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Mehdipour Ghazi M, Selnes P, Timón-Reina S, Tecelão S, Ingala S, Bjørnerud A, Kirsebom BE, Fladby T, Nielsen M. Comparative analysis of multimodal biomarkers for amyloid-beta positivity detection in Alzheimer's disease cohorts. Front Aging Neurosci 2024; 16:1345417. [PMID: 38469163 PMCID: PMC10925621 DOI: 10.3389/fnagi.2024.1345417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 02/12/2024] [Indexed: 03/13/2024] Open
Abstract
Introduction Efforts to develop cost-effective approaches for detecting amyloid pathology in Alzheimer's disease (AD) have gained significant momentum with a focus on biomarker classification. Recent research has explored non-invasive and readily accessible biomarkers, including magnetic resonance imaging (MRI) biomarkers and some AD risk factors. Methods In this comprehensive study, we leveraged a diverse dataset, encompassing participants with varying cognitive statuses from multiple sources, including cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and our in-house Dementia Disease Initiation (DDI) cohort. As brain amyloid plaques have been proposed as sufficient for AD diagnosis, our primary aim was to assess the effectiveness of multimodal biomarkers in identifying amyloid plaques, using deep machine learning methodologies. Results Our findings underscore the robustness of the utilized methods in detecting amyloid beta positivity across multiple cohorts. Additionally, we investigated the potential of demographic data to enhance MRI-based amyloid detection. Notably, the inclusion of demographic risk factors significantly improved our models' ability to detect amyloid-beta positivity, particularly in early-stage cases, exemplified by an average area under the ROC curve of 0.836 in the unimpaired DDI cohort. Discussion These promising, non-invasive, and cost-effective predictors of MRI biomarkers and demographic variables hold the potential for further refinement through considerations like APOE genotype and plasma markers.
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Affiliation(s)
- Mostafa Mehdipour Ghazi
- Department of Computer Science, Pioneer Centre for Artificial Intelligence, University of Copenhagen, Copenhagen, Denmark
| | - Per Selnes
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
| | | | - Sandra Tecelão
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | - Silvia Ingala
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Atle Bjørnerud
- Department of Physics, University of Oslo, Oslo, Norway
- Unit for Computational Radiology and Artificial Intelligence, Oslo University Hospital, Oslo, Norway
| | - Bjørn-Eivind Kirsebom
- Department of Neurology, University Hospital of North Norway, Tromsø, Norway
- Department of Psychology, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway
| | - Tormod Fladby
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
| | - Mads Nielsen
- Department of Computer Science, Pioneer Centre for Artificial Intelligence, University of Copenhagen, Copenhagen, Denmark
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Saint-Jalmes M, Fedyashov V, Beck D, Baldwin T, Faux NG, Bourgeat P, Fripp J, Masters CL, Goudey B. Disease progression modelling of Alzheimer's disease using probabilistic principal components analysis. Neuroimage 2023; 278:120279. [PMID: 37454702 DOI: 10.1016/j.neuroimage.2023.120279] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/27/2023] [Accepted: 07/12/2023] [Indexed: 07/18/2023] Open
Abstract
The recent biological redefinition of Alzheimer's Disease (AD) has spurred the development of statistical models that relate changes in biomarkers with neurodegeneration and worsening condition linked to AD. The ability to measure such changes may facilitate earlier diagnoses for affected individuals and help in monitoring the evolution of their condition. Amongst such statistical tools, disease progression models (DPMs) are quantitative, data-driven methods that specifically attempt to describe the temporal dynamics of biomarkers relevant to AD. Due to the heterogeneous nature of this disease, with patients of similar age experiencing different AD-related changes, a challenge facing longitudinal mixed-effects-based DPMs is the estimation of patient-realigning time-shifts. These time-shifts are indispensable for meaningful biomarker modelling, but may impact fitting time or vary with missing data in jointly estimated models. In this work, we estimate an individual's progression through Alzheimer's disease by combining multiple biomarkers into a single value using a probabilistic formulation of principal components analysis. Our results show that this variable, which summarises AD through observable biomarkers, is remarkably similar to jointly estimated time-shifts when we compute our scores for the baseline visit, on cross-sectional data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Reproducing the expected properties of clinical datasets, we confirm that estimated scores are robust to missing data or unavailable biomarkers. In addition to cross-sectional insights, we can model the latent variable as an individual progression score by repeating estimations at follow-up examinations and refining long-term estimates as more data is gathered, which would be ideal in a clinical setting. Finally, we verify that our score can be used as a pseudo-temporal scale instead of age to ignore some patient heterogeneity in cohort data and highlight the general trend in expected biomarker evolution in affected individuals.
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Affiliation(s)
- Martin Saint-Jalmes
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia.
| | - Victor Fedyashov
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Daniel Beck
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; School of Computing and Information Systems, The University of Melbourne, Australia
| | - Timothy Baldwin
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; School of Computing and Information Systems, The University of Melbourne, Australia; Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
| | - Noel G Faux
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Australia; Melbourne Data Analytics Platform, The University of Melbourne, Australia
| | | | - Jurgen Fripp
- CSIRO Health and Biosecurity, Brisbane, Australia
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Benjamin Goudey
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Australia
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Hao X, Li X, Zhang GQ, Tao C, Schulz P, Cui L. An ontology-based approach for harmonization and cross-cohort query of Alzheimer's disease data resources. BMC Med Inform Decis Mak 2023; 23:151. [PMID: 37542312 PMCID: PMC10401730 DOI: 10.1186/s12911-023-02250-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 07/26/2023] [Indexed: 08/06/2023] Open
Abstract
BACKGROUND In the United States, the National Alzheimer's Coordinating Center (NACC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) are two major data sharing resources for Alzheimer's Disease (AD) research. NACC and ADNI strive to make their data more FAIR (findable, interoperable, accessible and reusable) for the broader research community. However, there is limited work harmonizing and supporting cross-cohort interoperability of the two resources. METHOD In this paper, we leverage an ontology-based approach to harmonize data elements in the two resources and develop a web-based query system to search patient cohorts across the two resources. We first mapped data elements across NACC and ADNI, and performed value harmonization for the mapped data elements with inconsistent permissible values. Then we built an Alzheimer's Disease Data Element Ontology (ADEO) to model the mapped data elements in NACC and ADNI. We further developed a prototype cross-cohort query system to search patient cohorts across NACC and ADNI. RESULTS After manual review, we found 172 mappings between NACC and ADNI. These 172 mappings were further used to construct common concepts in ADEO. Our data element mapping and harmonization resulted in five files storing common concepts, variables in NACC and ADNI, mappings between variables and common concepts, permissible values of categorical type data elements, and coding inconsistency harmonization, respectively. Our cross-cohort query system consists of three core architectural elements: a web-based interface, an advanced query engine, and a backend MongoDB database. CONCLUSIONS In this work, ADEO has been specifically designed to facilitate data harmonization and cross-cohort query of NACC and ADNI data resources. Although our prototype cross-cohort query system was developed for exploring NACC and ADNI, its backend and frontend framework has been designed and implemented to be generally applicable to other domains for querying patient cohorts from multiple heterogeneous data sources.
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Affiliation(s)
- Xubing Hao
- McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - Xiaojin Li
- Department of Neurology, McGovern School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX USA
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - Guo-Qiang Zhang
- McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
- Department of Neurology, McGovern School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX USA
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - Cui Tao
- McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - Paul E. Schulz
- Department of Neurology, McGovern School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - The Alzheimer’s Disease Neuroimaging Initiative
- McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
- Department of Neurology, McGovern School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX USA
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - Licong Cui
- McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX USA
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Haulath K, Mohamed Basheer KP. TT self-weighted Deep-AD 3-Net: An AD stage and risk prediction. International Journal of Healthcare Management 2023. [DOI: 10.1080/20479700.2023.2175414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Affiliation(s)
- K. Haulath
- Department of Computer Science, EMEA College of Arts and Science, Kondotty, India
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Tarawneh R, Kasper RS, Sanford J, Phuah C, Hassenstab J, Cruchaga C. Vascular endothelial-cadherin as a marker of endothelial injury in preclinical Alzheimer disease. Ann Clin Transl Neurol 2022; 9:1926-1940. [PMID: 36342663 PMCID: PMC9735377 DOI: 10.1002/acn3.51685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 10/02/2022] [Accepted: 10/10/2022] [Indexed: 11/09/2022] Open
Abstract
OBJECTIVE Endothelial dysfunction is an early and prevalent pathology in Alzheimer disease (AD). We here investigate the value of vascular endothelial-cadherin (VEC) as a cerebrospinal fluid (CSF) marker of endothelial injury in preclinical AD. METHODS Cognitively normal participants (Clinical Dementia Rating [CDR] 0) from the Knight Washington University-ADRC were included in this study (n = 700). Preclinical Alzheimer's Cognitive Composite (PACC) scores, CSF VEC, tau, p-tau181, Aβ42/Aβ40, neurofilament light-chain (NFL) levels, and magnetic resonance imaging (MRI) assessments of white matter injury (WMI) were obtained from all participants. A subset of participants underwent brain amyloid imaging using positron emission tomography (amyloid-PET) (n = 534). Linear regression examined associations of CSF VEC with PACC and individual cognitive scores in preclinical AD. Mediation analyses examined whether CSF VEC mediated effects of CSF amyloid and tau markers on cognition in preclinical AD. RESULTS CSF VEC levels significantly correlated with PACC and individual cognitive scores in participants with amyloid (A+T±N±; n = 558) or those with amyloid and tau pathologies (A+T+N±; n = 259), after adjusting for covariates. CSF VEC also correlated with CSF measures of amyloid, tau, and neurodegeneration and global amyloid burden on amyloid-PET scans in our cohort. Importantly, our findings suggest that CSF VEC mediates associations of CSF Aβ42/Aβ40, p-tau181, and global amyloid burden with cognitive outcomes in preclinical AD. INTERPRETATION Our results support the utility of CSF VEC as a marker of endothelial injury in AD and highlight the importance of endothelial injury as an early pathology that contributes to cognitive impairment in even the earliest preclinical stages.
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Affiliation(s)
- Rawan Tarawneh
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA,Center for Memory and AgingUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Rachel S. Kasper
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Jessie Sanford
- Department of PsychiatryWashington University in St LouisSt. LouisMissouriUSA,NeuroGenomics and Informatics CenterWashington University in St LouisMissouriUSA
| | - Chia‐Ling Phuah
- NeuroGenomics and Informatics CenterWashington University in St LouisMissouriUSA,Department of NeurologyWashington University in St LouisSt. LouisMissouriUSA
| | - Jason Hassenstab
- Department of PsychologyWashington University in St LouisSt. LouisMissouriUSA
| | - Carlos Cruchaga
- Department of PsychiatryWashington University in St LouisSt. LouisMissouriUSA,NeuroGenomics and Informatics CenterWashington University in St LouisMissouriUSA
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Ghazi MM, Sorensen L, Ourselin S, Nielsen M. CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning From Sporadic Temporal Data. IEEE Trans Neural Netw Learn Syst 2022; PP:792-802. [PMID: 35666790 DOI: 10.1109/tnnls.2022.3177366] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Learning temporal patterns from multivariate longitudinal data is challenging especially in cases when data is sporadic, as often seen in, e.g., healthcare applications where the data can suffer from irregularity and asynchronicity as the time between consecutive data points can vary across features and samples, hindering the application of existing deep learning models that are constructed for complete, evenly spaced data with fixed sequence lengths. In this article, a novel deep learning-based model is developed for modeling multiple temporal features in sporadic data using an integrated deep learning architecture based on a recurrent neural network (RNN) unit and a continuous-time autoregressive (CAR) model. The proposed model, called CARRNN, uses a generalized discrete-time autoregressive (AR) model that is trainable end-to-end using neural networks modulated by time lags to describe the changes caused by the irregularity and asynchronicity. It is applied to time-series regression and classification tasks for Alzheimer's disease progression modeling, intensive care unit (ICU) mortality rate prediction, human activity recognition, and event-based digit recognition, where the proposed model based on a gated recurrent unit (GRU) in all cases achieves significantly better predictive performance than the state-of-the-art methods using RNNs, GRUs, and long short-term memory (LSTM) networks.
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Lim BY, Lai KW, Haiskin K, Kulathilake KASH, Ong ZC, Hum YC, Dhanalakshmi S, Wu X, Zuo X. Deep Learning Model for Prediction of Progressive Mild Cognitive Impairment to Alzheimer’s Disease Using Structural MRI. Front Aging Neurosci 2022; 14:876202. [PMID: 35721012 PMCID: PMC9201448 DOI: 10.3389/fnagi.2022.876202] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/11/2022] [Indexed: 01/23/2023] Open
Abstract
Alzheimer’s disease (AD) is an irreversible neurological disorder that affects the vast majority of dementia cases, leading patients to experience gradual memory loss and cognitive function decline. Despite the lack of a cure, early detection of Alzheimer’s disease permits the provision of preventive medication to slow the disease’s progression. The objective of this project is to develop a computer-aided method based on a deep learning model to distinguish Alzheimer’s disease (AD) from cognitively normal and its early stage, mild cognitive impairment (MCI), by just using structural MRI (sMRI). To attain this purpose, we proposed a multiclass classification method based on 3D T1-weight brain sMRI images from the ADNI database. Axial brain images were extracted from 3D MRI and fed into the convolutional neural network (CNN) for multiclass classification. Three separate models were tested: a CNN built from scratch, VGG-16, and ResNet-50. As a feature extractor, the VGG-16 and ResNet-50 convolutional bases trained on the ImageNet dataset were employed. To achieve classification, a new densely connected classifier was implemented on top of the convolutional bases.
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Affiliation(s)
- Bing Yan Lim
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- *Correspondence: Khin Wee Lai,
| | - Khairunnisa Haiskin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | | | - Zhi Chao Ong
- Department of Mechanical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Yan Chai Hum
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Petaling Jaya, Malaysia
| | - Samiappan Dhanalakshmi
- Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, College of Engineering and Technology, SRM Institute of Science and Technology, Chennai, India
| | - Xiang Wu
- School of Medical Information & Engineering, Xuzhou Medical University, Xuzhou, China
| | - Xiaowei Zuo
- Department of Psychiatry, The Affiliated Xuzhou Oriental Hospital of Xuzhou Medical University, Xuzhou, China
- Xiaowei Zuo,
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