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Jung M, Jung JS, Pfeifer J, Hartmann C, Ehrhardt T, Abid CL, Kintzel J, Puls A, Navarrete Santos A, Hollemann T, Riemann D, Rujescu D. Neuronal Stem Cells from Late-Onset Alzheimer Patients Show Altered Regulation of Sirtuin 1 Depending on Apolipoprotein E Indicating Disturbed Stem Cell Plasticity. Mol Neurobiol 2024; 61:1562-1579. [PMID: 37728850 PMCID: PMC10896791 DOI: 10.1007/s12035-023-03633-z] [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: 02/15/2023] [Accepted: 08/31/2023] [Indexed: 09/21/2023]
Abstract
Late-onset Alzheimer's disease (AD) is a complex multifactorial disease. The greatest known risk factor for late-onset AD is the E4 allele of the apolipoprotein E (APOE), while increasing age is the greatest known non-genetic risk factor. The cell type-specific functions of neural stem cells (NSCs), in particular their stem cell plasticity, remain poorly explored in the context of AD pathology. Here, we describe a new model that employs late-onset AD patient-derived induced pluripotent stem cells (iPSCs) to generate NSCs and to examine the role played by APOE4 in the expression of aging markers such as sirtuin 1 (SIRT1) in comparison to healthy subjects carrying APOE3. The effect of aging was investigated by using iPSC-derived NSCs from old age subjects as healthy matched controls. Transcript and protein analysis revealed that genes were expressed differently in NSCs from late-onset AD patients, e.g., exhibiting reduced autophagy-related protein 7 (ATG7), phosphatase and tensin homolog (PTEN), and fibroblast growth factor 2 (FGF2). Since SIRT1 expression differed between APOE3 and APOE4 NSCs, the suppression of APOE function in NSCs also repressed the expression of SIRT1. However, the forced expression of APOE3 by plasmids did not recover differently expressed genes. The altered aging markers indicate decreased plasticity of NSCs. Our study provides a suitable in vitro model to investigate changes in human NSCs associated with aging, APOE4, and late-onset AD.
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Affiliation(s)
- Matthias Jung
- Institute of Physiological Chemistry (IPC), Faculty of Medicine, Martin Luther University Halle-Wittenberg, Hollystrasse 1, 06114, Halle (Saale), Germany.
| | - Juliane-Susanne Jung
- Institute of Anatomy and Cell Biology, Faculty of Medicine, Martin Luther University Halle-Wittenberg, Grosse Steinstrasse 52, 06118, Halle (Saale), Germany
| | - Jenny Pfeifer
- Institute of Physiological Chemistry (IPC), Faculty of Medicine, Martin Luther University Halle-Wittenberg, Hollystrasse 1, 06114, Halle (Saale), Germany
| | - Carla Hartmann
- Institute of Physiological Chemistry (IPC), Faculty of Medicine, Martin Luther University Halle-Wittenberg, Hollystrasse 1, 06114, Halle (Saale), Germany
| | - Toni Ehrhardt
- Institute of Physiological Chemistry (IPC), Faculty of Medicine, Martin Luther University Halle-Wittenberg, Hollystrasse 1, 06114, Halle (Saale), Germany
| | - Chaudhry Luqman Abid
- Institute of Physiological Chemistry (IPC), Faculty of Medicine, Martin Luther University Halle-Wittenberg, Hollystrasse 1, 06114, Halle (Saale), Germany
| | - Jenny Kintzel
- Institute of Physiological Chemistry (IPC), Faculty of Medicine, Martin Luther University Halle-Wittenberg, Hollystrasse 1, 06114, Halle (Saale), Germany
| | - Anne Puls
- Institute of Physiological Chemistry (IPC), Faculty of Medicine, Martin Luther University Halle-Wittenberg, Hollystrasse 1, 06114, Halle (Saale), Germany
| | - Anne Navarrete Santos
- Institute of Anatomy and Cell Biology, Faculty of Medicine, Martin Luther University Halle-Wittenberg, Grosse Steinstrasse 52, 06118, Halle (Saale), Germany
| | - Thomas Hollemann
- Institute of Physiological Chemistry (IPC), Faculty of Medicine, Martin Luther University Halle-Wittenberg, Hollystrasse 1, 06114, Halle (Saale), Germany
| | - Dagmar Riemann
- Department Medical Immunology, Faculty of Medicine, Martin Luther University Halle-Wittenberg, Magdeburger Strasse 2, 06112, Halle (Saale), Germany
| | - Dan Rujescu
- Department of Psychiatry and Psychotherapy, Division of General Psychiatry, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
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Duff K, Hammers DB, Koppelmans V, King JB, Hoffman JM. Short-Term Practice Effects on Cognitive Tests Across the Late Life Cognitive Spectrum and How They Compare to Biomarkers of Alzheimer's Disease. J Alzheimers Dis 2024; 99:321-332. [PMID: 38669544 DOI: 10.3233/jad-231392] [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: 04/28/2024]
Abstract
Background Practice effects on cognitive testing in mild cognitive impairment (MCI) and Alzheimer's disease (AD) remain understudied, especially with how they compare to biomarkers of AD. Objective The current study sought to add to this growing literature. Methods Cognitively intact older adults (n = 68), those with amnestic MCI (n = 52), and those with mild AD (n = 45) completed a brief battery of cognitive tests at baseline and again after one week, and they also completed a baseline amyloid PET scan, a baseline MRI, and a baseline blood draw to obtain APOE ɛ4 status. Results The intact participants showed significantly larger baseline cognitive scores and practice effects than the other two groups on overall composite measures. Those with MCI showed significantly larger baseline scores and practice effects than AD participants on the composite. For amyloid deposition, the intact participants had significantly less tracer uptake, whereas MCI and AD participants were comparable. For total hippocampal volumes, all three groups were significantly different in the expected direction (intact > MCI > AD). For APOE ɛ4, the intact had significantly fewer copies of ɛ4 than MCI and AD. The effect sizes of the baseline cognitive scores and practice effects were comparable, and they were significantly larger than effect sizes of biomarkers in 7 of the 9 comparisons. Conclusion Baseline cognition and short-term practice effects appear to be sensitive markers in late life cognitive disorders, as they separated groups better than commonly-used biomarkers in AD. Further development of baseline cognition and short-term practice effects as tools for clinical diagnosis, prognostic indication, and enrichment of clinical trials seems warranted.
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Affiliation(s)
- Kevin Duff
- Department of Neurology, Layton Aging and Alzheimer's Disease Center, Oregon Health & Science University, Portland, OR, USA
- Department of Neurology, Center for Alzheimer's Care, Imaging and Research, University of Utah, Salt Lake City, UT, USA
| | - Dustin B Hammers
- Department of Neurology, Indiana University School of Medicine, Indiana, USA
| | | | - Jace B King
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA
| | - John M Hoffman
- Department of Neurology, Center for Alzheimer's Care, Imaging and Research, University of Utah, Salt Lake City, UT, USA
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA
- University of Utah Center for Quantitative Cancer Imaging, Huntsman Cancer Institute, Salt Lake City, UT, USA
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Wu L, Jin L, Li L, Yu K, Wu J, Lei Y, Jiang S, He J. An examination of Alzheimer's disease and white matter from 1981 to 2023: a Bibliometric and visual analysis. Front Neurol 2023; 14:1268566. [PMID: 38033779 PMCID: PMC10683644 DOI: 10.3389/fneur.2023.1268566] [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: 07/28/2023] [Accepted: 10/19/2023] [Indexed: 12/02/2023] Open
Abstract
Background Alzheimer's disease (AD) is characterized by the presence of gray matter lesions and alterations in white matter. This study aims to investigate the research related to white matter in the context of AD from a Bibliometric standpoint. Methods Regular and review articles focusing on the research pertaining to Alzheimer's disease (AD) and white matter were extracted from the Web of Science Core Collection (WOSCC) database, covering the period from its inception to 10th July 2023. The "Bibliometrix" R package was employed to summarize key findings, to quantify the occurrence of top keywords, and to visualize the collaborative network among countries. Furthermore, VOSviewer software was utilized to conduct co-authorship and co-occurrence analyses. CiteSpace was employed to identify the most influential references and keywords based on their citation bursts. The retrieval of AD- and white matter-related publications was conducted by the Web of Science Core Collection. Bibliometric analysis and visualization, including the examination of annual publication distribution, prominent countries, active institutions and authors, core journals, co-cited references, and keywords, were carried out by using VOSviewer, CiteSpace, the Bibliometrix Package, and the ggplot2 Package. The quality and impact of publications were assessed using the total global citation score and total local citation score. Results A total of 5,714 publications addressing the intersection of Alzheimer's disease (AD) and white matter were included in the analysis. The majority of publications originated from the United States, China, and the United Kingdom. Prominent journals were heavily featured in the publication output. In addition to "Alzheimer's disease" and "white matter," "mild cognitive impairment," "MRI" and "atrophy" had been frequently utilized as "keywords." Conclusion This Bibliometric investigation delineated a foundational knowledge framework that encompasses countries, institutions, authors, journals, and articles within the AD and white matter research domain spanning from 1981 to 2023. The outcomes provide a comprehensive perspective on the broader landscape of this research field.
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Affiliation(s)
- Linman Wu
- School of Mental Health, Wenzhou Medical University, Wenzhou, China
- Nanchong Mental Health Center of Sichuan Province, Nanchong, China
| | - Liuyin Jin
- Lishui Second People’s Hospital, Wenzhou Medical University, Lishui, China
| | - Lixia Li
- Nanchong Mental Health Center of Sichuan Province, Nanchong, China
| | - Kai Yu
- School of Mental Health, Wenzhou Medical University, Wenzhou, China
| | - Junnan Wu
- School of Mental Health, Wenzhou Medical University, Wenzhou, China
| | - Yuying Lei
- School of Mental Health, Wenzhou Medical University, Wenzhou, China
| | - Shulan Jiang
- School of Mental Health, Wenzhou Medical University, Wenzhou, China
| | - Jue He
- School of Mental Health, Wenzhou Medical University, Wenzhou, China
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Zheng W, Liu H, Li Z, Li K, Wang Y, Hu B, Dong Q, Wang Z. Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics. CNS Neurosci Ther 2023; 29:2457-2468. [PMID: 37002795 PMCID: PMC10401169 DOI: 10.1111/cns.14189] [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: 12/05/2022] [Revised: 03/07/2023] [Accepted: 03/13/2023] [Indexed: 08/05/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive decline, and mild cognitive impairment (MCI) is associated with a high risk of developing AD. Hippocampal morphometry analysis is believed to be the most robust magnetic resonance imaging (MRI) markers for AD and MCI. Multivariate morphometry statistics (MMS), a quantitative method of surface deformations analysis, is confirmed to have strong statistical power for evaluating hippocampus. AIMS We aimed to test whether surface deformation features in hippocampus can be employed for early classification of AD, MCI, and healthy controls (HC). METHODS We first explored the differences in hippocampus surface deformation among these three groups by using MMS analysis. Additionally, the hippocampal MMS features of selective patches and support vector machine (SVM) were used for the binary classification and triple classification. RESULTS By the results, we identified significant hippocampal deformation among the three groups, especially in hippocampal CA1. In addition, the binary classification of AD/HC, MCI/HC, AD/MCI showed good performances, and area under curve (AUC) of triple-classification model achieved 0.85. Finally, positive correlations were found between the hippocampus MMS features and cognitive performances. CONCLUSIONS The study revealed significant hippocampal deformation among AD, MCI, and HC. Additionally, we confirmed that hippocampal MMS can be used as a sensitive imaging biomarker for the early diagnosis of AD at the individual level.
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Affiliation(s)
- Weimin Zheng
- Department of Radiology, Aerospace Center Hospital, Beijing, China
| | - Honghong Liu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Zhigang Li
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona, USA
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Qunxi Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Zhiqun Wang
- Department of Radiology, Aerospace Center Hospital, Beijing, China
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Arenare G, Manca R, Caffarra P, Venneri A. Associations between Neuropsychiatric Symptoms and Alzheimer's Disease Biomarkers in People with Mild Cognitive Impairment. Brain Sci 2023; 13:1195. [PMID: 37626552 PMCID: PMC10452057 DOI: 10.3390/brainsci13081195] [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: 07/11/2023] [Revised: 08/03/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND Neuropsychiatric symptoms (NPS) are associated with faster decline in mild cognitive impairment (MCI). This study aimed to investigate the association between NPS severity and Alzheimer's disease (AD) biomarkers, i.e., amyloid-β (Aβ), phosphorylated tau protein (p-tau) and hippocampal volume ratio (HR), to characterise in more detail MCI patients with a poor prognosis. METHODS A total of 506 individuals with MCI and 99 cognitively unimpaired older adults were selected from the ADNI dataset. The patients were divided into three different groups based on their NPI-Q total scores: no NPS (n = 198), mild NPS (n = 160) and severe NPS (n = 148). Regression models were used to assess the association between the severity of NPS and each biomarker level and positivity status. RESULTS Cerebrospinal fluid Aβ levels were positively associated with older age and lower MMSE scores, while higher p-tau levels were associated with female sex and lower MMSE scores. Only patients with severe NPS had a lower HR (β = -0.18, p = 0.050), i.e., more pronounced medio-temporal atrophy, than those without NPS. DISCUSSION Only HR was associated with the presence of NPS, partially in line with previous evidence showing that severe NPS may be explained primarily by greater grey matter loss. Future longitudinal studies will be needed to ascertain the relevance of this finding.
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Affiliation(s)
- Giulia Arenare
- Department of Medicine and Surgery, University of Parma, 43125 Parma, Italy
| | - Riccardo Manca
- Department of Medicine and Surgery, University of Parma, 43125 Parma, Italy
- Department of Life Sciences, Brunel University London, Uxbridge UB8 3BH, UK
| | - Paolo Caffarra
- Department of Medicine and Surgery, University of Parma, 43125 Parma, Italy
| | - Annalena Venneri
- Department of Medicine and Surgery, University of Parma, 43125 Parma, Italy
- Department of Life Sciences, Brunel University London, Uxbridge UB8 3BH, UK
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Kloske CM, Barnum CJ, Batista AF, Bradshaw EM, Brickman AM, Bu G, Dennison J, Gearon MD, Goate AM, Haass C, Heneka MT, Hu WT, Huggins LKL, Jones NS, Koldamova R, Lemere CA, Liddelow SA, Marcora E, Marsh SE, Nielsen HM, Petersen KK, Petersen M, Piña-Escudero SD, Qiu WQ, Quiroz YT, Reiman E, Sexton C, Tansey MG, Tcw J, Teunissen CE, Tijms BM, van der Kant R, Wallings R, Weninger SC, Wharton W, Wilcock DM, Wishard TJ, Worley SL, Zetterberg H, Carrillo MC. APOE and immunity: Research highlights. Alzheimers Dement 2023; 19:2677-2696. [PMID: 36975090 DOI: 10.1002/alz.13020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 12/19/2022] [Indexed: 03/29/2023]
Abstract
INTRODUCTION At the Alzheimer's Association's APOE and Immunity virtual conference, held in October 2021, leading neuroscience experts shared recent research advances on and inspiring insights into the various roles that both the apolipoprotein E gene (APOE) and facets of immunity play in neurodegenerative diseases, including Alzheimer's disease and other dementias. METHODS The meeting brought together more than 1200 registered attendees from 62 different countries, representing the realms of academia and industry. RESULTS During the 4-day meeting, presenters illuminated aspects of the cross-talk between APOE and immunity, with a focus on the roles of microglia, triggering receptor expressed on myeloid cells 2 (TREM2), and components of inflammation (e.g., tumor necrosis factor α [TNFα]). DISCUSSION This manuscript emphasizes the importance of diversity in current and future research and presents an integrated view of innate immune functions in Alzheimer's disease as well as related promising directions in drug development.
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Affiliation(s)
| | | | - Andre F Batista
- Department of Neurology, Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Departments of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Elizabeth M Bradshaw
- Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - Adam M Brickman
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, G.H. Sergievsky Center, and Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA
| | - Guojun Bu
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Jessica Dennison
- Department of Psychiatry & Behavioral Sciences, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Mary D Gearon
- Department of Physiology, University of Kentucky, Lexington, Kentucky, USA
| | - Alison M Goate
- Department of Genetics & Genomic Sciences, Ronald M. Loeb Center for Alzheimer's disease, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Christian Haass
- Metabolic Biochemistry, Biomedical Center (BMC), Faculty of Medicine, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany 3 Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Michael T Heneka
- Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - William T Hu
- Department of Neurology, Rutgers-Robert Wood Johnson Medical School and Center for Healthy Aging, Rutgers Institute for Health, Health Care Policy, and Aging Research, New Brunswick, New Jersey, USA
| | - Lenique K L Huggins
- Department of Biology, Duke University, Durham, North Carolina, USA
- Yale School of Medicine, New Haven, Connecticut, USA
| | - Nahdia S Jones
- Interdisciplinary Program in Neuroscience, Georgetown University, Washington, District of Columbia, USA
| | - Radosveta Koldamova
- EOH, School of Public Health University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Cynthia A Lemere
- Department of Neurology, Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Departments of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Shane A Liddelow
- Neuroscience Institute and Departments of Neuroscience & Physiology and of Ophthalmology, NYU Grossman School of Medicine, New York, New York, USA
| | - Edoardo Marcora
- Ronald M. Loeb Center for Alzheimer's disease, Dept. of Genetics & Genomic Sciences, Dept. of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Samuel E Marsh
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Henrietta M Nielsen
- Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Kellen K Petersen
- The Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Melissa Petersen
- Department of Family Medicine, Institute of Translational Research, University of North Texas Health Science Center, Fort Worth, Texas, USA
| | - Stefanie D Piña-Escudero
- Global Brain Health Institute, Department of Neurology, University of California, San Francisco, California, USA
| | - Wei Qiao Qiu
- Boston University School of Medicine, Boston, Massachusetts, USA
| | - Yakeel T Quiroz
- Departments of Psychiatry and Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Eric Reiman
- Banner Alzheimer's Institute, Phoenix, Arizona, USA
- Banner Research, Phoenix, Arizona, USA
| | | | - Malú Gámez Tansey
- Department of Neuroscience and Center for Translational Research in Neurodegenerative Disease, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Julia Tcw
- Department of Pharmacology & Experimental Therapeutics, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Clinical Chemistry department, Amsterdam Neuroscience, Program Neurodegeneration, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Rik van der Kant
- Department of Functional Genomics, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Rebecca Wallings
- CTRND, Department of Neuroscience, University of Florida, Florida, USA
| | | | | | - Donna M Wilcock
- Sanders-Brown Center on Aging and Department of Physiology, University of Kentucky, Lexington, Kentucky, USA
| | - Tyler James Wishard
- Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California, USA
| | - Susan L Worley
- Independent science writer, Bryn Mawr, Pennsylvania, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
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Subramanyam Rallabandi V, Seetharaman K. Deep learning-based classification of healthy aging controls, mild cognitive impairment and Alzheimer’s disease using fusion of MRI-PET imaging. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Frizzell TO, Glashutter M, Liu CC, Zeng A, Pan D, Hajra SG, D’Arcy RC, Song X. Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review. Ageing Res Rev 2022; 77:101614. [PMID: 35358720 DOI: 10.1016/j.arr.2022.101614] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 03/02/2022] [Accepted: 03/24/2022] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast-growing effort in applying artificial intelligence (AI) to analyze these data. Here, we review and evaluate the AI studies in brain MRI analysis with synthesis. METHODS A systematic review of the literature, spanning the years from 2009 to 2020, was completed using the PubMed database. AI studies using MRI imaging to investigate normal aging, mild cognitive impairment, and AD-dementia were retrieved for review. Bias assessment was completed using the PROBAST criteria. RESULTS 97 relevant studies were included in the review. The studies were typically focused on the classification of AD, MCI, and normal aging (71% of the reported studies) and the prediction of MCI conversion to AD (25%). The best performance was achieved by using the deep learning-based convolution neural network algorithms (weighted average accuracy 89%), in contrast to 76-86% using Logistic Regression, Support Vector Machines, and other AI methods. DISCUSSION The synthesized evidence is paramount to developing sophisticated AI approaches to reliably capture and quantify multiple subtle MRI changes in the whole brain that exemplify the complexity and heterogeneity of AD and brain aging.
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Grueso S, Viejo-Sobera R. Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review. Alzheimers Res Ther 2021; 13:162. [PMID: 34583745 PMCID: PMC8480074 DOI: 10.1186/s13195-021-00900-w] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 09/12/2021] [Indexed: 01/18/2023]
Abstract
BACKGROUND An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer's disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer's disease dementia. METHODS We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer's disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. RESULTS Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer's disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. CONCLUSIONS Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.
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Affiliation(s)
- Sergio Grueso
- Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou 156, 08018, Barcelona, Spain.
| | - Raquel Viejo-Sobera
- Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya (UOC), Rambla del Poblenou 156, 08018, Barcelona, Spain
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Zhang L, Wang L, Gao J, Risacher SL, Yan J, Li G, Liu T, Zhu D. Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment. Med Image Anal 2021; 72:102082. [PMID: 34004495 DOI: 10.1016/j.media.2021.102082] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 03/20/2021] [Accepted: 04/13/2021] [Indexed: 01/22/2023]
Abstract
Multimodal fusion of different types of neural image data provides an irreplaceable opportunity to take advantages of complementary cross-modal information that may only partially be contained in single modality. To jointly analyze multimodal data, deep neural networks can be especially useful because many studies have suggested that deep learning strategy is very efficient to reveal complex and non-linear relations buried in the data. However, most deep models, e.g., convolutional neural network and its numerous extensions, can only operate on regular Euclidean data like voxels in 3D MRI. The interrelated and hidden structures that beyond the grid neighbors, such as brain connectivity, may be overlooked. Moreover, how to effectively incorporate neuroscience knowledge into multimodal data fusion with a single deep framework is understudied. In this work, we developed a graph-based deep neural network to simultaneously model brain structure and function in Mild Cognitive Impairment (MCI): the topology of the graph is initialized using structural network (from diffusion MRI) and iteratively updated by incorporating functional information (from functional MRI) to maximize the capability of differentiating MCI patients from elderly normal controls. This resulted in a new connectome by exploring "deep relations" between brain structure and function in MCI patients and we named it as Deep Brain Connectome. Though deep brain connectome is learned individually, it shows consistent patterns of alteration comparing to structural network at group level. With deep brain connectome, our developed deep model can achieve 92.7% classification accuracy on ADNI dataset.
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Affiliation(s)
- Lu Zhang
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA
| | - Li Wang
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA; Department of Mathematics, The University of Texas at Arlington, Arlington, TX 76019 USA
| | - Jean Gao
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Jingwen Yan
- School of Informatics and Computing, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Gang Li
- Biomedical Research Imaging Center and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7160, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA.
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11
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Zhao X, Ang CKE, Acharya UR, Cheong KH. Application of Artificial Intelligence techniques for the detection of Alzheimer’s disease using structural MRI images. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.02.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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12
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Elverman KH, Paitel ER, Figueroa CM, McKindles RJ, Nielson KA. Event-Related Potentials, Inhibition, and Risk for Alzheimer's Disease Among Cognitively Intact Elders. J Alzheimers Dis 2021; 80:1413-1428. [PMID: 33682720 DOI: 10.3233/jad-201559] [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: 01/28/2023]
Abstract
BACKGROUND Despite advances in understanding Alzheimer's disease (AD), prediction of AD prior to symptom onset remains severely limited, even when primary risk factors such as the apolipoprotein E (APOE) ɛ4 allele are known. OBJECTIVE Although executive dysfunction is highly prevalent and is a primary contributor to loss of independence in those with AD, few studies have examined neural differences underlying executive functioning as indicators of risk for AD prior to symptom onset, when intervention might be effective. METHODS This study examined event-related potential (ERP) differences during inhibitory control in 44 cognitively intact older adults (20 ɛ4+, 24 ɛ4-), relative to 41 young adults. All participants completed go/no-go and stop-signal tasks. RESULTS Overall, both older adult groups exhibited slower reaction times and longer ERP latencies compared to young adults. Older adults also had generally smaller N200 and P300 amplitudes, except at frontal electrodes and for N200 stop-signal amplitudes, which were larger in older adults. Considered with intact task accuracy, these findings suggest age-related neural compensation. Although ɛ4 did not distinguish elders during go or no-go tasks, this study uniquely showed that the more demanding stop-signal task was sensitive to ɛ4 differences, despite comparable task and neuropsychological performance with non-carriers. Specifically, ɛ4+ elders had slower frontal N200 latency and larger N200 amplitude, which was most robust at frontal sites, compared with ɛ4-. CONCLUSION N200 during a stop-signal task is sensitive to AD risk, prior to any evidence of cognitive dysfunction, suggesting that stop-signal ERPs may be an important protocol addition to neuropsychological testing.
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Affiliation(s)
| | | | | | - Ryan J McKindles
- Marquette University, Department of Biomedical Engineering, Milwaukee, WI, USA
| | - Kristy A Nielson
- Marquette University, Department of Psychology, Milwaukee, WI, USA.,Medical College of Wisconsin, Department of Neurology, Milwaukee, WI, USA
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13
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Halloway S, Schoeny ME, Barnes LL, Arvanitakis Z, Pressler SJ, Braun LT, Volgman AS, Gamboa C, Wilbur J. A study protocol for MindMoves: A lifestyle physical activity and cognitive training intervention to prevent cognitive impairment in older women with cardiovascular disease. Contemp Clin Trials 2021; 101:106254. [PMID: 33383230 PMCID: PMC7954878 DOI: 10.1016/j.cct.2020.106254] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 11/13/2020] [Accepted: 12/15/2020] [Indexed: 11/30/2022]
Abstract
INTRODUCTION Cognitive impairment (CI) and cardiovascular disease (CVD) disproportionately affect women compared to men, and CVD increases risk of CI. Physical activity and cognitive training can improve cognition in older adults and may have additive or synergistic effects. However, no combined intervention has targeted women with CVD or utilized a sustainable lifestyle approach. The purpose of the trial is to evaluate efficacy of MindMoves, a 24-week multimodal physical activity and cognitive training intervention, on cognition and serum biomarkers in older women with CVD. Three serum biomarkers (brain-derived neurotrophic factor [BDNF], vascular endothelial growth factor [VEGF], and insulin-like growth factor 1 [IGF-1]) were selected as a priori hypothesized indicators of the effects of physical activity and/or cognitive training on cognition. METHODS The study design is a randomized controlled trial with a 2 × 2 factorial design, to determine independent and combined efficacies of Mind (tablet-based cognitive training) and Move (lifestyle physical activity with goal-setting and group meetings) on change in cognition (primary outcome) and serum biomarkers (secondary outcomes). We will recruit 254 women aged ≥65 years with CVD and without CI from cardiology clinics. Women will be randomized to one of four conditions: (1) Mind, (2) Move, (3) MindMoves, or (4) usual care. Data will be obtained from participants at baseline, 24, 48, and 72 weeks. DISCUSSION This study will test efficacy of a lifestyle-focused intervention to prevent or delay cognitive impairment in older women with CVD and may identify relevant serum biomarkers that could be used as early indicators of intervention response.
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Affiliation(s)
- Shannon Halloway
- Rush University, College of Nursing, 600 S. Paulina, Suite 1080, Chicago, IL 60612, USA.
| | - Michael E Schoeny
- Rush University, College of Nursing, 600 S. Paulina, Suite 1080, Chicago, IL 60612, USA.
| | - Lisa L Barnes
- Rush Alzheimer's Disease Center, 1750 W. Harrison, Chicago, IL 60612, USA.
| | - Zoe Arvanitakis
- Rush Alzheimer's Disease Center, 1750 W. Harrison, Chicago, IL 60612, USA; Rush Medical College, 600 S. Paulina Street, Suite 524, Chicago, IL 60612, USA.
| | - Susan J Pressler
- Indiana University, School of Nursing, 600 Barnhill Drive, Indianapolis, IN 46202, USA.
| | - Lynne T Braun
- Rush University, College of Nursing, 600 S. Paulina, Suite 1080, Chicago, IL 60612, USA.
| | | | - Charlene Gamboa
- Rush University, College of Nursing, 600 S. Paulina, Suite 1080, Chicago, IL 60612, USA.
| | - JoEllen Wilbur
- Rush University, College of Nursing, 600 S. Paulina, Suite 1080, Chicago, IL 60612, USA.
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14
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Li C, Duara R, Loewenstein DA, Izquierdo W, Cabrerizo M, Barker W, Adjouadi M. Greater Regional Cortical Thickness is Associated with Selective Vulnerability to Atrophy in Alzheimer's Disease, Independent of Amyloid Load and APOE Genotype. J Alzheimers Dis 2020; 69:145-156. [PMID: 30958345 DOI: 10.3233/jad-180231] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Regional cortical thickness (rCTh) among cognitively normal (CN) adults (rCThCN) varies greatly between brain regions, as does the vulnerability to neurodegeneration. OBJECTIVE The goal of this study was to: 1) rank order rCThCN for various brain regions, and 2) explore their vulnerability to neurodegeneration in Alzheimer's disease (AD) within these brain regions. METHODS The relationship between rCTh among the CN group (rCThCN) and the percent difference in CTh (% CThDiff) in each region between the CN group and AD patients was examined. Pearson correlation analysis was performed accounting for amyloid-β (Aβ) protein and APOE genotype using 210 age, gender, and APOE matched CN (n = 105, age range: 56-90) and AD (n = 105, age range: 56-90) ADNI participants. RESULTS Strong positive correlations were observed between rCThCN and % CThDiff accounting for Aβ deposition and APOE status. Regions, such as the entorhinal cortex, which had the greatest CTh in the CN state, were also the regions which had the greatest % CThDiff. CONCLUSIONS Regions with the greatest CTh at the CN stage are found to aggregate in disease prone regions of AD, namely in the medial temporal lobe, including the temporal pole, ERC, parahippocampal gyrus, fusiform and the middle and inferior temporal gyrus. Although rCTh has been found to vary considerably across the different regions of the brain, our results indicate that regions with the greatest CTh at the CN stage are actually regions which have been found to be most vulnerable to neurodegeneration in AD.
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Affiliation(s)
- Chunfei Li
- Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Ranjan Duara
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami, FL, USA.,Florida Alzheimer's Disease Research Center at Gainesville, Miami Beach, Miami, USA.,Department of Neurology, University of Florida College of Medicine, Gainesville, FL, USA.,Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA
| | - David A Loewenstein
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami, FL, USA.,Florida Alzheimer's Disease Research Center at Gainesville, Miami Beach, Miami, USA.,Center on Aging and Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Walter Izquierdo
- Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Mercedes Cabrerizo
- Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Warren Barker
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami, FL, USA.,Florida Alzheimer's Disease Research Center at Gainesville, Miami Beach, Miami, USA
| | - Malek Adjouadi
- Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA.,Florida Alzheimer's Disease Research Center at Gainesville, Miami Beach, Miami, USA
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15
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Zheng W, Cui B, Sun Z, Li X, Han X, Yang Y, Li K, Hu L, Wang Z. Application of Generalized Split Linearized Bregman Iteration algorithm for Alzheimer's disease prediction. Aging (Albany NY) 2020; 12:6206-6224. [PMID: 32248185 PMCID: PMC7185109 DOI: 10.18632/aging.103017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 02/25/2020] [Indexed: 12/16/2022]
Abstract
In this paper, we applied a novel method for the detection of Alzheimer's disease (AD) based on a structural magnetic resonance imaging (sMRI) dataset. Specifically, the method involved a new classification algorithm of machine learning, named Generalized Split Linearized Bregman Iteration (GSplit LBI). It combines logistic regression and structural sparsity regularizations. In the study, 57 AD patients and 47 normal controls (NCs) were enrolled. We first extracted the entire brain gray matter volume values of all subjects and then used GSplit LBI to build a predictive classification model with a 10-fold full cross-validation method. The model accuracy achieved 90.44%. To further verify which voxels in the dataset have greater impact on the prediction results, we ranked the weight parameters and obtained the top 6% of the model parameters. To verify the generalization of model prediction and the stability of feature selection, we performed a cross-test on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and a Chinese dataset and achieved good performances on different cohorts. Conclusively, based on the sMRI dataset, our algorithm not only had good performance in a local cohort with high accuracy but also had good generalization of model prediction and stability of feature selection in different cohorts.
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Affiliation(s)
- Weimin Zheng
- Department of Radiology, Aerospace Center Hospital, Beijing 100049, China
| | - Bin Cui
- Department of Radiology, Aerospace Center Hospital, Beijing 100049, China
| | - Zeyu Sun
- Deepwise AI lab, Beijing 100080, China
| | - Xiuli Li
- Deepwise AI lab, Beijing 100080, China
| | - Xu Han
- Department of Radiology, Aerospace Center Hospital, Beijing 100049, China
| | - Yu Yang
- Beijing Huading Jialiang Technology Co, Beijing 100000, China
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Lingjing Hu
- Yanjing Medical College, Capital Medical University, Beijing 101300, China
| | - Zhiqun Wang
- Department of Radiology, Aerospace Center Hospital, Beijing 100049, China
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16
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A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10051894] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.
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17
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Lombardi G, Crescioli G, Cavedo E, Lucenteforte E, Casazza G, Bellatorre A, Lista C, Costantino G, Frisoni G, Virgili G, Filippini G. Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment. Cochrane Database Syst Rev 2020; 3:CD009628. [PMID: 32119112 PMCID: PMC7059964 DOI: 10.1002/14651858.cd009628.pub2] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year. OBJECTIVES To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI. SEARCH METHODS On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches. SELECTION CRITERIA We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter. DATA COLLECTION AND ANALYSIS Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available. MAIN RESULTS We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate. AUTHORS' CONCLUSIONS The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.
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Affiliation(s)
- Gemma Lombardi
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Giada Crescioli
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Enrica Cavedo
- Pitie‐Salpetriere Hospital, Sorbonne UniversityAlzheimer Precision Medicine (APM), AP‐HP47 boulevard de l'HopitalParisFrance75013
| | - Ersilia Lucenteforte
- University of PisaDepartment of Clinical and Experimental MedicineVia Savi 10PisaItaly56126
| | - Giovanni Casazza
- Università degli Studi di MilanoDipartimento di Scienze Biomediche e Cliniche "L. Sacco"via GB Grassi 74MilanItaly20157
| | | | - Chiara Lista
- Fondazione I.R.C.C.S. Istituto Neurologico Carlo BestaNeuroepidemiology UnitVia Celoria, 11MilanoItaly20133
| | - Giorgio Costantino
- Ospedale Maggiore Policlinico, Università degli Studi di MilanoUOC Pronto Soccorso e Medicina D'Urgenza, Fondazione IRCCS Ca' GrandaMilanItaly
| | | | - Gianni Virgili
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Graziella Filippini
- Carlo Besta Foundation and Neurological InstituteScientific Director’s Officevia Celoria, 11MilanItaly20133
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Ding Y, Luo C, Li C, Lan T, Qin Z. High-order correlation detecting in features for diagnosis of Alzheimer’s disease and mild cognitive impairment. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.101564] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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19
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Grassi M, Rouleaux N, Caldirola D, Loewenstein D, Schruers K, Perna G, Dumontier M. A Novel Ensemble-Based Machine Learning Algorithm to Predict the Conversion From Mild Cognitive Impairment to Alzheimer's Disease Using Socio-Demographic Characteristics, Clinical Information, and Neuropsychological Measures. Front Neurol 2019; 10:756. [PMID: 31379711 PMCID: PMC6646724 DOI: 10.3389/fneur.2019.00756] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 07/01/2019] [Indexed: 01/18/2023] Open
Abstract
Background: Despite the increasing availability in brain health related data, clinically translatable methods to predict the conversion from Mild Cognitive Impairment (MCI) to Alzheimer's disease (AD) are still lacking. Although MCI typically precedes AD, only a fraction of 20-40% of MCI individuals will progress to dementia within 3 years following the initial diagnosis. As currently available and emerging therapies likely have the greatest impact when provided at the earliest disease stage, the prompt identification of subjects at high risk for conversion to AD is of great importance in the fight against this disease. In this work, we propose a highly predictive machine learning algorithm, based only on non-invasively and easily in-the-clinic collectable predictors, to identify MCI subjects at risk for conversion to AD. Methods: The algorithm was developed using the open dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), employing a sample of 550 MCI subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. A restricted set of information regarding sociodemographic and clinical characteristics, neuropsychological test scores was used as predictors and several different supervised machine learning algorithms were developed and ensembled in final algorithm. A site-independent stratified train/test split protocol was used to provide an estimate of the generalized performance of the algorithm. Results: The final algorithm demonstrated an AUROC of 0.88, sensitivity of 77.7%, and a specificity of 79.9% on excluded test data. The specificity of the algorithm was 40.2% for 100% sensitivity. Conclusions: The algorithm we developed achieved sound and high prognostic performance to predict AD conversion using easily clinically derived information that makes the algorithm easy to be translated into practice. This indicates beneficial application to improve recruitment in clinical trials and to more selectively prescribe new and newly emerging early interventions to high AD risk patients.
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Affiliation(s)
- Massimiliano Grassi
- Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Nadine Rouleaux
- Faculty of Science and Engineering, Institute of Data Science, Maastricht University, Maastricht, Netherlands
| | - Daniela Caldirola
- Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - David Loewenstein
- Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center Miami Beach, Miami Beach, FL, United States
- Center for Cognitive Neuroscience and Aging, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Koen Schruers
- Research Institute of Mental Health and Neuroscience and Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Giampaolo Perna
- Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Department of Psychiatry and Behavioral Sciences, Miller School of Medicine, University of Miami, Miami, FL, United States
- Research Institute of Mental Health and Neuroscience and Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Michel Dumontier
- Faculty of Science and Engineering, Institute of Data Science, Maastricht University, Maastricht, Netherlands
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A clinically-translatable machine learning algorithm for the prediction of Alzheimer's disease conversion: further evidence of its accuracy via a transfer learning approach. Int Psychogeriatr 2019; 31:937-945. [PMID: 30426918 PMCID: PMC6517088 DOI: 10.1017/s1041610218001618] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
BACKGROUND In a previous study, we developed a highly performant and clinically-translatable machine learning algorithm for a prediction of three-year conversion to Alzheimer's disease (AD) in subjects with Mild Cognitive Impairment (MCI) and Pre-mild Cognitive Impairment. Further tests are necessary to demonstrate its accuracy when applied to subjects not used in the original training process. In this study, we aimed to provide preliminary evidence of this via a transfer learning approach. METHODS We initially employed the same baseline information (i.e. clinical and neuropsychological test scores, cardiovascular risk indexes, and a visual rating scale for brain atrophy) and the same machine learning technique (support vector machine with radial-basis function kernel) used in our previous study to retrain the algorithm to discriminate between participants with AD (n = 75) and normal cognition (n = 197). Then, the algorithm was applied to perform the original task of predicting the three-year conversion to AD in the sample of 61 MCI subjects that we used in the previous study. RESULTS Even after the retraining, the algorithm demonstrated a significant predictive performance in the MCI sample (AUC = 0.821, 95% CI bootstrap = 0.705-0.912, best balanced accuracy = 0.779, sensitivity = 0.852, specificity = 0.706). CONCLUSIONS These results provide a first indirect evidence that our original algorithm can also perform relevant generalized predictions when applied to new MCI individuals. This motivates future efforts to bring the algorithm to sufficient levels of optimization and trustworthiness that will allow its application in both clinical and research settings.
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21
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Li Z, Yang N, Lei X, Lin C, Li N, Jiang X, Wei X, Xu B. The association between the ApoE polymorphisms and the MRI-defined intracranial lesions in a cohort of southern China population. J Clin Lab Anal 2019; 33:e22950. [PMID: 31199015 PMCID: PMC6757122 DOI: 10.1002/jcla.22950] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 05/13/2019] [Accepted: 05/23/2019] [Indexed: 11/20/2022] Open
Abstract
Background The apolipoprotein E (APOE) ε4 allele is considered as a risk factor for Alzheimer's disease (AD). However, the association of APOE allele with MRI evidence of intracranial lesions has not been well understood. Methods Quantitative real‐time PCR was performed to detect the APOE genotype; MRI was examined for intracranial lesions. Their association was evaluated in a cohort of 226 AD patients and 2607 healthy individuals in southern China. Results The frequencies of ε2, ε3, and ε4 alleles were 8.0%, 82.9%, and 9.1% in the whole study population. The frequency of APOE‐ε4 allele was significantly higher in the AD subjects than that in the control group (14.4% vs 8.6%, P < 0.001). We found that brain atrophy occurred at a rate of 12.3% in ε4 allele group vs 8.5% in non‐ε4 genotype group, with a significance of P = 0.008. Severe brain atrophy occurred at a rate of 1.0% in ε4 allele group vs 0.2% in non‐ε4 genotype group (P = 0.011). The individuals carrying APOE ε4/ε4 had an odds ratio (OR) of 7.64 (P < 0.01) for developing AD, while the APOE ε3/ε4 gene carriers had an OR of 1.47 (P = 0.031) and the OR in APOE ε2/ε3 carriers is 0.81 (P = 0.372). Interestingly, we found that the risk of ε4/ε4 allele carrier developing AD was significantly higher in male (P < 0.001) than female (P = 0.478). Conclusion Compared to ε2 and ε3 alleles, the presence of APOE‐ε4 allele might increase the risk for AD in a dose‐dependent manner in southern China. Moreover, the presence of APOE‐ε4 allele results in a higher incidence of brain atrophy.
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Affiliation(s)
- Zhuoran Li
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Na Yang
- Department of Laboratory Medicine, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Xiuxia Lei
- Department of Laboratory Medicine, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Chuying Lin
- Department of Laboratory Medicine, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Nan Li
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xinqing Jiang
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Banglao Xu
- Department of Laboratory Medicine, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China
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22
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Shirzadi Z, Stefanovic B, Mutsaerts HJMM, Masellis M, MacIntosh BJ. Classifying cognitive impairment based on the spatial heterogeneity of cerebral blood flow images. J Magn Reson Imaging 2019; 50:858-867. [PMID: 30666734 DOI: 10.1002/jmri.26650] [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: 08/15/2018] [Revised: 12/28/2018] [Accepted: 12/29/2018] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND The spatial coefficient of variation (sCoV) of arterial spin-labeled (ASL) MRI can index cerebral blood flow spatial heterogeneity. This metric reflects delayed blood delivery-seen as a hyperintense ASL signal juxtaposed by hypointense regions. PURPOSE To investigate the use of ASL-sCoV in the classification of cognitively unimpaired (CU), mild cognitive impairment (MCI), and Alzheimer's disease (AD) cohorts. STUDY TYPE Prospective/cohort. POPULATION Baseline ASL images from AD neuroimaging initiative dataset in three groups of CU, MCI, and AD (N = 258). FIELD STRENGTH/SEQUENCE Pulsed ASL (PICORE QT2) images were acquired on 3 T Siemens systems (TE/TR = 12/3400 msec, TI1/2 = 700/1900 msec). ASSESSMENT ASL-sCoV was calculated in temporal, parietal, occipital, and frontal lobes as well as whole gray matter. STATISTICAL TESTS The primary analysis used an analysis of covariance to investigate sCoV and cognitive group (CU, MCI, AD) associations. We also evaluated the repeatability of sCoV by calculating within-subject agreement in a subgroup of CU participants with a repeat ASL. The secondary analyses assessed ventricular volume, amyloid burden, glucose uptake, ASL-sCoV, and regional CBF as cognitive group classifiers using logistic regression models and receiver operating characteristic analyses. RESULTS We found that global and temporal lobe sCoV differed between cognitive groups (P = 0.006). Post-hoc tests showed that temporal lobe sCoV was lower in CU than in MCI (Cohen's d = -0.36) or AD (Cohen's d = -1.36). We found that sCoV was moderately repeatable in CU (intersession intraclass correlation = 0.50; intrasession intraclass correlation = 0.88). Subsequent logistic regression analyses revealed that temporal lobe sCoV and amyloid uptake classified CU vs. MCI (P < 0.01; accuracy = 78%). Temporal lobe sCoV, amyloid, and glucose uptake classified CU vs. AD (P < 0.01; accuracy = 97%); glucose uptake significantly classified MCI vs. AD (P < 0.01; accuracy = 85%). DATA CONCLUSION We showed that ASL spatial heterogeneity can be used alongside AD neuroimaging markers to distinguish cognitive groups, in particular, cognitively unimpaired from cognitively impaired individuals. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:858-867.
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Affiliation(s)
- Zahra Shirzadi
- Department of Medical Biophysics, University of Toronto, ON, Canada.,Hurvitz Brain Sciences, Sunnybrook Research Institute, University of Toronto, ON, Canada
| | - Bojana Stefanovic
- Department of Medical Biophysics, University of Toronto, ON, Canada.,Hurvitz Brain Sciences, Sunnybrook Research Institute, University of Toronto, ON, Canada
| | - Henri J M M Mutsaerts
- Hurvitz Brain Sciences, Sunnybrook Research Institute, University of Toronto, ON, Canada.,Department of Radiology, VU Medical Center, Amsterdam, The Netherlands
| | - Mario Masellis
- Hurvitz Brain Sciences, Sunnybrook Research Institute, University of Toronto, ON, Canada.,Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, ON, Canada
| | - Bradley J MacIntosh
- Department of Medical Biophysics, University of Toronto, ON, Canada.,Hurvitz Brain Sciences, Sunnybrook Research Institute, University of Toronto, ON, Canada
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Grassi M, Perna G, Caldirola D, Schruers K, Duara R, Loewenstein DA. A Clinically-Translatable Machine Learning Algorithm for the Prediction of Alzheimer's Disease Conversion in Individuals with Mild and Premild Cognitive Impairment. J Alzheimers Dis 2018; 61:1555-1573. [PMID: 29355115 PMCID: PMC6326743 DOI: 10.3233/jad-170547] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Available therapies for Alzheimer's disease (AD) can only alleviate and delay the advance of symptoms, with the greatest impact eventually achieved when provided at an early stage. Thus, early identification of which subjects at high risk, e.g., with MCI, will later develop AD is of key importance. Currently available machine learning algorithms achieve only limited predictive accuracy or they are based on expensive and hard-to-collect information. OBJECTIVE The current study aims to develop an algorithm for a 3-year prediction of conversion to AD in MCI and PreMCI subjects based only on non-invasively and effectively collectable predictors. METHODS A dataset of 123 MCI/PreMCI subjects was used to train different machine learning techniques. Baseline information regarding sociodemographic characteristics, clinical and neuropsychological test scores, cardiovascular risk indexes, and a visual rating scale for brain atrophy was used to extract 36 predictors. Leave-pair-out-cross-validation was employed as validation strategy and a recursive feature elimination procedure was applied to identify a relevant subset of predictors. RESULTS 16 predictors were selected from all domains excluding sociodemographic information. The best model resulted a support vector machine with radial-basis function kernel (whole sample: AUC = 0.962, best balanced accuracy = 0.913; MCI sub-group alone: AUC = 0.914, best balanced accuracy = 0.874). CONCLUSIONS Our algorithm shows very high cross-validated performances that outperform the vast majority of the currently available algorithms, and all those which use only non-invasive and effectively assessable predictors. Further testing and optimization in independent samples will warrant its application in both clinical practice and clinical trials.
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Affiliation(s)
- Massimiliano Grassi
- Department of Clinical Neurosciences, Hermanas
Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano,
Como, Italy
| | - Giampaolo Perna
- Department of Clinical Neurosciences, Hermanas
Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano,
Como, Italy
- Research Institute of Mental Health and Neuroscience and
Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life
Sciences, University of Maastricht, Maastricht, Netherlands
- Department of Psychiatry and Behavioral Sciences, Miller
School of Medicine, University of Miami, Miami, FL, USA
- Mantovani Foundation, Arconate, Italy
| | - Daniela Caldirola
- Department of Clinical Neurosciences, Hermanas
Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano,
Como, Italy
| | - Koen Schruers
- Research Institute of Mental Health and Neuroscience and
Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life
Sciences, University of Maastricht, Maastricht, Netherlands
| | - Ranjan Duara
- Department of Neurology, Herbert Wertheim College of
Medicine, Florida International University of Miami, Miami, FL, USA
- Wien Center for Alzheimer’s Disease and Memory
Disorders, Mount Sinai Medical Center Miami Beach, FL, USA
- Courtesy Professor of Neurology, Department of Neurology,
University of Florida College of Medicine, Gainesville Florida,
USAaffiliations
| | - David A. Loewenstein
- Department of Psychiatry and Behavioral Sciences, Miller
School of Medicine, University of Miami, Miami, FL, USA
- Wien Center for Alzheimer’s Disease and Memory
Disorders, Mount Sinai Medical Center Miami Beach, FL, USA
- Center on Aging, Miller School of Medicine, University of
Miami, Miami, FL, USA
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Mathotaarachchi S, Pascoal TA, Shin M, Benedet AL, Kang MS, Beaudry T, Fonov VS, Gauthier S, Rosa-Neto P. Identifying incipient dementia individuals using machine learning and amyloid imaging. Neurobiol Aging 2017; 59:80-90. [DOI: 10.1016/j.neurobiolaging.2017.06.027] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 06/20/2017] [Accepted: 06/30/2017] [Indexed: 01/18/2023]
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25
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Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages. Neuroimage 2017; 155:530-548. [PMID: 28414186 PMCID: PMC5511557 DOI: 10.1016/j.neuroimage.2017.03.057] [Citation(s) in RCA: 298] [Impact Index Per Article: 42.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Revised: 03/25/2017] [Accepted: 03/28/2017] [Indexed: 01/18/2023] Open
Abstract
Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985-June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (FDG-PET) (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and addressed some future research directions.
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Affiliation(s)
- Saima Rathore
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, USA
| | - Mohamad Habes
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, USA
| | - Muhammad Aksam Iftikhar
- Department of Computer Science, Comsats Institute of Information technology, Lahore, Pakistan
| | - Amanda Shacklett
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, USA.
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26
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Li Y, Liu Y, Wang P, Wang J, Xu S, Qiu M. Dependency criterion based brain pathological age estimation of Alzheimer's disease patients with MR scans. Biomed Eng Online 2017; 16:50. [PMID: 28438167 PMCID: PMC5404315 DOI: 10.1186/s12938-017-0342-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Accepted: 04/19/2017] [Indexed: 12/20/2022] Open
Abstract
Objectives Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to the accelerated brain aging. Methods This paper considers this deviation and obtains it by maximizing the correlation between the estimated brain age and the class label rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to the prior knowledge. Secondly, use the support vector regression as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the correlation criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age. Results The experimental results showed that the separability of the samples was apparently improved. For normal control- Alzheimer’s disease (NC-AD), normal control- mild cognition impairment (NC-MCI), and mild cognition impairment—Alzheimer’s disease (MCI-AD), the average improvements were 0.164 (31.66%), 0.1284 (34.29%), and 0.0206 (7.1%), respectively. For NC-MCI-AD, the average improvement was 0.2002 (50.39%). The estimated brain pathological age could be not only more helpful for the classification of AD but also more precisely reflect the accelerated brain aging. Conclusion In conclusion, this paper proposes a new kind of brain age—brain pathological age and offers an estimation method for it that can distinguish different states of AD, thereby better reflecting accelerated brain aging. Besides, the brain pathological age is most helpful for feature reduction, thereby simplifying the relevant classification algorithm.
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Affiliation(s)
- Yongming Li
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China. .,Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, 400038, China. .,Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, 400044, China.
| | - Yuchuan Liu
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China
| | - Pin Wang
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China
| | - Jie Wang
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China
| | - Sha Xu
- College of Communication Engineering, Chongqing University, Shapingba District, Chongqing, 400044, China
| | - Mingguo Qiu
- Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, 400038, China
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27
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimers Dement 2017; 13:e1-e85. [PMID: 28342697 DOI: 10.1016/j.jalz.2016.11.007] [Citation(s) in RCA: 170] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS We used standard searches to find publications using ADNI data. RESULTS (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 2017; 145:137-165. [PMID: 27012503 PMCID: PMC5031516 DOI: 10.1016/j.neuroimage.2016.02.079] [Citation(s) in RCA: 518] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 02/03/2016] [Accepted: 02/25/2016] [Indexed: 01/18/2023] Open
Abstract
Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.
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Affiliation(s)
- Mohammad R Arbabshirani
- The Mind Research Network, Albuquerque, NM 87106, USA; Geisinger Health System, Danville, PA 17822, USA
| | - Sergey Plis
- The Mind Research Network, Albuquerque, NM 87106, USA
| | - Jing Sui
- The Mind Research Network, Albuquerque, NM 87106, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of ECE, University of New Mexico, Albuquerque, NM, USA
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Li C, Loewenstein DA, Duara R, Cabrerizo M, Barker W, Adjouadi M, Taheri S. The Relationship of Brain Amyloid Load and APOE Status to Regional Cortical Thinning and Cognition in the ADNI Cohort. J Alzheimers Dis 2017; 59:1269-1282. [PMID: 28731444 PMCID: PMC6310151 DOI: 10.3233/jad-170286] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND Both amyloid (Aβ) load and APOE4 allele are associated with neurodegenerative changes in Alzheimer's disease (AD) prone regions and with risk for cognitive impairment. OBJECTIVE To evaluate the unique and independent contribution of APOE4 allele status (E4+∖E4-), Aβ status (Amy+∖Amy-), and combined APOE4 and Aβ status on regional cortical thickness (CoTh) and cognition among participants diagnosed as cognitively normal (CN, n = 251), early mild cognitive impairment (EMCI, n = 207), late mild cognitive impairment (LMCI, n = 196), and mild AD (n = 162) from the ADNI. METHODS A series of two-way ANCOVAs with post-hoc Tukey HSD tests, controlling independently for Aβ and APOE4 status and age were examined. RESULTS Among LMCI and AD participants, cortical thinning was widespread in association with Amy+ status, whereas in association with E4+ status only in the inferior temporal and medial orbito-frontal regions. Among CN and EMCI participants, E4+ status, but not Amy+ status, was independently associated with increased CoTh, especially in limbic regions [e.g., in the entorhinal cortex, CoTh was 0.123 mm greater (p = 0.002) among E4+ than E4-participants]. Among CN and EMCI, both E4+ and Amy+ status were independently associated with cognitive impairment, which was greatest among those with combined E4 + and Amy+ status. CONCLUSION Decreased CoTh is independently associated with Amy+ status in many brain regions, but with E4+ status in very restricted number of brain regions. Among CN and EMCI participants, E4 + status is associated with increased CoTh, in medial and inferior temporal regions, although cognitive impairment at this state is independently associated with Amy+ and E4 + status. These findings imply a unique pathophysiological mechanism for E4 + status in AD and its progression.
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Affiliation(s)
- Chunfei Li
- Department of Electrical and Computer Engineering, Center for Advanced Technology and Education, Florida International University, Miami, FL, USA
| | - David A. Loewenstein
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
- Department of Psychiatry and Behavioral Sciences, Center on Aging and Miller School of Medicine, University of Miami, Miami, FL, USA
- Departments of Neurology, University of Florida College of Medicine, Gainesville, FL, USA and Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
- Departments of Neurology, University of Florida College of Medicine, Gainesville, FL, USA and Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA
- 1Florida ADRC (Florida Alzheimer’s Disease Research Center) at Gainesville, Miami Beach, Miami, FL, USA and Boca Raton, FL, USA
| | - Mercedes Cabrerizo
- Department of Electrical and Computer Engineering, Center for Advanced Technology and Education, Florida International University, Miami, FL, USA
| | - Warren Barker
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA
- 1Florida ADRC (Florida Alzheimer’s Disease Research Center) at Gainesville, Miami Beach, Miami, FL, USA and Boca Raton, FL, USA
| | - Malek Adjouadi
- Department of Electrical and Computer Engineering, Center for Advanced Technology and Education, Florida International University, Miami, FL, USA
- 1Florida ADRC (Florida Alzheimer’s Disease Research Center) at Gainesville, Miami Beach, Miami, FL, USA and Boca Raton, FL, USA
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Li Y, Li F, Wang P, Zhu X, Liu S, Qiu M, Zhang J, Zeng X. Estimating the brain pathological age of Alzheimer's disease patients from MR image data based on the separability distance criterion. Phys Med Biol 2016; 61:7162-7186. [PMID: 27649031 DOI: 10.1088/0031-9155/61/19/7162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Traditional age estimation methods are based on the same idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to accelerated brain aging. This paper considers this deviation and searches for it by maximizing the separability distance value rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to prior knowledge. Secondly, use the support vector regression (SVR) as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the separability distance criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age. The experimental results showed that the separability was apparently improved. For normal control-Alzheimer's disease (NC-AD), normal control-mild cognition impairment (NC-MCI), and MCI-AD, the average improvements were 0.178 (35.11%), 0.033 (14.47%), and 0.017 (39.53%), respectively. For NC-MCI-AD, the average improvement was 0.2287 (64.22%). The estimated brain pathological age could be not only more helpful to the classification of AD but also more precisely reflect accelerated brain aging. In conclusion, this paper offers a new method for brain age estimation that can distinguish different states of AD and can better reflect the extent of accelerated aging.
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Affiliation(s)
- Yongming Li
- College of Communication Engineering, Chongqing University, Chongqing 400044, People's Republic of China. Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing 400038, People's Republic of China
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Löwe LC, Gaser C, Franke K. The Effect of the APOE Genotype on Individual BrainAGE in Normal Aging, Mild Cognitive Impairment, and Alzheimer's Disease. PLoS One 2016; 11:e0157514. [PMID: 27410431 PMCID: PMC4943637 DOI: 10.1371/journal.pone.0157514] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Accepted: 05/30/2016] [Indexed: 01/28/2023] Open
Abstract
In our aging society, diseases in the elderly come more and more into focus. An important issue in research is Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) with their causes, diagnosis, treatment, and disease prediction. We applied the Brain Age Gap Estimation (BrainAGE) method to examine the impact of the Apolipoprotein E (APOE) genotype on structural brain aging, utilizing longitudinal magnetic resonance image (MRI) data of 405 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We tested for differences in neuroanatomical aging between carrier and non-carrier of APOE ε4 within the diagnostic groups and for longitudinal changes in individual brain aging during about three years follow-up. We further examined whether a combination of BrainAGE and APOE status could improve prediction accuracy of conversion to AD in MCI patients. The influence of the APOE status on conversion from MCI to AD was analyzed within all allelic subgroups as well as for ε4 carriers and non-carriers. The BrainAGE scores differed significantly between normal controls, stable MCI (sMCI) and progressive MCI (pMCI) as well as AD patients. Differences in BrainAGE changing rates over time were observed for APOE ε4 carrier status as well as in the pMCI and AD groups. At baseline and during follow-up, BrainAGE scores correlated significantly with neuropsychological test scores in APOE ε4 carriers and non-carriers, especially in pMCI and AD patients. Prediction of conversion was most accurate using the BrainAGE score as compared to neuropsychological test scores, even when the patient’s APOE status was unknown. For assessing the individual risk of coming down with AD as well as predicting conversion from MCI to AD, the BrainAGE method proves to be a useful and accurate tool even if the information of the patient’s APOE status is missing.
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Affiliation(s)
| | - Christian Gaser
- Structural Brain Mapping Group, Department of Neurology, University Hospital Jena, Jena, Germany
- Department of Psychiatry, University Hospital Jena, Jena, Germany
| | - Katja Franke
- Structural Brain Mapping Group, Department of Neurology, University Hospital Jena, Jena, Germany
- * E-mail:
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Keeney JTR, Ibrahimi S, Zhao L. Human ApoE Isoforms Differentially Modulate Glucose and Amyloid Metabolic Pathways in Female Brain: Evidence of the Mechanism of Neuroprotection by ApoE2 and Implications for Alzheimer's Disease Prevention and Early Intervention. J Alzheimers Dis 2016; 48:411-24. [PMID: 26402005 DOI: 10.3233/jad-150348] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Three major genetic isoforms of apolipoprotein E (ApoE), ApoE2, ApoE3, and ApoE4, exist in humans and lead to differences in susceptibility to Alzheimer's disease (AD). This study investigated the impact of human ApoE isoforms on brain metabolic pathways involved in glucose utilization and amyloid-β (Aβ) degradation, two major areas that are significantly perturbed in preclinical AD. Hippocampal RNA samples from middle-aged female mice with targeted human ApoE2, ApoE3, and ApoE4 gene replacement were comparatively analyzed with a qRT-PCR custom array for the expression of 85 genes involved in insulin/insulin-like growth factor (Igf) signaling. Consistent with its protective role against AD, ApoE2 brain exhibited the most metabolically robust profile among the three ApoE genotypes. When compared to ApoE2 brain, both ApoE3 and ApoE4 brains exhibited markedly reduced levels of Igf1, insulin receptor substrates (Irs), and facilitated glucose transporter 4 (Glut4), indicating reduced glucose uptake. Additionally, ApoE4 brain exhibited significantly decreased Pparg and insulin-degrading enzyme (Ide), indicating further compromised glucose metabolism and Aβ dysregulation associated with ApoE4. Protein analysis showed significantly decreased Igf1, Irs, and Glut4 in ApoE3 brain, and Igf1, Irs, Glut4, Pparg, and Ide in ApoE4 brain compared to ApoE2 brain. These data provide the first documented evidence that human ApoE isoforms differentially affect brain insulin/Igf signaling and downstream glucose and amyloid metabolic pathways, illustrating a potential mechanism for their differential risk in AD. A therapeutic strategy that enhances brain insulin/Igf1 signaling activity to a more robust ApoE2-like phenotype favoring both energy production and amyloid homeostasis holds promise for AD prevention and early intervention.
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Affiliation(s)
| | - Shaher Ibrahimi
- Department of Pharmacology and Toxicology, School of Pharmacy, University of Kansas, Lawrence, KS, USA
| | - Liqin Zhao
- Department of Pharmacology and Toxicology, School of Pharmacy, University of Kansas, Lawrence, KS, USA.,Neuroscience Graduate Program, University of Kansas, Lawrence, KS, USA
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Riedel BC, Thompson PM, Brinton RD. Age, APOE and sex: Triad of risk of Alzheimer's disease. J Steroid Biochem Mol Biol 2016; 160:134-47. [PMID: 26969397 PMCID: PMC4905558 DOI: 10.1016/j.jsbmb.2016.03.012] [Citation(s) in RCA: 365] [Impact Index Per Article: 45.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2015] [Revised: 03/02/2016] [Accepted: 03/06/2016] [Indexed: 02/06/2023]
Abstract
Age, apolipoprotein E ε4 (APOE) and chromosomal sex are well-established risk factors for late-onset Alzheimer's disease (LOAD; AD). Over 60% of persons with AD harbor at least one APOE-ε4 allele. The sex-based prevalence of AD is well documented with over 60% of persons with AD being female. Evidence indicates that the APOE-ε4 risk for AD is greater in women than men, which is particularly evident in heterozygous women carrying one APOE-ε4 allele. Paradoxically, men homozygous for APOE-ε4 are reported to be at greater risk for mild cognitive impairment and AD. Herein, we discuss the complex interplay between the three greatest risk factors for Alzheimer's disease, age, APOE-ε4 genotype and chromosomal sex. We propose that the convergence of these three risk factors, and specifically the bioenergetic aging perimenopause to menopause transition unique to the female, creates a risk profile for AD unique to the female. Further, we discuss the specific risk of the APOE-ε4 positive male which appears to emerge early in the aging process. Evidence for impact of the triad of AD risk factors is most evident in the temporal trajectory of AD progression and burden of pathology in relation to APOE genotype, age and sex. Collectively, the data indicate complex interactions between age, APOE genotype and gender that belies a one size fits all approach and argues for a precision medicine approach that integrates across the three main risk factors for Alzheimer's disease.
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Affiliation(s)
- Brandalyn C Riedel
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90089, USA
| | - Paul M Thompson
- USC Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA 90292, USA
| | - Roberta Diaz Brinton
- Department of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA.
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Influence of APOE Genotype on Alzheimer's Disease CSF Biomarkers in a Spanish Population. BIOMED RESEARCH INTERNATIONAL 2016; 2016:1390620. [PMID: 27092308 PMCID: PMC4820585 DOI: 10.1155/2016/1390620] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 10/09/2015] [Accepted: 11/25/2015] [Indexed: 11/18/2022]
Abstract
Objectives. To evaluate the association between apolipoprotein E (APOE) genotype and cerebrospinal fluid (CSF) levels of Alzheimer's disease (AD) biomarkers and to study the influence of APOE genotype on the development of AD in a Spanish population. Material and Methods. The study comprised 29 amnestic mild cognitive impairment (MCI) patients and 27 control subjects. Using ELISA methodology, CSF biomarkers and tau/Aβ ratios were obtained. ANOVA and adjusted odds ratios were calculated. Results. We observed the effect of APOE genotype and age on CSF AD variables. The progression to AD was more clearly influenced by CSF AD variables than by age or APOE status. Conclusions. APOE status influences CSF AD variables. However, the presence of APOE ε4 does not appear to be a deterministic factor for the development of AD, because CSF variables have a greater influence on progression to the disease. These results confirm previous observations and, to our knowledge, are the first published in a Spanish population.
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Liu H, Wei C, He H, Liu X. Evaluating Alzheimer's Disease Progression by Modeling Crosstalk Network Disruption. Front Neurosci 2016; 9:523. [PMID: 26834548 PMCID: PMC4718081 DOI: 10.3389/fnins.2015.00523] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2015] [Accepted: 12/27/2015] [Indexed: 11/20/2022] Open
Abstract
Aβ, tau, and P-tau have been widely accepted as reliable markers for Alzheimer's disease (AD). The crosstalk between these markers forms a complex network. AD may induce the integral variation and disruption of the network. The aim of this study was to develop a novel mathematic model based on a simplified crosstalk network to evaluate the disease progression of AD. The integral variation of the network is measured by three integral disruption parameters. The robustness of network is evaluated by network disruption probability. Presented results show that network disruption probability has a good linear relationship with Mini Mental State Examination (MMSE). The proposed model combined with Support vector machine (SVM) achieves a relative high 10-fold cross-validated performance in classification of AD vs. normal and mild cognitive impairment (MCI) vs. normal (95% accuracy, 95% sensitivity, 95% specificity for AD vs. normal; 90% accuracy, 94% sensitivity, 83% specificity for MCI vs. normal). This research evaluates the progression of AD and facilitates AD early diagnosis.
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Affiliation(s)
- Haochen Liu
- Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University Nanjing, China
| | - Chunxiang Wei
- Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University Nanjing, China
| | - Hua He
- Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University Nanjing, China
| | - Xiaoquan Liu
- Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University Nanjing, China
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Saykin AJ, Shen L, Yao X, Kim S, Nho K, Risacher SL, Ramanan VK, Foroud TM, Faber KM, Sarwar N, Munsie LM, Hu X, Soares HD, Potkin SG, Thompson PM, Kauwe JSK, Kaddurah-Daouk R, Green RC, Toga AW, Weiner MW. Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans. Alzheimers Dement 2015; 11:792-814. [PMID: 26194313 PMCID: PMC4510473 DOI: 10.1016/j.jalz.2015.05.009] [Citation(s) in RCA: 202] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Revised: 05/08/2015] [Accepted: 05/08/2015] [Indexed: 01/01/2023]
Abstract
INTRODUCTION Genetic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) have been crucial in advancing the understanding of Alzheimer's disease (AD) pathophysiology. Here, we provide an update on sample collection, scientific progress and opportunities, conceptual issues, and future plans. METHODS Lymphoblastoid cell lines and DNA and RNA samples from blood have been collected and banked, and data and biosamples have been widely disseminated. To date, APOE genotyping, genome-wide association study (GWAS), and whole exome and whole genome sequencing data have been obtained and disseminated. RESULTS ADNI genetic data have been downloaded thousands of times, and >300 publications have resulted, including reports of large-scale GWAS by consortia to which ADNI contributed. Many of the first applications of quantitative endophenotype association studies used ADNI data, including some of the earliest GWAS and pathway-based studies of biospecimen and imaging biomarkers, as well as memory and other clinical/cognitive variables. Other contributions include some of the first whole exome and whole genome sequencing data sets and reports in healthy controls, mild cognitive impairment, and AD. DISCUSSION Numerous genetic susceptibility and protective markers for AD and disease biomarkers have been identified and replicated using ADNI data and have heavily implicated immune, mitochondrial, cell cycle/fate, and other biological processes. Early sequencing studies suggest that rare and structural variants are likely to account for significant additional phenotypic variation. Longitudinal analyses of transcriptomic, proteomic, metabolomic, and epigenomic changes will also further elucidate dynamic processes underlying preclinical and prodromal stages of disease. Integration of this unique collection of multiomics data within a systems biology framework will help to separate truly informative markers of early disease mechanisms and potential novel therapeutic targets from the vast background of less relevant biological processes. Fortunately, a broad swath of the scientific community has accepted this grand challenge.
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Affiliation(s)
- Andrew J Saykin
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, 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.
| | - Li Shen
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Xiaohui Yao
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; School of Informatics and Computing, Indiana University, Purdue University - Indianapolis, Indianapolis, IN, USA
| | - Sungeun Kim
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kwangsik Nho
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Shannon L Risacher
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Vijay K Ramanan
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, 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
| | - Tatiana M Foroud
- 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
| | - Kelley M Faber
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | | | - Xiaolan Hu
- Bristol-Myers Squibb, Wallingford, CT, USA
| | | | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California - Irvine, Irvine, CA, USA
| | - Paul M Thompson
- Department of Neurology, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA; Imaging Genetics Center, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA
| | - John S K Kauwe
- Department of Biology, Brigham Young University, Provo, UT, USA; Department of Neuroscience, Brigham Young University, Provo, UT, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA; Duke Institute for Brain Sciences, Duke University, Durham, NC, USA
| | - Robert C Green
- Partners Center for Personalized Genetic Medicine, Boston, MA, USA; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute for Neuroimaging and Neuroinformatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Michael W Weiner
- Department of Radiology, University of California-San Francisco, San Francisco, CA, USA; Department of Medicine, University of California-San Francisco, San Francisco, CA, USA; Department of Psychiatry, University of California-San Francisco, San Francisco, CA, USA; Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center, San Francisco, CA, USA
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Abstract
Sporadic Alzheimer's disease (spAD) has three successive phases: preclinical, mild cognitive impairment, and dementia. Individuals in the preclinical phase are cognitively normal. Diagnosis of preclinical spAD requires evidence of pathologic brain changes provided by established biomarkers. Histopathologic features of spAD include (i) extra-cellular cerebral amyloid plaques and intracellular neurofibrillary tangles that embody hyperphosphorylated tau; and (ii) neuronal and synaptic loss. Amyloid-PET brain scans conducted during spAD's preclinical phase have disclosed abnormal accumulations of amyloid-beta (Aβ) in cognitively normal, high-risk individuals. However, this measure correlates poorly with changes in cognitive status. In contrast, MRI measures of brain atrophy consistently parallel cognitive deterioration. By the time dementia appears, amyloid deposition has already slowed or ceased. When a new treatment offers promise of arresting or delaying progression of preclinical spAD, its effectiveness must be inferred from intervention-correlated changes in biomarkers. Herein, differing tenets of the amyloid cascade hypothesis (ACH) and the mitochondrial cascade hypothesis (MCH) are compared. Adoption of the ACH suggests therapeutic research continue to focus on aspects of the amyloid pathways. Adoption of the MCH suggests research emphasis be placed on restoration and stabilization of mitochondrial function. Ketone ester (KE)-induced elevation of plasma ketone body (KB) levels improves mitochondrial metabolism and prevents or delays progression of AD-like pathologic changes in several AD animal models. Thus, as a first step, it is imperative to determine whether KE-caused hyperketonemia can bring about favorable changes in biomarkers of AD pathology in individuals who are in an early stage of AD's preclinical phase.
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Affiliation(s)
- Theodore B VanItallie
- Department of Medicine, St. Luke's Hospital, Columbia University College of Physicians & Surgeons, New York, NY 10025.
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Dodge HH, Mattek N, Gregor M, Bowman M, Seelye A, Ybarra O, Asgari M, Kaye JA. Social Markers of Mild Cognitive Impairment: Proportion of Word Counts in Free Conversational Speech. Curr Alzheimer Res 2015; 12:513-9. [PMID: 26027814 PMCID: PMC4526336 DOI: 10.2174/1567205012666150530201917] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Revised: 04/29/2015] [Accepted: 05/23/2015] [Indexed: 01/02/2023]
Abstract
BACKGROUND Detecting early signs of Alzheimer's disease (AD) and mild cognitive impairment (MCI) during the pre-symptomatic phase is becoming increasingly important for costeffective clinical trials and also for deriving maximum benefit from currently available treatment strategies. However, distinguishing early signs of MCI from normal cognitive aging is difficult. Biomarkers have been extensively examined as early indicators of the pathological process for AD, but assessing these biomarkers is expensive and challenging to apply widely among pre-symptomatic community dwelling older adults. Here we propose assessment of social markers, which could provide an alternative or complementary and ecologically valid strategy for identifying the pre-symptomatic phase leading to MCI and AD. METHODS The data came from a larger randomized controlled clinical trial (RCT), where we examined whether daily conversational interactions using remote video telecommunications software could improve cognitive functions of older adult participants. We assessed the proportion of words generated by participants out of total words produced by both participants and staff interviewers using transcribed conversations during the intervention trial as an indicator of how two people (participants and interviewers) interact with each other in one-on-one conversations. We examined whether the proportion differed between those with intact cognition and MCI, using first, generalized estimating equations with the proportion as outcome, and second, logistic regression models with cognitive status as outcome in order to estimate the area under ROC curve (ROC AUC). RESULTS Compared to those with normal cognitive function, MCI participants generated a greater proportion of words out of the total number of words during the timed conversation sessions (p=0.01). This difference remained after controlling for participant age, gender, interviewer and time of assessment (p=0.03). The logistic regression models showed the ROC AUC of identifying MCI (vs. normals) was 0.71 (95% Confidence Interval: 0.54 - 0.89) when average proportion of word counts spoken by subjects was included univariately into the model. CONCLUSION An ecologically valid social marker such as the proportion of spoken words produced during spontaneous conversations may be sensitive to transitions from normal cognition to MCI.
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Affiliation(s)
- Hiroko H Dodge
- Mail Code CR131, 3181 SW Sam Jackson Park Rd, Department of Neurology, Oregon Health & Science University, Portland, OR 97239-3098, USA.
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