustrates our strategy that encompasses the development of a modularized and open software tool for data processing, visualization and interactive exploration in diffusion imaging research and aims at reinforcing sustainable evaluation and progress in the field.Methods: In this paper, the usability and capabilities of a new application and toolkit component of the Medical Imaging and Interaction Toolkit (MITK, www.mitk.org), MITKDI, are demonstrated using in-vivo datasets.Results: MITK-DI provides a comprehensive software framework for high-performance data processing, analysis and interactive data exploration, which is designed in a modular, extensible fashion (using CTK) and in adherence to widely accepted coding standards (e.g. ITK, VTK). MITK-DI is available both as an open source software development toolkit and as a ready-to-use in stallable application.Conclusions: The open source release of the modular MITK-DI tools will increase verifiability and comparability within the research community and will also be an important step towards bringing many of the current techniques towards clinical application.
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Nir TM, Jahanshad N, Villalon-Reina JE, Isaev D, Zavaliangos-Petropulu A, Zhan L, Leow AD, Jack CR, Weiner MW, Thompson PM. Fractional anisotropy derived from the diffusion tensor distribution function boosts power to detect Alzheimer's disease deficits. Magn Reson Med 2017; 78:2322-2333. [PMID: 28266059 DOI: 10.1002/mrm.26623] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Revised: 11/21/2016] [Accepted: 01/08/2017] [Indexed: 12/30/2022]
Abstract
PURPOSE In diffusion MRI (dMRI), fractional anisotropy derived from the single-tensor model (FADTI ) is the most widely used metric to characterize white matter (WM) microarchitecture, despite known limitations in regions with crossing fibers. Due to time constraints when scanning patients in clinical settings, high angular resolution diffusion imaging acquisition protocols, often used to overcome these limitations, are still rare in clinical population studies. However, the tensor distribution function (TDF) may be used to model multiple underlying fibers by representing the diffusion profile as a probabilistic mixture of tensors. METHODS We compared the ability of standard FADTI and TDF-derived FA (FATDF ), calculated from a range of dMRI angular resolutions (41, 30, 15, and 7 gradient directions), to profile WM deficits in 251 individuals from the Alzheimer's Disease Neuroimaging Initiative and to detect associations with 1) Alzheimer's disease diagnosis, 2) Clinical Dementia Rating scores, and 3) average hippocampal volume. RESULTS Across angular resolutions and statistical tests, FATDF showed larger effect sizes than FADTI , particularly in regions preferentially affected by Alzheimer's disease, and was less susceptible to crossing fiber anomalies. CONCLUSION The TDF "corrected" form of FA may be a more sensitive and accurate alternative to the commonly used FADTI , even in clinical quality dMRI data. Magn Reson Med 78:2322-2333, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Talia M Nir
- Imaging Genetics Center, University of Southern California, Marina del Rey, California, USA
| | - Neda Jahanshad
- Imaging Genetics Center, University of Southern California, Marina del Rey, California, USA
| | - Julio E Villalon-Reina
- Imaging Genetics Center, University of Southern California, Marina del Rey, California, USA
| | - Dmitry Isaev
- Imaging Genetics Center, University of Southern California, Marina del Rey, California, USA
| | | | - Liang Zhan
- Computer Engineering Program, University of Wisconsin-Stout, Menomonie, Wisconsin, USA
| | - Alex D Leow
- Department of Psychiatry and Bioengineering, University of Illinois, Chicago, Illinois, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, Rochester, Minnesota, USA
| | - Michael W Weiner
- Department of Radiology, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Paul M Thompson
- Imaging Genetics Center, University of Southern California, Marina del Rey, California, USA
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Toepper M. Dissociating Normal Aging from Alzheimer's Disease: A View from Cognitive Neuroscience. J Alzheimers Dis 2017; 57:331-352. [PMID: 28269778 PMCID: PMC5366251 DOI: 10.3233/jad-161099] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/23/2017] [Indexed: 02/07/2023]
Abstract
Both normal aging and Alzheimer's disease (AD) are associated with changes in cognition, grey and white matter volume, white matter integrity, neural activation, functional connectivity, and neurotransmission. Obviously, all of these changes are more pronounced in AD and proceed faster providing the basis for an AD diagnosis. Since these differences are quantitative, however, it was hypothesized that AD might simply reflect an accelerated aging process. The present article highlights the different neurocognitive changes associated with normal aging and AD and shows that, next to quantitative differences, there are multiple qualitative differences as well. These differences comprise different neurocognitive dissociations as different cognitive deficit profiles, different weights of grey and white matter atrophy, and different gradients of structural decline. These qualitative differences clearly indicate that AD cannot be simply described as accelerated aging process but on the contrary represents a solid entity.
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Affiliation(s)
- Max Toepper
- Department of Psychiatry and Psychotherapy Bethel, Research Division, Evangelisches Krankenhaus Bielefeld (EvKB), Bielefeld, Germany
- Department of Psychiatry and Psychotherapy Bethel, Department of Geriatric Psychiatry, Evangelisches Krankenhaus Bielefeld (EvKB), Bielefeld, Germany
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Ma W, Li M, Gao F, Zhang X, Shi L, Yu L, Zhao B, Chen W, Wang G, Wang X. DTI Analysis of Presbycusis Using Voxel-Based Analysis. AJNR Am J Neuroradiol 2016; 37:2110-2114. [PMID: 27418468 DOI: 10.3174/ajnr.a4870] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Accepted: 05/09/2016] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Presbycusis is the most common sensory deficit in the aging population. A recent study reported using a DTI-based tractography technique to identify a lack of integrity in a portion of the auditory pathway in patients with presbycusis. The aim of our study was to investigate the white matter pathology of patients with presbycusis by using a voxel-based analysis that is highly sensitive to local intensity changes in DTI data. MATERIALS AND METHODS Fifteen patients with presbycusis and 14 age- and sex-matched healthy controls were scanned on a 3T scanner. Fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity were obtained from the DTI data. Intergroup statistics were implemented on these measurements, which were transformed to Montreal Neurological Institute coordinates by using a nonrigid image registration method called large deformation diffeomorphic metric mapping. RESULTS Increased axial diffusivity, radial diffusivity, and mean diffusivity and decreased fractional anisotropy were found near the right-side hearing-related areas in patients with presbycusis. Increased radial diffusivity and mean diffusivity were also found near a language-related area (Broca area) in patients with presbycusis. CONCLUSIONS Our findings could be important for exploring reliable imaging evidence of presbycusis and could complement an ROI-based approach.
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Affiliation(s)
- W Ma
- From the Department of Otolaryngology (W.M., L.S.)
- the Second Hospital of Shandong University, The Central Hospital of Jinan City (W.M., L.Y.)
| | - M Li
- College of Electronics and Information Engineering (M.L.), Sichuan University, Chengdu, China
| | - F Gao
- Shandong Medical Imaging Research Institute (F.G., X.Z., B.Z., G.W.), Shandong University, Jinan, China
| | - X Zhang
- Shandong Medical Imaging Research Institute (F.G., X.Z., B.Z., G.W.), Shandong University, Jinan, China
| | - L Shi
- From the Department of Otolaryngology (W.M., L.S.)
| | - L Yu
- the Second Hospital of Shandong University, The Central Hospital of Jinan City (W.M., L.Y.)
| | - B Zhao
- Shandong Medical Imaging Research Institute (F.G., X.Z., B.Z., G.W.), Shandong University, Jinan, China
| | - W Chen
- Philips Healthcare (W.C.), Shanghai, China
| | - G Wang
- Shandong Medical Imaging Research Institute (F.G., X.Z., B.Z., G.W.), Shandong University, Jinan, China
| | - X Wang
- the Department of Radiation Oncology (X.W.), University of Nebraska Medical Center, Omaha, Nebraska
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Liu D, Wang Z, Shu H, Zhang Z. Disrupted white matter integrity is associated with cognitive deficits in patients with amnestic mild cognitive impairment: An atlas-based study. SAGE Open Med 2016; 4:2050312116648812. [PMID: 27354916 PMCID: PMC4910535 DOI: 10.1177/2050312116648812] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Accepted: 03/31/2016] [Indexed: 11/16/2022] Open
Abstract
Objective: This study investigated white matter integrity in patients with amnestic mild cognitive impairment by diffusion tensor imaging. Methods: A total of 83 patients with amnestic mild cognitive impairment and 85 elderly healthy controls underwent neuropsychological testing and a diffusion tensor imaging scan. Whole-brain white matter data were parcellated into 50 regions based on the anatomical ICBM-DTI-81 atlas, and regional diffusion metrics consisting of fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity were calculated for each region. Diffusion tensor imaging indices were compared between groups, and it was determined that between-group differences were significantly correlated with neurocognitive performance. Results: Relative to the healthy controls group, the amnestic mild cognitive impairment group exhibited poorer cognitive performance in all neuropsychological tests except the complex figure test (p = 0.083) and showed decreased mean fractional anisotropy in the fornix, increased mean diffusivity in the fornix and bilateral uncinate fasciculus, elevated axial diffusivity in the fornix and genu of corpus callosum, and elevated radial diffusivity in the fornix and bilateral uncinate fasciculus (p < 0.05). Behaviorally, integrity of the bilateral uncinate fasciculus was correlated positively with episodic memory function, while left uncinate fasciculus integrity was positively associated with language function in the amnestic mild cognitive impairment group (p < 0.05). Conclusion: White matter abnormalities in neural pathways associated with memory were correlated with neurocognitive deficiencies in amnestic mild cognitive impairment. Given that amnestic mild cognitive impairment is putatively a prodromal syndrome for Alzheimer’s disease, this study furthers our understanding of the white matter changes associated with Alzheimer’s disease pathogenesis in the predementia stage.
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Affiliation(s)
- Duan Liu
- Department of Neurology, The Second People's Hospital Of Chengdu, Chengdu, China
| | - Zan Wang
- Department of Neurology, The Second People's Hospital Of Chengdu, Chengdu, China
| | - Hao Shu
- Department of Neurology, The Second People's Hospital Of Chengdu, Chengdu, China
| | - Zhijun Zhang
- Department of Neurology, The Second People's Hospital Of Chengdu, Chengdu, China
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Jelescu IO, Veraart J, Fieremans E, Novikov DS. Degeneracy in model parameter estimation for multi-compartmental diffusion in neuronal tissue. NMR IN BIOMEDICINE 2016; 29:33-47. [PMID: 26615981 PMCID: PMC4920129 DOI: 10.1002/nbm.3450] [Citation(s) in RCA: 188] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Revised: 10/28/2015] [Accepted: 10/30/2015] [Indexed: 05/05/2023]
Abstract
The ultimate promise of diffusion MRI (dMRI) models is specificity to neuronal microstructure, which may lead to distinct clinical biomarkers using noninvasive imaging. While multi-compartment models are a common approach to interpret water diffusion in the brain in vivo, the estimation of their parameters from the dMRI signal remains an unresolved problem. Practically, even when q space is highly oversampled, nonlinear fit outputs suffer from heavy bias and poor precision. So far, this has been alleviated by fixing some of the model parameters to a priori values, for improved precision at the expense of accuracy. Here we use a representative two-compartment model to show that fitting fails to determine the five model parameters from over 60 measurement points. For the first time, we identify the reasons for this poor performance. The first reason is the existence of two local minima in the parameter space for the objective function of the fitting procedure. These minima correspond to qualitatively different sets of parameters, yet they both lie within biophysically plausible ranges. We show that, at realistic signal-to-noise ratio values, choosing between the two minima based on the associated objective function values is essentially impossible. Second, there is an ensemble of very low objective function values around each of these minima in the form of a pipe. The existence of such a direction in parameter space, along which the objective function profile is very flat, explains the bias and large uncertainty in parameter estimation, and the spurious parameter correlations: in the presence of noise, the minimum can be randomly displaced by a very large amount along each pipe. Our results suggest that the biophysical interpretation of dMRI model parameters crucially depends on establishing which of the minima is closer to the biophysical reality and the size of the uncertainty associated with each parameter.
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Affiliation(s)
- Ileana O. Jelescu
- Correspondence to: I.O. Jelescu, Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA.
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Guo J, Bakshi V, Lin AL. Early Shifts of Brain Metabolism by Caloric Restriction Preserve White Matter Integrity and Long-Term Memory in Aging Mice. Front Aging Neurosci 2015; 7:213. [PMID: 26617514 PMCID: PMC4643125 DOI: 10.3389/fnagi.2015.00213] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2015] [Accepted: 10/30/2015] [Indexed: 12/15/2022] Open
Abstract
Preservation of brain integrity with age is highly associated with lifespan determination. Caloric restriction (CR) has been shown to increase longevity and healthspan in various species; however, its effects on preserving living brain functions in aging remain largely unexplored. In the study, we used multimodal, non-invasive neuroimaging (PET/MRI/MRS) to determine in vivo brain glucose metabolism, energy metabolites, and white matter structural integrity in young and old mice fed with either control or 40% CR diet. In addition, we determined the animals' memory and learning ability with behavioral assessments. Blood glucose, blood ketone bodies, and body weight were also measured. We found distinct patterns between normal aging and CR aging on brain functions - normal aging showed reductions in brain glucose metabolism, white matter integrity, and long-term memory, resembling human brain aging. CR aging, in contrast, displayed an early shift from glucose to ketone bodies metabolism, which was associated with preservations of brain energy production, white matter integrity, and long-term memory in aging mice. Among all the mice, we found a positive correlation between blood glucose level and body weight, but an inverse association between blood glucose level and lifespan. Our findings suggest that CR could slow down brain aging, in part due to the early shift of energy metabolism caused by lower caloric intake, and we were able to identify the age-dependent effects of CR non-invasively using neuroimaging. These results provide a rationale for CR-induced sustenance of brain health with extended longevity.
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Affiliation(s)
- Janet Guo
- Sanders-Brown Center on Aging, University of Kentucky , Lexington, KY , USA ; Department of Pharmacology and Nutritional Sciences, University of Kentucky , Lexington, KY , USA
| | - Vikas Bakshi
- Sanders-Brown Center on Aging, University of Kentucky , Lexington, KY , USA ; Department of Pharmacology and Nutritional Sciences, University of Kentucky , Lexington, KY , USA
| | - Ai-Ling Lin
- Sanders-Brown Center on Aging, University of Kentucky , Lexington, KY , USA ; Department of Pharmacology and Nutritional Sciences, University of Kentucky , Lexington, KY , USA ; Department of Biomedical Engineering, University of Kentucky , Lexington, KY , USA
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48
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de Groot M, Cremers LGM, Ikram MA, Hofman A, Krestin GP, van der Lugt A, Niessen WJ, Vernooij MW. White Matter Degeneration with Aging: Longitudinal Diffusion MR Imaging Analysis. Radiology 2015; 279:532-41. [PMID: 26536311 DOI: 10.1148/radiol.2015150103] [Citation(s) in RCA: 73] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
PURPOSE To determine longitudinally the rate of change in diffusion-tensor imaging (DTI) parameters of white matter microstructure with aging and to investigate whether cardiovascular risk factors influence this longitudinal change. MATERIALS AND METHODS This prospective population-based cohort study was approved by a dedicated ethics committee overseen by the national government, and all participants gave written informed consent. Community-dwelling participants without dementia were examined by using a research-dedicated 1.5-T magnetic resonance (MR) imager on two separate visits that were, on average, 2.0 years apart. Among 810 persons who were eligible for imaging at baseline, longitudinal imaging data were available for 501 persons (mean age, 69.9 years; age range, 64.1-91.1 years). Changes in normal-appearing white matter DTI characteristics in the tract centers were analyzed globally to investigate diffuse patterns of change and then locally by using voxelwise multilinear regression. The influence of cardiovascular risk factors was assessed by treating them as additional determinants in both analyses. RESULTS Over the 2.0-year follow-up interval, global fractional anisotropy (FA) decreased by 0.0042 (P < .001), while mean diffusivity (MD) increased by 8.1 × 10(-6) mm(2)/sec (P < .001). Voxelwise analysis of the brain white matter skeleton showed an average decrease of 0.0082 (Pmean = .002) in FA in 57% of skeleton voxels. The sensorimotor pathway, however, showed an increase of 0.0078 (Pmean = .009) in FA. MD increased by 10.8 × 10(-6)mm(2)/sec (Pmean < .001) on average in 79% of white matter skeleton voxels. Additionally, white matter degeneration was more pronounced in older persons. Cardiovascular risk factors were generally not associated with longitudinal changes in white matter microstructure. CONCLUSION Longitudinal diffusion analysis indicates widespread microstructural deterioration of the normal-appearing white matter in normal aging, with relative sparing of sensorimotor fibers.
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Affiliation(s)
- Marius de Groot
- From the Departments of Radiology (M.d.G., L.G.M.C., M.A.I., G.P.K., A.v.d.L., W.J.N., M.W.V.), Medical Informatics (M.d.G., W.J.N.), Epidemiology (M.d.G., L.G.M.C., M.A.I., A.H., M.W.V.), and Neurology (M.A.I.), Erasmus MC, University Medical Center Rotterdam, PO Box 2040, Room Ca220, 3000 CA Rotterdam, the Netherlands; and Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands (W.J.N.)
| | - Lotte G M Cremers
- From the Departments of Radiology (M.d.G., L.G.M.C., M.A.I., G.P.K., A.v.d.L., W.J.N., M.W.V.), Medical Informatics (M.d.G., W.J.N.), Epidemiology (M.d.G., L.G.M.C., M.A.I., A.H., M.W.V.), and Neurology (M.A.I.), Erasmus MC, University Medical Center Rotterdam, PO Box 2040, Room Ca220, 3000 CA Rotterdam, the Netherlands; and Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands (W.J.N.)
| | - M Arfan Ikram
- From the Departments of Radiology (M.d.G., L.G.M.C., M.A.I., G.P.K., A.v.d.L., W.J.N., M.W.V.), Medical Informatics (M.d.G., W.J.N.), Epidemiology (M.d.G., L.G.M.C., M.A.I., A.H., M.W.V.), and Neurology (M.A.I.), Erasmus MC, University Medical Center Rotterdam, PO Box 2040, Room Ca220, 3000 CA Rotterdam, the Netherlands; and Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands (W.J.N.)
| | - Albert Hofman
- From the Departments of Radiology (M.d.G., L.G.M.C., M.A.I., G.P.K., A.v.d.L., W.J.N., M.W.V.), Medical Informatics (M.d.G., W.J.N.), Epidemiology (M.d.G., L.G.M.C., M.A.I., A.H., M.W.V.), and Neurology (M.A.I.), Erasmus MC, University Medical Center Rotterdam, PO Box 2040, Room Ca220, 3000 CA Rotterdam, the Netherlands; and Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands (W.J.N.)
| | - Gabriel P Krestin
- From the Departments of Radiology (M.d.G., L.G.M.C., M.A.I., G.P.K., A.v.d.L., W.J.N., M.W.V.), Medical Informatics (M.d.G., W.J.N.), Epidemiology (M.d.G., L.G.M.C., M.A.I., A.H., M.W.V.), and Neurology (M.A.I.), Erasmus MC, University Medical Center Rotterdam, PO Box 2040, Room Ca220, 3000 CA Rotterdam, the Netherlands; and Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands (W.J.N.)
| | - Aad van der Lugt
- From the Departments of Radiology (M.d.G., L.G.M.C., M.A.I., G.P.K., A.v.d.L., W.J.N., M.W.V.), Medical Informatics (M.d.G., W.J.N.), Epidemiology (M.d.G., L.G.M.C., M.A.I., A.H., M.W.V.), and Neurology (M.A.I.), Erasmus MC, University Medical Center Rotterdam, PO Box 2040, Room Ca220, 3000 CA Rotterdam, the Netherlands; and Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands (W.J.N.)
| | - Wiro J Niessen
- From the Departments of Radiology (M.d.G., L.G.M.C., M.A.I., G.P.K., A.v.d.L., W.J.N., M.W.V.), Medical Informatics (M.d.G., W.J.N.), Epidemiology (M.d.G., L.G.M.C., M.A.I., A.H., M.W.V.), and Neurology (M.A.I.), Erasmus MC, University Medical Center Rotterdam, PO Box 2040, Room Ca220, 3000 CA Rotterdam, the Netherlands; and Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands (W.J.N.)
| | - Meike W Vernooij
- From the Departments of Radiology (M.d.G., L.G.M.C., M.A.I., G.P.K., A.v.d.L., W.J.N., M.W.V.), Medical Informatics (M.d.G., W.J.N.), Epidemiology (M.d.G., L.G.M.C., M.A.I., A.H., M.W.V.), and Neurology (M.A.I.), Erasmus MC, University Medical Center Rotterdam, PO Box 2040, Room Ca220, 3000 CA Rotterdam, the Netherlands; and Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands (W.J.N.)
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Colgan N, Siow B, O'Callaghan JM, Harrison IF, Wells JA, Holmes HE, Ismail O, Richardson S, Alexander DC, Collins EC, Fisher EM, Johnson R, Schwarz AJ, Ahmed Z, O'Neill MJ, Murray TK, Zhang H, Lythgoe MF. Application of neurite orientation dispersion and density imaging (NODDI) to a tau pathology model of Alzheimer's disease. Neuroimage 2015; 125:739-744. [PMID: 26505297 PMCID: PMC4692518 DOI: 10.1016/j.neuroimage.2015.10.043] [Citation(s) in RCA: 142] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2015] [Revised: 09/30/2015] [Accepted: 10/17/2015] [Indexed: 11/21/2022] Open
Abstract
Increased hyperphosphorylated tau and the formation of intracellular neurofibrillary tangles are associated with the loss of neurons and cognitive decline in Alzheimer's disease, and related neurodegenerative conditions. We applied two diffusion models, diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), to in vivo diffusion magnetic resonance images (dMRI) of a mouse model of human tauopathy (rTg4510) at 8.5 months of age. In grey matter regions with the highest degree of tau burden, microstructural indices provided by both NODDI and DTI discriminated the rTg4510 (TG) animals from wild type (WT) controls; however only the neurite density index (NDI) (the volume fraction that comprises axons or dendrites) from the NODDI model correlated with the histological measurements of the levels of hyperphosphorylated tau protein. Reductions in diffusion directionality were observed when implementing both models in the white matter region of the corpus callosum, with lower fractional anisotropy (DTI) and higher orientation dispersion (NODDI) observed in the TG animals. In comparison to DTI, histological measures of tau pathology were more closely correlated with NODDI parameters in this region. This in vivo dMRI study demonstrates that NODDI identifies potential tissue sources contributing to DTI indices and NODDI may provide greater specificity to pathology in Alzheimer's disease. We analyzed the microstructural changes in rTg4510 and wild type mice at 8.5 months. We correlated microstructural findings with histological measures of tau burden We compare two diffusion MR models: DTI and NODDI. Both models revealed changes in tissue microstructure due to tau pathology. The NODDI metrics demonstrated a good correlation with histological measures of tau burden.
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Affiliation(s)
- N Colgan
- UCL Centre for Advanced Biomedical Imaging , Division of Medicine, University College London, UK; Department of Medical Physics and Bioengineering, Saolta University Health Care Group, University Hospital Galway, Newcastle Road, Galway, H91 YR71, Ireland.
| | - B Siow
- UCL Centre for Advanced Biomedical Imaging , Division of Medicine, University College London, UK; Department of Computer Science & Centre for Medical Image Computing, University College London, UK
| | - J M O'Callaghan
- UCL Centre for Advanced Biomedical Imaging , Division of Medicine, University College London, UK
| | - I F Harrison
- UCL Centre for Advanced Biomedical Imaging , Division of Medicine, University College London, UK
| | - J A Wells
- UCL Centre for Advanced Biomedical Imaging , Division of Medicine, University College London, UK
| | - H E Holmes
- UCL Centre for Advanced Biomedical Imaging , Division of Medicine, University College London, UK
| | - O Ismail
- UCL Centre for Advanced Biomedical Imaging , Division of Medicine, University College London, UK
| | - S Richardson
- UCL Centre for Advanced Biomedical Imaging , Division of Medicine, University College London, UK; Department of Computer Science & Centre for Medical Image Computing, University College London, UK
| | - D C Alexander
- Department of Computer Science & Centre for Medical Image Computing, University College London, UK
| | - E C Collins
- Eli Lilly & Co. Ltd, Lilly Corporate Center, Indianapolis, IN 46285, USA
| | - E M Fisher
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square London, UK
| | - R Johnson
- Eli Lilly & Co. Ltd, Lilly Corporate Center, Indianapolis, IN 46285, USA
| | - A J Schwarz
- Eli Lilly & Co. Ltd, Lilly Corporate Center, Indianapolis, IN 46285, USA
| | - Z Ahmed
- Eli Lilly & Co. Ltd, Erl Wood Manor, Windlesham, Surrey GU20 6PH, UK
| | - M J O'Neill
- Eli Lilly & Co. Ltd, Erl Wood Manor, Windlesham, Surrey GU20 6PH, UK
| | - T K Murray
- Eli Lilly & Co. Ltd, Erl Wood Manor, Windlesham, Surrey GU20 6PH, UK
| | - H Zhang
- Department of Computer Science & Centre for Medical Image Computing, University College London, UK
| | - M F Lythgoe
- UCL Centre for Advanced Biomedical Imaging , Division of Medicine, University College London, UK
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50
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Nowrangi MA, Okonkwo O, Lyketsos C, Oishi K, Mori S, Albert M, Mielke MM. Atlas-based diffusion tensor imaging correlates of executive function. J Alzheimers Dis 2015; 44:585-98. [PMID: 25318544 DOI: 10.3233/jad-141937] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Impairment in executive function (EF) is commonly found in Alzheimer's disease (AD) and mild cognitive impairment (MCI). Atlas-based diffusion tensor imaging (DTI) methods may be useful in relating regional integrity to EF measures in MCI and AD. Sixty-six participants (25 normal controls, 22 MCI, and 19 AD) received DTI scans and clinical evaluation. DTI scans were applied to a pre-segmented atlas and fractional anisotropy (FA) and mean diffusivity (MD) were calculated. ANOVA was used to assess group differences in frontal, parietal, and cerebellar regions. For regions differing between groups (p < 0.01), linear regression examined the relationship between EF scores and regional FA and MD. Anisotropy and diffusivity in frontal and parietal lobe white matter structures were associated with EF scores in MCI and only frontal lobe structures in AD. EF was more strongly associated with FA than MD. The relationship between EF and anisotropy and diffusivity was strongest in MCI. These results suggest that regional white matter integrity is compromised in MCI and AD and that FA may be a better correlate of EF than MD.
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Affiliation(s)
- Milap A Nowrangi
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine and Johns Hopkins Bayview Medical Center, Baltimore, MD, USA
| | - Ozioma Okonkwo
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Constantine Lyketsos
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine and Johns Hopkins Bayview Medical Center, Baltimore, MD, USA
| | - Kenichi Oishi
- Department of Radiology Johns Hopkins University School of Medicine and Johns Hopkins Bayview Medical Center, Baltimore, MD, USA
| | - Susumu Mori
- Department of Radiology Johns Hopkins University School of Medicine and Johns Hopkins Bayview Medical Center, Baltimore, MD, USA
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine and Johns Hopkins Bayview Medical Center, Baltimore, MD, USA
| | - Michelle M Mielke
- Department of Health Sciences Research, Division of Epidemiology and Department of Neurology, Mayo Clinic, Rochester, MN, USA
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