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Delvenne A, Gobom J, Schindler SE, Kate MT, Reus LM, Dobricic V, Tijms BM, Benzinger TLS, Cruchaga C, Teunissen CE, Ramakers I, Martinez-Lage P, Tainta M, Vandenberghe R, Schaeverbeke J, Engelborghs S, Roeck ED, Popp J, Peyratout G, Tsolaki M, Freund-Levi Y, Lovestone S, Streffer J, Barkhof F, Bertram L, Blennow K, Zetterberg H, Visser PJ, Vos SJB. CSF proteomic profiles of neurodegeneration biomarkers in Alzheimer's disease. Alzheimers Dement 2024. [PMID: 38970402 DOI: 10.1002/alz.14103] [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: 04/03/2024] [Revised: 06/05/2024] [Accepted: 06/06/2024] [Indexed: 07/08/2024]
Abstract
INTRODUCTION We aimed to unravel the underlying pathophysiology of the neurodegeneration (N) markers neurogranin (Ng), neurofilament light (NfL), and hippocampal volume (HCV), in Alzheimer's disease (AD) using cerebrospinal fluid (CSF) proteomics. METHODS Individuals without dementia were classified as A+ (CSF amyloid beta [Aβ]42), T+ (CSF phosphorylated tau181), and N+ or N- based on Ng, NfL, or HCV separately. CSF proteomics were generated and compared between groups using analysis of covariance. RESULTS Only a few individuals were A+T+Ng-. A+T+Ng+ and A+T+NfL+ showed different proteomic profiles compared to A+T+Ng- and A+T+NfL-, respectively. Both Ng+ and NfL+ were associated with neuroplasticity, though in opposite directions. Compared to A+T+HCV-, A+T+HCV+ showed few proteomic changes, associated with oxidative stress. DISCUSSION Different N markers are associated with distinct neurodegenerative processes and should not be equated. N markers may differentially complement disease staging beyond amyloid and tau. Our findings suggest that Ng may not be an optimal N marker, given its low incongruency with tau pathophysiology. HIGHLIGHTS In Alzheimer's disease, neurogranin (Ng)+, neurofilament light (NfL)+, and hippocampal volume (HCV)+ showed differential protein expression in cerebrospinal fluid. Ng+ and NfL+ were associated with neuroplasticity, although in opposite directions. HCV+ showed few proteomic changes, related to oxidative stress. Neurodegeneration (N) markers may differentially refine disease staging beyond amyloid and tau. Ng might not be an optimal N marker, as it relates more closely to tau.
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Affiliation(s)
- Aurore Delvenne
- Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Johan Gobom
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Mara Ten Kate
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Lianne M Reus
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Valerija Dobricic
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam University Medical Centers (AUMC), Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Inez Ramakers
- Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | | | - Mikel Tainta
- Fundación CITA-Alzhéimer Fundazioa, Donostia, Spain
| | - Rik Vandenberghe
- Neurology Service, University Hospitals Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Jolien Schaeverbeke
- Neurology Service, University Hospitals Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology and Bru-BRAIN, Universitair Ziekenhuis Brussel and NEUR Research Group, Center for Neurosciences (C4N), Vrije Universiteit Brussel, Brussels, Belgium
| | - Ellen De Roeck
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
| | - Julius Popp
- Old Age Psychiatry, University Hospital Lausanne, Lausanne, Switzerland
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatry University Hospital Zürich, Zürich, Switzerland
| | | | - Magda Tsolaki
- 1st Department of Neurology, AHEPA University Hospital, Medical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Makedonia, Thessaloniki, Greece
| | - Yvonne Freund-Levi
- Department of Neurobiology, Caring Sciences and Society (NVS), Division of Clinical Geriatrics, Karolinska Institutet, Huddinge, Stockholm, Sweden
- Department of Psychiatry in Region Örebro County and School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Department of Old Age Psychiatry, Psychology & Neuroscience, King's College, London, UK
| | - Simon Lovestone
- University of Oxford, United Kingdom (currently at Johnson and Johnson Medical Ltd., Oxford, UK
| | - Johannes Streffer
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- H. Lundbeck A/S, Valby, Denmark
| | - Frederik Barkhof
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Paris Brain Institute, ICM, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, and Department of Neurology, Institute on Aging and Brain Disorders, University of Science and Technology of China and First Affiliated Hospital of USTC, Hefei, P.R. China
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Pieter Jelle Visser
- Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Huddinge, Stockholm, Sweden
| | - Stephanie J B Vos
- Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
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Zhuang X, Cordes D, Bender AR, Nandy R, Oh EC, Kinney J, Caldwell JZK, Cummings J, Miller J. Classifying Alzheimer's Disease Neuropathology Using Clinical and MRI Measurements. J Alzheimers Dis 2024:JAD231321. [PMID: 38943387 DOI: 10.3233/jad-231321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2024]
Abstract
Background Computer-aided machine learning models are being actively developed with clinically available biomarkers to diagnose Alzheimer's disease (AD) in living persons. Despite considerable work with cross-sectional in vivo data, many models lack validation against postmortem AD neuropathological data. Objective Train machine learning models to classify the presence or absence of autopsy-confirmed severe AD neuropathology using clinically available features. Methods AD neuropathological status are assessed at postmortem for participants from the National Alzheimer's Coordinating Center (NACC). Clinically available features are utilized, including demographics, Apolipoprotein E(APOE) genotype, and cortical thicknesses derived from ante-mortem MRI scans encompassing AD meta regions of interest (meta-ROI). Both logistic regression and random forest models are trained to identify linearly and nonlinearly separable features between participants with the presence (N = 91, age-at-MRI = 73.6±9.24, 38 women) or absence (N = 53, age-at-MRI = 68.93±19.69, 24 women) of severe AD neuropathology. The trained models are further validated in an external data set against in vivo amyloid biomarkers derived from PET imaging (amyloid-positive: N = 71, age-at-MRI = 74.17±6.37, 26 women; amyloid-negative: N = 73, age-at-MRI = 71.59±6.80, 41 women). Results Our models achieve a cross-validation accuracy of 84.03% in classifying the presence or absence of severe AD neuropathology, and an external-validation accuracy of 70.14% in classifying in vivo amyloid positivity status. Conclusions Our models show that clinically accessible features, including APOE genotype and cortical thinning encompassing AD meta-ROIs, are able to classify both postmortem confirmed AD neuropathological status and in vivo amyloid status with reasonable accuracies. These results suggest the potential utility of AD meta-ROIs in determining AD neuropathological status in living persons.
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Affiliation(s)
- Xiaowei Zhuang
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
- Interdisciplinary Neuroscience PhD Program, University of Nevada Las Vegas, Las Vegas, NV, USA
- Laboratory of Neurogenetics and Precision Medicine, University of Nevada, Las Vegas, Las Vegas, NV, USA
| | - Dietmar Cordes
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
- University of Colorado Boulder, Boulder, CO, USA
| | - Andrew R Bender
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | - Rajesh Nandy
- Department of Biostatistics & Epidemiology, School of Public Health, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Edwin C Oh
- Interdisciplinary Neuroscience PhD Program, University of Nevada Las Vegas, Las Vegas, NV, USA
- Laboratory of Neurogenetics and Precision Medicine, University of Nevada, Las Vegas, Las Vegas, NV, USA
- Department of Internal Medicine, School of Medicine, University of Nevada, Las Vegas, Las Vegas, NV, USA
| | - Jefferson Kinney
- Interdisciplinary Neuroscience PhD Program, University of Nevada Las Vegas, Las Vegas, NV, USA
- Department of Brain Health, Chambers-Grundy Center for Transformative Neuroscience, School of Integrated Health Sciences, University of Nevada, Las Vegas, Las Vegas, NV, USA
| | | | - Jeffrey Cummings
- Department of Brain Health, Chambers-Grundy Center for Transformative Neuroscience, School of Integrated Health Sciences, University of Nevada, Las Vegas, Las Vegas, NV, USA
| | - Justin Miller
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
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Hata M, Miyazaki Y, Mori K, Yoshiyama K, Akamine S, Kanemoto H, Gotoh S, Omori H, Hirashima A, Satake Y, Suehiro T, Takahashi S, Ikeda M. Utilizing portable electroencephalography to screen for pathology of Alzheimer's disease: a methodological advancement in diagnosis of neurodegenerative diseases. Front Psychiatry 2024; 15:1392158. [PMID: 38855641 PMCID: PMC11157607 DOI: 10.3389/fpsyt.2024.1392158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024] Open
Abstract
Background The current biomarker-supported diagnosis of Alzheimer's disease (AD) is hindered by invasiveness and cost issues. This study aimed to address these challenges by utilizing portable electroencephalography (EEG). We propose a novel, non-invasive, and cost-effective method for identifying AD, using a sample of patients with biomarker-verified AD, to facilitate early and accessible disease screening. Methods This study included 35 patients with biomarker-verified AD, confirmed via cerebrospinal fluid sampling, and 35 age- and sex-balanced healthy volunteers (HVs). All participants underwent portable EEG recordings, focusing on 2-minute resting-state EEG epochs with closed eyes state. EEG recordings were transformed into scalogram images, which were analyzed using "vision Transformer(ViT)," a cutting-edge deep learning model, to differentiate patients from HVs. Results The application of ViT to the scalogram images derived from portable EEG data demonstrated a significant capability to distinguish between patients with biomarker-verified AD and HVs. The method achieved an accuracy of 73%, with an area under the receiver operating characteristic curve of 0.80, indicating robust performance in identifying AD pathology using neurophysiological measures. Conclusions Our findings highlight the potential of portable EEG combined with advanced deep learning techniques as a transformative tool for screening of biomarker-verified AD. This study not only contributes to the neurophysiological understanding of AD but also opens new avenues for the development of accessible and non-invasive diagnostic methods. The proposed approach paves the way for future clinical applications, offering a promising solution to the limitations of advanced diagnostic practices for dementia.
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Affiliation(s)
- Masahiro Hata
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yuki Miyazaki
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kohji Mori
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kenji Yoshiyama
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Shoshin Akamine
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hideki Kanemoto
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Shiho Gotoh
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hisaki Omori
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Psychiatry, Esaka Hospital, Osaka, Japan
| | - Atsuya Hirashima
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Psychiatry, Esaka Hospital, Osaka, Japan
| | - Yuto Satake
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Takashi Suehiro
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Shun Takahashi
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Occupational Therapy, Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka, Japan
- Clinical Research and Education Center, Asakayama General Hospital, Osaka, Japan
- Department of Neuropsychiatry, Wakayama Medical University, Wakayama, Japan
| | - Manabu Ikeda
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
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Hojjati SH, Babajani-Feremi A. Seeing beyond the symptoms: biomarkers and brain regions linked to cognitive decline in Alzheimer's disease. Front Aging Neurosci 2024; 16:1356656. [PMID: 38813532 PMCID: PMC11135344 DOI: 10.3389/fnagi.2024.1356656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 04/08/2024] [Indexed: 05/31/2024] Open
Abstract
Objective Early Alzheimer's disease (AD) diagnosis remains challenging, necessitating specific biomarkers for timely detection. This study aimed to identify such biomarkers and explore their associations with cognitive decline. Methods A cohort of 1759 individuals across cognitive aging stages, including healthy controls (HC), mild cognitive impairment (MCI), and AD, was examined. Utilizing nine biomarkers from structural MRI (sMRI), diffusion tensor imaging (DTI), and positron emission tomography (PET), predictions were made for Mini-Mental State Examination (MMSE), Clinical Dementia Rating Scale Sum of Boxes (CDRSB), and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS). Biomarkers included four sMRI (e.g., average thickness [ATH]), four DTI (e.g., mean diffusivity [MD]), and one PET Amyloid-β (Aβ) measure. Ensemble regression tree (ERT) technique with bagging and random forest approaches were applied in four groups (HC/MCI, HC/AD, MCI/AD, and HC/MCI/AD). Results Aβ emerged as a robust predictor of cognitive scores, particularly in late-stage AD. Volumetric measures, notably ATH, consistently correlated with cognitive scores across early and late disease stages. Additionally, ADAS demonstrated links to various neuroimaging biomarkers in all subject groups, highlighting its efficacy in monitoring brain changes throughout disease progression. ERT identified key brain regions associated with cognitive scores, such as the right transverse temporal region for Aβ, left and right entorhinal cortex, left inferior temporal gyrus, and left middle temporal gyrus for ATH, and the left uncinate fasciculus for MD. Conclusion This study underscores the importance of an interdisciplinary approach in understanding AD mechanisms, offering potential contributions to early biomarker development.
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Affiliation(s)
- Seyed Hani Hojjati
- Department of Radiology, Weill Cornell Medicine, Brain Health Imaging Institute, New York, NY, United States
| | - Abbas Babajani-Feremi
- Department of Neurology, University of Florida, Gainesville, FL, United States
- Magnetoencephalography (MEG) Lab, The Norman Fixel Institute of Neurological Diseases, University of Florida Health, Gainesville, FL, United States
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Momota Y, Bun S, Hirano J, Kamiya K, Ueda R, Iwabuchi Y, Takahata K, Yamamoto Y, Tezuka T, Kubota M, Seki M, Shikimoto R, Mimura Y, Kishimoto T, Tabuchi H, Jinzaki M, Ito D, Mimura M. Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders. Sci Rep 2024; 14:7633. [PMID: 38561395 PMCID: PMC10984960 DOI: 10.1038/s41598-024-58223-3] [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: 09/12/2023] [Accepted: 03/26/2024] [Indexed: 04/04/2024] Open
Abstract
Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer's disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.
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Affiliation(s)
- Yuki Momota
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
- Department of Functional Brain Imaging Research, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage-Ku, Chiba-Shi, Chiba, 263-8555, Japan
| | - Shogyoku Bun
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
| | - Jinichi Hirano
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
| | - Kei Kamiya
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Ryo Ueda
- Office of Radiation Technology, Keio University Hospital, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Yu Iwabuchi
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Keisuke Takahata
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
- Department of Functional Brain Imaging Research, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage-Ku, Chiba-Shi, Chiba, 263-8555, Japan
| | - Yasuharu Yamamoto
- Department of Functional Brain Imaging Research, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage-Ku, Chiba-Shi, Chiba, 263-8555, Japan
| | - Toshiki Tezuka
- Department of Neurology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Masahito Kubota
- Department of Neurology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Morinobu Seki
- Department of Neurology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Ryo Shikimoto
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Yu Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Taishiro Kishimoto
- Psychiatry Department, Donald and Barbara Zucker School of Medicine, Hempstead, NY, 11549, USA
- Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine, Mori JP Tower F7, 1-3-1 Azabudai, Minato-ku, Tokyo, 106-0041, Japan
| | - Hajime Tabuchi
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Masahiro Jinzaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Daisuke Ito
- Department of Physiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
- Memory Center, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Masaru Mimura
- Center for Preventive Medicine, Keio University, Mori JP Tower 7th Floor, 1-3-1 Azabudai, Minato-ku, Tokyo, 106-0041, Japan
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Mehdipour Ghazi M, Selnes P, Timón-Reina S, Tecelão S, Ingala S, Bjørnerud A, Kirsebom BE, Fladby T, Nielsen M. Comparative analysis of multimodal biomarkers for amyloid-beta positivity detection in Alzheimer's disease cohorts. Front Aging Neurosci 2024; 16:1345417. [PMID: 38469163 PMCID: PMC10925621 DOI: 10.3389/fnagi.2024.1345417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 02/12/2024] [Indexed: 03/13/2024] Open
Abstract
Introduction Efforts to develop cost-effective approaches for detecting amyloid pathology in Alzheimer's disease (AD) have gained significant momentum with a focus on biomarker classification. Recent research has explored non-invasive and readily accessible biomarkers, including magnetic resonance imaging (MRI) biomarkers and some AD risk factors. Methods In this comprehensive study, we leveraged a diverse dataset, encompassing participants with varying cognitive statuses from multiple sources, including cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and our in-house Dementia Disease Initiation (DDI) cohort. As brain amyloid plaques have been proposed as sufficient for AD diagnosis, our primary aim was to assess the effectiveness of multimodal biomarkers in identifying amyloid plaques, using deep machine learning methodologies. Results Our findings underscore the robustness of the utilized methods in detecting amyloid beta positivity across multiple cohorts. Additionally, we investigated the potential of demographic data to enhance MRI-based amyloid detection. Notably, the inclusion of demographic risk factors significantly improved our models' ability to detect amyloid-beta positivity, particularly in early-stage cases, exemplified by an average area under the ROC curve of 0.836 in the unimpaired DDI cohort. Discussion These promising, non-invasive, and cost-effective predictors of MRI biomarkers and demographic variables hold the potential for further refinement through considerations like APOE genotype and plasma markers.
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Affiliation(s)
- Mostafa Mehdipour Ghazi
- Department of Computer Science, Pioneer Centre for Artificial Intelligence, University of Copenhagen, Copenhagen, Denmark
| | - Per Selnes
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
| | | | - Sandra Tecelão
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | - Silvia Ingala
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Atle Bjørnerud
- Department of Physics, University of Oslo, Oslo, Norway
- Unit for Computational Radiology and Artificial Intelligence, Oslo University Hospital, Oslo, Norway
| | - Bjørn-Eivind Kirsebom
- Department of Neurology, University Hospital of North Norway, Tromsø, Norway
- Department of Psychology, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway
| | - Tormod Fladby
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
| | - Mads Nielsen
- Department of Computer Science, Pioneer Centre for Artificial Intelligence, University of Copenhagen, Copenhagen, Denmark
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Bisi N, Pinzi L, Rastelli G, Tonali N. Early Diagnosis of Neurodegenerative Diseases: What Has Been Undertaken to Promote the Transition from PET to Fluorescence Tracers. Molecules 2024; 29:722. [PMID: 38338465 PMCID: PMC10856728 DOI: 10.3390/molecules29030722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
Alzheimer's Disease (AD) and Parkinson's Disease (PD) represent two among the most frequent neurodegenerative diseases worldwide. A common hallmark of these pathologies is the misfolding and consequent aggregation of amyloid proteins into soluble oligomers and insoluble β-sheet-rich fibrils, which ultimately lead to neurotoxicity and cell death. After a hundred years of research on the subject, this is the only reliable histopathological feature in our hands. Since AD and PD are diagnosed only once neuronal death and the first symptoms have appeared, the early detection of these diseases is currently impossible. At present, there is no effective drug available, and patients are left with symptomatic and inconclusive therapies. Several reasons could be associated with the lack of effective therapeutic treatments. One of the most important factors is the lack of selective probes capable of detecting, as early as possible, the most toxic amyloid species involved in the onset of these pathologies. In this regard, chemical probes able to detect and distinguish among different amyloid aggregates are urgently needed. In this article, we will review and put into perspective results from ex vivo and in vivo studies performed on compounds specifically interacting with such early species. Following a general overview on the three different amyloid proteins leading to insoluble β-sheet-rich amyloid deposits (amyloid β1-42 peptide, Tau, and α-synuclein), a list of the advantages and disadvantages of the approaches employed to date is discussed, with particular attention paid to the translation of fluorescence imaging into clinical applications. Furthermore, we also discuss how the progress achieved in detecting the amyloids of one neurodegenerative disease could be leveraged for research into another amyloidosis. As evidenced by a critical analysis of the state of the art, substantial work still needs to be conducted. Indeed, the early diagnosis of neurodegenerative diseases is a priority, and we believe that this review could be a useful tool for better investigating this field.
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Affiliation(s)
- Nicolò Bisi
- Université Paris-Saclay, CNRS, BioCIS, Bat. Henri Moissan, 17, Av. des Sciences, 91400 Orsay, France
| | - Luca Pinzi
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125 Modena, Italy; (L.P.); (G.R.)
| | - Giulio Rastelli
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125 Modena, Italy; (L.P.); (G.R.)
| | - Nicolò Tonali
- Université Paris-Saclay, CNRS, BioCIS, Bat. Henri Moissan, 17, Av. des Sciences, 91400 Orsay, France
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8
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Huang L, Li Q, Lu Y, Pan F, Cui L, Wang Y, Miao Y, Chen T, Li Y, Wu J, Chen X, Jia J, Guo Q. Consensus on rapid screening for prodromal Alzheimer's disease in China. Gen Psychiatr 2024; 37:e101310. [PMID: 38313393 PMCID: PMC10836380 DOI: 10.1136/gpsych-2023-101310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/19/2023] [Indexed: 02/06/2024] Open
Abstract
Alzheimer's disease (AD) is a common cause of dementia, characterised by cerebral amyloid-β deposition, pathological tau and neurodegeneration. The prodromal stage of AD (pAD) refers to patients with mild cognitive impairment (MCI) and evidence of AD's pathology. At this stage, disease-modifying interventions should be used to prevent the progression to dementia. Given the inherent heterogeneity of MCI, more specific biomarkers are needed to elucidate the underlying AD's pathology. Although the uses of cerebrospinal fluid and positron emission tomography are widely accepted methods for detecting AD's pathology, their clinical applications are limited by their high costs and invasiveness, particularly in low-income areas in China. Therefore, to improve the early detection of Alzheimer's disease (AD) pathology through cost-effective screening methods, a panel of 45 neurologists, psychiatrists and gerontologists was invited to establish a formal consensus on the screening of pAD in China. The supportive evidence and grades of recommendations are based on a systematic literature review and focus group discussion. National meetings were held to allow participants to review, vote and provide their expert opinions to reach a consensus. A majority (two-thirds) decision was used for questions for which consensus could not be reached. Recommended screening methods are presented in this publication, including neuropsychological assessment, peripheral biomarkers and brain imaging. In addition, a general workflow for screening pAD in China is established, which will help clinicians identify individuals at high risk and determine therapeutic targets.
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Affiliation(s)
- Lin Huang
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qinjie Li
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yao Lu
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fengfeng Pan
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Liang Cui
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Wang
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ya Miao
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianlu Chen
- Center for Translational Medicine and Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yatian Li
- Shanghai BestCovered, Shanghai, China
| | | | - Xiaochun Chen
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jianping Jia
- Department of Neurology, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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9
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Chen Y, Su Y, Wu J, Chen K, Atri A, Caselli RJ, Reiman EM, Wang Y. Combining Blood-Based Biomarkers and Structural MRI Measurements to Distinguish Persons with and without Significant Amyloid Plaques. J Alzheimers Dis 2024; 98:1415-1426. [PMID: 38578889 DOI: 10.3233/jad-231162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2024]
Abstract
Background Amyloid-β (Aβ) plaques play a pivotal role in Alzheimer's disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing Aβ plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of Aβ plaques. Objective To develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma Aβ42/40 ratio. Methods We extracted hippocampal multivariate morphometry statistics from MR images and together with plasma Aβ42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimer's Institute (BAI), including prediction accuracy, precision, recall rate, F1 score, and AUC score. Results Results from ADNI (mean age 72.6, Aβ+ rate 49.5%) and BAI (mean age 66.2, Aβ+ rate 36.9%) datasets revealed the integrated multimodal (IMM) model's superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma Aβ42/40 (0.73 and 0.81) predictors. CONCLUSIONS Our IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimer's disease diagnostics, leveraging diverse pathologic features related to Aβ plaques and structural MRI.
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Affiliation(s)
- Yanxi Chen
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Jianfeng Wu
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Kewei Chen
- College of Health Solutions, Arizona State University, Tempe, AZ, USA
| | - Alireza Atri
- Banner Alzheimer's Institute, Phoenix, AZ, USA
- Banner Sun Health Research Institute, Sun City, AZ, USA
- Department of Neurology, Center for Brain/Mind Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | - Yalin Wang
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
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10
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Lew CO, Zhou L, Mazurowski MA, Doraiswamy PM, Petrella JR. MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum. Radiology 2023; 309:e222441. [PMID: 37815445 PMCID: PMC10623183 DOI: 10.1148/radiol.222441] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 09/03/2023] [Accepted: 09/11/2023] [Indexed: 10/11/2023]
Abstract
Background PET can be used for amyloid-tau-neurodegeneration (ATN) classification in Alzheimer disease, but incurs considerable cost and exposure to ionizing radiation. MRI currently has limited use in characterizing ATN status. Deep learning techniques can detect complex patterns in MRI data and have potential for noninvasive characterization of ATN status. Purpose To use deep learning to predict PET-determined ATN biomarker status using MRI and readily available diagnostic data. Materials and Methods MRI and PET data were retrospectively collected from the Alzheimer's Disease Imaging Initiative. PET scans were paired with MRI scans acquired within 30 days, from August 2005 to September 2020. Pairs were randomly split into subsets as follows: 70% for training, 10% for validation, and 20% for final testing. A bimodal Gaussian mixture model was used to threshold PET scans into positive and negative labels. MRI data were fed into a convolutional neural network to generate imaging features. These features were combined in a logistic regression model with patient demographics, APOE gene status, cognitive scores, hippocampal volumes, and clinical diagnoses to classify each ATN biomarker component as positive or negative. Area under the receiver operating characteristic curve (AUC) analysis was used for model evaluation. Feature importance was derived from model coefficients and gradients. Results There were 2099 amyloid (mean patient age, 75 years ± 10 [SD]; 1110 male), 557 tau (mean patient age, 75 years ± 7; 280 male), and 2768 FDG PET (mean patient age, 75 years ± 7; 1645 male) and MRI pairs. Model AUCs for the test set were as follows: amyloid, 0.79 (95% CI: 0.74, 0.83); tau, 0.73 (95% CI: 0.58, 0.86); and neurodegeneration, 0.86 (95% CI: 0.83, 0.89). Within the networks, high gradients were present in key temporal, parietal, frontal, and occipital cortical regions. Model coefficients for cognitive scores, hippocampal volumes, and APOE status were highest. Conclusion A deep learning algorithm predicted each component of PET-determined ATN status with acceptable to excellent efficacy using MRI and other available diagnostic data. © RSNA, 2023 Supplemental material is available for this article.
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Affiliation(s)
- Christopher O. Lew
- From the Department of Radiology, Division of Neuroradiology,
Alzheimer Disease Imaging Research Laboratory (C.O.L., J.R.P.), and
Neurocognitive Disorders Program, Departments of Psychiatry and Medicine
(P.M.D.), Duke University Medical Center, DUMC-Box 3808, Durham, NC 27710-3808;
and Duke Institute for Brain Sciences (P.M.D.) and Department of Electrical and
Computer Engineering, Department of Computer Science, Department of
Biostatistics and Bioinformatics (L.Z., M.A.M.), Duke University, Durham,
NC
| | - Longfei Zhou
- From the Department of Radiology, Division of Neuroradiology,
Alzheimer Disease Imaging Research Laboratory (C.O.L., J.R.P.), and
Neurocognitive Disorders Program, Departments of Psychiatry and Medicine
(P.M.D.), Duke University Medical Center, DUMC-Box 3808, Durham, NC 27710-3808;
and Duke Institute for Brain Sciences (P.M.D.) and Department of Electrical and
Computer Engineering, Department of Computer Science, Department of
Biostatistics and Bioinformatics (L.Z., M.A.M.), Duke University, Durham,
NC
| | - Maciej A. Mazurowski
- From the Department of Radiology, Division of Neuroradiology,
Alzheimer Disease Imaging Research Laboratory (C.O.L., J.R.P.), and
Neurocognitive Disorders Program, Departments of Psychiatry and Medicine
(P.M.D.), Duke University Medical Center, DUMC-Box 3808, Durham, NC 27710-3808;
and Duke Institute for Brain Sciences (P.M.D.) and Department of Electrical and
Computer Engineering, Department of Computer Science, Department of
Biostatistics and Bioinformatics (L.Z., M.A.M.), Duke University, Durham,
NC
| | - P. Murali Doraiswamy
- From the Department of Radiology, Division of Neuroradiology,
Alzheimer Disease Imaging Research Laboratory (C.O.L., J.R.P.), and
Neurocognitive Disorders Program, Departments of Psychiatry and Medicine
(P.M.D.), Duke University Medical Center, DUMC-Box 3808, Durham, NC 27710-3808;
and Duke Institute for Brain Sciences (P.M.D.) and Department of Electrical and
Computer Engineering, Department of Computer Science, Department of
Biostatistics and Bioinformatics (L.Z., M.A.M.), Duke University, Durham,
NC
| | - Jeffrey R. Petrella
- From the Department of Radiology, Division of Neuroradiology,
Alzheimer Disease Imaging Research Laboratory (C.O.L., J.R.P.), and
Neurocognitive Disorders Program, Departments of Psychiatry and Medicine
(P.M.D.), Duke University Medical Center, DUMC-Box 3808, Durham, NC 27710-3808;
and Duke Institute for Brain Sciences (P.M.D.) and Department of Electrical and
Computer Engineering, Department of Computer Science, Department of
Biostatistics and Bioinformatics (L.Z., M.A.M.), Duke University, Durham,
NC
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11
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Coomans EM, Tomassen J, Ossenkoppele R, Tijms BM, Lorenzini L, ten Kate M, Collij LE, Heeman F, Rikken RM, van der Landen SM, den Hollander ME, Golla SSV, Yaqub M, Windhorst AD, Barkhof F, Scheltens P, de Geus EJC, Visser PJ, van Berckel BNM, den Braber A. Genetically identical twin-pair difference models support the amyloid cascade hypothesis. Brain 2023; 146:3735-3746. [PMID: 36892415 PMCID: PMC10473566 DOI: 10.1093/brain/awad077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 02/03/2023] [Accepted: 02/23/2023] [Indexed: 03/10/2023] Open
Abstract
The amyloid cascade hypothesis has strongly impacted the Alzheimer's disease research agenda and clinical trial designs over the past decades, but precisely how amyloid-β pathology initiates the aggregation of neocortical tau remains unclear. We cannot exclude the possibility of a shared upstream process driving both amyloid-β and tau in an independent manner instead of there being a causal relationship between amyloid-β and tau. Here, we tested the premise that if a causal relationship exists, then exposure should be associated with outcome both at the individual level as well as within identical twin-pairs, who are strongly matched on genetic, demographic and shared environmental background. Specifically, we tested associations between longitudinal amyloid-β PET and cross-sectional tau PET, neurodegeneration and cognitive decline using genetically identical twin-pair difference models, which provide the unique opportunity of ruling out genetic and shared environmental effects as potential confounders in an association. We included 78 cognitively unimpaired identical twins with [18F]flutemetamol (amyloid-β)-PET, [18F]flortaucipir (tau)-PET, MRI (hippocampal volume) and cognitive data (composite memory). Associations between each modality were tested at the individual level using generalized estimating equation models, and within identical twin-pairs using within-pair difference models. Mediation analyses were performed to test for directionality in the associations as suggested by the amyloid cascade hypothesis. At the individual level, we observed moderate-to-strong associations between amyloid-β, tau, neurodegeneration and cognition. The within-pair difference models replicated results observed at the individual level with comparably strong effect sizes. Within-pair differences in amyloid-β were strongly associated with within-pair differences in tau (β = 0.68, P < 0.001), and moderately associated with within-pair differences in hippocampal volume (β = -0.37, P = 0.03) and memory functioning (β = -0.57, P < 0.001). Within-pair differences in tau were moderately associated with within-pair differences in hippocampal volume (β = -0.53, P < 0.001) and strongly associated with within-pair differences in memory functioning (β = -0.68, P < 0.001). Mediation analyses showed that of the total twin-difference effect of amyloid-β on memory functioning, the proportion mediated through pathways including tau and hippocampal volume was 69.9%, which was largely attributable to the pathway leading from amyloid-β to tau to memory functioning (proportion mediated, 51.6%). Our results indicate that associations between amyloid-β, tau, neurodegeneration and cognition are unbiased by (genetic) confounding. Furthermore, effects of amyloid-β on neurodegeneration and cognitive decline were fully mediated by tau. These novel findings in this unique sample of identical twins are compatible with the amyloid cascade hypothesis and thereby provide important new knowledge for clinical trial designs.
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Affiliation(s)
- Emma M Coomans
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
| | - Jori Tomassen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
- Clinical Memory Research Unit, Lund University, 205 02 Lund, Sweden
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
| | - Luigi Lorenzini
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
| | - Mara ten Kate
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
| | - Fiona Heeman
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 405 30 Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden
| | - Roos M Rikken
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
| | - Sophie M van der Landen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
| | - Marijke E den Hollander
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
| | - Sandeep S V Golla
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
| | - Albert D Windhorst
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London WC1N 3BG, UK
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
- Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, 6200 MD Maastricht, The Netherlands
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Bart N M van Berckel
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, 1081 HV Amsterdam, The Netherlands
| | - Anouk den Braber
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HV Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden
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12
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Cumplido-Mayoral I, García-Prat M, Operto G, Falcon C, Shekari M, Cacciaglia R, Milà-Alomà M, Lorenzini L, Ingala S, Meije Wink A, Mutsaerts HJMM, Minguillón C, Fauria K, Molinuevo JL, Haller S, Chetelat G, Waldman A, Schwarz AJ, Barkhof F, Suridjan I, Kollmorgen G, Bayfield A, Zetterberg H, Blennow K, Suárez-Calvet M, Vilaplana V, Gispert JD. Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer's disease and neurodegeneration stratified by sex. eLife 2023; 12:e81067. [PMID: 37067031 PMCID: PMC10181824 DOI: 10.7554/elife.81067] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 04/10/2023] [Indexed: 04/18/2023] Open
Abstract
Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer's disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD, and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging in non-demented individuals with abnormal levels of biomarkers of AD and axonal injury.
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Affiliation(s)
- Irene Cumplido-Mayoral
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
| | - Marina García-Prat
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
| | - Grégory Operto
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- CIBER Fragilidad y Envejecimiento Saludable (CIBERFES)MadridFrance
| | - Carles Falcon
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN)MadridSpain
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
| | - Raffaele Cacciaglia
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- CIBER Fragilidad y Envejecimiento Saludable (CIBERFES)MadridFrance
| | - Marta Milà-Alomà
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- CIBER Fragilidad y Envejecimiento Saludable (CIBERFES)MadridFrance
| | - Luigi Lorenzini
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Henk JMM Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Carolina Minguillón
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- CIBER Fragilidad y Envejecimiento Saludable (CIBERFES)MadridFrance
| | - Karine Fauria
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- CIBER Fragilidad y Envejecimiento Saludable (CIBERFES)MadridFrance
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
| | - Sven Haller
- CIRD Centre d'Imagerie Rive DroiteGenevaSwitzerland
| | - Gael Chetelat
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and BrainCyceronFrance
| | - Adam Waldman
- Centre for Dementia Prevention, Edinburgh Imaging, and UK Dementia Research Institute at The University of EdinburghEdinburghUnited Kingdom
| | | | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
- Institutes of Neurology and Healthcare Engineering, University College LondonLondonUnited Kingdom
| | | | | | | | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, University of GothenburgMölndalSweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University HospitalMölndalSweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
- Hong Kong Center for Neurodegenerative DiseasesHong KongChina
- UK Dementia Research Institute at UCLLondonUnited Kingdom
| | - Kaj Blennow
- Institute of Neuroscience and Physiology, University of GothenburgMölndalSweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University HospitalMölndalSweden
| | - Marc Suárez-Calvet
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- CIBER Fragilidad y Envejecimiento Saludable (CIBERFES)MadridFrance
- Servei de Neurologia, Hospital del MarBarcelonaSpain
| | - Verónica Vilaplana
- Department of Signal Theory and Communications, Universitat Politècnica de CatalunyaBarcelonaSpain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN)MadridSpain
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13
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Wu J, Su Y, Zhu W, Mallak NJ, Lepore N, Reiman EM, Caselli RJ, Thompson PM, Chen K, Wang Y. Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding. J Alzheimers Dis 2023; 91:637-651. [PMID: 36463452 PMCID: PMC9940990 DOI: 10.3233/jad-220812] [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] [Indexed: 11/30/2022]
Abstract
BACKGROUND Amyloid-β (Aβ) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the "ATN framework" of AD. Current methods to detect Aβ/tau pathology include cerebrospinal fluid (invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (promising but mainly still in development). OBJECTIVE To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements. METHODS With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction. RESULTS We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer's Disease Neuroimaging Initiative. Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics. CONCLUSION The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects.
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Affiliation(s)
- Jianfeng Wu
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Yi Su
- Banner Alzheimer’s Institute, Phoenix, AZ, USA
| | - Wenhui Zhu
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Negar Jalili Mallak
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Natasha Lepore
- CIBORG Lab, Department of Radiology Children’s Hospital Los Angeles, Los Angeles, CA, USA
| | | | | | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Kewei Chen
- Banner Alzheimer’s Institute, Phoenix, AZ, USA
| | - Yalin Wang
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
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14
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Giraldo DL, Smith RE, Struyfs H, Niemantsverdriet E, De Roeck E, Bjerke M, Engelborghs S, Romero E, Sijbers J, Jeurissen B. Investigating Tissue-Specific Abnormalities in Alzheimer's Disease with Multi-Shell Diffusion MRI. J Alzheimers Dis 2022; 90:1771-1791. [PMID: 36336929 PMCID: PMC9789487 DOI: 10.3233/jad-220551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Most studies using diffusion-weighted MRI (DW-MRI) in Alzheimer's disease (AD) have focused their analyses on white matter (WM) microstructural changes using the diffusion (kurtosis) tensor model. Although recent works have addressed some limitations of the tensor model, such as the representation of crossing fibers and partial volume effects with cerebrospinal fluid (CSF), the focus remains in modeling and analyzing the WM. OBJECTIVE In this work, we present a brain analysis approach for DW-MRI that disentangles multiple tissue compartments as well as micro- and macroscopic effects to investigate differences between groups of subjects in the AD continuum and controls. METHODS By means of the multi-tissue constrained spherical deconvolution of multi-shell DW-MRI, underlying brain tissue is modeled with a WM fiber orientation distribution function along with the contributions of gray matter (GM) and CSF to the diffusion signal. From this multi-tissue model, a set of measures capturing tissue diffusivity properties and morphology are extracted. Group differences were interrogated following fixel-, voxel-, and tensor-based morphometry approaches while including strong FWE control across multiple comparisons. RESULTS Abnormalities related to AD stages were detected in WM tracts including the splenium, cingulum, longitudinal fasciculi, and corticospinal tract. Changes in tissue composition were identified, particularly in the medial temporal lobe and superior longitudinal fasciculus. CONCLUSION This analysis framework constitutes a comprehensive approach allowing simultaneous macro and microscopic assessment of WM, GM, and CSF, from a single DW-MRI dataset.
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Affiliation(s)
- Diana L. Giraldo
- Computer Imaging and Medical Applications Laboratory - Cim@Lab, Universidad Nacional de Colombia, Bogotá, Colombia,imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium,μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Robert E. Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia,The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Hanne Struyfs
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Ellis Niemantsverdriet
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Ellen De Roeck
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
| | - Maria Bjerke
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium,Laboratory of Neurochemistry, Department of Clinical Chemistry, and Center for Neurosciences (C4N), Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium,Department of Neurology, and Center for Neurosciences (C4N), Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Eduardo Romero
- Computer Imaging and Medical Applications Laboratory - Cim@Lab, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Jan Sijbers
- imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium,μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Ben Jeurissen
- imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium,μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium,Lab for Equilibrium Investigations and Aerospace, Department of Physics, University of Antwerp, Antwerp, Belgium,Correspondence to: Ben Jeurissen, PhD, imec - Vision Lab, Department of Physics, University of Antwerp (CDE), Universiteitsplein 1, Building N, 2610 Antwerp, Belgium. Tel.: +32 3 265 24 77; E-mail:
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15
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Kline A, Wang H, Li Y, Dennis S, Hutch M, Xu Z, Wang F, Cheng F, Luo Y. Multimodal machine learning in precision health: A scoping review. NPJ Digit Med 2022; 5:171. [PMID: 36344814 PMCID: PMC9640667 DOI: 10.1038/s41746-022-00712-8] [Citation(s) in RCA: 65] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 10/14/2022] [Indexed: 11/09/2022] Open
Abstract
Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.
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Affiliation(s)
- Adrienne Kline
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Hanyin Wang
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Yikuan Li
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Saya Dennis
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Meghan Hutch
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Cornell University, New York, 10065, NY, USA
| | - Fei Wang
- Department of Population Health Sciences, Cornell University, New York, 10065, NY, USA
| | - Feixiong Cheng
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, 44195, OH, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, 60201, IL, USA.
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16
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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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17
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Selection of single domain anti-transferrin receptor antibodies for blood-brain barrier transcytosis using a neurotensin based assay and histological assessment of target engagement in a mouse model of Alzheimer's related amyloid-beta pathology. PLoS One 2022; 17:e0276107. [PMID: 36256604 PMCID: PMC9578589 DOI: 10.1371/journal.pone.0276107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 09/29/2022] [Indexed: 11/05/2022] Open
Abstract
The blood-brain barrier (BBB) presents a major obstacle in developing specific diagnostic imaging agents for many neurological disorders. In this study we aimed to generate single domain anti-mouse transferrin receptor antibodies (anti-mTfR VHHs) to mediate BBB transcytosis as components of novel MRI molecular contrast imaging agents. Anti-mTfR VHHs were produced by immunizing a llama with mTfR, generation of a VHH phage display library, immunopanning, and in vitro characterization of candidates. Site directed mutagenesis was used to generate additional variants. VHH fusions with neurotensin (NT) allowed rapid, hypothermia-based screening for VHH-mediated BBB transcytosis in wild-type mice. One anti-mTfR VHH variant was fused with an anti-amyloid-beta (Aβ) VHH dimer and labeled with fluorescent dye for direct assessment of in vivo target engagement in a mouse model of AD-related Aβ plaque pathology. An anti-mTfR VHH called M1 and variants had binding affinities to mTfR of <1nM to 1.52nM. The affinity of the VHH binding to mTfR correlated with the efficiency of the VHH-NT induced hypothermia effects after intravenous injection of 600 nmol/kg body weight, ranging from undetectable for nonbinding mutants to -6°C for the best mutants. The anti-mTfR VHH variant M1P96H with the strongest hypothermia effect was fused to the anti-Aβ VHH dimer and labeled with Alexa647; the dye-labeled VHH fusion construct still bound both mTfR and Aβ plaques at concentrations as low as 0.22 nM. However, after intravenous injection at 600 nmol/kg body weight into APP/PS1 transgenic mice, there was no detectible labeling of plaques above control levels. Thus, NT-induced hypothermia did not correlate with direct target engagement in cortex, likely because the concentration required for NT-induced hypothermia was lower than the concentration required to produce in situ labeling. These findings reveal an important dissociation between NT-induced hypothermia, presumably mediated by hypothalamus, and direct engagement with Aβ-plaques in cortex. Additional methods to assess anti-mTfR VHH BBB transcytosis will need to be developed for anti-mTfR VHH screening and the development of novel MRI molecular contrast agents.
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18
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Behl T, Kaur I, Sehgal A, Singh S, Albarrati A, Albratty M, Najmi A, Meraya AM, Bungau S. The road to precision medicine: Eliminating the "One Size Fits All" approach in Alzheimer's disease. Biomed Pharmacother 2022; 153:113337. [PMID: 35780617 DOI: 10.1016/j.biopha.2022.113337] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 06/18/2022] [Accepted: 06/24/2022] [Indexed: 11/29/2022] Open
Abstract
The expeditious advancement of Alzheimer's Disease (AD) is a threat to the global healthcare system, that is further supplemented by therapeutic failure. The prevalence of this disorder has been expected to quadrupole by 2050, thereby exerting a tremendous economic pressure on medical sector, worldwide. Thus, there is a dire need of a change in conventional approaches and adopt a novel methodology of disease prevention, treatment and diagnosis. Precision medicine offers a personalized approach to disease management, It is dependent upon genetic, environmental and lifestyle factors associated with the individual, aiding to develop tailored therapeutics. Precision Medicine Initiatives are launched, worldwide, to facilitate the integration of personalized models and clinical medicine. The review aims to provide a comprehensive understanding of the neuroinflammatory processes causing AD, giving a brief overview of the disease interventions. This is further followed by the role of precision medicine in AD, constituting the genetic perspectives, operation of personalized form of medicine and optimization of clinical trials with the 3 R's, showcasing an in-depth understanding of this novel approach in varying aspects of the healthcare industry, to provide an opportunity to the global AD researchers to elucidate suitable therapeutic regimens in clinically and pathologically complex diseases, like AD.
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Affiliation(s)
- Tapan Behl
- Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India.
| | - Ishnoor Kaur
- Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India
| | - Aayush Sehgal
- Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India
| | - Sukhbir Singh
- Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, India
| | - Ali Albarrati
- Rehabilitation Health Sciences College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Mohammed Albratty
- Department of Pharmaceutical Chemistry, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Asim Najmi
- Department of Pharmaceutical Chemistry and Pharmacognosy, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Abdulkarim M Meraya
- Pharmacy Practice Research Unit, Department of Clinical Pharmacy, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Simona Bungau
- Department of Pharmacy, Faculty of Medicine and Pharmacy, University of Oradea, Oradea, Romania; Doctoral School of Biomedical Sciences, University of Oradea, Oradea, Romania.
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19
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Kim S, Kim SW, Noh Y, Lee PH, Na DL, Seo SW, Seong JK. Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model. Front Aging Neurosci 2022; 14:869387. [PMID: 35783130 PMCID: PMC9247505 DOI: 10.3389/fnagi.2022.869387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 05/16/2022] [Indexed: 01/18/2023] Open
Abstract
ObjectiveAnalyzing neuroimages being useful method in the field of neuroscience and neurology and solving the incompatibilities across protocols and vendors have become a major problem. We referred to this incompatibility as “center effects,” and in this study, we attempted to correct such center effects of cortical feature obtained from multicenter magnetic resonance images (MRIs).MethodsFor MRI of a total of 4,321 multicenter subjects, the harmonized w-score was calculated by correcting biological covariates such as age, sex, years of education, and intercranial volume (ICV) as fixed effects and center information as a random effect. Afterward, we performed classification tasks using principal component analysis (PCA) and linear discriminant analysis (LDA) to check whether the center effect was successfully corrected from the harmonized w-score.ResultsFirst, an experiment was conducted to predict the dataset origin of a random subject sampled from two different datasets, and it was confirmed that the prediction accuracy of linear mixed effect (LME) model-based w-score was significantly closer to the baseline than that of raw cortical thickness. As a second experiment, we classified the data of the normal and patient groups of each dataset, and LME model-based w-score, which is biological-feature-corrected values, showed higher classification accuracy than the raw cortical thickness data. Afterward, to verify the compatibility of the dataset used for LME model training and the dataset that is not, intraobject comparison and w-score RMSE calculation process were performed.ConclusionThrough comparison between the LME model-based w-score and existing methods and several classification tasks, we showed that the LME model-based w-score sufficiently corrects the center effects while preserving the disease effects from the dataset. We also showed that the preserved disease effects have a match with well-known disease atrophy patterns such as Alzheimer’s disease or Parkinson’s disease. Finally, through intrasubject comparison, we found that the difference between centers decreases in the LME model-based w-score compared with the raw cortical thickness and thus showed that our model well-harmonizes the data that are not used for the model training.
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Affiliation(s)
- SeungWook Kim
- Department of Bio-Convergence Engineering, Korea University, Seoul, South Korea
| | - Sung-Woo Kim
- Department of Bio-Convergence Engineering, Korea University, Seoul, South Korea
| | - Young Noh
- Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, South Korea
| | - Phil Hyu Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Samsung Alzheimer Research Center, Center for Clinical Epidemiology, Samsung Medical Center, Seoul, South Korea
- Department of Health Sciences and Technology, Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea
- *Correspondence: Sang Won Seo,
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Korea University, Seoul, South Korea
- Department of Artificial Intelligence, Korea University, Seoul, South Korea
- Interdisciplinary Program in Precision Public Health, Korea University, Seoul, South Korea
- Joon-Kyung Seong,
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20
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Predictive Scale for Amyloid PET Positivity Based on Clinical and MRI Variables in Patients with Amnestic Mild Cognitive Impairment. J Clin Med 2022; 11:jcm11123433. [PMID: 35743503 PMCID: PMC9224873 DOI: 10.3390/jcm11123433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 06/11/2022] [Accepted: 06/12/2022] [Indexed: 12/05/2022] Open
Abstract
The presence of amyloid-β (Aβ) deposition is considered important in patients with amnestic mild cognitive impairment (aMCI), since they can progress to Alzheimer’s disease dementia. Amyloid positron emission tomography (PET) has been used for detecting Aβ deposition, but its high cost is a significant barrier for clinical usage. Therefore, we aimed to develop a new predictive scale for amyloid PET positivity using easily accessible tools. Overall, 161 aMCI patients were recruited from six memory clinics and underwent neuropsychological tests, brain magnetic resonance imaging (MRI), apolipoprotein E (APOE) genotype testing, and amyloid PET. Among the potential predictors, verbal and visual memory tests, medial temporal lobe atrophy, APOE genotype, and age showed significant differences between the Aβ-positive and Aβ-negative groups and were combined to make a model for predicting amyloid PET positivity with the area under the curve (AUC) of 0.856. Based on the best model, we developed the new predictive scale comprising integers, which had an optimal cutoff score ≥ 3. The new predictive scale was validated in another cohort of 98 participants and showed a good performance with AUC of 0.835. This new predictive scale with accessible variables may be useful for predicting Aβ positivity in aMCI patients in clinical practice.
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21
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Delvenne A, Gobom J, Tijms B, Bos I, Reus LM, Dobricic V, Kate MT, Verhey F, Ramakers I, Scheltens P, Teunissen CE, Vandenberghe R, Schaeverbeke J, Gabel S, Popp J, Peyratout G, Martinez-Lage P, Tainta M, Tsolaki M, Freund-Levi Y, Lovestone S, Streffer J, Barkhof F, Bertram L, Blennow K, Zetterberg H, Visser PJ, Vos SJB. Cerebrospinal fluid proteomic profiling of individuals with mild cognitive impairment and suspected non-Alzheimer's disease pathophysiology. Alzheimers Dement 2022; 19:807-820. [PMID: 35698882 DOI: 10.1002/alz.12713] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 04/06/2022] [Accepted: 05/12/2022] [Indexed: 11/09/2022]
Abstract
BACKGROUND Suspected non-Alzheimer's disease pathophysiology (SNAP) is a biomarker concept that encompasses individuals with neuronal injury but without amyloidosis. We aim to investigate the pathophysiology of SNAP, defined as abnormal tau without amyloidosis, in individuals with mild cognitive impairment (MCI) by cerebrospinal fluid (CSF) proteomics. METHODS Individuals were classified based on CSF amyloid beta (Aβ)1-42 (A) and phosphorylated tau (T), as cognitively normal A-T- (CN), MCI A-T+ (MCI-SNAP), and MCI A+T+ (MCI-AD). Proteomics analyses, Gene Ontology (GO), brain cell expression, and gene expression analyses in brain regions of interest were performed. RESULTS A total of 96 proteins were decreased in MCI-SNAP compared to CN and MCI-AD. These proteins were enriched for extracellular matrix (ECM), hemostasis, immune system, protein processing/degradation, lipids, and synapse. Fifty-one percent were enriched for expression in the choroid plexus. CONCLUSION The pathophysiology of MCI-SNAP (A-T+) is distinct from that of MCI-AD. Our findings highlight the need for a different treatment in MCI-SNAP compared to MCI-AD.
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Affiliation(s)
- Aurore Delvenne
- Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Johan Gobom
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Betty Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands
| | - Isabelle Bos
- Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands
| | - Lianne M Reus
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands
| | - Valerija Dobricic
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany
| | - Mara Ten Kate
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Frans Verhey
- Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Inez Ramakers
- Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam University Medical Centers (AUMC), Amsterdam Neuroscience, the Netherlands
| | - Rik Vandenberghe
- Neurology Service, University Hospitals Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Jolien Schaeverbeke
- Neurology Service, University Hospitals Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Silvy Gabel
- Neurology Service, University Hospitals Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Julius Popp
- Old Age Psychiatry, University Hospital Lausanne, Lausanne, Switzerland
- Department of Geriatric Psychiatry, Psychiatry University Hospital Zürich, Zürich, Switzerland
| | | | | | - Mikel Tainta
- Fundación CITA-Alzhéimer Fundazioa, San Sebastian, Spain
| | - Magda Tsolaki
- 1st Department of Neurology, AHEPA University Hospital, Medical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Makedonia, Thessaloniki, Greece
| | - Yvonne Freund-Levi
- Department of Neurobiology, Caring Sciences and Society (NVS), Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry in Region Örebro County and School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Department of Old Age Psychiatry, Psychology & Neuroscience, King's College, London, UK
| | - Simon Lovestone
- University of Oxford, Oxford, United Kingdom (currently at Johnson and Johnson Medical Ltd.), London, UK
| | - Johannes Streffer
- Institute Born-Bunge, Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Belgium
- UCB Biopharma SPRL, Brain-l'Alleud, Belgium
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Institutes of Neurology & Healthcare Engineering, UCL London, London, UK
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - Pieter Jelle Visser
- Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden
| | - Stephanie J B Vos
- Department of Psychiatry and Neuropsychology, Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
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22
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Homann J, Osburg T, Ohlei O, Dobricic V, Deecke L, Bos I, Vandenberghe R, Gabel S, Scheltens P, Teunissen CE, Engelborghs S, Frisoni G, Blin O, Richardson JC, Bordet R, Lleó A, Alcolea D, Popp J, Clark C, Peyratout G, Martinez-Lage P, Tainta M, Dobson RJB, Legido-Quigley C, Sleegers K, Van Broeckhoven C, Wittig M, Franke A, Lill CM, Blennow K, Zetterberg H, Lovestone S, Streffer J, ten Kate M, Vos SJB, Barkhof F, Visser PJ, Bertram L. Genome-Wide Association Study of Alzheimer's Disease Brain Imaging Biomarkers and Neuropsychological Phenotypes in the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Dataset. Front Aging Neurosci 2022; 14:840651. [PMID: 35386118 PMCID: PMC8979334 DOI: 10.3389/fnagi.2022.840651] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/15/2022] [Indexed: 12/24/2022] Open
Abstract
Alzheimer's disease (AD) is the most frequent neurodegenerative disease with an increasing prevalence in industrialized, aging populations. AD susceptibility has an established genetic basis which has been the focus of a large number of genome-wide association studies (GWAS) published over the last decade. Most of these GWAS used dichotomized clinical diagnostic status, i.e., case vs. control classification, as outcome phenotypes, without the use of biomarkers. An alternative and potentially more powerful study design is afforded by using quantitative AD-related phenotypes as GWAS outcome traits, an analysis paradigm that we followed in this work. Specifically, we utilized genotype and phenotype data from n = 931 individuals collected under the auspices of the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study to perform a total of 19 separate GWAS analyses. As outcomes we used five magnetic resonance imaging (MRI) traits and seven cognitive performance traits. For the latter, longitudinal data from at least two timepoints were available in addition to cross-sectional assessments at baseline. Our GWAS analyses revealed several genome-wide significant associations for the neuropsychological performance measures, in particular those assayed longitudinally. Among the most noteworthy signals were associations in or near EHBP1 (EH domain binding protein 1; on chromosome 2p15) and CEP112 (centrosomal protein 112; 17q24.1) with delayed recall as well as SMOC2 (SPARC related modular calcium binding 2; 6p27) with immediate recall in a memory performance test. On the X chromosome, which is often excluded in other GWAS, we identified a genome-wide significant signal near IL1RAPL1 (interleukin 1 receptor accessory protein like 1; Xp21.3). While polygenic score (PGS) analyses showed the expected strong associations with SNPs highlighted in relevant previous GWAS on hippocampal volume and cognitive function, they did not show noteworthy associations with recent AD risk GWAS findings. In summary, our study highlights the power of using quantitative endophenotypes as outcome traits in AD-related GWAS analyses and nominates several new loci not previously implicated in cognitive decline.
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Affiliation(s)
- Jan Homann
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Tim Osburg
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Olena Ohlei
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Valerija Dobricic
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Laura Deecke
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Isabelle Bos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, Netherlands
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam University Medical Centers, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Neurology Service, University Hospital Leuven, Leuven, Belgium
| | - Silvy Gabel
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Philip Scheltens
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam University Medical Centers, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Charlotte E. Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, Netherlands
| | - Sebastiaan Engelborghs
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology and Center for Neurosciences, Universitair Ziekenhuis Brussel and Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Giovanni Frisoni
- Department of Psychiatry, University of Geneva, Geneva, Switzerland
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Olivier Blin
- Institut Neurosciences Timone, AIX Marseille University, Marseille, France
| | - Jill C. Richardson
- Neurosciences Therapeutic Area, GlaxoSmithKline R&D, Stevenage, United Kingdom
| | - Regis Bordet
- Lille Neuroscience and Cognition, University of Lille, Inserm, CHU Lille, Lille, France
| | - Alberto Lleó
- Memory Unit, Neurology Department, Hospital de Sant Pau, Barcelona and Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Daniel Alcolea
- Memory Unit, Neurology Department, Hospital de Sant Pau, Barcelona and Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Julius Popp
- Department of Geriatric Psychiatry, University Hospital of Psychiatry Zurich, Zurich, Switzerland
- Old Age Psychiatry, Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Christopher Clark
- Department of Geriatric Psychiatry, University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | - Gwendoline Peyratout
- Old Age Psychiatry, Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Pablo Martinez-Lage
- Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Spain
| | - Mikel Tainta
- Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Spain
| | - Richard J. B. Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Cristina Legido-Quigley
- Steno Diabetes Center, Copenhagen, Denmark
- King’s College London, Institute of Pharmaceutical Sciences, London, United Kingdom
| | - Kristel Sleegers
- Complex Genetics of Alzheimer’s Disease Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Christine Van Broeckhoven
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Michael Wittig
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Christina M. Lill
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, United Kingdom
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, Queen Square, London, United Kingdom
- UK Dementia Research Institute at University College London, London, United Kingdom
| | - Simon Lovestone
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Johannes Streffer
- Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
- Janssen R&D, LLC. Beerse, Belgium
| | - Mara ten Kate
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Stephanie J. B. Vos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam Neuroscience, Amsterdam, Netherlands
- Institutes of Neurology and Healthcare Engineering, University College London, London, United Kingdom
| | - Pieter Jelle Visser
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, Netherlands
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
- Department of Psychology, University of Oslo, Oslo, Norway
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23
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Seo Y, Jang H, Lee H. Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease. Life (Basel) 2022; 12:life12020275. [PMID: 35207561 PMCID: PMC8879055 DOI: 10.3390/life12020275] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 02/05/2022] [Accepted: 02/09/2022] [Indexed: 01/18/2023] Open
Abstract
Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.
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Affiliation(s)
| | | | - Hyejoo Lee
- Correspondence: ; Tel.: +82-2-3410-1233; Fax: +82-2-3410-0052
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24
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Li B, Jang I, Riphagen J, Almaktoum R, Yochim KM, Ances BM, Bookheimer SY, Salat DH. Identifying individuals with Alzheimer's disease-like brains based on structural imaging in the Human Connectome Project Aging cohort. Hum Brain Mapp 2021; 42:5535-5546. [PMID: 34582057 PMCID: PMC8559490 DOI: 10.1002/hbm.25626] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 11/28/2022] Open
Abstract
Given the difficulty in factoring out typical age effects from subtle Alzheimer's disease (AD) effects on brain structure, identification of very early, as well as younger preclinical “at‐risk” individuals has unique challenges. We examined whether age‐correction procedures could be used to better identify individuals at very early potential risk from adults who did not have any existing cognitive diagnosis. First, we obtained cross‐sectional age effects for each structural feature using data from a selected portion of the Human Connectome Project Aging (HCP‐A) cohort. After age detrending, we weighted AD structural deterioration with patterns quantified from data of the Alzheimer's Disease Neuroimaging Initiative. Support vector machine was then used to classify individuals with brains that most resembled atrophy in AD across the entire HCP‐A sample. Additionally, we iteratively adjusted the pipeline by removing individuals classified as AD‐like from the HCP‐A cohort to minimize atypical brain structural contributions to the age detrending. The classifier had a mean cross‐validation accuracy of 94.0% for AD recognition. It also could identify mild cognitive impairment with more severe AD‐specific biomarkers and worse cognition. In an independent HCP‐A cohort, 8.8% were identified as AD‐like, and they trended toward worse cognition. An “AD risk” score derived from the machine learning models also significantly correlated with cognition. This work provides a proof of concept for the potential to use structural brain imaging to identify asymptomatic individuals at young ages who show structural brain patterns similar to AD and are potentially at risk for a future clinical disorder.
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Affiliation(s)
- Binyin Li
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ikbeom Jang
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Joost Riphagen
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.,Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, The Netherlands
| | - Randa Almaktoum
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Kathryn Morrison Yochim
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Beau M Ances
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, University of California, Los Angeles, California, USA
| | - David H Salat
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, Massachusetts, USA
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25
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Wu J, Dong Q, Zhang J, Su Y, Wu T, Caselli RJ, Reiman EM, Ye J, Lepore N, Chen K, Thompson PM, Wang Y. Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology. Front Neurosci 2021; 15:762458. [PMID: 34899166 PMCID: PMC8655732 DOI: 10.3389/fnins.2021.762458] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 11/01/2021] [Indexed: 12/03/2022] Open
Abstract
Amyloid-β (Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer's disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. One of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research focuses in the AD pathophysiological progress. This work proposes a novel framework, Federated Morphometry Feature Selection (FMFS) model, to examine subtle aspects of hippocampal morphometry that are associated with Aβ/tau burden in the brain, measured using positron emission tomography (PET). FMFS is comprised of hippocampal surface-based feature calculation, patch-based feature selection, federated group LASSO regression, federated screening rule-based stability selection, and region of interest (ROI) identification. FMFS was tested on two Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts to understand hippocampal alterations that relate to Aβ/tau depositions. Each cohort included pairs of MRI and PET for AD, mild cognitive impairment (MCI), and cognitively unimpaired (CU) subjects. Experimental results demonstrated that FMFS achieves an 89× speedup compared to other published state-of-the-art methods under five independent hypothetical institutions. In addition, the subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal Aβ/tau. As potential biomarkers for Aβ/tau pathology, the features from the identified ROIs had greater power for predicting cognitive assessment and for survival analysis than five other imaging biomarkers. All the results indicate that FMFS is an efficient and effective tool to reveal associations between Aβ/tau burden and hippocampal morphometry.
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Affiliation(s)
- Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
- Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Yi Su
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
| | - Teresa Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Richard J. Caselli
- Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, United States
| | - Eric M. Reiman
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Natasha Lepore
- CIBORG Lab, Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, CA, United States
| | - Kewei Chen
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, United States
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
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26
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Relationship between Amyloid-β Deposition and the Coupling between Structural and Functional Brain Networks in Patients with Mild Cognitive Impairment and Alzheimer's Disease. Brain Sci 2021; 11:brainsci11111535. [PMID: 34827535 PMCID: PMC8615711 DOI: 10.3390/brainsci11111535] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/17/2021] [Accepted: 11/17/2021] [Indexed: 01/02/2023] Open
Abstract
Previous studies have demonstrated that the accumulation of amyloid-β (Aβ) pathologies has distinctive stage-specific effects on the structural and functional brain networks along the Alzheimer's disease (AD) continuum. A more comprehensive account of both types of brain network may provide a better characterization of the stage-specific effects of Aβ pathologies. A potential candidate for this joint characterization is the coupling between the structural and functional brain networks (SC-FC coupling). We therefore investigated the effect of Aβ accumulation on global SC-FC coupling in patients with mild cognitive impairment (MCI), AD, and healthy controls. Patients with MCI were dichotomized according to their level of Aβ pathology seen in 18F-flutemetamol PET-CT scans-namely, Aβ-negative and Aβ-positive. Our results show that there was no difference in global SC-FC coupling between different cohorts. During the prodromal AD stage, there was a significant negative correlation between the level of Aβ pathology and the global SC-FC coupling of MCI patients with positive Aβ, but no significant correlation for MCI patients with negative Aβ. During the AD dementia stage, the correlation between Aβ pathology and global SC-FC coupling in patients with AD was positive. Our results suggest that Aβ pathology has distinctive stage-specific effects on global coupling between the structural and functional brain networks along the AD continuum.
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27
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Tsolaki M, Tsatali M, Gkioka M, Poptsi E, Tsolaki A, Papaliagkas V, Tabakis IM, Lazarou I, Makri M, Kazis D, Papagiannopoulos S, Kiryttopoulos A, Koutsouraki E, Tegos T. Memory Clinics and Day Care Centers in Thessaloniki, Northern Greece: 30 Years of Clinical Practice and Experience. Front Neurol 2021; 12:683131. [PMID: 34512506 PMCID: PMC8425245 DOI: 10.3389/fneur.2021.683131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 06/25/2021] [Indexed: 11/17/2022] Open
Abstract
Background: This review describes the diagnostic and interventional procedures conducted in two university memory clinics (established network of G. Papanikolaou Hospital: 1988–2017 and AHEPA hospital: 2017–today) and 2 day care centers (established network of DCCs: 2005–today) in North Greece and their contribution in the scientific field of dementia. The aims of this work are (1) to provide a diagnosis and treatment protocol established in the network of memory clinics and DCCs and (2) to present further research conducted in the aforementioned network during the last 30 years of clinical practice. Methods: The guidelines to set a protocol demand a series of actions as follows: (1) set the diagnosis criteria, neuropsychological assessment, laboratory examinations, and examination of neurophysiological, neuroimaging, cerebrospinal fluid, blood, and genetic markers; and (2) apply non-pharmacological interventions according to the needs and specialized psychosocial interventions of the patient to the caregivers of the patient. Results: In addition to the guidelines followed in memory clinics at the 1st and 3rd Department of Neurology and two DCCs, a database of patients, educational programs, and further participation in international research programs, including clinical trials, make our contribution in the dementia field strong. Conclusion: In the current paper, we provide useful guidelines on how major and minor neurocognitive disorders are being treated in Thessaloniki, Greece, describing successful practices which have been adapted in the last 30 years.
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Affiliation(s)
- Magda Tsolaki
- Greek Association of Alzheimer's Disease and Related Disorders (GAADRD), Thessaloniki, Greece.,1st University Department of Neurology UH "AHEPA", School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI - AUTh) Balkan Center, Buildings A & B, Aristotle University of Thessaloniki, Thessaloniki, Greece.,3rd University Department of Neurology "G. Papanikolaou" Hospital, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Marianna Tsatali
- Greek Association of Alzheimer's Disease and Related Disorders (GAADRD), Thessaloniki, Greece
| | - Mara Gkioka
- Greek Association of Alzheimer's Disease and Related Disorders (GAADRD), Thessaloniki, Greece.,1st University Department of Neurology UH "AHEPA", School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Eleni Poptsi
- Greek Association of Alzheimer's Disease and Related Disorders (GAADRD), Thessaloniki, Greece
| | - Anthoula Tsolaki
- Greek Association of Alzheimer's Disease and Related Disorders (GAADRD), Thessaloniki, Greece.,1st University Department of Neurology UH "AHEPA", School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vasileios Papaliagkas
- Greek Association of Alzheimer's Disease and Related Disorders (GAADRD), Thessaloniki, Greece.,Department of Biomedical Sciences International Hellenic University, Thessaloniki, Greece
| | - Irene-Maria Tabakis
- Greek Association of Alzheimer's Disease and Related Disorders (GAADRD), Thessaloniki, Greece
| | - Ioulietta Lazarou
- 1st University Department of Neurology UH "AHEPA", School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Marina Makri
- Greek Association of Alzheimer's Disease and Related Disorders (GAADRD), Thessaloniki, Greece.,1st University Department of Neurology UH "AHEPA", School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitrios Kazis
- 3rd University Department of Neurology "G. Papanikolaou" Hospital, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Sotirios Papagiannopoulos
- 3rd University Department of Neurology "G. Papanikolaou" Hospital, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Kiryttopoulos
- 1st University Department of Neurology UH "AHEPA", School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Efrosyni Koutsouraki
- 1st University Department of Neurology UH "AHEPA", School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Thomas Tegos
- 1st University Department of Neurology UH "AHEPA", School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Belkhiria C, Vergara RC, Martinez M, Delano PH, Delgado C. Neural links between facial emotion recognition and cognitive impairment in presbycusis. Int J Geriatr Psychiatry 2021; 36:1171-1178. [PMID: 33503682 DOI: 10.1002/gps.5501] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 10/05/2020] [Accepted: 01/22/2021] [Indexed: 12/29/2022]
Abstract
OBJECTIVES Facial emotion recognition (FER) is impaired in people with dementia and with severe to profound hearing loss, probably reflecting common neural changes. Here, we aim to study the association between brain structures and FER impairment in mild to moderate age-related hearing loss participants. METHODS We evaluated FER in a cross-sectional cohort of 111 Chilean nondemented elderly participants. They were assessed for FER in seven different categories using 35 facial stimuli. We collected pure-tone average (PTA) audiometric thresholds, cognitive and neuropsychiatric assessments, and morphometric brain imaging using a 3-Tesla MRI. RESULTS According to PTA threshold levels, participants were classified as controls (≤25 dB, n = 56) or presbycusis (>25 dB, n = 55), with an average PTA of 17.08 ± 4.8 dB HL and 36.27 ± 9.5 dB HL respectively. Poorer total FER score was correlated with worse hearing thresholds (r = -0.23, p < 0.05) in participants with presbycusis. Multiple regression models explained 57 % of the variability of FER in presbycusis and 10% in controls. In both groups, the main determinant of FER was cognitive performance. In the brain structure of presbycusis participants, FER was correlated with the atrophy of the right insula, right hippocampus, bilateral cingulate cortex and multiple areas of the temporal cortex. In controls, FER was only associated with bilateral middle temporal cortex volume. CONCLUSIONS FER impairment in presbycusis is distinctively associated with atrophy of neural structures engaged in the perceptual and conceptual level of face emotion processing.
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Affiliation(s)
- Chama Belkhiria
- Neuroscience Department, Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - Rodrigo C Vergara
- Neuroscience Department, Facultad de Medicina, Universidad de Chile, Santiago, Chile
- Kinesiology Department, Facultad de Artes y Educación Física, Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile
| | - Melissa Martinez
- Neurology and Neurosurgery Department, Hospital Clínico de la Universidad de Chile, Santiago, Chile
| | - Paul H Delano
- Neuroscience Department, Facultad de Medicina, Universidad de Chile, Santiago, Chile
- Otolaryngology Department, Hospital Clínico de la Universidad de Chile, Santiago, Chile
- Centro Avanzado de Ingeniería Eléctrica y Electrónica, AC3E, Universidad Técnica Federico Santa María, Valparaíso, Chile
- Biomedical Neuroscience Institute, Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - Carolina Delgado
- Neuroscience Department, Facultad de Medicina, Universidad de Chile, Santiago, Chile
- Neurology and Neurosurgery Department, Hospital Clínico de la Universidad de Chile, Santiago, Chile
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In vivo multi-parametric manganese-enhanced MRI for detecting amyloid plaques in rodent models of Alzheimer's disease. Sci Rep 2021; 11:12419. [PMID: 34127752 PMCID: PMC8203664 DOI: 10.1038/s41598-021-91899-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/28/2021] [Indexed: 12/14/2022] Open
Abstract
Amyloid plaques are a hallmark of Alzheimer's disease (AD) that develop in its earliest stages. Thus, non-invasive detection of these plaques would be invaluable for diagnosis and the development and monitoring of treatments, but this remains a challenge due to their small size. Here, we investigated the utility of manganese-enhanced MRI (MEMRI) for visualizing plaques in transgenic rodent models of AD across two species: 5xFAD mice and TgF344-AD rats. Animals were given subcutaneous injections of MnCl2 and imaged in vivo using a 9.4 T Bruker scanner. MnCl2 improved signal-to-noise ratio but was not necessary to detect plaques in high-resolution images. Plaques were visible in all transgenic animals and no wild-types, and quantitative susceptibility mapping showed that they were more paramagnetic than the surrounding tissue. This, combined with beta-amyloid and iron staining, indicate that plaque MR visibility in both animal models was driven by plaque size and iron load. Longitudinal relaxation rate mapping revealed increased manganese uptake in brain regions of high plaque burden in transgenic animals compared to their wild-type littermates. This was limited to the rhinencephalon in the TgF344-AD rats, while it was most significantly increased in the cortex of the 5xFAD mice. Alizarin Red staining suggests that manganese bound to plaques in 5xFAD mice but not in TgF344-AD rats. Multi-parametric MEMRI is a simple, viable method for detecting amyloid plaques in rodent models of AD. Manganese-induced signal enhancement can enable higher-resolution imaging, which is key to visualizing these small amyloid deposits. We also present the first in vivo evidence of manganese as a potential targeted contrast agent for imaging plaques in the 5xFAD model of AD.
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30
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Kim S, Lee P, Oh KT, Byun MS, Yi D, Lee JH, Kim YK, Ye BS, Yun MJ, Lee DY, Jeong Y. Deep learning-based amyloid PET positivity classification model in the Alzheimer's disease continuum by using 2-[ 18F]FDG PET. EJNMMI Res 2021; 11:56. [PMID: 34114091 PMCID: PMC8192639 DOI: 10.1186/s13550-021-00798-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 06/02/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Considering the limited accessibility of amyloid position emission tomography (PET) in patients with dementia, we proposed a deep learning (DL)-based amyloid PET positivity classification model from PET images with 2-deoxy-2-[fluorine-18]fluoro-D-glucose (2-[18F]FDG). METHODS We used 2-[18F]FDG PET datasets from the Alzheimer's Disease Neuroimaging Initiative and Korean Brain Aging Study for the Early diagnosis and prediction of Alzheimer's disease for model development. Moreover, we used an independent dataset from another hospital. A 2.5-D deep learning architecture was constructed using 291 submodules and three axes images as the input. We conducted the voxel-wise analysis to assess the regions with substantial differences in glucose metabolism between the amyloid PET-positive and PET-negative participants. This facilitated an understanding of the deep model classification. In addition, we compared these regions with the classification probability from the submodules. RESULTS There were 686 out of 1433 (47.9%) and 50 out of 100 (50%) amyloid PET-positive participants in the training and internal validation datasets and the external validation datasets, respectively. With 50 times iterations of model training and validation, the model achieved an AUC of 0.811 (95% confidence interval (CI) of 0.803-0.819) and 0.798 (95% CI, 0.789-0.807) on the internal and external validation datasets, respectively. The area under the curve (AUC) was 0.860 when tested with the model with the highest value (0.864) on the external validation dataset. Moreover, it had 75.0% accuracy, 76.0% sensitivity, 74.0% specificity, and 75.0% F1-score. We found an overlap between the regions within the default mode network, thus generating high classification values. CONCLUSION The proposed model based on the 2-[18F]FDG PET imaging data and a DL framework might successfully classify amyloid PET positivity in clinical practice, without performing amyloid PET, which have limited accessibility.
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Affiliation(s)
- Suhong Kim
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- Korea Advanced Institute of Science and Technology (KAIST), KI for Health Science Technology, Daejeon, Republic of Korea
| | - Peter Lee
- Korea Advanced Institute of Science and Technology (KAIST), KI for Health Science Technology, Daejeon, Republic of Korea
| | - Kyeong Taek Oh
- Department of Medical Engineering, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Min Soo Byun
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea
| | - Jun Ho Lee
- Department of Neuropsychiatry, National Center for Mental Health, Seoul, Republic of Korea
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
| | - Byoung Seok Ye
- Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Mi Jin Yun
- Department of Nuclear Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
| | - Dong Young Lee
- Department of Neuropsychiatry, National Center for Mental Health, Seoul, Republic of Korea.
- Department of Psychiatry, Seoul National University College of Medicine, 101 Daehak-ro, Joungno-gu, Seoul, 03080, Republic of Korea.
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
| | - Yong Jeong
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
- Korea Advanced Institute of Science and Technology (KAIST), KI for Health Science Technology, Daejeon, Republic of Korea.
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31
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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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A machine learning approach to screen for preclinical Alzheimer's disease. Neurobiol Aging 2021; 105:205-216. [PMID: 34102381 DOI: 10.1016/j.neurobiolaging.2021.04.024] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/06/2021] [Accepted: 04/23/2021] [Indexed: 11/22/2022]
Abstract
Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on 18F-florbetapir and 18F-fluorodeoxyglucose PET, respectively. We used a nested cross-validation approach with non-invasive features (electroencephalography [EEG], APOE4 genotype, demographic, neuropsychological and MRI data) to predict: 1/ amyloid status; 2/ neurodegeneration status; 3/ decline to prodromal AD at 5-year follow-up. Importantly, EEG was most strongly predictive of neurodegeneration, even when reducing the number of channels from 224 down to 4, as 4-channel EEG best predicted neurodegeneration (negative predictive value [NPV] = 82%, positive predictive value [PPV] = 38%, 77% specificity, 45% sensitivity). The combination of demographic, neuropsychological data, APOE4 and hippocampal volumetry most strongly predicted amyloid (80% NPV, 41% PPV, 70% specificity, 58% sensitivity) and most strongly predicted decline to prodromal AD at 5 years (97% NPV, 14% PPV, 83% specificity, 50% sensitivity). Thus, machine learning can help to screen patients at high risk of preclinical AD using non-invasive and affordable biomarkers.
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33
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Ashford MT, Veitch DP, Neuhaus J, Nosheny RL, Tosun D, Weiner MW. The search for a convenient procedure to detect one of the earliest signs of Alzheimer's disease: A systematic review of the prediction of brain amyloid status. Alzheimers Dement 2021; 17:866-887. [PMID: 33583100 DOI: 10.1002/alz.12253] [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: 08/03/2020] [Revised: 10/10/2020] [Accepted: 11/03/2020] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Convenient, cost-effective tests for amyloid beta (Aβ) are needed to identify those at higher risk for developing Alzheimer's disease (AD). This systematic review evaluates recent models that predict dichotomous Aβ. (PROSPERO: CRD42020144734). METHODS We searched Embase and identified 73 studies from 29,581 for review. We assessed study quality using established tools, extracted information, and reported results narratively. RESULTS We identified few high-quality studies due to concerns about Aβ determination and analytical issues. The most promising convenient, inexpensive classifiers consist of age, apolipoprotein E genotype, cognitive measures, and/or plasma Aβ. Plasma Aβ may be sufficient if pre-analytical variables are standardized and scalable assays developed. Some models lowered costs associated with clinical trial recruitment or clinical screening. DISCUSSION Conclusions about models are difficult due to study heterogeneity and quality. Promising prediction models used demographic, cognitive/neuropsychological, imaging, and plasma Aβ measures. Further studies using standardized Aβ determination, and improved model validation are required.
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Affiliation(s)
- Miriam T Ashford
- Department of Veterans Affairs Medical Center, Northern California Institute for Research and Education, San Francisco, California, USA.,Department of Veterans Affairs Medical Center, Center for Imaging and Neurodegenerative Diseases, San Francisco, California, USA
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Northern California Institute for Research and Education, San Francisco, California, USA
| | - John Neuhaus
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
| | - Rachel L Nosheny
- Department of Veterans Affairs Medical Center, Center for Imaging and Neurodegenerative Diseases, San Francisco, California, USA.,Department of Psychiatry, University of California San Francisco, San Francisco, California, USA
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Michael W Weiner
- Department of Veterans Affairs Medical Center, Northern California Institute for Research and Education, San Francisco, California, USA.,Department of Veterans Affairs Medical Center, Center for Imaging and Neurodegenerative Diseases, San Francisco, California, USA.,Department of Psychiatry, University of California San Francisco, San Francisco, California, USA.,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA.,Department of Medicine, University of California San Francisco, San Francisco, California, USA.,Department of Neurology, University of California San Francisco, San Francisco, California, USA
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Tosun D, Veitch D, Aisen P, Jack CR, Jagust WJ, Petersen RC, Saykin AJ, Bollinger J, Ovod V, Mawuenyega KG, Bateman RJ, Shaw LM, Trojanowski JQ, Blennow K, Zetterberg H, Weiner MW. Detection of β-amyloid positivity in Alzheimer's Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers. Brain Commun 2021; 3:fcab008. [PMID: 33842885 PMCID: PMC8023542 DOI: 10.1093/braincomms/fcab008] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 12/02/2020] [Accepted: 12/04/2020] [Indexed: 01/18/2023] Open
Abstract
In vivo gold standard for the ante-mortem assessment of brain β-amyloid pathology is currently β-amyloid positron emission tomography or cerebrospinal fluid measures of β-amyloid42 or the β-amyloid42/β-amyloid40 ratio. The widespread acceptance of a biomarker classification scheme for the Alzheimer's disease continuum has ignited interest in more affordable and accessible approaches to detect Alzheimer's disease β-amyloid pathology, a process that often slows down the recruitment into, and adds to the cost of, clinical trials. Recently, there has been considerable excitement concerning the value of blood biomarkers. Leveraging multidisciplinary data from cognitively unimpaired participants and participants with mild cognitive impairment recruited by the multisite biomarker study of Alzheimer's Disease Neuroimaging Initiative, here we assessed to what extent plasma β-amyloid42/β-amyloid40, neurofilament light and phosphorylated-tau at threonine-181 biomarkers detect the presence of β-amyloid pathology, and to what extent the addition of clinical information such as demographic data, APOE genotype, cognitive assessments and MRI can assist plasma biomarkers in detecting β-amyloid-positivity. Our results confirm plasma β-amyloid42/β-amyloid40 as a robust biomarker of brain β-amyloid-positivity (area under curve, 0.80-0.87). Plasma phosphorylated-tau at threonine-181 detected β-amyloid-positivity only in the cognitively impaired with a moderate area under curve of 0.67, whereas plasma neurofilament light did not detect β-amyloid-positivity in either group of participants. Clinical information as well as MRI-score independently detected positron emission tomography β-amyloid-positivity in both cognitively unimpaired and impaired (area under curve, 0.69-0.81). Clinical information, particularly APOE ε4 status, enhanced the performance of plasma biomarkers in the detection of positron emission tomography β-amyloid-positivity by 0.06-0.14 units of area under curve for cognitively unimpaired, and by 0.21-0.25 units for cognitively impaired; and further enhancement of these models with an MRI-score of β-amyloid-positivity yielded an additional improvement of 0.04-0.11 units of area under curve for cognitively unimpaired and 0.05-0.09 units for cognitively impaired. Taken together, these multi-disciplinary results suggest that when combined with clinical information, plasma phosphorylated-tau at threonine-181 and neurofilament light biomarkers, and an MRI-score could effectively identify β-amyloid+ cognitively unimpaired and impaired (area under curve, 0.80-0.90). Yet, when the MRI-score is considered in combination with clinical information, plasma phosphorylated-tau at threonine-181 and plasma neurofilament light have minimal added value for detecting β-amyloid-positivity. Our systematic comparison of β-amyloid-positivity detection models identified effective combinations of demographics, APOE, global cognition, MRI and plasma biomarkers. Promising minimally invasive and low-cost predictors such as plasma biomarkers of β-amyloid42/β-amyloid40 may be improved by age and APOE genotype.
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Affiliation(s)
- Duygu Tosun
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Dallas Veitch
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute (ATRI), Keck School of Medicine, University of Southern California, San Diego, CA, USA
| | | | - William J Jagust
- School of Public Health and Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Ronald C Petersen
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - James Bollinger
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
| | - Vitaliy Ovod
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
| | - Kwasi G Mawuenyega
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - Michael W Weiner
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
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35
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Hamatani S, Hirano Y, Sugawara A, Isobe M, Kodama N, Yoshihara K, Moriguchi Y, Ando T, Endo Y, Takahashi J, Nohara N, Takamura T, Hori H, Noda T, Tose K, Watanabe K, Adachi H, Gondo M, Takakura S, Fukudo S, Shimizu E, Yoshiuchi K, Sato Y, Sekiguchi A. Eating Disorder Neuroimaging Initiative (EDNI): a multicentre prospective cohort study protocol for elucidating the neural effects of cognitive-behavioural therapy for eating disorders. BMJ Open 2021; 11:e042685. [PMID: 33495256 PMCID: PMC7839914 DOI: 10.1136/bmjopen-2020-042685] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
INTRODUCTION Anorexia nervosa is a refractory psychiatric disorder with a mortality rate of 5.9% and standardised mortality ratio of 5.35, which is much higher than other psychiatric disorders. The standardised mortality ratio of bulimia nervosa is 1.49; however, it is characterised by suicidality resulting in a shorter time to death. While there is no current validated drug treatment for eating disorders in Japan, cognitive-behavioural therapy (CBT) is a well-established and commonly used treatment. CBT is also recommended in the Japanese Guidelines for the Treatment of Eating Disorders (2012) and has been covered by insurance since 2018. However, the neural mechanisms responsible for the effect of CBT have not been elucidated, and the use of biomarkers such as neuroimaging data would be beneficial. METHODS AND ANALYSIS The Eating Disorder Neuroimaging Initiative is a multisite prospective cohort study. We will longitudinally collect data from 72 patients with eating disorders (anorexia nervosa and bulimia nervosa) and 70 controls. Data will be collected at baseline, after 21-41 sessions of CBT and 12 months later. We will assess longitudinal changes in neural circuit function, clinical data, gene expression and psychological measures by therapeutic intervention and analyse the relationship among them using machine learning methods. ETHICS AND DISSEMINATION The study was approved by The Ethical Committee of the National Center of Neurology and Psychiatry (A2019-072). We will obtain written informed consent from all patients who participate in the study after they had been fully informed about the study protocol. All imaging, demographic and clinical data are shared between the participating sites and will be made publicly available in 2024. TRIAL REGISTRATION NUMBER UMIN000039841.
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Affiliation(s)
- Sayo Hamatani
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Yoshiyuki Hirano
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University and University of Fukui, Chiba, Japan
| | - Ayako Sugawara
- Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Masanori Isobe
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Naoki Kodama
- Division of Psychosomatic Medicine, Department of Neurology, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Kazufumi Yoshihara
- Department of Psychosomatic Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoshiya Moriguchi
- Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Tetsuya Ando
- Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Yuka Endo
- Department of Psychosomatic Medicine, Tohoku University Hospital, Sendai, Japan
| | - Jumpei Takahashi
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
| | - Nobuhiro Nohara
- Department of Stress Sciences and Psychosomatic Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tsunehiko Takamura
- Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Hiroaki Hori
- Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Tomomi Noda
- Japan Society for the Promotion of Science, Tokyo, Japan
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Keima Tose
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Keita Watanabe
- Department of Radiology, University of Occupational and Environmental Health School of Medicine, Kitakyushu, Japan
| | - Hiroaki Adachi
- Division of Psychosomatic Medicine, Department of Neurology, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Motoharu Gondo
- Department of Psychosomatic Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Department of Psychosomatic Medicine, Kitakyushu Municipal Medical Center, Fukuoka, Japan
| | - Shu Takakura
- Department of Psychosomatic Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Shin Fukudo
- Department of Psychosomatic Medicine, Tohoku University Hospital, Sendai, Japan
- Department of Behavioral Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Eiji Shimizu
- Research Center for Child Mental Development, Chiba University, Chiba, Japan
- Department of Cognitive Behavioral Physiology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Kazuhiro Yoshiuchi
- Department of Stress Sciences and Psychosomatic Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yasuhiro Sato
- Department of Psychosomatic Medicine, Tohoku University Hospital, Sendai, Japan
| | - Atsushi Sekiguchi
- Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
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36
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Park YW, Choi D, Park M, Ahn SJ, Ahn SS, Suh SH, Lee SK. Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging. J Alzheimers Dis 2021; 79:483-491. [PMID: 33337361 DOI: 10.3233/jad-200734] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Noninvasive identification of amyloid-β (Aβ) is important for better clinical management of mild cognitive impairment (MCI) patients. OBJECTIVE To investigate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ42 status when integrated with clinical profiles. METHODS A total of 407 MCI subjects from the Alzheimer's Disease Neuroimaging Initiative were allocated to training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampus were extracted from magnetic resonance imaging (MRI). A cut-off of <192 pg/mL was applied to define CSF Aβ42 status. After feature selection, random forest with subsampling methods were utilized to develop three models with which to predict CSF Aβ42: 1) a radiomics model; 2) a clinical model based on clinical profiles; and 3) a combined model based on radiomics and clinical profiles. The prediction performances thereof were validated in the test set. A prediction model using hippocampus volume was also developed and validated. RESULTS The best-performing radiomics model showed an area under the curve (AUC) of 0.674 in the test set. The best-performing clinical model showed an AUC of 0.758 in the test set. The best-performing combined model showed an AUC of 0.823 in the test set. The hippocampal volume model showed a lower performance, with an AUC of 0.543 in the test set. CONCLUSION Radiomics models from MRI can help predict CSF Aβ42 status in MCI patients and potentially triage the patients for invasive and costly Aβ tests.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Dongmin Choi
- Department of Computer Science, Yonsei University, Seoul, Korea
| | - Mina Park
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Jun Ahn
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Sang Hyun Suh
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
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Spatial patterns of correlation between cortical amyloid and cortical thickness in a tertiary clinical population with memory deficit. Sci Rep 2020; 10:20717. [PMID: 33244036 PMCID: PMC7693188 DOI: 10.1038/s41598-020-77503-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 11/11/2020] [Indexed: 12/28/2022] Open
Abstract
To estimate regional Alzheimer disease (AD) pathology burden clinically, analysis methods that enable tracking brain amyloid or tau positron emission tomography (PET) with magnetic resonance imaging (MRI) measures are needed. We therefore developed a robust MRI analysis method to identify brain regions that correlate linearly with regional amyloid burden in congruent PET images. This method was designed to reduce data variance and improve the sensitivity of the detection of cortical thickness-amyloid correlation by using whole brain modeling, nonlinear image coregistration, and partial volume correction. Using this method, a cross-sectional analysis of 75 tertiary memory clinic AD patients was performed to test our hypothesis that regional amyloid burden and cortical thickness are inversely correlated in medial temporal neocortical regions. Medial temporal cortical thicknesses were not correlated with their regional amyloid burden, whereas cortical thicknesses in the lateral temporal, lateral parietal, and frontal regions were inversely correlated with amyloid burden. This study demonstrates the robustness of our technique combining whole brain modeling, nonlinear image coregistration, and partial volume correction to track the differential correlation between regional amyloid burden and cortical thinning in specific brain regions. This method could be used with amyloid and tau PET to assess corresponding cortical thickness changes.
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38
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Visser PJ, Reus LM, Gobom J, Jansen I, Dicks E, Tsolaki M, Verhey FRJ, Popp J, Martinez-Lage P, Vandenberghe R, Lleó A, Molinuevo JL, Engelborghs S, Freund-Levi Y, Froelich L, Sleegers K, Dobricic V, Hong S, Lovestone S, Streffer J, Vos SJB, Bos I, Smit AB, Blennow K, Scheltens P, Teunissen CE, Bertram L, Zetterberg H, Tijms BM. Cerebrospinal fluid total tau levels indicate aberrant neuronal plasticity in Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020. [PMID: 33173883 DOI: 10.1101/2020.10.29.20211920] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Alzheimer's disease (AD) is characterised by abnormal amyloid beta and tau processing. Previous studies reported that cerebrospinal fluid (CSF) total tau (t-tau) levels vary between patients. Here we show that CSF t-tau variability is associated with distinct impairments in neuronal plasticity mediated by gene repression factors SUZ12 and REST. AD individuals with abnormal t-tau levels have increased CSF concentrations of plasticity proteins regulated by SUZ12 and REST. AD individuals with normal t-tau, on the contrary, have decreased concentrations of these plasticity proteins and increased concentrations in proteins associated with blood-brain and blood CSF-barrier dysfunction. Genomic analyses suggested that t-tau levels in part depend on genes involved in gene expression. The distinct plasticity abnormalities in AD as signaled by t-tau urge the need for personalised treatment.
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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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de Leeuw FA, Karamujić-Čomić H, Tijms BM, Peeters CFW, Kester MI, Scheltens P, Ahmad S, Vojinovic D, Adams HHH, Hankemeier T, Bos D, van der Lugt A, Vernooij MW, Ikram MA, Amin N, Barkhof F, Teunissen CE, van Duijn CM, van der Flier WM. Circulating metabolites are associated with brain atrophy and white matter hyperintensities. Alzheimers Dement 2020; 17:205-214. [PMID: 32886448 PMCID: PMC7984157 DOI: 10.1002/alz.12180] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 07/25/2020] [Accepted: 08/08/2020] [Indexed: 01/01/2023]
Abstract
Introduction Our aim was to study whether systemic metabolites are associated with magnetic resonance imaging (MRI) measures of brain and hippocampal atrophy and white matter hyperintensities (WMH). Methods We studied associations of 143 plasma‐based metabolites with MRI measures of brain and hippocampal atrophy and WMH in three independent cohorts (n = 3962). We meta‐analyzed the results of linear regression analyses to determine the association of metabolites with MRI measures. Results Higher glucose levels and lower levels of three small high density lipoprotein (HDL) particles were associated with brain atrophy. Higher glucose levels were associated with WMH. Discussion Glucose levels were associated with brain atrophy and WMH, and small HDL particle levels were associated with brain atrophy. Circulating metabolites may aid in developing future intervention trials.
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Affiliation(s)
- Francisca A de Leeuw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Hata Karamujić-Čomić
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Carel F W Peeters
- Department of Epidemiology & Biostatistics, Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, The Netherlands
| | - Maartje I Kester
- Department of Neurology, Flevoziekenhuis, Almere, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Shahzad Ahmad
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Dina Vojinovic
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Hieab H H Adams
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands.,Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Thomas Hankemeier
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.,Division of Analytical Biosciences, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Leiden, The Netherlands.,Translational Epidemiology, Faculty Science, Leiden University, Leiden, The Netherlands
| | - Daniel Bos
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Najaf Amin
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Institutes of Neurology & Healthcare Engineering, UCL London, London, UK
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.,Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Epidemiology & Biostatistics, Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, The Netherlands
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41
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Pekkala T, Hall A, Ngandu T, van Gils M, Helisalmi S, Hänninen T, Kemppainen N, Liu Y, Lötjönen J, Paajanen T, Rinne JO, Soininen H, Kivipelto M, Solomon A. Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline. Front Aging Neurosci 2020; 12:228. [PMID: 32848707 PMCID: PMC7406705 DOI: 10.3389/fnagi.2020.00228] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 06/29/2020] [Indexed: 12/28/2022] Open
Abstract
The importance of early interventions in Alzheimer's disease (AD) emphasizes the need to accurately and efficiently identify at-risk individuals. Although many dementia prediction models have been developed, there are fewer studies focusing on detection of brain pathology. We developed a model for identification of amyloid-PET positivity using data on demographics, vascular factors, cognition, APOE genotype, and structural MRI, including regional brain volumes, cortical thickness and a visual medial temporal lobe atrophy (MTA) rating. We also analyzed the relative importance of different factors when added to the overall model. The model used baseline data from the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) exploratory PET sub-study. Participants were at risk for dementia, but without dementia or cognitive impairment. Their mean age was 71 years. Participants underwent a brain 3T MRI and PiB-PET imaging. PiB images were visually determined as positive or negative. Cognition was measured using a modified version of the Neuropsychological Test Battery. Body mass index (BMI) and hypertension were used as cardiovascular risk factors in the model. Demographic factors included age, gender and years of education. The model was built using the Disease State Index (DSI) machine learning algorithm. Of the 48 participants, 20 (42%) were rated as Aβ positive. Compared with the Aβ negative group, the Aβ positive group had a higher proportion of APOE ε4 carriers (53 vs. 14%), lower executive functioning, lower brain volumes, and higher visual MTA rating. AUC [95% CI] for the complete model was 0.78 [0.65-0.91]. MRI was the most effective factor, especially brain volumes and visual MTA rating but not cortical thickness. APOE was nearly as effective as MRI in improving detection of amyloid positivity. The model with the best performance (AUC 0.82 [0.71-0.93]) was achieved by combining APOE and MRI. Our findings suggest that combining demographic data, vascular risk factors, cognitive performance, APOE genotype, and brain MRI measures can help identify Aβ positivity. Detecting amyloid positivity could reduce invasive and costly assessments during the screening process in clinical trials.
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Affiliation(s)
- Timo Pekkala
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Anette Hall
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Tiia Ngandu
- Public Health Promotion Unit, Finnish Institute for Health and Welfare, Helsinki, Finland.,Division of Clinical Geriatrics, Center for Alzheimer Research, NVS, Karolinska Institutet, Stockholm, Sweden
| | - Mark van Gils
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | - Seppo Helisalmi
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Tuomo Hänninen
- Neurocenter/Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Nina Kemppainen
- Turku PET Centre, University of Turku, Turku, Finland.,Division of Clinical Neurosciences, Turku University Hospital, Turku, Finland
| | - Yawu Liu
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland.,Department of Clinical Radiology, Kuopio University Hospital, Kuopio, Finland
| | | | - Teemu Paajanen
- Finnish Institute of Occupational Health, Helsinki, Finland
| | - Juha O Rinne
- Turku PET Centre, University of Turku, Turku, Finland.,Division of Clinical Neurosciences, Turku University Hospital, Turku, Finland
| | - Hilkka Soininen
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland.,Neurocenter/Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Miia Kivipelto
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland.,Division of Clinical Geriatrics, Center for Alzheimer Research, NVS, Karolinska Institutet, Stockholm, Sweden.,Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland.,Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, United Kingdom
| | - Alina Solomon
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland.,Division of Clinical Geriatrics, Center for Alzheimer Research, NVS, Karolinska Institutet, Stockholm, Sweden
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Belkhiria C, Vergara RC, San Martin S, Leiva A, Martinez M, Marcenaro B, Andrade M, Delano PH, Delgado C. Insula and Amygdala Atrophy Are Associated With Functional Impairment in Subjects With Presbycusis. Front Aging Neurosci 2020; 12:102. [PMID: 32410980 PMCID: PMC7198897 DOI: 10.3389/fnagi.2020.00102] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Accepted: 03/26/2020] [Indexed: 01/07/2023] Open
Abstract
Hearing loss is an important risk factor for dementia. However, the mechanisms that relate these disorders are still unknown. As a proxy of this relationship, we studied the structural brain changes associated with functional impairment in activities of daily living in subjects with age related hearing loss, or presbycusis. One hundred eleven independent, non-demented subjects older than 65 years recruited in the ANDES cohort were evaluated using a combined approach including (i) audiological tests: hearing thresholds and cochlear function measured by pure tone averages and the distortion product otoacoustic emissions respectively; (ii) behavioral variables: cognitive, neuropsychiatric, and functional impairment in activities of daily living measured by validated questionnaires; and (iii) structural brain imaging—assessed by magnetic resonance imaging at 3 Tesla. The mean age of the recruited subjects (69 females) was 73.95 ± 5.47 years (mean ± SD) with an average educational level of 9.44 ± 4.2 years of schooling. According to the audiometric hearing thresholds and presence of otoacoustic emissions, we studied three groups: controls with normal hearing (n = 36), presbycusis with preserved cochlear function (n = 33), and presbycusis with cochlear dysfunction (n = 38). We found a significant association (R2D = 0.17) between the number of detected otoacoustic emissions and apathy symptoms. The presbycusis with cochlear dysfunction group had worse performance than controls in global cognition, language and executive functions, and severe apathy symptoms than the other groups. The neuropsychiatric symptoms and language deficits were the main determinants of functional impairment in both groups of subjects with presbycusis. Atrophy of insula, amygdala, and other temporal areas were related with functional impairment, apathy, and language deficits in the presbycusis with cochlear dysfunction group. We conclude that (i) the neuropsychiatric symptoms had a major effect on functional loss in subjects with presbycusis, (ii) cochlear dysfunction is relevant for the association between hearing loss and behavioral impairment, and (iii) atrophy of the insula and amygdala among other temporal areas are related with hearing loss and behavioral impairment.
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Affiliation(s)
- Chama Belkhiria
- Neuroscience Department, Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - Rodrigo C Vergara
- Neuroscience Department, Facultad de Medicina, Universidad de Chile, Santiago, Chile.,Kinesiology Department, Facultad de Artes y Educación Física, Universidad Metropolitana de Ciencias de la Educación, Santiago, Chile
| | - Simón San Martin
- Neuroscience Department, Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - Alexis Leiva
- Neuroscience Department, Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - Melissa Martinez
- Neurology and Neurosurgery Department, Hospital Clínico de la Universidad de Chile, Santiago, Chile
| | - Bruno Marcenaro
- Neuroscience Department, Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - Maricarmen Andrade
- Internal Medicine Department, Clínica Universidad de los Andes, Santiago, Chile
| | - Paul H Delano
- Neuroscience Department, Facultad de Medicina, Universidad de Chile, Santiago, Chile.,Otolaryngology Department, Hospital Clínico de la Universidad de Chile, Santiago, Chile.,Centro Avanzado de Ingeniería Eléctrica y Electrónica, AC3E, Universidad Técnica Federico Santa María, Valparaíso, Chile.,Biomedical Neuroscience Institute, Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - Carolina Delgado
- Neuroscience Department, Facultad de Medicina, Universidad de Chile, Santiago, Chile.,Neurology and Neurosurgery Department, Hospital Clínico de la Universidad de Chile, Santiago, Chile
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Reimand J, de Wilde A, Teunissen CE, Zwan M, Windhorst AD, Boellaard R, Barkhof F, van der Flier WM, Scheltens P, van Berckel BNM, Ossenkoppele R, Bouwman F. PET and CSF amyloid-β status are differently predicted by patient features: information from discordant cases. Alzheimers Res Ther 2019; 11:100. [PMID: 31810489 PMCID: PMC6898919 DOI: 10.1186/s13195-019-0561-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 11/21/2019] [Indexed: 12/21/2022]
Abstract
BACKGROUND Amyloid-β PET and CSF Aβ42 yield discordant results in 10-20% of memory clinic patients, possibly providing unique information. Although the predictive power of demographic, clinical, genetic, and imaging features for amyloid positivity has previously been investigated, it is unknown whether these features differentially predict amyloid-β status based on PET or CSF or whether this differs by disease stage. METHODS We included 768 patients (subjective cognitive decline (SCD, n = 194), mild cognitive impairment (MCI, n = 127), dementia (AD and non-AD, n = 447) with amyloid-β PET and CSF Aβ42 measurement within 1 year. Ninety-seven (13%) patients had discordant PET/CSF amyloid-β status. We performed parallel random forest models predicting separately PET and CSF status using 17 patient features (demographics, APOE4 positivity, CSF (p)tau, cognitive performance, and MRI visual ratings) in the total patient group and stratified by syndrome diagnosis. Thereafter, we selected features with the highest variable importance measure (VIM) as input for logistic regression models, where amyloid status on either PET or CSF was predicted by (i) the selected patient feature and (ii) the patient feature adjusted for the status of the other amyloid modality. RESULTS APOE4, CSF tau, and p-tau had the highest VIM for PET and CSF in all groups. In the amyloid-adjusted logistic regression models, p-tau was a significant predictor for PET-amyloid in SCD (OR = 1.02 [1.01-1.04], pFDR = 0.03), MCI (OR = 1.05 [1.02-1.07], pFDR < 0.01), and dementia (OR = 1.04 [1.03-1.05], pFDR < 0.001), but not for CSF-amyloid. APOE4 (OR = 3.07 [1.33-7.07], punc < 0.01) was associated with CSF-amyloid in SCD, while it was only predictive for PET-amyloid in MCI (OR = 9.44 [2.93, 30.39], pFDR < 0.01). Worse MMSE scores (OR = 1.21 [1.03-1.41], punc = 0.02) were associated to CSF-amyloid status in SCD, whereas worse memory (OR = 1.17 [1.05-1.31], pFDR = 0.02) only predicted PET positivity in dementia. CONCLUSION Amyloid status based on either PET or CSF was predicted by different patient features, and this varied by disease stage, suggesting that PET-CSF discordance yields unique information. The stronger associations of both APOE4 carriership and worse memory z-scores with CSF-amyloid in SCD suggest that CSF-amyloid is more sensitive early in the disease course. The higher predictive value of CSF p-tau for a positive PET scan suggests that PET is more specific to AD pathology.
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Affiliation(s)
- Juhan Reimand
- Department of Neurology & Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands.
- Department of Health Technologies, Tallinn University of Technology, Tallinn, Estonia.
- Radiology Centre, North Estonia Medical Centre, Tallinn, Estonia.
| | - Arno de Wilde
- Department of Neurology & Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Marissa Zwan
- Department of Neurology & Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands
| | - Albert D Windhorst
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Ronald Boellaard
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Wiesje M van der Flier
- Department of Neurology & Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands
- Department of Epidemiology & Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Philip Scheltens
- Department of Neurology & Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands
| | - Bart N M van Berckel
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Rik Ossenkoppele
- Department of Neurology & Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Femke Bouwman
- Department of Neurology & Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, P.O. Box 7057, 1007 MB, Amsterdam, The Netherlands
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Maserejian N, Bian S, Wang W, Jaeger J, Syrjanen JA, Aakre J, Jack CR, Mielke MM, Gao F. Practical algorithms for amyloid β probability in subjective or mild cognitive impairment. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2019; 11:180. [PMID: 31700988 PMCID: PMC6827360 DOI: 10.1016/j.dadm.2019.09.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
INTRODUCTION Practical algorithms predicting the probability of amyloid pathology among patients with subjective cognitive decline or mild cognitive impairment may help clinical decisions regarding confirmatory biomarker testing for Alzheimer's disease. METHODS Algorithm feature selection was conducted with Alzheimer's Disease Neuroimaging Initiative and Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing data. Probability algorithms were developed in Alzheimer's Disease Neuroimaging Initiative using nested cross-validation accompanied by stratified subsampling to obtain 1000 internally validated decision trees. Semi-independent validation was conducted using Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing. Independent external validation was conducted in the population-based Mayo Clinic Study of Aging. RESULTS Two algorithms were developed using age and normalized immediate recall z-scores, with or without apolipoprotein E ε4 carrier status. Both algorithms had robust performance across data sets and when substituting different recall memory tests. DISCUSSION The statistical framework resulted in robust probability estimation. Application of these algorithms may assist in clinical decision-making for further testing to diagnose amyloid pathology.
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Affiliation(s)
| | - Shijia Bian
- Department of Biostatistics, Biogen, Cambridge, MA, USA
| | - Wenting Wang
- Department of Biostatistics, Biogen, Cambridge, MA, USA
| | - Judith Jaeger
- CognitionMetrics, Wilmington, DE, USA
- Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, NY, USA
| | | | - Jeremiah Aakre
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Michelle M. Mielke
- Department of Health Sciences Research, Professor of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Feng Gao
- Department of Biostatistics, Biogen, Cambridge, MA, USA
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Shi L, Westwood S, Baird AL, Winchester L, Dobricic V, Kilpert F, Hong S, Franke A, Hye A, Ashton NJ, Morgan AR, Bos I, Vos SJB, Buckley NJ, Kate MT, Scheltens P, Vandenberghe R, Gabel S, Meersmans K, Engelborghs S, De Roeck EE, Sleegers K, Frisoni GB, Blin O, Richardson JC, Bordet R, Molinuevo JL, Rami L, Wallin A, Kettunen P, Tsolaki M, Verhey F, Lleó A, Alcolea D, Popp J, Peyratout G, Martinez-Lage P, Tainta M, Johannsen P, Teunissen CE, Freund-Levi Y, Frölich L, Legido-Quigley C, Barkhof F, Blennow K, Zetterberg H, Baker S, Morgan BP, Streffer J, Visser PJ, Bertram L, Lovestone S, Nevado-Holgado AJ. Discovery and validation of plasma proteomic biomarkers relating to brain amyloid burden by SOMAscan assay. Alzheimers Dement 2019; 15:1478-1488. [PMID: 31495601 PMCID: PMC6880298 DOI: 10.1016/j.jalz.2019.06.4951] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 06/11/2019] [Accepted: 06/23/2019] [Indexed: 11/09/2022]
Abstract
Introduction Plasma proteins have been widely studied as candidate biomarkers to predict brain amyloid deposition to increase recruitment efficiency in secondary prevention clinical trials for Alzheimer's disease. Most such biomarker studies are targeted to specific proteins or are biased toward high abundant proteins. Methods 4001 plasma proteins were measured in two groups of participants (discovery group = 516, replication group = 365) selected from the European Medical Information Framework for Alzheimer's disease Multimodal Biomarker Discovery study, all of whom had measures of amyloid. Results A panel of proteins (n = 44), along with age and apolipoprotein E (APOE) ε4, predicted brain amyloid deposition with good performance in both the discovery group (area under the curve = 0.78) and the replication group (area under the curve = 0.68). Furthermore, a causal relationship between amyloid and tau was confirmed by Mendelian randomization. Discussion The results suggest that high-dimensional plasma protein testing could be a useful and reproducible approach for measuring brain amyloid deposition.
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Affiliation(s)
- Liu Shi
- Department of Psychiatry, University of Oxford, Oxford, UK.
| | - Sarah Westwood
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Alison L Baird
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - Valerija Dobricic
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, Lübeck, Germany
| | - Fabian Kilpert
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, Lübeck, Germany
| | - Shengjun Hong
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, Lübeck, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Abdul Hye
- Maurice Wohl Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Nicholas J Ashton
- Maurice Wohl Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK; Department of Psychiatry and Neurochemistry, University of Gothenburg, Mölndal, Sweden; Wallenberg Centre for Molecular & Translational Medicine, University of Gothenburg, Gothenburg, Sweden; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK
| | - Angharad R Morgan
- Dementia Research Institute Cardiff, Cardiff University, Cardiff, UK
| | - Isabelle Bos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | - Stephanie J B Vos
- Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands
| | - Noel J Buckley
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Mara Ten Kate
- Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands
| | - Philip Scheltens
- Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands
| | | | - Silvy Gabel
- University Hospital Leuven, Leuven, Belgium; Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Belgium
| | - Karen Meersmans
- University Hospital Leuven, Leuven, Belgium; Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Belgium
| | - Sebastiaan Engelborghs
- Center for Neurosciences, Vrije Universiteit Brussel (VUB), Brussels, Belgium; Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium; Institute Born-Bunge, University of Antwerp, Antwerp, Belgium; Department of Neurology, VUB University Hospital Brussels (UZ Brussel), Brussels, Belgium
| | - Ellen E De Roeck
- Center for Neurosciences, Vrije Universiteit Brussel (VUB), Brussels, Belgium; Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium; Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Kristel Sleegers
- Institute Born-Bunge, University of Antwerp, Antwerp, Belgium; Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Edegem, Belgium
| | - Giovanni B Frisoni
- University of Geneva, Geneva, Switzerland; IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Olivier Blin
- AIX Marseille University, INS, Ap-hm, Marseille, France
| | | | - Régis Bordet
- University of Lille, Inserm, CHU Lille, Lille, France
| | - José L Molinuevo
- Alzheimer's disease & other cognitive disorders unit, Hopsital Clínic-IDIBAPS, Barcelona, Spain; Barcelona Beta Brain Research Center, Universitat Pompeu Fabra, Barcelona, Spain
| | - Lorena Rami
- Barcelona Beta Brain Research Center, Universitat Pompeu Fabra, Barcelona, Spain
| | - Anders Wallin
- Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Petronella Kettunen
- Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Magda Tsolaki
- 1st Department of Neurology, AHEPA University Hospital, Makedonia, Thessaloniki, Greece
| | - Frans Verhey
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | - Alberto Lleó
- Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Daniel Alcolea
- Department of Neurology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Julius Popp
- University Hospital of Lausanne, Lausanne, Switzerland; Geriatric Psychiatry, Department of Mental Health and Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | | | | | | | - Peter Johannsen
- Danish Dementia Research Centre, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands
| | - Yvonne Freund-Levi
- Department of Neurobiology, Caring Sciences and Society (NVS), Division of Clinical Geriatrics, Karolinska Institutet, and Department of Geriatric Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden; Department of Psychiatry in Region Örebro County and School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden; Department of Old Age Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Lutz Frölich
- Department of Geriatric Psychiatry, Zentralinstitut für Seelische Gesundheit, University of Heidelberg, Mannheim, Germany
| | - Cristina Legido-Quigley
- Kings College London, London, UK; The Systems Medicine Group, Steno Diabetes Center, Gentofte, Denmark
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherland; UCL Institutes of Neurology and Healthcare Engineering, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; UK Dementia Research Institute at UCL, London, UK; Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | | | - B Paul Morgan
- Dementia Research Institute Cardiff, Cardiff University, Cardiff, UK
| | - Johannes Streffer
- Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium; Janssen R&D, LLC, Beerse, Belgium
| | - Pieter Jelle Visser
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands; Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands; Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, Lübeck, Germany; Department of Psychology, University of Oslo, Oslo, Norway
| | - Simon Lovestone
- Department of Psychiatry, University of Oxford, Oxford, UK; Janssen-Cilag UK, formerly Department of Psychiatry, University of Oxford, Oxford, UK
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Petrone PM, Casamitjana A, Falcon C, Artigues M, Operto G, Cacciaglia R, Molinuevo JL, Vilaplana V, Gispert JD. Prediction of amyloid pathology in cognitively unimpaired individuals using voxel-wise analysis of longitudinal structural brain MRI. ALZHEIMERS RESEARCH & THERAPY 2019; 11:72. [PMID: 31421683 PMCID: PMC6698344 DOI: 10.1186/s13195-019-0526-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 07/23/2019] [Indexed: 01/01/2023]
Abstract
Background Magnetic resonance imaging (MRI) has unveiled specific alterations at different stages of Alzheimer’s disease (AD) pathophysiologic continuum constituting what has been established as “AD signature”. To what extent MRI can detect amyloid-related cerebral changes from structural MRI in cognitively unimpaired individuals is still an area open for exploration. Method Longitudinal 3D-T1 MRI scans were acquired from a subset of the ADNI cohort comprising 403 subjects: 79 controls (Ctrls), 50 preclinical AD (PreAD), and 274 MCI and dementia due to AD (MCI/AD). Amyloid CSF was used as gold-standard measure with established cutoffs (< 192 pg/mL) to establish diagnostic categories. Cognitively unimpaired individuals were defined as Ctrls if were amyloid negative and PreAD otherwise. The MCI/AD group was amyloid positive. Only subjects with the same diagnostic category at baseline and follow-up visits were considered for the study. Longitudinal morphometric analysis was performed using SPM12 to calculate Jacobian determinant maps. Statistical analysis was carried out on these Jacobian maps to identify structural changes that were significantly different between diagnostic categories. A machine learning classifier was applied on Jacobian determinant maps to predict the presence of abnormal amyloid levels in cognitively unimpaired individuals. The performance of this classifier was evaluated using receiver operating characteristic curve analysis and as a function of the follow-up time between MRI scans. We applied a cost function to assess the benefit of using this classifier in the triaging of individuals in a clinical trial-recruitment setting. Results The optimal follow-up time for classification of Ctrls vs PreAD was Δt > 2.5 years, and hence, only subjects within this temporal span are used for evaluation (15 Ctrls, 10 PreAD). The longitudinal voxel-based classifier achieved an AUC = 0.87 (95%CI 0.72–0.97). The brain regions that showed the highest discriminative power to detect amyloid abnormalities were the medial, inferior, and lateral temporal lobes; precuneus; caudate heads; basal forebrain; and lateral ventricles. Conclusions Our work supports that machine learning applied to longitudinal brain volumetric changes can be used to predict, with high precision, the presence of amyloid abnormalities in cognitively unimpaired subjects. Used as a triaging method to identify a fixed number of amyloid-positive individuals, this longitudinal voxel-wise classifier is expected to avoid 55% of unnecessary CSF and/or PET scans and reduce economic cost by 40%. Electronic supplementary material The online version of this article (10.1186/s13195-019-0526-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Paula M Petrone
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, C/ Wellington 30, 08005, Barcelona, Spain
| | - Adrià Casamitjana
- Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, C/ Jordi Girona 1-3, edifici D5 Campus Nord UPC, 08034, Barcelona, Spain
| | - Carles Falcon
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, C/ Wellington 30, 08005, Barcelona, Spain.,Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, 28029, Spain
| | - Miquel Artigues
- Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, C/ Jordi Girona 1-3, edifici D5 Campus Nord UPC, 08034, Barcelona, Spain
| | - Grégory Operto
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, C/ Wellington 30, 08005, Barcelona, Spain
| | - Raffaele Cacciaglia
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, C/ Wellington 30, 08005, Barcelona, Spain
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, C/ Wellington 30, 08005, Barcelona, Spain.,CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Verónica Vilaplana
- Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, C/ Jordi Girona 1-3, edifici D5 Campus Nord UPC, 08034, Barcelona, Spain.
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, C/ Wellington 30, 08005, Barcelona, Spain. .,CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain. .,Universitat Pompeu Fabra, Barcelona, Spain.
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Archetti D, Ingala S, Venkatraghavan V, Wottschel V, Young AL, Bellio M, Bron EE, Klein S, Barkhof F, Alexander DC, Oxtoby NP, Frisoni GB, Redolfi A. Multi-study validation of data-driven disease progression models to characterize evolution of biomarkers in Alzheimer's disease. NEUROIMAGE-CLINICAL 2019; 24:101954. [PMID: 31362149 PMCID: PMC6675943 DOI: 10.1016/j.nicl.2019.101954] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 06/24/2019] [Accepted: 07/19/2019] [Indexed: 11/18/2022]
Abstract
Understanding the sequence of biological and clinical events along the course of Alzheimer's disease provides insights into dementia pathophysiology and can help participant selection in clinical trials. Our objective is to train two data-driven computational models for sequencing these events, the Event Based Model (EBM) and discriminative-EBM (DEBM), on the basis of well-characterized research data, then validate the trained models on subjects from clinical cohorts characterized by less-structured data-acquisition protocols. Seven independent data cohorts were considered totalling 2389 cognitively normal (CN), 1424 mild cognitive impairment (MCI) and 743 Alzheimer's disease (AD) patients. The Alzheimer's Disease Neuroimaging Initiative (ADNI) data set was used as training set for the constriction of disease models while a collection of multi-centric data cohorts was used as test set for validation. Cross-sectional information related to clinical, cognitive, imaging and cerebrospinal fluid (CSF) biomarkers was used. Event sequences obtained with EBM and DEBM showed differences in the ordering of single biomarkers but according to both the first biomarkers to become abnormal were those related to CSF, followed by cognitive scores, while structural imaging showed significant volumetric decreases at later stages of the disease progression. Staging of test set subjects based on sequences obtained with both models showed good linear correlation with the Mini Mental State Examination score (R2EBM = 0.866; R2DEBM = 0.906). In discriminant analyses, significant differences (p-value ≤ 0.05) between the staging of subjects from training and test sets were observed in both models. No significant difference between the staging of subjects from the training and test was observed (p-value > 0.05) when considering a subset composed by 562 subjects for which all biomarker families (cognitive, imaging and CSF) are available. Event sequence obtained with DEBM recapitulates the heuristic models in a data-driven fashion and is clinically plausible. We demonstrated inter-cohort transferability of two disease progression models and their robustness in detecting AD phases. This is an important step towards the adoption of data-driven statistical models into clinical domain. Data-driven event sequences describe evolution of relevant biomarkers in AD. Agreement between event sequences and heuristic AD progression models Accuracy in classifying subjects from clinical cohorts up to 91% Staging of subjects and MMSE scores of individuals show linear relation. Transferability of AD progression models based on research data to clinical cohorts
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Affiliation(s)
- Damiano Archetti
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands.
| | - Vikram Venkatraghavan
- Biomedical Imaging Group Rotterdam, Depts. of Medical Informatics & Radiology, Erasmus MC, The Netherlands.
| | - Viktor Wottschel
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands.
| | - Alexandra L Young
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK.
| | - Maura Bellio
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK.
| | - Esther E Bron
- Biomedical Imaging Group Rotterdam, Depts. of Medical Informatics & Radiology, Erasmus MC, The Netherlands.
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Depts. of Medical Informatics & Radiology, Erasmus MC, The Netherlands.
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands; Institutes of Neurology and Healthcare Engineering, UCL, London, UK.
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK.
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK.
| | - Giovanni B Frisoni
- University of Geneva, Geneva, Switzerland; IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
| | - Alberto Redolfi
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
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von Arnim CAF, Bartsch T, Jacobs AH, Holbrook J, Bergmann P, Zieschang T, Polidori MC, Dodel R. Diagnosis and treatment of cognitive impairment. Z Gerontol Geriatr 2019; 52:309-315. [PMID: 31161337 DOI: 10.1007/s00391-019-01560-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 05/07/2019] [Indexed: 01/14/2023]
Abstract
As a result of the aging population dementia is a growing challenge, especially in healthcare. Nevertheless, cognitive disorders are often not systematically evaluated, especially during hospital stays for other reasons; however, cognitive impairment is associated with a number of geriatric syndromes, including falls, delirium, dysphagia and lack of adherence to treatment plans. This article considers the current state of diagnosis and treatment of dementia. Non-pharmacological therapeutic approaches as well as current and future pharmacological treatment options are discussed. The drugs of choice for the symptomatic treatment of cognitive deficits in Alzheimer's disease and Parkinson-associated dementia are cholinesterase inhibitors and memantine; there is no specific pharmacological treatment for other types of dementia. Prevention and treatment of cardiovascular risk factors can potentially retard the progression of possibly all forms of dementia.
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Affiliation(s)
- Christine A F von Arnim
- Clinic for Neurogeriatrics and Neurological Rehabilitation, RKU, University and Rehabilitation Hospital Ulm, Oberer Eselsberg 45, 89081, Ulm, Germany.
| | | | | | - Jill Holbrook
- Clinic for Neurogeriatrics and Neurological Rehabilitation, RKU, University and Rehabilitation Hospital Ulm, Oberer Eselsberg 45, 89081, Ulm, Germany
| | | | - Tania Zieschang
- University Clinic of Geriatric Medicine, Oldenburg University, Oldenburg, Germany
| | - M Cristina Polidori
- Ageing Clinical Research, Dpt. II Internal Medicine, University Hospital of Cologne, Cologne, Germany
| | - Richard Dodel
- Geriatric Medicine, University Duisburg-Essen, Essen, Germany
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Ansart M, Epelbaum S, Gagliardi G, Colliot O, Dormont D, Dubois B, Hampel H, Durrleman S. Reduction of recruitment costs in preclinical AD trials: validation of automatic pre-screening algorithm for brain amyloidosis. Stat Methods Med Res 2019; 29:151-164. [PMID: 30698081 DOI: 10.1177/0962280218823036] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
We propose a method for recruiting asymptomatic Amyloid positive individuals in clinical trials, using a two-step process. We first select during a pre-screening phase a subset of individuals which are more likely to be amyloid positive based on the automatic analysis of data acquired during routine clinical practice, before doing a confirmatory PET-scan to these selected individuals only. This method leads to an increased number of recruitments and to a reduced number of PET-scans, resulting in a decrease in overall recruitment costs. We validate our method on three different cohorts, and consider five different classification algorithms for the pre-screening phase. We show that the best results are obtained using solely cognitive, genetic and socio-demographic features, as the slight increased performance when using MRI or longitudinal data is balanced by the cost increase they induce. We show that the proposed method generalizes well when tested on an independent cohort, and that the characteristics of the selected set of individuals are identical to the characteristics of a population selected in a standard way. The proposed approach shows how Machine Learning can be used effectively in practice to optimize recruitment costs in clinical trials.
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Affiliation(s)
- Manon Ansart
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
| | - Stéphane Epelbaum
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France
| | - Geoffroy Gagliardi
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France
| | - Olivier Colliot
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France
- AP-HP, Pitié-Salpêtrière hospital, Department of Neuroradiology, Paris, France
| | - Didier Dormont
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
- AP-HP, Pitié-Salpêtrière hospital, Department of Neuroradiology, Paris, France
| | - Bruno Dubois
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France
| | - Harald Hampel
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Paris, France
- AXA Research Fund & Sorbonne University Chair, Paris, France
- Sorbonne University, GRC no 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Paris, France
| | - Stanley Durrleman
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France
- Inria, Aramis project-team, Paris, France
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