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Han T, Peng Y, Du Y, Li Y, Wang Y, Sun W, Cui L, Peng Q. Mining Alzheimer's disease clinical data: reducing effects of natural aging for predicting progression and identifying subtypes. Front Neurosci 2024; 18:1388391. [PMID: 39206114 PMCID: PMC11351280 DOI: 10.3389/fnins.2024.1388391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 07/26/2024] [Indexed: 09/04/2024] Open
Abstract
Introduction Because Alzheimer's disease (AD) has significant heterogeneity in encephalatrophy and clinical manifestations, AD research faces two critical challenges: eliminating the impact of natural aging and extracting valuable clinical data for patients with AD. Methods This study attempted to address these challenges by developing a novel machine-learning model called tensorized contrastive principal component analysis (T-cPCA). The objectives of this study were to predict AD progression and identify clinical subtypes while minimizing the influence of natural aging. Results We leveraged a clinical variable space of 872 features, including almost all AD clinical examinations, which is the most comprehensive AD feature description in current research. T-cPCA yielded the highest accuracy in predicting AD progression by effectively minimizing the confounding effects of natural aging. Discussion The representative features and pathogenic circuits of the four primary AD clinical subtypes were discovered. Confirmed by clinical doctors in Tangdu Hospital, the plaques (18F-AV45) distribution of typical patients in the four clinical subtypes are consistent with representative brain regions found in four AD subtypes, which further offers novel insights into the underlying mechanisms of AD pathogenesis.
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Affiliation(s)
- Tian Han
- Systems Engineering Institute, School of Automation, Xi’an Jiaotong University, Xi’an, China
| | - Yunhua Peng
- Center for Mitochondrial Biology and Medicine, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi’an Jiaotong University, Xi’an, China
| | - Ying Du
- Department of Neurology, Tangdu Hospital, Fourth Military Medical University, Xi’an, China
| | - Yunbo Li
- Department of Nuclear Medicine, Tangdu Hospital, Fourth Military Medical University, Xi’an, China
| | - Ying Wang
- Systems Engineering Institute, School of Automation, Xi’an Jiaotong University, Xi’an, China
| | - Wentong Sun
- Systems Engineering Institute, School of Automation, Xi’an Jiaotong University, Xi’an, China
| | - Lanxin Cui
- Systems Engineering Institute, School of Automation, Xi’an Jiaotong University, Xi’an, China
| | - Qinke Peng
- Systems Engineering Institute, School of Automation, Xi’an Jiaotong University, Xi’an, China
- School of Future Technology, Xi’an Jiaotong University, Xi’an, China
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Bi XA, Wang Y, Luo S, Chen K, Xing Z, Xu L. Hypergraph Structural Information Aggregation Generative Adversarial Networks for Diagnosis and Pathogenetic Factors Identification of Alzheimer's Disease With Imaging Genetic Data. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7420-7434. [PMID: 36264725 DOI: 10.1109/tnnls.2022.3212700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease with profound pathogenetic causes. Imaging genetic data analysis can provide comprehensive insights into its causes. To fully utilize the multi-level information in the data, this article proposes a hypergraph structural information aggregation model, and constructs a novel deep learning method named hypergraph structural information aggregation generative adversarial networks (HSIA-GANs) for the automatic sample classification and accurate feature extraction. Specifically, HSIA-GAN is composed of generator and discriminator. The generator has three main functions. First, vertex graph and edge graph are constructed based on the input hypergraph to present the low-order relations. Second, the low-order structural information of hypergraph is extracted by the designed vertex convolution layers and edge convolution layers. Finally, the synthetic hypergraph is generated as the input of the discriminator. The discriminator can extract the high-order structural information directly from hypergraph through vertex-edge convolution, fuse the high and low-order structural information, and finalize the results through the full connection (FC) layers. Based on the data acquired from AD neuroimaging initiative, HSIA-GAN shows significant advantages in three classification tasks, and extracts discriminant features conducive to better disease classification.
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Andreatta Maduro P, Guimarães MP, de Sousa Rodrigues M, Pereira Rolim Coimbra Pinto AP, da Mota Junior AA, Lima Rocha AS, Matoso JMD, Bavaresco Gambassi B, Schwingel PA. Comparing the Efficacy of Two Cognitive Screening Tools in Identifying Gray and White Matter Brain Damage among Older Adults. J Aging Res 2024; 2024:5527225. [PMID: 38690079 PMCID: PMC11060871 DOI: 10.1155/2024/5527225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 03/19/2024] [Accepted: 04/12/2024] [Indexed: 05/02/2024] Open
Abstract
Background Ageing is associated with structural changes in brain regions and functional decline in cognitive domains. Noninvasive tools for identifying structural damage in the brains of older adults are relevant for early treatment. Aims This study aims to evaluate and compare the accuracy of the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA©) in identifying gray and white matter brain damage in older individuals with varying degrees of cognitive impairment. Methods Ninety older adults (62 women) with an average age of 69 ± 7 years were enrolled and categorized as having no cognitive impairment (NCI), mild cognitive impairment (MCI), or moderate cognitive impairment (MoCI). Magnetic resonance imaging (MRI) was utilized to assess the number, volume, and distribution of brain damage. The Fazekas and Scheltens scales were applied to the brain MRIs, and inferential statistics were employed to compare variables among the groups. Results Cognitive impairment was observed in 56.7% of the participants (95% confidence interval (CI): 46.4-66.4%), with thirty-six older adults (40%) classified as MCI and 15 (17%) as MoCI. Cognitive impairment and medial temporal lobe (MTL) atrophy were found to be associated (p=0.001), exhibiting higher mean volume scales of the MTL atrophied area in the MoCI group (p < 0.001). The MMSE accurately revealed MTL atrophy based on the Scheltens (p < 0.05) and Fazekas (p < 0.05) scales. At the same time, the MoCA accurately identified periventricular white matter (PWM) abnormalities according to the Fazekas scale (p < 0.05). Conclusions The MMSE and MoCA screening tools effectively identified gray and white matter brain damage in older adults with varying degrees of cognitive impairment. Lower MMSE scores are associated with MTL atrophy and lesions, and lower MoCA scores are related to PWM lesions. The concurrent use of MMSE and MoCA is recommended for assessing structural changes in distinct brain regions.
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Affiliation(s)
- Paula Andreatta Maduro
- Post-Graduation Program in Health Sciences (PPGCS), University of Pernambuco (UPE), Recife, PE 50100-130, Brazil
- Human Performance Research Laboratory (LAPEDH), UPE, Petrolina, PE 56328-900, Brazil
- University Hospital of the Federal University of Vale do São Francisco (HU-UNIVASF), Brazilian Hospital Services Company (EBSERH), Petrolina, PE 56304-205, Brazil
| | | | - Mateus de Sousa Rodrigues
- Human Performance Research Laboratory (LAPEDH), UPE, Petrolina, PE 56328-900, Brazil
- University Hospital of the Federal University of Vale do São Francisco (HU-UNIVASF), Brazilian Hospital Services Company (EBSERH), Petrolina, PE 56304-205, Brazil
| | - Ana Paula Pereira Rolim Coimbra Pinto
- University Hospital of the Federal University of Vale do São Francisco (HU-UNIVASF), Brazilian Hospital Services Company (EBSERH), Petrolina, PE 56304-205, Brazil
| | - Américo Alves da Mota Junior
- Human Performance Research Laboratory (LAPEDH), UPE, Petrolina, PE 56328-900, Brazil
- University Hospital of the Federal University of Vale do São Francisco (HU-UNIVASF), Brazilian Hospital Services Company (EBSERH), Petrolina, PE 56304-205, Brazil
| | - Alaine Souza Lima Rocha
- Post-Graduation Program in Health Sciences (PPGCS), University of Pernambuco (UPE), Recife, PE 50100-130, Brazil
- Human Performance Research Laboratory (LAPEDH), UPE, Petrolina, PE 56328-900, Brazil
- Department of Physical Therapy, Federal University of Ceará (UFC), Fortaleza, CE 60430-450, Brazil
| | - Juliana Magalhães Duarte Matoso
- Human Performance Research Laboratory (LAPEDH), UPE, Petrolina, PE 56328-900, Brazil
- Department of Clinical Medicine, Pedro Ernesto University Hospital, State University of Rio de Janeiro (UERJ), Rio de Janeiro, RJ 20551-030, Brazil
| | - Bruno Bavaresco Gambassi
- Human Performance Research Laboratory (LAPEDH), UPE, Petrolina, PE 56328-900, Brazil
- Post-Graduation Program in Management of Health Programs and Services (PPGGPSS), CEUMA University (UNICEUMA), São Luís, MA 65075-120, Brazil
| | - Paulo Adriano Schwingel
- Post-Graduation Program in Health Sciences (PPGCS), University of Pernambuco (UPE), Recife, PE 50100-130, Brazil
- Human Performance Research Laboratory (LAPEDH), UPE, Petrolina, PE 56328-900, Brazil
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Jomeiri A, Navin AH, Shamsi M. Longitudinal MRI analysis using a hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction. Behav Brain Res 2024; 463:114900. [PMID: 38341100 DOI: 10.1016/j.bbr.2024.114900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 12/16/2023] [Accepted: 02/06/2024] [Indexed: 02/12/2024]
Abstract
Alzheimer's disease is a progressive neurological disorder characterized by brain atrophy and cell death, leading to cognitive decline and impaired functioning. Previous research has primarily focused on using cross-sectional data for Alzheimer's disease identification, but analyzing longitudinal sequential MR images is crucial for improved diagnostic accuracy and understanding disease progression. However, existing deep learning models face challenges in learning spatial and temporal features from such data. To address these challenges, this study presents a novel hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction using longitudinal MRI analysis. The proposed framework combines Convolutional DenseNet for spatial information extraction and joined BiLSTM layers for capturing temporal characteristics and relationships between longitudinal images at different time points. This approach overcomes issues like overfitting, vanishing gradients, and incomplete patient data. We evaluated the model on 684 longitudinal MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including normal controls, individuals with mild cognitive impairment, and Alzheimer's disease patients. The results demonstrate high classification accuracy, with 95.28% for AD/CN, 88.19% for NC/MCI, 83.51% for sMCI/pMCI, and 92.14% for MCI/AD. These findings highlight the substantial improvement in Alzheimer's disease diagnosis achieved through the utilization of longitudinal MRI images. The contributions of this study lie in both the deep learning and medical domains. In the deep learning domain, our hybrid framework effectively learns spatial and temporal features from longitudinal data, addressing the challenges associated with multi-dimensional and sequential time series data. In the medical domain, our study emphasizes the importance of analyzing baseline and longitudinal MR images for accurate diagnosis and understanding disease progression.
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Affiliation(s)
- Alireza Jomeiri
- Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran
| | - Ahmad Habibizad Navin
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
| | - Mahboubeh Shamsi
- Department of Engineering, Qom University of Technology, Qom, Iran
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Platero C, Tohka J, Strange B. Estimating Dementia Onset: AT(N) Profiles and Predictive Modeling in Mild Cognitive Impairment Patients. Curr Alzheimer Res 2024; 20:778-790. [PMID: 38425106 DOI: 10.2174/0115672050295317240223162312] [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: 01/04/2024] [Revised: 02/06/2024] [Accepted: 02/15/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND Mild Cognitive Impairment (MCI) usually precedes the symptomatic phase of dementia and constitutes a window of opportunities for preventive therapies. OBJECTIVES The objective of this study was to predict the time an MCI patient has left to reach dementia and obtain the most likely natural history in the progression of MCI towards dementia. METHODS This study was conducted on 633 MCI patients and 145 subjects with dementia through 4726 visits over 15 years from Alzheimer Disease Neuroimaging Initiative (ADNI) cohort. A combination of data from AT(N) profiles at baseline and longitudinal predictive modeling was applied. A data-driven approach was proposed for categorical diagnosis prediction and timeline estimation of cognitive decline progression, which combined supervised and unsupervised learning techniques. RESULTS A reduced vector of only neuropsychological measures was selected for training the models. At baseline, this approach had high performance in detecting subjects at high risk of converting from MCI to dementia in the coming years. Furthermore, a Disease Progression Model (DPM) was built and also verified using three metrics. As a result of the DPM focused on the studied population, it was inferred that amyloid pathology (A+) appears about 7 years before dementia, and tau pathology (T+) and neurodegeneration (N+) occur almost simultaneously, between 3 and 4 years before dementia. In addition, MCI-A+ subjects were shown to progress more rapidly to dementia compared to MCI-A- subjects. CONCLUSION Based on proposed natural histories and cross-sectional and longitudinal analysis of AD markers, the results indicated that only a single cerebrospinal fluid sample is necessary during the prodromal phase of AD. Prediction from MCI into dementia and its timeline can be achieved exclusively through neuropsychological measures.
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Affiliation(s)
- Carlos Platero
- Health Science Technology Group, Technical University of Madrid, 28012 Madrid, Spain
| | - Jussi Tohka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, FI-70211 Kuopio, Finland
| | - Bryan Strange
- Laboratory for Clinical Neuroscience, CTB, Technical University of Madrid, IdISSC, Madrid, Spain
- Alzheimer Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, Madrid, Spain
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Yu K, Hao L, Bu F, Guo Y, Duan Y, Hu R, Lu J, Li P. Knowledge structure and emerging trends of cognitive impairment induced by sleep deprivation: A bibliometric analysis based on CiteSpace and VOSviewer from 2000 to 2022. Medicine (Baltimore) 2023; 102:e34776. [PMID: 37800755 PMCID: PMC10552981 DOI: 10.1097/md.0000000000034776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/26/2023] [Indexed: 10/07/2023] Open
Abstract
This paper implements a bibliometric approach to investigate the research hotspots and future research directions in the relevant field literature. It also offers research ideas and methods for preventing and treating cognitive impairment induced by sleep deprivation in the clinical setting. The evolution of various clusters in the field is summarized through Citespace's projection function for keywords in the literature. CiteSpace and Vosviewer are utilized to analyze and visualize the attributes of the articles, including number of publications, citation frequency, country/region, institution, journal, authors, keywords, and references, from the 2280 publications obtained. A total of 2280 publications were collected, with the number of papers and citations in the field continuously increasing year by year. The most influential country in this field is the United States, and the University of Washington is the most influential institution. The most authoritative journal in the field is identified as SLEEP. Sleep deprivation, prefrontal cortex, and performance are the current topics of interest. The article with the strongest citation burst, lasting from 2015 to 2018, is "Sleep Drives Metabolite Clearance from the Adult Brain." The most influential article and co-cited reference, "Neurocognitive Consequences of Sleep Deprivation," highlights that sleep deprivation from various causes may lead to cognitive impairment. Future research should investigate all forms of cognitive impairment resulting from sleep deprivation. The findings of this study will assist researchers in improving their knowledge structure, identifying research hotspots, and revealing future directions in the field.
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Affiliation(s)
- Kai Yu
- The First Affiliated Hospital of Inner Mongolia Medical University, Inner Mongolia, China
- Urology Department, The First Hospital of Jilin University Changchun, Changchun, China
| | - Lei Hao
- Department of Pathophysiology, Inner Mongolia Medical University, Inner Mongolia, China
| | - Fan Bu
- The First Affiliated Hospital of Inner Mongolia Medical University, Inner Mongolia, China
| | - Yuanzhi Guo
- The First Affiliated Hospital of Inner Mongolia Medical University, Inner Mongolia, China
| | - Yaqi Duan
- The First Affiliated Hospital of Inner Mongolia Medical University, Inner Mongolia, China
| | - Rui Hu
- Urology Department, The First Hospital of Jilin University Changchun, Changchun, China
| | - Ji Lu
- Urology Department, The First Hospital of Jilin University Changchun, Changchun, China
| | - Peng Li
- The First Affiliated Hospital of Inner Mongolia Medical University, Inner Mongolia, China
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Mulyadi AW, Jung W, Oh K, Yoon JS, Lee KH, Suk HI. Estimating explainable Alzheimer's disease likelihood map via clinically-guided prototype learning. Neuroimage 2023; 273:120073. [PMID: 37037063 DOI: 10.1016/j.neuroimage.2023.120073] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 03/03/2023] [Accepted: 03/30/2023] [Indexed: 04/12/2023] Open
Abstract
Identifying Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristics make AD biomarker identification from structural brain imaging (e.g., structural MRI) scans quite challenging. Using clinically-guided prototype learning, we propose a novel deep-learning approach through eXplainable AD Likelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs. Specifically, we establish a set of topologically-aware prototypes onto the clusters of latent clinical features, uncovering an AD spectrum manifold. Considering this pseudo map as an enriched reference, we employ an estimating network to approximate the AD likelihood map over a 3D sMRI scan. Additionally, we promote the explainability of such a likelihood map by revealing a comprehensible overview from clinical and morphological perspectives. During the inference, this estimated likelihood map served as a substitute for unseen sMRI scans for effectively conducting the downstream task while providing thorough explainable states.
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Affiliation(s)
- Ahmad Wisnu Mulyadi
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Wonsik Jung
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Kwanseok Oh
- Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea
| | - Jee Seok Yoon
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Kun Ho Lee
- Gwangju Alzheimer's & Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea; Department of Biomedical Science, Chosun University, Gwangju 61452, Republic of Korea; Korea Brain Research Institute, Daegu 41062, Republic of Korea
| | - Heung-Il Suk
- Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
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Platero C. Categorical predictive and disease progression modeling in the early stage of Alzheimer's disease. J Neurosci Methods 2022; 374:109581. [PMID: 35346695 DOI: 10.1016/j.jneumeth.2022.109581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 03/02/2022] [Accepted: 03/21/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND A preclinical stage of Alzheimer's disease (AD) precedes the symptomatic phases of mild cognitive impairment (MCI) and dementia, which constitutes a window of opportunities for preventive therapies or delaying dementia onset. NEW METHOD We propose to use categorical predictive models based on survival analysis with longitudinal data which are capable of determining subsets of markers to classify cognitively unimpaired (CU) subjects who progress into MCI/dementia or not. Subsequently, the proposed combination of markers was used to construct disease progression models (DPMs), which reveal long-term pathological trajectories from short-term clinical data. The proposed methodology was applied to a population recruited by the ADNI. RESULTS A very small subset of standard MRI-based data, CSF markers and cognitive measures was used to predict CU-to-MCI/dementia progression. The longitudinal data of these selected markers were used to construct DPMs using the algorithms of growth models by alternating conditional expectation (GRACE) and the latent time joint mixed effects model (LTJMM). The results show that the natural history of the proposed cognitive decline classifies the subjects well according to the clinical groups and shows a moderate correlation between the conversion times and their estimates by the algorithms. COMPARISON WITH EXISTING METHODS Unlike the training of the DPM algorithms without preselection of the markers, here, it is proposed to construct and evaluate the DPMs using the subsets of markers defined by the categorical predictive models. CONCLUSIONS The estimates of the natural history of the proposed cognitive decline from GRACE were more robust than those using LTJMM. The transition from normal to cognitive decline is mostly associated with an increase in temporal atrophy, worsening of clinical scores and pTAU/Aβ. Furthermore, pTAU/Aβ, Everyday Cognition score and the normalized volume of the entorhinal cortex show alterations of more than 20% fifteen years before the onset of cognitive decline.
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Affiliation(s)
- Carlos Platero
- Health Science Technology Group, Technical University of Madrid, Ronda de Valencia 3, 28012 Madrid, Spain
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Khajehpiri B, Moghaddam HA, Forouzanfar M, Lashgari R, Ramos-Cejudo J, Osorio RS, Ardekani BA. Survival Analysis in Cognitively Normal Subjects and in Patients with Mild Cognitive Impairment Using a Proportional Hazards Model with Extreme Gradient Boosting Regression. J Alzheimers Dis 2021; 85:837-850. [PMID: 34864679 DOI: 10.3233/jad-215266] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Evaluating the risk of Alzheimer's disease (AD) in cognitively normal (CN) and patients with mild cognitive impairment (MCI) is extremely important. While MCI-to-AD progression risk has been studied extensively, few studies estimate CN-to-MCI conversion risk. The Cox proportional hazards (PH), a widely used survival analysis model, assumes a linear predictor-risk relationship. Generalizing the PH model to more complex predictor-risk relationships may increase risk estimation accuracy. OBJECTIVE The aim of this study was to develop a PH model using an Xgboost regressor, based on demographic, genetic, neuropsychiatric, and neuroimaging predictors to estimate risk of AD in patients with MCI, and the risk of MCI in CN subjects. METHODS We replaced the Cox PH linear model with an Xgboost regressor to capture complex interactions between predictors, and non-linear predictor-risk associations. We endeavored to limit model inputs to noninvasive and more widely available predictors in order to facilitate future applicability in a wider setting. RESULTS In MCI-to-AD (n = 882), the Xgboost model achieved a concordance index (C-index) of 84.5%. When the model was used for MCI risk prediction in CN (n = 100) individuals, the C-index was 73.3%. In both applications, the C-index was statistically significantly higher in the Xgboost in comparison to the Cox PH model. CONCLUSION Using non-linear regressors such as Xgboost improves AD dementia risk assessment in CN and MCI. It is possible to achieve reasonable risk stratification using predictors that are relatively low-cost in terms of time, invasiveness, and availability. Future strategies for improving AD dementia risk estimation are discussed.
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Affiliation(s)
- Boshra Khajehpiri
- Machine Vision and Medical Image Processing(MVMIP) Laboratory, Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Hamid Abrishami Moghaddam
- Machine Vision and Medical Image Processing(MVMIP) Laboratory, Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Mohamad Forouzanfar
- Machine Vision and Medical Image Processing(MVMIP) Laboratory, Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran.,Department of Systems Engineering, École deTechnologie Supérieure, Université duQuébec, Montreal, Quebec, Canada
| | - Reza Lashgari
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Jaime Ramos-Cejudo
- Department of Psychiatry, New York University(NYU) Grossman School of Medicine, New York, NY, USA
| | - Ricardo S Osorio
- TheNathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.,Department of Psychiatry, New York University(NYU) Grossman School of Medicine, New York, NY, USA
| | - Babak A Ardekani
- TheNathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
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Lu P, Colliot O. Multilevel Survival Modeling with Structured Penalties for Disease Prediction from Imaging Genetics data. IEEE J Biomed Health Inform 2021; 26:798-808. [PMID: 34329174 DOI: 10.1109/jbhi.2021.3100918] [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: 11/06/2022]
Abstract
This paper introduces a framework for disease prediction from multimodal genetic and imaging data. We propose a multilevel survival model which allows predicting the time of occurrence of a future disease state in patients initially exhibiting mild symptoms. This new multilevel setting allows modeling the interactions between genetic and imaging variables. This is in contrast with classical additive models which treat all modalities in the same manner and can result in undesirable elimination of specific modalities when their contributions are unbalanced. Moreover, the use of a survival model allows overcoming the limitations of previous approaches based on classification which consider a fixed time frame. Furthermore, we introduce specific penalties taking into account the structure of the different types of data, such as a group lasso penalty over the genetic modality and a L2-penalty over the imaging modality. Finally, we propose a fast optimization algorithm, based on a proximal gradient method. The approach was applied to the prediction of Alzheimer's disease (AD) among patients with mild cognitive impairment (MCI) based on genetic (single nucleotide polymorphisms - SNP) and imaging (anatomical MRI measures) data from the ADNI database. The experiments demonstrate the effectiveness of the method for predicting the time of conversion to AD. It revealed how genetic variants and brain imaging alterations interact in the prediction of future disease status. The approach is generic and could potentially be useful for the prediction of other diseases.
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Abuhmed T, El-Sappagh S, Alonso JM. Robust hybrid deep learning models for Alzheimer’s progression detection. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106688] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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