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Capogna E, Sørensen Ø, Watne LO, Roe J, Strømstad M, Idland AV, Halaas NB, Blennow K, Zetterberg H, Walhovd KB, Fjell AM, Vidal-Piñeiro D. Subtypes of brain change in aging and their associations with cognition and Alzheimer's disease biomarkers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583291. [PMID: 38496633 PMCID: PMC10942348 DOI: 10.1101/2024.03.04.583291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Structural brain changes underly cognitive changes in older age and contribute to inter-individual variability in cognition. Here, we assessed how changes in cortical thickness, surface area, and subcortical volume, are related to cognitive change in cognitively unimpaired older adults using structural magnetic resonance imaging (MRI) data-driven clustering. Specifically, we tested (1) which brain structural changes over time predict cognitive change in older age (2) whether these are associated with core cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers phosphorylated tau (p-tau) and amyloid-β (Aβ42), and (3) the degree of overlap between clusters derived from different structural features. In total 1899 cognitively healthy older adults (50 - 93 years) were followed up to 16 years with neuropsychological and structural MRI assessments, a subsample of which (n = 612) had CSF p-tau and Aβ42 measurements. We applied Monte-Carlo Reference-based Consensus clustering to identify subgroups of older adults based on structural brain change patterns over time. Four clusters for each brain feature were identified, representing the degree of longitudinal brain decline. Each brain feature provided a unique contribution to brain aging as clusters were largely independent across modalities. Cognitive change and baseline cognition were best predicted by cortical area change, whereas higher levels of p-tau and Aβ42 were associated with changes in subcortical volume. These results provide insights into the link between changes in brain morphology and cognition, which may translate to a better understanding of different aging trajectories.
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Affiliation(s)
- Elettra Capogna
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Øystein Sørensen
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Leiv Otto Watne
- Department of Geriatric Medicine, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
| | - James Roe
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Marie Strømstad
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Ane Victoria Idland
- Oslo Delirium Research Group, Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Nathalie Bodd Halaas
- Oslo Delirium Research Group, Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Campus UllevÅl, University of Oslo, Oslo, Norway
| | - Kaj Blennow
- Institute of Neuroscience and Physiology, the Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Paris Brain Institute, ICM, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, and Department of Neurology, Institute on Aging and Brain Disorders, University of Science and Technology of China and First Affiliated Hospital of USTC, Hefei, P.R. China
| | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, the Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Center for Neurodegenerative Diseases, Hong Kong, China
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Kristine Beate Walhovd
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
- Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Anders Martin Fjell
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
- Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Didac Vidal-Piñeiro
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
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Walhovd KB, Lövden M, Fjell AM. Timing of lifespan influences on brain and cognition. Trends Cogn Sci 2023; 27:901-915. [PMID: 37563042 DOI: 10.1016/j.tics.2023.07.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/04/2023] [Accepted: 07/04/2023] [Indexed: 08/12/2023]
Abstract
Modifiable risk and protective factors for boosting brain and cognitive development and preventing neurodegeneration and cognitive decline are embraced in neuroimaging studies. We call for sobriety regarding the timing and quantity of such influences on brain and cognition. Individual differences in the level of brain and cognition, many of which present already at birth and early in development, appear stable, larger, and more pervasive than differences in change across the lifespan. Incorporating early-life factors, including genetics, and investigating both level and change will reduce the risk of ascribing undue importance and causality to proximate factors in adulthood and older age. This has implications for both mechanistic understanding and prevention.
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Affiliation(s)
- Kristine B Walhovd
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Norway; Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
| | - Martin Lövden
- Department of Psychology, University of Gothenburg, Gothenburg, Sweden
| | - Anders M Fjell
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Norway; Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
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Shaji S, Palanisamy R, Swaminathan R. Structural irregularities in MR corpus callosal images and their association with cerebrospinal fluid biomarkers in Mild Cognitive Impairments. Neurosci Lett 2023; 810:137329. [PMID: 37301466 DOI: 10.1016/j.neulet.2023.137329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 05/15/2023] [Accepted: 06/05/2023] [Indexed: 06/12/2023]
Abstract
In this study, irregularity measures from MR images of corpus callosal brain structures in healthy and Mild Cognitive Impairment (MCI) conditions are extracted and their association with Cerebrospinal Fluid (CSF) biomarkers are analyzed. For this, MR images of healthy controls, Early MCI (EMCI) and Late MCI (LMCI) subjects are considered from a public database. The considered images are preprocessed and corpus callosal structure is segmented. Structural irregularity measures are extracted from the segmented regions using Fourier analysis. Statistical tests are performed to identify the significant features which can characterize the MCI stages. Association of these measures with CSF amyloid beta and tau concentrations are further investigated. Results demonstrate that Fourier spectral analysis is able to characterize the non-periodic variations in the corpus callosal structures of healthy, EMCI and LMCI MR images. The callosal irregularity measures increase as the disease progresses from healthy to LMCI. Phosphorylated tau concentrations in CSF demonstrate a positive correlation with irregularity measures across the diagnostic groups. Significant association of callosal measures and amyloid beta levels are found to be absent in MCI stages. As corpus callosal structural irregularities due to early MCI condition and their association with CSF markers remain uncharacterized in the literature, this study seems to be clinically significant for the timely intervention of pre-symptomatic MCI stages.
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Affiliation(s)
- Sreelakshmi Shaji
- Non-Invasive Imaging and Diagnostic Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India.
| | - Rohini Palanisamy
- Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai, India.
| | - Ramakrishnan Swaminathan
- Non-Invasive Imaging and Diagnostic Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India.
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Hayes JP, Pierce ME, Brown E, Salat D, Logue MW, Constantinescu J, Valerio K, Miller MW, Sherva R, Huber BR, Milberg W, McGlinchey R. Genetic Risk for Alzheimer Disease and Plasma Tau Are Associated With Accelerated Parietal Cortex Thickness Change in Middle-Aged Adults. Neurol Genet 2023; 9:e200053. [PMID: 36742995 PMCID: PMC9893442 DOI: 10.1212/nxg.0000000000200053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 11/21/2022] [Indexed: 02/04/2023]
Abstract
Background and Objectives Neuroimaging and biomarker studies in Alzheimer disease (AD) have shown well-characterized patterns of cortical thinning and altered biomarker concentrations of tau and β-amyloid (Aβ). However, earlier identification of AD has great potential to advance clinical care and determine candidates for drug trials. The extent to which AD risk markers relate to cortical thinning patterns in midlife is unknown. The first objective of this study was to examine cortical thickness change associated with genetic risk for AD among middle-aged military veterans. The second objective was to determine the relationship between plasma tau and Aβ and change in brain cortical thickness among veterans stratified by genetic risk for AD. Methods Participants consisted of post-9/11 veterans (N = 155) who were consecutively enrolled in the Translational Research Center for TBI and Stress Disorders prospective longitudinal cohort and were assessed for mild traumatic brain injury (TBI) and posttraumatic disorder (PTSD). Genome-wide polygenic risk scores (PRSs) for AD were calculated using summary results from the International Genomics of Alzheimer's Disease Project. T-tau and Aβ40 and Aβ42 plasma assays were run using Simoa technology. Whole-brain MRI cortical thickness change estimates were obtained using the longitudinal stream of FreeSurfer. Follow-up moderation analyses examined the AD PRS × plasma interaction on change in cortical thickness in AD-vulnerable regions. Results Higher AD PRS, signifying greater genetic risk for AD, was associated with accelerated cortical thickness change in a right hemisphere inferior parietal cortex cluster that included the supramarginal gyrus, angular gyrus, and intraparietal sulcus. Higher tau, but not Aβ42/40 ratio, was associated with greater cortical thickness change among those with higher AD PRS. Mild TBI and PTSD were not associated with cortical thickness change. Discussion Plasma tau, particularly when combined with genetic stratification for AD risk, can be a useful indicator of brain change in midlife. Accelerated inferior parietal cortex changes in midlife may be an important factor to consider as a marker of AD-related brain alterations.
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Affiliation(s)
- Jasmeet Pannu Hayes
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Meghan E Pierce
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Emma Brown
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - David Salat
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Mark W Logue
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Julie Constantinescu
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Kate Valerio
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Mark W Miller
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Richard Sherva
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Bertrand Russell Huber
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - William Milberg
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
| | - Regina McGlinchey
- Department of Psychology (J.P.H., K.V.), The Ohio State University, & Chronic Brain Injury Program, The Ohio State University, Columbus; Translational Research Center for TBI and Stress Disorders (TRACTS) (M.E.P., E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Department of Psychiatry (M.E.P., M.W.L., M.W.M., B.R.H.), Boston University School of Medicine, MA; Neuroimaging Research for Veterans (NeRVe) Center (E.B., D.S., J.C., W.M., R.M.), VA Boston Healthcare System, MA; Brain Aging and Dementia (BAnD) Laboratory (D.S.), A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown; National Center for PTSD (M.W.L., M.W.M., B.R.H.), Behavioral Sciences Division, VA Boston Healthcare System, MA; Boston University School of Medicine (M.W.L., R.S.), Biomedical Genetics, MA; Boston University School of Public Health (M.W.L.), Department of Biostatistics, MA; Department of Neurology (B.R.H.), Boston University School of Medicine, MA; Geriatric Research (W.M., R.M.), Education, and Clinical Center (GRECC), VA Boston Healthcare System, MA; and Department of Psychiatry (W.M., R.M.), Harvard Medical School, Boston, MA
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Stark J, Hiersche KJ, Yu JC, Hasselbach AN, Abdi H, Hayes SM. Partial Least Squares Regression Analysis of Alzheimer's Disease Biomarkers, Modifiable Health Variables, and Cognitive Change in Older Adults with Mild Cognitive Impairment. J Alzheimers Dis 2023; 93:633-651. [PMID: 37066909 PMCID: PMC10999056 DOI: 10.3233/jad-221084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
BACKGROUND Prior work has shown that certain modifiable health, Alzheimer's disease (AD) biomarker, and demographic variables are associated with cognitive performance. However, less is known about the relative importance of these different domains of variables in predicting longitudinal change in cognition. OBJECTIVE Identify novel relationships between modifiable physical and health variables, AD biomarkers, and slope of cognitive change over two years in a cohort of older adults with mild cognitive impairment (MCI). METHODS Metrics of cardiometabolic risk, stress, inflammation, neurotrophic/growth factors, and AD pathology were assessed in 123 older adults with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (mean age = 73.9; SD = 7.6; mean education = 16.0; SD = 3.0). Partial least squares regression (PLSR)-a multivariate method which creates components that best predict an outcome-was used to identify whether these physiological variables were important in predicting slope of change in episodic memory or executive function over two years. RESULTS At two-year follow-up, the two PLSR models predicted, respectively, 20.0% and 19.6% of the variance in change in episodic memory and executive function. Baseline levels of AD biomarkers were important in predicting change in both episodic memory and executive function. Baseline education and neurotrophic/growth factors were important in predicting change in episodic memory, whereas cardiometabolic variables such as blood pressure and cholesterol were important in predicting change in executive function. CONCLUSION These data-driven analyses highlight the impact of AD biomarkers on cognitive change and further clarify potential domain specific relationships with predictors of cognitive change.
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Affiliation(s)
- Jessica Stark
- Department of Psychology, The Ohio State University, Columbus, OH, USA
| | - Kelly J Hiersche
- Department of Psychology, The Ohio State University, Columbus, OH, USA
| | - Ju-Chi Yu
- Centre for Addiction and Mental Health, Toronto, Canada
| | | | - Hervé Abdi
- Department of Psychology, The University of Texas at Dallas, Dallas, TX, USA
| | - Scott M Hayes
- Department of Psychology, The Ohio State University, Columbus, OH, USA
- Department of Psychology, The Ohio State University, Columbus, OH, USA
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Dang M, Sang F, Long S, Chen Y. The Aging Patterns of Brain Structure, Function, and Energy Metabolism. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1419:85-97. [PMID: 37418208 DOI: 10.1007/978-981-99-1627-6_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
The normal aging process brings changes in brain structure, function, and energy metabolism, which are presumed to contribute to the age-related decline in brain function and cognitive ability. This chapter aims to summarize the aging patterns of brain structure, function, and energy metabolism to distinguish them from the pathological changes associated with neurodegenerative diseases and explore protective factors in aging. We first described the normal atrophy pattern of cortical gray matter with age, which is negatively affected by some neurodegenerative diseases and is protected by a healthy lifestyle, such as physical exercise. Next, we summarized the main types of age-related white matter lesions, including white matter atrophy and hyperintensity. Age-related white matter changes mainly occurred in the frontal lobe, and white matter lesions in posterior regions may be an early sign of Alzheimer's disease. In addition, the relationship between brain activity and various cognitive functions during aging was discussed based on electroencephalography, magnetoencephalogram, and functional magnetic resonance imaging. An age-related reduction in occipital activity is coupled with increased frontal activity, which supports the posterior-anterior shift in aging (PASA) theory. Finally, we discussed the relationship between amyloid-β deposition and tau accumulation in the brain, as pathological manifestations of neurodegenerative disease and aging.
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Affiliation(s)
- Mingxi Dang
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China
| | - Feng Sang
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China
| | - Shijie Long
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China
| | - Yaojing Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China.
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China.
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7
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Conditional Kaplan–Meier Estimator with Functional Covariates for Time-to-Event Data. STATS 2022. [DOI: 10.3390/stats5040066] [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] Open
Abstract
Due to the wide availability of functional data from multiple disciplines, the studies of functional data analysis have become popular in the recent literature. However, the related development in censored survival data has been relatively sparse. In this work, we consider the problem of analyzing time-to-event data in the presence of functional predictors. We develop a conditional generalized Kaplan–Meier (KM) estimator that incorporates functional predictors using kernel weights and rigorously establishes its asymptotic properties. In addition, we propose to select the optimal bandwidth based on a time-dependent Brier score. We then carry out extensive numerical studies to examine the finite sample performance of the proposed functional KM estimator and bandwidth selector. We also illustrated the practical usage of our proposed method by using a data set from Alzheimer’s Disease Neuroimaging Initiative data.
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8
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Li Y, Hsu W. A classification for complex imbalanced data in disease screening and early diagnosis. Stat Med 2022; 41:3679-3695. [PMID: 35603639 PMCID: PMC9541048 DOI: 10.1002/sim.9442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 04/11/2022] [Accepted: 05/10/2022] [Indexed: 11/09/2022]
Abstract
Imbalanced classification has drawn considerable attention in the statistics and machine learning literature. Typically, traditional classification methods often perform poorly when a severely skewed class distribution is observed, not to mention under a high-dimensional longitudinal data structure. Given the ubiquity of big data in modern health research, it is expected that imbalanced classification in disease diagnosis may encounter an additional level of difficulty that is imposed by such a complex data structure. In this article, we propose a nonparametric classification approach for imbalanced data in longitudinal and high-dimensional settings. Technically, the functional principal component analysis is first applied for feature extraction under the longitudinal structure. The univariate exponential loss function coupled with group LASSO penalty is then adopted into the classification procedure in high-dimensional settings. Along with a good improvement in imbalanced classification, our approach provides a meaningful feature selection for interpretation while enjoying a remarkably lower computational complexity. The proposed method is illustrated on the real data application of Alzheimer's disease early detection and its empirical performance in finite sample size is extensively evaluated by simulations.
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Affiliation(s)
- Yiming Li
- Department of StatisticsKansas State UniversityManhattanKansasUSA
| | - Wei‐Wen Hsu
- Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health SciencesUniversity of CincinnatiCincinnatiOhioUSA
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9
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Chang M, Brainerd CJ. Predicting conversion from mild cognitive impairment to Alzheimer's disease with multimodal latent factors. J Clin Exp Neuropsychol 2022; 44:316-335. [PMID: 36036715 DOI: 10.1080/13803395.2022.2115015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Abstract
INTRODUCTION We studied the ability of latent factor scores to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) and investigated whether multimodal factor scores improve predictive power, relative to single-modal factor scores. METHOD We conducted exploratory factor analyses (EFAs) and confirmatory factor analyses (CFAs) of the baseline data of MCI subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) to generate factor scores for three data modalities: neuropsychological (NP), magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF). Factor scores from single or multiple modalities were entered in logistic regression models to predict MCI to AD conversion for 160 ADNI subjects over a 2-year interval. RESULTS NP factors attained an area under the curve (AUC) of .80, with a sensitivity of .66 and a specificity of .77. MRI factors reached a comparable level of performance (AUC = .80, sensitivity = .66, specificity = .78), whereas CSF factors produced weaker prediction (AUC = .70, sensitivity = .56, specificity = .79). Combining NP factors with MRI or CSF factors produced better prediction than either MRI or CSF factors alone. Similarly, adding MRI factors to NP or CSF factors produced improvements in prediction relative to NP or CSF factors alone. However, adding CSF factors to either NP or MRI factors produced no improvement in prediction. CONCLUSIONS Latent factor scores provided good accuracy for predicting MCI to AD conversion. Adding NP or MRI factors to factors from other modalities enhanced predictive power but adding CSF factors did not.
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Affiliation(s)
- Minyu Chang
- Department of Psychology and Human Neuroscience Institute, Cornell University, Ithaca, New York, USA
| | - C J Brainerd
- Department of Psychology and Human Neuroscience Institute, Cornell University, Ithaca, New York, USA
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10
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Gherardelli C, Cisternas P, Inestrosa NC. Lithium Enhances Hippocampal Glucose Metabolism in an In Vitro Mice Model of Alzheimer's Disease. Int J Mol Sci 2022; 23:ijms23158733. [PMID: 35955868 PMCID: PMC9368914 DOI: 10.3390/ijms23158733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 07/24/2022] [Accepted: 07/26/2022] [Indexed: 11/16/2022] Open
Abstract
Impaired cerebral glucose metabolism is an early event that contributes to the pathogenesis of Alzheimer's disease (AD). Importantly, restoring glucose availability by pharmacological agents or genetic manipulation has been shown to protect against Aβ toxicity, ameliorate AD pathology, and increase lifespan. Lithium, a therapeutic agent widely used as a treatment for mood disorders, has been shown to attenuate AD pathology and promote glucose metabolism in skeletal muscle. However, despite its widespread use in neuropsychiatric disorders, lithium's effects on the brain have been poorly characterized. Here we evaluated the effect of lithium on glucose metabolism in hippocampal neurons from wild-type (WT) and APPSwe/PS1ΔE9 (APP/PS1) mice. Our results showed that lithium significantly stimulates glucose uptake and replenishes ATP levels by preferential oxidation of glucose through glycolysis in neurons from WT mice. This increase was also accompanied by a strong increase in glucose transporter 3 (Glut3), the major carrier responsible for glucose uptake in neurons. Similarly, using hippocampal slices from APP-PS1 mice, we demonstrate that lithium increases glucose uptake, glycolytic rate, and the ATP:ADP ratio in a process that also involves the activation of AMPK. Together, our findings indicate that lithium stimulates glucose metabolism and can act as a potential therapeutic agent in AD.
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Affiliation(s)
- Camila Gherardelli
- Centro de Envejecimiento y Regeneración (CARE-UC), Departamento de Biología Celular y Molecular, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
| | - Pedro Cisternas
- Instituto de Ciencias de la Salud, Universidad de O’Higgins, Rancagua 2820000, Chile
| | - Nibaldo C. Inestrosa
- Centro de Envejecimiento y Regeneración (CARE-UC), Departamento de Biología Celular y Molecular, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
- Centro de Excelencia en Biomedicina de Magallanes (CEBIMA), Universidad de Magallanes, Punta Arenas 6210427, Chile
- Correspondence: ; Tel.: +56-966078961
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11
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Vacher M, Porter T, Milicic L, Bourgeat P, Dore V, Villemagne VL, Laws SM, Doecke JD. A Targeted Association Study of Blood-Brain Barrier Gene SNPs and Brain Atrophy. J Alzheimers Dis 2022; 86:1817-1829. [DOI: 10.3233/jad-210644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: The blood-brain barrier (BBB) is formed by a high-density lining of endothelial cells, providing a border between circulating blood and the brain interstitial fluid. This structure plays a key role in protecting the brain microenvironment by restricting passage of certain molecules and circulating pathogens. Objective: To identify associations between brain volumetric changes and a set of 355 BBB-related single nucleotide polymorphisms (SNP). Method: In a population of 721 unrelated individuals, linear mixed effect models were used to assess if specific variants were linked to regional rates of atrophy over a 12-year time span. Four brain regions were investigated, including cortical grey matter, cortical white matter, ventricle, and hippocampus. Further, we also investigated the potential impact of history of hypertension, diabetes, and the incidence of stroke on regional brain volume change. Results: History of hypertension, diabetes, and stroke was not associated with longitudinal brain volume change. However, we identified a series of genetic variants associated with regional brain volume changes. The associations were independent of variation due to the APOEɛ4 allele and were significant post correction for multiple comparisons. Conclusion: This study suggests that key genes involved in the regulation of BBB integrity may be associated with longitudinal changes in specific brain regions. The derived polygenic risk scores indicate that these interactions are multigenic. Further research needs to be conducted to investigate how BBB functions maybe compromised by genetic variation.
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Affiliation(s)
- Michael Vacher
- CSIRO Health and Biosecurity, Australian e-Health Research Centre, Floreat, Western Australia, Australia
- Centre for Precision Health, Edith Cowan University, Joondalup, Western Australia, Australia
- Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia
| | - Tenielle Porter
- Centre for Precision Health, Edith Cowan University, Joondalup, Western Australia, Australia
- Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia
- School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia
| | - Lidija Milicic
- Centre for Precision Health, Edith Cowan University, Joondalup, Western Australia, Australia
- Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia
| | - Pierrick Bourgeat
- CSIRO Health and Biosecurity, Australian e-Health Research Centre, Herston, Queensland, Australia
| | - Vincent Dore
- CSIRO Health and Biosecurity, Australian e-Health Research Centre, Herston, Queensland, Australia
| | - Victor L Villemagne
- Department of Molecular Imaging & Therapy and Centre for PET, Austin Health, Heidelberg, Victoria, Australia
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Simon M. Laws
- Centre for Precision Health, Edith Cowan University, Joondalup, Western Australia, Australia
- Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia
- School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia
| | - James D. Doecke
- CSIRO Health and Biosecurity, Australian e-Health Research Centre, Herston, Queensland, Australia
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12
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Assessment of Retinal Microvascular Alterations in Individuals with Amnestic and Non-Amnestic Mild Cognitive Impairment using Optical Coherence Tomography Angiography. Retina 2022; 42:1338-1346. [DOI: 10.1097/iae.0000000000003458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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13
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Jacobs HIL, O'Donnell A, Satizabal CL, Lois C, Kojis D, Hanseeuw BJ, Thibault E, Sanchez JS, Buckley RF, Yang Q, DeCarli C, Killiany R, Sargurupremraj M, Sperling RA, Johnson KA, Beiser AS, Seshadri S. Associations Between Brainstem Volume and Alzheimer's Disease Pathology in Middle-Aged Individuals of the Framingham Heart Study. J Alzheimers Dis 2022; 86:1603-1609. [PMID: 35213372 PMCID: PMC9038711 DOI: 10.3233/jad-215372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The brainstem is among the first regions to accumulate Alzheimer's disease (AD)-related hyperphosphorylated tau pathology during aging. We aimed to examine associations between brainstem volume and neocortical amyloid-β or tau pathology in 271 middle-aged clinically normal individuals of the Framingham Heart Study who underwent MRI and PET imaging. Lower volume of the medulla, pons, or midbrain was associated with greater neocortical amyloid burden. No associations were detected between brainstem volumes and tau deposition. Our results support the hypothesis that lower brainstem volumes are associated with initial AD-related processes and may signal preclinical AD pathology.
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Affiliation(s)
- Heidi I L Jacobs
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, Netherlands
- Gordon Center for Medical Imaging, Boston, MA, USA
| | - Adrienne O'Donnell
- Boston University School of Public Health, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Claudia L Satizabal
- The Framingham Heart Study, Framingham, MA, USA
- Boston University School of Medicine, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Cristina Lois
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Gordon Center for Medical Imaging, Boston, MA, USA
| | - Daniel Kojis
- Boston University School of Public Health, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Bernard J Hanseeuw
- Massachusetts General Hospital, Boston, MA, USA
- Gordon Center for Medical Imaging, Boston, MA, USA
- Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Emma Thibault
- Massachusetts General Hospital, Boston, MA, USA
- Gordon Center for Medical Imaging, Boston, MA, USA
| | - Justin S Sanchez
- Massachusetts General Hospital, Boston, MA, USA
- Gordon Center for Medical Imaging, Boston, MA, USA
| | - Rachel F Buckley
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Melbourne School of Psychological Sciences, University of Melbourne, Victoria, Australia
| | - Qiong Yang
- Boston University School of Public Health, Boston, MA, USA
| | | | - Ron Killiany
- Boston University School of Medicine, Boston, MA, USA
| | - Muralidharan Sargurupremraj
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Reisa A Sperling
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | - Keith A Johnson
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Gordon Center for Medical Imaging, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | - Alexa S Beiser
- Boston University School of Public Health, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Sudha Seshadri
- The Framingham Heart Study, Framingham, MA, USA
- Boston University School of Medicine, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
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14
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Riemer M, Wolbers T, van Rijn H. Age-related changes in time perception: The impact of naturalistic environments and retrospective judgements on timing performance. Q J Exp Psychol (Hove) 2021; 74:2002-2012. [PMID: 34024221 PMCID: PMC8450996 DOI: 10.1177/17470218211023362] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/23/2021] [Accepted: 04/19/2021] [Indexed: 01/02/2023]
Abstract
Reduced timing abilities have been reported in older adults and are associated with pathological cognitive decline. However, time perception experiments often lack ecological validity. Especially the reduced complexity of experimental stimuli and the participants' awareness of the time-related nature of the task can influence lab-assessed timing performance and thereby conceal age-related differences. An approximation of more naturalistic paradigms can provide important information about age-related changes in timing abilities. To determine the impact of higher ecological validity on timing experiments, we implemented a paradigm that allowed us to test (1) the effect of embedding the to-be-timed stimuli within a naturalistic visual scene and (2) the effect of retrospective time judgements, which are more common in real life than prospective judgements. The results show that compared with out-of-context stimuli, younger adults benefit from a naturalistic embedding of stimuli (reflected in higher precision and less errors), whereas the performance of older adults is reduced when confronted with naturalistic stimuli. Differences between retrospective and prospective time judgements were not modulated by age. We conclude that, potentially driven by difficulties in suppressing temporally irrelevant environmental information, the contextual embedding of naturalistic stimuli can affect the degree to which age influences the performance in time perception tasks.
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Affiliation(s)
- Martin Riemer
- Department of Experimental Psychology, University of Groningen, Groningen, The Netherlands
- Aging & Cognition Research Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany
| | - Thomas Wolbers
- Aging & Cognition Research Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany
| | - Hedderik van Rijn
- Department of Experimental Psychology, University of Groningen, Groningen, The Netherlands
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15
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Huang Y, Li L, Jiang J. Radiogenomics of Alzheimer's disease: exploring gene related metabolic imaging markers. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5772-5775. [PMID: 34892431 DOI: 10.1109/embc46164.2021.9630690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and considerably determined by genetic factors. Fluorodeoxyglucose positron emission tomography (FDG-PET) can reflect the functional state of glucose metabolism in the brain, and radiomic features of FDG-PET were considered as important imaging markers in AD. However, radiomic features are not highly interpretable, especially lack of explanation of underlying biological and molecular mechanisms. Therefore, this study used radiogenomics analysis to explore prognostic metabolic imaging markers by associating radiomics features and genetic data. In the study, we used the FDG-PET images and genotype data of 389 subjects (Cohort B) enrolled in the ADNI, including 109 AD, 134 healthy controls (HCs), 72 MCI non-converters (MCI-nc) and 74 MCI converters (MCI-c). Firstly, we performed a Genome-wide association study (GWAS) on the genotype data of 998 subjects (Cohort A), including 632 AD and 366 HCs after quality control (QC) steps to identify susceptibility loci as the gene features. Secondly, radiomics features were extracted from the preprocessed PET images. Thirdly, two-sample t-test, rank sum test and F-score were regarded as the feature selection step to select effective radiomic features. Fourthly, a support vector machine (SVM) was used to test the ability of the radiomic features to classify HCs, MCI and AD patients. Finally, we performed the Spearman correlation analysis on the genetic data and radiomic features. As a result, we identified rs429358 and rs2075650 as genome-wide significant signals. The radiomic approach achieved good classification abilities. Two prognostic FDG-PET radiomic features in the amygdala were proven to be correlated with the genetic data.
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16
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Brain age and Alzheimer's-like atrophy are domain-specific predictors of cognitive impairment in Parkinson's disease. Neurobiol Aging 2021; 109:31-42. [PMID: 34649002 DOI: 10.1016/j.neurobiolaging.2021.08.020] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 08/27/2021] [Accepted: 08/31/2021] [Indexed: 11/20/2022]
Abstract
Recently, it was shown that patients with Parkinson's disease (PD) who exhibit an "Alzheimer's disease (AD)-like" pattern of brain atrophy are at greater risk for future cognitive decline. This study aimed to investigate whether this association is domain-specific and whether atrophy associated with brain aging also relates to cognitive impairment in PD. SPARE-AD, an MRI index capturing AD-like atrophy, and atrophy-based estimates of brain age were computed from longitudinal structural imaging data of 178 PD patients and 84 healthy subjects from the LANDSCAPE cohort. All patients underwent an extensive neuropsychological test battery. Patients diagnosed with mild cognitive impairment or dementia were found to have higher SPARE-AD scores as compared to patients with normal cognition and healthy controls. All patient groups showed increased brain age. SPARE-AD predicted impairment in memory, language and executive functions, whereas advanced brain age was associated with deficits in attention and working memory. Data suggest that SPARE-AD and brain age are differentially related to domain-specific cognitive decline in PD. The underlying pathomechanisms remain to be determined.
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17
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Contador J, Pérez-Millán A, Tort-Merino A, Balasa M, Falgàs N, Olives J, Castellví M, Borrego-Écija S, Bosch B, Fernández-Villullas G, Ramos-Campoy O, Antonell A, Bargalló N, Sanchez-Valle R, Sala-Llonch R, Lladó A. Longitudinal brain atrophy and CSF biomarkers in early-onset Alzheimer's disease. NEUROIMAGE-CLINICAL 2021; 32:102804. [PMID: 34474317 PMCID: PMC8405839 DOI: 10.1016/j.nicl.2021.102804] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 08/17/2021] [Accepted: 08/20/2021] [Indexed: 01/09/2023]
Abstract
There is evidence of longitudinal atrophy in posterior brain areas in early-onset Alzheimer's disease (EOAD; aged < 65 years), but no studies have been conducted in an EOAD cohort with fluid biomarkers characterization. We used 3T-MRI and Freesurfer 6.0 to investigate cortical and subcortical gray matter loss at two years in 12 EOAD patients (A + T + N + ) compared to 19 controls (A-T-N-) from the Hospital Clínic Barcelona cohort. We explored group differences in atrophy patterns and we correlated atrophy and baseline CSF-biomarkers levels in EOAD. We replicated the correlation analyses in 14 EOAD (A + T + N + ) and 55 late-onset AD (LOAD; aged ≥ 75 years; A + T + N + ) participants from the Alzheimer's disease Neuroimaging Initiative. We found that EOAD longitudinal atrophy spread with a posterior-to-anterior gradient and beyond hippocampus/amygdala. In EOAD, higher initial CSF NfL levels correlated with higher ventricular volumes at baseline. On the other hand, higher initial CSF Aβ42 levels (within pathological range) predicted higher rates of cortical loss in EOAD. In EOAD and LOAD subjects, higher CSF t-tau values at baseline predicted higher rates of subcortical atrophy. CSF p-tau did not show any significant correlation. In conclusion, posterior cortices, hippocampus and amygdala capture EOAD atrophy from early stages. CSF Aβ42 might predict cortical thinning and t-tau/NfL subcortical atrophy.
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Affiliation(s)
- José Contador
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona 08036, Spain
| | - Agnès Pérez-Millán
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona 08036, Spain
| | - Adrià Tort-Merino
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona 08036, Spain
| | - Mircea Balasa
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona 08036, Spain; Atlantic Fellow for Equity in Brain Health, Global Brain Heath Institute
| | - Neus Falgàs
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona 08036, Spain; Atlantic Fellow for Equity in Brain Health, Global Brain Heath Institute; Department of Neurology, Memory & Aging Center, Weill Institute for Neurosciences, University of California, 675 Nelson Rising Lane, Suite 190, San Francisco, CA 94158, USA
| | - Jaume Olives
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona 08036, Spain
| | - Magdalena Castellví
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona 08036, Spain
| | - Sergi Borrego-Écija
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona 08036, Spain
| | - Beatriz Bosch
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona 08036, Spain
| | - Guadalupe Fernández-Villullas
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona 08036, Spain
| | - Oscar Ramos-Campoy
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona 08036, Spain
| | - Anna Antonell
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona 08036, Spain
| | - Nuria Bargalló
- Image Diagnostic Centre, IDIBAPS, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Raquel Sanchez-Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona 08036, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas. CIBERNED, Spain
| | - Roser Sala-Llonch
- Institute of Neurosciences. Department of Biomedicine, Faculty of Medicine, University of Barcelona, Barcelona, 08036, Spain; Biomedical Imaging Group, Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain
| | - Albert Lladó
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona 08036, Spain; Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas. CIBERNED, Spain.
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Moradi K, Faghani S, Abdolalizadeh A, Khomeijani-Farahani M, Ashraf-Ganjouei A. Biological Features of Reversion from Mild Cognitive Impairment to Normal Cognition: A Study of Cerebrospinal Fluid Markers and Brain Volume. J Alzheimers Dis Rep 2021; 5:179-186. [PMID: 33981955 PMCID: PMC8075565 DOI: 10.3233/adr-200229] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/04/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Mild cognitive impairment (MCI) is a transitional condition between normal cognition and dementia. Although a significant proportion of the population with MCI experience reversion to normal cognition, it is still poorly understood. OBJECTIVE This study was designed to extend the present evidence regarding the difference between stable and reverting MCI by including whole brain atrophy measures as possible parameters involved. METHODS 405 patients diagnosed with MCI at baseline were selected. After one-year follow-up period, 337 patients (83.2%) were categorized as stable MCI and 68 patients (16.8%) reverted to cognitively normal status (reversion group). Several baseline biomarkers including cerebrospinal fluid (CSF) biomarkers of AD, including Aβ42, t-tau, and p-tau and MRI-based atrophy measurements were compared. RESULTS Participants with stable MCI demonstrated greater brain atrophy as well as lower Aβ and higher tau proteins in the CSF. The atrophy rate was found to be associated with CSF biomarkers merely in the stable group, after adjustment for confounding variables. CONCLUSION These findings provide novel evidence regarding the biological perspective of the reversion phenomenon in individuals with MCI.
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Affiliation(s)
- Kamyar Moradi
- Students Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, Tehran, Iran
| | - Shahriar Faghani
- Students Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, Tehran, Iran
| | - AmirHussein Abdolalizadeh
- Students Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Khomeijani-Farahani
- Students Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Ashraf-Ganjouei
- Students Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Interdisciplinary Neuroscience Research Program (INRP), Tehran University of Medical Sciences, Tehran, Iran
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Buvaneswari PR, Gayathri R. Deep Learning-Based Segmentation in Classification of Alzheimer’s Disease. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-020-05193-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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20
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Chang CW, Juan YS, Yang YH, Lee HY. The Relationship Between Lower Urinary Tract Symptoms and Severity of Alzheimer's Disease. Am J Alzheimers Dis Other Demen 2021; 36:1533317521992657. [PMID: 33635087 PMCID: PMC10623918 DOI: 10.1177/1533317521992657] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
INTRODUCTION Urinary incontinence (UI) is more prevalent in elderly populations with dementia than those without dementia. Alzheimer's disease (AD) is the most common cause of dementia. Urge UI, the most common type of UI in AD patients, causes more morbidity and mortality. However, it is inconvenient to obtain the report of urodynamic study from AD patient to diagnose urinary incontinence. Nevertheless, it is easier to obtain subjective or objective questionnaires from the patients or the caregivers. The data collected from the questionnaires are used to evaluate if severity of dementia is associated with urge UI and other lower urinary tract symptoms (LUTs). PATIENTS AND METHODS A total of 43 AD patients were enrolled in this study, all of whom were checked post-void residual (PVR) urine amount by sonography after voiding. The severity of dementia was evaluated by questionnaire including Cognitive Abilities Screening Instrument (CASI), Mini Mental Status Examination (MMSE), Clinical Dementia Rating (CDR), and Clinical Dementia Rating Sub-of-Box (CDR-SB). The LUTs were assessed with International Consultation of Incontinence Questionnaire (ICIQ) and Overactive bladder symptom scores (OABSS) questionnaire. Independent t test and Pearson's correlation analysis were calculated. RESULTS The average age in both AD with/without urge UI patients is 78 years old. The scores of CDR-SB, OABSS and ICIQ are significantly different in these 2 groups (p = 0.023, p = 0.003, p = 0.001; respectively). However, the neurophysiological scores of CASI, MMSE, CDR, CDR-SB is not correlated with OABSS (r = 0.047, p = 0.382; r = 0.074, p = 0.317; r = 0.087, p = 0.288; r = 0.112, p = 0.237; respectively). Interestingly, if we separate each individual symptom of OAB, there is a significant correlation between CDR-SB and urge UI score (r = 0.314, p = 0.023). CONCLUSIONS Higher lower urinary tract symptom scores are noted in AD patients with urge UI. The CDR-SB score is highly correlated with urge UI in AD patients.
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Affiliation(s)
- Che-Wei Chang
- Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung
- Department of Urology, Kaohsiung Municipal Siaogang Hospital, Kaohsiung
- Department of Urology, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung
| | - Yung-Shun Juan
- Department of Urology, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung
- Department of Urology, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung
| | - Yuan-Han Yang
- Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung
| | - Hsiang-Ying Lee
- Department of Urology, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung
- Department of Urology, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung
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21
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Cisbani G, Bazinet RP. The role of peripheral fatty acids as biomarkers for Alzheimer's disease and brain inflammation. Prostaglandins Leukot Essent Fatty Acids 2021; 164:102205. [PMID: 33271431 DOI: 10.1016/j.plefa.2020.102205] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 11/06/2020] [Accepted: 11/07/2020] [Indexed: 12/31/2022]
Abstract
Alzheimer's disease (AD) is a complex and heterogeneous neurodegenerative disease. A wide range of techniques have been proposed to facilitate early diagnosis of AD, including biomarkers from the cerebrospinal fluid and blood. Although phosphorylated tau and amyloid beta are amongst the most promising biomarkers of AD, other peripheral biomarkers have been identified and in this review we synthesize the current knowledge on circulating fatty acids. Fatty acids are involved in different biological process including neurotransmission and inflammation. Interestingly, some fatty acids appear to be modulated during disease progression, including long chain saturated fatty acids, and polyunsaturated fatty acids, such as docosahexaenoic acid . However, discrepant results have been reported in the literature and there is the need for further validation and method standardization. Nonetheless, our literature review suggests that fatty acid analyses could potentially provide a valuable source of data to further inform the pathology and progression of AD.
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Affiliation(s)
- Giulia Cisbani
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada.
| | - Richard P Bazinet
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada.
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22
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Rabin JS, Neal TE, Nierle HE, Sikkes SAM, Buckley RF, Amariglio RE, Papp KV, Rentz DM, Schultz AP, Johnson KA, Sperling RA, Hedden T. Multiple markers contribute to risk of progression from normal to mild cognitive impairment. NEUROIMAGE-CLINICAL 2020; 28:102400. [PMID: 32919366 PMCID: PMC7491146 DOI: 10.1016/j.nicl.2020.102400] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 08/17/2020] [Accepted: 08/25/2020] [Indexed: 11/24/2022]
Abstract
OBJECTIVE To identify a parsimonious set of markers that optimally predicts subsequent clinical progression from normal to mild cognitive impairment (MCI). METHODS 250 clinically normal adults (mean age = 73.6 years, SD = 6.0) from the Harvard Aging Brain Study were assessed at baseline on a wide set of markers, including magnetic resonance imaging markers of gray matter thickness and volume, white matter lesions, fractional anisotropy, resting state functional connectivity, positron emission tomography markers of glucose metabolism and β-amyloid (Aβ) burden, and a measure of vascular risk. Participants were also tested annually on a battery of clinical and cognitive tests (median follow-up = 5.0 years, SD = 1.66). We applied least absolute shrinkage and selection operator (LASSO) Cox models to determine the minimum set of non-redundant markers that predicts subsequent clinical progression from normal to MCI, adjusting for age, sex, and education. RESULTS 23 participants (9.2%) progressed to MCI over the study period (mean years of follow-up to diagnosis = 3.96, SD = 1.89). Progression was predicted by several brain markers, including reduced entorhinal thickness (hazard ratio, HR = 1.73), greater Aβ burden (HR = 1.58), lower default network connectivity (HR = 1.42), and smaller hippocampal volume (HR = 1.30). When cognitive test scores were added to the model, the aforementioned neuroimaging markers remained significant and lower striatum volume as well as lower scores on baseline memory and processing speed tests additionally contributed to progression. CONCLUSION Among a large set of brain, vascular and cognitive markers, a subset of markers independently predicted progression from normal to MCI. These markers may enhance risk stratification by identifying clinically normal individuals who are most likely to develop clinical symptoms and would likely benefit most from therapeutic intervention.
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Affiliation(s)
- Jennifer S Rabin
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02144, USA; Harquail Centre for Neuromodulation and Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada; Department of Medicine (Neurology), University of Toronto, Toronto, ON M5S 3H2, Canada
| | - Taylor E Neal
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02144, USA; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Hannah E Nierle
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02144, USA; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sietske A M Sikkes
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit, The Netherlands; Department of Epidemiology and Biostatistics, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, The Netherlands; Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02144, USA
| | - Rachel F Buckley
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02144, USA; Florey Institutes of Neuroscience and Mental Health, Melbourne and Melbourne School of Psychological Science, University of Melbourne, Melbourne, Australia; Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Rebecca E Amariglio
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02144, USA; Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Kathryn V Papp
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02144, USA; Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Dorene M Rentz
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02144, USA; Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Aaron P Schultz
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02144, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - Keith A Johnson
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA; Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Boston, MA 02144, USA
| | - Reisa A Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02144, USA; Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - Trey Hedden
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA.
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Cavedoni S, Chirico A, Pedroli E, Cipresso P, Riva G. Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality. Front Hum Neurosci 2020; 14:245. [PMID: 32848660 PMCID: PMC7396670 DOI: 10.3389/fnhum.2020.00245] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 06/02/2020] [Indexed: 01/16/2023] Open
Abstract
Elderly people affected by Mild Cognitive Impairment (MCI) usually report a perceived decline in cognitive functions that deeply impacts their quality of life. This subtle waning, although it cannot be diagnosable as dementia, is noted by caregivers on the basis of their relative’s behaviors. Crucially, if this condition is also not detected in time by clinicians, it can easily turn into dementia. Thus, early detection of MCI is strongly needed. Classical neuropsychological measures – underlying a categorical model of diagnosis - could be integrated with a dimensional assessment approach involving Virtual Reality (VR) and Artificial Intelligence (AI). VR can be used to create highly ecologically controlled simulations resembling the daily life contexts in which patients’ daily instrumental activities (IADL) usually take place. Clinicians can record patients’ kinematics, particularly gait, while performing IADL (Digital Biomarkers). Then, Artificial Intelligence employs Machine Learning (ML) to analyze them in combination with clinical and neuropsychological data. This integrated computational approach would enable the creation of a predictive model to identify specific patterns of cognitive and motor impairment in MCI. Therefore, this new dimensional cognitive-behavioral assessment would reveal elderly people’s neural alterations and impaired cognitive functions, typical of MCI and dementia, even in early stages for more time-sensitive interventions.
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Affiliation(s)
- Silvia Cavedoni
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy
| | - Alice Chirico
- Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy
| | - Elisa Pedroli
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy.,Faculty of Psychology, eCampus University, Novedrate, Italy
| | - Pietro Cipresso
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy.,Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy
| | - Giuseppe Riva
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy.,Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy
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Casamitjana A, Petrone P, Molinuevo JL, Gispert JD, Vilaplana V. Projection to Latent Spaces Disentangles Pathological Effects on Brain Morphology in the Asymptomatic Phase of Alzheimer's Disease. Front Neurol 2020; 11:648. [PMID: 32849173 PMCID: PMC7399334 DOI: 10.3389/fneur.2020.00648] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 06/02/2020] [Indexed: 01/14/2023] Open
Abstract
Alzheimer's disease (AD) continuum is defined as a cascade of several neuropathological processes that can be measured using biomarkers, such as cerebrospinal fluid (CSF) levels of Aβ, p-tau, and t-tau. In parallel, brain anatomy can be characterized through imaging techniques, such as magnetic resonance imaging (MRI). In this work we relate both sets of measurements and seek associations between biomarkers and the brain structure that can be indicative of AD progression. The goal is to uncover underlying multivariate effects of AD pathology on regional brain morphological information. For this purpose, we used the projection to latent structures (PLS) method. Using PLS, we found a low dimensional latent space that best describes the covariance between both sets of measurements on the same subjects. Possible confounder effects (age and sex) on brain morphology are included in the model and regressed out using an orthogonal PLS model. We looked for statistically significant correlations between brain morphology and CSF biomarkers that explain part of the volumetric variance at each region-of-interest (ROI). Furthermore, we used a clustering technique to discover a small set of CSF-related patterns describing the AD continuum. We applied this technique to the study of subjects in the whole AD continuum, from the pre-clinical asymptomatic stages all the way through to the symptomatic groups. Subsequent analyses involved splitting the course of the disease into diagnostic categories: cognitively unimpaired subjects (CU), mild cognitively impaired subjects (MCI), and subjects with dementia (AD-dementia), where all symptoms were due to AD.
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Affiliation(s)
- Adrià Casamitjana
- Image and Video Processing Unit, Department of Signal Theory and Communications, UPCBarcelona Tech, Barcelona, Spain
| | - Paula Petrone
- Barcelonabeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - José Luis Molinuevo
- Barcelonabeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain.,Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain
| | - Juan Domingo Gispert
- Barcelonabeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain.,CIBER de Bioengeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Verónica Vilaplana
- Image and Video Processing Unit, Department of Signal Theory and Communications, UPCBarcelona Tech, Barcelona, Spain
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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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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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La Joie R, Visani AV, Baker SL, Brown JA, Bourakova V, Cha J, Chaudhary K, Edwards L, Iaccarino L, Janabi M, Lesman-Segev OH, Miller ZA, Perry DC, O'Neil JP, Pham J, Rojas JC, Rosen HJ, Seeley WW, Tsai RM, Miller BL, Jagust WJ, Rabinovici GD. Prospective longitudinal atrophy in Alzheimer's disease correlates with the intensity and topography of baseline tau-PET. Sci Transl Med 2020; 12:eaau5732. [PMID: 31894103 PMCID: PMC7035952 DOI: 10.1126/scitranslmed.aau5732] [Citation(s) in RCA: 300] [Impact Index Per Article: 75.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 09/13/2019] [Accepted: 11/13/2019] [Indexed: 12/16/2022]
Abstract
β-Amyloid plaques and tau-containing neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's disease (AD) and are thought to play crucial roles in a neurodegenerative cascade leading to dementia. Both lesions can now be visualized in vivo using positron emission tomography (PET) radiotracers, opening new opportunities to study disease mechanisms and improve patients' diagnostic and prognostic evaluation. In a group of 32 patients at early symptomatic AD stages, we tested whether β-amyloid and tau-PET could predict subsequent brain atrophy measured using longitudinal magnetic resonance imaging acquired at the time of PET and 15 months later. Quantitative analyses showed that the global intensity of tau-PET, but not β-amyloid-PET, signal predicted the rate of subsequent atrophy, independent of baseline cortical thickness. Additional investigations demonstrated that the specific distribution of tau-PET signal was a strong indicator of the topography of future atrophy at the single patient level and that the relationship between baseline tau-PET and subsequent atrophy was particularly strong in younger patients. These data support disease models in which tau pathology is a major driver of local neurodegeneration and highlight the relevance of tau-PET as a precision medicine tool to help predict individual patient's progression and design future clinical trials.
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Affiliation(s)
- Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
| | - Adrienne V Visani
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Suzanne L Baker
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jesse A Brown
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Viktoriya Bourakova
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jungho Cha
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Kiran Chaudhary
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Lauren Edwards
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Leonardo Iaccarino
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Mustafa Janabi
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Orit H Lesman-Segev
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Zachary A Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - David C Perry
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - James P O'Neil
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Julie Pham
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Julio C Rojas
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Howard J Rosen
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Richard M Tsai
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - William J Jagust
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
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Gupta Y, Lama RK, Kwon GR. Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers. Front Comput Neurosci 2019; 13:72. [PMID: 31680923 PMCID: PMC6805777 DOI: 10.3389/fncom.2019.00072] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 10/01/2019] [Indexed: 01/15/2023] Open
Abstract
Alzheimer's disease (AD), including its mild cognitive impairment (MCI) phase that may or may not progress into the AD, is the most ordinary form of dementia. It is extremely important to correctly identify patients during the MCI stage because this is the phase where AD may or may not develop. Thus, it is crucial to predict outcomes during this phase. Thus far, many researchers have worked on only using a single modality of a biomarker for the diagnosis of AD or MCI. Although recent studies show that a combination of one or more different biomarkers may provide complementary information for the diagnosis, it also increases the classification accuracy distinguishing between different groups. In this paper, we propose a novel machine learning-based framework to discriminate subjects with AD or MCI utilizing a combination of four different biomarkers: fluorodeoxyglucose positron emission tomography (FDG-PET), structural magnetic resonance imaging (sMRI), cerebrospinal fluid (CSF) protein levels, and Apolipoprotein-E (APOE) genotype. The Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset was used in this study. In total, there were 158 subjects for whom all four modalities of biomarker were available. Of the 158 subjects, 38 subjects were in the AD group, 82 subjects were in MCI groups (including 46 in MCIc [MCI converted; conversion to AD within 24 months of time period], and 36 in MCIs [MCI stable; no conversion to AD within 24 months of time period]), and the remaining 38 subjects were in the healthy control (HC) group. For each image, we extracted 246 regions of interest (as features) using the Brainnetome template image and NiftyReg toolbox, and later we combined these features with three CSF and two APOE genotype features obtained from the ADNI website for each subject using early fusion technique. Here, a different kernel-based multiclass support vector machine (SVM) classifier with a grid-search method was applied. Before passing the obtained features to the classifier, we have used truncated singular value decomposition (Truncated SVD) dimensionality reduction technique to reduce high dimensional features into a lower-dimensional feature. As a result, our combined method achieved an area under the receiver operating characteristic (AU-ROC) curve of 98.33, 93.59, 96.83, 94.64, 96.43, and 95.24% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIs, AD vs. MCIc, HC vs. MCIc, and HC vs. MCIs subjects which are high relative to single modality results and other state-of-the-art approaches. Moreover, combined multimodal methods have improved the classification performance over the unimodal classification.
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Nosheny RL, Insel PS, Mattsson N, Tosun D, Buckley S, Truran D, Schuff N, Aisen PS, Weiner MW. Associations among amyloid status, age, and longitudinal regional brain atrophy in cognitively unimpaired older adults. Neurobiol Aging 2019; 82:110-119. [PMID: 31437719 PMCID: PMC7198229 DOI: 10.1016/j.neurobiolaging.2019.07.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 06/28/2019] [Accepted: 07/07/2019] [Indexed: 01/18/2023]
Abstract
The goal of this study was to compare regional brain atrophy patterns in cognitively unimpaired (CU) older adults with and without brain accumulation of amyloid-β (Aβ) to elucidate contributions of Aβ, age, and other variables to atrophy rates. In 80 CU participants from the Alzheimer's Disease Neuroimaging Initiative, we determined effects of Aβ and age on longitudinal, regional atrophy rates, while accounting for confounding variables including sex, APOE ε4 genotype, white matter lesions, and cerebrospinal fluid total and phosphorylated tau levels. We not only found overlapping patterns of atrophy in Aβ+ versus Aβ- participants but also identified regions where atrophy pattern differed between the 2 groups. Higher Aβ load was associated with increased longitudinal atrophy in the entorhinal cortex, amygdala, and hippocampus, even when accounting for age and other variables. Age was associated with atrophy in insula, fusiform gyrus, and isthmus cingulate, even when accounting for Aβ. We found age by Aβ interactions in the postcentral gyrus and lateral orbitofrontal cortex. These results elucidate the separate and related effects of age, Aβ, and other important variables on longitudinal brain atrophy rates in CU older adults.
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Affiliation(s)
- Rachel L Nosheny
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Psychiatry, University of California, CA, USA.
| | - Philip S Insel
- Department of Clinical Sciences Malmö, Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Niklas Mattsson
- Department of Clinical Sciences Malmö, Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Duygu Tosun
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology and Biomedical Imaging, University of California, CA, USA
| | - Shannon Buckley
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Diana Truran
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - N Schuff
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine of USC, San Diego, CA, USA
| | - Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Psychiatry, University of California, CA, USA; Department of Radiology and Biomedical Imaging, University of California, CA, USA
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Yagi T, Kanekiyo M, Ito J, Ihara R, Suzuki K, Iwata A, Iwatsubo T, Aoshima K. Identification of prognostic factors to predict cognitive decline of patients with early Alzheimer's disease in the Japanese Alzheimer's Disease Neuroimaging Initiative study. ALZHEIMERS & DEMENTIA-TRANSLATIONAL RESEARCH & CLINICAL INTERVENTIONS 2019; 5:364-373. [PMID: 31440579 PMCID: PMC6698925 DOI: 10.1016/j.trci.2019.06.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Introduction The objective of this study was to determine the factors including neuropsychological test performances and cerebrospinal fluid (CSF) biomarkers which can predict disease progression of early Alzheimer's disease (AD) in a Japanese population. Methods The group classification on early AD population in both Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) and North American ADNI (NA-ADNI) was performed using the inclusion criteria including brain amyloid positivity on positron emission tomography or CSF. Participants with early AD from each cohort were stratified into two groups based on a cutoff 1.0 of Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) change at month 24 (m24): participants in "progress group" have CDR-SB change ≥ 1.0 and participants in "stable group" have CDR-SB change < 1.0. Then, we performed identification of prognostic factors from baseline items including neuropsychological scores (Assessment Scale-Cognitive Subscale[ADAS-cog 13], Mini-Mental State Examination (MMSE), CDR, FAQ, and Geriatric Depression Scale ), CSF markers (t-tau, p-tau, and beta-amyloid 1-42), vital signs (body weight, pulse rate, etc.,), by using two statistical approaches, Welch's t-test and simple linear regression by ordinary least squares. Comparisons between participants with J-ADNI and participants with NA-ADNI were also performed. Results Trends of CDR-SB changes were very similar between J-ADNI and NA-ADNI early AD population enrolled in this study. Baseline levels of CSF t-tau, p-tau, Mini-Mental State Examination, FAQ, and ADAS-cog13 were identified as prognostic factors in both J-ADNI and NA-ADNI. Based on a detailed subscale analysis on ADAS-cog13, four subscales (Q1: word recall, Q3: construction, Q4: delayed word recall, and Q8: word recognition) were identified as prognostic factors in both J-ADNI and NA-ADNI. Discussion Characterizing population with early AD can provide benefits for promoting efficiency in conducting AD clinical trials for disease-modifying treatments. Thus, implementing these prognostic factors into clinical trials may be potentially a good method to enrich participants with early AD who are suitable for evaluating treatment effects.
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Affiliation(s)
- Takuya Yagi
- Eisai Co., Ltd., Koishikawa, Bunkyo-ku, Tokyo, Japan
| | | | - Junichi Ito
- Eisai Co., Ltd., Tokodai, Tsukuba-shi, Ibaraki, Japan
| | - Ryoko Ihara
- Unit for Early and Exploratory Clinical Development, The University of Tokyo Hospital, Tokyo, Japan
| | - Kazushi Suzuki
- Unit for Early and Exploratory Clinical Development, The University of Tokyo Hospital, Tokyo, Japan
| | - Atsushi Iwata
- Department of Neurology, The University of Tokyo Hospital, Tokyo, Japan
| | - Takeshi Iwatsubo
- Department of Neuropathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ken Aoshima
- Eisai Co., Ltd., Koishikawa, Bunkyo-ku, Tokyo, Japan
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Casamitjana A, Petrone P, Molinuevo JL, Gispert JD, Vilaplana V. Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer's Disease: A Predictive Study. IEEE J Biomed Health Inform 2019; 24:365-376. [PMID: 31380776 DOI: 10.1109/jbhi.2019.2932565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Magnetic resonance imaging (MRI) provides high resolution brain morphological information and is used as a biomarker in neurodegenerative diseases. Population studies of brain morphology often seek to identify pathological structural changes related to different diagnostic categories (e.g.: controls, mild cognitive impairment or dementia) which normally describe highly heterogeneous groups with a single categorical variable. Instead, multiple biomarkers are used as a proxy for pathology and are more powerful in capturing structural variability. Hence, using the joint modeling of brain morphology and biomarkers, we aim at describing structural changes related to any brain condition by means of few underlying processes. In this regard, we use a multivariate approach based on Projection to Latent Structures in its regression variant (PLSR) to study structural changes related to aging and AD pathology. MRI volumetric and cortical thickness measurements are used for brain morphology and cerebrospinal fluid (CSF) biomarkers (t-tau, p-tau and amyloid-beta) are used as a proxy for AD pathology. By relating both sets of measurements, PLSR finds a low-dimensional latent space describing AD pathological effects on brain structure. The proposed framework allows us to separately model aging effects on brain morphology as a confounder variable orthogonal to the pathological effect. The predictive power of the associated latent spaces (i.e., the capacity of predicting biomarker values) is assessed in a cross-validation framework.
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Liedes H, Lötjönen J, Kortelainen JM, Novak G, van Gils M, Gordon MF. Multivariate Prediction of Hippocampal Atrophy in Alzheimer's Disease. J Alzheimers Dis 2019; 68:1453-1468. [PMID: 30909211 DOI: 10.3233/jad-180484] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Hippocampal atrophy (HA) is one of the biomarkers for Alzheimer's disease (AD). OBJECTIVE To identify the best biomarkers and develop models for prediction of HA over 24 months using baseline data. METHODS The study included healthy elderly controls, subjects with mild cognitive impairment, and subjects with AD, obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI 1) and the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing (AIBL) databases. Predictor variables included cognitive and neuropsychological tests, amyloid-β, tau, and p-tau from cerebrospinal fluid samples, apolipoprotein E, and features extracted from magnetic resonance images (MRI). Least-mean-squares regression with elastic net regularization and least absolute deviation regression models were tested using cross-validation in ADNI 1. The generalizability of the models including only MRI features was evaluated by training the models with ADNI 1 and testing them with AIBL. The models including the full set of variables were not evaluated with AIBL because not all needed variables were available in it. RESULTS The models including the full set of variables performed better than the models including only MRI features (root-mean-square error (RMSE) 1.76-1.82 versus 1.93-2.08). The MRI-only models performed well when applied to the independent validation cohort (RMSE 1.66-1.71). In the prediction of dichotomized HA (fast versus slow), the models achieved a reasonable prediction accuracy (0.79-0.87). CONCLUSIONS These models can potentially help identifying subjects predicted to have a faster HA rate. This can help in selection of suitable patients into clinical trials testing disease-modifying drugs for AD.
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Affiliation(s)
- Hilkka Liedes
- VTT Technical Research Centre of Finland Ltd, Tampere, Finland
| | - Jyrki Lötjönen
- VTT Technical Research Centre of Finland Ltd, Tampere, Finland.,Combinostics Ltd, Tampere, Finland
| | | | - Gerald Novak
- Janssen Pharmaceutical Research and Development, Titusville, NJ, USA
| | - Mark van Gils
- VTT Technical Research Centre of Finland Ltd, Tampere, Finland
| | - Mark Forrest Gordon
- Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA.,Current affiliation: Teva Pharmaceuticals, Inc., Frazer, PA, USA
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Zhang Y, Liu S. Analysis of structural brain MRI and multi-parameter classification for Alzheimer's disease. ACTA ACUST UNITED AC 2018. [PMID: 28622141 DOI: 10.1515/bmt-2016-0239] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Incorporating with machine learning technology, neuroimaging markers which extracted from structural Magnetic Resonance Images (sMRI), can help distinguish Alzheimer's Disease (AD) patients from Healthy Controls (HC). In the present study, we aim to investigate differences in atrophic regions between HC and AD and apply machine learning methods to classify these two groups. T1-weighted sMRI scans of 158 patients with AD and 145 age-matched HC were acquired from the ADNI database. Five kinds of parameters (i.e. cortical thickness, surface area, gray matter volume, curvature and sulcal depth) were obtained through the preprocessing steps. The recursive feature elimination (RFE) method for support vector machine (SVM) and leave-one-out cross validation (LOOCV) were applied to determine the optimal feature dimensions. Each kind of parameter was trained by SVM algorithm to acquire a classifier, which was used to classify HC and AD ultimately. Moreover, the ROC curves were depicted for testing the classifiers' performance and the SVM classifiers of two-dimensional spaces took the top two important features as classification features for separating HC and AD to the maximum extent. The results showed that the decreased cortical thickness and gray matter volume dramatically exhibited the trend of atrophy. The key differences between AD and HC existed in the cortical thickness and gray matter volume of the entorhinal cortex and medial orbitofrontal cortex. In terms of classification results, an optimal accuracy of 90.76% was obtained via multi-parameter combination (i.e. cortical thickness, gray matter volume and surface area). Meanwhile, the receiver operating characteristic (ROC) curves and area under the curve (AUC) were also verified multi-parameter combination could reach a better classification performance (AUC=0.94) after the SVM-RFE method. The results could be well prove that multi-parameter combination could provide more useful classified features from multivariate anatomical structure than single parameter. In addition, as cortical thickness and multi-parameter combination contained more important classified information with fewer feature dimensions after feature selection, it could be optimum to separate HC from AD to take the top two important features of them to construct SVM classifiers in two-dimensional space. The proposed work is a promising approach suggesting an important role for machine-learning based diagnostic image analysis for clinical practice.
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Affiliation(s)
- Yingteng Zhang
- School of Mathematics, South China University of Technology, Guangzhou 510640, China
| | - Shenquan Liu
- School of Mathematics, South China University of Technology, Guangzhou 510640, China
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34
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Gifford KA, Liu D, Neal JE, Babicz MA, Thompson JL, Walljasper LE, Wiggins ME, Turchan M, Pechman KR, Osborn KE, Acosta LMY, Bell SP, Hohman TJ, Libon DJ, Blennow K, Zetterberg H, Jefferson AL. The 12-Word Philadelphia Verbal Learning Test Performances in Older Adults: Brain MRI and Cerebrospinal Fluid Correlates and Regression-Based Normative Data. Dement Geriatr Cogn Dis Extra 2018; 8:476-491. [PMID: 30631339 PMCID: PMC6323369 DOI: 10.1159/000494209] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 10/01/2018] [Indexed: 11/19/2022] Open
Abstract
Background/Aims This study evaluated neuroimaging and biological correlates, psychometric properties, and regression-based normative data of the 12-word Philadelphia Verbal Learning Test (PVLT), a list-learning test. Methods Vanderbilt Memory and Aging Project participants free of clinical dementia and stroke (n = 230, aged 73 ± 7 years) completed a neuropsychological protocol and brain MRI. A subset (n = 111) underwent lumbar puncture for analysis of Alzheimer's disease (AD) and axonal integrity cerebrospinal fluid (CSF) biomarkers. Regression models related PVLT indices to MRI and CSF biomarkers adjusting for age, sex, race/ethnicity, education, APOE-ε4 carrier status, cognitive status, and intracranial volume (MRI models). Secondary analyses were restricted to participants with normal cognition (NC; n = 127), from which regression-based normative data were generated. Results Lower PVLT performances were associated with smaller medial temporal lobe volumes (p < 0.05) and higher CSF tau concentrations (p < 0.04). Among NC, PVLT indices were associated with white matter hyperintensities on MRI and an axonal injury biomarker (CSF neurofilament light; p < 0.03). Conclusion The PVLT appears sensitive to markers of neurodegeneration, including temporal regions affected by AD. Conversely, in cognitively normal older adults, PVLT performance seems to relate to white matter disease and axonal injury, perhaps reflecting non-AD pathways to cognitive change. Enhanced normative data enrich the clinical utility of this tool.
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Affiliation(s)
- Katherine A Gifford
- Vanderbilt Memory and Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Dandan Liu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jacquelyn E Neal
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Michelle A Babicz
- Vanderbilt Memory and Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Psychology, University of Houston, Houston, Texas, USA
| | - Jennifer L Thompson
- Vanderbilt Memory and Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lily E Walljasper
- Vanderbilt Memory and Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Margaret E Wiggins
- Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida, USA
| | - Maxim Turchan
- Vanderbilt Memory and Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kimberly R Pechman
- Vanderbilt Memory and Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Katie E Osborn
- Vanderbilt Memory and Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lealani Mae Y Acosta
- Vanderbilt Memory and Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Susan P Bell
- Vanderbilt Memory and Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Divisions of Cardiovascular and Geriatric Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - David J Libon
- Department of Geriatrics and Gerontology and Psychology, New Jersey Institute for Successful Aging, School of Osteopathic Medicine, Rowan University, Stratford, New Jersey, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Molecular Neuroscience, UCL Institute of Neurology, London, United Kingdom.,UK Dementia Research Institute at UCL, London, United Kingdom
| | - Angela L Jefferson
- Vanderbilt Memory and Alzheimer's Center, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Xu L, Liang G, Liao C, Chen GD, Chang CC. An Efficient Classifier for Alzheimer's Disease Genes Identification. Molecules 2018; 23:molecules23123140. [PMID: 30501121 PMCID: PMC6321377 DOI: 10.3390/molecules23123140] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 11/17/2018] [Accepted: 11/19/2018] [Indexed: 11/16/2022] Open
Abstract
Alzheimer’s disease (AD) is considered to one of 10 key diseases leading to death in humans. AD is considered the main cause of brain degeneration, and will lead to dementia. It is beneficial for affected patients to be diagnosed with the disease at an early stage so that efforts to manage the patient can begin as soon as possible. Most existing protocols diagnose AD by way of magnetic resonance imaging (MRI). However, because the size of the images produced is large, existing techniques that employ MRI technology are expensive and time-consuming to perform. With this in mind, in the current study, AD is predicted instead by the use of a support vector machine (SVM) method based on gene-coding protein sequence information. In our proposed method, the frequency of two consecutive amino acids is used to describe the sequence information. The accuracy of the proposed method for identifying AD is 85.7%, which is demonstrated by the obtained experimental results. The experimental results also show that the sequence information of gene-coding proteins can be used to predict AD.
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Affiliation(s)
- Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518055, China.
| | - Guangmin Liang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518055, China.
| | - Changrui Liao
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
| | - Gin-Den Chen
- Department of Obstetrics and Gynecology, Chung Shan Medical University Hospital, Taichung 40201, Taiwan.
| | - Chi-Chang Chang
- School of Medical Informatics, Chung Shan Medical University, Taichung 40201, Taiwan.
- IT Office, Chung Shan Medical University Hospital, Taichung 40201, Taiwan.
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36
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Feature Selection and Transfer Learning for Alzheimer’s Disease Clinical Diagnosis. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8081372] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background and Purpose: A majority studies on diagnosis of Alzheimer’s Disease (AD) are based on an assumption: the training and testing data are drawn from the same distribution. However, in the diagnosis of AD and mild cognitive impairment (MCI), this identical-distribution assumption may not hold. To solve this problem, we utilize the transfer learning method into the diagnosis of AD. Methods: The MR (Magnetic Resonance) images were segmented using spm-Dartel toolbox and registrated with Automatic Anatomical Labeling (AAL) atlas, then the gray matter (GM) tissue volume of the anatomical region were computed as characteristic parameter. The information gain was introduced for feature selection. The TrAdaboost algorithm was used to classify AD, MCI, and normal controls (NC) data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, meanwhile, the “knowledge” learned from ADNI was transferred to AD samples from local hospital. The classification accuracy, sensitivity and specificity were calculated and compared with four classical algorithms. Results: In the experiment of transfer task: AD to MCI, 177 AD and 40NC subjects were grouped as training data; 245 MCI and 45 remaining NC subjects were combined as testing data, the highest accuracy achieved 85.4%, higher than the other four classical algorithms. Meanwhile, feature selection that is based on information gain reduced the features from 90 to 7, controlled the redundancy efficiently. In the experiment of transfer task: ADNI to local hospital data, the highest accuracy achieved 93.7%, and the specificity achieved 100%. Conclusions: The experimental results showed that our algorithm has a clear advantage over classic classification methods with higher accuracy and less fluctuation.
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Yao X, Yan J, Liu K, Kim S, Nho K, Risacher SL, Greene CS, Moore JH, Saykin AJ, Shen L. Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules. Bioinformatics 2018; 33:3250-3257. [PMID: 28575147 DOI: 10.1093/bioinformatics/btx344] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 05/26/2017] [Indexed: 11/13/2022] Open
Abstract
Motivation Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity. Results We propose a novel module identification framework for imaging genetic studies using the tissue-specific functional interaction network. Our method includes three steps: (i) re-prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; (ii) detect densely connected modules based on interactions among top re-prioritized genes; and (iii) identify phenotype-relevant modules enriched by top GWAS findings. We demonstrate our method on the GWAS of [18F]FDG-PET measures in the amygdala region using the imaging genetic data from the Alzheimer's Disease Neuroimaging Initiative, and map the GWAS results onto the amygdala-specific functional interaction network. The proposed network-based GWAS method can effectively detect densely connected modules enriched by top GWAS findings. Tissue-specific functional network can provide precise context to help explore the collective effects of genes with biologically meaningful interactions specific to the studied phenotype. Availability and implementation The R code and sample data are freely available at http://www.iu.edu/shenlab/tools/gwasmodule/. Contact shenli@iu.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiaohui Yao
- Department of BioHealth Informatics, Indiana University School of Informatics & Computing, Indianapolis, IN 46202, USA.,Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Jingwen Yan
- Department of BioHealth Informatics, Indiana University School of Informatics & Computing, Indianapolis, IN 46202, USA.,Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Kefei Liu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Sungeun Kim
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.,Department of Electrical and Computer Engineering, SUNY Oswego, NY 13126, USA
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jason H Moore
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Li Shen
- Department of BioHealth Informatics, Indiana University School of Informatics & Computing, Indianapolis, IN 46202, USA.,Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
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Ding X, Bucholc M, Wang H, Glass DH, Wang H, Clarke DH, Bjourson AJ, Dowey LRC, O'Kane M, Prasad G, Maguire L, Wong-Lin K. A hybrid computational approach for efficient Alzheimer's disease classification based on heterogeneous data. Sci Rep 2018; 8:9774. [PMID: 29950585 PMCID: PMC6021389 DOI: 10.1038/s41598-018-27997-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 06/12/2018] [Indexed: 12/20/2022] Open
Abstract
There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer's disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approach for AD classification and evaluate it on the heterogeneous longitudinal AIBL dataset. Specifically, using clinical dementia rating as an index of AD severity, the most important indicators (mini-mental state examination, logical memory recall, grey matter and cerebrospinal volumes from MRI and active voxels from PiB-PET brain scans, ApoE, and age) can be automatically identified from parallel data mining algorithms. In this work, Bayesian network modelling across different time points is used to identify and visualize time-varying relationships among the significant features, and importantly, in an efficient way using only coarse-grained data. Crucially, our approach suggests key data features and their appropriate combinations that are relevant for AD severity classification with high accuracy. Overall, our study provides insights into AD developments and demonstrates the potential of our approach in supporting efficient AD diagnosis.
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Affiliation(s)
- Xuemei Ding
- Intelligent Systems Research Centre, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland, UK.
- Faculty of Mathematics and Informatics, Fujian Normal University, Fuzhou, China.
| | - Magda Bucholc
- Intelligent Systems Research Centre, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland, UK
| | - Haiying Wang
- School of Computing and Mathematics, Ulster University, Jordanstown Campus, Northern Ireland, UK
| | - David H Glass
- School of Computing and Mathematics, Ulster University, Jordanstown Campus, Northern Ireland, UK
| | - Hui Wang
- School of Computing and Mathematics, Ulster University, Jordanstown Campus, Northern Ireland, UK
| | - Dave H Clarke
- Clarke Analytics Ltd., 6 Dernville, Annabella Mallow, Cork, Ireland
| | - Anthony John Bjourson
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, C-TRIC, Ulster University, Altnagelvin Hospital, Derry~Londonderry, Northern Ireland, UK
| | - Le Roy C Dowey
- C-TRIC, Altnagelvin Hospital campus, Derry~Londonderry, Northern Ireland, UK
- School of Biomedical Sciences, Ulster University, Coleraine Campus, Northern Ireland, UK
| | - Maurice O'Kane
- C-TRIC, Altnagelvin Hospital campus, Derry~Londonderry, Northern Ireland, UK
| | - Girijesh Prasad
- Intelligent Systems Research Centre, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland, UK
| | - Liam Maguire
- Intelligent Systems Research Centre, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland, UK
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland, UK.
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39
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Gifford KA, Liu D, Neal JE, Acosta LMY, Bell SP, Wiggins ME, Wisniewski KM, Godfrey M, Logan LA, Hohman TJ, Pechman KR, Libon DJ, Blennow K, Zetterberg H, Jefferson AL. Validity and Normative Data for the Biber Figure Learning Test: A Visual Supraspan Memory Measure. Assessment 2018; 27:1320-1334. [PMID: 29809069 DOI: 10.1177/1073191118773870] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The Biber Figure Learning Test (BFLT), a visuospatial serial figure learning test, was evaluated for biological correlates and psychometric properties, and normative data were generated. Nondemented individuals (n = 332, 73 ± 7, 41% female) from the Vanderbilt Memory & Aging Project completed a comprehensive neuropsychological protocol. Adjusted regression models related BFLT indices to structural brain magnetic resonance imaging and cerebrospinal fluid (CSF) markers of brain health. Regression-based normative data were generated. Lower BFLT performances (Total Learning, Delayed Recall, Recognition) related to smaller medial temporal lobe volumes and higher CSF tau concentrations but not CSF amyloid. BFLT indices were most strongly correlated with other measures of verbal and nonverbal memory and visuospatial skills. The BFLT provides a comprehensive assessment of all aspects of visuospatial learning and memory and is sensitive to biomarkers of unhealthy brain aging. Enhanced normative data enriches the clinical utility of this visual serial figure learning test for use with older adults.
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Affiliation(s)
| | - Dandan Liu
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | - Susan P Bell
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | | | - Laura A Logan
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | | | - Kaj Blennow
- University of Gothenburg, Mölndal, Sweden.,Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- University of Gothenburg, Mölndal, Sweden.,Sahlgrenska University Hospital, Mölndal, Sweden.,UCL Institute of Neurology, Queen Square, London, UK.,UK Dementia Research Institute at UCL, London, UK
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40
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Kim J, Lee B. Identification of Alzheimer's disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine. Hum Brain Mapp 2018; 39:3728-3741. [PMID: 29736986 DOI: 10.1002/hbm.24207] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 04/18/2018] [Accepted: 04/25/2018] [Indexed: 01/06/2023] Open
Abstract
Different modalities such as structural MRI, FDG-PET, and CSF have complementary information, which is likely to be very useful for diagnosis of AD and MCI. Therefore, it is possible to develop a more effective and accurate AD/MCI automatic diagnosis method by integrating complementary information of different modalities. In this paper, we propose multi-modal sparse hierarchical extreme leaning machine (MSH-ELM). We used volume and mean intensity extracted from 93 regions of interest (ROIs) as features of MRI and FDG-PET, respectively, and used p-tau, t-tau, and A β 42 as CSF features. In detail, high-level representation was individually extracted from each of MRI, FDG-PET, and CSF using a stacked sparse extreme learning machine auto-encoder (sELM-AE). Then, another stacked sELM-AE was devised to acquire a joint hierarchical feature representation by fusing the high-level representations obtained from each modality. Finally, we classified joint hierarchical feature representation using a kernel-based extreme learning machine (KELM). The results of MSH-ELM were compared with those of conventional ELM, single kernel support vector machine (SK-SVM), multiple kernel support vector machine (MK-SVM) and stacked auto-encoder (SAE). Performance was evaluated through 10-fold cross-validation. In the classification of AD vs. HC and MCI vs. HC problem, the proposed MSH-ELM method showed mean balanced accuracies of 96.10% and 86.46%, respectively, which is much better than those of competing methods. In summary, the proposed algorithm exhibits consistently better performance than SK-SVM, ELM, MK-SVM and SAE in the two binary classification problems (AD vs. HC and MCI vs. HC).
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Affiliation(s)
- Jongin Kim
- Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea
| | - Boreom Lee
- Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea
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Yang P, Ni D, Chen S, Wang T, Wu D, Lei B. Multi-task fused sparse learning for mild cognitive impairment identification. Technol Health Care 2018; 26:437-448. [PMID: 29710750 PMCID: PMC6004967 DOI: 10.3233/thc-174587] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Brain functional connectivity network (BFCN) has been widely applied to identify biomarkers for the brain function understanding and brain diseases analysis. OBJECTIVE Building a biologically meaningful brain network is a crucial work in these applications. For this task, sparse learning has been widely applied for the network construction. If multiple time-point data is added to the brain imaging application, the disease progression pattern in the longitudinal analysis can be better revealed. METHODS A novel longitudinal analysis for MCI classification is devised based on resting-state functional magnetic resonating imaging (rs-fMRI). Specifically, this paper proposes a novel multi-task learning method to integrate fused penalty by regularization. In addition, a novel objective function is developed for fused sparse learning via smoothness constraint. RESULTS The proposed method achieves the best classification performance with an accuracy of 95.74% for baseline and 93.64% for year 1 data. CONCLUSIONS The experimental results show that our proposed method achieves quite promising classification performance.
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Affiliation(s)
- Peng Yang
- School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China
| | - Dong Ni
- School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China
| | - Siping Chen
- School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China
| | - Tianfu Wang
- School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China
| | - Donghui Wu
- Department of Geriatric Psychiatry, Shenzhen Kangning Hospital, and Shenzhen Mental Health Center, Shenzhen, Guangdong, China
| | - Baiying Lei
- School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China
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Longitudinal structural cerebral changes related to core CSF biomarkers in preclinical Alzheimer's disease: A study of two independent datasets. NEUROIMAGE-CLINICAL 2018; 19:190-201. [PMID: 30023169 PMCID: PMC6050455 DOI: 10.1016/j.nicl.2018.04.016] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 03/08/2018] [Accepted: 04/14/2018] [Indexed: 12/21/2022]
Abstract
Alzheimer's disease (AD) is characterized by an accumulation of β-amyloid (Aβ42) accompanied by brain atrophy and cognitive decline. Several recent studies have shown that Aβ42 accumulation is associated with gray matter (GM) changes prior to the development of cognitive impairment, in the so-called preclinical stage of the AD (pre-AD). It also has been proved that the GM atrophy profile is not linear, both in normal ageing but, especially, on AD. However, several other factors may influence this association and may have an impact on the generalization of results from different samples. In this work, we estimate differences in rates of GM volume change in cognitively healthy elders in association with baseline core cerebrospinal fluid (CSF) AD biomarkers, and assess to what these differences are sample dependent. We report the dependence of atrophy rates, measured in a two-year interval, on Aβ42, computed both over continuous and categorical values of Aβ42, at voxel-level (p < 0.001; k < 100) and corrected for sex, age and education. Analyses were performed jointly and separately, on two samples. The first sample was formed of 31 individuals (22 Ctrl and 9 pre-AD), aged 60–80 and recruited at the Hospital Clinic of Barcelona. The second sample was a replica of the first one with subjects selected from the ADNI dataset. We also investigated the dependence of the GM atrophy rate on the basal levels of continuous p-tau and on the p-tau/Aβ42 ratio. Correlation analyses on the whole sample showed a dependence of GM atrophy rates on Aβ42 in medial and orbital frontal, precuneus, cingulate, medial temporal regions and cerebellum. Correlations with p-tau were located in the left hippocampus, parahippocampus and striatal nuclei whereas correlation with p-tau/Aβ42 was mainly found in ventral and medial temporal areas. Regarding analyses performed separately, we found a substantial discrepancy of results between samples, illustrating the complexities of comparing two independent datasets even when using the same inclusion criteria. Such discrepancies may lead to significant differences in the sample size needed to detect a particular reduction on cerebral atrophy rates in prevention trials. Higher cognitive reserve and more advanced pathological progression in the ADNI sample could partially account for the observed discrepancies. Taken together, our findings in these two samples highlight the importance of comparing and merging independent datasets to draw more robust and generalizable conclusions on the structural changes in the preclinical stages of AD. GM atrophy rates depends differently on values of CSF Aβ42 than on CSF p-tau in the preclinical stage of AD. Discrepant results were obtained. Although nominally equivalent, samples might reflect different time-windows in the AD continuum. It is necessary a further effort to standardize CSF-biomarkers measures and thresholds to make different samples to be directly comparable.
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Key Words
- AD, Alzheimer's disease
- ADNI, Alzheimer's Disease Neuroimaging Initiative
- Alzheimer's disease
- Aβ42, amyloid beta
- CDR, Clinical Dementia Rating
- CSF biomarkers
- CSF, Cerebro-Spinal Fluid
- Ctrl, control
- DI, divergences of the longitudinal deformations
- ELISA, Enzyme-Linked ImmunoSorbent Assay
- FWE, Family Wise Error
- GM, gray matter
- HCB, Hospital Clinic Barcelona
- L, left
- Longitudinal VBM
- MMSE, Mini Mental State examination
- PLR, pairwise longitudinal registration
- Preclinical Alzheimer's disease
- R, right
- ROI, region of interest
- TIV, total intracranial volume
- VBM, voxel-based morphometry
- WM, white matter
- p-tau, phosphorylated tau
- preAD, preclinical Alzheimer's disease
- t-tau, total tau
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Lawrence E, Vegvari C, Ower A, Hadjichrysanthou C, De Wolf F, Anderson RM. A Systematic Review of Longitudinal Studies Which Measure Alzheimer's Disease Biomarkers. J Alzheimers Dis 2018; 59:1359-1379. [PMID: 28759968 PMCID: PMC5611893 DOI: 10.3233/jad-170261] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Alzheimer’s disease (AD) is a progressive and fatal neurodegenerative disease, with no effective treatment or cure. A gold standard therapy would be treatment to slow or halt disease progression; however, knowledge of causation in the early stages of AD is very limited. In order to determine effective endpoints for possible therapies, a number of quantitative surrogate markers of disease progression have been suggested, including biochemical and imaging biomarkers. The dynamics of these various surrogate markers over time, particularly in relation to disease development, are, however, not well characterized. We reviewed the literature for studies that measured cerebrospinal fluid or plasma amyloid-β and tau, or took magnetic resonance image or fluorodeoxyglucose/Pittsburgh compound B-positron electron tomography scans, in longitudinal cohort studies. We summarized the properties of the major cohort studies in various countries, commonly used diagnosis methods and study designs. We have concluded that additional studies with repeat measures over time in a representative population cohort are needed to address the gap in knowledge of AD progression. Based on our analysis, we suggest directions in which research could move in order to advance our understanding of this complex disease, including repeat biomarker measurements, standardization and increased sample sizes.
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Affiliation(s)
- Emma Lawrence
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Carolin Vegvari
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Alison Ower
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | | | - Frank De Wolf
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.,Janssen Prevention Center, Leiden, The Netherlands
| | - Roy M Anderson
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
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Rojas JC, Bang J, Lobach IV, Tsai RM, Rabinovici GD, Miller BL, Boxer AL. CSF neurofilament light chain and phosphorylated tau 181 predict disease progression in PSP. Neurology 2017; 90:e273-e281. [PMID: 29282336 DOI: 10.1212/wnl.0000000000004859] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Accepted: 10/16/2017] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE To determine the ability of CSF biomarkers to predict disease progression in progressive supranuclear palsy (PSP). METHODS We compared the ability of baseline CSF β-amyloid1-42, tau, phosphorylated tau 181 (p-tau), and neurofilament light chain (NfL) concentrations, measured by INNO-BIA AlzBio3 or ELISA, to predict 52-week changes in clinical (PSP Rating Scale [PSPRS] and Schwab and England Activities of Daily Living [SEADL]), neuropsychological, and regional brain volumes on MRI using linear mixed effects models controlled for age, sex, and baseline disease severity, and Fisher F density curves to compare effect sizes in 50 patients with PSP. Similar analyses were done using plasma NfL measured by single molecule arrays in 141 patients. RESULTS Higher CSF NfL concentration predicted more rapid decline (biomarker × time interaction) over 52 weeks in PSPRS (p = 0.004, false discovery rate-corrected) and SEADL (p = 0.008), whereas lower baseline CSF p-tau predicted faster decline on PSPRS (p = 0.004). Higher CSF tau concentrations predicted faster decline by SEADL (p = 0.004). The CSF NfL/p-tau ratio was superior for predicting change in PSPRS, compared to p-tau (p = 0.003) or NfL (p = 0.001) alone. Higher NfL concentrations in CSF or blood were associated with greater superior cerebellar peduncle atrophy (fixed effect, p ≤ 0.029 and 0.008, respectively). CONCLUSIONS Both CSF p-tau and NfL correlate with disease severity and rate of disease progression in PSP. The inverse correlation of p-tau with disease severity suggests a potentially different mechanism of tau pathology in PSP as compared to Alzheimer disease.
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Affiliation(s)
- Julio C Rojas
- From the Memory and Aging Center, Department of Neurology (J.C.R., R.M.T., G.D.R., B.L.M., A.L.B.), and Department of Epidemiology and Biostatistics, Division of Biostatistics (I.V.L.), University of California, San Francisco; and Department of Neurology (J.B.), Johns Hopkins University School of Medicine, Baltimore, MD.
| | - Jee Bang
- From the Memory and Aging Center, Department of Neurology (J.C.R., R.M.T., G.D.R., B.L.M., A.L.B.), and Department of Epidemiology and Biostatistics, Division of Biostatistics (I.V.L.), University of California, San Francisco; and Department of Neurology (J.B.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Iryna V Lobach
- From the Memory and Aging Center, Department of Neurology (J.C.R., R.M.T., G.D.R., B.L.M., A.L.B.), and Department of Epidemiology and Biostatistics, Division of Biostatistics (I.V.L.), University of California, San Francisco; and Department of Neurology (J.B.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Richard M Tsai
- From the Memory and Aging Center, Department of Neurology (J.C.R., R.M.T., G.D.R., B.L.M., A.L.B.), and Department of Epidemiology and Biostatistics, Division of Biostatistics (I.V.L.), University of California, San Francisco; and Department of Neurology (J.B.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Gil D Rabinovici
- From the Memory and Aging Center, Department of Neurology (J.C.R., R.M.T., G.D.R., B.L.M., A.L.B.), and Department of Epidemiology and Biostatistics, Division of Biostatistics (I.V.L.), University of California, San Francisco; and Department of Neurology (J.B.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Bruce L Miller
- From the Memory and Aging Center, Department of Neurology (J.C.R., R.M.T., G.D.R., B.L.M., A.L.B.), and Department of Epidemiology and Biostatistics, Division of Biostatistics (I.V.L.), University of California, San Francisco; and Department of Neurology (J.B.), Johns Hopkins University School of Medicine, Baltimore, MD
| | - Adam L Boxer
- From the Memory and Aging Center, Department of Neurology (J.C.R., R.M.T., G.D.R., B.L.M., A.L.B.), and Department of Epidemiology and Biostatistics, Division of Biostatistics (I.V.L.), University of California, San Francisco; and Department of Neurology (J.B.), Johns Hopkins University School of Medicine, Baltimore, MD
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d'Oleire Uquillas F, Jacobs HIL, Hanseeuw B, Marshall GA, Properzi M, Schultz AP, LaPoint MR, Johnson KA, Sperling RA, Vannini P. Interactive versus additive relationships between regional cortical thinning and amyloid burden in predicting clinical decline in mild AD and MCI individuals. NEUROIMAGE-CLINICAL 2017; 17:388-396. [PMID: 29159051 PMCID: PMC5683806 DOI: 10.1016/j.nicl.2017.10.034] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 09/09/2017] [Accepted: 10/28/2017] [Indexed: 11/13/2022]
Abstract
The biological mechanisms that link Beta-amyloid (Aβ) plaque deposition, neurodegeneration, and clinical decline in Alzheimer's disease (AD) dementia, have not been completely elucidated. Here we studied whether amyloid accumulation and neurodegeneration, independently or interactively, predict clinical decline over time in a group of memory impaired older individuals [diagnosed with either amnestic mild cognitive impairment (MCI), or mild AD dementia]. We found that baseline Aβ-associated cortical thinning across clusters encompassing lateral and medial temporal and parietal cortices was related to higher baseline Clinical Dementia Rating Sum-of-Boxes (CDR-SB). Baseline Aβ-associated cortical thinning also predicted CDR-SB over time. Notably, the association between CDR-SB change and cortical thickness values from the right lateral temporo-parietal cortex and right precuneus was driven by individuals with high Aβ burden. In contrast, the association between cortical thickness in the medial temporal lobe (MTL) and clinical decline was similar for individuals with high or low Aβ burden. Furthermore, amyloid pathology was a stronger predictor for clinical decline than MTL thickness. While this study validates previous findings relating AD biomarkers of neurodegeneration to clinical impairment, here we show that regions outside the MTL may be more vulnerable and specific to AD dementia. Additionally, excluding mild AD individuals revealed that these relationships remained, suggesting that lower cortical thickness values in specific regions, vulnerable to amyloid pathology, predict clinical decline already at the prodromal stage. Aβ burden is associated with cortical thinning in a pattern consistent with AD. Interaction between Aβ and neocortical thinning predicts clinical decline. MTL thickness predicts clinical decline regardless of Aβ burden. Amyloid pathology is a stronger predictor for clinical decline than MTL thickness.
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Affiliation(s)
| | - Heidi I L Jacobs
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Bernard Hanseeuw
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Department of Neurology, Saint-Luc University Hospital, Institute of Neuroscience, Université Catholique de Louvain, Brussels, Belgium
| | - Gad A Marshall
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael Properzi
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Aaron P Schultz
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Molly R LaPoint
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Keith A Johnson
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Reisa A Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Patrizia Vannini
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Discriminative multi-task feature selection for multi-modality classification of Alzheimer's disease. Brain Imaging Behav 2017; 10:739-49. [PMID: 26311394 DOI: 10.1007/s11682-015-9437-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Recently, multi-task based feature selection methods have been used in multi-modality based classification of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). However, in traditional multi-task feature selection methods, some useful discriminative information among subjects is usually not well mined for further improving the subsequent classification performance. Accordingly, in this paper, we propose a discriminative multi-task feature selection method to select the most discriminative features for multi-modality based classification of AD/MCI. Specifically, for each modality, we train a linear regression model using the corresponding modality of data, and further enforce the group-sparsity regularization on weights of those regression models for joint selection of common features across multiple modalities. Furthermore, we propose a discriminative regularization term based on the intra-class and inter-class Laplacian matrices to better use the discriminative information among subjects. To evaluate our proposed method, we perform extensive experiments on 202 subjects, including 51 AD patients, 99 MCI patients, and 52 healthy controls (HC), from the baseline MRI and FDG-PET image data of the Alzheimer's Disease Neuroimaging Initiative (ADNI). The experimental results show that our proposed method not only improves the classification performance, but also has potential to discover the disease-related biomarkers useful for diagnosis of disease, along with the comparison to several state-of-the-art methods for multi-modality based AD/MCI classification.
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Zhu X, Suk HI, Lee SW, Shen D. Canonical feature selection for joint regression and multi-class identification in Alzheimer's disease diagnosis. Brain Imaging Behav 2017; 10:818-28. [PMID: 26254746 DOI: 10.1007/s11682-015-9430-4] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Fusing information from different imaging modalities is crucial for more accurate identification of the brain state because imaging data of different modalities can provide complementary perspectives on the complex nature of brain disorders. However, most existing fusion methods often extract features independently from each modality, and then simply concatenate them into a long vector for classification, without appropriate consideration of the correlation among modalities. In this paper, we propose a novel method to transform the original features from different modalities to a common space, where the transformed features become comparable and easy to find their relation, by canonical correlation analysis. We then perform the sparse multi-task learning for discriminative feature selection by using the canonical features as regressors and penalizing a loss function with a canonical regularizer. In our experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we use Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images to jointly predict clinical scores of Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) and also identify multi-class disease status for Alzheimer's disease diagnosis. The experimental results showed that the proposed canonical feature selection method helped enhance the performance of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods.
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Affiliation(s)
- Xiaofeng Zhu
- Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Heung-Il Suk
- Department of Brain and Cognitive Engineering, Korea University, Seongbuk-gu, Republic of Korea
| | - Seong-Whan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seongbuk-gu, Republic of Korea
| | - Dinggang Shen
- Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Brain and Cognitive Engineering, Korea University, Seongbuk-gu, Republic of Korea.
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Frölich L, Peters O, Lewczuk P, Gruber O, Teipel SJ, Gertz HJ, Jahn H, Jessen F, Kurz A, Luckhaus C, Hüll M, Pantel J, Reischies FM, Schröder J, Wagner M, Rienhoff O, Wolf S, Bauer C, Schuchhardt J, Heuser I, Rüther E, Henn F, Maier W, Wiltfang J, Kornhuber J. Incremental value of biomarker combinations to predict progression of mild cognitive impairment to Alzheimer's dementia. ALZHEIMERS RESEARCH & THERAPY 2017; 9:84. [PMID: 29017593 PMCID: PMC5634868 DOI: 10.1186/s13195-017-0301-7] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 08/30/2017] [Indexed: 01/24/2023]
Abstract
Background The progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) dementia can be predicted by cognitive, neuroimaging, and cerebrospinal fluid (CSF) markers. Since most biomarkers reveal complementary information, a combination of biomarkers may increase the predictive power. We investigated which combination of the Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR)-sum-of-boxes, the word list delayed free recall from the Consortium to Establish a Registry of Dementia (CERAD) test battery, hippocampal volume (HCV), amyloid-beta1–42 (Aβ42), amyloid-beta1–40 (Aβ40) levels, the ratio of Aβ42/Aβ40, phosphorylated tau, and total tau (t-Tau) levels in the CSF best predicted a short-term conversion from MCI to AD dementia. Methods We used 115 complete datasets from MCI patients of the “Dementia Competence Network”, a German multicenter cohort study with annual follow-up up to 3 years. MCI was broadly defined to include amnestic and nonamnestic syndromes. Variables known to predict progression in MCI patients were selected a priori. Nine individual predictors were compared by receiver operating characteristic (ROC) curve analysis. ROC curves of the five best two-, three-, and four-parameter combinations were analyzed for significant superiority by a bootstrapping wrapper around a support vector machine with linear kernel. The incremental value of combinations was tested for statistical significance by comparing the specificities of the different classifiers at a given sensitivity of 85%. Results Out of 115 subjects, 28 (24.3%) with MCI progressed to AD dementia within a mean follow-up period of 25.5 months. At baseline, MCI-AD patients were no different from stable MCI in age and gender distribution, but had lower educational attainment. All single biomarkers were significantly different between the two groups at baseline. ROC curves of the individual predictors gave areas under the curve (AUC) between 0.66 and 0.77, and all single predictors were statistically superior to Aβ40. The AUC of the two-parameter combinations ranged from 0.77 to 0.81. The three-parameter combinations ranged from AUC 0.80–0.83, and the four-parameter combination from AUC 0.81–0.82. None of the predictor combinations was significantly superior to the two best single predictors (HCV and t-Tau). When maximizing the AUC differences by fixing sensitivity at 85%, the two- to four-parameter combinations were superior to HCV alone. Conclusion A combination of two biomarkers of neurodegeneration (e.g., HCV and t-Tau) is not superior over the single parameters in identifying patients with MCI who are most likely to progress to AD dementia, although there is a gradual increase in the statistical measures across increasing biomarker combinations. This may have implications for clinical diagnosis and for selecting subjects for participation in clinical trials.
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Affiliation(s)
- Lutz Frölich
- Department of Geriatric Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Zentralinstitut für Seelische Gesundheit, Quadrat J5, D-68159, Mannheim, Germany.
| | - Oliver Peters
- Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Charité, Berlin, Germany
| | - Piotr Lewczuk
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander-University of Erlangen-Nuremberg, Nuremberg, Germany.,Department of Neurodegeneration Diagnostics, Medical University of Bialystok, Bialystok, Poland
| | - Oliver Gruber
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, and German Center for Neurodegenerative Diseases (DZNE), Research Site Göttingen, Göttingen, Germany
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany.,Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University of Munich, Munich, Germany
| | - Hermann J Gertz
- Department of Psychiatry, University of Leipzig, Leipzig, Germany
| | - Holger Jahn
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg, Hamburg, Germany
| | - Frank Jessen
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany.,German Center for Neurodegenerative Diseases (DZNE), Cologne/Bonn, Germany.,Department of Psychiatry and Psychotherapy, Medical Faculty University of Cologne, Cologne, Germany
| | - Alexander Kurz
- Department of Psychiatry and Psychotherapy, Technical University of Munich, Munich, Germany
| | - Christian Luckhaus
- Department of Psychiatry and Psychotherapy, University of Düsseldorf, Düsseldorf, Germany
| | - Michael Hüll
- Center for Psychiatry, Clinic for Geriatric Psychiatry and Psychotherapy Emmendingen and Department of Psychiatry and Psychotherapy, University of Freiburg, Freiburg, Germany
| | - Johannes Pantel
- Institute of General Medicine University of Frankfurt, Frankfurt am Main, Germany
| | - Friedel M Reischies
- Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Charité, Berlin, Germany
| | - Johannes Schröder
- Section for Geriatric Psychiatry Research, Department for Psychiatry, University of Heidelberg, Heidelberg, Germany
| | - Michael Wagner
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Otto Rienhoff
- Department of Medical Informatics, University of Göttingen, Göttingen, Germany
| | - Stefanie Wolf
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, and German Center for Neurodegenerative Diseases (DZNE), Research Site Göttingen, Göttingen, Germany
| | | | | | - Isabella Heuser
- Department of Psychiatry and Psychotherapy, Campus Benjamin Franklin, Charité, Berlin, Germany
| | - Eckart Rüther
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, and German Center for Neurodegenerative Diseases (DZNE), Research Site Göttingen, Göttingen, Germany
| | - Fritz Henn
- Department of Geriatric Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Zentralinstitut für Seelische Gesundheit, Quadrat J5, D-68159, Mannheim, Germany
| | - Wolfgang Maier
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, and German Center for Neurodegenerative Diseases (DZNE), Research Site Göttingen, Göttingen, Germany
| | - Johannes Kornhuber
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander-University of Erlangen-Nuremberg, Nuremberg, Germany.,Department of Neurodegeneration Diagnostics, Medical University of Bialystok, Bialystok, Poland
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50
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Doan NT, Engvig A, Zaske K, Persson K, Lund MJ, Kaufmann T, Cordova-Palomera A, Alnæs D, Moberget T, Brækhus A, Barca ML, Nordvik JE, Engedal K, Agartz I, Selbæk G, Andreassen OA, Westlye LT. Distinguishing early and late brain aging from the Alzheimer's disease spectrum: consistent morphological patterns across independent samples. Neuroimage 2017; 158:282-295. [PMID: 28666881 DOI: 10.1016/j.neuroimage.2017.06.070] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 05/12/2017] [Accepted: 06/23/2017] [Indexed: 11/30/2022] Open
Abstract
Alzheimer's disease (AD) is a debilitating age-related neurodegenerative disorder. Accurate identification of individuals at risk is complicated as AD shares cognitive and brain features with aging. We applied linked independent component analysis (LICA) on three complementary measures of gray matter structure: cortical thickness, area and gray matter density of 137 AD, 78 mild (MCI) and 38 subjective cognitive impairment patients, and 355 healthy adults aged 18-78 years to identify dissociable multivariate morphological patterns sensitive to age and diagnosis. Using the lasso classifier, we performed group classification and prediction of cognition and age at different age ranges to assess the sensitivity and diagnostic accuracy of the LICA patterns in relation to AD, as well as early and late healthy aging. Three components showed high sensitivity to the diagnosis and cognitive status of AD, with different relationships with age: one reflected an anterior-posterior gradient in thickness and gray matter density and was uniquely related to diagnosis, whereas the other two, reflecting widespread cortical thickness and medial temporal lobe volume, respectively, also correlated significantly with age. Repeating the LICA decomposition and between-subject analysis on ADNI data, including 186 AD, 395 MCI and 220 age-matched healthy controls, revealed largely consistent brain patterns and clinical associations across samples. Classification results showed that multivariate LICA-derived brain characteristics could be used to predict AD and age with high accuracy (area under ROC curve up to 0.93 for classification of AD from controls). Comparison between classifiers based on feature ranking and feature selection suggests both common and unique feature sets implicated in AD and aging, and provides evidence of distinct age-related differences in early compared to late aging.
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Affiliation(s)
- Nhat Trung Doan
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway.
| | - Andreas Engvig
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Medicine, Diakonhjemmet Hospital, Oslo, Norway
| | - Krystal Zaske
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Karin Persson
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway; Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway
| | - Martina Jonette Lund
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Tobias Kaufmann
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Aldo Cordova-Palomera
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Dag Alnæs
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Torgeir Moberget
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Anne Brækhus
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway; Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway
| | - Maria Lage Barca
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway; Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway
| | | | - Knut Engedal
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway; Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Geir Selbæk
- Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway; Centre for Old Age Psychiatric Research, Innlandet Hospital Trust, Ottestad, Norway
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Lars T Westlye
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
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