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Pinheiro FI, Araújo-Filho I, do Rego ACM, de Azevedo EP, Cobucci RN, Guzen FP. Hepatopancreatic metabolic disorders and their implications in the development of Alzheimer's disease and vascular dementia. Ageing Res Rev 2024; 96:102250. [PMID: 38417711 DOI: 10.1016/j.arr.2024.102250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/07/2024] [Accepted: 02/22/2024] [Indexed: 03/01/2024]
Abstract
Dementia has been faced with significant public health challenges and economic burdens that urges the need to develop safe and effective interventions. In recent years, an increasing number of studies have focused on the relationship between dementia and liver and pancreatic metabolic disorders that result in diseases such as diabetes, obesity, hypertension and dyslipidemia. Previous reports have shown that there is a plausible correlation between pathologies caused by hepatopancreatic dysfunctions and dementia. Glucose, insulin and IGF-1 metabolized in the liver and pancreas probably have an important influence on the pathophysiology of the most common dementias: Alzheimer's and vascular dementia. This current review highlights recent studies aimed at identifying convergent mechanisms, such as insulin resistance and other diseases, linked to altered hepatic and pancreatic metabolism, which are capable of causing brain changes that ultimately lead to dementia.
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Affiliation(s)
- Francisco I Pinheiro
- Postgraduate Program in Biotechnology, Health School, Potiguar University (UnP), Natal, RN, Brazil; Department of Surgical, Federal University of Rio Grande do Norte, Natal 59010-180, Brazil; Institute of Education, Research and Innovation of the Liga Norte Rio-Grandense Against Cancer
| | - Irami Araújo-Filho
- Postgraduate Program in Biotechnology, Health School, Potiguar University (UnP), Natal, RN, Brazil; Department of Surgical, Federal University of Rio Grande do Norte, Natal 59010-180, Brazil; Postgraduate Program in Health Sciences, Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil
| | - Amália C M do Rego
- Postgraduate Program in Biotechnology, Health School, Potiguar University (UnP), Natal, RN, Brazil; Institute of Education, Research and Innovation of the Liga Norte Rio-Grandense Against Cancer
| | - Eduardo P de Azevedo
- Postgraduate Program in Biotechnology, Health School, Potiguar University (UnP), Natal, RN, Brazil
| | - Ricardo N Cobucci
- Postgraduate Program in Biotechnology, Health School, Potiguar University (UnP), Natal, RN, Brazil; Postgraduate Program in Health Sciences, Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil; Postgraduate Program in Science Applied to Women`s Health, Medical School, Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil
| | - Fausto P Guzen
- Postgraduate Program in Biotechnology, Health School, Potiguar University (UnP), Natal, RN, Brazil; Postgraduate Program in Health and Society, Department of Biomedical Sciences, Faculty of Health Sciences, State University of Rio Grande do Norte (UERN), Mossoró, Brazil; Postgraduate Program in Physiological Sciences, Department of Biomedical Sciences, Faculty of Health Sciences, State University of Rio Grande do Norte (UERN), Mossoró, Brazil.
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Xiao P, Li Q, Gui H, Xu B, Zhao X, Wang H, Tao L, Chen H, Wang H, Lv F, Luo T, Cheng O, Luo J, Man Y, Xiao Z, Fang W. Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease. Neurol Sci 2024:10.1007/s10072-024-07472-1. [PMID: 38528280 DOI: 10.1007/s10072-024-07472-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/14/2024] [Indexed: 03/27/2024]
Abstract
BACKGROUND Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear. OBJECTIVE The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD). METHODS Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs. RESULTS A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics. CONCLUSIONS These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.
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Affiliation(s)
- Pan Xiao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Qin Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Honge Gui
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Bintao Xu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Xiaole Zhao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Hongyu Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Li Tao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Huiyue Chen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Hansheng Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Tianyou Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Oumei Cheng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jin Luo
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yun Man
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zheng Xiao
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weidong Fang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
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El Haffaf LM, Ronat L, Cannizzaro A, Hanganu A. Associations Between Hyperactive Neuropsychiatric Symptoms and Brain Morphology in Mild Cognitive Impairment and Alzheimer's Disease. J Alzheimers Dis 2024; 97:841-853. [PMID: 38143342 DOI: 10.3233/jad-220857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2023]
Abstract
BACKGROUND Hyperactive neuropsychiatric symptoms (NPS) (i.e., agitation, disinhibition, and irritability) are among the most challenging symptoms to manage in Alzheimer's disease (AD). However, their underlying brain correlates have been poorly studied. OBJECTIVE We aimed to investigate the associations between the total score of hyperactive NPS and brain structures in participants with AD, mild cognitive impairment (MCI), and cognitively normal older adults (CN). METHODS Neuropsychiatric and 3T MRI data from 216 AD, 564 MCI, and 660 CN participants were extracted from the Alzheimer's Disease Neuroimaging Initiative database. To define NPS and brain structures' associations, we fitted a general linear model (GLM) in two ways: 1) an overall GLM including all three groups (AD, MCI, CN) and 2) three pair-wise GLMs (AD versus MCI, MCI versus CN, AD versus CN). The cortical changes as a function of NPS total score were investigated using multiple regression analyses. RESULTS Results from the overall GLM include associations between 1) agitation and the right parietal supramarginal surface area in the MCI-CN contrast, 2) disinhibition and the cortical thickness of the right frontal pars opercularis and temporal inferior in the AD-MCI contrast, and 3) irritability and the right frontal pars opercularis, frontal superior, and temporal superior volumes in the MCI-CN contrast. CONCLUSIONS Our study shows that each hyperactive NPS is associated with distinct brain regions in AD, MCI, and CN (groups with different levels of cognitive performance). This suggests that each NPS is associated with a unique signature of brain morphology, including variations in volume, thickness, or area.
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Affiliation(s)
- Lyna Mariam El Haffaf
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS du Centre-Sud-de-l'Ile-de-Montreal, Montréal, QC, Canada
- Département de Psychologie, Faculté des Arts et des Sciences, Université de Montréal, Montréal, QC, Canada
| | - Lucas Ronat
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS du Centre-Sud-de-l'Ile-de-Montreal, Montréal, QC, Canada
- Département de Médecine, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada
| | - Adriana Cannizzaro
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS du Centre-Sud-de-l'Ile-de-Montreal, Montréal, QC, Canada
- Département de Psychologie, Faculté des Arts et des Sciences, Université de Montréal, Montréal, QC, Canada
| | - Alexandru Hanganu
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS du Centre-Sud-de-l'Ile-de-Montreal, Montréal, QC, Canada
- Département de Psychologie, Faculté des Arts et des Sciences, Université de Montréal, Montréal, QC, Canada
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Cannizzaro A, Ronat L, El Haffaf LM, Hanganu A. Associations between neuropsychiatric symptoms of affective and vegetative domains and brain morphology in aging people with mild cognitive impairment and Alzheimer's disease. Int J Geriatr Psychiatry 2023; 38:e5952. [PMID: 37351584 DOI: 10.1002/gps.5952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 06/05/2023] [Indexed: 06/24/2023]
Abstract
OBJECTIVE Neuropsychiatric symptoms (NPS) are common in mild cognitive impairment (MCI) and even more in Alzheimer's disease (AD). The symptom-based cluster including nighttime disturbances, depression, appetite changes, anxiety, and apathy (affective and vegetative symptoms) was associated with an increased risk of dementia in MCI and has common neuroanatomical associations. Our objective was to investigate the differences in brain morphology associations with affective and vegetative symptoms between three groups: cognitively normal older adults (CN), MCI and AD. MATERIAL AND METHODS Alzheimer's Disease Neuroimaging Initiative data of 223 CN, 367 MCI and 175 AD, including cortical volumes, surface areas and thicknesses and severity scores of the five NPS were analyzed. A whole-brain vertex-wise general linear model was performed to test for intergroup differences (CN-MCI, CN-AD, AD-MCI) in brain morphology associations with five NPS. Multiple regressions were conducted to investigate cortical change as a function of NPS severity in the AD-MCI contrast. RESULTS We found (1) signature differences in nighttime disturbances associations with prefrontal regions in AD-MCI, (2) signature differences in NPS associations with temporal regions in AD-MCI for depression and in CN-AD for anxiety, (3) decreased temporal metrics in MCI as nighttime disturbances and depression severity increased, (4) decreased pars triangularis metrics in AD as nighttime disturbances and apathy severity increased. CONCLUSION Each NPS seems to have a signature on brain morphology. Affective and vegetative NPS were primarily associated with prefrontal and temporal regions. These signatures open the possibility of potential future assessments of links between brain morphology and NPS on an individual level.
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Affiliation(s)
- Adriana Cannizzaro
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
- Faculté des Arts et des Sciences, Département de Psychologie, Université de Montréal, Montreal, Quebec, Canada
| | - Lucas Ronat
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
- Faculté de Médecine, Département de Médecine, Université de Montréal, Montreal, Quebec, Canada
| | - Lyna Mariam El Haffaf
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
- Faculté des Arts et des Sciences, Département de Psychologie, Université de Montréal, Montreal, Quebec, Canada
| | - Alexandru Hanganu
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
- Faculté des Arts et des Sciences, Département de Psychologie, Université de Montréal, Montreal, Quebec, Canada
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Bachmann T, Schroeter ML, Chen K, Reiman EM, Weise CM. Longitudinal changes in surface based brain morphometry measures in amnestic mild cognitive impairment and Alzheimer's Disease. Neuroimage Clin 2023; 38:103371. [PMID: 36924681 PMCID: PMC10025277 DOI: 10.1016/j.nicl.2023.103371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 12/14/2022] [Accepted: 03/06/2023] [Indexed: 03/11/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is associated with marked brain atrophy. While commonly used structural MRI imaging methods do not account for the complexity of human brain morphology, little is known about the longitudinal changes of cortical geometry and their relationship with cognitive decline in subjects with AD. METHODS Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to perform two-sample t-tests to investigate longitudinal changes of cortical thickness (CTh) and three surface-based morphometry measures: fractal dimension (i.e. cortical complexity; FD), gyrification index (GI), and sulcal depth (SD) in subjects with AD, amnestic mild cognitive impairment (aMCI) in comparison to cognitively unimpaired controls (CU) in baseline and 2-year follow-up sMRI scans. In addition, correlations of the morphological measures with two-year cognitive decline as assessed by the modified AD Assessment Scale-Cognitive Subscale (ADAS-Cog 11) were calculated via regression analyses. RESULTS Compared to CU, both AD and aMCI showed marked decreases in CTh. In contrast, analyses of FD and GI yielded a more nuanced decline of the respective measures with some areas showing increases in FD and GI. Overall changes in FD and GI were more pronounced in AD as compared to aMCI. Analyses of SD yielded widespread decreases. Interestingly, cognitive decline corresponded well with CTh declines in aMCI but not AD, whereas changes in FD corresponded with AD only but not aMCI, whereas GI and SD were associated with cognitive decline in aMCI and AD. CONCLUSION Patterns of longitudinal changes in FD, GI and SD were only partially overlapping with CTh reductions. In AD, surface-based morphometry measures for brain-surface complexity showed better correspondence than CTh with cognitive decline over a two-year period of time. Being drawn from measures reflecting changes in more intricate aspects of human brain morphology, these data provide new insight into the complexity of AD-related brain atrophy and its relationship with cognitive decline.
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Affiliation(s)
- Tobias Bachmann
- University of Leipzig Medical Center, Department of Neurology, Germany.
| | - Matthias L Schroeter
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, Leipzig, Germany; Clinic of Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA; School of Mathematics and Statistics (KC), Neurodegenerative Disease Research Center (EMR), Arizona State University, USA; Department of Neurology, College of Medicine - Phoenix (KC), Department of Psychiatry (EMR), University of Arizona, USA
| | - Eric M Reiman
- Banner Alzheimer's Institute, Phoenix, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA; Department of Neurology, College of Medicine - Phoenix (KC), Department of Psychiatry (EMR), University of Arizona, USA; Neurogenomics Division, Translational Genomics Research Institute, University of Arizona, and Arizona State University, Phoenix, AZ, USA; Banner-Arizona State University Neurodegenerative Disease Research Center, BioDesign Institute, Arizona State, University, Tempe, AZ, USA
| | - Christopher M Weise
- University of Leipzig Medical Center, Department of Neurology, Germany; University of Halle Medical Center, Department of Neurology, Germany
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Ryu DW, Hong YJ, Cho JH, Kwak K, Lee JM, Shim YS, Youn YC, Yang DW. Automated brain volumetric program measuring regional brain atrophy in diagnosis of mild cognitive impairment and Alzheimer's disease dementia. Brain Imaging Behav 2022; 16:2086-2096. [PMID: 35697957 DOI: 10.1007/s11682-022-00678-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2022] [Indexed: 11/27/2022]
Abstract
A quantitative analysis of brain volume can assist in the diagnosis of Alzheimer's disease (AD) which is ususally accompanied by brain atrophy. With an automated analysis program Quick Brain Volumetry (QBraVo) developed for volumetric measurements, we measured regional volumes and ratios to evaluate their performance in discriminating AD dementia (ADD) and mild cognitive impairment (MCI) patients from normal controls (NC). Validation of QBraVo was based on intra-rater and inter-rater reliability with a manual measurement. The regional volumes and ratios to total intracranial volume (TIV) and to total brain volume (TBV) or total cerebrospinal fluid volume (TCV) were compared among subjects. The regional volume to total cerebellar volume ratio named Standardized Atrophy Volume Ratio (SAVR) was calculated to compare brain atrophy. Diagnostic performances to distinguish among NC, MCI, and ADD were compared between MMSE, SAVR, and the predictive model. In total, 56 NCs, 44 MCI, and 45 ADD patients were enrolled. The average run time of QBraVo was 5 min 36 seconds. Intra-rater reliability was 0.999. Inter-rater reliability was high for TBV, TCV, and TIV (R = 0.97, 0.89 and 0.93, respectively). The medial temporal SAVR showed the highest performance for discriminating ADD from NC (AUC = 0.808, diagnostic accuracy = 80.2%). The predictive model using both MMSE and medial temporal SAVR improved the diagnostic performance for MCI in NC (AUC = 0.844, diagnostic accuracy = 79%). Our results demonstrated QBraVo is a fast and accurate method to measure brain volume. The regional volume calculated as SAVR could help to diagnose ADD and MCI and increase diagnostic accuracy for MCI.
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Affiliation(s)
- Dong-Woo Ryu
- Department of Neurology, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea
| | - Yun Jeong Hong
- Department of Neurology, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea
| | - Jung Hee Cho
- Department of Neurology, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea
| | - Kichang Kwak
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
| | - Yong S Shim
- Department of Neurology, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea
| | - Young Chul Youn
- Department of Neurology, Chung-Ang University Hospital, Seoul, Republic of Korea
| | - Dong Won Yang
- Department of Neurology, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea.
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Classification of Parkinson's disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach. Brain Imaging Behav 2022; 16:2150-2163. [PMID: 35650376 DOI: 10.1007/s11682-022-00685-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/02/2022] [Indexed: 11/02/2022]
Abstract
To investigate the value of combining amplitude of low-frequency fluctuations-based radiomics and the support vector machine classifier method in distinguishing patients with Parkinson's disease from healthy controls. A total of 123 patients with Parkinson's disease and 90 healthy controls from three centers with functional and structural MRI images were included in this study. We extracted radiomics features using the Brainnetome 246 atlas from the mean amplitude of low-frequency fluctuations maps. Two-sample t-tests and recursive feature elimination combined with support vector machine method were applied for feature selection and dimensionality reduction. We used support vector machine classifier to construct model and identify the discriminative features. The automated anatomical labeling 90 atlas and fivefold cross-validation were used to evaluate the robustness and generalization of the classifier. We found our model obtained a high classification performance with an accuracy of 78.07%, and AUC, sensitivity, and specificity of 0.8597, 78.80%, and 76.08%, respectively. We detected 7 discriminative brain subregions. The fivefold cross-validation and automated anatomical labeling 90 atlas also got high classification accuracy, and we found Brainnetome 246 atlas achieved a higher classification performance than the automated anatomical labeling 90 atlas both with tenfold and fivefold cross-validation. Our findings may help the early diagnosis of Parkinson's disease and provide support for research on Parkinson's disease mechanisms and clinical evaluation.
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Li K, Qu H, Ma M, Xia C, Cai M, Han F, Zhang Q, Gu X, Ma Q. Correlation Between Brain Structure Atrophy and Plasma Amyloid-β and Phosphorylated Tau in Patients With Alzheimer’s Disease and Amnestic Mild Cognitive Impairment Explored by Surface-Based Morphometry. Front Aging Neurosci 2022; 14:816043. [PMID: 35547625 PMCID: PMC9083065 DOI: 10.3389/fnagi.2022.816043] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 02/28/2022] [Indexed: 12/27/2022] Open
Abstract
ObjectiveTo investigate the changes in the cortical thickness of the region of interest (ROI) and plasma Aβ40, Aβ42, and phosphorylated Tau (P-Tau) concentrations in patients with Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI) as the disease progressed with surface-based morphometry (SBM), to analyze the correlation between ROI cortical thickness and measured plasma indexes and neuropsychological scales, and to explore the clinical value of ROI cortical thickness combined with plasma Aβ40, Aβ42, and P-Tau in the early recognition and diagnosis of AD.MethodsThis study enrolled 33 patients with AD, 48 patients with aMCI, and 33 healthy controls (normal control, NC). Concentration changes in plasma Aβ42, Aβ40, and P-Tau collected in each group were analyzed. Meanwhile, the whole brain T1 structure images (T1WI-3D-MPRAGE) of each group of patients were collected, and T1 image in AD-aMCI, AD-NC, and aMCI-NC group were analyzed and processed by SBM technology to obtain brain regions with statistical differences as clusters, and the cortical thickness of each cluster was extracted. Multivariate ordered logistic regression analysis was used to screen out the measured plasma indexes and the indexes with independent risk factors in the cortical thickness of each cluster. Three comparative receiver operating characteristic (ROC) curves of AD-aMCI, AD-NC, and aMCI-NC groups were plotted, respectively, to explore the diagnostic value of multi-factor combined prediction for cognitive impairment. The relationship between cortical thickness and plasma indexes, and between cortical thickness and Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores were clarified by Pearson correlation analysis.ResultsPlasma Aβ40, Aβ42, and P-Tau proteins in the NC, aMCI, and AD groups increased with the progression of AD (P < 0.01); cortical thickness reductions in the AD-aMCI groups and AD-NC groups mainly involved the bilateral superior temporal gyrus, transverse temporal gyrus, superior marginal gyrus, insula, right entorhinal cortex, right fusiform gyrus, and cingulate gyrus. However, there were no statistical significances in cortical thickness reductions in the aMCI and NC groups. The cortical thickness of the ROI was negatively correlated with plasma Aβ40, Aβ42, and P-Tau concentrations (P < 0.05), and the cortical thickness of the ROI was positively correlated with MMSE and MoCA scores. Independent risk factors such as Aβ40, Aβ42, P-Tau, and AD-NC cluster 1R (right superior temporal gyrus, temporal pole, entorhinal cortex, transverse temporal gyrus, fusiform gyrus, superior marginal gyrus, middle temporal gyrus, and inferior temporal gyrus) were combined to plot ROC curves. The diagnostic efficiency of plasma indexes was higher than that of cortical thickness indexes, the diagnostic efficiency of ROC curves after the combination of cortical thickness and plasma indexes was higher than that of cortical thickness or plasma indexes alone.ConclusionPlasma Aβ40, Aβ42, and P-Tau may be potential biomarkers for early prediction of AD. As the disease progressed, AD patients developed cortical atrophy characterized by atrophy of the medial temporal lobe. The combined prediction of these region and plasma Aβ40, Aβ42, and P-Tau had a higher diagnostic value than single-factor prediction for cognitive decline.
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Affiliation(s)
- Kaidi Li
- Department of Neurology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China
| | - Hang Qu
- Department of Imaging, Yangzhou First People’s Hospital, Affiliated Hospital of Yangzhou University, Yangzhou, China
| | - Mingyi Ma
- Department of Molecular and Cellular Biology, University of Illinois Urbana-Champaign, Urbana, IL, United States
| | - Chenyu Xia
- Department of Neurology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Ming Cai
- Department of Neurology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Fang Han
- Department of Imaging, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Qing Zhang
- Department of Imaging, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Xinyi Gu
- Department of Neurology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Qiang Ma
- Department of Neurology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
- *Correspondence: Qiang Ma,
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Nanni L, Interlenghi M, Brahnam S, Salvatore C, Papa S, Nemni R, Castiglioni I. Comparison of Transfer Learning and Conventional Machine Learning Applied to Structural Brain MRI for the Early Diagnosis and Prognosis of Alzheimer's Disease. Front Neurol 2020; 11:576194. [PMID: 33250847 PMCID: PMC7674838 DOI: 10.3389/fneur.2020.576194] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 09/30/2020] [Indexed: 01/22/2023] Open
Abstract
Alzheimer's Disease (AD) is the most common neurodegenerative disease, with 10% prevalence in the elder population. Conventional Machine Learning (ML) was proven effective in supporting the diagnosis of AD, while very few studies investigated the performance of deep learning and transfer learning in this complex task. In this paper, we evaluated the potential of ensemble transfer-learning techniques, pretrained on generic images and then transferred to structural brain MRI, for the early diagnosis and prognosis of AD, with respect to a fusion of conventional-ML approaches based on Support Vector Machine directly applied to structural brain MRI. Specifically, more than 600 subjects were obtained from the ADNI repository, including AD, Mild Cognitive Impaired converting to AD (MCIc), Mild Cognitive Impaired not converting to AD (MCInc), and cognitively-normal (CN) subjects. We used T1-weighted cerebral-MRI studies to train: (1) an ensemble of five transfer-learning architectures pretrained on generic images; (2) a 3D Convolutional Neutral Network (CNN) trained from scratch on MRI volumes; and (3) a fusion of two conventional-ML classifiers derived from different feature extraction/selection techniques coupled to SVM. The AD-vs-CN, MCIc-vs-CN, MCIc-vs-MCInc comparisons were investigated. The ensemble transfer-learning approach was able to effectively discriminate AD from CN with 90.2% AUC, MCIc from CN with 83.2% AUC, and MCIc from MCInc with 70.6% AUC, showing comparable or slightly lower results with the fusion of conventional-ML systems (AD from CN with 93.1% AUC, MCIc from CN with 89.6% AUC, and MCIc from MCInc with AUC in the range of 69.1-73.3%). The deep-learning network trained from scratch obtained lower performance than either the fusion of conventional-ML systems and the ensemble transfer-learning, due to the limited sample of images used for training. These results open new prospective on the use of transfer learning combined with neuroimages for the automatic early diagnosis and prognosis of AD, even if pretrained on generic images.
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Affiliation(s)
- Loris Nanni
- Department of Information Engineering, University of Padua, Padua, Italy
| | - Matteo Interlenghi
- Institute of Molecular Bioimaging and Physiology, National Research Council of Italy (IBFM-CNR), Milan, Italy
| | - Sheryl Brahnam
- Department of IT and Cybersecurity, Missouri State University, Springfield, MO, United States
| | - Christian Salvatore
- Department of Science, Technology and Society, Scuola Universitaria Superiore IUSS Pavia, Pavia, Italy
- DeepTrace Technologies S.R.L., Milan, Italy
| | - Sergio Papa
- Centro Diagnostico Italiano S.p.A., Milan, Italy
| | | | - Isabella Castiglioni
- Institute of Molecular Bioimaging and Physiology, National Research Council of Italy (IBFM-CNR), Milan, Italy
- Department of Physics “G. Occhialini”, University of Milano Bicocca, Milan, Italy
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10
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β-amyloid and tau drive early Alzheimer's disease decline while glucose hypometabolism drives late decline. Commun Biol 2020; 3:352. [PMID: 32632135 PMCID: PMC7338410 DOI: 10.1038/s42003-020-1079-x] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 06/15/2020] [Indexed: 12/31/2022] Open
Abstract
Clinical trials focusing on therapeutic candidates that modify β-amyloid (Aβ) have repeatedly failed to treat Alzheimer’s disease (AD), suggesting that Aβ may not be the optimal target for treating AD. The evaluation of Aβ, tau, and neurodegenerative (A/T/N) biomarkers has been proposed for classifying AD. However, it remains unclear whether disturbances in each arm of the A/T/N framework contribute equally throughout the progression of AD. Here, using the random forest machine learning method to analyze participants in the Alzheimer’s Disease Neuroimaging Initiative dataset, we show that A/T/N biomarkers show varying importance in predicting AD development, with elevated biomarkers of Aβ and tau better predicting early dementia status, and biomarkers of neurodegeneration, especially glucose hypometabolism, better predicting later dementia status. Our results suggest that AD treatments may also need to be disease stage-oriented with Aβ and tau as targets in early AD and glucose metabolism as a target in later AD. Here the authors analyze the Alzheimer’s Disease Neuroimaging Initiative dataset using random forest machine learning methods and determine that Aβ and tau biomarkers are better predictors of early dementia status, while glucose hypometabolism is a better predictor of later dementia status. These results suggest the need for stage-oriented Alzheimer’s disease treatments.
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11
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Young PNE, Estarellas M, Coomans E, Srikrishna M, Beaumont H, Maass A, Venkataraman AV, Lissaman R, Jiménez D, Betts MJ, McGlinchey E, Berron D, O'Connor A, Fox NC, Pereira JB, Jagust W, Carter SF, Paterson RW, Schöll M. Imaging biomarkers in neurodegeneration: current and future practices. Alzheimers Res Ther 2020; 12:49. [PMID: 32340618 PMCID: PMC7187531 DOI: 10.1186/s13195-020-00612-7] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 04/01/2020] [Indexed: 12/12/2022]
Abstract
There is an increasing role for biological markers (biomarkers) in the understanding and diagnosis of neurodegenerative disorders. The application of imaging biomarkers specifically for the in vivo investigation of neurodegenerative disorders has increased substantially over the past decades and continues to provide further benefits both to the diagnosis and understanding of these diseases. This review forms part of a series of articles which stem from the University College London/University of Gothenburg course "Biomarkers in neurodegenerative diseases". In this review, we focus on neuroimaging, specifically positron emission tomography (PET) and magnetic resonance imaging (MRI), giving an overview of the current established practices clinically and in research as well as new techniques being developed. We will also discuss the use of machine learning (ML) techniques within these fields to provide additional insights to early diagnosis and multimodal analysis.
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Affiliation(s)
- Peter N E Young
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mar Estarellas
- Centre for Medical Image Computing (CMIC), Department of Computer Science & Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Emma Coomans
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Meera Srikrishna
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Helen Beaumont
- Neuroscience and Aphasia Research Unit, Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Ashwin V Venkataraman
- Division of Brain Sciences, Imperial College London, London, UK
- United Kingdom Dementia Research Institute, Imperial College London, London, UK
| | - Rikki Lissaman
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff, UK
| | - Daniel Jiménez
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
- Department of Neurological Sciences, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Matthew J Betts
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | | | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Antoinette O'Connor
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - William Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, USA
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Stephen F Carter
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Wolfson Molecular Imaging Centre, Division of Neuroscience and Experimental Psychology, MAHSC, University of Manchester, Manchester, UK
| | - Ross W Paterson
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden.
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK.
- Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
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12
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Tan X, Liu Y, Li Y, Wang P, Zeng X, Yan F, Li X. Localized instance fusion of MRI data of Alzheimer's disease for classification based on instance transfer ensemble learning. Biomed Eng Online 2018; 17:49. [PMID: 29716598 PMCID: PMC5930507 DOI: 10.1186/s12938-018-0489-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 04/23/2018] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Diagnosis of Alzheimer's disease (AD) is very important, and MRI is an effective imaging mode of Alzheimer's disease. There are many existing studies on the diagnosis of Alzheimer's disease based on MRI data. However, there are no studies on the transfer learning between different datasets (including different subjects), thereby improving the sample size of target dataset indirectly. METHODS Therefore, a new framework method is proposed in this paper to solve this problem. First, gravity transfer is used to transfer the source domain data closer to the target data set. Secondly, the best deviation between the transferred source domain samples and the target domain samples is searched by instance transfer learning algorithm (ITL) based on wrapper mode, thereby obtaining optimal transferred domain samples. Finally, the optimal transferred domain samples and the target domain training samples are combined for classification. If the source data and the target data have different features, a feature growing algorithm is proposed to solve this problem. RESULTS The experimental results show that the proposed method is effective regardless of different kernel functions, different number of samples and different parameters. Besides, the transferred source domain samples by ITL algorithm can enlarge the target domain training samples and assist to improve the classification accuracy significantly. CONCLUSIONS Therefore, the study can enlarge the samples of AD by instance transfer learning, thereby being helpful for the small sample problems of AD. Since the proposed algorithm is a framework algorithm, the study is heuristics to the relevant researchers.
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Affiliation(s)
- Xiaoheng Tan
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China
| | - Yuchuan Liu
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China
| | - Yongming Li
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China. .,Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, 400038, China.
| | - Pin Wang
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China
| | - Xiaoping Zeng
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China
| | - Fang Yan
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China
| | - Xinke Li
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China
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