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Burkhart MC, Lee LY, Vaghari D, Toh AQ, Chong E, Chen C, Tiňo P, Kourtzi Z. Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction. Sci Rep 2024; 14:10755. [PMID: 38729989 PMCID: PMC11087538 DOI: 10.1038/s41598-024-60914-w] [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: 12/13/2023] [Accepted: 04/29/2024] [Indexed: 05/12/2024] Open
Abstract
Predicting the course of neurodegenerative disorders early has potential to greatly improve clinical management and patient outcomes. A key challenge for early prediction in real-world clinical settings is the lack of labeled data (i.e., clinical diagnosis). In contrast to supervised classification approaches that require labeled data, we propose an unsupervised multimodal trajectory modeling (MTM) approach based on a mixture of state space models that captures changes in longitudinal data (i.e., trajectories) and stratifies individuals without using clinical diagnosis for model training. MTM learns the relationship between states comprising expensive, invasive biomarkers (β-amyloid, grey matter density) and readily obtainable cognitive observations. MTM training on trajectories stratifies individuals into clinically meaningful clusters more reliably than MTM training on baseline data alone and is robust to missing data (i.e., cognitive data alone or single assessments). Extracting an individualized cognitive health index (i.e., MTM-derived cluster membership index) allows us to predict progression to AD more precisely than standard clinical assessments (i.e., cognitive tests or MRI scans alone). Importantly, MTM generalizes successfully from research cohort to real-world clinical data from memory clinic patients with missing data, enhancing the clinical utility of our approach. Thus, our multimodal trajectory modeling approach provides a cost-effective and non-invasive tool for early dementia prediction without labeled data (i.e., clinical diagnosis) with strong potential for translation to clinical practice.
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Affiliation(s)
- Michael C Burkhart
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Liz Y Lee
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Delshad Vaghari
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK
| | - An Qi Toh
- Department of Pharmacology, Memory, Aging, and Cognition Center, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Eddie Chong
- Department of Pharmacology, Memory, Aging, and Cognition Center, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Christopher Chen
- Department of Pharmacology, Memory, Aging, and Cognition Center, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Peter Tiňo
- School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK
| | - Zoe Kourtzi
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK.
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Chen P, Zhang S, Zhao K, Kang X, Rittman T, Liu Y. Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review. Brain Res 2024; 1823:148675. [PMID: 37979603 DOI: 10.1016/j.brainres.2023.148675] [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: 08/02/2023] [Revised: 10/19/2023] [Accepted: 11/07/2023] [Indexed: 11/20/2023]
Abstract
Neurodegenerative diseases are associated with heterogeneity in genetics, pathology, and clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical prognosis and stratifying patients for disease modifying treatments. Recently, data-driven methods based on neuroimaging have been applied to investigate the subtyping of neurodegenerative disease, helping to disentangle this heterogeneity. We reviewed brain-based subtyping studies in aging and representative neurodegenerative diseases, including Alzheimer's disease, mild cognitive impairment, frontotemporal dementia, and Lewy body dementia, from January 2000 to November 2022. We summarized clustering methods, validation, robustness, reproducibility, and clinical relevance of 71 eligible studies in the present study. We found vast variations in approaches between studies, including ten neuroimaging modalities, 24 cluster algorithms, and 41 methods of cluster number determination. The clinical relevance of subtyping studies was evaluated by summarizing the analysis method of clinical measurements, showing a relatively low clinical utility in the current studies. Finally, we conclude that future studies of heterogeneity in neurodegenerative disease should focus on validation, comparison between subtyping approaches, and prioritise clinical utility.
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Affiliation(s)
- Pindong Chen
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Department of Clinical Neurosciences, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Shirui Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiaopeng Kang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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Tekkesinoglu S, Pudas S. Explaining graph convolutional network predictions for clinicians-An explainable AI approach to Alzheimer's disease classification. Front Artif Intell 2024; 6:1334613. [PMID: 38259822 PMCID: PMC10801225 DOI: 10.3389/frai.2023.1334613] [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: 11/07/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction Graph-based representations are becoming more common in the medical domain, where each node defines a patient, and the edges signify associations between patients, relating individuals with disease and symptoms in a node classification task. In this study, a Graph Convolutional Networks (GCN) model was utilized to capture differences in neurocognitive, genetic, and brain atrophy patterns that can predict cognitive status, ranging from Normal Cognition (NC) to Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD), on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Elucidating model predictions is vital in medical applications to promote clinical adoption and establish physician trust. Therefore, we introduce a decomposition-based explanation method for individual patient classification. Methods Our method involves analyzing the output variations resulting from decomposing input values, which allows us to determine the degree of impact on the prediction. Through this process, we gain insight into how each feature from various modalities, both at the individual and group levels, contributes to the diagnostic result. Given that graph data contains critical information in edges, we studied relational data by silencing all the edges of a particular class, thereby obtaining explanations at the neighborhood level. Results Our functional evaluation showed that the explanations remain stable with minor changes in input values, specifically for edge weights exceeding 0.80. Additionally, our comparative analysis against SHAP values yielded comparable results with significantly reduced computational time. To further validate the model's explanations, we conducted a survey study with 11 domain experts. The majority (71%) of the responses confirmed the correctness of the explanations, with a rating of above six on a 10-point scale for the understandability of the explanations. Discussion Strategies to overcome perceived limitations, such as the GCN's overreliance on demographic information, were discussed to facilitate future adoption into clinical practice and gain clinicians' trust as a diagnostic decision support system.
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Affiliation(s)
| | - Sara Pudas
- Department of Integrative Medical Biology (IMB), Umeå University, Umeå, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
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Borchert RJ, Azevedo T, Badhwar A, Bernal J, Betts M, Bruffaerts R, Burkhart MC, Dewachter I, Gellersen HM, Low A, Lourida I, Machado L, Madan CR, Malpetti M, Mejia J, Michopoulou S, Muñoz-Neira C, Pepys J, Peres M, Phillips V, Ramanan S, Tamburin S, Tantiangco HM, Thakur L, Tomassini A, Vipin A, Tang E, Newby D, Ranson JM, Llewellyn DJ, Veldsman M, Rittman T. Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review. Alzheimers Dement 2023; 19:5885-5904. [PMID: 37563912 DOI: 10.1002/alz.13412] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 05/18/2023] [Accepted: 06/02/2023] [Indexed: 08/12/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. METHODS We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. RESULTS A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DISCUSSION The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HIGHLIGHTS There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.
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Affiliation(s)
- Robin J Borchert
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Tiago Azevedo
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - AmanPreet Badhwar
- Department of Pharmacology and Physiology, University of Montreal, Montreal, Canada
- Centre de recherche de l'Institut Universitaire de Gériatrie (CRIUGM), Montreal, Canada
| | - Jose Bernal
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Matthew Betts
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences, University of Magdeburg, Magdeburg, Germany
| | - Rose Bruffaerts
- Computational Neurology, Experimental Neurobiology Unit, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
| | | | - Ilse Dewachter
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Audrey Low
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | | | - Luiza Machado
- Department of Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Maura Malpetti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Jhony Mejia
- Department of Biomedical Engineering, Universidad de Los Andes, Bogotá, Colombia
| | - Sofia Michopoulou
- Imaging Physics, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Carlos Muñoz-Neira
- Research into Memory, Brain sciences and dementia Group (ReMemBr Group), Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Artificial Intelligence & Computational Neuroscience Group (AICN Group), Sheffield Institute for Translational Neuroscience (SITraN), Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Jack Pepys
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Marion Peres
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - Siddharth Ramanan
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | - Lokendra Thakur
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, UK
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Alessandro Tomassini
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | | | - Eugene Tang
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Danielle Newby
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- Alan Turing Institute, London, UK
| | - Michele Veldsman
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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Winchester LM, Harshfield EL, Shi L, Badhwar A, Khleifat AA, Clarke N, Dehsarvi A, Lengyel I, Lourida I, Madan CR, Marzi SJ, Proitsi P, Rajkumar AP, Rittman T, Silajdžić E, Tamburin S, Ranson JM, Llewellyn DJ. Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia. Alzheimers Dement 2023; 19:5860-5871. [PMID: 37654029 PMCID: PMC10840606 DOI: 10.1002/alz.13390] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/13/2023] [Accepted: 06/19/2023] [Indexed: 09/02/2023]
Abstract
With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.
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Affiliation(s)
| | - Eric L Harshfield
- Department of Clinical Neurosciences, Stroke Research Group, University of Cambridge, Cambridge, UK
| | - Liu Shi
- Novo Nordisk Research Centre Oxford (NNRCO), Headington, UK
| | - AmanPreet Badhwar
- Département de Pharmacologie et Physiologie, Institut de Génie Biomédical, Faculté de Médecine, Université de Montréal, Montreal, Canada
- Centre de recherche de l'Institut Universitaire de Gériatrie (CRIUGM), Montreal, Canada
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Natasha Clarke
- Centre de recherche de l'Institut Universitaire de Gériatrie (CRIUGM), Montreal, Canada
| | - Amir Dehsarvi
- School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Imre Lengyel
- Wellcome-Wolfson Institute of Experimental Medicine, Queen's University, Belfast, UK
| | - Ilianna Lourida
- Health and Community Sciences, University of Exeter Medical School, Exeter, UK
| | | | - Sarah J Marzi
- UK Dementia Research Institute at Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Petroula Proitsi
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Anto P Rajkumar
- Institute of Mental Health, Mental Health and Clinical Neurosciences academic unit, University of Nottingham, Nottingham, UK, Mental health services of older people, Nottinghamshire healthcare NHS foundation trust, Nottingham, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Edina Silajdžić
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Janice M Ranson
- Health and Community Sciences, University of Exeter Medical School, Exeter, UK
| | - David J Llewellyn
- Health and Community Sciences, University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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Lamontagne-Caron R, Desrosiers P, Potvin O, Doyon N, Duchesne S. Predicting cognitive decline in a low-dimensional representation of brain morphology. Sci Rep 2023; 13:16793. [PMID: 37798311 PMCID: PMC10556003 DOI: 10.1038/s41598-023-43063-4] [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: 11/07/2022] [Accepted: 09/19/2023] [Indexed: 10/07/2023] Open
Abstract
Identifying early signs of neurodegeneration due to Alzheimer's disease (AD) is a necessary first step towards preventing cognitive decline. Individual cortical thickness measures, available after processing anatomical magnetic resonance imaging (MRI), are sensitive markers of neurodegeneration. However, normal aging cortical decline and high inter-individual variability complicate the comparison and statistical determination of the impact of AD-related neurodegeneration on trajectories. In this paper, we computed trajectories in a 2D representation of a 62-dimensional manifold of individual cortical thickness measures. To compute this representation, we used a novel, nonlinear dimension reduction algorithm called Uniform Manifold Approximation and Projection (UMAP). We trained two embeddings, one on cortical thickness measurements of 6237 cognitively healthy participants aged 18-100 years old and the other on 233 mild cognitively impaired (MCI) and AD participants from the longitudinal database, the Alzheimer's Disease Neuroimaging Initiative database (ADNI). Each participant had multiple visits ([Formula: see text]), one year apart. The first embedding's principal axis was shown to be positively associated ([Formula: see text]) with participants' age. Data from ADNI is projected into these 2D spaces. After clustering the data, average trajectories between clusters were shown to be significantly different between MCI and AD subjects. Moreover, some clusters and trajectories between clusters were more prone to host AD subjects. This study was able to differentiate AD and MCI subjects based on their trajectory in a 2D space with an AUC of 0.80 with 10-fold cross-validation.
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Affiliation(s)
- Rémi Lamontagne-Caron
- Département de médecine, Université Laval, Quebec, QC, G1V 0A6, Canada.
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada.
| | - Patrick Desrosiers
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Quebec, QC, G1V 0A6, Canada
- Département de physique, de génie physique et d'optique, Université Laval, Quebec, QC, G1V 0A6, Canada
| | | | - Nicolas Doyon
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Quebec, QC, G1V 0A6, Canada
- Département de mathématiques et de statistique, Université Laval, Quebec, QC, G1V 0A6, Canada
| | - Simon Duchesne
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Département de radiologie et médecine nucléaire, Université Laval, Quebec, QC, G1V 0A6, Canada
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Ge X, Cui K, Qin Y, Chen D, Han H, Yu H. Screening strategies and dynamic risk prediction models for Alzheimer's disease. J Psychiatr Res 2023; 166:92-99. [PMID: 37757706 DOI: 10.1016/j.jpsychires.2023.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/16/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND Characterizing the progression from Mild cognitive impairment (MCI) to Alzheimer's disease (AD) is essential for early AD prevention and targeted intervention. Our goal was to construct precise screening schemes for individuals with different risk of AD and to establish prognosis models for them. METHODS We constructed a retrospective cohort by reviewing individuals with baseline diagnosis of MCI and at least one follow-up visits between November 2005 and May 2021. They were stratified into high-risk and low-risk groups with longitudinal cognitive trajectory. Then, we established a screening framework and obtained optimal screening strategies for two risk groups. Cox and random survival forest (RSF) models were developed for dynamic prognosis prediction. RESULTS In terms of screening strategies, the combination of Clinical Dementia Rating Sum of Boxes (CDRSB) and hippocampus volume was recommended for the high-risk MCI group, while the combination of Alzheimer's Disease Assessment Scale Cognitive 13 items (ADAS13) and FAQ was recommended for low-risk MCI group. The concordance index (C-index) of the Cox model for the high-risk group was 0.844 (95% CI: 0.815-0.873) and adjustments for demographic information and APOE ε4. The RSF model incorporating longitudinal ADAS13, FAQ, and demographic information and APOE ε4 performed for the low-risk group. CONCLUSION This precise screening scheme will optimize allocation of medical resources and reduce the economic burden on individuals with low risk of MCI. Moreover, dynamic prognosis models may be helpful for early identification of individuals at risk and clinical decisions, which will promote the secondary prevention of AD.
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Affiliation(s)
- Xiaoyan Ge
- Department of Health Statistics, School of Public Health, Jinzhou Medical University, 40 SongPo Road, Jinzhou, China.
| | - Kai Cui
- Department of Health Statistics, School of Public Health, Jinzhou Medical University, 40 SongPo Road, Jinzhou, China.
| | - Yao Qin
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China.
| | - Durong Chen
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China.
| | - Hongjuan Han
- Department of Mathematics, School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China.
| | - Hongmei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China; Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, 56 XinJian South Road, Taiyuan, China.
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Li R, Harshfield EL, Bell S, Burkhart M, Tuladhar AM, Hilal S, Tozer DJ, Chappell FM, Makin SD, Lo JW, Wardlaw JM, de Leeuw FE, Chen C, Kourtzi Z, Markus HS. Predicting incident dementia in cerebral small vessel disease: comparison of machine learning and traditional statistical models. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2023; 5:100179. [PMID: 37593075 PMCID: PMC10428032 DOI: 10.1016/j.cccb.2023.100179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/07/2023] [Accepted: 08/08/2023] [Indexed: 08/19/2023]
Abstract
Background Cerebral small vessel disease (SVD) contributes to 45% of dementia cases worldwide, yet we lack a reliable model for predicting dementia in SVD. Past attempts largely relied on traditional statistical approaches. Here, we investigated whether machine learning (ML) methods improved prediction of incident dementia in SVD from baseline SVD-related features over traditional statistical methods. Methods We included three cohorts with varying SVD severity (RUN DMC, n = 503; SCANS, n = 121; HARMONISATION, n = 265). Baseline demographics, vascular risk factors, cognitive scores, and magnetic resonance imaging (MRI) features of SVD were used for prediction. We conducted both survival analysis and classification analysis predicting 3-year dementia risk. For each analysis, several ML methods were evaluated against standard Cox or logistic regression. Finally, we compared the feature importance ranked by different models. Results We included 789 participants without missing data in the survival analysis, amongst whom 108 (13.7%) developed dementia during a median follow-up of 5.4 years. Excluding those censored before three years, we included 750 participants in the classification analysis, amongst whom 48 (6.4%) developed dementia by year 3. Comparing statistical and ML models, only regularised Cox/logistic regression outperformed their statistical counterparts overall, but not significantly so in survival analysis. Baseline cognition was highly predictive, and global cognition was the most important feature. Conclusions When using baseline SVD-related features to predict dementia in SVD, the ML survival or classification models we evaluated brought little improvement over traditional statistical approaches. The benefits of ML should be evaluated with caution, especially given limited sample size and features.
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Affiliation(s)
- Rui Li
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, United Kingdom of Great Britain and Northern Ireland
| | - Eric L. Harshfield
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, United Kingdom of Great Britain and Northern Ireland
- Heart and Lung Research Institute, University of Cambridge, United Kingdom of Great Britain and Northern Ireland
| | - Steven Bell
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, United Kingdom of Great Britain and Northern Ireland
- Heart and Lung Research Institute, University of Cambridge, United Kingdom of Great Britain and Northern Ireland
- Precision Breast Cancer Institute, Department of Oncology, University of Cambridge, United Kingdom of Great Britain and Northern Ireland
| | - Michael Burkhart
- Adaptive Brain Lab, Department of Psychology, University of Cambridge, United Kingdom of Great Britain and Northern Ireland
| | - Anil M. Tuladhar
- Department of Neurology, Donders Centre for Medical Neuroscience, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Saima Hilal
- Memory Aging and Cognition Centre, Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Daniel J. Tozer
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, United Kingdom of Great Britain and Northern Ireland
| | - Francesca M. Chappell
- Centre for Clinical Brain Sciences, University of Edinburgh, United Kingdom of Great Britain and Northern Ireland
| | - Stephen D.J. Makin
- Centre for Rural Health, Institute of Applied Health Sciences, University of Aberdeen, United Kingdom of Great Britain and Northern Ireland
| | - Jessica W. Lo
- Centre for Healthy Brain Ageing, University of New South Wales, Sydney, Australia
| | - Joanna M. Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, United Kingdom of Great Britain and Northern Ireland
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Centre for Medical Neuroscience, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Christopher Chen
- Memory Aging and Cognition Centre, Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Zoe Kourtzi
- Adaptive Brain Lab, Department of Psychology, University of Cambridge, United Kingdom of Great Britain and Northern Ireland
| | - Hugh S. Markus
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, United Kingdom of Great Britain and Northern Ireland
- Heart and Lung Research Institute, University of Cambridge, United Kingdom of Great Britain and Northern Ireland
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Zhang W, Zheng X, Tang Z, Wang H, Li R, Xie Z, Yan J, Zhang X, Yu Q, Wang F, Li Y. Combination of Paper and Electronic Trail Making Tests for Automatic Analysis of Cognitive Impairment: Development and Validation Study. J Med Internet Res 2023; 25:e42637. [PMID: 37294606 PMCID: PMC10337362 DOI: 10.2196/42637] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/06/2022] [Accepted: 05/23/2023] [Indexed: 06/10/2023] Open
Abstract
BACKGROUND Computer-aided detection, used in the screening and diagnosing of cognitive impairment, provides an objective, valid, and convenient assessment. Particularly, digital sensor technology is a promising detection method. OBJECTIVE This study aimed to develop and validate a novel Trail Making Test (TMT) using a combination of paper and electronic devices. METHODS This study included community-dwelling older adult individuals (n=297), who were classified into (1) cognitively healthy controls (HC; n=100 participants), (2) participants diagnosed with mild cognitive impairment (MCI; n=98 participants), and (3) participants with Alzheimer disease (AD; n=99 participants). An electromagnetic tablet was used to record each participant's hand-drawn stroke. A sheet of A4 paper was placed on top of the tablet to maintain the traditional interaction style for participants who were not familiar or comfortable with electronic devices (such as touchscreens). In this way, all participants were instructed to perform the TMT-square and circle. Furthermore, we developed an efficient and interpretable cognitive impairment-screening model to automatically analyze cognitive impairment levels that were dependent on demographic characteristics and time-, pressure-, jerk-, and template-related features. Among these features, novel template-based features were based on a vector quantization algorithm. First, the model identified a candidate trajectory as the standard answer (template) from the HC group. The distance between the recorded trajectories and reference was computed as an important evaluation index. To verify the effectiveness of our method, we compared the performance of a well-trained machine learning model using the extracted evaluation index with conventional demographic characteristics and time-related features. The well-trained model was validated using follow-up data (HC group: n=38; MCI group: n=32; and AD group: n=22). RESULTS We compared 5 candidate machine learning methods and selected random forest as the ideal model with the best performance (accuracy: 0.726 for HC vs MCI, 0.929 for HC vs AD, and 0.815 for AD vs MCI). Meanwhile, the well-trained classifier achieved better performance than the conventional assessment method, with high stability and accuracy of the follow-up data. CONCLUSIONS The study demonstrated that a model combining both paper and electronic TMTs increases the accuracy of evaluating participants' cognitive impairment compared to conventional paper-based feature assessment.
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Affiliation(s)
- Wei Zhang
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiaoran Zheng
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Zeshen Tang
- Department of Computer Science and Technolgy, College of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Haoran Wang
- Department of Computer Science and Technolgy, College of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Renren Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Zengmai Xie
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jiaxin Yan
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiaochen Zhang
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Qing Yu
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Fei Wang
- Department of Neurosurgery, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yunxia Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 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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11
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Sha Z, Zhao Z, Li N, Xiao S, Li O, Zhang J, Li Z, Xu J. Efficacy and safety of Yi Shen Fang granules in elderly people with MCI: study protocol for a multicentre, randomized, double-blind, parallel-group, controlled trial. BMC Complement Med Ther 2023; 23:101. [PMID: 37013520 PMCID: PMC10068223 DOI: 10.1186/s12906-023-03940-x] [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: 02/07/2023] [Accepted: 03/24/2023] [Indexed: 04/05/2023] Open
Abstract
BACKGROUND Mild cognitive impairment (MCI) is a transitional state between normal ageing and dementia. Most MCI patients will progress to dementia within 5 years; therefore, early intervention for MCI is important for delaying the occurrence and progression of dementia. Yi Shen Fang (YSF) granules are a promising traditional Chinese medicine (TCM) treatment that shows great neuroprotective potential against cognitive impairment, as evidenced in clinical and basic studies. This trial aims to systematically evaluate the efficacy and safety of YSF granules in elderly people with MCI. METHODS This study is a multicentre, randomized, double-blind, parallel-group, controlled trial. Based on the results of previous clinical trials, 280 elderly patients with MCI will be randomly divided into a treatment group (n = 140) and control group (n = 140). The study will last 33 weeks, including 1 week of screening, 8 weeks of intervention, and 24 weeks of follow-up. The primary outcomes will be the changes in Montreal Cognitive Assessment (MoCA) and Memory and Executive Screening (MES) scores before and after the intervention. The secondary outcome measures will be homocysteine (HCY) levels, Functional Assessment Questionnaire (FAQ) scores and event-related potential (ERP) detection in typical cases. The TCM symptom scale is a combined measure of syndrome differentiation and treatment. During this study, the classifications and characteristics of adverse events, the times of occurrence and disappearance, the measures of treatment, their impact on the primary disease, and outcomes will be reported truthfully. DISCUSSION This study will provide valuable clinical evidence that YSF can help to improve the cognitive function of elderly people with MCI, and the results will be disseminated via conferences and publications. TRIAL REGISTRATION Chinese Clinical Trial Registry, ChiCTR2000036807. Registered on August 25, 2020.
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Affiliation(s)
- Zhongwei Sha
- Department of Mental Diseases, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zhenghao Zhao
- Department of Mental Diseases, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Nana Li
- Department of Mental Diseases, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Shuyun Xiao
- Department of Mental Diseases, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ou Li
- Department of Mental Diseases, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jie Zhang
- Department of Mental Diseases, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zhimin Li
- Department of Mental Diseases, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jian Xu
- Department of Mental Diseases, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
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12
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Ranson JM, Bucholc M, Lyall D, Newby D, Winchester L, Oxtoby NP, Veldsman M, Rittman T, Marzi S, Skene N, Al Khleifat A, Foote IF, Orgeta V, Kormilitzin A, Lourida I, Llewellyn DJ. Harnessing the potential of machine learning and artificial intelligence for dementia research. Brain Inform 2023; 10:6. [PMID: 36829050 PMCID: PMC9958222 DOI: 10.1186/s40708-022-00183-3] [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: 05/31/2022] [Accepted: 12/26/2022] [Indexed: 02/26/2023] Open
Abstract
Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine.
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Affiliation(s)
- Janice M Ranson
- University of Exeter Medical School, College House, St Luke's Campus, Heavitree Road, Exeter, EX1 2LU, UK.
| | - Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK
| | - Donald Lyall
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Danielle Newby
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - Neil P Oxtoby
- Department of Computer Science, UCL Centre for Medical Image Computing, University College London, London, UK
| | | | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Sarah Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Nathan Skene
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, King's College London, London, UK
| | | | - Vasiliki Orgeta
- Division of Psychiatry, University College London, London, UK
| | | | - Ilianna Lourida
- University of Exeter Medical School, College House, St Luke's Campus, Heavitree Road, Exeter, EX1 2LU, UK
| | - David J Llewellyn
- University of Exeter Medical School, College House, St Luke's Campus, Heavitree Road, Exeter, EX1 2LU, UK
- The Alan Turing Institute, London, UK
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13
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Sensi SL, Russo M, Tiraboschi P. Biomarkers of diagnosis, prognosis, pathogenesis, response to therapy: Convergence or divergence? Lessons from Alzheimer's disease and synucleinopathies. HANDBOOK OF CLINICAL NEUROLOGY 2023; 192:187-218. [PMID: 36796942 DOI: 10.1016/b978-0-323-85538-9.00015-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Alzheimer's disease (AD) is the most common disorder associated with cognitive impairment. Recent observations emphasize the pathogenic role of multiple factors inside and outside the central nervous system, supporting the notion that AD is a syndrome of many etiologies rather than a "heterogeneous" but ultimately unifying disease entity. Moreover, the defining pathology of amyloid and tau coexists with many others, such as α-synuclein, TDP-43, and others, as a rule, not an exception. Thus, an effort to shift our AD paradigm as an amyloidopathy must be reconsidered. Along with amyloid accumulation in its insoluble state, β-amyloid is becoming depleted in its soluble, normal states, as a result of biological, toxic, and infectious triggers, requiring a shift from convergence to divergence in our approach to neurodegeneration. These aspects are reflected-in vivo-by biomarkers, which have become increasingly strategic in dementia. Similarly, synucleinopathies are primarily characterized by abnormal deposition of misfolded α-synuclein in neurons and glial cells and, in the process, depleting the levels of the normal, soluble α-synuclein that the brain needs for many physiological functions. The soluble to insoluble conversion also affects other normal brain proteins, such as TDP-43 and tau, accumulating in their insoluble states in both AD and dementia with Lewy bodies (DLB). The two diseases have been distinguished by the differential burden and distribution of insoluble proteins, with neocortical phosphorylated tau deposition more typical of AD and neocortical α-synuclein deposition peculiar to DLB. We propose a reappraisal of the diagnostic approach to cognitive impairment from convergence (based on clinicopathologic criteria) to divergence (based on what differs across individuals affected) as a necessary step for the launch of precision medicine.
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Affiliation(s)
- Stefano L Sensi
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy; Molecular Neurology Unit, Center for Advanced Studies and Technology-CAST and ITAB Institute for Advanced Biotechnology, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.
| | - Mirella Russo
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy; Molecular Neurology Unit, Center for Advanced Studies and Technology-CAST and ITAB Institute for Advanced Biotechnology, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Pietro Tiraboschi
- Division of Neurology V-Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
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14
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Pan D, Zeng A, Yang B, Lai G, Hu B, Song X, Jiang T. Deep Learning for Brain MRI Confirms Patterned Pathological Progression in Alzheimer's Disease. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2204717. [PMID: 36575159 PMCID: PMC9951348 DOI: 10.1002/advs.202204717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Deep learning (DL) on brain magnetic resonance imaging (MRI) data has shown excellent performance in differentiating individuals with Alzheimer's disease (AD). However, the value of DL in detecting progressive structural MRI (sMRI) abnormalities linked to AD pathology has yet to be established. In this study, an interpretable DL algorithm named the Ensemble of 3-dimensional convolutional neural network (Ensemble 3DCNN) with enhanced parsing techniques is proposed to investigate the longitudinal trajectories of whole-brain sMRI changes denoting AD onset and progression. A set of 2369 T1-weighted images from the multi-centre Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies cohorts are applied to model derivation, validation, testing, and pattern analysis. An Ensemble-3DCNN-based P-score is generated, based on which multiple brain regions, including amygdala, insular, parahippocampal, and temporal gyrus, exhibit early and connected progressive neurodegeneration. Complex individual variability in the sMRI is also observed. This study combining non-invasive sMRI and interpretable DL in detecting patterned sMRI changes confirmed AD pathological progression, shedding new light on predicting AD progression using whole-brain sMRI.
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Affiliation(s)
- Dan Pan
- School of Electronics and InformationGuangdong Polytechnic Normal UniversityGuangzhou510665China
| | - An Zeng
- Faculty of Computers, Guangdong University of TechnologyGuangzhou510006China
| | - Baoyao Yang
- Faculty of Computers, Guangdong University of TechnologyGuangzhou510006China
| | - Gangyong Lai
- Faculty of Computers, Guangdong University of TechnologyGuangzhou510006China
| | - Bing Hu
- Department of RadiologyThe Third Affiliated Hospital of SUN Yat‐sen UniversityGuangzhou510630China
| | - Xiaowei Song
- Clinical Research CentreSurrey Memorial HospitalFraser HealthSurreyBritish ColumbiaV3V 1Z2Canada
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
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López-Ojeda W, Hurley RA. Medical Metaverse, Part 2: Artificial Intelligence Algorithms and Large Language Models in Psychiatry and Clinical Neurosciences. J Neuropsychiatry Clin Neurosci 2023; 35:316-320. [PMID: 37840258 DOI: 10.1176/appi.neuropsych.20230117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Affiliation(s)
- Wilfredo López-Ojeda
- Veterans Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center (MIRECC) and Research and Academic Affairs Service Line, W.G. Hefner Veterans Affairs Medical Center, Salisbury, N.C. (López-Ojeda, Hurley); Department of Psychiatry and Behavioral Medicine (López-Ojeda, Hurley) and Department of Radiology (Hurley), Wake Forest School of Medicine, Winston-Salem, N.C.; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston (Hurley)
| | - Robin A Hurley
- Veterans Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center (MIRECC) and Research and Academic Affairs Service Line, W.G. Hefner Veterans Affairs Medical Center, Salisbury, N.C. (López-Ojeda, Hurley); Department of Psychiatry and Behavioral Medicine (López-Ojeda, Hurley) and Department of Radiology (Hurley), Wake Forest School of Medicine, Winston-Salem, N.C.; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston (Hurley)
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16
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Liu KY, Thambisetty M, Howard R. How can secondary dementia prevention trials of Alzheimer's disease be clinically meaningful? Alzheimers Dement 2022; 19:10.1002/alz.12788. [PMID: 36161763 PMCID: PMC10039957 DOI: 10.1002/alz.12788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 07/06/2022] [Accepted: 08/09/2022] [Indexed: 11/08/2022]
Abstract
After clinical trial failures in symptomatic Alzheimer's disease (AD), our field has moved to earlier intervention in cognitively normal individuals with biomarker evidence of AD. This offers potential for dementia prevention, but mainly low and variable rates of progression to AD dementia reduce the usefulness of trials' data in decision making by potential prescribers. With results from several Phase 3 secondary prevention studies anticipated within the next few years and the Food and Drug Administration's recent endorsement of amyloid beta as a surrogate outcome biomarker for AD clinical trials, it is time to question the clinical significance of changes in biomarkers, adequacy of current trial durations, and criteria for treatment success if cognitively unimpaired patients and their doctors are to meaningfully evaluate the potential value of new agents. We argue for a change of direction toward trial designs that can unambiguously inform clinical decision making about dementia risk and progression.
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Affiliation(s)
- Kathy Y. Liu
- Division of Psychiatry, University College London, London, UK
| | - Madhav Thambisetty
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, USA
| | - Robert Howard
- Division of Psychiatry, University College London, London, UK
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17
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Shen Y, Lu Q, Zhang T, Yan H, Mansouri N, Osipowicz K, Tanglay O, Young I, Doyen S, Lu X, Zhang X, Sughrue ME, Wang T. Use of machine learning to identify functional connectivity changes in a clinical cohort of patients at risk for dementia. Front Aging Neurosci 2022; 14:962319. [PMID: 36118683 PMCID: PMC9475065 DOI: 10.3389/fnagi.2022.962319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveProgressive conditions characterized by cognitive decline, including mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are clinical conditions representing a major risk factor to develop dementia, however, the diagnosis of these pre-dementia conditions remains a challenge given the heterogeneity in clinical trajectories. Earlier diagnosis requires data-driven approaches for improved and targeted treatment modalities.MethodsNeuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 35 patients with SCD, 19 with MCI, and 36 age-matched healthy controls (HC). A recently developed machine learning technique, Hollow Tree Super (HoTS) was utilized to classify subjects into diagnostic categories based on their FC, and derive network and parcel-based FC features contributing to each model. The same approach was used to identify features associated with performance in a range of neuropsychological tests. We concluded our analysis by looking at changes in PageRank centrality (a measure of node hubness) between the diagnostic groups.ResultsSubjects were classified into diagnostic categories with a high area under the receiver operating characteristic curve (AUC-ROC), ranging from 0.73 to 0.84. The language networks were most notably associated with classification. Several central networks and sensory brain regions were predictors of poor performance in neuropsychological tests, suggesting maladaptive compensation. PageRank analysis highlighted that basal and limbic deep brain region, along with the frontal operculum demonstrated a reduction in centrality in both SCD and MCI patients compared to controls.ConclusionOur methods highlight the potential to explore the underlying neural networks contributing to the cognitive changes and neuroplastic responses in prodromal dementia.
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Affiliation(s)
- Ying Shen
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Rehabilitation Medicine, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| | - Qian Lu
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Rehabilitation Medicine, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| | - Tianjiao Zhang
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hailang Yan
- Department of Radiology, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| | | | | | - Onur Tanglay
- Omniscient Neurotechnology, Sydney, NSW, Australia
| | | | | | - Xi Lu
- Department of Rehabilitation Medicine, China-Japan Friendship Hospital, Beijing, China
| | - Xia Zhang
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi’an, China
- Shenzhen Xijia Medical Technology Company, Shenzhen, China
| | - Michael E. Sughrue
- Omniscient Neurotechnology, Sydney, NSW, Australia
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi’an, China
- Michael E. Sughrue,
| | - Tong Wang
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Tong Wang,
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GC-CNNnet: Diagnosis of Alzheimer’s Disease with PET Images Using Genetic and Convolutional Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7413081. [PMID: 35983158 PMCID: PMC9381254 DOI: 10.1155/2022/7413081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 06/01/2022] [Accepted: 06/10/2022] [Indexed: 11/17/2022]
Abstract
There is a wide variety of effects of Alzheimer's disease (AD), a neurodegenerative disease that can lead to cognitive decline, deterioration of daily life, and behavioral and psychological changes. A polymorphism of the ApoE gene ε 4 is considered a genetic risk factor for Alzheimer's disease. The purpose of this paper is to demonstrate that single-nucleotide polymorphic markers (SNPs) have a causal relationship with quantitative PET imaging traits. Additionally, the classification of AD is based on the frequency of brain tissue variations in PET images using a combination of k-nearest-neighbor (KNN), support vector machine (SVM), linear discrimination analysis (LDA), and convolutional neural network (CNN) techniques. According to the results, the suggested SNPs appear to be associated with quantitative traits more strongly than the SNPs in the ApoE genes. Regarding the classification result, the highest accuracy is obtained by the CNN with 91.1%. These results indicate that the KNN and CNN methods are beneficial in diagnosing AD. Nevertheless, the LDA and SVM are demonstrated with a lower level of accuracy.
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Multiple Cognitive and Behavioral Factors Link Association Between Brain Structure and Functional Impairment of Daily Instrumental Activities in Older Adults. J Int Neuropsychol Soc 2022; 28:673-686. [PMID: 34308821 DOI: 10.1017/s1355617721000916] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
OBJECTIVE Functional impairment in daily activity is a cornerstone in distinguishing the clinical progression of dementia. Multiple indicators based on neuroimaging and neuropsychological instruments are used to assess the levels of impairment and disease severity; however, it remains unclear how multivariate patterns of predictors uniquely predict the functional ability and how the relative importance of various predictors differs. METHOD In this study, 881 older adults with subjective cognitive complaints, mild cognitive impairment (MCI), and dementia with Alzheimer's type completed brain structural magnetic resonance imaging (MRI), neuropsychological assessment, and a survey of instrumental activities of daily living (IADL). We utilized the partial least square (PLS) method to identify latent components that are predictive of IADL. RESULTS The result showed distinct brain components (gray matter density of cerebellar, medial temporal, subcortical, limbic, and default network regions) and cognitive-behavioral components (general cognitive abilities, processing speed, and executive function, episodic memory, and neuropsychiatric symptoms) were predictive of IADL. Subsequent path analysis showed that the effect of brain structural components on IADL was largely mediated by cognitive and behavioral components. When comparing hierarchical regression models, the brain structural measures minimally added the explanatory power of cognitive and behavioral measures on IADL. CONCLUSION Our finding suggests that cerebellar structure and orbitofrontal cortex, alongside with medial temporal lobe, play an important role in the maintenance of functional status in older adults with or without dementia. Moreover, the significance of brain structural volume affects real-life functional activities via disruptions in multiple cognitive and behavioral functions.
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Lombardi A, Diacono D, Amoroso N, Biecek P, Monaco A, Bellantuono L, Pantaleo E, Logroscino G, De Blasi R, Tangaro S, Bellotti R. A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer's Disease. Brain Inform 2022; 9:17. [PMID: 35882684 PMCID: PMC9325942 DOI: 10.1186/s40708-022-00165-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/03/2022] [Indexed: 11/11/2022] Open
Abstract
In clinical practice, several standardized neuropsychological tests have been designed to assess and monitor the neurocognitive status of patients with neurodegenerative diseases such as Alzheimer's disease. Important research efforts have been devoted so far to the development of multivariate machine learning models that combine the different test indexes to predict the diagnosis and prognosis of cognitive decline with remarkable results. However, less attention has been devoted to the explainability of these models. In this work, we present a robust framework to (i) perform a threefold classification between healthy control subjects, individuals with cognitive impairment, and subjects with dementia using different cognitive indexes and (ii) analyze the variability of the explainability SHAP values associated with the decisions taken by the predictive models. We demonstrate that the SHAP values can accurately characterize how each index affects a patient's cognitive status. Furthermore, we show that a longitudinal analysis of SHAP values can provide effective information on Alzheimer's disease progression.
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Affiliation(s)
- Angela Lombardi
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Domenico Diacono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Przemysław Biecek
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Loredana Bellantuono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento di Scienze mediche di base, Neuroscienze e Organi di senso, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Ester Pantaleo
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Giancarlo Logroscino
- Dipartimento di Scienze mediche di base, Neuroscienze e Organi di senso, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Pia Fondazione “Card. G. Panico”, Tricase, Italy
| | | | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Dipartimento di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
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21
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Budgett RF, Bakker G, Sergeev E, Bennett KA, Bradley SJ. Targeting the Type 5 Metabotropic Glutamate Receptor: A Potential Therapeutic Strategy for Neurodegenerative Diseases? Front Pharmacol 2022; 13:893422. [PMID: 35645791 PMCID: PMC9130574 DOI: 10.3389/fphar.2022.893422] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 04/18/2022] [Indexed: 01/13/2023] Open
Abstract
The type 5 metabotropic glutamate receptor, mGlu5, has been proposed as a potential therapeutic target for the treatment of several neurodegenerative diseases. In preclinical neurodegenerative disease models, novel allosteric modulators have been shown to improve cognitive performance and reduce disease-related pathology. A common pathological hallmark of neurodegenerative diseases is a chronic neuroinflammatory response, involving glial cells such as astrocytes and microglia. Since mGlu5 is expressed in astrocytes, targeting this receptor could provide a potential mechanism by which neuroinflammatory processes in neurodegenerative disease may be modulated. This review will discuss current evidence that highlights the potential of mGlu5 allosteric modulators to treat neurodegenerative diseases, including Alzheimer’s disease, Huntington’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. Furthermore, this review will explore the role of mGlu5 in neuroinflammatory responses, and the potential for this G protein-coupled receptor to modulate neuroinflammation.
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Affiliation(s)
- Rebecca F Budgett
- The Centre for Translational Pharmacology, Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | | | | | | | - Sophie J Bradley
- The Centre for Translational Pharmacology, Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom.,Sosei Heptares, Cambridge, United Kingdom
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22
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Dong N, Fu C, Li R, Zhang W, Liu M, Xiao W, Taylor HM, Nicholas PJ, Tanglay O, Young IM, Osipowicz KZ, Sughrue ME, Doyen SP, Li Y. Machine Learning Decomposition of the Anatomy of Neuropsychological Deficit in Alzheimer’s Disease and Mild Cognitive Impairment. Front Aging Neurosci 2022; 14:854733. [PMID: 35592700 PMCID: PMC9110794 DOI: 10.3389/fnagi.2022.854733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/22/2022] [Indexed: 12/03/2022] Open
Abstract
Objective Alzheimer’s Disease (AD) is a progressive condition characterized by cognitive decline. AD is often preceded by mild cognitive impairment (MCI), though the diagnosis of both conditions remains a challenge. Early diagnosis of AD, and prediction of MCI progression require data-driven approaches to improve patient selection for treatment. We used a machine learning tool to predict performance in neuropsychological tests in AD and MCI based on functional connectivity using a whole-brain connectome, in an attempt to identify network substrates of cognitive deficits in AD. Methods Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI, and diffusion weighted imaging scans were obtained from 149 MCI, and 85 AD patients; and 140 cognitively unimpaired geriatric participants. A novel machine learning tool, Hollow Tree Super (HoTS) was utilized to extract feature importance from each machine learning model to identify brain regions that were associated with deficit and absence of deficit for 11 neuropsychological tests. Results 11 models attained an area under the receiver operating curve (AUC-ROC) greater than 0.65, while five models had an AUC-ROC ≥ 0.7. 20 parcels of the Human Connectome Project Multimodal Parcelation Atlas matched to poor performance in at least two neuropsychological tests, while 14 parcels were associated with good performance in at least two tests. At a network level, most parcels predictive of both presence and absence of deficit were affiliated with the Central Executive Network, Default Mode Network, and the Sensorimotor Networks. Segregating predictors by the cognitive domain associated with each test revealed areas of coherent overlap between cognitive domains, with the parcels providing possible markers to screen for cognitive impairment. Conclusion Approaches such as ours which incorporate whole-brain functional connectivity and harness feature importance in machine learning models may aid in identifying diagnostic and therapeutic targets in AD.
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Affiliation(s)
- Ningxin Dong
- Department of Medical Imaging, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Changyong Fu
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Renren Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Wei Zhang
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Meng Liu
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Weixin Xiao
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | | | | | - Onur Tanglay
- Omniscient Neurotechnology, Sydney, NSW, Australia
| | | | | | - Michael E. Sughrue
- Omniscient Neurotechnology, Sydney, NSW, Australia
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi’an, China
| | | | - Yunxia Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- *Correspondence: Yunxia Li,
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23
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Relationship between parental history of dementia, motor-cognitive and executive function performance in African American women. J Neurol Sci 2022; 439:120305. [DOI: 10.1016/j.jns.2022.120305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 04/19/2022] [Accepted: 05/28/2022] [Indexed: 11/23/2022]
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24
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Nicholas PJ, To A, Tanglay O, Young IM, Sughrue ME, Doyen S. Using a ResNet-18 Network to Detect Features of Alzheimer’s Disease on Functional Magnetic Resonance Imaging: A Failed Replication. Comment on Odusami et al. Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network. Diagnostics 2021, 11, 1071. Diagnostics (Basel) 2022; 12:diagnostics12051094. [PMID: 35626249 PMCID: PMC9139912 DOI: 10.3390/diagnostics12051094] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 02/01/2022] [Accepted: 02/17/2022] [Indexed: 11/30/2022] Open
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25
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Giorgio J, Jagust WJ, Baker S, Landau SM, Tino P, Kourtzi Z. A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation. Nat Commun 2022; 13:1887. [PMID: 35393421 PMCID: PMC8989879 DOI: 10.1038/s41467-022-28795-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 02/11/2022] [Indexed: 01/21/2023] Open
Abstract
The early stages of Alzheimer’s disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future pathological tau accumulation. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal lobe atrophy, tau and APOE 4) at mildly impaired and asymptomatic stages of AD. Using baseline non-tau markers we derive a prognostic index that: (a) stratifies patients based on future pathological tau accumulation, (b) predicts individualised regional future rate of tau accumulation, and (c) translates predictions from deep phenotyping patient cohorts to cognitively normal individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design targeting the earliest stages of AD. The authors present a machine learning approach that combines baseline multimodal data to accurately predict individualised trajectories of future pathological tau accumulation at asymptomatic and mildly impaired stages of Alzheimer’s disease.
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Affiliation(s)
- Joseph Giorgio
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA.,Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Suzanne Baker
- Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Susan M Landau
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Peter Tino
- School of Computer Science, University of Birmingham, Birmingham, UK
| | - Zoe Kourtzi
- Department of Psychology, University of Cambridge, Cambridge, UK.
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26
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The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence. Biomedicines 2022; 10:biomedicines10020315. [PMID: 35203524 PMCID: PMC8869403 DOI: 10.3390/biomedicines10020315] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 02/05/2023] Open
Abstract
Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.
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27
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Imaging Clinical Subtypes and Associated Brain Networks in Alzheimer’s Disease. Brain Sci 2022; 12:brainsci12020146. [PMID: 35203910 PMCID: PMC8869882 DOI: 10.3390/brainsci12020146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 11/17/2022] Open
Abstract
Alzheimer’s disease (AD) does not present uniform symptoms or a uniform rate of progression in all cases. The classification of subtypes can be based on clinical symptoms or patterns of pathological brain alterations. Imaging techniques may allow for the identification of AD subtypes and their differentiation from other neurodegenerative diseases already at an early stage. In this review, the strengths and weaknesses of current clinical imaging methods are described. These include positron emission tomography (PET) to image cerebral glucose metabolism and pathological amyloid or tau deposits. Magnetic resonance imaging (MRI) is more widely available than PET. It provides information on structural or functional changes in brain networks and their relation to AD subtypes. Amyloid PET provides a very early marker of AD but does not distinguish between AD subtypes. Regional patterns of pathology related to AD subtypes are observed with tau and glucose PET, and eventually as atrophy patterns on MRI. Structural and functional network changes occur early in AD but have not yet provided diagnostic specificity.
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28
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Pena D, Suescun J, Schiess M, Ellmore TM, Giancardo L. Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer's Disease Conversion. Front Neurosci 2022; 15:744190. [PMID: 35046766 PMCID: PMC8761739 DOI: 10.3389/fnins.2021.744190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 11/09/2021] [Indexed: 01/21/2023] Open
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient’s ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current computational approaches are focused on predicting whether or not a subject with MCI will convert to AD in the future. To our knowledge, limited attention has been given to the development of automated computer-assisted diagnosis (CAD) systems able to provide an AD conversion diagnosis in MCI patient cohorts followed longitudinally. This is important as these CAD systems could be used by primary care providers to monitor patients with MCI. The method outlined in this paper addresses this gap and presents a computationally efficient pre-processing and prediction pipeline, and is designed for recognizing patterns associated with AD conversion. We propose a new approach that leverages longitudinal data that can be easily acquired in a clinical setting (e.g., T1-weighted magnetic resonance images, cognitive tests, and demographic information) to identify the AD conversion point in MCI subjects with AUC = 84.7. In contrast, cognitive tests and demographics alone achieved AUC = 80.6, a statistically significant difference (n = 669, p < 0.05). We designed a convolutional neural network that is computationally efficient and requires only linear registration between imaging time points. The model architecture combines Attention and Inception architectures while utilizing both cross-sectional and longitudinal imaging and clinical information. Additionally, the top brain regions and clinical features that drove the model’s decision were investigated. These included the thalamus, caudate, planum temporale, and the Rey Auditory Verbal Learning Test. We believe our method could be easily translated into the healthcare setting as an objective AD diagnostic tool for patients with MCI.
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29
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Ranson JM, Rittman T, Hayat S, Brayne C, Jessen F, Blennow K, van Duijn C, Barkhof F, Tang E, Mummery CJ, Stephan BCM, Altomare D, Frisoni GB, Ribaldi F, Molinuevo JL, Scheltens P, Llewellyn DJ. Modifiable risk factors for dementia and dementia risk profiling. A user manual for Brain Health Services-part 2 of 6. Alzheimers Res Ther 2021; 13:169. [PMID: 34635138 PMCID: PMC8507172 DOI: 10.1186/s13195-021-00895-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 08/26/2021] [Indexed: 12/14/2022]
Abstract
We envisage the development of new Brain Health Services to achieve primary and secondary dementia prevention. These services will complement existing memory clinics by targeting cognitively unimpaired individuals, where the focus is on risk profiling and personalized risk reduction interventions rather than diagnosing and treating late-stage disease. In this article, we review key potentially modifiable risk factors and genetic risk factors and discuss assessment of risk factors as well as additional fluid and imaging biomarkers that may enhance risk profiling. We then outline multidomain measures and risk profiling and provide practical guidelines for Brain Health Services, with consideration of outstanding uncertainties and challenges. Users of Brain Health Services should undergo risk profiling tailored to their age, level of risk, and availability of local resources. Initial risk assessment should incorporate a multidomain risk profiling measure. For users aged 39-64, we recommend the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) Dementia Risk Score, whereas for users aged 65 and older, we recommend the Brief Dementia Screening Indicator (BDSI) and the Australian National University Alzheimer's Disease Risk Index (ANU-ADRI). The initial assessment should also include potentially modifiable risk factors including sociodemographic, lifestyle, and health factors. If resources allow, apolipoprotein E ɛ4 status testing and structural magnetic resonance imaging should be conducted. If this initial assessment indicates a low dementia risk, then low intensity interventions can be implemented. If the user has a high dementia risk, additional investigations should be considered if local resources allow. Common variant polygenic risk of late-onset AD can be tested in middle-aged or older adults. Rare variants should only be investigated in users with a family history of early-onset dementia in a first degree relative. Advanced imaging with 18-fluorodeoxyglucose positron emission tomography (FDG-PET) or amyloid PET may be informative in high risk users to clarify the nature and burden of their underlying pathologies. Cerebrospinal fluid biomarkers are not recommended for this setting, and blood-based biomarkers need further validation before clinical use. As new technologies become available, advances in artificial intelligence are likely to improve our ability to combine diverse data to further enhance risk profiling. Ultimately, Brain Health Services have the potential to reduce the future burden of dementia through risk profiling, risk communication, personalized risk reduction, and cognitive enhancement interventions.
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Affiliation(s)
- Janice M Ranson
- College of Medicine and Health, University of Exeter, Exeter, UK
- Deep Dementia Phenotyping (DEMON) Network, Exeter, UK
| | - Timothy Rittman
- Deep Dementia Phenotyping (DEMON) Network, Exeter, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Shabina Hayat
- Department of Public Health and Primary Care, Cambridge Public Health, University of Cambridge, Cambridge, UK
| | - Carol Brayne
- Department of Public Health and Primary Care, Cambridge Public Health, University of Cambridge, Cambridge, UK
| | - Frank Jessen
- Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Cologne, Germany
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Cornelia van Duijn
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Eugene Tang
- Deep Dementia Phenotyping (DEMON) Network, Exeter, UK
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Catherine J Mummery
- Deep Dementia Phenotyping (DEMON) Network, Exeter, UK
- Dementia Research Centre, Institute of Neurology, University College London, and National Hospital for Neurology and Neurosurgery, University College London Hospital, London, UK
| | - Blossom C M Stephan
- Institute of Mental Health, Division of Psychiatry and Applied Psychology, School of Medicine, Nottingham University, Nottingham, UK
| | - Daniele Altomare
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Memory Clinic, Geneva University Hospitals, Geneva, Switzerland
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), Saint John of God Clinical Research Centre, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Life Science Partners, Amsterdam, The Netherlands
| | - David J Llewellyn
- College of Medicine and Health, University of Exeter, Exeter, UK.
- Deep Dementia Phenotyping (DEMON) Network, Exeter, UK.
- Alan Turing Institute, London, UK.
- 2.04 College House, St Luke's Campus, University of Exeter Medical School, Exeter, EX1 2 LU, UK.
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Butler LM, Houghton R, Abraham A, Vassilaki M, Durán-Pacheco G. Comorbidity Trajectories Associated With Alzheimer's Disease: A Matched Case-Control Study in a United States Claims Database. Front Neurosci 2021; 15:749305. [PMID: 34690684 PMCID: PMC8531650 DOI: 10.3389/fnins.2021.749305] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 09/21/2021] [Indexed: 12/02/2022] Open
Abstract
Background: Trajectories of comorbidities among individuals at risk of Alzheimer's disease (AD) may differ from those aging without AD clinical syndrome. Therefore, characterizing the comorbidity burden and pattern associated with AD risk may facilitate earlier detection, enable timely intervention, and help slow the rate of cognitive and functional decline in AD. This case-control study was performed to compare the prevalence of comorbidities between AD cases and controls during the 5 years prior to diagnosis (or index date for controls); and to identify comorbidities with a differential time-dependent prevalence trajectory during the 5 years prior to AD diagnosis. Methods: Incident AD cases and individually matched controls were identified in a United States claims database between January 1, 2000 and December 31, 2016. AD status and comorbidities were defined based on the presence of diagnosis codes in administrative claims records. Generalized estimating equations were used to assess evidence of changes over time and between AD and controls. A principal component analysis and hierarchical clustering was performed to identify groups of AD-related comorbidities with respect to prevalence changes over time (or trajectory), and differences between AD and controls. Results: Data from 186,064 individuals in the IBM MarketScan Commercial Claims and Medicare Supplementary databases were analyzed (93,032 AD cases and 93,032 non-AD controls). In total, there were 177 comorbidities with a ≥ 5% prevalence. Five main clusters of comorbidities were identified. Clusters differed between AD cases and controls in the overall magnitude of association with AD, in their diverging time trajectories, and in comorbidity prevalence. Three clusters contained comorbidities that notably increased in frequency over time in AD cases but not in controls during the 5-year period before AD diagnosis. Comorbidities in these clusters were related to the early signs and/or symptoms of AD, psychiatric and mood disorders, cerebrovascular disease, history of hazard and injuries, and metabolic, cardiovascular, and respiratory complaints. Conclusion: We demonstrated a greater comorbidity burden among those who later developed AD vs. controls, and identified comorbidity clusters that could distinguish these two groups. Further investigation of comorbidity burden is warranted to facilitate early detection of individuals at risk of developing AD.
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Affiliation(s)
| | | | | | - Maria Vassilaki
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
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31
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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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32
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Dansson HV, Stempfle L, Egilsdóttir H, Schliep A, Portelius E, Blennow K, Zetterberg H, Johansson FD. Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer's disease. ALZHEIMERS RESEARCH & THERAPY 2021; 13:151. [PMID: 34488882 PMCID: PMC8422748 DOI: 10.1186/s13195-021-00886-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 08/08/2021] [Indexed: 11/10/2022]
Abstract
BACKGROUND In Alzheimer's disease, amyloid- β (A β) peptides aggregate in the lowering CSF amyloid levels - a key pathological hallmark of the disease. However, lowered CSF amyloid levels may also be present in cognitively unimpaired elderly individuals. Therefore, it is of great value to explain the variance in disease progression among patients with A β pathology. METHODS A cohort of n=2293 participants, of whom n=749 were A β positive, was selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to study heterogeneity in disease progression for individuals with A β pathology. The analysis used baseline clinical variables including demographics, genetic markers, and neuropsychological data to predict how the cognitive ability and AD diagnosis of subjects progressed using statistical models and machine learning. Due to the relatively low prevalence of A β pathology, models fit only to A β-positive subjects were compared to models fit to an extended cohort including subjects without established A β pathology, adjusting for covariate differences between the cohorts. RESULTS A β pathology status was determined based on the A β42/A β40 ratio. The best predictive model of change in cognitive test scores for A β-positive subjects at the 2-year follow-up achieved an R2 score of 0.388 while the best model predicting adverse changes in diagnosis achieved a weighted F1 score of 0.791. A β-positive subjects declined faster on average than those without A β pathology, but the specific level of CSF A β was not predictive of progression rate. When predicting cognitive score change 4 years after baseline, the best model achieved an R2 score of 0.325 and it was found that fitting models to the extended cohort improved performance. Moreover, using all clinical variables outperformed the best model based only on a suite of cognitive test scores which achieved an R2 score of 0.228. CONCLUSION Our analysis shows that CSF levels of A β are not strong predictors of the rate of cognitive decline in A β-positive subjects when adjusting for other variables. Baseline assessments of cognitive function accounts for the majority of variance explained in the prediction of 2-year decline but is insufficient for achieving optimal results in longer-term predictions. Predicting changes both in cognitive test scores and in diagnosis provides multiple perspectives of the progression of potential AD subjects.
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Affiliation(s)
- Hákon Valur Dansson
- Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
| | - Lena Stempfle
- Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden.
| | - Hildur Egilsdóttir
- Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
| | - Alexander Schliep
- Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
| | - Erik Portelius
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.,UK Dementia Research Institute, UCL, London, UK
| | - Fredrik D Johansson
- Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
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Fabrizio C, Termine A, Caltagirone C, Sancesario G. Artificial Intelligence for Alzheimer's Disease: Promise or Challenge? Diagnostics (Basel) 2021; 11:1473. [PMID: 34441407 PMCID: PMC8391160 DOI: 10.3390/diagnostics11081473] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 08/12/2021] [Accepted: 08/12/2021] [Indexed: 01/23/2023] Open
Abstract
Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer's disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.
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Affiliation(s)
- Carlo Fabrizio
- Laboratory of Experimental and Behavioral Neurophysiology, IRCCS Santa Lucia Foundation, 00143 Rome, Italy; (C.F.); (A.T.)
| | - Andrea Termine
- Laboratory of Experimental and Behavioral Neurophysiology, IRCCS Santa Lucia Foundation, 00143 Rome, Italy; (C.F.); (A.T.)
| | - Carlo Caltagirone
- Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy;
| | - Giulia Sancesario
- Biobank, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
- European Center for Brain Research, Experimental Neuroscience, 00143 Rome, Italy
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Apolipoprotein E allele 4 effects on Single-Subject Gray Matter Networks in Mild Cognitive Impairment. NEUROIMAGE: CLINICAL 2021; 32:102799. [PMID: 34469849 PMCID: PMC8405842 DOI: 10.1016/j.nicl.2021.102799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 07/23/2021] [Accepted: 08/17/2021] [Indexed: 11/23/2022] Open
Abstract
There is evidence that gray matter networks are disrupted in Mild Cognitive Impairment (MCI) and associated with cognitive impairment and faster disease progression. However, it remains unknown how these alterations are related to the presence of Apolipoprotein E isoform E4 (ApoE4), the most prominent genetic risk factor for late-onset Alzheimer's disease (AD). To investigate this topic at the individual level, we explore the impact of ApoE4 and the disease progression on the Single-Subject Gray Matter Networks (SSGMNets) using the graph theory approach. Our data sample comprised 200 MCI patients selected from the ADNI database, classified as non-Converters and Converters (will progress into AD). Each group included 50 ApoE4-positive ('Carriers', ApoE4 + ) and 50 ApoE4-negative ('non-Carriers', ApoE4-). The SSGMNets were estimated from structural MRIs at two-time points: baseline and conversion. We investigated whether altered network topological measures at baseline and their rate of change (RoC) between baseline and conversion time points were associated with ApoE4 and disease progression. We also explored the correlation of SSGMNets attributes with general cognition score (MMSE), memory (ADNI-MEM), and CSF-derived biomarkers of AD (Aβ42, T-tau, and P-tau). Our results showed that ApoE4 and the disease progression modulated the global topological network properties independently but not in their RoC. MCI converters showed a lower clustering index in several regions associated with neurodegeneration in AD. The SSGMNets' topological organization was revealed to be able to predict cognitive and memory measures. The findings presented here suggest that SSGMNets could indeed be used to identify MCI ApoE4 Carriers with a high risk for AD progression.
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Cummings J, Aisen P, Apostolova LG, Atri A, Salloway S, Weiner M. Aducanumab: Appropriate Use Recommendations. J Prev Alzheimers Dis 2021; 8:398-410. [PMID: 34585212 PMCID: PMC8835345 DOI: 10.14283/jpad.2021.41] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Aducanumab has been approved by the US Food and Drug Administration for treatment of Alzheimer's disease (AD). Clinicians require guidance on the appropriate use of this new therapy. An Expert Panel was assembled to construct Appropriate Use Recommendations based on the participant populations, conduct of the pivotal trials of aducanumab, updated Prescribing Information, and expert consensus. Aducanumab is an amyloid-targeting monoclonal antibody delivered by monthly intravenous infusions. The pivotal trials included patients with early AD (mild cognitive impairment due to AD and mild AD dementia) who had confirmed brain amyloid using amyloid positron tomography. The Expert Panel recommends that use of aducanumab be restricted to this population in which efficacy and safety have been studied. Aducanumab is titrated to a dose of 10 mg/kg over a 6-month period. The Expert Panel recommends that the aducanumab be titrated to the highest dose to maximize the opportunity for efficacy. Aducanumab can substantially increase the incidence of amyloid-related imaging abnormalities (ARIA) with brain effusion or hemorrhage. Dose interruption or treatment discontinuation is recommended for symptomatic ARIA and for moderate-severe ARIA. The Expert Panel recommends MRIs prior to initiating therapy, during the titration of the drug, and at any time the patient has symptoms suggestive of ARIA. Recommendations are made for measures less cumbersome than those used in trials for the assessment of effectiveness in the practice setting. The Expert Panel emphasized the critical importance of engaging in a process of patient-centered informed decision-making that includes comprehensive discussions and clear communication with the patient and care partner regarding the requirements for therapy, the expected outcome of therapy, potential risks and side effects, and the required safety monitoring, as well as uncertainties regarding individual responses and benefits.
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Affiliation(s)
- J Cummings
- Jeffrey Cummings, MD, ScD, 1380 Opal Valley Street, Henderson, NV 89052, , T: 702-902-3939
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Geerts H, Spiros A. Simulating the Effects of Common Comedications and Genotypes on Alzheimer's Cognitive Trajectory Using a Quantitative Systems Pharmacology Approach. J Alzheimers Dis 2020; 78:413-424. [PMID: 33016912 DOI: 10.3233/jad-200688] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND Many Alzheimer's disease patients in clinical practice are on polypharmacy for treatment of comorbidities. OBJECTIVE While pharmacokinetic interactions between drugs have been relatively well established with corresponding treatment guidelines, many medications and common genotype variants also affect central brain circuits involved in cognitive trajectory, leading to complex pharmacodynamic interactions and a large variability in clinical trials. METHODS We applied a mechanism-based and ADAS-Cog calibrated Quantitative Systems Pharmacology biophysical model of neuronal circuits relevant for cognition in Alzheimer's disease, to standard-of-care cholinergic therapy with COMTVal158Met, 5-HTTLPR rs25531, and APOE genotypes and with benzodiazepines, antidepressants, and antipsychotics, all together 9,585 combinations. RESULTS The model predicts a variability of up to 14 points on ADAS-Cog at baseline (COMTVV 5-HTTLPRss APOE 4/4 combination is worst) and a four-fold range for the rate of progression. The progression rate is inversely proportional to baseline ADAS-Cog. Antidepressants, benzodiazepines, first-generation more than second generation, and most antipsychotics with the exception of aripiprazole worsen the outcome when added to standard-of-care in mild cases. Low dose second-generation benzodiazepines revert the negative effects of risperidone and olanzapine, but only in mild stages. Non APOE4 carriers with a COMTMM and 5HTTLPRLL are predicted to have the best cognitive performance at baseline but deteriorate somewhat faster over time. However, this effect is significantly modulated by comedications. CONCLUSION Once these simulations are validated, the platform can in principle provide optimal treatment guidance in clinical practice at an individual patient level, identify negative pharmacodynamic interactions with novel targets and address protocol amendments in clinical trials.
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Chen Q, Baran TM, Rooks B, O'Banion MK, Mapstone M, Zhang Z, Lin F. Cognitively supernormal older adults maintain a unique structural connectome that is resistant to Alzheimer's pathology. Neuroimage Clin 2020; 28:102413. [PMID: 32971466 PMCID: PMC7511768 DOI: 10.1016/j.nicl.2020.102413] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 08/30/2020] [Accepted: 09/02/2020] [Indexed: 11/20/2022]
Abstract
Studying older adults with excellent cognitive capacities (Supernormals) provides a unique opportunity for identifying factors related to cognitive success - a critical topic across lifespan. There is a limited understanding of Supernormals' neural substrates, especially whether any of them attends shaping and supporting superior cognitive function or confer resistance to age-related neurodegeneration such as Alzheimer's disease (AD). Here, applying a state-of-the-art diffusion imaging processing pipeline and finite mixture modelling, we longitudinally examine the structural connectome of Supernormals. We find a unique structural connectome, containing the connections between frontal, cingulate, parietal, temporal, and subcortical regions in the same hemisphere that remains stable over time in Supernormals, relatively to typical agers. The connectome significantly classifies positive vs. negative AD pathology at 72% accuracy in a new sample mixing Supernormals, typical agers, and AD risk [amnestic mild cognitive impairment (aMCI)] subjects. Among this connectome, the mean diffusivity of the connection between right isthmus cingulate cortex and right precuneus most robustly contributes to predicting AD pathology across samples. The mean diffusivity of this connection links negatively to global cognition in those Supernormals with positive AD pathology. But this relationship does not exist in typical agers or aMCI. Our data suggest the presence of a structural connectome supporting cognitive success. Cingulate to precuneus white matter integrity may be useful as a structural marker for monitoring neurodegeneration and may provide critical information for understanding how some older adults maintain or excel cognitively in light of significant AD pathology.
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Affiliation(s)
- Quanjing Chen
- Elaine C. Hubbard Center for Nursing Research on Aging, School of Nursing, University of Rochester Medical Center, United States; Department of Psychiatry, School of Medicine and Dentistry, University of Rochester Medical Center, United States.
| | - Timothy M Baran
- Department of Imaging Sciences, School of Medicine and Dentistry, University of Rochester Medical Center, United States; Department of Biomedical Engineering, University of Rochester, United States
| | - Brian Rooks
- Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester Medical Center, United States
| | - M Kerry O'Banion
- Department of Neuroscience, School of Medicine and Dentistry, University of Rochester Medical Center, United States
| | - Mark Mapstone
- Department of Neurology, University of California-Irvine, United States
| | - Zhengwu Zhang
- Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester Medical Center, United States
| | - Feng Lin
- Elaine C. Hubbard Center for Nursing Research on Aging, School of Nursing, University of Rochester Medical Center, United States; Department of Psychiatry, School of Medicine and Dentistry, University of Rochester Medical Center, United States; Department of Neuroscience, School of Medicine and Dentistry, University of Rochester Medical Center, United States; Department of Neurology, School of Medicine and Dentistry, University of Rochester Medical Center, United States; Department of Brain and Cognitive Sciences, University of Rochester, United States.
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Rittman T. Neurological update: neuroimaging in dementia. J Neurol 2020; 267:3429-3435. [PMID: 32638104 PMCID: PMC7578138 DOI: 10.1007/s00415-020-10040-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 06/26/2020] [Accepted: 06/30/2020] [Indexed: 12/18/2022]
Abstract
Neuroimaging for dementia has made remarkable progress in recent years, shedding light on diagnostic subtypes of dementia, predicting prognosis and monitoring pathology. This review covers some updates in the understanding of dementia using structural imaging, positron emission tomography (PET), structural and functional connectivity, and using big data and artificial intelligence. Progress with neuroimaging methods allows neuropathology to be examined in vivo, providing a suite of biomarkers for understanding neurodegeneration and for application in clinical trials. In addition, we highlight quantitative susceptibility imaging as an exciting new technique that may prove to be a sensitive biomarker for a range of neurodegenerative diseases. There are challenges in translating novel imaging techniques to clinical practice, particularly in developing standard methodologies and overcoming regulatory issues. It is likely that clinicians will need to lead the way if these obstacles are to be overcome. Continued efforts applying neuroimaging to understand mechanisms of neurodegeneration and translating them to clinical practice will complete a revolution in neuroimaging.
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Affiliation(s)
- Timothy Rittman
- Department of Neurosciences, University of Cambridge, Cambridge, UK.
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