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Lin L, Wu Y, Liu L, Sun S, Wu S. Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data. Bioengineering (Basel) 2024; 11:647. [PMID: 39061729 PMCID: PMC11273398 DOI: 10.3390/bioengineering11070647] [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: 05/17/2024] [Revised: 06/14/2024] [Accepted: 06/21/2024] [Indexed: 07/28/2024] Open
Abstract
The intricate dynamics of brain aging, especially the neurodegenerative mechanisms driving accelerated (ABA) and resilient brain aging (RBA), are pivotal in neuroscience. Understanding the temporal dynamics of these phenotypes is crucial for identifying vulnerabilities to cognitive decline and neurodegenerative diseases. Currently, there is a lack of comprehensive understanding of the temporal dynamics and neuroimaging biomarkers linked to ABA and RBA. This study addressed this gap by utilizing a large-scale UK Biobank (UKB) cohort, with the aim to elucidate brain aging heterogeneity and establish the foundation for targeted interventions. Employing Lasso regression on multimodal neuroimaging data, structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rsfMRI), we predicted the brain age and classified individuals into ABA and RBA cohorts. Our findings identified 1949 subjects (6.2%) as representative of the ABA subpopulation and 3203 subjects (10.1%) as representative of the RBA subpopulation. Additionally, the Discriminative Event-Based Model (DEBM) was applied to estimate the sequence of biomarker changes across aging trajectories. Our analysis unveiled distinct central ordering patterns between the ABA and RBA cohorts, with profound implications for understanding cognitive decline and vulnerability to neurodegenerative disorders. Specifically, the ABA cohort exhibited early degeneration in four functional networks and two cognitive domains, with cortical thinning initially observed in the right hemisphere, followed by the temporal lobe. In contrast, the RBA cohort demonstrated initial degeneration in the three functional networks, with cortical thinning predominantly in the left hemisphere and white matter microstructural degeneration occurring at more advanced stages. The detailed aging progression timeline constructed through our DEBM analysis positioned subjects according to their estimated stage of aging, offering a nuanced view of the aging brain's alterations. This study holds promise for the development of targeted interventions aimed at mitigating age-related cognitive decline.
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Affiliation(s)
- Lan Lin
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (Y.W.); (L.L.); (S.W.)
| | - Yutong Wu
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (Y.W.); (L.L.); (S.W.)
| | - Lingyu Liu
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (Y.W.); (L.L.); (S.W.)
| | - Shen Sun
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Shuicai Wu
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (Y.W.); (L.L.); (S.W.)
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2
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Liu L, Sun S, Kang W, Wu S, Lin L. A review of neuroimaging-based data-driven approach for Alzheimer's disease heterogeneity analysis. Rev Neurosci 2024; 35:121-139. [PMID: 37419866 DOI: 10.1515/revneuro-2023-0033] [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: 03/21/2023] [Accepted: 06/18/2023] [Indexed: 07/09/2023]
Abstract
Alzheimer's disease (AD) is a complex form of dementia and due to its high phenotypic variability, its diagnosis and monitoring can be quite challenging. Biomarkers play a crucial role in AD diagnosis and monitoring, but interpreting these biomarkers can be problematic due to their spatial and temporal heterogeneity. Therefore, researchers are increasingly turning to imaging-based biomarkers that employ data-driven computational approaches to examine the heterogeneity of AD. In this comprehensive review article, we aim to provide health professionals with a comprehensive view of past applications of data-driven computational approaches in studying AD heterogeneity and planning future research directions. We first define and offer basic insights into different categories of heterogeneity analysis, including spatial heterogeneity, temporal heterogeneity, and spatial-temporal heterogeneity. Then, we scrutinize 22 articles relating to spatial heterogeneity, 14 articles relating to temporal heterogeneity, and five articles relating to spatial-temporal heterogeneity, highlighting the strengths and limitations of these strategies. Furthermore, we discuss the importance of understanding spatial heterogeneity in AD subtypes and their clinical manifestations, biomarkers for abnormal orderings and AD stages, the recent advancements in spatial-temporal heterogeneity analysis for AD, and the emerging role of omics data integration in advancing personalized diagnosis and treatment for AD patients. By emphasizing the significance of understanding AD heterogeneity, we hope to stimulate further research in this field to facilitate the development of personalized interventions for AD patients.
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Affiliation(s)
- Lingyu Liu
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Shen Sun
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Wenjie Kang
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Shuicai Wu
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Lan Lin
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
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3
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Young AL, Oxtoby NP, Garbarino S, Fox NC, Barkhof F, Schott JM, Alexander DC. Data-driven modelling of neurodegenerative disease progression: thinking outside the black box. Nat Rev Neurosci 2024; 25:111-130. [PMID: 38191721 DOI: 10.1038/s41583-023-00779-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/30/2023] [Indexed: 01/10/2024]
Abstract
Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to 'black box' machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.
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Affiliation(s)
- Alexandra L Young
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Neil P Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
| | - Sara Garbarino
- Life Science Computational Laboratory, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Frederik Barkhof
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Daniel C Alexander
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
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4
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De Francesco S, Crema C, Archetti D, Muscio C, Reid RI, Nigri A, Bruzzone MG, Tagliavini F, Lodi R, D'Angelo E, Boeve B, Kantarci K, Firbank M, Taylor JP, Tiraboschi P, Redolfi A. Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA. Sci Rep 2023; 13:17355. [PMID: 37833302 PMCID: PMC10575864 DOI: 10.1038/s41598-023-43706-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023] Open
Abstract
Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis.
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Affiliation(s)
- Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
| | - Claudio Crema
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Damiano Archetti
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Cristina Muscio
- ASST Bergamo Ovest, Bergamo, Italy
- Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic and Foundation, Rochester, Minnesota, USA
| | - Anna Nigri
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Maria Grazia Bruzzone
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Fabrizio Tagliavini
- Scientific Directorate, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | - Brad Boeve
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Michael Firbank
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle Upon Tyne, UK
| | - John-Paul Taylor
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle Upon Tyne, UK
| | - Pietro Tiraboschi
- Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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5
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Wijeratne PA, Eshaghi A, Scotton WJ, Kohli M, Aksman L, Oxtoby NP, Pustina D, Warner JH, Paulsen JS, Scahill RI, Sampaio C, Tabrizi SJ, Alexander DC. The temporal event-based model: Learning event timelines in progressive diseases. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:1-19. [PMID: 37719837 PMCID: PMC10503481 DOI: 10.1162/imag_a_00010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 07/15/2023] [Indexed: 09/19/2023]
Abstract
Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer's disease (AD) and Huntington's disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of 80 % with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions.
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Affiliation(s)
- Peter A. Wijeratne
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
- Department of Informatics, University of Sussex, Brighton, United Kingdom
| | - Arman Eshaghi
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London, London, United Kingdom
| | - William J. Scotton
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London, London, United Kingdom
| | - Maitrei Kohli
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Leon Aksman
- Keck School of Medicine, University of Southern California, Los Angeles, California, United States
| | - Neil P. Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Dorian Pustina
- CHDI Management/CHDI Foundation, Princeton, New Jersey, United States
| | - John H. Warner
- CHDI Management/CHDI Foundation, Princeton, New Jersey, United States
| | - Jane S. Paulsen
- Departments of Neurology and Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, Iowa, United States
| | - Rachael I. Scahill
- Huntington’s Disease Centre, Department of Neurodegenerative Disease, University College London, Queen Square, London, United Kingdom
| | - Cristina Sampaio
- CHDI Management/CHDI Foundation, Princeton, New Jersey, United States
| | - Sarah J. Tabrizi
- Huntington’s Disease Centre, Department of Neurodegenerative Disease, University College London, Queen Square, London, United Kingdom
| | - Daniel C. Alexander
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
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6
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Ying J, Reboreda A, Yoshida M, Brandon MP. Grid cell disruption in a mouse model of early Alzheimer's disease reflects reduced integration of self-motion cues. Curr Biol 2023:S0960-9822(23)00547-X. [PMID: 37220744 DOI: 10.1016/j.cub.2023.04.065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 04/20/2023] [Accepted: 04/27/2023] [Indexed: 05/25/2023]
Abstract
Converging evidence from human and rodent studies suggests that disrupted grid cell coding in the medial entorhinal cortex (MEC) underlies path integration behavioral deficits during early Alzheimer's disease (AD). However, grid cell firing relies on both self-motion cues and environmental features, and it remains unclear whether disrupted grid coding can account for specific path integration deficits reported during early AD. Here, we report in the J20 transgenic amyloid beta (Aβ) mouse model of early AD that grid cells were spatially unstable toward the center of the arena, had qualitatively different spatial components that aligned parallel to the borders of the environment, and exhibited impaired integration of distance traveled via reduced theta phase precession. Our results suggest that disrupted early AD grid coding reflects reduced integration of self-motion cues but not environmental information via geometric boundaries, providing evidence that grid cell impairments underlie path integration deficits during early AD.
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Affiliation(s)
- Johnson Ying
- Department of Psychiatry, Douglas Hospital Research Centre, McGill University, Montreal, QC H4H 1R3, Canada; Integrated Program in Neuroscience, McGill University, Montreal, QC H3A 0G4, Canada
| | - Antonio Reboreda
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany; Leibniz Institute for Neurobiology (LIN), Magdeburg 39120, Germany
| | - Motoharu Yoshida
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany; Leibniz Institute for Neurobiology (LIN), Magdeburg 39120, Germany; Center for Behavioral Brain Sciences (CBBS), Magdeburg 39106, Germany
| | - Mark P Brandon
- Department of Psychiatry, Douglas Hospital Research Centre, McGill University, Montreal, QC H4H 1R3, Canada; Integrated Program in Neuroscience, McGill University, Montreal, QC H3A 0G4, Canada.
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7
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Maheux E, Koval I, Ortholand J, Birkenbihl C, Archetti D, Bouteloup V, Epelbaum S, Dufouil C, Hofmann-Apitius M, Durrleman S. Forecasting individual progression trajectories in Alzheimer's disease. Nat Commun 2023; 14:761. [PMID: 36765056 PMCID: PMC9918533 DOI: 10.1038/s41467-022-35712-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 12/19/2022] [Indexed: 02/12/2023] Open
Abstract
The anticipation of progression of Alzheimer's disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials.
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Affiliation(s)
- Etienne Maheux
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Igor Koval
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Juliette Ortholand
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Colin Birkenbihl
- Department of bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115, Germany
| | - Damiano Archetti
- IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Vincent Bouteloup
- Université de Bordeaux, CNRS UMR 5293, Institut des Maladies Neurodégénératives, Bordeaux, France
- Centre Hospitalier Universitaire (CHU) de Bordeaux, pôle de neurosciences cliniques, centre mémoire de ressources et de recherche, Bordeaux, France
| | - Stéphane Epelbaum
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Institut de la mémoire et de la maladie d'Alzheimer (IM2A), center of excellence of neurodegenerative diseases (CoEN), department of Neurology, DMU Neurosciences, Paris, France
| | - Carole Dufouil
- Université de Bordeaux, CNRS UMR 5293, Institut des Maladies Neurodégénératives, Bordeaux, France
- Centre Hospitalier Universitaire (CHU) de Bordeaux, pôle de neurosciences cliniques, centre mémoire de ressources et de recherche, Bordeaux, France
| | - Martin Hofmann-Apitius
- Department of bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115, Germany
| | - Stanley Durrleman
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France.
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8
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MRI data quality assessment for the RIN - Neuroimaging Network using the ACR phantoms. Phys Med 2022; 104:93-100. [PMID: 36379160 DOI: 10.1016/j.ejmp.2022.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 09/20/2022] [Accepted: 10/08/2022] [Indexed: 11/13/2022] Open
Abstract
PURPOSE Generating big-data is becoming imperative with the advent of machine learning. RIN-Neuroimaging Network addresses this need by developing harmonized protocols for multisite studies to identify quantitative MRI (qMRI) biomarkers for neurological diseases. In this context, image quality control (QC) is essential. Here, we present methods and results of how the RIN performs intra- and inter-site reproducibility of geometrical and image contrast parameters, demonstrating the relevance of such QC practice. METHODS American College of Radiology (ACR) large and small phantoms were selected. Eighteen sites were equipped with a 3T scanner that differed by vendor, hardware/software versions, and receiver coils. The standard ACR protocol was optimized (in-plane voxel, post-processing filters, receiver bandwidth) and repeated monthly. Uniformity, ghosting, geometric accuracy, ellipse's ratio, slice thickness, and high-contrast detectability tests were performed using an automatic QC script. RESULTS Measures were mostly within the ACR tolerance ranges for both T1- and T2-weighted acquisitions, for all scanners, regardless of vendor, coil, and signal transmission chain type. All measurements showed good reproducibility over time. Uniformity and slice thickness failed at some sites. Scanners that upgraded the signal transmission chain showed a decrease in geometric distortion along the slice encoding direction. Inter-vendor differences were observed in uniformity and geometric measurements along the slice encoding direction (i.e. ellipse's ratio). CONCLUSIONS Use of the ACR phantoms highlighted issues that triggered interventions to correct performance at some sites and to improve the longitudinal stability of the scanners. This is relevant for establishing precision levels for future multisite studies of qMRI biomarkers.
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9
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Redolfi A, Archetti D, De Francesco S, Crema C, Tagliavini F, Lodi R, Ghidoni R, Gandini Wheeler-Kingshott CAM, Alexander DC, D'Angelo E. Italian, European, and international neuroinformatics efforts: An overview. Eur J Neurosci 2022. [PMID: 36310103 DOI: 10.1111/ejn.15854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/18/2022] [Accepted: 10/18/2022] [Indexed: 12/15/2022]
Abstract
Neuroinformatics is a research field that focusses on software tools capable of identifying, analysing, modelling, organising and sharing multiscale neuroscience data. Neuroinformatics has exploded in the last two decades with the emergence of the Big Data phenomenon, characterised by the so-called 3Vs (volume, velocity and variety), which provided neuroscientists with an improved ability to acquire and process data faster and more cheaply thanks to technical improvements in clinical, genomic and radiological technologies. This situation has led to a 'data deluge', as neuroscientists can routinely collect more study data in a few days than they could in a year just a decade ago. To address this phenomenon, several neuroimaging-focussed neuroinformatics platforms have emerged, funded by national or transnational agencies, with the following goals: (i) development of tools for archiving and organising analytical data (XNAT, REDCap and LabKey); (ii) development of data-driven models evolving from reductionist approaches to multidimensional models (RIN, IVN, HBD, EuroPOND, E-DADS and GAAIN BRAIN); and (iii) development of e-infrastructures to provide sufficient computational power and storage resources (neuGRID, HBP-EBRAINS, LONI and CONP). Although the scenario is still fragmented, there are technological and economical attempts at both national and international levels to introduce high standards for open and Findable, Accessible, Interoperable and Reusable (FAIR) neuroscience worldwide.
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Affiliation(s)
- Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Damiano Archetti
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Claudio Crema
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fabrizio Tagliavini
- Scientific Directorate, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Raffaele Lodi
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Roberta Ghidoni
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Center, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London, UK.,Department of Computer Science, University College London, London, UK
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
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10
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Ren G, Yu K, Xie Z, Wang P, Zhang W, Huang Y, Wang Y, Wu X. Current Applications of Machine Learning in Spine: From Clinical View. Global Spine J 2022; 12:1827-1840. [PMID: 34628966 PMCID: PMC9609532 DOI: 10.1177/21925682211035363] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES This review aims to present current applications of machine learning (ML) in spine domain to clinicians. METHODS We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. RESULTS Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. CONCLUSIONS ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.
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Affiliation(s)
- GuanRui Ren
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Kun Yu
- Nanjing Jiangbei Hospital, Nanjing,
Jiangsu, China
| | - ZhiYang Xie
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - PeiYang Wang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Wei Zhang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Yong Huang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - YunTao Wang
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,YunTao Wang, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
| | - XiaoTao Wu
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,XiaoTao Wu, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
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11
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van der Ende EL, Bron EE, Poos JM, Jiskoot LC, Panman JL, Papma JM, Meeter LH, Dopper EGP, Wilke C, Synofzik M, Heller C, Swift IJ, Sogorb-Esteve A, Bouzigues A, Borroni B, Sanchez-Valle R, Moreno F, Graff C, Laforce R, Galimberti D, Masellis M, Tartaglia MC, Finger E, Vandenberghe R, Rowe JB, de Mendonça A, Tagliavini F, Santana I, Ducharme S, Butler CR, Gerhard A, Levin J, Danek A, Otto M, Pijnenburg YAL, Sorbi S, Zetterberg H, Niessen WJ, Rohrer JD, Klein S, van Swieten JC, Venkatraghavan V, Seelaar H. A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia. Brain 2022; 145:1805-1817. [PMID: 34633446 PMCID: PMC9166533 DOI: 10.1093/brain/awab382] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 08/22/2021] [Accepted: 09/09/2021] [Indexed: 11/17/2022] Open
Abstract
Several CSF and blood biomarkers for genetic frontotemporal dementia have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain and phosphorylated neurofilament heavy chain), synapse dysfunction [neuronal pentraxin 2 (NPTX2)], astrogliosis (glial fibrillary acidic protein) and complement activation (C1q, C3b). Determining the sequence in which biomarkers become abnormal over the course of disease could facilitate disease staging and help identify mutation carriers with prodromal or early-stage frontotemporal dementia, which is especially important as pharmaceutical trials emerge. We aimed to model the sequence of biomarker abnormalities in presymptomatic and symptomatic genetic frontotemporal dementia using cross-sectional data from the Genetic Frontotemporal dementia Initiative (GENFI), a longitudinal cohort study. Two-hundred and seventy-five presymptomatic and 127 symptomatic carriers of mutations in GRN, C9orf72 or MAPT, as well as 247 non-carriers, were selected from the GENFI cohort based on availability of one or more of the aforementioned biomarkers. Nine presymptomatic carriers developed symptoms within 18 months of sample collection ('converters'). Sequences of biomarker abnormalities were modelled for the entire group using discriminative event-based modelling (DEBM) and for each genetic subgroup using co-initialized DEBM. These models estimate probabilistic biomarker abnormalities in a data-driven way and do not rely on previous diagnostic information or biomarker cut-off points. Using cross-validation, subjects were subsequently assigned a disease stage based on their position along the disease progression timeline. CSF NPTX2 was the first biomarker to become abnormal, followed by blood and CSF neurofilament light chain, blood phosphorylated neurofilament heavy chain, blood glial fibrillary acidic protein and finally CSF C3b and C1q. Biomarker orderings did not differ significantly between genetic subgroups, but more uncertainty was noted in the C9orf72 and MAPT groups than for GRN. Estimated disease stages could distinguish symptomatic from presymptomatic carriers and non-carriers with areas under the curve of 0.84 (95% confidence interval 0.80-0.89) and 0.90 (0.86-0.94) respectively. The areas under the curve to distinguish converters from non-converting presymptomatic carriers was 0.85 (0.75-0.95). Our data-driven model of genetic frontotemporal dementia revealed that NPTX2 and neurofilament light chain are the earliest to change among the selected biomarkers. Further research should investigate their utility as candidate selection tools for pharmaceutical trials. The model's ability to accurately estimate individual disease stages could improve patient stratification and track the efficacy of therapeutic interventions.
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Affiliation(s)
- Emma L van der Ende
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Esther E Bron
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Jackie M Poos
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Lize C Jiskoot
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Jessica L Panman
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Janne M Papma
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Lieke H Meeter
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Elise G P Dopper
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Carlo Wilke
- German Center for Neurodegenerative Diseases (DZNE), 72076 Tübingen, Germany
- Department of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, 72076 Tübingen, Germany
| | - Matthis Synofzik
- German Center for Neurodegenerative Diseases (DZNE), 72076 Tübingen, Germany
- Department of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, 72076 Tübingen, Germany
| | - Carolin Heller
- UK Dementia Research Institute at University College London, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
| | - Imogen J Swift
- UK Dementia Research Institute at University College London, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
| | - Aitana Sogorb-Esteve
- UK Dementia Research Institute at University College London, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
| | - Arabella Bouzigues
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
| | - Barbara Borroni
- Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
| | - Raquel Sanchez-Valle
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clinic, IDIBAPS, University of Barcelona, 08036 Barcelona, Spain
| | - Fermin Moreno
- Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, 20014 Gipuzkoa, Spain
- Neuroscience Area, Biodonostia Health Research Institute, San Sebastian, Gipuzkoa, Spain
| | - Caroline Graff
- Center for Alzheimer Research, Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Bioclinicum, Karolinska Institutet, 17176 Solna, Sweden
- Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, 17176 Solna, Sweden
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, Université Laval, G1Z 1J4 Québec, Canada
| | - Daniela Galimberti
- Centro Dino Ferrari, University of Milan, 20122 Milan, Italy
- Neurodegenerative Diseases Unit, Fondazione IRCCS, Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Mario Masellis
- Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, ON M4N 3M5 Toronto, Canada
| | - Maria Carmela Tartaglia
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, M5S 1A8 Toronto, Canada
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, University of Western Ontario, ON N6A 3K7 London, Ontario, Canada
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
| | - James B Rowe
- Cambridge University Centre for Frontotemporal Dementia, University of Cambridge, CB2 0SZ Cambridge, UK
| | | | | | - Isabel Santana
- Center for Neuroscience and Cell Biology, Faculty of Medicine, University of Coimbra, 3004-504 Coimbra, Portugal
| | - Simon Ducharme
- McConnell Brain Imaging Centre, Montreal Neurological Institute and McGill University Health Centre, McGill University, 3801 Montreal, Québec, Canada
| | - Christopher R Butler
- Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, OX3 9DU Oxford, UK
- Department of Brain Sciences, Imperial College London, SW7 2AZ London, UK
| | - Alexander Gerhard
- Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, M20 3LJ Manchester, UK
- Department of Nuclear Medicine and Geriatric Medicine, University Hospital Essen, 45 147 Essen, Germany
| | - Johannes Levin
- Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität München, 81377 Munich, Germany
- German Center for Neurodegenerative Diseases, 81377 Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany
| | - Adrian Danek
- Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität München, 81377 Munich, Germany
| | - Markus Otto
- Department of Neurology, University of Ulm, 89081 Ulm, Germany
| | - Yolande A L Pijnenburg
- Department of Neurology, Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center, 1105 AZ Amsterdam, The Netherlands
| | - Sandro Sorbi
- Department of Neurofarba, University of Florence, 50139 Florence, Italy
| | - Henrik Zetterberg
- UK Dementia Research Institute at University College London, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, 405 30 Mölndal, Sweden
| | - Wiro J Niessen
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Jonathan D Rohrer
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
| | - Stefan Klein
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - John C van Swieten
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Vikram Venkatraghavan
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Harro Seelaar
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
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12
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Golriz Khatami S, Salimi Y, Hofmann-Apitius M, Oxtoby NP, Birkenbihl C. Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer's disease. Alzheimers Res Ther 2022; 14:55. [PMID: 35443691 PMCID: PMC9020023 DOI: 10.1186/s13195-022-01001-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 04/06/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Previous models of Alzheimer's disease (AD) progression were primarily hypothetical or based on data originating from single cohort studies. However, cohort datasets are subject to specific inclusion and exclusion criteria that influence the signals observed in their collected data. Furthermore, each study measures only a subset of AD-relevant variables. To gain a comprehensive understanding of AD progression, the heterogeneity and robustness of estimated progression patterns must be understood, and complementary information contained in cohort datasets be leveraged. METHODS We compared ten event-based models that we fit to ten independent AD cohort datasets. Additionally, we designed and applied a novel rank aggregation algorithm that combines partially overlapping, individual event sequences into a meta-sequence containing the complementary information from each cohort. RESULTS We observed overall consistency across the ten event-based model sequences (average pairwise Kendall's tau correlation coefficient of 0.69 ± 0.28), despite variance in the positioning of mainly imaging variables. The changes described in the aggregated meta-sequence are broadly consistent with the current understanding of AD progression, starting with cerebrospinal fluid amyloid beta, followed by tauopathy, memory impairment, FDG-PET, and ultimately brain deterioration and impairment of visual memory. CONCLUSION Overall, the event-based models demonstrated similar and robust disease cascades across independent AD cohorts. Aggregation of data-driven results can combine complementary strengths and information of patient-level datasets. Accordingly, the derived meta-sequence draws a more complete picture of AD pathology compared to models relying on single cohorts.
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Affiliation(s)
- Sepehr Golriz Khatami
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany.
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany.
| | - Yasamin Salimi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
| | - Neil P Oxtoby
- Centre for Medical Image Computing and Department of Computer Science, University College London, Gower St, London, WC1E 6BT, UK
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
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13
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Ying J, Keinath AT, Lavoie R, Vigneault E, El Mestikawy S, Brandon MP. Disruption of the grid cell network in a mouse model of early Alzheimer's disease. Nat Commun 2022; 13:886. [PMID: 35173173 PMCID: PMC8850598 DOI: 10.1038/s41467-022-28551-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 01/27/2022] [Indexed: 11/28/2022] Open
Abstract
Early-onset familial Alzheimer’s disease (AD) is marked by an aggressive buildup of amyloid beta (Aβ) proteins, yet the neural circuit operations impacted during the initial stages of Aβ pathogenesis remain elusive. Here, we report a coding impairment of the medial entorhinal cortex (MEC) grid cell network in the J20 transgenic mouse model of familial AD that over-expresses Aβ throughout the hippocampus and entorhinal cortex. Grid cells showed reduced spatial periodicity, spatial stability, and synchrony with interneurons and head-direction cells. In contrast, the spatial coding of non-grid cells within the MEC, and place cells within the hippocampus, remained intact. Grid cell deficits emerged at the earliest incidence of Aβ fibril deposition and coincided with impaired spatial memory performance in a path integration task. These results demonstrate that widespread Aβ-mediated damage to the entorhinal-hippocampal circuit results in an early impairment of the entorhinal grid cell network. It remains poorly understood how the onset of Alzheimer’s disease affects spatial cognition. Here, the authors report that spatial coding in grid cells deteriorates over time in a mouse model of Alzheimer’s disease during the early stages of pathology while place cell and head direction coding remain intact.
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Affiliation(s)
- Johnson Ying
- Department of Psychiatry, Douglas Hospital Research Centre, McGill University, Montreal, QC, Canada.,Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Alexandra T Keinath
- Department of Psychiatry, Douglas Hospital Research Centre, McGill University, Montreal, QC, Canada
| | - Raphael Lavoie
- Department of Psychiatry, Douglas Hospital Research Centre, McGill University, Montreal, QC, Canada
| | - Erika Vigneault
- Department of Psychiatry, Douglas Hospital Research Centre, McGill University, Montreal, QC, Canada
| | - Salah El Mestikawy
- Department of Psychiatry, Douglas Hospital Research Centre, McGill University, Montreal, QC, Canada.,Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Mark P Brandon
- Department of Psychiatry, Douglas Hospital Research Centre, McGill University, Montreal, QC, Canada. .,Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada.
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14
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SimulAD: A dynamical model for personalized simulation and disease staging in Alzheimer’s disease. Neurobiol Aging 2022; 113:73-83. [DOI: 10.1016/j.neurobiolaging.2021.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 12/13/2021] [Accepted: 12/30/2021] [Indexed: 11/17/2022]
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15
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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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16
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De Francesco S, Galluzzi S, Vanacore N, Festari C, Rossini PM, Cappa SF, Frisoni GB, Redolfi A. Norms for Automatic Estimation of Hippocampal Atrophy and a Step Forward for Applicability to the Italian Population. Front Neurosci 2021; 15:656808. [PMID: 34262425 PMCID: PMC8273578 DOI: 10.3389/fnins.2021.656808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 06/03/2021] [Indexed: 11/23/2022] Open
Abstract
INTRODUCTION Hippocampal volume is one of the main biomarkers of Alzheimer's Dementia (AD). Over the years, advanced tools that performed automatic segmentation of Magnetic Resonance Imaging (MRI) T13D scans have been developed, such as FreeSurfer (FS) and ACM-Adaboost (AA). Hippocampal volume is considered abnormal when it is below the 5th percentile of the normative population. The aim of this study was to set norms, established from the Alzheimer's Disease Neuroimaging Initiative (ADNI) population, for hippocampal volume measured with FS v.6.0 and AA tools in the neuGRID platform (www.neugrid2.eu) and demonstrate their applicability for the Italian population. METHODS Norms were set from a large group of 545 healthy controls belonging to ADNI. For each pipeline, subjects with segmentation errors were discarded, resulting in 532 valid segmentations for FS and 421 for AA (age range 56-90 years). The comparability of ADNI and the Italian Brain Normative Archive (IBNA), representative of the Italian general population, was assessed testing clinical variables, neuropsychological scores and normalized hippocampal volumes. Finally, percentiles were validated using the Italian Alzheimer's disease Repository Without Borders (ARWiBo) as external independent data set to evaluate FS and AA generalizability. RESULTS Hippocampal percentiles were checked with the chi-square goodness of fit test. P-values were not significant, showing that FS and AA algorithm distributions fitted the data well. Clinical, neuropsychological and volumetric features were similar in ADNI and IBNA (p > 0.01). Hippocampal volumes measured with both FS and AA were associated with age (p < 0.001). The 5th percentile thresholds, indicating left/right hippocampal atrophy were respectively: (i) below 3,223/3,456 mm3 at 56 years and 2,506/2,415 mm3 at 90 years for FS; (ii) below 4,583/4,873 mm3 at 56 years and 3,831/3,870 mm3 at 90 years for AA. The average volumes computed on 100 cognitively intact healthy controls (CN) selected from ARWiBo were close to the 50th percentiles, while those for 100 AD patients were close to the abnormal percentiles. DISCUSSION Norms generated from ADNI through the automatic FS and AA segmentation tools may be used as normative references for Italian patients with suspected AD.
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Affiliation(s)
- Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Samantha Galluzzi
- Laboratory of Alzheimer’s Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Nicola Vanacore
- National Center for Disease Prevention and Health Promotion, National Institute of Health, Rome, Italy
| | - Cristina Festari
- Laboratory of Alzheimer’s Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Paolo Maria Rossini
- Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
| | - Stefano F. Cappa
- IRCCS Mondino Foundation, Pavia, Italy
- IUSS Cognitive Neuroscience (ICoN) Center, University School for Advanced Studies, Pavia, Italy
| | - Giovanni B. Frisoni
- Laboratory of Alzheimer’s Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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17
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Bron EE, Klein S, Papma JM, Jiskoot LC, Venkatraghavan V, Linders J, Aalten P, De Deyn PP, Biessels GJ, Claassen JAHR, Middelkoop HAM, Smits M, Niessen WJ, van Swieten JC, van der Flier WM, Ramakers IHGB, van der Lugt A. Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease. NEUROIMAGE-CLINICAL 2021; 31:102712. [PMID: 34118592 PMCID: PMC8203808 DOI: 10.1016/j.nicl.2021.102712] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 05/26/2021] [Accepted: 05/27/2021] [Indexed: 11/18/2022]
Abstract
This work validates the generalizability of MRI-based classification of Alzheimer’s disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the Alzheimer’s Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia. AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924–0.955) and CNN (0.933; 95%CI: 0.918–0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p<0.01 for McNemar’s test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855–0.932) and CNN (0.876; 95%CI: 0.836–0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p=0.01). Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice.
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Affiliation(s)
- Esther E Bron
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
| | - Stefan Klein
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Janne M Papma
- Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Lize C Jiskoot
- Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Vikram Venkatraghavan
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Jara Linders
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Pauline Aalten
- Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Peter Paul De Deyn
- Department of Neurology and Alzheimer Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Geert Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Huub A M Middelkoop
- Department of Neurology & Neuropsychology, Leiden University Medical Center, Leiden, The Netherlands; Institute of Psychology, Health, Medical and Neuropsychology Unit, Leiden University, The Netherlands
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Wiro J Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Imaging Physics, Applied Sciences, Delft University of Technology, The Netherlands
| | | | | | - Inez H G B Ramakers
- Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
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18
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Venkatraghavan V, Vinke EJ, Bron EE, Niessen WJ, Arfan Ikram M, Klein S, Vernooij MW. Progression along data-driven disease timelines is predictive of Alzheimer's disease in a population-based cohort. Neuroimage 2021; 238:118233. [PMID: 34091030 DOI: 10.1016/j.neuroimage.2021.118233] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 04/11/2021] [Accepted: 06/01/2021] [Indexed: 11/15/2022] Open
Abstract
Data-driven disease progression models have provided important insight into the timeline of brain changes in AD phenotypes. However, their utility in predicting the progression of pre-symptomatic AD in a population-based setting has not yet been investigated. In this study, we investigated if the disease timelines constructed in a case-controlled setting, with subjects stratified according to APOE status, are generalizable to a population-based cohort, and if progression along these disease timelines is predictive of AD. Seven volumetric biomarkers derived from structural MRI were considered. We estimated APOE-specific disease timelines of changes in these biomarkers using a recently proposed method called co-initialized discriminative event-based modeling (co-init DEBM). This method can also estimate a disease stage for new subjects by calculating their position along the disease timelines. The model was trained and cross-validated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and tested on the population-based Rotterdam Study (RS) cohort. We compared the diagnostic and prognostic value of the disease stage in the two cohorts. Furthermore, we investigated if the rate of change of disease stage in RS participants with longitudinal MRI data was predictive of AD. In ADNI, the estimated disease timeslines for ϵ4 non-carriers and carriers were found to be significantly different from one another (p<0.001). The estimate disease stage along the respective timelines distinguished AD subjects from controls with an AUC of 0.83 in both APOEϵ4 non-carriers and carriers. In the RS cohort, we obtained an AUC of 0.83 and 0.85 in ϵ4 non-carriers and carriers, respectively. Progression along the disease timelines as estimated by the rate of change of disease stage showed a significant difference (p<0.005) for subjects with pre-symptomatic AD as compared to the general aging population in RS. It distinguished pre-symptomatic AD subjects with an AUC of 0.81 in APOEϵ4 non-carriers and 0.88 in carriers, which was better than any individual volumetric biomarker, or its rate of change, could achieve. Our results suggest that co-init DEBM trained on case-controlled data is generalizable to a population-based cohort setting and that progression along the disease timelines is predictive of the development of AD in the general population. We expect that this approach can help to identify at-risk individuals from the general population for targeted clinical trials as well as to provide biomarker based objective assessment in such trials.
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Affiliation(s)
- Vikram Venkatraghavan
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands
| | - Elisabeth J Vinke
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, The Netherlands
| | - Esther E Bron
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands
| | - Wiro J Niessen
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands; Quantitative Imaging Group, Dept. of Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, The Netherlands
| | - Stefan Klein
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands
| | - Meike W Vernooij
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, The Netherlands.
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19
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Archetti D, Young AL, Oxtoby NP, Ferreira D, Mårtensson G, Westman E, Alexander DC, Frisoni GB, Redolfi A. Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease. Front Big Data 2021; 4:661110. [PMID: 34095821 PMCID: PMC8173213 DOI: 10.3389/fdata.2021.661110] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 05/04/2021] [Indexed: 01/15/2023] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disorder which spans several years from preclinical manifestations to dementia. In recent years, interest in the application of machine learning (ML) algorithms to personalized medicine has grown considerably, and a major challenge that such models face is the transferability from the research settings to clinical practice. The objective of this work was to demonstrate the transferability of the Subtype and Stage Inference (SuStaIn) model from well-characterized research data set, employed as training set, to independent less-structured and heterogeneous test sets representative of the clinical setting. The training set was composed of MRI data of 1043 subjects from the Alzheimer’s disease Neuroimaging Initiative (ADNI), and the test set was composed of data from 767 subjects from OASIS, Pharma-Cog, and ViTA clinical datasets. Both sets included subjects covering the entire spectrum of AD, and for both sets volumes of relevant brain regions were derived from T1-3D MRI scans processed with Freesurfer v5.3 cross-sectional stream. In order to assess the predictive value of the model, subpopulations of subjects with stable mild cognitive impairment (MCI) and MCIs that progressed to AD dementia (pMCI) were identified in both sets. SuStaIn identified three disease subtypes, of which the most prevalent corresponded to the typical atrophy pattern of AD. The other SuStaIn subtypes exhibited similarities with the previously defined hippocampal sparing and limbic predominant atrophy patterns of AD. Subject subtyping proved to be consistent in time for all cohorts and the staging provided by the model was correlated with cognitive performance. Classification of subjects on the basis of a combination of SuStaIn subtype and stage, mini mental state examination and amyloid-β1-42 cerebrospinal fluid concentration was proven to predict conversion from MCI to AD dementia on par with other novel statistical algorithms, with ROC curves that were not statistically different for the training and test sets and with area under curve respectively equal to 0.77 and 0.76. This study proves the transferability of a SuStaIn model for AD from research data to less-structured clinical cohorts, and indicates transferability to the clinical setting.
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Affiliation(s)
- Damiano Archetti
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Department of Computer Science, UCL Centre for Medical Image Computing, London, United Kingdom
| | - Neil P Oxtoby
- Department of Computer Science, UCL Centre for Medical Image Computing, London, United Kingdom
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.,Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Gustav Mårtensson
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Daniel C Alexander
- Department of Computer Science, UCL Centre for Medical Image Computing, London, United Kingdom
| | - Giovanni B Frisoni
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland.,Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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20
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Koval I, Bône A, Louis M, Lartigue T, Bottani S, Marcoux A, Samper-González J, Burgos N, Charlier B, Bertrand A, Epelbaum S, Colliot O, Allassonnière S, Durrleman S. AD Course Map charts Alzheimer's disease progression. Sci Rep 2021; 11:8020. [PMID: 33850174 PMCID: PMC8044144 DOI: 10.1038/s41598-021-87434-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 03/22/2021] [Indexed: 02/06/2023] Open
Abstract
Alzheimer's disease (AD) is characterized by the progressive alterations seen in brain images which give rise to the onset of various sets of symptoms. The variability in the dynamics of changes in both brain images and cognitive impairments remains poorly understood. This paper introduces AD Course Map a spatiotemporal atlas of Alzheimer's disease progression. It summarizes the variability in the progression of a series of neuropsychological assessments, the propagation of hypometabolism and cortical thinning across brain regions and the deformation of the shape of the hippocampus. The analysis of these variations highlights strong genetic determinants for the progression, like possible compensatory mechanisms at play during disease progression. AD Course Map also predicts the patient's cognitive decline with a better accuracy than the 56 methods benchmarked in the open challenge TADPOLE. Finally, AD Course Map is used to simulate cohorts of virtual patients developing Alzheimer's disease. AD Course Map offers therefore new tools for exploring the progression of AD and personalizing patients care.
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Affiliation(s)
- Igor Koval
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
- Centre de Mathématiques Appliquées, Ecole Polytechnique, Palaiseau, France
| | - Alexandre Bône
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Maxime Louis
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Thomas Lartigue
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
- Centre de Mathématiques Appliquées, Ecole Polytechnique, Palaiseau, France
| | - Simona Bottani
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Arnaud Marcoux
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Jorge Samper-González
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Ninon Burgos
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Benjamin Charlier
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
- Laboratoire Alexandre Grotendieck, Université de Montpellier, Montpellier, France
| | - Anne Bertrand
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Stéphane Epelbaum
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Olivier Colliot
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France
- Inria, Aramis project-team, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Stéphanie Allassonnière
- Centre de Recherche des Cordeliers, Université Paris Descartes, Paris, France
- Centre de Mathématiques Appliquées, Ecole Polytechnique, Palaiseau, France
| | - Stanley Durrleman
- Institut du Cerveau et de la Moelle épinière (ICM) & Inserm, U 1127 & CNRS, UMR 7225, Sorbonne Université, 75013, Paris, France.
- Inria, Aramis project-team, Paris, France.
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21
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Buckley RF. Recent Advances in Imaging of Preclinical, Sporadic, and Autosomal Dominant Alzheimer's Disease. Neurotherapeutics 2021; 18:709-727. [PMID: 33782864 PMCID: PMC8423933 DOI: 10.1007/s13311-021-01026-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2021] [Indexed: 12/25/2022] Open
Abstract
Observing Alzheimer's disease (AD) pathological changes in vivo with neuroimaging provides invaluable opportunities to understand and predict the course of disease. Neuroimaging AD biomarkers also allow for real-time tracking of disease-modifying treatment in clinical trials. With recent neuroimaging advances, along with the burgeoning availability of longitudinal neuroimaging data and big-data harmonization approaches, a more comprehensive evaluation of the disease has shed light on the topographical staging and temporal sequencing of the disease. Multimodal imaging approaches have also promoted the development of data-driven models of AD-associated pathological propagation of tau proteinopathies. Studies of autosomal dominant, early sporadic, and late sporadic courses of the disease have shed unique insights into the AD pathological cascade, particularly with regard to genetic vulnerabilities and the identification of potential drug targets. Further, neuroimaging markers of b-amyloid, tau, and neurodegeneration have provided a powerful tool for validation of novel fluid cerebrospinal and plasma markers. This review highlights some of the latest advances in the field of human neuroimaging in AD across these topics, particularly with respect to positron emission tomography and structural and functional magnetic resonance imaging.
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Affiliation(s)
- Rachel F Buckley
- Department of Neurology, Massachusetts General Hospital & Brigham and Women's, Harvard Medical School, Boston, MA, USA.
- Melbourne School of Psychological Sciences and Florey Institutes, University of Melbourne, Melbourne, VIC, Australia.
- Department of Neurology, Massachusetts General Hospital, 149 13th St, Charlestown, MA, 02129, USA.
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22
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Analyzing the effect of APOE on Alzheimer's disease progression using an event-based model for stratified populations. Neuroimage 2020; 227:117646. [PMID: 33338617 DOI: 10.1016/j.neuroimage.2020.117646] [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] [Received: 09/15/2020] [Revised: 11/12/2020] [Accepted: 12/10/2020] [Indexed: 02/08/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia and is phenotypically heterogeneous. APOE is a triallelic gene which correlates with phenotypic heterogeneity in AD. In this work, we determined the effect of APOE alleles on the disease progression timeline of AD using a discriminative event-based model (DEBM). Since DEBM is a data-driven model, stratification into smaller disease subgroups would lead to more inaccurate models as compared to fitting the model on the entire dataset. Hence our secondary aim is to propose and evaluate novel approaches in which we split the different steps of DEBM into group-aspecific and group-specific parts, where the entire dataset is used to train the group-aspecific parts and only the data from a specific group is used to train the group-specific parts of the DEBM. We performed simulation experiments to benchmark the accuracy of the proposed approaches and to select the optimal approach. Subsequently, the chosen approach was applied to the baseline data of 417 cognitively normal, 235 mild cognitively impaired who convert to AD within 3 years, and 342 AD patients from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset to gain new insights into the effect of APOE carriership on the disease progression timeline of AD. In the ε4 carrier group, the model predicted with high confidence that CSF Amyloidβ42 and the cognitive score of Alzheimer's Disease Assessment Scale (ADAS) are early biomarkers. Hippocampus was the earliest volumetric biomarker to become abnormal, closely followed by the CSF Phosphorylated Tau181 (PTAU) biomarker. In the homozygous ε3 carrier group, the model predicted a similar ordering among CSF biomarkers. However, the volume of the fusiform gyrus was identified as one of the earliest volumetric biomarker. While the findings in the ε4 carrier and the homozygous ε3 carrier groups fit the current understanding of progression of AD, the finding in the ε2 carrier group did not. The model predicted, with relatively low confidence, CSF Neurogranin as one of the earliest biomarkers along with cognitive score of Mini-Mental State Examination (MMSE). Amyloid β42 was found to become abnormal after PTAU. The presented models could aid understanding of the disease, and in selecting homogeneous group of presymptomatic subjects at-risk of developing symptoms for clinical trials.
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23
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Ansart M, Epelbaum S, Bassignana G, Bône A, Bottani S, Cattai T, Couronné R, Faouzi J, Koval I, Louis M, Thibeau-Sutre E, Wen J, Wild A, Burgos N, Dormont D, Colliot O, Durrleman S. Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review. Med Image Anal 2020; 67:101848. [PMID: 33091740 DOI: 10.1016/j.media.2020.101848] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 08/17/2020] [Accepted: 08/31/2020] [Indexed: 11/25/2022]
Abstract
We performed a systematic review of studies focusing on the automatic prediction of the progression of mild cognitive impairment to Alzheimer's disease (AD) dementia, and a quantitative analysis of the methodological choices impacting performance. This review included 172 articles, from which 234 experiments were extracted. For each of them, we reported the used data set, the feature types, the algorithm type, performance and potential methodological issues. The impact of these characteristics on the performance was evaluated using a multivariate mixed effect linear regressions. We found that using cognitive, fluorodeoxyglucose-positron emission tomography or potentially electroencephalography and magnetoencephalography variables significantly improved predictive performance compared to not including them, whereas including other modalities, in particular T1 magnetic resonance imaging, did not show a significant effect. The good performance of cognitive assessments questions the wide use of imaging for predicting the progression to AD and advocates for exploring further fine domain-specific cognitive assessments. We also identified several methodological issues, including the absence of a test set, or its use for feature selection or parameter tuning in nearly a fourth of the papers. Other issues, found in 15% of the studies, cast doubts on the relevance of the method to clinical practice. We also highlight that short-term predictions are likely not to be better than predicting that subjects stay stable over time. These issues highlight the importance of adhering to good practices for the use of machine learning as a decision support system for the clinical practice.
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Affiliation(s)
- Manon Ansart
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France.
| | - Stéphane Epelbaum
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France; Institute of Memory and Alzheimer's Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, F-75013, France
| | - Giulia Bassignana
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Alexandre Bône
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Simona Bottani
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Tiziana Cattai
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France; Dept. of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, Italy
| | - Raphaël Couronné
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Johann Faouzi
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Igor Koval
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Maxime Louis
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Elina Thibeau-Sutre
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Junhao Wen
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Adam Wild
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Ninon Burgos
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France
| | - Didier Dormont
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France; AP-HP, Pitié-Salpêtrière hospital, Department of Neuroradiology, Paris, France
| | - Olivier Colliot
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France; Inria, Aramis project-team, Paris, F-75013, France; Institute of Memory and Alzheimer's Disease (IM2A), Centre of excellence of neurodegenerative disease (CoEN), National Reference Center for Rare or Early Dementias, Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, F-75013, France; AP-HP, Pitié-Salpêtrière hospital, Department of Neuroradiology, Paris, France
| | - Stanley Durrleman
- Inria, Aramis project-team, Paris, F-75013, France; Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France; Inserm, U 1127, Paris, F-75013, France; CNRS, UMR 7225, Paris, F-75013, France; Sorbonne Université, Paris, F-75013, France
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24
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Redolfi A, De Francesco S, Palesi F, Galluzzi S, Muscio C, Castellazzi G, Tiraboschi P, Savini G, Nigri A, Bottini G, Bruzzone MG, Ramusino MC, Ferraro S, Gandini Wheeler-Kingshott CAM, Tagliavini F, Frisoni GB, Ryvlin P, Demonet JF, Kherif F, Cappa SF, D'Angelo E. Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts. Front Neurol 2020; 11:1021. [PMID: 33071930 PMCID: PMC7538836 DOI: 10.3389/fneur.2020.01021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 08/04/2020] [Indexed: 12/13/2022] Open
Abstract
Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify-CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from "slight" to "significant" in 80% of the cases. Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology.
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Affiliation(s)
- Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | - Samantha Galluzzi
- Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Cristina Muscio
- Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Gloria Castellazzi
- IRCCS Mondino Foundation, Pavia, Italy
- NMR Research Unit, Queen Square MS Center, Department of Neuroinflammation, UCL Institute of Neurology, London, United Kingdom
- Department of Computer, Electrical and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Pietro Tiraboschi
- Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | - Anna Nigri
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Gabriella Bottini
- Neuropsychology Center, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Maria Grazia Bruzzone
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Matteo Cotta Ramusino
- IRCCS Mondino Foundation, Pavia, Italy
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Stefania Ferraro
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
- NMR Research Unit, Queen Square MS Center, Department of Neuroinflammation, UCL Institute of Neurology, London, United Kingdom
| | - Fabrizio Tagliavini
- Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Giovanni B. Frisoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Leenaards Memory Center, Center Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Jean-François Demonet
- Department of Clinical Neurosciences, Leenaards Memory Center, Center Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Ferath Kherif
- Department of Clinical Neurosciences, Leenaards Memory Center, Center Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Stefano F. Cappa
- IRCCS Mondino Foundation, Pavia, Italy
- University School of Advanced Studies, Pavia, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
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25
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Fransquet PD, Lacaze P, Saffery R, Phung J, Parker E, Shah R, Murray A, Woods RL, Ryan J. Blood DNA methylation signatures to detect dementia prior to overt clinical symptoms. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12056. [PMID: 32671182 PMCID: PMC7346866 DOI: 10.1002/dad2.12056] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 05/27/2020] [Accepted: 05/27/2020] [Indexed: 12/13/2022]
Abstract
INTRODUCTION This study determined whether blood DNA methylation (DNAm) patterns differentiate individuals with presymptomatic dementia compared to controls. METHODS DNAm was measured in 73 individuals prior to dementia diagnosis and 87 cognitively healthy controls matched for age, sex, smoking, education, and baseline cognition. DNAm was also measured at 3 years follow-up in 25 dementia cases, and 24 controls. RESULTS Cases and controls differed in DNAm (unadjusted P < .01) at the time of diagnosis (n = 28,787 probes), and pre-diagnosis (n = 15,111 probes), with cg01404610 (General transcription factor IIA subunit 1 gene) significant after correction for multiple testing. Overall, 1150 probes overlapped between analyses (methylation differences from -10.6% to +11.0%), and effect sizes increased from pre-diagnosis to diagnosis. DISCUSSION Discernible blood DNAm signatures are in dementia cases before the appearance of overt clinical symptoms. Blood-based methylation may serve as a potential biomarker of dementia, but further investigation is needed to determine their true clinical utility.
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Affiliation(s)
- Peter Daniel Fransquet
- School of Public Health and Preventive MedicineMonash UniversityMelbourneVictoriaAustralia
| | - Paul Lacaze
- School of Public Health and Preventive MedicineMonash UniversityMelbourneVictoriaAustralia
| | - Richard Saffery
- Department of PaediatricsMurdoch Children's Research InstituteThe University of MelbourneParkvilleVictoriaAustralia
| | - James Phung
- School of Public Health and Preventive MedicineMonash UniversityMelbourneVictoriaAustralia
| | - Emily Parker
- School of Public Health and Preventive MedicineMonash UniversityMelbourneVictoriaAustralia
| | - Raj Shah
- Department of Family Medicine and Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
| | - Anne Murray
- Berman Center for Outcomes and Clinical ResearchHennepin Healthcare, Division of GeriatricsDepartment of MedicineHennepin Healthcare Research InstituteUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Robyn L. Woods
- School of Public Health and Preventive MedicineMonash UniversityMelbourneVictoriaAustralia
| | - Joanne Ryan
- School of Public Health and Preventive MedicineMonash UniversityMelbourneVictoriaAustralia
- PSNRECUniversité de MontpellierINSERMMontpellierFrance
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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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