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Shi Y, Lin F, Li Y, Wang Y, Chen X, Meng F, Ye Q, Cai G. Association of pro-inflammatory diet with increased risk of all-cause dementia and Alzheimer's dementia: a prospective study of 166,377 UK Biobank participants. BMC Med 2023; 21:266. [PMID: 37480061 PMCID: PMC10362711 DOI: 10.1186/s12916-023-02940-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 06/13/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND Increasing evidence suggests an association between pro-inflammatory diets and cognitive function. However, only a few studies based on small sample sizes have explored the association between pro-inflammatory diets and dementia using the dietary inflammatory index (DII). Additionally, the relationship between DII and different subtypes of dementia, such as Alzheimer's dementia and vascular dementia, remains largely unexplored. Given the changes in brain structure already observed in patients with dementia, we also investigated the association between DII and magnetic resonance imaging (MRI) measures of brain structure to provide some hints to elucidate the potential mechanisms between pro-inflammatory diet and cognitive decline. METHODS A total of 166,377 UK Biobank participants without dementia at baseline were analyzed. DII calculations were based on the information collected by the 24-h recall questionnaire. Brain structural anatomy and tissue-specific volumes were measured using brain MRI. Cox proportional hazards models, competing risk models, and restricted cubic spline were applied to assess the longitudinal associations. The generalized linear model was used to assess the association between DII and MRI measurements. RESULTS During a median follow-up time of 9.46 years, a total of 1372 participants developed dementia. The incidence of all-cause dementia increased by 4.6% for each additional unit of DII [hazard ratio (HR): 1.046]. Besides, DII displayed a "J-shaped" non-linear association with Alzheimer's dementia (Pnonlinear = 0.003). When DII was above 1.30, an increase in DII was significantly associated with an increased risk of Alzheimer's dementia (HR: 1.391, 95%CI: 1.085-1.784, P = 0.009). For brain MRI, the total volume of white matter hyperintensities increased with an increase in DII, whereas the volume of gray matter in the hippocampus decreased. CONCLUSIONS In this cohort study, higher DII was associated with a higher risk of all-cause dementia and Alzheimer's dementia. However, our findings suggested that the association with DII and vascular and frontotemporal dementia was not significant.
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Affiliation(s)
- Yisen Shi
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou, 350001, China
| | - Fabin Lin
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou, 350001, China
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Yueping Li
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou, 350001, China
| | - Yingqing Wang
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou, 350001, China
| | - Xiaochun Chen
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou, 350001, China
| | - Fangang Meng
- Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China.
| | - Qinyong Ye
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China.
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China.
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou, 350001, China.
| | - Guoen Cai
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China.
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, 29 Xinquan Road, Fuzhou, 350001, China.
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, 88 Jiaotong Road, Fuzhou, 350001, China.
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Nguyen HD, Clément M, Mansencal B, Coupé P. Towards better interpretable and generalizable AD detection using collective artificial intelligence. Comput Med Imaging Graph 2023; 104:102171. [PMID: 36640484 DOI: 10.1016/j.compmedimag.2022.102171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 12/24/2022] [Accepted: 12/24/2022] [Indexed: 01/03/2023]
Abstract
Alzheimer's Disease is the most common cause of dementia. Accurate diagnosis and prognosis of this disease are essential to design an appropriate treatment plan, increasing the life expectancy of the patient. Intense research has been conducted on the use of machine learning to identify Alzheimer's Disease from neuroimaging data, such as structural magnetic resonance imaging. In recent years, advances of deep learning in computer vision suggest a new research direction for this problem. Current deep learning-based approaches in this field, however, have a number of drawbacks, including the interpretability of model decisions, a lack of generalizability information and a lower performance compared to traditional machine learning techniques. In this paper, we design a two-stage framework to overcome these limitations. In the first stage, an ensemble of 125 U-Nets is used to grade the input image, producing a 3D map that reflects the disease severity at voxel-level. This map can help to localize abnormal brain areas caused by the disease. In the second stage, we model a graph per individual using the generated grading map and other information about the subject. We propose to use a graph convolutional neural network classifier for the final classification. As a result, our framework demonstrates comparative performance to the state-of-the-art methods in different datasets for both diagnosis and prognosis. We also demonstrate that the use of a large ensemble of U-Nets offers a better generalization capacity for our framework.
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Affiliation(s)
- Huy-Dung Nguyen
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400 Talence, France.
| | - Michaël Clément
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400 Talence, France
| | - Boris Mansencal
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400 Talence, France
| | - Pierrick Coupé
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400 Talence, France
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3
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Xiao Y, Wang J, Huang K, Gao L, Yao S. Progressive structural and covariance connectivity abnormalities in patients with Alzheimer's disease. Front Aging Neurosci 2023; 14:1064667. [PMID: 36688148 PMCID: PMC9853893 DOI: 10.3389/fnagi.2022.1064667] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 12/13/2022] [Indexed: 01/07/2023] Open
Abstract
Background Alzheimer's disease (AD) is one of most prevalent neurodegenerative diseases worldwide and characterized by cognitive decline and brain structure atrophy. While studies have reported substantial grey matter atrophy related to progression of AD, it remains unclear about brain regions with progressive grey matter atrophy, covariance connectivity, and the associations with cognitive decline in AD patients. Objective This study aims to investigate the grey matter atrophy, structural covariance connectivity abnormalities, and the correlations between grey matter atrophy and cognitive decline during AD progression. Materials We analyzed neuroimaging data of healthy controls (HC, n = 45) and AD patients (n = 40) at baseline (AD-T1) and one-year follow-up (AD-T2) obtained from the Alzheimer's Disease Neuroimaging Initiative. We investigated AD-related progressive changes of grey matter volume, covariance connectivity, and the clinical relevance to further understand the pathological progression of AD. Results The results showed clear patterns of grey matter atrophy in inferior frontal gyrus, prefrontal cortex, lateral temporal gyrus, posterior cingulate cortex, insula, hippocampus, caudate, and thalamus in AD patients. There was significant atrophy in bilateral superior temporal gyrus (STG) and left caudate in AD patients over a one-year period, and the grey matter volume decrease in right STG and left caudate was correlated with cognitive decline. Additionally, we found reduced structural covariance connectivity between right STG and left caudate in AD patients. Using AD-related grey matter atrophy as features, there was high discrimination accuracy of AD patients from HC, and AD patients at different time points.
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Affiliation(s)
- Yaqiong Xiao
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China
| | - Kaiyu Huang
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Lei Gao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Shun Yao
- Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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4
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Schilling KG, Archer D, Yeh FC, Rheault F, Cai LY, Shafer A, Resnick SM, Hohman T, Jefferson A, Anderson AW, Kang H, Landman BA. Short superficial white matter and aging: a longitudinal multi-site study of 1293 subjects and 2711 sessions. AGING BRAIN 2023; 3:100067. [PMID: 36817413 PMCID: PMC9937516 DOI: 10.1016/j.nbas.2023.100067] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
It is estimated that short association fibers running immediately beneath the cortex may make up as much as 60% of the total white matter volume. However, these have been understudied relative to the long-range association, projection, and commissural fibers of the brain. This is largely because of limitations of diffusion MRI fiber tractography, which is the primary methodology used to non-invasively study the white matter connections. Inspired by recent anatomical considerations and methodological improvements in superficial white matter (SWM) tractography, we aim to characterize changes in these fiber systems in cognitively normal aging, which provide insight into the biological foundation of age-related cognitive changes, and a better understanding of how age-related pathology differs from healthy aging. To do this, we used three large, longitudinal and cross-sectional datasets (N = 1293 subjects, 2711 sessions) to quantify microstructural features and length/volume features of several SWM systems. We find that axial, radial, and mean diffusivities show positive associations with age, while fractional anisotropy has negative associations with age in SWM throughout the entire brain. These associations were most pronounced in the frontal, temporal, and temporoparietal regions. Moreover, measures of SWM volume and length decrease with age in a heterogenous manner across the brain, with different rates of change in inter-gyri and intra-gyri SWM, and at slower rates than well-studied long-range white matter pathways. These features, and their variations with age, provide the background for characterizing normal aging, and, in combination with larger association pathways and gray matter microstructural features, may provide insight into fundamental mechanisms associated with aging and cognition.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Derek Archer
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Francois Rheault
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Leon Y Cai
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Andrea Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Timothy Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN
| | - Angela Jefferson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam W Anderson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University, Nashville, TN, United States
| | - Bennett A Landman
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
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Aging and white matter microstructure and macrostructure: a longitudinal multi-site diffusion MRI study of 1218 participants. Brain Struct Funct 2022; 227:2111-2125. [PMID: 35604444 DOI: 10.1007/s00429-022-02503-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 04/22/2022] [Indexed: 11/02/2022]
Abstract
Quantifying the microstructural and macrostructural geometrical features of the human brain's connections is necessary for understanding normal aging and disease. Here, we examine brain white matter diffusion magnetic resonance imaging data from one cross-sectional and two longitudinal data sets totaling in 1218 subjects and 2459 sessions of people aged 50-97 years. Data was drawn from well-established cohorts, including the Baltimore Longitudinal Study of Aging data set, Cambridge Centre for Ageing Neuroscience data set, and the Vanderbilt Memory & Aging Project. Quantifying 4 microstructural features and, for the first time, 11 macrostructure-based features of volume, area, and length across 120 white matter pathways, we apply linear mixed effect modeling to investigate changes in pathway-specific features over time, and document large age associations within white matter. Conventional diffusion tensor microstructure indices are the most age-sensitive measures, with positive age associations for diffusivities and negative age associations with anisotropies, with similar patterns observed across all pathways. Similarly, pathway shape measures also change with age, with negative age associations for most length, surface area, and volume-based features. A particularly novel finding of this study is that while trends were homogeneous throughout the brain for microstructure features, macrostructural features demonstrated heterogeneity across pathways, whereby several projection, thalamic, and commissural tracts exhibited more decline with age compared to association and limbic tracts. The findings from this large-scale study provide a comprehensive overview of the age-related decline in white matter and demonstrate that macrostructural features may be more sensitive to heterogeneous white matter decline. Therefore, leveraging macrostructural features may be useful for studying aging and could facilitate comparisons in a variety of diseases or abnormal conditions.
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Wang J, Zheng L, Wang Z, Wu X, Ma N, Zhang T, Chen K, Biswal BB, Yang Q, Ma H. Alteration of Behavioral Inhibitory Control in High-Altitude Immigrants. Front Behav Neurosci 2021; 15:712278. [PMID: 34955775 PMCID: PMC8703013 DOI: 10.3389/fnbeh.2021.712278] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 11/09/2021] [Indexed: 11/23/2022] Open
Abstract
Behavioral inhibitory control (BIC) acts as a key cognitive ability, which is essential for humans to withhold inappropriate behaviors. Meanwhile, many studies reported that long-term exposure to high altitude (HA) may affect cognitive ability. However, it is not clear whether long-term exposure to HAs may affect the BIC of an individual. To clarify the role of altitude in the behavioral control of adults and the underlying neural mechanism, we explored the BIC neural activity profiles of healthy immigrants from low-altitude (LA) regions to HA regions. Combining a two-choice oddball paradigm and electrophysiological techniques, this study monitored the N2 and P3 event-related components and neural oscillations across LA and HA groups. Results showed longer reaction times (RTs) for the HA group than the LA group. Relative to the LA group, lower N2 and P3 amplitudes were observed for the HA group. Significant positive correlations were also found between P3 amplitude and theta/delta band power across both groups. Importantly, lower theta/delta band powers were only observed for the HA group under the deviant condition. Collectively, these findings suggest that long-term exposure to HAs may attenuate BIC during the response inhibition stage and provide valuable insights into the neurocognitive implications of environmental altitude on BIC.
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Affiliation(s)
- Jiazheng Wang
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Liqin Zheng
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Zedong Wang
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiao Wu
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Ning Ma
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, School of Psychology, South China Normal University, Guangzhou, China
| | - Tao Zhang
- Center for Mental Health Development and Research, Xihua University, Chengdu, China
| | - Kai Chen
- Center for Mental Health Development and Research, Xihua University, Chengdu, China
| | - Bharat B Biswal
- MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Qun Yang
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
| | - Hailin Ma
- Plateau Brain Science Research Center, Tibet University, Lhasa, China.,Plateau Brain Science Research Center, South China Normal University, Guangzhou, China
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Knudsen LV, Gazerani P, Michel TM, Vafaee MS. The role of multimodal MRI in mild cognitive impairment and Alzheimer's disease. J Neuroimaging 2021; 32:148-157. [PMID: 34752671 DOI: 10.1111/jon.12940] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/28/2021] [Accepted: 09/28/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND AND PURPOSE Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD), where neurodegeneration is not as considerable, thereby potentially increasing the effect of treatments. Therefore, highly sensitive and specific classification of subjects with MCI is necessary, where various MRI modalities have displayed promise. METHODS Structural, diffusion, and resting-state (RS) functional MRI analyses were performed on the AD (n = 26), MCI (n = 5), and healthy control (HC) (n = 14) group. Structural analysis was performed via voxel-based morphometry (VBM) and volumetric subcortical segmentation analysis. Fractional anisotropy and mean diffusivity were estimated during the diffusion analysis. RS analysis investigated seed-based functional connectivity. Classification via support vector machine was performed to evaluate which MRI modality most accurately differentiated the groups. Multiple linear regression was conducted to evaluate the MRI modalities correlation with clinical assessment scores. RESULTS Classification of MCI and HC displayed highest accuracy based on diffusion MRI, which besides demonstrated high correlation with clinical scores. Classification was equally accurate in AD, when using VBM or diffusion tensor imaging measures. Yet, more variance was explained by VBM measures in the clinical assessment scores of the AD group. CONCLUSIONS This study highlights the potential of diffusion MRI in differentiating MCI from HC and AD. However, the results need to be interpreted with caution as sample size and artifacts in the MRI data probably influenced the results.
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Affiliation(s)
- Laust Vind Knudsen
- Research Unit for Psychiatry, Odense University Hospital, Odense, Denmark
| | - Parisa Gazerani
- Department of Health Science and Technology, Faculty of Medicine, Aalborg University, Aalborg, Denmark.,Department of Life Sciences and Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, 0130, Norway
| | - Tanja Maria Michel
- Research Unit for Psychiatry, Odense University Hospital, Odense, Denmark
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Xia T, Chartsias A, Wang C, Tsaftaris SA. Learning to synthesise the ageing brain without longitudinal data. Med Image Anal 2021; 73:102169. [PMID: 34311421 DOI: 10.1016/j.media.2021.102169] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 07/01/2021] [Accepted: 07/09/2021] [Indexed: 12/21/2022]
Abstract
How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data) a multivariate auto-regressive function which given an image and a desired target age generates an output image. While collecting data for faces may be easier, collecting longitudinal brain data is not trivial. We propose a deep learning-based method that learns to simulate subject-specific brain ageing trajectories without relying on longitudinal data. Our method synthesises images conditioned on two factors: age (a continuous variable), and status of Alzheimer's Disease (AD, an ordinal variable). With an adversarial formulation we learn the joint distribution of brain appearance, age and AD status, and define reconstruction losses to address the challenging problem of preserving subject identity. We compare with several benchmarks using two widely used datasets. We evaluate the quality and realism of synthesised images using ground-truth longitudinal data and a pre-trained age predictor. We show that, despite the use of cross-sectional data, our model learns patterns of gray matter atrophy in the middle temporal gyrus in patients with AD. To demonstrate generalisation ability, we train on one dataset and evaluate predictions on the other. In conclusion, our model shows an ability to separate age, disease influence and anatomy using only 2D cross-sectional data that should be useful in large studies into neurodegenerative disease, that aim to combine several data sources. To facilitate such future studies by the community at large our code is made available at https://github.com/xiat0616/BrainAgeing.
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Affiliation(s)
- Tian Xia
- Institute for Digital Communications, School of Engineering, University of Edinburgh, West Mains Rd, Edinburgh EH9 3FB, UK.
| | - Agisilaos Chartsias
- Institute for Digital Communications, School of Engineering, University of Edinburgh, West Mains Rd, Edinburgh EH9 3FB, UK
| | - Chengjia Wang
- The BHF Centre for Cardiovascular Science, Edinburgh EH16 4TJ, UK
| | - Sotirios A Tsaftaris
- Institute for Digital Communications, School of Engineering, University of Edinburgh, West Mains Rd, Edinburgh EH9 3FB, UK; The Alan Turing Institute, London NW1 2DB, UK
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9
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Wang P, Zhou B, Yao H, Xie S, Feng F, Zhang Z, Guo Y, An N, Zhou Y, Zhang X, Liu Y. Aberrant Hippocampal Functional Connectivity Is Associated with Fornix White Matter Integrity in Alzheimer's Disease and Mild Cognitive Impairment. J Alzheimers Dis 2021; 75:1153-1168. [PMID: 32390630 DOI: 10.3233/jad-200066] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common cause of dementia in older individuals, and amnestic mild cognitive impairment (aMCI) is currently considered the prodromal stage of AD. The hippocampus and fornix interact functionally and structurally, with the fornix being the major efferent white matter tract from the hippocampus. OBJECTIVE The main aim of this study was to examine the impairments present in subjects with AD or aMCI and the relationship of these impairments with the microstructure of the fornix and the functional connectivity (FC) and gray matter volume of the hippocampus. METHODS Forty-four AD, 34 aMCI, and 41 age- and gender-matched normal controls (NCs) underwent neuropsychological assessments and multimode MRI. We chose the bilateral hippocampi as the region of interest in which gray matter alterations and FC with the whole brain were assessed and the fornix body as the region of interest in which the microstructural integrity of the white matter was observed. We also evaluated the relationship among gray matter alterations, the abnormal FC of the hippocampus and the integrity of the fornix in AD/aMCIResults:Compared to the NC group, the AD and aMCI groups demonstrated decreased gray matter volume, reduced FC between the bilateral hippocampi and several brain regions in the default mode network and control network, and damaged integrity of the fornix body (decreased fractional anisotropy and increased diffusivity). We also found that left hippocampal FC with some regions, the integrity of the fornix body, and cognition ability were significantly correlated. Therefore, our findings suggest that damage to white matter integrity may partially explain the reduced resting-state FC of the hippocampus in AD and aMCI. CONCLUSION AD and aMCI are diseases of disconnectivity including not only functional but also structural disconnectivity. Damage to white matter integrity may partially explain the reduced resting-state FC in AD and aMCI. These findings have significant implications for diagnostics and modeling and provide insights for understanding the disconnection syndrome in AD.
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Affiliation(s)
- Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Nankai University, Tianjin, China.,Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Bo Zhou
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Hongxiang Yao
- Department of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Sangma Xie
- 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.,College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Feng Feng
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Zengqiang Zhang
- Hainan Hospital of Chinese PLA General Hospital, Sanya, China
| | - Yan'e Guo
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Ningyu An
- Department of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Nankai University, Tianjin, China
| | - Xi Zhang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yong Liu
- 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.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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LV YUTING, ZHAO WENSHUO, YAO XUFENG, XU SONG, TANG ZHIXIAN, FAN YIFENG, HUANG GANG. ANALYSES OF BRAIN CORTICAL CHANGES OF ALZHEIMER’S DISEASE. J MECH MED BIOL 2021. [DOI: 10.1142/s021951942140025x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Alzheimer’s disease (AD) produces complicated cortical changes in gray matter (GM) of the human brain. However, alterations in the brain cortex have not been clearly addressed. In our study, a cohort of 236 cases MR data enrolled from the ADNI database was categorized into three groups of normal controls (NCs), mild cognitive impairment (MCI) and AD. The GM morphological differences were investigated among the three groups using the magnetic resonance (MR) GM characteristics of gray matter volume (GMV), cortical thickness (CT), cortical surface area (CSA) and local gyrification index (LGI) at the three levels of whole brain, bilateral hemispheres and critical brain regions. Totally, there were six critical brain regions for GMV, 11 for CT, 2 for CSA and 59 for LGI among the three groups for the no-division groups. Also, there were 11 critical brain regions for GMV, 15 for CT, 8 for CSA, 3 for LGI for female sub-groups and 4 critical brain regions for GMV, 11 for CT, 1 for CSA, 3 for LGI for male sub-groups. The four measured cortical characteristics showed reliable capability in the morphological description of GM changes of AD. In conclusion, the cortical characteristics of GMV, CT, CSA and LGI of critical brain regions showed valuable indications for GM changes of AD, and those characteristics could be used as imaging markers for AD prediction.
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Affiliation(s)
- YUTING LV
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, P. R. China
| | - WENSHUO ZHAO
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
| | - XUFENG YAO
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, P. R. China
| | - SONG XU
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
| | - ZHIXIAN TANG
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, P. R. China
| | - YIFENG FAN
- School of Medical Imaging, Hangzhou Medical College, Hangzhou 310053, P. R. China
| | - GANG HUANG
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, P. R. China
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11
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Cheng R, Chen L, Liu X, Luo T, Gong J, Jiang P. Changes in Gray Matter Asymmetries of the Fusiform and Parahippocampal Gyruses in Patients With Subcortical Ischemic Vascular Disease. Front Neurol 2021; 11:603977. [PMID: 33551966 PMCID: PMC7859431 DOI: 10.3389/fneur.2020.603977] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 12/23/2020] [Indexed: 11/13/2022] Open
Abstract
Objective: Changes in the normal asymmetry of the human brain often mean pathology. Current studies on the correlation between asymmetry and cognitive impairment have focused on Alzheimer's disease (AD) and AD-related mild cognitive impairment (MCI). The purpose of this study was to investigate changes in gray matter asymmetry and their relationship with cognitive impairment in patients with subcortical ischemic vascular disease (SIVD) by using voxel-based morphological measurements. Methods: Fifty-nine SIVD patients with (subcortical vascular cognitive impairment, SVCI, N = 30) and without (pre-SVCI, N = 29) cognitive impairment and 30 normal controls (NC, N = 30) underwent high-resolution structural MRI and neuropsychological examinations. The differences in gray matter asymmetry among the three groups were estimated by using one-way ANOVA. Moreover, partial correlation analysis was performed to explore the relationships between the asymmetry index (AI) values and cognitive assessments controlled for age, sex, and education. Results: The gray matter asymmetries in the fusiform and parahippocampal gyruses of the SVCI group were significantly different from those of the NC group and the pre-SVCI group, while no differences were found between the NC group and the pre-SVCI group in the same areas. More specifically, in the fusiform and parahippocampal gyruses, the SVCI group displayed a dramatic rightward asymmetry, whereas the NC group and pre-SVCI group exhibited a marked leftward asymmetry. The results of the correlation analysis showed that the "mean AI" in significant cluster was strongly correlated with the changes in cognitive outcomes. Conclusion: This study demonstrated different lateralization in the fusiform and parahippocampal gyruses of SIVD patients with cognitive impairment compared to healthy subjects and SIVD patients without cognitive decline. Our findings may contribute to better understanding the possible mechanism of cognitive impairment in patients with SIVD, and they suggest the possibility of using gray matter asymmetry as a biomarker for disease progression.
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Affiliation(s)
- Runtian Cheng
- The Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Li Chen
- The Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiaoshuang Liu
- The Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Tianyou Luo
- The Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Junwei Gong
- The Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Peiling Jiang
- The Department of Radiology, The Second Affiliated Hospital of Military Medical University, Chongqing, China
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12
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Li TR, Wu Y, Jiang JJ, Lin H, Han CL, Jiang JH, Han Y. Radiomics Analysis of Magnetic Resonance Imaging Facilitates the Identification of Preclinical Alzheimer's Disease: An Exploratory Study. Front Cell Dev Biol 2020; 8:605734. [PMID: 33344457 PMCID: PMC7744815 DOI: 10.3389/fcell.2020.605734] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Accepted: 11/09/2020] [Indexed: 12/12/2022] Open
Abstract
Diagnosing Alzheimer's disease (AD) in the preclinical stage offers opportunities for early intervention; however, there is currently a lack of convenient biomarkers to facilitate the diagnosis. Using radiomics analysis, we aimed to determine whether the features extracted from multiparametric magnetic resonance imaging (MRI) can be used as potential biomarkers. This study was part of the Sino Longitudinal Study on Cognitive Decline project (NCT03370744), a prospective cohort study. All participants were cognitively healthy at baseline. Cohort 1 (n = 183) was divided into individuals with preclinical AD (n = 78) and controls (n = 105) using amyloid-positron emission tomography, and this cohort was used as the training dataset (80%) and validation dataset (the remaining 20%); cohort 2 (n = 51) was selected retrospectively and divided into "converters" and "nonconverters" according to individuals' future cognitive status, and this cohort was used as a separate test dataset; cohort three included 37 converters (13 from the Alzheimer's Disease Neuroimaging Initiative) and was used as another test set for independent longitudinal research. We extracted radiomics features from multiparametric MRI scans from each participant, using t-tests, autocorrelation tests, and three independent selection algorithms. We then established two classification models (support vector machine [SVM] and random forest [RF]) to verify the efficiency of the retained features. Five-fold cross-validation and 100 repetitions were carried out for the above process. Furthermore, the acquired stable high-frequency features were tested in cohort three by paired two-sample t-tests and survival analyses to identify whether their levels changed with cognitive decline and impact conversion time. The SVM and RF models both showed excellent classification efficiency, with an average accuracy of 89.7-95.9% and 87.1-90.8% in the validation set and 81.9-89.1% and 83.2-83.7% in the test set, respectively. Three stable high-frequency features were identified, all based on the structural MRI modality: the large zone high-gray-level emphasis feature of the right posterior cingulate gyrus, the variance feature of the left superior parietal gyrus, and the coarseness feature of the left posterior cingulate gyrus; their levels were correlated with amyloid-β deposition and predicted future cognitive decline (areas under the curve 0.649-0.761). In addition, levels of the variance feature at baseline decreased with cognitive decline and could affect the conversion time (p < 0.05). In conclusion, this exploratory study shows that the radiomics features of multiparametric MRI scans could represent potential biomarkers of preclinical AD.
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Affiliation(s)
- Tao-Ran Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yue Wu
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Juan-Juan Jiang
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Hua Lin
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Chun-Lei Han
- Turku PET Centre and Turku University Hospital, Turku, Finland
| | - Jie-Hui Jiang
- Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China
- National Clinical Research Center for Geriatric Disorders, Beijing, China
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13
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Wang W, Wong L, Shi L, Luo Y, Liang Z, Dong C, Song Q, Liu T, Zhang Q, Liu A, Miao Y, Wu J. Association of impaired fasting glucose and Type 2 Diabetes Mellitus with brain volume changes in Alzheimer's Disease patients analyzed by MRI: a retrospective study. PeerJ 2020; 8:e9801. [PMID: 32913679 PMCID: PMC7456526 DOI: 10.7717/peerj.9801] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 08/03/2020] [Indexed: 12/27/2022] Open
Abstract
Objectives Alzheimer’s disease (AD), impaired fasting glucose (IFG), and Type 2 diabetes mellitus (T2DM) were reported associated with smaller brain volumes. Nevertheless, the association of hyperglycemia with brain volume changes in AD patients remains unclear. To investigate this issue, structural magnetic resonance imaging was used to compare brain volumes among AD patients with different fasting glucose levels. Methods Eighty-five AD patients were divided into three groups based on their fasting glucose level as suggested by the American Diabetes Association: normal fasting glucose group (AD_NFG, n = 45), AD_IFG group (n = 15), and AD_T2DM group (n = 25). Sagittal 3D T1-weighted images were obtained to calculate the brain volume. Brain parenchyma and 33 brain structures were automatically segmented. Each regional volume was analyzed among groups. For regions with statistical significance, partial correlation analysis was used to evaluate their relationships with fasting glucose level, corrected for Mini-Mental State Examination score, age, education level, cholesterol, triglyceride, and blood pressure. Results Compared with the AD_IFG and AD_NFG groups, the volume of pons in AD_T2DM group was significantly smaller. Fasting glucose was negatively correlated with pontine volume. Conclusions T2DM may exacerbate pontine atrophy in AD patients, and fasting glucose level is associated with pontine volume.
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Affiliation(s)
- Weiwei Wang
- Tianjin Medical University, Tianjin, China.,Radiology Department, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Leongtim Wong
- BrainNow Research Institute, Shenzhen, Guangdong Province, China
| | - Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen, Guangdong Province, China
| | - Zhanhua Liang
- Neurology Department, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Chunbo Dong
- Neurology Department, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Qingwei Song
- Radiology Department, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Tieli Liu
- Radiology Department, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Qing Zhang
- Radiology Department, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning Province, China
| | - Ailian Liu
- Radiology Department, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Yanwei Miao
- Radiology Department, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Jianlin Wu
- Radiology Department, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning Province, China
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14
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Dou X, Yao H, Feng F, Wang P, Zhou B, Jin D, Yang Z, Li J, Zhao C, Wang L, An N, Liu B, Zhang X, Liu Y. Characterizing white matter connectivity in Alzheimer's disease and mild cognitive impairment: An automated fiber quantification analysis with two independent datasets. Cortex 2020; 129:390-405. [PMID: 32574842 DOI: 10.1016/j.cortex.2020.03.032] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 12/13/2019] [Accepted: 03/31/2020] [Indexed: 12/28/2022]
Abstract
Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia. Diffusion tensor imaging (DTI) has been widely used to show structural integrity and delineate white matter (WM) degeneration in AD. The automated fiber quantification (AFQ) method is a fully automated approach that can rapidly and reliably identify major WM fiber tracts and evaluate WM properties. The main aim of this study was to assess WM integrity and abnormities in a cohort of patients with amnestic mild cognitive impairment (aMCI) and AD as well as normal controls (NCs). For this purpose, we first used AFQ to identify 20 major WM tracts and assessed WM integrity and abnormalities in a cohort of 120 subjects (39 NCs, 34 aMCI patients and 47 AD patients) in a discovery dataset and 122 subjects (43 NCs, 37 aMCI patients and 42 AD patients) in a replicated dataset. Pointwise differences along WM tracts were identified in the discovery dataset and simultaneously confirmed in the replicated dataset. Next, we investigated the utility of DTI measures along WM tracts as features to distinguish patients with AD from NCs via multilevel cross validation using a support vector machine. Correlation analysis revealed the identified microstructural WM alterations and classification output to be highly associated with cognitive ability in the patient groups, suggesting that they may be a robust biomarker of AD. This systematic study provides a pipeline to examine WM integrity and its potential clinical application in AD and may be useful for studying other neurological and psychiatric disorders.
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Affiliation(s)
- Xuejiao Dou
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China
| | - Hongxiang Yao
- Department of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Feng Feng
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, 300350, China; Department of Neurology, Nankai University Huanhu Hospital, Tianjin, China
| | - Bo Zhou
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Dan Jin
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China
| | - Zhengyi Yang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jin Li
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Cui Zhao
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Luning Wang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Ningyu An
- Department of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Bing Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xi Zhang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China.
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
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15
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Farina FR, Emek-Savaş DD, Rueda-Delgado L, Boyle R, Kiiski H, Yener G, Whelan R. A comparison of resting state EEG and structural MRI for classifying Alzheimer's disease and mild cognitive impairment. Neuroimage 2020; 215:116795. [PMID: 32278090 DOI: 10.1016/j.neuroimage.2020.116795] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 03/27/2020] [Accepted: 03/28/2020] [Indexed: 12/31/2022] Open
Abstract
Alzheimer's disease (AD) is the leading cause of dementia, accounting for 70% of cases worldwide. By 2050, dementia prevalence will have tripled, with most new cases occurring in low- and middle-income countries. Mild cognitive impairment (MCI) is a stage between healthy aging and dementia, marked by cognitive deficits that do not impair daily living. People with MCI are at increased risk of dementia, with an average progression rate of 39% within 5 years. There is urgent need for low-cost, accessible and objective methods to facilitate early dementia detection. Electroencephalography (EEG) has potential to address this need due to its low cost and portability. Here, we collected resting state EEG, structural MRI (sMRI) and rich neuropsychological data from older adults (55+ years) with AD, amnestic MCI (aMCI) and healthy controls (~60 per group). We evaluated a range of candidate EEG markers (i.e., frequency band power and functional connectivity) for AD and aMCI classification and compared their performance with sMRI. We also tested a combined EEG and cognitive classification model (using Mini-Mental State Examination; MMSE). sMRI outperformed resting state EEG at classifying AD (AUCs = 1.00 vs 0.76, respectively). However, both EEG and sMRI were only moderately good at distinguishing aMCI from healthy aging (AUCs = 0.67-0.73), and neither method achieved sensitivity above 70%. The addition of EEG to MMSE scores had no added benefit relative to MMSE scores alone. This is the first direct comparison of EEG and sMRI for classification of AD and aMCI.
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Affiliation(s)
- F R Farina
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland.
| | - D D Emek-Savaş
- Department of Psychology, Faculty of Letters, Dokuz Eylul University, Izmir, 35160, Turkey; Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, 35340, Turkey; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland
| | - L Rueda-Delgado
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - R Boyle
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - H Kiiski
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - G Yener
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, 35340, Turkey; Department of Neurology, Dokuz Eylul University Medical School, Izmir, 35340, Turkey
| | - R Whelan
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland.
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Ding Y, Luo C, Li C, Lan T, Qin Z. High-order correlation detecting in features for diagnosis of Alzheimer’s disease and mild cognitive impairment. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.101564] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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17
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Li J, Jin D, Li A, Liu B, Song C, Wang P, Wang D, Xu K, Yang H, Yao H, Zhou B, Bejanin A, Chetelat G, Han T, Lu J, Wang Q, Yu C, Zhang X, Zhou Y, Zhang X, Jiang T, Liu Y, Han Y. ASAF: altered spontaneous activity fingerprinting in Alzheimer's disease based on multisite fMRI. Sci Bull (Beijing) 2019; 64:998-1010. [PMID: 36659811 DOI: 10.1016/j.scib.2019.04.034] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 03/22/2019] [Accepted: 03/25/2019] [Indexed: 01/21/2023]
Abstract
Several monocentric studies have noted alterations in spontaneous brain activity in Alzheimer's disease (AD), although there is no consensus on the altered amplitude of low-frequency fluctuations in AD patients. The main aim of the present study was to identify a reliable and reproducible abnormal brain activity pattern in AD. The amplitude of local brain activity (AM), which can provide fast mapping of spontaneous brain activity across the whole brain, was evaluated based on multisite rs-fMRI data for 688 subjects (215 normal controls (NCs), 221 amnestic mild cognitive impairment (aMCI) 252 AD). Two-sample t-tests were used to detect group differences between AD patients and NCs from the same site. Differences in the AM maps were statistically analyzed via the Stouffer's meta-analysis. Consistent regions of lower spontaneous brain activity in the default mode network and increased activity in the bilateral hippocampus/parahippocampus, thalamus, caudate nucleus, orbital part of the middle frontal gyrus and left fusiform were observed in the AD patients compared with those in NCs. Significant correlations (P < 0.05, Bonferroni corrected) between the normalized amplitude index and Mini-Mental State Examination scores were found in the identified brain regions, which indicates that the altered brain activity was associated with cognitive decline in the patients. Multivariate analysis and leave-one-site-out cross-validation led to a 78.49% prediction accuracy for single-patient classification. The altered activity patterns of the identified brain regions were largely correlated with the FDG-PET results from another independent study. These results emphasized the impaired brain activity to provide a robust and reproducible imaging signature of AD.
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Affiliation(s)
- Jiachen Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Dan Jin
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ang Li
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bing Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan 250012, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin 300350, China; Institute of Geriatrics and Gerontology, Chinese PLA General Hospital, Beijing 100853, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital, Ji'nan 250012, China
| | - Kaibin Xu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Hongxiang Yao
- Department of Radiology, Chinese PLA General Hospital, Beijing 100853, China
| | - Bo Zhou
- Institute of Geriatrics and Gerontology, Chinese PLA General Hospital, Beijing 100853, China
| | - Alexandre Bejanin
- Université Normandie, Inserm, Université de Caen-Normandie, Inserm UMR-S U1237, GIP Cyceron, Caen 14000, France
| | - Gael Chetelat
- Université Normandie, Inserm, Université de Caen-Normandie, Inserm UMR-S U1237, GIP Cyceron, Caen 14000, France
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Qing Wang
- Department of Radiology, Qilu Hospital, Ji'nan 250012, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Xinqing Zhang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Xi Zhang
- Institute of Geriatrics and Gerontology, Chinese PLA General Hospital, Beijing 100853, China
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China; Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing 100053, China; Beijing Institute of Geriatrics, Beijing 100053, China; National Clinical Research Center for Geriatric Disorders, Beijing 100053, China.
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Alzheimer Disease-associated Cortical Atrophy Does not Differ Between Chinese and Whites. Alzheimer Dis Assoc Disord 2019; 33:186-193. [PMID: 31094707 DOI: 10.1097/wad.0000000000000315] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PURPOSE To assess whether there are differences in Alzheimer disease (AD)-associated atrophy regions in Chinese and white patients with AD versus cognitively normal older adults, and to test whether associations between clinical severity and gray matter volume are similar or different across these ethnic groups in a cross-sectional analysis. MATERIALS AND METHODS Chinese and white patients with AD, individuals with mild cognitive impairment, and cognitively normal controls (46 white and 48 Chinese) were clinically evaluated at an academic center within 1 year of magnetic resonance imaging acquisition. Clinical severity was assessed using the Clinical Dementia Rating Sum of Boxes and cortical atrophy was measured using voxel-based morphometry as well as Freesurfer. Chinese and white cohorts were demographically matched for age, sex, and education. RESULTS Clinical severity by diagnosis was similar across ethnicities. Chinese and white patient groups showed similar amounts of atrophy in the regions most affected in AD after accounting for demographic variables and head size. There was no significant difference between ethnic groups when compared by atrophy and clinical severity. CONCLUSIONS Our study suggests that Chinese and white patients with AD, when matched demographically, are clinically and neuroanatomically similar on normalized measures of cortical atrophy and clinical severity.
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19
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López-Sanz D, Suárez-Méndez I, Bernabé R, Pasquín N, Rodríguez-Mañas L, Maestú F, Walter S. Scoping Review of Neuroimaging Studies Investigating Frailty and Frailty Components. Front Med (Lausanne) 2018; 5:284. [PMID: 30349819 PMCID: PMC6186819 DOI: 10.3389/fmed.2018.00284] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 09/17/2018] [Indexed: 01/10/2023] Open
Abstract
Background: Neuroimaging techniques are a cornerstone for diagnosing and investigating cognitive decline and dementia in the elderly. In frailty research, the physical as opposed to the cognitive domain of the aging process, neuroimaging studies are less common. Here we systematically review the use of neuroimaging techniques in frailty research. Methods: We searched PUBMED for any publication reporting the association between neuroimaging markers and frailty, following Fried's original definition, as well as its determining phenotypes: gait speed, grip strength, fatigue and recent weight loss in the non-diseased population older than 65 years. Results: The search returned a total of 979 abstracts which were independently screened by 3 reviewers. In total, 17 studies met the inclusion criteria. Of these, 12 studies evaluated gait speed, 2 grip strength, and 3 frailty (2 Fried Frailty, 1 Frailty Index). An association between increased burden of white matter lesions, lower fractional anisotropy, and higher diffusivity has been associated consistently to frailty and worse performance in the different frailty components. Conclusions: White matter lesions were significantly associated to frailty and frailty components thus highlighting the potential utility of neuroimaging in unraveling the underlying mechanisms of this state. However, considering small sample size and design effects, it is not possible to completely rule out reverse causality between frailty and neuroimaging findings. More studies are needed to clarify this important clinical question.
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Affiliation(s)
- David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM), Madrid, Spain.,Department of Experimental Psychology, Complutense University of Madrid (UCM), Madrid, Spain
| | - Isabel Suárez-Méndez
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM), Madrid, Spain
| | - Raquel Bernabé
- Fundación Para la Investigación Biomédica, Getafe University Hospital, Madrid, Spain
| | - Natalia Pasquín
- Fundación Para la Investigación Biomédica, Getafe University Hospital, Madrid, Spain
| | - Leocadio Rodríguez-Mañas
- Fundación Para la Investigación Biomédica, Getafe University Hospital, Madrid, Spain.,Geriatrics Department, Getafe University Hospital, Madrid, Spain.,Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM), Madrid, Spain.,Department of Experimental Psychology, Complutense University of Madrid (UCM), Madrid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain
| | - Stefan Walter
- Fundación Para la Investigación Biomédica, Getafe University Hospital, Madrid, Spain.,Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain.,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States
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20
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Improved Gray Matter Atrophy Detection in Alzheimer Disease in Chinese Populations Using Chinese Brain Template. Alzheimer Dis Assoc Disord 2018; 32:309-313. [DOI: 10.1097/wad.0000000000000264] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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21
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Feng F, Wang P, Zhao K, Zhou B, Yao H, Meng Q, Wang L, Zhang Z, Ding Y, Wang L, An N, Zhang X, Liu Y. Radiomic Features of Hippocampal Subregions in Alzheimer's Disease and Amnestic Mild Cognitive Impairment. Front Aging Neurosci 2018; 10:290. [PMID: 30319396 PMCID: PMC6167420 DOI: 10.3389/fnagi.2018.00290] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 09/03/2018] [Indexed: 12/27/2022] Open
Abstract
Alzheimer's disease (AD) is characterized by progressive dementia, especially in episodic memory, and amnestic mild cognitive impairment (aMCI) is associated with a high risk of developing AD. Hippocampal atrophy/shape changes are believed to be the most robust magnetic resonance imaging (MRI) markers for AD and aMCI. Radiomics, a method of texture analysis, can quantitatively examine a large set of features and has previously been successfully applied to evaluate imaging biomarkers for AD. To test whether radiomic features in the hippocampus can be employed for early classification of AD and aMCI, 1692 features from the caudal and head parts of the bilateral hippocampus were extracted from 38 AD patients, 33 aMCI patients and 45 normal controls (NCs). One way analysis of variance (ANOVA) showed that 111 features exhibited statistically significant group differences (P < 0.01, Bonferroni corrected). Among these features, 98 were significantly correlated with Mini-Mental State Examination (MMSE) scores in AD and aMCI subjects (P < 0.01). The support vector machine (SVM) model demonstrated that radiomic features allowed us to distinguish AD from NC with an accuracy of 86.75% (specificity = 88.89% and sensitivity = 84.21%) and an area under curve (AUC) of 0.93. In conclusion, these findings provide evidence showing that radiomic features are beneficial in detecting early cognitive decline, and SVM classification analysis provides encouraging evidence for using hippocampal radiomic features as a potential biomarker for clinical applications in AD.
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Affiliation(s)
- Feng Feng
- Department of Neurology, Nanlou Division, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, China
- Department of Neurology, The General Hospital of the PLA Rocket Force, Beijing, China
| | - Pan Wang
- Department of Neurology, Nanlou Division, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, China
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
| | - Kun Zhao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Bo Zhou
- Department of Neurology, Nanlou Division, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Hongxiang Yao
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Qingqing Meng
- Department of Neurology, Nanlou Division, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Lei Wang
- Department of Neurology, The General Hospital of the PLA Rocket Force, Beijing, China
| | - Zengqiang Zhang
- Department of Neurology, Nanlou Division, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, China
- Hainan Branch of Chinese PLA General Hospital, Sanya, China
| | - Yanhui Ding
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Luning Wang
- Department of Neurology, Nanlou Division, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Ningyu An
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Xi Zhang
- Department of Neurology, Nanlou Division, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
- National Laboratory of Pattern Recognition, Institute of Automation, 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
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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22
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Schverer M, Lanfumey L, Baulieu EE, Froger N, Villey I. Neurosteroids: non-genomic pathways in neuroplasticity and involvement in neurological diseases. Pharmacol Ther 2018; 191:190-206. [PMID: 29953900 DOI: 10.1016/j.pharmthera.2018.06.011] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Neurosteroids are neuroactive brain-born steroids. They can act through non-genomic and/or through genomic pathways. Genomic pathways are largely described for steroid hormones: the binding to nuclear receptors leads to transcription regulation. Pregnenolone, Dehydroepiandrosterone, their respective sulfate esters and Allopregnanolone have no corresponding nuclear receptor identified so far whereas some of their non-genomic targets have been identified. Neuroplasticity is the capacity that neuronal networks have to change their structure and function in response to biological and/or environmental signals; it is regulated by several mechanisms, including those that involve neurosteroids. In this review, after a description of their biosynthesis, the effects of Pregnenolone, Dehydroepiandrosterone, their respective sulfate esters and Allopregnanolone on their targets will be exposed. We then shall highlight that neurosteroids, by acting on these targets, can regulate neurogenesis, structural and functional plasticity. Finally, we will discuss the therapeutic potential of neurosteroids in the pathophysiology of neurological diseases in which alterations of neuroplasticity are associated with changes in neurosteroid levels.
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Affiliation(s)
- Marina Schverer
- Inserm U894, Centre de Psychiatrie et Neurosciences, Université Paris Descartes, 75014 Paris, France
| | - Laurence Lanfumey
- Inserm U894, Centre de Psychiatrie et Neurosciences, Université Paris Descartes, 75014 Paris, France.
| | - Etienne-Emile Baulieu
- MAPREG SAS, Le Kremlin-Bicêtre, France; Inserm UMR 1195, Université Paris-Saclay, Le Kremlin Bicêtre, France
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23
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Li Q, Wu X, Xu L, Chen K, Yao L. Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals Using Multi-feature Kernel Discriminant Dictionary Learning. Front Comput Neurosci 2018; 11:117. [PMID: 29375356 PMCID: PMC5767247 DOI: 10.3389/fncom.2017.00117] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 12/19/2017] [Indexed: 01/03/2023] Open
Abstract
Accurate classification of either patients with Alzheimer's disease (AD) or patients with mild cognitive impairment (MCI), the prodromal stage of AD, from cognitively unimpaired (CU) individuals is important for clinical diagnosis and adequate intervention. The current study focused on distinguishing AD or MCI from CU based on the multi-feature kernel supervised within-Class-similar discriminative dictionary learning algorithm (MKSCDDL), which we introduced in a previous study, demonstrating that MKSCDDL had superior performance in face recognition. Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir-PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were all included for classification of AD vs. CU, MCI vs. CU, as well as AD vs. MCI (113 AD patients, 110 MCI patients, and 117 CU subjects). By adopting MKSCDDL, we achieved a classification accuracy of 98.18% for AD vs. CU, 78.50% for MCI vs. CU, and 74.47% for AD vs. MCI, which in each instance was superior to results obtained using several other state-of-the-art approaches (MKL, JRC, mSRC, and mSCDDL). In addition, testing time results outperformed other high quality methods. Therefore, the results suggested that the MKSCDDL procedure is a promising tool for assisting early diagnosis of diseases using neuroimaging data.
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Affiliation(s)
- Qing Li
- Department of Electronics, College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - Xia Wu
- Department of Electronics, College of Information Science and Technology, Beijing Normal University, Beijing, China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Lele Xu
- Department of Electronics, College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - Kewei Chen
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, United States
| | - Li Yao
- Department of Electronics, College of Information Science and Technology, Beijing Normal University, Beijing, China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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24
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Xiao Z, Ding Y, Lan T, Zhang C, Luo C, Qin Z. Brain MR Image Classification for Alzheimer's Disease Diagnosis Based on Multifeature Fusion. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:1952373. [PMID: 28611848 PMCID: PMC5458434 DOI: 10.1155/2017/1952373] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Revised: 03/15/2017] [Accepted: 04/02/2017] [Indexed: 01/02/2023]
Abstract
We propose a novel classification framework to precisely identify individuals with Alzheimer's disease (AD) or mild cognitive impairment (MCI) from normal controls (NC). The proposed method combines three different features from structural MR images: gray-matter volume, gray-level cooccurrence matrix, and Gabor feature. These features can obtain both the 2D and 3D information of brains, and the experimental results show that a better performance can be achieved through the multifeature fusion. We also analyze the multifeatures combination correlation technologies and improve the SVM-RFE algorithm through the covariance method. The results of comparison experiments on public Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrate the effectiveness of the proposed method. Besides, it also indicates that multifeatures combination is better than the single-feature method. The proposed features selection algorithm could effectively extract the optimal features subset in order to improve the classification performance.
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Affiliation(s)
- Zhe Xiao
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan Province 610054, China
| | - Yi Ding
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan Province 610054, China
| | - Tian Lan
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan Province 610054, China
| | - Cong Zhang
- China Gas Turbine Establishment, Mianyang, Sichuan 621000, China
| | - Chuanji Luo
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan Province 610054, China
| | - Zhiguang Qin
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan Province 610054, China
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25
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Wang C, Ding Y, Shen B, Gao D, An J, Peng K, Hou G, Zou L, Jiang M, Qiu S. Altered Gray Matter Volume in Stable Chronic Obstructive Pulmonary Disease with Subclinical Cognitive Impairment: an Exploratory Study. Neurotox Res 2016; 31:453-463. [PMID: 28005183 DOI: 10.1007/s12640-016-9690-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Revised: 12/05/2016] [Accepted: 12/12/2016] [Indexed: 12/31/2022]
Abstract
Gray matter volume deficits have been identified in cognitively impaired patients with chronic obstructive pulmonary disease (COPD). However, it remains unknown whether the gray matter volume is altered in COPD patients with subclinical cognitive impairment. To determine whether any gray matter abnormalities are present in these patients, neuropsychological tests and structural MRI data were analyzed from 60 patients with COPD and 60 age-, gender-, education-, and handedness-matched normal controls (NCs). The COPD patients had similar Mini-Mental State Examination (MMSE) scores compared with the NCs. However, they had reduced Montreal Cognitive Assessment (MoCA) scores for visuospatial and executive and naming and memory functions (P < 0.001). Voxel-based morphometry (VBM) analysis revealed that the COPD patients had significantly lowered gray matter volumes in several brain regions, including the left precuneus (PrCU), bilateral calcarine (CAL), right superior temporal gyrus/middle temporal gyrus (STG/MTG), bilateral fusiform gyrus (FG), and right inferior parietal lobule (IPL) (P < 0.01, corrected). Importantly, the forced vital capacity (FVC) was found to be associated with the gray matter volume in the calcarine. The present study confirmed that brain structural changes were present in stable COPD patients with subclinical cognitive impairment. These findings may provide new insights into the pathogenesis of COPD.
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Affiliation(s)
- Chunrong Wang
- Department of Radiology, Nanfang Hospital Affiliated to Southern Medical University, Guangzhou, Guangdong, 510515, China
- Department of Radiology, Nanshan Hospital Affiliated to Guangdong Medical University, Shenzhen, Guangdong, 518052, China
| | - Yanhui Ding
- School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong, 250014, China
| | - Bixian Shen
- Department of Radiology, Nanshan Hospital Affiliated to Guangdong Medical University, Shenzhen, Guangdong, 518052, China
| | - Dehong Gao
- Department of Radiology, Nanshan Hospital Affiliated to Guangdong Medical University, Shenzhen, Guangdong, 518052, China
| | - Jie An
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510405, China
| | - Kewen Peng
- Department of Radiology, Nanshan Hospital Affiliated to Guangdong Medical University, Shenzhen, Guangdong, 518052, China
| | - Gangqiang Hou
- Department of Radiology, Nanshan Hospital Affiliated to Guangdong Medical University, Shenzhen, Guangdong, 518052, China
| | - Liqiu Zou
- Department of Radiology, Nanshan Hospital Affiliated to Guangdong Medical University, Shenzhen, Guangdong, 518052, China
| | - Mei Jiang
- Department of Radiology, Nanshan Hospital Affiliated to Guangdong Medical University, Shenzhen, Guangdong, 518052, China
| | - Shijun Qiu
- Department of Radiology, Nanfang Hospital Affiliated to Southern Medical University, Guangzhou, Guangdong, 510515, China.
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510405, China.
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26
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Wang P, Zhou B, Yao H, Zhan Y, Zhang Z, Cui Y, Xu K, Ma J, Wang L, An N, Zhang X, Liu Y, Jiang T. Aberrant intra- and inter-network connectivity architectures in Alzheimer's disease and mild cognitive impairment. Sci Rep 2015; 5:14824. [PMID: 26439278 PMCID: PMC4594099 DOI: 10.1038/srep14824] [Citation(s) in RCA: 84] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Accepted: 09/04/2015] [Indexed: 01/21/2023] Open
Abstract
Alzheimer’s disease (AD) patients and those with high-risk mild cognitive impairment are increasingly considered to have dysfunction syndromes. Large-scale network studies based on neuroimaging techniques may provide additional insight into AD pathophysiology. The aim of the present study is to evaluate the impaired network functional connectivity with the disease progression. For this purpose, we explored altered functional connectivities based on previously well-defined brain areas that comprise the five key functional systems [the default mode network (DMN), dorsal attention network (DAN), control network (CON), salience network (SAL), sensorimotor network (SMN)] in 35 with AD and 27 with mild cognitive impairment (MCI) subjects, compared with 27 normal cognitive subjects. Based on three levels of analysis, we found that intra- and inter-network connectivity were impaired in AD. Importantly, the interaction between the sensorimotor and attention functions was first attacked at the MCI stage and then extended to the key functional systems in the AD individuals. Lower cognitive ability (lower MMSE scores) was significantly associated with greater reductions in intra- and inter-network connectivity across all patient groups. These profiles indicate that aberrant intra- and inter-network dysfunctions might be potential biomarkers or predictors of AD progression and provide new insight into AD pathophysiology.
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Affiliation(s)
- Pan Wang
- Department of Neurology, Institute of Geriatrics and Gerontology, Chinese PLA General Hospital, Beijing, 100853, China.,Department of Neurology, Tianjin Huanhu Hospital, Tianjin, 300060, China
| | - Bo Zhou
- Department of Neurology, Institute of Geriatrics and Gerontology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Hongxiang Yao
- Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Yafeng Zhan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Zengqiang Zhang
- Department of Neurology, Institute of Geriatrics and Gerontology, Chinese PLA General Hospital, Beijing, 100853, China.,Hainan Branch of Chinese PLA General Hospital, Sanya, 572014, China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Kaibin Xu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Luning Wang
- Department of Neurology, Institute of Geriatrics and Gerontology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Ningyu An
- Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Xi Zhang
- Department of Neurology, Institute of Geriatrics and Gerontology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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27
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Wang WY, Yu JT, Liu Y, Yin RH, Wang HF, Wang J, Tan L, Radua J, Tan L. Voxel-based meta-analysis of grey matter changes in Alzheimer's disease. Transl Neurodegener 2015; 4:6. [PMID: 25834730 PMCID: PMC4381413 DOI: 10.1186/s40035-015-0027-z] [Citation(s) in RCA: 82] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2014] [Accepted: 03/18/2015] [Indexed: 01/18/2023] Open
Abstract
Background Voxel-based morphometry (VBM) using structural brain MRI has been widely used for the assessment of impairment in Alzheimer’s disease (AD), but previous studies in VBM studies on AD remain inconsistent. Objective We conducted meta-analyses to integrate the reported studies to determine the consistent grey matter alterations in AD based on VBM method. Methods The PubMed, ISI Web of Science, EMBASE and Medline database were searched for articles between 1995 and June 2014. Manual searches were also conducted, and authors of studies were contacted for additional data. Coordinates were extracted from clusters with significant grey matter difference between AD patients and healthy controls (HC). Meta-analysis was performed using a new improved voxel-based meta-analytic method, Effect Size Signed Differential Mapping (ES-SDM). Results Thirty data-sets comprising 960 subjects with AD and 1195 HC met inclusion criteria. Grey matter volume (GMV) reduction at 334 coordinates in AD and no GMV increase were found in the current meta-analysis. Significant reductions in GMV were robustly localized in the limbic regions (left parahippocampl gyrus and left posterior cingulate gyrus). In addition, there were GM decreases in right fusiform gyrus and right superior frontal gyrus. The findings remain largely unchanged in the jackknife sensitivity analyses. Conclusions Our meta-analysis clearly identified GMV atrophy in AD. These findings confirm that the most prominent and replicable structural abnormalities in AD are in the limbic regions and contributes to the understanding of pathophysiology underlying AD.
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Affiliation(s)
- Wen-Ying Wang
- Department of Neurology, Qingdao Municipal Hospital, School of Medicine, Qingdao University, No.5 Donghai Middle Road, Qingdao, Shandong Province 266071 China
| | - Jin-Tai Yu
- Department of Neurology, Qingdao Municipal Hospital, School of Medicine, Qingdao University, No.5 Donghai Middle Road, Qingdao, Shandong Province 266071 China ; College of Medicine and Pharmaceutics, Ocean University of China, Qingdao, 266011 China ; Department of Neurology, Qingdao Municipal Hospital, Nanjing Medical University, Nanjing, 266071 China
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China
| | - Rui-Hua Yin
- Department of Neurology, Qingdao Municipal Hospital, School of Medicine, Qingdao University, No.5 Donghai Middle Road, Qingdao, Shandong Province 266071 China
| | - Hui-Fu Wang
- Department of Neurology, Qingdao Municipal Hospital, Nanjing Medical University, Nanjing, 266071 China
| | - Jun Wang
- Department of Neurology, Qingdao Municipal Hospital, School of Medicine, Qingdao University, No.5 Donghai Middle Road, Qingdao, Shandong Province 266071 China
| | - Lin Tan
- College of Medicine and Pharmaceutics, Ocean University of China, Qingdao, 266011 China
| | - Joaquim Radua
- Department of Psychosis Studies, Institute of Psychiatry, King's College London, London, UK ; Research Unit, FIDMAG Germanes Hospitala'ries-CIBERSAM, Sant Boi de Llobregat, Barcelona, Spain
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, School of Medicine, Qingdao University, No.5 Donghai Middle Road, Qingdao, Shandong Province 266071 China ; College of Medicine and Pharmaceutics, Ocean University of China, Qingdao, 266011 China ; Department of Neurology, Qingdao Municipal Hospital, Nanjing Medical University, Nanjing, 266071 China
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28
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Cai Y, Liu J, Zhang L, Liao M, Zhang Y, Wang L, Peng H, He Z, Li Z, Li W, Lu S, Ding Y, Li L. Grey matter volume abnormalities in patients with bipolar I depressive disorder and unipolar depressive disorder: a voxel-based morphometry study. Neurosci Bull 2014; 31:4-12. [PMID: 25502401 DOI: 10.1007/s12264-014-1485-5] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2014] [Accepted: 09/14/2014] [Indexed: 12/26/2022] Open
Abstract
Bipolar disorder and unipolar depressive disorder (UD) may be different in brain structure. In the present study, we performed voxel-based morphometry (VBM) to quantify the grey matter volumes in 23 patients with bipolar I depressive disorder (BP1) and 23 patients with UD, and 23 age-, gender-, and education-matched healthy controls (HCs) using magnetic resonance imaging. We found that compared with the HC and UD groups, the BP1 group showed reduced grey matter volumes in the right inferior frontal gyrus and middle cingulate gyrus, while the UD group showed reduced volume in the right inferior frontal gyrus compared to HCs. In addition, correlation analyses revealed that the grey matter volumes of these regions were negatively correlated with the Hamilton depression rating scores. Taken together, the results of our study suggest that decreased grey matter volume of the right inferior frontal gyrus is a common abnormality in BP1 and UD, and decreased grey matter volume in the right middle cingulate gyrus may be specific to BP1.
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Affiliation(s)
- Yi Cai
- Mental Health Institute of the Second Xiangya Hospital, National Technology Institute of Psychiatry Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Changsha, 410011, China
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