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Amini S, Hao B, Yang J, Karjadi C, Kolachalama VB, Au R, Paschalidis IC. Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models. Alzheimers Dement 2024. [PMID: 38924662 DOI: 10.1002/alz.13886] [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: 07/18/2023] [Revised: 03/01/2024] [Accepted: 04/19/2024] [Indexed: 06/28/2024]
Abstract
INTRODUCTION Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials. METHODS We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases. RESULTS Our best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI-to-AD progression within 6 years. DISCUSSION The proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy-to-administer screening tool for MCI-to-AD progression prediction, facilitating development of remote assessment. HIGHLIGHTS Voice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment. The study leveraged AI methods for speech recognition and processed the resulting text using language models. The developed AI-powered pipeline can lead to fully automated assessment that could enable remote and cost-effective screening and prognosis for Alzehimer's disease.
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Affiliation(s)
- Samad Amini
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Boran Hao
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Jingmei Yang
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Cody Karjadi
- Framingham Heart Study, Boston University, Framingham, Massachusetts, USA
| | - Vijaya B Kolachalama
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA
- Faculty of Computing & Data Sciences, Boston University, Boston, Massachusetts, USA
- Department of Computer Science, Boston University, Boston, Massachusetts, USA
| | - Rhoda Au
- Framingham Heart Study, Boston University, Framingham, Massachusetts, USA
- Departments of Anatomy & Neurobiology, Neurology, and Epidemiology, Boston University School of Medicine and School of Public Health, Boston, Massachusetts, USA
| | - Ioannis C Paschalidis
- Department of Electrical & Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Faculty of Computing & Data Sciences, Boston University, Boston, Massachusetts, USA
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Mitrovska A, Safari P, Ritter K, Shariati B, Fischer JK. Secure federated learning for Alzheimer's disease detection. Front Aging Neurosci 2024; 16:1324032. [PMID: 38515517 PMCID: PMC10954782 DOI: 10.3389/fnagi.2024.1324032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/22/2024] [Indexed: 03/23/2024] Open
Abstract
Machine Learning (ML) is considered a promising tool to aid and accelerate diagnosis in various medical areas, including neuroimaging. However, its success is set back by the lack of large-scale public datasets. Indeed, medical institutions possess a large amount of data; however, open-sourcing is prevented by the legal requirements to protect the patient's privacy. Federated Learning (FL) is a viable alternative that can overcome this issue. This work proposes training an ML model for Alzheimer's Disease (AD) detection based on structural MRI (sMRI) data in a federated setting. We implement two aggregation algorithms, Federated Averaging (FedAvg) and Secure Aggregation (SecAgg), and compare their performance with the centralized ML model training. We simulate heterogeneous environments and explore the impact of demographical (sex, age, and diagnosis) and imbalanced data distributions. The simulated heterogeneous environments allow us to observe these statistical differences' effect on the ML models trained using FL and highlight the importance of studying such differences when training ML models for AD detection. Moreover, as part of the evaluation, we demonstrate the increased privacy guarantees of FL with SecAgg via simulated membership inference attacks.
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Affiliation(s)
- Angela Mitrovska
- Fraunhofer-Institut fur Nachrichtentechnik, Heinrich-Hertz-Institute (HHI), Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Pooyan Safari
- Fraunhofer-Institut fur Nachrichtentechnik, Heinrich-Hertz-Institute (HHI), Berlin, Germany
| | - Kerstin Ritter
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charite – Universitatsmedizin Berlin (corporate member of Freie Universitat Berlin, Humboldt-Universitat zu Berlin, and Berlin Institute of Health), Berlin, Germany
| | - Behnam Shariati
- Fraunhofer-Institut fur Nachrichtentechnik, Heinrich-Hertz-Institute (HHI), Berlin, Germany
| | - Johannes Karl Fischer
- Fraunhofer-Institut fur Nachrichtentechnik, Heinrich-Hertz-Institute (HHI), Berlin, Germany
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Chen Y, Pan Y, Xia Y, Yuan Y. Disentangle First, Then Distill: A Unified Framework for Missing Modality Imputation and Alzheimer's Disease Diagnosis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3566-3578. [PMID: 37450359 DOI: 10.1109/tmi.2023.3295489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Multi-modality medical data provide complementary information, and hence have been widely explored for computer-aided AD diagnosis. However, the research is hindered by the unavoidable missing-data problem, i.e., one data modality was not acquired on some subjects due to various reasons. Although the missing data can be imputed using generative models, the imputation process may introduce unrealistic information to the classification process, leading to poor performance. In this paper, we propose the Disentangle First, Then Distill (DFTD) framework for AD diagnosis using incomplete multi-modality medical images. First, we design a region-aware disentanglement module to disentangle each image into inter-modality relevant representation and intra-modality specific representation with emphasis on disease-related regions. To progressively integrate multi-modality knowledge, we then construct an imputation-induced distillation module, in which a lateral inter-modality transition unit is created to impute representation of the missing modality. The proposed DFTD framework has been evaluated against six existing methods on an ADNI dataset with 1248 subjects. The results show that our method has superior performance in both AD-CN classification and MCI-to-AD prediction tasks, substantially over-performing all competing methods.
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Vik A, Kociński M, Rye I, Lundervold AJ, Lundervold AS. Functional activity level reported by an informant is an early predictor of Alzheimer's disease. BMC Geriatr 2023; 23:205. [PMID: 37003981 PMCID: PMC10067216 DOI: 10.1186/s12877-023-03849-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 02/24/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND Loss of autonomy in day-to-day functioning is one of the feared outcomes of Alzheimer's disease (AD), and relatives may have been worried by subtle behavioral changes in ordinary life situations long before these changes are given medical attention. In the present study, we ask if such subtle changes should be given weight as an early predictor of a future AD diagnosis. METHODS Longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to define a group of adults with a mild cognitive impairment (MCI) diagnosis remaining stable across several visits (sMCI, n=360; 55-91 years at baseline), and a group of adults who over time converted from having an MCI diagnosis to an AD diagnosis (cAD, n=320; 55-88 years at baseline). Eleven features were used as input in a Random Forest (RF) binary classifier (sMCI vs. cAD) model. This model was tested on an unseen holdout part of the dataset, and further explored by three different permutation-driven importance estimates and a comprehensive post hoc machine learning exploration. RESULTS The results consistently showed that measures of daily life functioning, verbal memory function, and a volume measure of hippocampus were the most important predictors of conversion from an MCI to an AD diagnosis. Results from the RF classification model showed a prediction accuracy of around 70% in the test set. Importantly, the post hoc analyses showed that even subtle changes in everyday functioning noticed by a close informant put MCI patients at increased risk for being on a path toward the major cognitive impairment of an AD diagnosis. CONCLUSION The results showed that even subtle changes in everyday functioning should be noticed when reported by relatives in a clinical evaluation of patients with MCI. Information of these changes should also be included in future longitudinal studies to investigate different pathways from normal cognitive aging to the cognitive decline characterizing different stages of AD and other neurodegenerative disorders.
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Affiliation(s)
- Alexandra Vik
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Marek Kociński
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ingrid Rye
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Astri J Lundervold
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Alexander S Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway.
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway.
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Khobo IL, Jankiewicz M, Holmes MJ, Little F, Cotton MF, Laughton B, van der Kouwe AJW, Moreau A, Nwosu E, Meintjes EM, Robertson FC. Multimodal magnetic resonance neuroimaging measures characteristic of early cART-treated pediatric HIV: A feature selection approach. Hum Brain Mapp 2022; 43:4128-4144. [PMID: 35575438 PMCID: PMC9374890 DOI: 10.1002/hbm.25907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 04/03/2022] [Accepted: 04/26/2022] [Indexed: 11/09/2022] Open
Abstract
Children with perinatally acquired HIV (CPHIV) have poor cognitive outcomes despite early combination antiretroviral therapy (cART). While CPHIV-related brain alterations can be investigated separately using proton magnetic resonance spectroscopy (1 H-MRS), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI), a set of multimodal MRI measures characteristic of children on cART has not been previously identified. We used the embedded feature selection of a logistic elastic-net (EN) regularization to select neuroimaging measures that distinguish CPHIV from controls and measured their classification performance via the area under the receiver operating characteristic curve (AUC) using repeated cross validation. We also wished to establish whether combining MRI modalities improved the models. In single modality analysis, sMRI volumes performed best followed by DTI, whereas individual EN models on spectroscopic, gyrification, and cortical thickness measures showed no class discrimination capability. Adding DTI and 1 H-MRS in basal measures to sMRI volumes produced the highest classification performancevalidation accuracy = 85 % AUC = 0.80 . The best multimodal MRI set consisted of 22 DTI and sMRI volume features, which included reduced volumes of the bilateral globus pallidus and amygdala, as well as increased mean diffusivity (MD) and radial diffusivity (RD) in the right corticospinal tract in cART-treated CPHIV. Consistent with previous studies of CPHIV, select subcortical volumes obtained from sMRI provide reasonable discrimination between CPHIV and controls. This may give insight into neuroimaging measures that are relevant in understanding the effects of HIV on the brain, thereby providing a starting point for evaluating their link with cognitive performance in CPHIV.
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Affiliation(s)
- Isaac L. Khobo
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Marcin Jankiewicz
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Cape Universities Body Imaging CenterUniversity of Cape TownCape TownSouth Africa
| | - Martha J. Holmes
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Francesca Little
- Department of Statistical SciencesUniversity of Cape TownCape TownSouth Africa
| | - Mark F. Cotton
- Department of Pediatrics & Child Health, Family Center for Research with Ubuntu, Tygerberg HospitalStellenbosch UniversityCape TownSouth Africa
| | - Barbara Laughton
- Department of Pediatrics & Child Health, Family Center for Research with Ubuntu, Tygerberg HospitalStellenbosch UniversityCape TownSouth Africa
| | - Andre J. W. van der Kouwe
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- A.A. Martinos Centre for Biomedical ImagingMassachusetts General HospitalBostonMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
| | | | - Emmanuel Nwosu
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
| | - Ernesta M. Meintjes
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Cape Universities Body Imaging CenterUniversity of Cape TownCape TownSouth Africa
| | - Frances C. Robertson
- Division of Biomedical Engineering, Department of Human Biology, Biomedical Engineering Research CenterUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Cape Universities Body Imaging CenterUniversity of Cape TownCape TownSouth Africa
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Dutta P, Patra AP, Saha S. DeePROG: Deep Attention-Based Model for Diseased Gene Prognosis by Fusing Multi-Omics Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2770-2781. [PMID: 34166198 DOI: 10.1109/tcbb.2021.3090302] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
An in-depth exploration of gene prognosis using different methodologies aids in understanding various biological regulations of genes in disease pathobiology and molecular functions. Interpreting gene functions at biological and molecular levels remains a daunting yet crucial task in domains such as drug design, personalized medicine, and next-generation diagnostics. Recent advancements in omics technologies have produced diverse heterogeneous genomic datasets like micro-array gene expression, miRNA expression, DNA sequence, 3D structures, which are significant resources for understanding the gene functions. In this paper, we propose a novel self-attention based deep multi-modal model, named DeePROG, for the prognosis of disease affected genes based on heterogeneous omics data. We use three NCBI datasets covering three modalities, namely gene expression profile, the underlying DNA sequence, and the 3D protein structures. To extract useful features from each modality, we develop several context-specific deep learning models. Besides, we develop three attention-based deep bi-modal architectures along with DeePROG to leverage the prognosis of the underlying biomedical data. We assess the performance of the models' in terms of computational assessment of function annotation (CAFA2) metrics. Moreover, we analyze the results in terms of receiver operating characteristics (ROC) curve in high-class imbalance data setting and perform statistical significance tests in terms of Welch's t-test. Experiment results show that DeePROG significantly outperforms baseline models across in terms of performance metrics. The source code and all preprocessed datasets used in this study are available at https://github.com/duttaprat/DeePROG.
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El-Sappagh S, Saleh H, Ali F, Amer E, Abuhmed T. Two-stage deep learning model for Alzheimer’s disease detection and prediction of the mild cognitive impairment time. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07263-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Addressing the Missing Data Challenge in Multi-Modal Datasets for the Diagnosis of Alzheimer’s Disease. J Neurosci Methods 2022; 375:109582. [DOI: 10.1016/j.jneumeth.2022.109582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 11/18/2022]
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Lange C, Mäurer A, Suppa P, Apostolova I, Steffen IG, Grothe MJ, Buchert R. Brain FDG PET for Short- to Medium-Term Prediction of Further Cognitive Decline and Need for Assisted Living in Acutely Hospitalized Geriatric Patients With Newly Detected Clinically Uncertain Cognitive Impairment. Clin Nucl Med 2022; 47:123-129. [PMID: 35006106 DOI: 10.1097/rlu.0000000000003981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
PURPOSE The aim of this study was to evaluate brain FDG PET for short- to medium-term prediction of cognitive decline, need for assisted living, and survival in acutely hospitalized geriatric patients with newly detected clinically uncertain cognitive impairment (CUCI). MATERIALS AND METHODS The study included 96 patients (62 females, 81.4 ± 5.4 years) hospitalized due to (sub)acute admission indications with newly detected CUCI (German Clinical Trials Register DRKS00005041). FDG PET was categorized as "neurodegenerative" (DEG+) or "nonneurodegenerative" (DEG-) based on visual inspection by 2 independent readers. In addition, each individual PET was tested voxel-wise against healthy controls (P < 0.001 uncorrected). The resulting total hypometabolic volume (THV) served as reader-independent measure of the spatial extent of neuronal dysfunction/degeneration. FDG PET findings at baseline were tested for association with the change in living situation and change in vital status 12 to 24 months after PET. The association with the annual change of the CDR-SB (Clinical Dementia Rating Sum of Boxes) after PET was tested in a subsample of 72 patients. RESULTS The mean time between PET and follow-up did not differ between DEG+ and DEG- patients (1.37 ± 0.27 vs 1.41 ± 0.27 years, P = 0.539). Annual change of CDR-SB was higher in DEG+ compared with DEG- patients (2.78 ± 2.44 vs 0.99 ± 1.81, P = 0.001), and it was positively correlated with THV (age-corrected Spearman ρ = 0.392, P = 0.001). DEG+ patients moved from at home to assisted living significantly earlier than DEG- patients (P = 0.050). Survival was not associated with DEG status or with THV. CONCLUSIONS In acutely hospitalized geriatric patients with newly detected CUCI, the brain FDG PET can contribute to the prediction of further cognitive/functional decline and the need for assisted living within 1 to 2 years.
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Affiliation(s)
- Catharina Lange
- From the Department of Nuclear Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin
| | | | | | - Ivayla Apostolova
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg
| | - Ingo G Steffen
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Michel J Grothe
- Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain
| | - Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg
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Ai R, Jin X, Tang B, Yang G, Niu Z, Fang EF. Aging and Alzheimer’s Disease. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_74] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Drouin SM, McFall GP, Potvin O, Bellec P, Masellis M, Duchesne S, Dixon RA. Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes. J Alzheimers Dis 2022; 88:97-115. [PMID: 35570482 PMCID: PMC9277685 DOI: 10.3233/jad-215289] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/11/2022] [Indexed: 11/15/2022]
Abstract
BACKGROUND Hippocampal atrophy is a well-known biomarker of neurodegeneration, such as that observed in Alzheimer's disease (AD). Although distributions of hippocampal volume trajectories for asymptomatic individuals often reveal substantial heterogeneity, it is unclear whether interpretable trajectory classes can be objectively detected and used for prediction analyses. OBJECTIVE To detect and predict hippocampal trajectory classes in a computationally competitive context using established AD-related risk factors/biomarkers. METHODS We used biomarker/risk factor and longitudinal MRI data in asymptomatic adults from the AD Neuroimaging Initiative (n = 351; Mean = 75 years; 48.7% female). First, we applied latent class growth analyses to left (LHC) and right (RHC) hippocampal trajectory distributions to identify distinct classes. Second, using random forest analyses, we tested 38 multi-modal biomarkers/risk factors for their relative importance in discriminating the lower (potentially elevated atrophy risk) from the higher (potentially reduced risk) class. RESULTS For both LHC and RHC trajectory distribution analyses, we observed three distinct trajectory classes. Three biomarkers/risk factors predicted membership in LHC and RHC lower classes: male sex, higher education, and lower plasma Aβ1-42. Four additional factors selectively predicted membership in the lower LHC class: lower plasma tau and Aβ1-40, higher depressive symptomology, and lower body mass index. CONCLUSION Data-driven analyses of LHC and RHC trajectories detected three classes underlying the heterogeneous distributions. Machine learning analyses determined three common and four unique biomarkers/risk factors discriminating the higher and lower LHC/RHC classes. Our sequential analytic approach produced evidence that the dynamics of preclinical hippocampal trajectories can be predicted by AD-related biomarkers/risk factors from multiple modalities.
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Affiliation(s)
- Shannon M. Drouin
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
| | - G. Peggy McFall
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| | | | - Pierre Bellec
- Département de Psychologie, Université de Montréal, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada
| | - Mario Masellis
- Hurvitz Brain Sciences Research Program, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Medicine (Neurology), University of Toronto, Toronto, ON, Canada
| | - Simon Duchesne
- CERVO Brain Research Centre, Quebec, QC, Canada
- Radiology and Nuclear Medicine Department, Université Laval, Quebec, QC, Canada
| | - Roger A. Dixon
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
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González DA, Gonzales MM, Jennette KJ, Soble JR, Fongang B. Cognitive screening with functional assessment improves diagnostic accuracy and attenuates bias. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12250. [PMID: 34934799 PMCID: PMC8652409 DOI: 10.1002/dad2.12250] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/17/2021] [Accepted: 09/21/2021] [Indexed: 02/04/2023]
Abstract
INTRODUCTION Cognitive screening measures often lack sensitivity and are hampered by inequities across ethnoracial groups. A multitrait multimethod (MTMM) classification may attenuate these shortcomings. METHODS A sample of 7227 participants across the diagnostic spectrum were selected from the National Alzheimer's Coordinating Center cohort. Random forest ensemble methods were used to predict diagnosis across the sample and within Black American (n = 1025) and non-Hispanic White groups (n = 5263) based on: (1) a demographically corrected Montreal Cognitive Assessment (MoCA), (2) MoCA and Functional Assessment Questionnaire (FAQ), (3) MoCA and FAQ with demographic correction. RESULTS The MTMM approach with demographic correction had the highest diagnostic accuracy for the cognitively unimpaired (area under curve [AUC] [95% confidence interval (CI)]): 0.906 [0.892, 0.920]) and mild cognitive impairment (AUC: 0.835 [0.810, 0.860]) groups and reduced racial disparities. DISCUSSION With further validation, the MTMM approach combining cognitive screening and functional status assessment may serve to improve diagnostic accuracy and extend opportunities for early intervention with greater equity.
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Affiliation(s)
- David Andrés González
- Department of NeurologyUniversity of Texas Health Science Center at San AntonioSan AntonioTexasUSA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative DiseasesUniversity of Texas Health Science Center at San AntonioSan AntonioTexasUSA
| | - Mitzi M. Gonzales
- Department of NeurologyUniversity of Texas Health Science Center at San AntonioSan AntonioTexasUSA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative DiseasesUniversity of Texas Health Science Center at San AntonioSan AntonioTexasUSA
| | - Kyle J. Jennette
- Department of PsychiatryUniversity of Illinois College of MedicineChicagoIllinoisUSA
| | - Jason R. Soble
- Department of PsychiatryUniversity of Illinois College of MedicineChicagoIllinoisUSA
- Department of NeurologyUniversity of Illinois College of MedicineChicagoIllinoisUSA
| | - Bernard Fongang
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative DiseasesUniversity of Texas Health Science Center at San AntonioSan AntonioTexasUSA
- Department of Biochemistry and Structural BiologyUniversity of Texas Health Science Center at San AntonioSan AntonioTexasUSA
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Grueso S, Viejo-Sobera R. Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review. Alzheimers Res Ther 2021; 13:162. [PMID: 34583745 PMCID: PMC8480074 DOI: 10.1186/s13195-021-00900-w] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 09/12/2021] [Indexed: 01/18/2023]
Abstract
BACKGROUND An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer's disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer's disease dementia. METHODS We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer's disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. RESULTS Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer's disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. CONCLUSIONS Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.
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Affiliation(s)
- Sergio Grueso
- Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou 156, 08018, Barcelona, Spain.
| | - Raquel Viejo-Sobera
- Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou 156, 08018, Barcelona, Spain
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14
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Wu J, Dong Q, Gui J, Zhang J, Su Y, Chen K, Thompson PM, Caselli RJ, Reiman EM, Ye J, Wang Y. Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases. Front Neurosci 2021; 15:669595. [PMID: 34421510 PMCID: PMC8377280 DOI: 10.3389/fnins.2021.669595] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 07/15/2021] [Indexed: 01/04/2023] Open
Abstract
Biomarker assisted preclinical/early detection and intervention in Alzheimer’s disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (Aβ) plaques in the human brain. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that magnetic resonance imaging (MRI)-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain Aβ burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse-coding and Max-Pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each individual subject. Then we apply these individual representations and a binary random forest classifier to predict brain Aβ positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate Aβ positivity in people with mild cognitive impairment (MCI) [Accuracy (ACC) = 0.89 (ADNI)] and in cognitively unimpaired (CU) individuals [ACC = 0.79 (ADNI) and ACC = 0.81 (OASIS)]. These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM) and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.
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Affiliation(s)
- Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States.,Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China
| | - Jie Gui
- School of Cyber Science and Engineering, Southeast University, Nanjing, China
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, United States
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, United States
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, United States
| | - Richard J Caselli
- Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, United States
| | - Eric M Reiman
- Banner Alzheimer's Institute, Phoenix, AZ, United States
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
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15
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Amini M, Pedram MM, Moradi A, Jamshidi M, Ouchani M. Single and Combined Neuroimaging Techniques for Alzheimer's Disease Detection. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:9523039. [PMID: 34335726 PMCID: PMC8292054 DOI: 10.1155/2021/9523039] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 06/04/2021] [Accepted: 06/30/2021] [Indexed: 02/07/2023]
Abstract
Alzheimer's disease (AD) consists of the gradual process of decreasing volume and quality of neuron connection in the brain, which consists of gradual synaptic integrity and loss of cognitive functions. In recent years, there has been significant attention in AD classification and early detection with machine learning algorithms. There are different neuroimaging techniques for capturing data and using it for the classification task. Input data as images will help machine learning models to detect different biomarkers for AD classification. This marker has a more critical role for AD detection than other diseases because beta-amyloid can extract complex structures with some metal ions. Most researchers have focused on using 3D and 4D convolutional neural networks for AD classification due to reasonable amounts of data. Also, combination neuroimaging techniques like functional magnetic resonance imaging and positron emission tomography for AD detection have recently gathered much attention. However, gathering a combination of data can be expensive, complex, and tedious. For time consumption reasons, most patients prefer to throw one of the neuroimaging techniques. So, in this review article, we have surveyed different research studies with various neuroimaging techniques and ML methods to see the effect of using combined data as input. The result has shown that the use of the combination method would increase the accuracy of AD detection. Also, according to the sensitivity metrics from different machine learning methods, MRI and fMRI showed promising results.
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Affiliation(s)
- Morteza Amini
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Shahid Beheshti University, Tehran, Iran
| | - Mir Mohsen Pedram
- Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Tehran, Iran
| | - Alireza Moradi
- Department of Clinical Psychology, Faculty of Psychology and Educational Science, Kharazmi University, Tehran, Iran
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
| | - Mahdieh Jamshidi
- Department of Mathematical Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
| | - Mahshad Ouchani
- Institute for Cognitive and Brain Science, Shahid Beheshti University, Tehran, Iran
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16
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Ning Z, Xiao Q, Feng Q, Chen W, Zhang Y. Relation-Induced Multi-Modal Shared Representation Learning for Alzheimer's Disease Diagnosis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1632-1645. [PMID: 33651685 DOI: 10.1109/tmi.2021.3063150] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The fusion of multi-modal data (e.g., magnetic resonance imaging (MRI) and positron emission tomography (PET)) has been prevalent for accurate identification of Alzheimer's disease (AD) by providing complementary structural and functional information. However, most of the existing methods simply concatenate multi-modal features in the original space and ignore their underlying associations which may provide more discriminative characteristics for AD identification. Meanwhile, how to overcome the overfitting issue caused by high-dimensional multi-modal data remains appealing. To this end, we propose a relation-induced multi-modal shared representation learning method for AD diagnosis. The proposed method integrates representation learning, dimension reduction, and classifier modeling into a unified framework. Specifically, the framework first obtains multi-modal shared representations by learning a bi-directional mapping between original space and shared space. Within this shared space, we utilize several relational regularizers (including feature-feature, feature-label, and sample-sample regularizers) and auxiliary regularizers to encourage learning underlying associations inherent in multi-modal data and alleviate overfitting, respectively. Next, we project the shared representations into the target space for AD diagnosis. To validate the effectiveness of our proposed approach, we conduct extensive experiments on two independent datasets (i.e., ADNI-1 and ADNI-2), and the experimental results demonstrate that our proposed method outperforms several state-of-the-art methods.
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17
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A machine learning approach to screen for preclinical Alzheimer's disease. Neurobiol Aging 2021; 105:205-216. [PMID: 34102381 DOI: 10.1016/j.neurobiolaging.2021.04.024] [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: 12/04/2020] [Revised: 04/06/2021] [Accepted: 04/23/2021] [Indexed: 11/22/2022]
Abstract
Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on 18F-florbetapir and 18F-fluorodeoxyglucose PET, respectively. We used a nested cross-validation approach with non-invasive features (electroencephalography [EEG], APOE4 genotype, demographic, neuropsychological and MRI data) to predict: 1/ amyloid status; 2/ neurodegeneration status; 3/ decline to prodromal AD at 5-year follow-up. Importantly, EEG was most strongly predictive of neurodegeneration, even when reducing the number of channels from 224 down to 4, as 4-channel EEG best predicted neurodegeneration (negative predictive value [NPV] = 82%, positive predictive value [PPV] = 38%, 77% specificity, 45% sensitivity). The combination of demographic, neuropsychological data, APOE4 and hippocampal volumetry most strongly predicted amyloid (80% NPV, 41% PPV, 70% specificity, 58% sensitivity) and most strongly predicted decline to prodromal AD at 5 years (97% NPV, 14% PPV, 83% specificity, 50% sensitivity). Thus, machine learning can help to screen patients at high risk of preclinical AD using non-invasive and affordable biomarkers.
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18
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Ding Y, Zhao K, Che T, Du K, Sun H, Liu S, Zheng Y, Li S, Liu B, Liu Y. Quantitative Radiomic Features as New Biomarkers for Alzheimer's Disease: An Amyloid PET Study. Cereb Cortex 2021; 31:3950-3961. [PMID: 33884402 DOI: 10.1093/cercor/bhab061] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/29/2021] [Accepted: 02/22/2021] [Indexed: 12/20/2022] Open
Abstract
Growing evidence indicates that amyloid-beta (Aβ) accumulation is one of the most common neurobiological biomarkers in Alzheimer's disease (AD). The primary aim of this study was to explore whether the radiomic features of Aβ positron emission tomography (PET) images are used as predictors and provide a neurobiological foundation for AD. The radiomics features of Aβ PET imaging of each brain region of the Brainnetome Atlas were computed for classification and prediction using a support vector machine model. The results showed that the area under the receiver operating characteristic curve (AUC) was 0.93 for distinguishing AD (N = 291) from normal control (NC; N = 334). Additionally, the AUC was 0.83 for the prediction of mild cognitive impairment (MCI) converting (N = 88) (vs. no conversion, N = 100) to AD. In the MCI and AD groups, the systemic analysis demonstrated that the classification outputs were significantly associated with clinical measures (apolipoprotein E genotype, polygenic risk scores, polygenic hazard scores, cerebrospinal fluid Aβ, and Tau, cognitive ability score, the conversion time for progressive MCI subjects and cognitive changes). These findings provide evidence that the radiomic features of Aβ PET images can serve as new biomarkers for clinical applications in AD/MCI, further providing evidence for predicting whether MCI subjects will convert to AD.
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Affiliation(s)
- Yanhui Ding
- School of Information Science and Engineering, Shandong Normal University, Ji'nan 250014, China
| | - Kun Zhao
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China.,Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tongtong Che
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Kai Du
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Hongzan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Shu Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Ji'nan 250014, China
| | - Shuyu Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Bing Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China.,Pazhou Lab, Guangzhou 510330, China.,School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
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19
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Ai R, Jin X, Tang B, Yang G, Niu Z, Fang EF. Ageing and Alzheimer’s Disease. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_74-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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20
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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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21
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Naik B, Mehta A, Shah M. Denouements of machine learning and multimodal diagnostic classification of Alzheimer's disease. Vis Comput Ind Biomed Art 2020; 3:26. [PMID: 33151420 PMCID: PMC7642580 DOI: 10.1186/s42492-020-00062-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 10/16/2020] [Indexed: 12/19/2022] Open
Abstract
Alzheimer's disease (AD) is the most common type of dementia. The exact cause and treatment of the disease are still unknown. Different neuroimaging modalities, such as magnetic resonance imaging (MRI), positron emission tomography, and single-photon emission computed tomography, have played a significant role in the study of AD. However, the effective diagnosis of AD, as well as mild cognitive impairment (MCI), has recently drawn large attention. Various technological advancements, such as robots, global positioning system technology, sensors, and machine learning (ML) algorithms, have helped improve the diagnostic process of AD. This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls. Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.
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Affiliation(s)
- Binny Naik
- Department of Computer Engineering, Indus University, Ahmedabad, Gujarat, 382115, India
| | - Ashir Mehta
- Department of Computer Engineering, Indus University, Ahmedabad, Gujarat, 382115, India
| | - Manan Shah
- Department of Chemical Engineering, School of Technology, Pandit Deendayal Petroleum University, Gandhinagar, Gujarat, 382007, India.
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22
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Gupta Y, Kim JI, Kim BC, Kwon GR. Classification and Graphical Analysis of Alzheimer's Disease and Its Prodromal Stage Using Multimodal Features From Structural, Diffusion, and Functional Neuroimaging Data and the APOE Genotype. Front Aging Neurosci 2020; 12:238. [PMID: 32848713 PMCID: PMC7406801 DOI: 10.3389/fnagi.2020.00238] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 07/08/2020] [Indexed: 12/26/2022] Open
Abstract
Graphical, voxel, and region-based analysis has become a popular approach to studying neurodegenerative disorders such as Alzheimer's disease (AD) and its prodromal stage [mild cognitive impairment (MCI)]. These methods have been used previously for classification or discrimination of AD in subjects in a prodromal stage called stable MCI (MCIs), which does not convert to AD but remains stable over a period of time, and converting MCI (MCIc), which converts to AD, but the results reported across similar studies are often inconsistent. Furthermore, the classification accuracy for MCIs vs. MCIc is limited. In this study, we propose combining different neuroimaging modalities (sMRI, FDG-PET, AV45-PET, DTI, and rs-fMRI) with the apolipoprotein-E genotype to form a multimodal system for the discrimination of AD, and to increase the classification accuracy. Initially, we used two well-known analyses to extract features from each neuroimage for the discrimination of AD: whole-brain parcelation analysis (or region-based analysis), and voxel-wise analysis (or voxel-based morphometry). We also investigated graphical analysis (nodal and group) for all six binary classification groups (AD vs. HC, MCIs vs. MCIc, AD vs. MCIc, AD vs. MCIs, HC vs. MCIc, and HC vs. MCIs). Data for a total of 129 subjects (33 AD, 30 MCIs, 31 MCIc, and 35 HCs) for each imaging modality were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) homepage. These data also include two APOE genotype data points for the subjects. Moreover, we used the 2-mm AICHA atlas with the NiftyReg registration toolbox to extract 384 brain regions from each PET (FDG and AV45) and sMRI image. For the rs-fMRI images, we used the DPARSF toolbox in MATLAB for the automatic extraction of data and the results for REHO, ALFF, and fALFF. We also used the pyClusterROI script for the automatic parcelation of each rs-fMRI image into 200 brain regions. For the DTI images, we used the FSL (Version 6.0) toolbox for the extraction of fractional anisotropy (FA) images to calculate a tract-based spatial statistic. Moreover, we used the PANDA toolbox to obtain 50 white-matter-region-parcellated FA images on the basis of the 2-mm JHU-ICBM-labeled template atlas. To integrate the different modalities and different complementary information into one form, and to optimize the classifier, we used the multiple kernel learning (MKL) framework. The obtained results indicated that our multimodal approach yields a significant improvement in accuracy over any single modality alone. The areas under the curve obtained by the proposed method were 97.78, 96.94, 95.56, 96.25, 96.67, and 96.59% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIc, AD vs. MCIs, HC vs. MCIc, and HC vs. MCIs binary classification, respectively. Our proposed multimodal method improved the classification result for MCIs vs. MCIc groups compared with the unimodal classification results. Our study found that the (left/right) precentral region was present in all six binary classification groups (this region can be considered the most significant region). Furthermore, using nodal network topology, we found that FDG, AV45-PET, and rs-fMRI were the most important neuroimages, and showed many affected regions relative to other modalities. We also compared our results with recently published results.
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Affiliation(s)
- Yubraj Gupta
- Department of Information and Communication Engineering, Chosun University, Gwangju, South Korea
| | - Ji-In Kim
- Department of Information and Communication Engineering, Chosun University, Gwangju, South Korea
| | - Byeong Chae Kim
- Department of Neurology, Chonnam National University Medical School, Gwangju, South Korea
| | - Goo-Rak Kwon
- Department of Information and Communication Engineering, Chosun University, Gwangju, South Korea
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23
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Lu X, Chen J, Shu H, Wang Z, Shi Y, Yuan Y, Xie C, Liao W, Su F, Shi Y, Zhang Z. Predicting conversion to Alzheimer's disease among individual high-risk patients using the Characterizing AD Risk Events index model. CNS Neurosci Ther 2020; 26:720-729. [PMID: 32243064 PMCID: PMC7298996 DOI: 10.1111/cns.13371] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 02/29/2020] [Accepted: 03/12/2020] [Indexed: 12/17/2022] Open
Abstract
AIMS Both amnestic mild cognitive impairment (aMCI) and remitted late-onset depression (rLOD) confer a high risk of developing Alzheimer's disease (AD). This study aims to determine whether the Characterizing AD Risk Events (CARE) index model can effectively predict conversion in individuals at high risk for AD development either in an independent aMCI population or in an rLOD population. METHODS The CARE index model was constructed based on the event-based probabilistic framework fusion of AD biomarkers to differentiate individuals progressing to AD from cognitively stable individuals in the aMCI population (27 stable subjects, 6 progressive subjects) and rLOD population (29 stable subjects, 10 progressive subjects) during the follow-up period. RESULTS AD diagnoses were predicted in the aMCI population with a balanced accuracy of 80.6%, a sensitivity of 83.3%, and a specificity of 77.8%. They were also predicted in the rLOD population with a balanced accuracy of 74.5%, a sensitivity of 80.0%, and a specificity of 69.0%. In addition, the CARE index scores were observed to be negatively correlated with the composite Z scores for episodic memory (R2 = .17, P < .001) at baseline in the combined high-risk population (N = 72). CONCLUSIONS The CARE index model can be used for the prediction of conversion to AD in both aMCI and rLOD populations effectively. Additionally, it can be used to monitor the disease severity of patients.
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Affiliation(s)
- Xiang Lu
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
| | - Jiu Chen
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
- Institute of NeuropsychiatryThe Affiliated Brain Hospital of Nanjing Medical UniversityNanjingChina
| | - Hao Shu
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
| | - Zan Wang
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
| | - Yong‐mei Shi
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
| | - Yong‐gui Yuan
- Department of Psychosomatics and PsychiatryAffiliated ZhongDa Hospital of Southeast UniversityNanjingChina
| | - Chun‐ming Xie
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
| | - Wen‐xiang Liao
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
| | - Fan Su
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
| | - Ya‐chen Shi
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
| | - Zhi‐jun Zhang
- Department of NeurologySchool of MedicineAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
- Department of PsychologyXinxiang Medical UniversityXinxiangChina
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24
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Chen J, Chen G, Shu H, Chen G, Ward BD, Wang Z, Liu D, Antuono PG, Li SJ, Zhang Z. Predicting progression from mild cognitive impairment to Alzheimer's disease on an individual subject basis by applying the CARE index across different independent cohorts. Aging (Albany NY) 2020; 11:2185-2201. [PMID: 31078129 PMCID: PMC6520016 DOI: 10.18632/aging.101883] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Accepted: 03/20/2019] [Indexed: 01/04/2023]
Abstract
The purposes of this study are to investigate whether the Characterizing Alzheimer's disease Risk Events (CARE) index can accurately predict progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) on an individual subject basis, and to investigate whether this model can be generalized to an independent cohort. Using an event-based probabilistic model approach to integrate widely available biomarkers from behavioral data and brain structural and functional imaging, we calculated the CARE index. We then applied the CARE index to identify which MCI individuals from the ADNI dataset progressed to AD during a three-year follow-up period. Subsequently, the CARE index was generalized to the prediction of MCI individuals from an independent Nanjing Aging and Dementia Study (NADS) dataset during the same time period. The CARE index achieved high prediction performance with 80.4% accuracy, 75% sensitivity, 82% specificity, and 0.809 area under the receiver operating characteristic (ROC) curve (AUC) on MCI subjects from the ADNI dataset over three years, and a highly validated prediction performance with 87.5% accuracy, 81% sensitivity, 90% specificity, and 0.861 AUC on MCI subjects from the NADS dataset. In conclusion, the CARE index is highly accurate, sufficiently robust, and generalized for predicting which MCI individuals will develop AD over a three-year period. This suggests that the CARE index can be usefully applied to select individuals with MCI for clinical trials and to identify which individuals will convert from MCI to AD for administration of early disease-modifying treatment.
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Affiliation(s)
- Jiu Chen
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China.,Institute of Neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China.,Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Gang Chen
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Hao Shu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China.,Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Guangyu Chen
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - B Douglas Ward
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Zan Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Duan Liu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Piero G Antuono
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Shi-Jiang Li
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China.,Department of Psychology, Xinxiang Medical University, Xinxiang, China
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- A complete listing of ADNI investigators can be found at at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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25
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Battista P, Salvatore C, Berlingeri M, Cerasa A, Castiglioni I. Artificial intelligence and neuropsychological measures: The case of Alzheimer's disease. Neurosci Biobehav Rev 2020; 114:211-228. [PMID: 32437744 DOI: 10.1016/j.neubiorev.2020.04.026] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 04/03/2020] [Accepted: 04/23/2020] [Indexed: 12/19/2022]
Abstract
One of the current challenges in the field of Alzheimer's disease (AD) is to identify patients with mild cognitive impairment (MCI) that will convert to AD. Artificial intelligence, in particular machine learning (ML), has established as one of more powerful approach to extract reliable predictors and to automatically classify different AD phenotypes. It is time to accelerate the translation of this knowledge in clinical practice, mainly by using low-cost features originating from the neuropsychological assessment. We performed a meta-analysis to assess the contribution of ML and neuropsychological measures for the automated classification of MCI patients and the prediction of their conversion to AD. The pooled sensitivity and specificity of patients' classifications was obtained by means of a quantitative bivariate random-effect meta-analytic approach. Although a high heterogeneity was observed, the results of meta-analysis show that ML applied to neuropsychological measures can lead to a successful automatic classification, being more specific as screening rather than prognosis tool. Relevant categories of neuropsychological tests can be extracted by ML that maximize the classification accuracy.
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Affiliation(s)
- Petronilla Battista
- Scientific Clinical Institutes Maugeri IRCCS, Institute of Bari, Pavia, Italy.
| | - Christian Salvatore
- Department of Science, Technology and Society, Scuola Universitaria Superiore IUSS Pavia, Piazza della Vittoria 15, 27100 Pavia, Italy; DeepTrace Technologies S.r.l., Via Conservatorio 17, 20122 Milan, Italy.
| | - Manuela Berlingeri
- Department of Humanistic Studies, University of Urbino Carlo Bo, Urbino, Italy; Institute for Biomedical Research and Innovation, National Research Council, 87050 Mangone (CS), Italy; NeuroMi, Milan Centre for Neuroscience, Milan, Italy.
| | - Antonio Cerasa
- Department of Physics "Giuseppe Occhialini", University of Milano Bicocca, Milan, Italy; S. Anna Institute and Research in Advanced Neurorehabilitation (RAN), Crotone, Italy.
| | - Isabella Castiglioni
- Center of Developmental Neuropsychology, Area Vasta 1, ASUR Marche, Pesaro, Italy; Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Segrate, Milan, Italy.
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26
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Franzmeier N, Koutsouleris N, Benzinger T, Goate A, Karch CM, Fagan AM, McDade E, Duering M, Dichgans M, Levin J, Gordon BA, Lim YY, Masters CL, Rossor M, Fox NC, O'Connor A, Chhatwal J, Salloway S, Danek A, Hassenstab J, Schofield PR, Morris JC, Bateman RJ, Ewers M. Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning. Alzheimers Dement 2020; 16:501-511. [PMID: 32043733 PMCID: PMC7222030 DOI: 10.1002/alz.12032] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 10/21/2019] [Accepted: 11/25/2019] [Indexed: 11/22/2022]
Abstract
Introduction: Developing cross-validated multi-biomarker models for the prediction of the rate of cognitive decline in Alzheimer’s disease (AD) is a critical yet unmet clinical challenge. Methods: We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid-PET and fluorodeoxyglucose positron-emission tomography (FDG-PET) to predict rates of cognitive decline. Prediction models were trained in autosomal-dominant Alzheimer’s disease (ADAD, n = 121) and subsequently cross-validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model-based risk enrichment was estimated. Results: A model combining all biomarker modalities and established in ADAD predicted the 4-year rate of decline in global cognition (R2 = 24%) and memory (R2 =25%) in sporadic AD. Model-based risk-enrichment reduced the sample size required for detecting simulated intervention effects by 50%–75%. Discussion: Our independently validated machine-learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD.
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Affiliation(s)
- Nicolai Franzmeier
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Tammie Benzinger
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.,Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Alison Goate
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Ronald M. Loeb Center for Alzheimer's Disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Celeste M Karch
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.,Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Anne M Fagan
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.,Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Eric McDade
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Marco Duering
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany.,Munich Cluster for Systems Neurology, Munich, Germany.,German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Johannes Levin
- Munich Cluster for Systems Neurology, Munich, Germany.,German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Brian A Gordon
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.,Mallinckrodt Institute of Radiology, Washington University, St. Louis, Missouri, USA.,Department of Psychological and Brain Sciences, Washington University, St. Louis, Missouri, USA
| | - Yen Ying Lim
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Colin L Masters
- The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Martin Rossor
- Dementia Research Centre, University College London, Queen Square, London, UK
| | - Nick C Fox
- Dementia Research Centre, University College London, Queen Square, London, UK
| | - Antoinette O'Connor
- Dementia Research Centre, University College London, Queen Square, London, UK
| | - Jasmeer Chhatwal
- Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Stephen Salloway
- Department of Neurology, Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Adrian Danek
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Jason Hassenstab
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Psychological and Brain Sciences, Washington University, St. Louis, Missouri, USA
| | - Peter R Schofield
- Neuroscience Research Australia, Randwick, New South Wales, Australia.,School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - John C Morris
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Randall J Bateman
- Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.,Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
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- ADNI Consortium members are listed in the appendix
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- DIAN Consortium members are listed in the appendix
| | - Michael Ewers
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany
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27
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Gupta Y, Lama RK, Kwon GR. Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers. Front Comput Neurosci 2019; 13:72. [PMID: 31680923 PMCID: PMC6805777 DOI: 10.3389/fncom.2019.00072] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 10/01/2019] [Indexed: 01/15/2023] Open
Abstract
Alzheimer's disease (AD), including its mild cognitive impairment (MCI) phase that may or may not progress into the AD, is the most ordinary form of dementia. It is extremely important to correctly identify patients during the MCI stage because this is the phase where AD may or may not develop. Thus, it is crucial to predict outcomes during this phase. Thus far, many researchers have worked on only using a single modality of a biomarker for the diagnosis of AD or MCI. Although recent studies show that a combination of one or more different biomarkers may provide complementary information for the diagnosis, it also increases the classification accuracy distinguishing between different groups. In this paper, we propose a novel machine learning-based framework to discriminate subjects with AD or MCI utilizing a combination of four different biomarkers: fluorodeoxyglucose positron emission tomography (FDG-PET), structural magnetic resonance imaging (sMRI), cerebrospinal fluid (CSF) protein levels, and Apolipoprotein-E (APOE) genotype. The Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset was used in this study. In total, there were 158 subjects for whom all four modalities of biomarker were available. Of the 158 subjects, 38 subjects were in the AD group, 82 subjects were in MCI groups (including 46 in MCIc [MCI converted; conversion to AD within 24 months of time period], and 36 in MCIs [MCI stable; no conversion to AD within 24 months of time period]), and the remaining 38 subjects were in the healthy control (HC) group. For each image, we extracted 246 regions of interest (as features) using the Brainnetome template image and NiftyReg toolbox, and later we combined these features with three CSF and two APOE genotype features obtained from the ADNI website for each subject using early fusion technique. Here, a different kernel-based multiclass support vector machine (SVM) classifier with a grid-search method was applied. Before passing the obtained features to the classifier, we have used truncated singular value decomposition (Truncated SVD) dimensionality reduction technique to reduce high dimensional features into a lower-dimensional feature. As a result, our combined method achieved an area under the receiver operating characteristic (AU-ROC) curve of 98.33, 93.59, 96.83, 94.64, 96.43, and 95.24% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIs, AD vs. MCIc, HC vs. MCIc, and HC vs. MCIs subjects which are high relative to single modality results and other state-of-the-art approaches. Moreover, combined multimodal methods have improved the classification performance over the unimodal classification.
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28
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Bucholc M, Ding X, Wang H, Glass DH, Wang H, Prasad G, Maguire LP, Bjourson AJ, McClean PL, Todd S, Finn DP, Wong-Lin K. A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual. EXPERT SYSTEMS WITH APPLICATIONS 2019; 130:157-171. [PMID: 31402810 PMCID: PMC6688646 DOI: 10.1016/j.eswa.2019.04.022] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R 2 = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.
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Affiliation(s)
- Magda Bucholc
- Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
| | - Xuemei Ding
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
- Fujian Provincial Engineering Technology Research Centre for Public Service Big Data Mining and Application, College of Mathematics and Informatics, Fujian Normal University, Fuzhou, Fujian, 350108, China
| | - Haiying Wang
- School of Computing and Mathematics, Ulster University, Jordanstown campus, Northern Ireland, United Kingdom
| | - David H. Glass
- School of Computing and Mathematics, Ulster University, Jordanstown campus, Northern Ireland, United Kingdom
| | - Hui Wang
- School of Computing and Mathematics, Ulster University, Jordanstown campus, Northern Ireland, United Kingdom
| | - Girijesh Prasad
- Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
| | - Liam P. Maguire
- Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
| | - Anthony J. Bjourson
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Northern Ireland, United Kingdom
| | - Paula L. McClean
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Northern Ireland, United Kingdom
| | - Stephen Todd
- Altnagelvin Area Hospital, Western Health and Social Care Trust, Northern Ireland, United Kingdom
| | - David P. Finn
- Pharmacology and Therapeutics, School of Medicine, and NCBES Galway Neuroscience Centre, National University of Ireland, Galway, Republic of Ireland
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
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29
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Böhle M, Eitel F, Weygandt M, Ritter K. Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification. Front Aging Neurosci 2019; 11:194. [PMID: 31417397 PMCID: PMC6685087 DOI: 10.3389/fnagi.2019.00194] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 07/15/2019] [Indexed: 12/16/2022] Open
Abstract
Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer's disease (AD) detection based on structural magnetic resonance imaging (MRI) data. However, the network decisions are often perceived as being highly non-transparent, making it difficult to apply these algorithms in clinical routine. In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data. Similarly to other visualization methods, LRP produces a heatmap in the input space indicating the importance/relevance of each voxel contributing to the final classification outcome. In contrast to susceptibility maps produced by guided backpropagation ("Which change in voxels would change the outcome most?"), the LRP method is able to directly highlight positive contributions to the network classification in the input space. In particular, we show that (1) the LRP method is very specific for individuals ("Why does this person have AD?") with high inter-patient variability, (2) there is very little relevance for AD in healthy controls and (3) areas that exhibit a lot of relevance correlate well with what is known from literature. To quantify the latter, we compute size-corrected metrics of the summed relevance per brain area, e.g., relevance density or relevance gain. Although these metrics produce very individual "fingerprints" of relevance patterns for AD patients, a lot of importance is put on areas in the temporal lobe including the hippocampus. After discussing several limitations such as sensitivity toward the underlying model and computation parameters, we conclude that LRP might have a high potential to assist clinicians in explaining neural network decisions for diagnosing AD (and potentially other diseases) based on structural MRI data.
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Affiliation(s)
- Moritz Böhle
- Berlin Institute of Health, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Fabian Eitel
- Berlin Institute of Health, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Martin Weygandt
- Berlin Institute of Health, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Excellence Cluster NeuroCure Berlin, Berlin, Germany
| | - Kerstin Ritter
- Berlin Institute of Health, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Bernstein Center for Computational Neuroscience, Berlin, Germany
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30
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Nanni L, Brahnam S, Salvatore C, Castiglioni I. Texture descriptors and voxels for the early diagnosis of Alzheimer's disease. Artif Intell Med 2019; 97:19-26. [PMID: 31202396 DOI: 10.1016/j.artmed.2019.05.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 05/10/2019] [Accepted: 05/16/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND AND OBJECTIVE Early and accurate diagnosis of Alzheimer's Disease (AD) is critical since early treatment effectively slows the progression of the disease thereby adding productive years to those afflicted by this disease. A major problem encountered in the classification of MRI for the automatic diagnosis of AD is the so-called curse-of-dimensionality, which is a consequence of the high dimensionality of MRI feature vectors and the low number of training patterns available in most MRI datasets relevant to AD. METHODS A method for performing early diagnosis of AD is proposed that combines a set of SVMs trained on different texture descriptors (which reduce dimensionality) extracted from slices of Magnetic Resonance Image (MRI) with a set of SVMs trained on markers built from the voxels of MRIs. The dimension of the voxel-based features is reduced by using different feature selection algorithms, each of which trains a separate SVM. These two sets of SVMs are then combined by weighted-sum rule for a final decision. RESULTS Experimental results show that 2D texture descriptors improve the performance of state-of-the-art voxel-based methods. The evaluation of our system on the four ADNI datasets demonstrates the efficacy of the proposed ensemble and demonstrates a contribution to the accurate prediction of AD. CONCLUSIONS Ensembles of texture descriptors combine partially uncorrelated information with respect to standard approaches based on voxels, feature selection, and classification by SVM. In other words, the fusion of a system based on voxels and an ensemble of texture descriptors enhances the performance of voxel-based approaches.
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Affiliation(s)
- Loris Nanni
- Department of Information Engineering, University of Padua, Via Gradenigo, 6/A, 35131 Padua, Italy.
| | - Sheryl Brahnam
- Department of Management and Computer Information Systems, Glass Hall, Room 387, Missouri State University, Springfield, MO 65804, USA.
| | - Christian Salvatore
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.lli Cervi, 93, 20090 Segrate, Milano, Italy.
| | - Isabella Castiglioni
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.lli Cervi, 93, 20090 Segrate, Milano, Italy.
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31
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Collazos-Huertas D, Cárdenas-Peña D, Castellanos-Dominguez G. Instance-Based Representation Using Multiple Kernel Learning for Predicting Conversion to Alzheimer Disease. Int J Neural Syst 2019; 29:1850042. [DOI: 10.1142/s0129065718500429] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The early detection of Alzheimer’s disease and quantification of its progression poses multiple difficulties for machine learning algorithms. Two of the most relevant issues are related to missing data and results interpretability. To deal with both issues, we introduce a methodology to predict conversion of mild cognitive impairment patients to Alzheimer’s from structural brain MRI volumes. First, we use morphological measures of each brain structure to build an instance-based feature mapping that copes with missed follow-up visits. Then, the extracted multiple feature mappings are combined into a single representation through the convex combination of reproducing kernels. The weighting parameters per structure are tuned based on the maximization of the centered-kernel alignment criterion. We evaluate the proposed methodology on a couple of well-known classification machines employing the ADNI database devoted to assessing the combined prognostic value of several AD biomarkers. The obtained experimental results show that our proposed method of Instance-based representation using multiple kernel learning enables detecting mild cognitive impairment as well as predicting conversion to Alzheimers disease within three years from the initial screening. Besides, the brain structures with larger combination weights are directly related to memory and cognitive functions.
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Affiliation(s)
- D. Collazos-Huertas
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Km 9 Vía al Aeropuerto la Nubia, Manizales, Colombia
| | - D. Cárdenas-Peña
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Km 9 Vía al Aeropuerto la Nubia, Manizales, Colombia
| | - G. Castellanos-Dominguez
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Km 9 Vía al Aeropuerto la Nubia, Manizales, Colombia
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32
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Ye C, Mori S, Chan P, Ma T. Connectome-wide network analysis of white matter connectivity in Alzheimer's disease. Neuroimage Clin 2019; 22:101690. [PMID: 30825712 PMCID: PMC6396432 DOI: 10.1016/j.nicl.2019.101690] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2018] [Revised: 01/04/2019] [Accepted: 01/25/2019] [Indexed: 01/06/2023]
Abstract
A multivariate analytical strategy may pinpoint the structural connectivity patterns associated with Alzheimer's disease (AD) pathology in connectome-wide association studies. Diffusion magnetic resonance imaging data from 161 participants including subjects with healthy controls, AD, stable and converting mild cognitive impairment, were selected for group-wise comparisons. A multivariate distance matrix regression (MDMR) analysis was performed to detect abnormality in brain structural network along with disease progression. Based on the seed regions returned by the MDMR analysis, supervised learning was applied to evaluate the disease predictive performance. Nine brain regions, including the left orbital part of superior and middle frontal gyrus, the bilateral supplementary motor area, the bilateral insula, the left hippocampus, the left putamen, and the left thalamus demonstrated extremely significant structural pattern changes along with the progression of AD. The disease classification was more efficient when based on the key connectivity related to these seed regions than when based on whole-brain structural connectivity. MDMR analysis reveals brain network reorganization caused by AD pathology. The key structural connectivity detected in this study exhibits promising distinguishing capability to predict prodromal AD patients.
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Affiliation(s)
- Chenfei Ye
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong Province, China; Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | - Susumu Mori
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Piu Chan
- National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, China; Department of Neurobiology, Neurology and Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing Institute of Geriatrics, Beijing, China; Clinical Center for Parkinson's Disease, Capital Medical University, Beijing, China; Key Laboratory for Neurodegenerative Disease of the Ministry of Education, Beijing Key Laboratory for Parkinson's Disease, Parkinson Disease Center of Beijing Institute for Brain Disorders, Beijing, China
| | - Ting Ma
- Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong Province, China; Peng Cheng Laboratory, Shenzhen, Guangdong, China; National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
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33
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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: 53] [Impact Index Per Article: 8.8] [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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Lange C, Suppa P, Pietrzyk U, Makowski MR, Spies L, Peters O, Buchert R. Prediction of Alzheimer's Dementia in Patients with Amnestic Mild Cognitive Impairment in Clinical Routine: Incremental Value of Biomarkers of Neurodegeneration and Brain Amyloidosis Added Stepwise to Cognitive Status. J Alzheimers Dis 2018; 61:373-388. [PMID: 29154285 DOI: 10.3233/jad-170705] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
The aim of this study was to evaluate the incremental benefit of biomarkers for prediction of Alzheimer's disease dementia (ADD) in patients with mild cognitive impairment (MCI) when added stepwise in the order of their collection in clinical routine. The model started with cognitive status characterized by the ADAS-13 score. Hippocampus volume (HV), cerebrospinal fluid (CSF) phospho-tau (pTau), and the FDG t-sum score in an AD meta-region-of-interest were compared as neurodegeneration markers. CSF-Aβ1-42 was used as amyloidosis marker. The incremental prognostic benefit from these markers was assessed by stepwise Kaplan-Meier survival analysis in 402 ADNI MCI subjects. Predefined cutoffs were used to dichotomize patients as 'negative' or 'positive' for AD characteristic alteration with respect to each marker. Among the neurodegeneration markers, CSF-pTau provided the best incremental risk stratification when added to ADAS-13. FDG PET outperformed HV only in MCI subjects with relatively preserved cognition. Adding CSF-Aβ provided further risk stratification in pTau-positive subjects, independent of their cognitive status. Stepwise integration of biomarkers allows stepwise refinement of risk estimates for MCI-to-ADD progression. Incremental benefit strongly depends on the patient's status according to the preceding diagnostic steps. The stepwise Kaplan-Meier curves might be useful to optimize diagnostic workflow in individual patients.
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Affiliation(s)
- Catharina Lange
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.,School of Mathematics and Natural Science, University of Wuppertal, Wuppertal, Germany
| | - Per Suppa
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.,jung diagnostics GmbH, Hamburg, Germany
| | - Uwe Pietrzyk
- School of Mathematics and Natural Science, University of Wuppertal, Wuppertal, Germany.,Institute of Neuroscience and Medicine, Forschungszentrum Jülich, Jülich, Germany
| | - Marcus R Makowski
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Oliver Peters
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ralph Buchert
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Department of Diagnostic and Interventional Radiology and Nuclear Medicine, Center for Radiology and Endoscopy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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35
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Salvatore C, Cerasa A, Castiglioni I. MRI Characterizes the Progressive Course of AD and Predicts Conversion to Alzheimer's Dementia 24 Months Before Probable Diagnosis. Front Aging Neurosci 2018; 10:135. [PMID: 29881340 PMCID: PMC5977985 DOI: 10.3389/fnagi.2018.00135] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 04/23/2018] [Indexed: 12/16/2022] Open
Abstract
There is no disease-modifying treatment currently available for AD, one of the more impacting neurodegenerative diseases affecting more than 47.5 million people worldwide. The definition of new approaches for the design of proper clinical trials is highly demanded in order to achieve non-confounding results and assess more effective treatment. In this study, a cohort of 200 subjects was obtained from the Alzheimer's Disease Neuroimaging Initiative. Subjects were followed-up for 24 months, and classified as AD (50), progressive-MCI to AD (50), stable-MCI (50), and cognitively normal (50). Structural T1-weighted MRI brain studies and neuropsychological measures of these subjects were used to train and optimize an artificial-intelligence classifier to distinguish mild-AD patients who need treatment (AD + pMCI) from subjects who do not need treatment (sMCI + CN). The classifier was able to distinguish between the two groups 24 months before AD definite diagnosis using a combination of MRI brain studies and specific neuropsychological measures, with 85% accuracy, 83% sensitivity, and 87% specificity. The combined-approach model outperformed the classification using MRI data alone (72% classification accuracy, 69% sensitivity, and 75% specificity). The patterns of morphological abnormalities localized in the temporal pole and medial-temporal cortex might be considered as biomarkers of clinical progression and evolution. These regions can be already observed 24 months before AD definite diagnosis. The best neuropsychological predictors mainly included measures of functional abilities, memory and learning, working memory, language, visuoconstructional reasoning, and complex attention, with a particular focus on some of the sub-scores of the FAQ and AVLT tests.
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Affiliation(s)
- Christian Salvatore
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Milan, Italy
| | - Antonio Cerasa
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Catanzaro, Italy
| | - Isabella Castiglioni
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Milan, Italy
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36
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Liu X, Chen K, Wu T, Weidman D, Lure F, Li J. Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease. Transl Res 2018; 194:56-67. [PMID: 29352978 PMCID: PMC5875456 DOI: 10.1016/j.trsl.2018.01.001] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 01/01/2018] [Accepted: 01/02/2018] [Indexed: 01/24/2023]
Abstract
Alzheimer's disease (AD) is a major neurodegenerative disease and the most common cause of dementia. Currently, no treatment exists to slow down or stop the progression of AD. There is converging belief that disease-modifying treatments should focus on early stages of the disease, that is, the mild cognitive impairment (MCI) and preclinical stages. Making a diagnosis of AD and offering a prognosis (likelihood of converting to AD) at these early stages are challenging tasks but possible with the help of multimodality imaging, such as magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG)-positron emission topography (PET), amyloid-PET, and recently introduced tau-PET, which provides different but complementary information. This article is a focused review of existing research in the recent decade that used statistical machine learning and artificial intelligence methods to perform quantitative analysis of multimodality image data for diagnosis and prognosis of AD at the MCI or preclinical stages. We review the existing work in 3 subareas: diagnosis, prognosis, and methods for handling modality-wise missing data-a commonly encountered problem when using multimodality imaging for prediction or classification. Factors contributing to missing data include lack of imaging equipment, cost, difficulty of obtaining patient consent, and patient drop-off (in longitudinal studies). Finally, we summarize our major findings and provide some recommendations for potential future research directions.
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Affiliation(s)
- Xiaonan Liu
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, Arizona
| | - Teresa Wu
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona
| | | | | | - Jing Li
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona.
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37
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Ritter K, Lange C, Weygandt M, Mäurer A, Roberts A, Estrella M, Suppa P, Spies L, Prasad V, Steffen I, Apostolova I, Bittner D, Gövercin M, Brenner W, Mende C, Peters O, Seybold J, Fiebach JB, Steinhagen-Thiessen E, Hampel H, Haynes JD, Buchert R. Combination of Structural MRI and FDG-PET of the Brain Improves Diagnostic Accuracy in Newly Manifested Cognitive Impairment in Geriatric Inpatients. J Alzheimers Dis 2018; 54:1319-1331. [PMID: 27567842 DOI: 10.3233/jad-160380] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
BACKGROUND The cause of cognitive impairment in acutely hospitalized geriatric patients is often unclear. The diagnostic process is challenging but important in order to treat potentially life-threatening etiologies or identify underlying neurodegenerative disease. OBJECTIVE To evaluate the add-on diagnostic value of structural and metabolic neuroimaging in newly manifested cognitive impairment in elderly geriatric inpatients. METHODS Eighty-one inpatients (55 females, 81.6±5.5 y) without history of cognitive complaints prior to hospitalization were recruited in 10 acute geriatrics clinics. Primary inclusion criterion was a clinical hypothesis of Alzheimer's disease (AD), cerebrovascular disease (CVD), or mixed AD+CVD etiology (MD), which remained uncertain after standard diagnostic workup. Additional procedures performed after enrollment included detailed neuropsychological testing and structural MRI and FDG-PET of the brain. An interdisciplinary expert team established the most probable etiologic diagnosis (non-neurodegenerative, AD, CVD, or MD) integrating all available data. Automatic multimodal classification based on Random Undersampling Boosting was used for rater-independent assessment of the complementary contribution of the additional diagnostic procedures to the etiologic diagnosis. RESULTS Automatic 4-class classification based on all diagnostic routine standard procedures combined reproduced the etiologic expert diagnosis in 31% of the patients (p = 0.100, chance level 25%). Highest accuracy by a single modality was achieved by MRI or FDG-PET (both 45%, p≤0.001). Integration of all modalities resulted in 76% accuracy (p≤0.001). CONCLUSION These results indicate substantial improvement of diagnostic accuracy in uncertain de novo cognitive impairment in acutely hospitalized geriatric patients with the integration of structural MRI and brain FDG-PET into the diagnostic process.
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Affiliation(s)
- Kerstin Ritter
- Berlin Center for Advanced Neuroimaging, Bernstein Center for Computational Neuroscience, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Catharina Lange
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Martin Weygandt
- Berlin Center for Advanced Neuroimaging, Bernstein Center for Computational Neuroscience, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Anja Mäurer
- Evangelisches Geriatriezentrum Berlin, Berlin, Germany
| | - Anna Roberts
- Evangelisches Geriatriezentrum Berlin, Berlin, Germany
| | - Melanie Estrella
- Geriatric Research Group, Department of Geriatric Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Per Suppa
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Jung Diagnostics GmbH, Hamburg, Germany
| | | | - Vikas Prasad
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ingo Steffen
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ivayla Apostolova
- Department of Radiology and Nuclear Medicine, University Hospital Magdeburg, Magdeburg, Germany
| | - Daniel Bittner
- Department of Neurology, University Hospital Magdeburg, Magdeburg, Germany
| | - Mehmet Gövercin
- Geriatric Research Group, Department of Geriatric Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Winfried Brenner
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Oliver Peters
- Department of Psychiatry and Psychotherapy Charité Benjamin Franklin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Joachim Seybold
- Evangelisches Geriatriezentrum Berlin, Berlin, Germany.,Department of Internal Medicine/Infectious Diseases and Pulmonary Medicine, Charité - Universitätsmedizin Berlin, Germany
| | | | | | - Harald Hampel
- AXA Research Fund & UPMC Chair, Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, Institut de la Mémoire et de la Maladied' Alzheimer (IM2A) & Institut du Cerveau et de la Moelleépinière (ICM), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - John-Dylan Haynes
- Berlin Center for Advanced Neuroimaging, Bernstein Center for Computational Neuroscience, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ralph Buchert
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
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38
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Rasero J, Amoroso N, La Rocca M, Tangaro S, Bellotti R, Stramaglia S. Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer's disease. PLoS One 2017; 12:e0187281. [PMID: 29135998 PMCID: PMC5685585 DOI: 10.1371/journal.pone.0187281] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Accepted: 10/17/2017] [Indexed: 01/21/2023] Open
Abstract
Alzheimer’s disease (AD) is the most common form of dementia among older people and increasing longevity ensures its prevalence will rise even further. Whether AD originates by disconnecting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. An important related challenge is to predict whether a given subject, with a mild cognitive impairment (MCI), will convert or not to AD. Here, our aim is to characterize the structural connectivity pattern of MCI and AD subjects using the multivariate distance matrix regression (MDMR) analysis, and to compare it to those of healthy subjects. MDMR is a technique developed in genomics that has been recently applied to functional brain network data, and here applied to identify brain nodes with different connectivity patterns, in controls and patients, because of brain atrophy. We address this issue at the macroscale by looking to differences in individual structural MRI brain networks, obtained from MR images according to a recently proposed definition of connectivity which measures the image similarity between patches at different locations in the brain. In particular, using data from ADNI, we selected four groups of subjects (all of them matched by age and sex): HC (healthy control participants), ncMCI (mild cognitive impairment not converting to AD), cMCI (mild cognitive impairment converting to AD) and AD. Next, we built structural MRI brain networks and performed group comparison for all the pairs of groups. Our results were three-fold: (i) considering the comparison of HC with the three other groups, the number of significant brain regions was 4 for ncMCI, 290 for cMCI and 74 for AD, out of a total of 549 regions; hence, in terms of the structural MRI connectivity here adopted, cMCI subjects have the maximal altered pattern w.r.t. healthy conditions. (ii) Eight and seven nodes were significant for the comparisons AD-ncMCI and AD-cMCI, respectively; six nodes, among them, were significant in both comparisons and these nodes form a connected brain region (corresponding to hippocampus, amygdala, Parahippocampal Gyrus, Planum Polare, Frontal Orbital Cortex, Temporal Pole and subcallosal cortex) showing reduced strength of connectivity in the MCI stages; (iii) The connectivity maps of cMCI and ncMCI subjects significantly differ from the connectome of healthy subjects in three regions all corresponding to Frontal Orbital Cortex.
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Affiliation(s)
- Javier Rasero
- Biocruces Health Research Institute. Hospital Universitario de Cruces. E-48903, Barakaldo, Spain
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
| | - Nicola Amoroso
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
| | - Marianna La Rocca
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
| | | | - Roberto Bellotti
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
- TIRES-Center of Innovative Technologies for Signal Detection and Processing, Università degli Studi Aldo Moro, Bari, Italy
| | - Sebastiano Stramaglia
- Dipartimento di Fisica, Universitá degli Studi Aldo Moro, Via Orabona,4, 70126 Bari, Italy
- INFN, Sezione di Bari, via Orabona 4, 70126 Bari, Italy
- TIRES-Center of Innovative Technologies for Signal Detection and Processing, Università degli Studi Aldo Moro, Bari, Italy
- * E-mail:
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39
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Rasero J, Alonso-Montes C, Diez I, Olabarrieta-Landa L, Remaki L, Escudero I, Mateos B, Bonifazi P, Fernandez M, Arango-Lasprilla JC, Stramaglia S, Cortes JM. Group-Level Progressive Alterations in Brain Connectivity Patterns Revealed by Diffusion-Tensor Brain Networks across Severity Stages in Alzheimer's Disease. Front Aging Neurosci 2017; 9:215. [PMID: 28736521 PMCID: PMC5500648 DOI: 10.3389/fnagi.2017.00215] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Accepted: 06/20/2017] [Indexed: 01/22/2023] Open
Abstract
Alzheimer's disease (AD) is a chronically progressive neurodegenerative disease highly correlated to aging. Whether AD originates by targeting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. Here, we aim to provide an answer to this question at the group-level by looking at differences in diffusion-tensor brain networks. In particular, making use of data from Alzheimer's Disease Neuroimaging Initiative (ADNI), four different groups were defined (all of them matched by age, sex and education level): G1 (N1 = 36, healthy control subjects, Control), G2 (N2 = 36, early mild cognitive impairment, EMCI), G3 (N3 = 36, late mild cognitive impairment, LMCI) and G4 (N4 = 36, AD). Diffusion-tensor brain networks were compared across three disease stages: stage I (Control vs. EMCI), stage II (Control vs. LMCI) and stage III (Control vs. AD). The group comparison was performed using the multivariate distance matrix regression analysis, a technique that was born in genomics and was recently proposed to handle brain functional networks, but here applied to diffusion-tensor data. The results were threefold: First, no significant differences were found in stage I. Second, significant differences were found in stage II in the connectivity pattern of a subnetwork strongly associated to memory function (including part of the hippocampus, amygdala, entorhinal cortex, fusiform gyrus, inferior and middle temporal gyrus, parahippocampal gyrus and temporal pole). Third, a widespread disconnection across the entire AD brain was found in stage III, affecting more strongly the same memory subnetwork appearing in stage II, plus the other new subnetworks, including the default mode network, medial visual network, frontoparietal regions and striatum. Our results are consistent with a scenario where progressive alterations of connectivity arise as the disease severity increases and provide the brain areas possibly involved in such a degenerative process. Further studies applying the same strategy to longitudinal data are needed to fully confirm this scenario.
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Affiliation(s)
- Javier Rasero
- Dipartimento Interateneo di Fisica, Istituto Nazionale di Fisica Nucleare, Universita degli Studi di BariBari, Italy
- Biocruces Health Research InstituteBarakaldo, Spain
| | | | - Ibai Diez
- Biocruces Health Research InstituteBarakaldo, Spain
| | | | | | - Iñaki Escudero
- Biocruces Health Research InstituteBarakaldo, Spain
- Radiology Service, Cruces University HospitalBarakaldo, Spain
| | - Beatriz Mateos
- Biocruces Health Research InstituteBarakaldo, Spain
- Radiology Service, Cruces University HospitalBarakaldo, Spain
| | - Paolo Bonifazi
- Biocruces Health Research InstituteBarakaldo, Spain
- IKERBASQUE: The Basque Foundation for ScienceBilbao, Spain
| | - Manuel Fernandez
- Biocruces Health Research InstituteBarakaldo, Spain
- Neurology Service, Cruces University HospitalBarakaldo, Spain
| | | | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Istituto Nazionale di Fisica Nucleare, Universita degli Studi di BariBari, Italy
- Basque Center for Applied MathematicsBilbao, Spain
| | - Jesus M. Cortes
- Biocruces Health Research InstituteBarakaldo, Spain
- IKERBASQUE: The Basque Foundation for ScienceBilbao, Spain
- Department of Cell Biology and Histology, University of the Basque CountryLeioa, Spain
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40
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Pagani M, Nobili F, Morbelli S, Arnaldi D, Giuliani A, Öberg J, Girtler N, Brugnolo A, Picco A, Bauckneht M, Piva R, Chincarini A, Sambuceti G, Jonsson C, De Carli F. Early identification of MCI converting to AD: a FDG PET study. Eur J Nucl Med Mol Imaging 2017; 44:2042-2052. [PMID: 28664464 DOI: 10.1007/s00259-017-3761-x] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 06/13/2017] [Indexed: 01/02/2023]
Abstract
PURPOSE Mild cognitive impairment (MCI) is a transitional pathological stage between normal ageing (NA) and Alzheimer's disease (AD). Although subjects with MCI show a decline at different rates, some individuals remain stable or even show an improvement in their cognitive level after some years. We assessed the accuracy of FDG PET in discriminating MCI patients who converted to AD from those who did not. METHODS FDG PET was performed in 42 NA subjects, 27 MCI patients who had not converted to AD at 5 years (nc-MCI; mean follow-up time 7.5 ± 1.5 years), and 95 MCI patients who converted to AD within 5 years (MCI-AD; mean conversion time 1.8 ± 1.1 years). Relative FDG uptake values in 26 meta-volumes of interest were submitted to ANCOVA and support vector machine analyses to evaluate regional differences and discrimination accuracy. RESULTS The MCI-AD group showed significantly lower FDG uptake values in the temporoparietal cortex than the other two groups. FDG uptake values in the nc-MCI group were similar to those in the NA group. Support vector machine analysis discriminated nc-MCI from MCI-AD patients with an accuracy of 89% (AUC 0.91), correctly detecting 93% of the nc-MCI patients. CONCLUSION In MCI patients not converting to AD within a minimum follow-up time of 5 years and MCI patients converting within 5 years, baseline FDG PET and volume-based analysis identified those who converted with an accuracy of 89%. However, further analysis is needed in patients with amnestic MCI who convert to a dementia other than AD.
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Affiliation(s)
- Marco Pagani
- Institute of Cognitive Sciences and Technologies, CNR, Via Palestro 32, 00185, Rome, Italy. .,Department of Nuclear Medicine, Karolinska Hospital Stockholm, Stockholm, Sweden.
| | - Flavio Nobili
- Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Silvia Morbelli
- Department of Nuclear Medicine, Department of Health Science (DISSAL), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Dario Arnaldi
- Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Alessandro Giuliani
- Environment and Health Department, Istituto Superiore di Sanità, Rome, Italy
| | - Johanna Öberg
- Department of Hospital Physics, Karolinska Hospital, Stockholm, Sweden
| | - Nicola Girtler
- Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy.,Clinical Psychology, IRCCS AOU San Martino-IST, Genoa, Italy
| | - Andrea Brugnolo
- Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Agnese Picco
- Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Matteo Bauckneht
- Department of Nuclear Medicine, Department of Health Science (DISSAL), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Roberta Piva
- Department of Nuclear Medicine, Department of Health Science (DISSAL), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Andrea Chincarini
- National Institute of Nuclear Physics (INFN), Genoa section, Genoa, Italy
| | - Gianmario Sambuceti
- Department of Nuclear Medicine, Department of Health Science (DISSAL), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Cathrine Jonsson
- Medical Radiation Physics and Nuclear Medicine, Imaging and Physiology, Karolinska University Hospital, Stockholm, Sweden
| | - Fabrizio De Carli
- Institute of Molecular Bioimaging and Physiology, CNR - Genoa Unit, AOU San Martino-IST, Genoa, Italy
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41
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Golbabaei S, Dadashi A, Soltanian-Zadeh H. Measures of the brain functional network that correlate with Alzheimer's neuropsychological test scores: An fMRI and graph analysis study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:5554-5557. [PMID: 28269515 DOI: 10.1109/embc.2016.7591985] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Neural degeneration in Alzheimer's disease (AD) leads to structural topology deformation that in turn changes brain functionality. The main aim of the present study is to find the brain's functional connectivity network (FCN) correlates of Alzheimer's psychological test scores. To this end, the brain's FCN is extracted from the resting state functional magnetic resonance images (rs-fMRI) of healthy controls and patients with AD and represented as a graph. Then, network measures are calculated from the graphs. The correlations between the brain network measures and five AD psychological assessment test scores are evaluated. The results show positive correlation between the Mini-Mental State Examination (MMSE) and nodal strength in the left insula and negative correlation between the Clinical Dementia Rating (CDR) score and local efficiency of left olfactory cortex and also eigenvector centrality of left supramarginal cortex. The Neuropsychiatric Inventory Questionnaire (NPI-Q) also seems to be correlated with network measures of the left superior parietal gyrus. Moreover, notable decreased continuity are spotted in the limbic system. These measures can be used to provide an objective tool for AD diagnosis.
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42
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Lange C, Suppa P, Frings L, Brenner W, Spies L, Buchert R. Optimization of Statistical Single Subject Analysis of Brain FDG PET for the Prognosis of Mild Cognitive Impairment-to-Alzheimer's Disease Conversion. J Alzheimers Dis 2016; 49:945-959. [PMID: 26577523 DOI: 10.3233/jad-150814] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Positron emission tomography (PET) with the glucose analog F-18-fluorodeoxyglucose (FDG) is widely used in the diagnosis of neurodegenerative diseases. Guidelines recommend voxel-based statistical testing to support visual evaluation of the PET images. However, the performance of voxel-based testing strongly depends on each single preprocessing step involved. OBJECTIVE To optimize the processing pipeline of voxel-based testing for the prognosis of dementia in subjects with amnestic mild cognitive impairment (MCI). METHODS The study included 108 ADNI MCI subjects grouped as 'stable MCI' (n = 77) or 'MCI-to-AD converter' according to their diagnostic trajectory over 3 years. Thirty-two ADNI normals served as controls. Voxel-based testing was performed with the statistical parametric mapping software (SPM8) starting with default settings. The following modifications were added step-by-step: (i) motion correction, (ii) custom-made FDG template, (iii) different reference regions for intensity scaling, and (iv) smoothing was varied between 8 and 18 mm. The t-sum score for hypometabolism within a predefined AD mask was compared between the different settings using receiver operating characteristic (ROC) analysis with respect to differentiation between 'stable MCI' and 'MCI-to-AD converter'. The area (AUC) under the ROC curve was used as performance measure. RESULTS The default setting provided an AUC of 0.728. The modifications of the processing pipeline improved the AUC up to 0.832 (p = 0.046). Improvement of the AUC was confirmed in an independent validation sample of 241 ADNI MCI subjects (p = 0.048). CONCLUSION The prognostic value of voxel-based single subject analysis of brain FDG PET in MCI subjects can be improved considerably by optimizing the processing pipeline.
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Affiliation(s)
- Catharina Lange
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Per Suppa
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.,jung diagnostics GmbH, Hamburg, Germany
| | - Lars Frings
- Department of Nuclear Medicine, University of Freiburg, Freiburg, Germany
| | - Winfried Brenner
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Ralph Buchert
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
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DADM: The first 2 years of the Alzheimer Association's open access journal to support the research and development of novel biomarkers and diagnostic approaches. Alzheimers Dement 2016; 12:755-7. [PMID: 27370207 DOI: 10.1016/j.jalz.2016.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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