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Baumeister H, Vogel JW, Insel PS, Kleineidam L, Wolfsgruber S, Stark M, Gellersen HM, Yakupov R, Schmid MC, Lüsebrink F, Brosseron F, Ziegler G, Freiesleben SD, Preis L, Schneider LS, Spruth EJ, Altenstein S, Lohse A, Fliessbach K, Vogt IR, Bartels C, Schott BH, Rostamzadeh A, Glanz W, Incesoy EI, Butryn M, Janowitz D, Rauchmann BS, Kilimann I, Goerss D, Munk MH, Hetzer S, Dechent P, Ewers M, Scheffler K, Wuestefeld A, Strandberg O, van Westen D, Mattsson-Carlgren N, Janelidze S, Stomrud E, Palmqvist S, Spottke A, Laske C, Teipel S, Perneczky R, Buerger K, Schneider A, Priller J, Peters O, Ramirez A, Wiltfang J, Heneka MT, Wagner M, Düzel E, Jessen F, Hansson O, Berron D. A generalizable data-driven model of atrophy heterogeneity and progression in memory clinic settings. Brain 2024:awae118. [PMID: 38654513 DOI: 10.1093/brain/awae118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 02/02/2024] [Accepted: 03/03/2024] [Indexed: 04/26/2024] Open
Abstract
Memory clinic patients are a heterogeneous population representing various aetiologies of pathological aging. It is unknown if divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer's disease (AD) patients, are prevalent and clinically meaningful in this group of older adults. To uncover distinct atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to baseline structural MRI data from 813 participants enrolled in the DELCODE cohort (mean ± SD age = 70.67 ± 6.07 years, 52% females). Participants were cognitively unimpaired (CU; n = 285) or fulfilled diagnostic criteria for subjective cognitive decline (SCD; n = 342), mild cognitive impairment (MCI; n = 118), or dementia of the Alzheimer's type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid AD biomarker levels, the Preclinical Alzheimer Cognitive Composite (PACC-5), as well as episodic memory and executive functioning. PACC-5 trajectories over up to 240 weeks were examined. To test if baseline atrophy subtype and stage predicted clinical trajectories before manifest cognitive impairment, we analysed PACC-5 trajectories and MCI conversion rates of CU and SCD participants. Limbic-predominant and hippocampal-sparing atrophy subtypes were identified. Limbic-predominant atrophy first affected the medial temporal lobes, followed by further temporal and, finally, the remaining cortical regions. At baseline, this subtype was related to older age, more pathological AD biomarker levels, APOE ε4 carriership, and an amnestic cognitive impairment. Hippocampal-sparing atrophy initially occurred outside the temporal lobe with the medial temporal lobe spared up to advanced atrophy stages. This atrophy pattern also affected individuals with positive AD biomarkers and was associated with more generalised cognitive impairment. Limbic-predominant atrophy, in all and in only unimpaired participants, was linked to more negative longitudinal PACC-5 slopes than observed in participants without or with hippocampal-sparing atrophy and increased the risk of MCI conversion. SuStaIn modelling was repeated in a sample from the Swedish BioFINDER-2 cohort. Highly similar atrophy progression patterns and associated cognitive profiles were identified. Cross-cohort model generalizability, both on the subject and group level, were excellent, indicating reliable performance in previously unseen data. The proposed model is a promising tool for capturing heterogeneity among older adults at early at-risk states for AD in applied settings. The implementation of atrophy subtype- and stage-specific end-points may increase the statistical power of pharmacological trials targeting early AD.
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Affiliation(s)
- Hannah Baumeister
- German Center for Neurodegenerative Diseases (DZNE), 39120, Magdeburg, Germany
| | - Jacob W Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42, Lund, Sweden
| | - Philip S Insel
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, 94143, San Francisco, CA, USA
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases (DZNE), 53127, Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127, Bonn, Germany
| | - Steffen Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), 53127, Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127, Bonn, Germany
| | - Melina Stark
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127, Bonn, Germany
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases (DZNE), 39120, Magdeburg, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), 39120, Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, 39120, Magdeburg, Germany
| | - Matthias C Schmid
- German Center for Neurodegenerative Diseases (DZNE), 53127, Bonn, Germany
- Institute for Medical Biometry, University Hospital Bonn, 53127, Bonn, Germany
| | - Falk Lüsebrink
- German Center for Neurodegenerative Diseases (DZNE), 39120, Magdeburg, Germany
| | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), 53127, Bonn, Germany
| | - Gabriel Ziegler
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, 39120, Magdeburg, Germany
| | - Silka D Freiesleben
- German Center for Neurodegenerative Diseases (DZNE), 10117, Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117, Berlin, Germany
| | - Lukas Preis
- Department of Psychiatry and Neurosciences, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117, Berlin, Germany
| | - Luisa-Sophie Schneider
- Department of Psychiatry and Neurosciences, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117, Berlin, Germany
| | - Eike J Spruth
- German Center for Neurodegenerative Diseases (DZNE), 10117, Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117, Berlin, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), 10117, Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117, Berlin, Germany
| | - Andrea Lohse
- Department of Psychiatry and Neurosciences, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117, Berlin, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), 53127, Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127, Bonn, Germany
| | - Ina R Vogt
- German Center for Neurodegenerative Diseases (DZNE), 53127, Bonn, Germany
| | - Claudia Bartels
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075, Göttingen, Germany
| | - Björn H Schott
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075, Göttingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), 37075, Göttingen, Germany
- Leibniz Institute for Neurobiology, 39118, Magdeburg, Germany
| | - Ayda Rostamzadeh
- Department of Psychiatry, Medical Faculty, University of Cologne, 50937, Cologne, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), 39120, Magdeburg, Germany
| | - Enise I Incesoy
- German Center for Neurodegenerative Diseases (DZNE), 39120, Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, 39120, Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, 39120, Magdeburg, Germany
| | - Michaela Butryn
- German Center for Neurodegenerative Diseases (DZNE), 39120, Magdeburg, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität, 81377, Munich, Germany
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-Universität, 80336, Munich, Germany
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, S10 2HQ, Sheffield, UK
- Department of Neuroradiology, Ludwig-Maximilians-Universität, 81377, Munich, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), 18147, Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, 18147, Rostock, Germany
| | - Doreen Goerss
- German Center for Neurodegenerative Diseases (DZNE), 18147, Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, 18147, Rostock, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), 72076, Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, 72076, Tübingen, Germany
| | - Stefan Hetzer
- Berlin Center for Advanced Neuroimaging, Charité - Universitätsmedizin Berlin, 10117, Berlin, Germany
| | - Peter Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Göttingen, 37075, Göttingen, Germany
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität, 81377, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377, Munich, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, 72076, Tübingen, Germany
| | - Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42, Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42, Lund, Sweden
| | - Danielle van Westen
- Diagnostic Radiology, Institution of Clinical Sciences Lund, Lund University, 211 84, Lund, Sweden
- Image and Function, Skåne University Hospital, 211 84, Lund, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund University, 211 84, Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, 22184, Lund, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42, Lund, Sweden
- Memory Clinic, Skåne University Hospital, 205 02, Malmö, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42, Lund, Sweden
- Memory Clinic, Skåne University Hospital, 205 02, Malmö, Sweden
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), 53127, Bonn, Germany
- Department of Neurology, University of Bonn, 53127, Bonn, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), 72076, Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, 72076, Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research, 72076, Tübingen, Germany
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), 18147, Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, 18147, Rostock, Germany
| | - Robert Perneczky
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-Universität, 80336, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), 81377, Munich, Germany
- Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, SW7 2AZ, London, UK
| | - Katharina Buerger
- Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität, 81377, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 81377, Munich, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), 53127, Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127, Bonn, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), 10117, Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Technical University of Munich, 81675, Munich, Germany
- University of Edinburgh and UK DRI, EH16 4SB, Edinburgh, UK
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), 10117, Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117, Berlin, Germany
| | - Alfredo Ramirez
- German Center for Neurodegenerative Diseases (DZNE), 53127, Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127, Bonn, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931, Cologne, Germany
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, University of Cologne, 50931, Cologne, Germany
- Department of Psychiatry & Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, The University of Texas at San Antonio, 78229, San Antonio, TX, US
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, 37075, Göttingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), 37075, Göttingen, Germany
- Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, 3810-193, Aveiro, Portugal
| | - Michael T Heneka
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 4362, Belvaux, Luxembourg
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), 53127, Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, 53127, Bonn, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), 39120, Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, 39120, Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), Otto-von-Guericke University Magdeburg, 39106, Magdeburg, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), 53127, Bonn, Germany
- Department of Psychiatry, Medical Faculty, University of Cologne, 50937, Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931, Cologne, Germany
- Center for Behavioral Brain Sciences (CBBS), Otto-von-Guericke University Magdeburg, 39106, Magdeburg, Germany
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42, Lund, Sweden
- Memory Clinic, Skåne University Hospital, 205 02, Malmö, Sweden
| | - David Berron
- German Center for Neurodegenerative Diseases (DZNE), 39120, Magdeburg, Germany
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 222 42, Lund, Sweden
- Center for Behavioral Brain Sciences (CBBS), Otto-von-Guericke University Magdeburg, 39106, Magdeburg, Germany
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2
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Therriault J, Schindler SE, Salvadó G, Pascoal TA, Benedet AL, Ashton NJ, Karikari TK, Apostolova L, Murray ME, Verberk I, Vogel JW, La Joie R, Gauthier S, Teunissen C, Rabinovici GD, Zetterberg H, Bateman RJ, Scheltens P, Blennow K, Sperling R, Hansson O, Jack CR, Rosa-Neto P. Biomarker-based staging of Alzheimer disease: rationale and clinical applications. Nat Rev Neurol 2024; 20:232-244. [PMID: 38429551 DOI: 10.1038/s41582-024-00942-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/05/2024] [Indexed: 03/03/2024]
Abstract
Disease staging, whereby the spatial extent and load of brain pathology are used to estimate the severity of Alzheimer disease (AD), is pivotal to the gold-standard neuropathological diagnosis of AD. Current in vivo diagnostic frameworks for AD are based on abnormal concentrations of amyloid-β and tau in the cerebrospinal fluid or on PET scans, and breakthroughs in molecular imaging have opened up the possibility of in vivo staging of AD. Focusing on the key principles of disease staging shared across several areas of medicine, this Review highlights the potential for in vivo staging of AD to transform our understanding of preclinical AD, refine enrolment criteria for trials of disease-modifying therapies and aid clinical decision-making in the era of anti-amyloid therapeutics. We provide a state-of-the-art review of recent biomarker-based AD staging systems and highlight their contributions to the understanding of the natural history of AD. Furthermore, we outline hypothetical frameworks to stage AD severity using more accessible fluid biomarkers. In addition, by applying amyloid PET-based staging to recently published anti-amyloid therapeutic trials, we highlight how biomarker-based disease staging frameworks could illustrate the numerous pathological changes that have already taken place in individuals with mildly symptomatic AD. Finally, we discuss challenges related to the validation and standardization of disease staging and provide a forward-looking perspective on potential clinical applications.
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Affiliation(s)
- Joseph Therriault
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montreal, Quebec, Canada.
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Tharick A Pascoal
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andréa Lessa Benedet
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation, London, UK
| | - Thomas K Karikari
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Liana Apostolova
- Department of Neurology, University of Indiana School of Medicine, Indianapolis, IN, USA
| | | | - Inge Verberk
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Jacob W Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Clinical Sciences, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Serge Gauthier
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Charlotte Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Philip Scheltens
- Alzheimer Centre Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Reisa Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | | | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
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3
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Salvadó G, Horie K, Barthélemy NR, Vogel JW, Pichet Binette A, Chen CD, Aschenbrenner AJ, Gordon BA, Benzinger TLS, Holtzman DM, Morris JC, Palmqvist S, Stomrud E, Janelidze S, Ossenkoppele R, Schindler SE, Bateman RJ, Hansson O. Disease staging of Alzheimer's disease using a CSF-based biomarker model. Nat Aging 2024:10.1038/s43587-024-00599-y. [PMID: 38514824 DOI: 10.1038/s43587-024-00599-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 02/20/2024] [Indexed: 03/23/2024]
Abstract
Biological staging of individuals with Alzheimer's disease (AD) may improve diagnostic and prognostic workup of dementia in clinical practice and the design of clinical trials. In this study, we used the Subtype and Stage Inference (SuStaIn) algorithm to establish a robust biological staging model for AD using cerebrospinal fluid (CSF) biomarkers. Our analysis involved 426 participants from BioFINDER-2 and was validated in 222 participants from the Knight Alzheimer Disease Research Center cohort. SuStaIn identified a singular biomarker sequence and revealed that five CSF biomarkers effectively constituted a reliable staging model (ordered: Aβ42/40, pT217/T217, pT205/T205, MTBR-tau243 and non-phosphorylated mid-region tau). The CSF stages (0-5) demonstrated a correlation with increased abnormalities in other AD-related biomarkers, such as Aβ-PET and tau-PET, and aligned with longitudinal biomarker changes reflective of AD progression. Higher CSF stages at baseline were associated with an elevated hazard ratio of clinical decline. This study highlights a common molecular pathway underlying AD pathophysiology across all patients, suggesting that a single CSF collection can accurately indicate the presence of AD pathologies and characterize the stage of disease progression. The proposed staging model has implications for enhancing diagnostic and prognostic assessments in both clinical practice and the design of clinical trials.
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Affiliation(s)
- Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
| | - Kanta Horie
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Eisai, Inc., Nutley, NJ, USA
| | - Nicolas R Barthélemy
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Jacob W Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Clinical Science, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Charles D Chen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Andrew J Aschenbrenner
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - David M Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J Bateman
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
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4
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Mastenbroek SE, Vogel JW, Collij LE, Serrano GE, Tremblay C, Young AL, Arce RA, Shill HA, Driver-Dunckley ED, Mehta SH, Belden CM, Atri A, Choudhury P, Barkhof F, Adler CH, Ossenkoppele R, Beach TG, Hansson O. Disease progression modelling reveals heterogeneity in trajectories of Lewy-type α-synuclein pathology. bioRxiv 2023:2023.12.05.569878. [PMID: 38106128 PMCID: PMC10723322 DOI: 10.1101/2023.12.05.569878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Lewy body (LB) disorders, characterized by the aggregation of misfolded α-synuclein proteins, exhibit notable clinical heterogeneity. This may be due to variations in accumulation patterns of LB neuropathology. By applying data-driven disease progression modelling to regional neuropathological LB density scores from 814 brain donors, we describe three inferred trajectories of LB pathology that were characterized by differing clinicopathological presentation and longitudinal antemortem clinical progression. Most donors (81.9%) showed earliest pathology in the olfactory bulb, followed by accumulation in either limbic (60.8%) or brainstem (21.1%) regions. The remaining donors (18.1%) exhibited the first abnormalities in brainstem regions. Early limbic pathology was associated with Alzheimer's disease-associated characteristics. Meanwhile, brainstem-first pathology was associated with progressive motor impairment and substantial LB pathology outside of the brain. Our data provides evidence for heterogeneity in the temporal spread of LB pathology, possibly explaining some of the clinical disparities observed in LBDs.
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Affiliation(s)
- Sophie E. Mastenbroek
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain imaging, Amsterdam, the Netherlands
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
| | - Jacob W. Vogel
- Department of Clinical Sciences Malmö, Faculty of Medicine, SciLifLab, Lund University, Lund, Sweden
| | - Lyduine E. Collij
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain imaging, Amsterdam, the Netherlands
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
| | - Geidy E. Serrano
- Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Cecilia Tremblay
- Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Alexandra L. Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Richard A. Arce
- Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Holly A. Shill
- Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Erika D. Driver-Dunckley
- Department of Neurology, Parkinson’s Disease and Movement Disorders Center, Mayo Clinic, Scottsdale, Arizona, United States of America
| | - Shyamal H. Mehta
- Department of Neurology, Parkinson’s Disease and Movement Disorders Center, Mayo Clinic, Scottsdale, Arizona, United States of America
| | - Christine M. Belden
- Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, United States of America
- Department of Neurology, Center for Mind/Brain Medicine, Brigham & Women’s Hospital & Harvard Medical School, Boston, Massachusetts, United States of America
| | - Parichita Choudhury
- Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain imaging, Amsterdam, the Netherlands
- Institutes of Neurology & Healthcare Engineering, University College London, London, United Kingdom
| | - Charles H. Adler
- Department of Neurology, Parkinson’s Disease and Movement Disorders Center, Mayo Clinic, Scottsdale, Arizona, United States of America
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Thomas G. Beach
- Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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5
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Shen T, Vogel JW, Duda J, Phillips JS, Cook PA, Gee J, Elman L, Quinn C, Amado DA, Baer M, Massimo L, Grossman M, Irwin DJ, McMillan CT. Novel data-driven subtypes and stages of brain atrophy in the ALS-FTD spectrum. Transl Neurodegener 2023; 12:57. [PMID: 38062485 PMCID: PMC10701950 DOI: 10.1186/s40035-023-00389-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 11/15/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND TDP-43 proteinopathies represent a spectrum of neurological disorders, anchored clinically on either end by amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). The ALS-FTD spectrum exhibits a diverse range of clinical presentations with overlapping phenotypes, highlighting its heterogeneity. This study was aimed to use disease progression modeling to identify novel data-driven spatial and temporal subtypes of brain atrophy and its progression in the ALS-FTD spectrum. METHODS We used a data-driven procedure to identify 13 anatomic clusters of brain volume for 57 behavioral variant FTD (bvFTD; with either autopsy-confirmed TDP-43 or TDP-43 proteinopathy-associated genetic variants), 103 ALS, and 47 ALS-FTD patients with likely TDP-43. A Subtype and Stage Inference (SuStaIn) model was trained to identify subtypes of individuals along the ALS-FTD spectrum with distinct brain atrophy patterns, and we related subtypes and stages to clinical, genetic, and neuropathological features of disease. RESULTS SuStaIn identified three novel subtypes: two disease subtypes with predominant brain atrophy in either prefrontal/somatomotor regions or limbic-related regions, and a normal-appearing group without obvious brain atrophy. The limbic-predominant subtype tended to present with more impaired cognition, higher frequencies of pathogenic variants in TBK1 and TARDBP genes, and a higher proportion of TDP-43 types B, E and C. In contrast, the prefrontal/somatomotor-predominant subtype had higher frequencies of pathogenic variants in C9orf72 and GRN genes and higher proportion of TDP-43 type A. The normal-appearing brain group showed higher frequency of ALS relative to ALS-FTD and bvFTD patients, higher cognitive capacity, higher proportion of lower motor neuron onset, milder motor symptoms, and lower frequencies of genetic pathogenic variants. The overall SuStaIn stages also correlated with evidence for clinical progression including longer disease duration, higher King's stage, and cognitive decline. Additionally, SuStaIn stages differed across clinical phenotypes, genotypes and types of TDP-43 pathology. CONCLUSIONS Our findings suggest distinct neurodegenerative subtypes of disease along the ALS-FTD spectrum that can be identified in vivo, each with distinct brain atrophy, clinical, genetic and pathological patterns.
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Affiliation(s)
- Ting Shen
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, 222 42, Lund, Sweden
| | - Jeffrey Duda
- Penn Image Computing and Science Lab (PICSL), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jeffrey S Phillips
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Philip A Cook
- Penn Image Computing and Science Lab (PICSL), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - James Gee
- Penn Image Computing and Science Lab (PICSL), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lauren Elman
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Colin Quinn
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Defne A Amado
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Michael Baer
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lauren Massimo
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - David J Irwin
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Corey T McMillan
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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6
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Aksman LM, Oxtoby NP, Scelsi MA, Wijeratne PA, Young AL, Alves IL, Collij LE, Vogel JW, Barkhof F, Alexander DC, Altmann A. A data-driven study of Alzheimer's disease related amyloid and tau pathology progression. Brain 2023; 146:4935-4948. [PMID: 37433038 PMCID: PMC10690020 DOI: 10.1093/brain/awad232] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 06/12/2023] [Accepted: 06/29/2023] [Indexed: 07/13/2023] Open
Abstract
Amyloid-β is thought to facilitate the spread of tau throughout the neocortex in Alzheimer's disease, though how this occurs is not well understood. This is because of the spatial discordance between amyloid-β, which accumulates in the neocortex, and tau, which accumulates in the medial temporal lobe during ageing. There is evidence that in some cases amyloid-β-independent tau spreads beyond the medial temporal lobe where it may interact with neocortical amyloid-β. This suggests that there may be multiple distinct spatiotemporal subtypes of Alzheimer's-related protein aggregation, with potentially different demographic and genetic risk profiles. We investigated this hypothesis, applying data-driven disease progression subtyping models to post-mortem neuropathology and in vivo PET-based measures from two large observational studies: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Religious Orders Study and Rush Memory and Aging Project (ROSMAP). We consistently identified 'amyloid-first' and 'tau-first' subtypes using cross-sectional information from both studies. In the amyloid-first subtype, extensive neocortical amyloid-β precedes the spread of tau beyond the medial temporal lobe, while in the tau-first subtype, mild tau accumulates in medial temporal and neocortical areas prior to interacting with amyloid-β. As expected, we found a higher prevalence of the amyloid-first subtype among apolipoprotein E (APOE) ε4 allele carriers while the tau-first subtype was more common among APOE ε4 non-carriers. Within tau-first APOE ε4 carriers, we found an increased rate of amyloid-β accumulation (via longitudinal amyloid PET), suggesting that this rare group may belong within the Alzheimer's disease continuum. We also found that tau-first APOE ε4 carriers had several fewer years of education than other groups, suggesting a role for modifiable risk factors in facilitating amyloid-β-independent tau. Tau-first APOE ε4 non-carriers, in contrast, recapitulated many of the features of primary age-related tauopathy. The rate of longitudinal amyloid-β and tau accumulation (both measured via PET) within this group did not differ from normal ageing, supporting the distinction of primary age-related tauopathy from Alzheimer's disease. We also found reduced longitudinal subtype consistency within tau-first APOE ε4 non-carriers, suggesting additional heterogeneity within this group. Our findings support the idea that amyloid-β and tau may begin as independent processes in spatially disconnected regions, with widespread neocortical tau resulting from the local interaction of amyloid-β and tau. The site of this interaction may be subtype-dependent: medial temporal lobe in amyloid-first, neocortex in tau-first. These insights into the dynamics of amyloid-β and tau may inform research and clinical trials that target these pathologies.
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Affiliation(s)
- Leon M Aksman
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Marzia A Scelsi
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
| | - Peter A Wijeratne
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | | | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1007MB, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Jacob W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
- Brain Research Center, Amsterdam 1081 GN, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1007MB, The Netherlands
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
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7
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Seidlitz J, Mallard TT, Vogel JW, Lee YH, Warrier V, Ball G, Hansson O, Hernandez LM, Mandal AS, Wagstyl K, Lombardo MV, Courchesne E, Glessner JT, Satterthwaite TD, Bethlehem RAI, Bernstock JD, Tasaki S, Ng B, Gaiteri C, Smoller JW, Ge T, Gur RE, Gandal MJ, Alexander-Bloch AF. The molecular genetic landscape of human brain size variation. Cell Rep 2023; 42:113439. [PMID: 37963017 DOI: 10.1016/j.celrep.2023.113439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 06/13/2023] [Accepted: 10/27/2023] [Indexed: 11/16/2023] Open
Abstract
Human brain size changes dynamically through early development, peaks in adolescence, and varies up to 2-fold among adults. However, the molecular genetic underpinnings of interindividual variation in brain size remain unknown. Here, we leveraged postmortem brain RNA sequencing and measurements of brain weight (BW) in 2,531 individuals across three independent datasets to identify 928 genome-wide significant associations with BW. Genes associated with higher or lower BW showed distinct neurodevelopmental trajectories and spatial patterns that mapped onto functional and cellular axes of brain organization. Expression of BW genes was predictive of interspecies differences in brain size, and bioinformatic annotation revealed enrichment for neurogenesis and cell-cell communication. Genome-wide, transcriptome-wide, and phenome-wide association analyses linked BW gene sets to neuroimaging measurements of brain size and brain-related clinical traits. Cumulatively, these results represent a major step toward delineating the molecular pathways underlying human brain size variation in health and disease.
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Affiliation(s)
- Jakob Seidlitz
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA
| | - Jacob W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Younga H Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA
| | - Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge CB2 1TN, UK; Department of Psychology, University of Cambridge, Cambridge CB2 1TN, UK
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Melbourne, VIC 3052, Australia
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö P663+Q9, Sweden; Memory Clinic, Skåne University Hospital, Malmö P663+Q9, Sweden
| | - Leanna M Hernandez
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024, USA
| | - Ayan S Mandal
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - Michael V Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy
| | - Eric Courchesne
- Department of Neuroscience, University of California, San Diego, San Diego, CA 92093, USA; Autism Center of Excellence, University of California, San Diego, San Diego, CA 92093, USA
| | - Joseph T Glessner
- The Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | | | - Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard University, Boston, MA 02115, USA; Department of Neurosurgery, Boston Children's Hospital, Harvard University, Boston, MA 02115, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Bernard Ng
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Raquel E Gur
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael J Gandal
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron F Alexander-Bloch
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
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8
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Linguiti S, Vogel JW, Sydnor VJ, Pines A, Wellman N, Basbaum A, Eickhoff CR, Eickhoff SB, Edwards RR, Larsen B, McKinstry-Wu A, Scott JC, Roalf DR, Sharma V, Strain EC, Corder G, Dworkin RH, Satterthwaite TD. Functional imaging studies of acute administration of classic psychedelics, ketamine, and MDMA: Methodological limitations and convergent results. Neurosci Biobehav Rev 2023; 154:105421. [PMID: 37802267 DOI: 10.1016/j.neubiorev.2023.105421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/13/2023] [Accepted: 10/02/2023] [Indexed: 10/08/2023]
Abstract
Functional magnetic resonance imaging (fMRI) is increasingly used to non-invasively study the acute impact of psychedelics on the human brain. While fMRI is a promising tool for measuring brain function in response to psychedelics, it also has known methodological challenges. We conducted a systematic review of fMRI studies examining acute responses to experimentally administered psychedelics in order to identify convergent findings and characterize heterogeneity in the literature. We reviewed 91 full-text papers; these studies were notable for substantial heterogeneity in design, task, dosage, drug timing, and statistical approach. Data recycling was common, with 51 unique samples across 91 studies. Fifty-seven studies (54%) did not meet contemporary standards for Type I error correction or control of motion artifact. Psilocybin and LSD were consistently reported to moderate the connectivity architecture of the sensorimotor-association cortical axis. Studies also consistently reported that ketamine administration increased activation in the dorsomedial prefrontal cortex. Moving forward, use of best practices such as pre-registration, standardized image processing and statistical testing, and data sharing will be important in this rapidly developing field.
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Affiliation(s)
- Sophia Linguiti
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Jacob W Vogel
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States; Department of Clinical Sciences, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States; Department of Psychiatry, Stanford University, Stanford, CA, United States
| | - Nick Wellman
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Allan Basbaum
- Department of Anatomy, University of California, San Francisco, United States
| | - Claudia R Eickhoff
- Institute of Neuroscience and Medicine, (INM-1, INM-7), Research Centre Jülich, Jülich, Germany; Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, (INM-1, INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Robert R Edwards
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Andrew McKinstry-Wu
- Department of Anesthesiology and Critical Care, Neuroscience of Unconsciousness and Reanimation Research Alliance (NEURRAL), University of Pennsylvania, Philadelphia, United States
| | - J Cobb Scott
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States; VISN4 Mental Illness Research, Education, and Clinical Center at the Corporal Michael J. Crescenz VA (Veterans Affairs) Medical Center, Philadelphia, PA, United States
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Vaishnavi Sharma
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Eric C Strain
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, 5510 Nathan Shock Drive, Baltimore, MD, United States
| | - Gregory Corder
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Robert H Dworkin
- Department of Anesthesiology and Perioperative Medicine, University of Rochester School of Medicine and Dentistry, Rochester, NY, United States
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Philadelphia, PA, United States; Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.
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9
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Vogel JW, Corriveau-Lecavalier N, Franzmeier N, Pereira JB, Brown JA, Maass A, Botha H, Seeley WW, Bassett DS, Jones DT, Ewers M. Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight. Nat Rev Neurosci 2023; 24:620-639. [PMID: 37620599 DOI: 10.1038/s41583-023-00731-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.
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Affiliation(s)
- Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, Lund, Sweden.
| | - Nick Corriveau-Lecavalier
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Acadamy, University of Gothenburg, Mölndal and Gothenburg, Sweden
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Neuro Division, Department of Clinical Neurosciences, Karolinska Institute, Stockholm, Sweden
| | - Jesse A Brown
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, CA, USA
| | - Dani S Bassett
- Departments of Bioengineering, Electrical and Systems Engineering, Physics and Astronomy, Neurology and Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.
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10
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Shen T, Vogel JW, Duda J, Phillips JS, Cook PA, Gee J, Elman L, Quinn C, Amado DA, Baer M, Massimo L, Grossman M, Irwin DJ, McMillan CT. Novel data-driven subtypes and stages of brain atrophy in the ALS-FTD spectrum. Res Sq 2023:rs.3.rs-3183113. [PMID: 37609205 PMCID: PMC10441467 DOI: 10.21203/rs.3.rs-3183113/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Background TDP-43 proteinopathies represents a spectrum of neurological disorders, anchored clinically on either end by amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). The ALS-FTD spectrum exhibits a diverse range of clinical presentations with overlapping phenotypes, highlighting its heterogeneity. This study aimed to use disease progression modeling to identify novel data-driven spatial and temporal subtypes of brain atrophy and its progression in the ALS-FTD spectrum. Methods We used a data-driven procedure to identify 13 anatomic clusters of brain volumes for 57 behavioral variant FTD (bvFTD; with either autopsy-confirmed TDP-43 or TDP-43 proteinopathy-associated genetic variants), 103 ALS, and 47 ALS-FTD patients with likely TDP-43. A Subtype and Stage Inference (SuStaIn) model was trained to identify subtypes of individuals along the ALS-FTD spectrum with distinct brain atrophy patterns, and we related subtypes and stages to clinical, genetic, and neuropathological features of disease. Results SuStaIn identified three novel subtypes: two disease subtypes with predominant brain atrophy either in prefrontal/somatomotor regions or limbic-related regions, and a normal-appearing group without obvious brain atrophy. The Limbic-predominant subtype tended to present with more impaired cognition, higher frequencies of pathogenic variants in TBK1 and TARDBP genes, and a higher proportion of TDP-43 type B, E and C. In contrast, the Prefrontal/Somatomotor-predominant subtype had higher frequencies of pathogenic variants in C9orf72 and GRN genes and higher proportion of TDP-43 type A. The normal-appearing brain group showed higher frequency of ALS relative to ALS-FTD and bvFTD patients, higher cognitive capacity, higher proportion of lower motor neuron onset, milder motor symptoms, and lower frequencies of genetic pathogenic variants. Overall SuStaIn stages also correlated with evidence for clinical progression including longer disease duration, higher King's stage, and cognitive decline. Additionally, SuStaIn stages differed across clinical phenotypes, genotypes and types of TDP-43 pathology. Conclusions Our findings suggest distinct neurodegenerative subtypes of disease along the ALS-FTD spectrum that can be identified in vivo, each with distinct brain atrophy, clinical, genetic and pathological patterns.
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Affiliation(s)
- Ting Shen
- University of Pennsylvania Perelman School of Medicine
| | | | - Jeffrey Duda
- University of Pennsylvania Perelman School of Medicine
| | | | - Philip A Cook
- University of Pennsylvania Perelman School of Medicine
| | - James Gee
- University of Pennsylvania Perelman School of Medicine
| | - Lauren Elman
- University of Pennsylvania Perelman School of Medicine
| | - Colin Quinn
- University of Pennsylvania Perelman School of Medicine
| | - Defne A Amado
- University of Pennsylvania Perelman School of Medicine
| | - Michael Baer
- University of Pennsylvania Perelman School of Medicine
| | | | | | - David J Irwin
- University of Pennsylvania Perelman School of Medicine
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11
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Salvadó G, Horie K, Barthélemy NR, Vogel JW, Binette AP, Chen CD, Aschenbrenner AJ, Gordon BA, Benzinger TL, Holtzman DM, Morris JC, Palmqvist S, Stomrud E, Janelidze S, Ossenkoppele R, Schindler SE, Bateman RJ, Hansson O. Novel CSF tau biomarkers can be used for disease staging of sporadic Alzheimer's disease. medRxiv 2023:2023.07.14.23292650. [PMID: 37503281 PMCID: PMC10370223 DOI: 10.1101/2023.07.14.23292650] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Biological staging of individuals with Alzheimer's disease (AD) may improve diagnostic and prognostic work-up of dementia in clinical practice and the design of clinical trials. Here, we created a staging model using the Subtype and Stage Inference (SuStaIn) algorithm by evaluating cerebrospinal fluid (CSF) amyloid-β (Aβ) and tau biomarkers in 426 participants from BioFINDER-2, that represent the entire spectrum of AD. The model composition and main analyses were replicated in 222 participants from the Knight ADRC cohort. SuStaIn revealed in the two cohorts that the data was best explained by a single biomarker sequence (one subtype), and that five CSF biomarkers (ordered: Aβ42/40, tau phosphorylation occupancies at the residues 217 and 205 [pT217/T217 and pT205/T205], microtubule-binding region of tau containing the residue 243 [MTBR-tau243], and total tau) were sufficient to create an accurate disease staging model. Increasing CSF stages (0-5) were associated with increased abnormality in other AD-related biomarkers, such as Aβ- and tau-PET, and aligned with different phases of longitudinal biomarker changes consistent with current models of AD progression. Higher CSF stages at baseline were associated with higher hazard ratio of clinical decline. Our findings indicate that a common pathophysiologic molecular pathway develops across all AD patients, and that a single CSF collection is sufficient to reliably indicate the presence of both AD pathologies and the degree and stage of disease progression.
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Affiliation(s)
- Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Kanta Horie
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Eisai Inc., Nutley, NJ, United States
| | - Nicolas R. Barthélemy
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Jacob W. Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Clinical Science, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Charles D. Chen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Andrew J Aschenbrenner
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian A. Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L.S. Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - David M. Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Suzanne E. Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J. Bateman
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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12
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Young AL, Vogel JW, Robinson JL, McMillan CT, Ossenkoppele R, Wolk DA, Irwin DJ, Elman L, Grossman M, Lee VMY, Lee EB, Hansson O. Data-driven neuropathological staging and subtyping of TDP-43 proteinopathies. Brain 2023; 146:2975-2988. [PMID: 37150879 PMCID: PMC10317181 DOI: 10.1093/brain/awad145] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/27/2023] [Accepted: 04/16/2023] [Indexed: 05/09/2023] Open
Abstract
TAR DNA-binding protein-43 (TDP-43) accumulation is the primary pathology underlying several neurodegenerative diseases. Charting the progression and heterogeneity of TDP-43 accumulation is necessary to better characterize TDP-43 proteinopathies, but current TDP-43 staging systems are heuristic and assume each syndrome is homogeneous. Here, we use data-driven disease progression modelling to derive a fine-grained empirical staging system for the classification and differentiation of frontotemporal lobar degeneration due to TDP-43 (FTLD-TDP, n = 126), amyotrophic lateral sclerosis (ALS, n = 141) and limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) with and without Alzheimer's disease (n = 304). The data-driven staging of ALS and FTLD-TDP complement and extend previously described human-defined staging schema for ALS and behavioural variant frontotemporal dementia. In LATE-NC individuals, progression along data-driven stages was positively associated with age, but negatively associated with age in individuals with FTLD-TDP. Using only regional TDP-43 severity, our data driven model distinguished individuals diagnosed with ALS, FTLD-TDP or LATE-NC with a cross-validated accuracy of 85.9%, with misclassifications associated with mixed pathological diagnosis, age and genetic mutations. Adding age and SuStaIn stage to this model increased accuracy to 92.3%. Our model differentiates LATE-NC from FTLD-TDP, though some overlap was observed between late-stage LATE-NC and early-stage FTLD-TDP. We further tested for the presence of subtypes with distinct regional TDP-43 progression patterns within each diagnostic group, identifying two distinct cortical-predominant and brainstem-predominant subtypes within FTLD-TDP and a further two subcortical-predominant and corticolimbic-predominant subtypes within ALS. The FTLD-TDP subtypes exhibited differing proportions of TDP-43 type, while there was a trend for age differing between ALS subtypes. Interestingly, a negative relationship between age and SuStaIn stage was seen in the brainstem/subcortical-predominant subtype of each proteinopathy. No subtypes were observed for the LATE-NC group, despite aggregating individuals with and without Alzheimer's disease and a larger sample size for this group. Overall, we provide an empirical pathological TDP-43 staging system for ALS, FTLD-TDP and LATE-NC, which yielded accurate classification. We further demonstrate that there is substantial heterogeneity amongst ALS and FTLD-TDP progression patterns that warrants further investigation in larger cross-cohort studies.
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Affiliation(s)
- Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, WC1V 6LJ, UK
| | - Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, SE-222 42 Lund, Sweden
- Clinical Memory Research Unit, Lund University, SE-222 42 Lund, Sweden
| | - John L Robinson
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Corey T McMillan
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Lund University, SE-222 42 Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
| | - David A Wolk
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - David J Irwin
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lauren Elman
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Virginia M Y Lee
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Edward B Lee
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, SE-222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, SE-205 02 Malmö, Sweden
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13
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Leuzy A, Binette AP, Vogel JW, Klein G, Borroni E, Tonietto M, Strandberg O, Mattsson-Carlgren N, Palmqvist S, Pontecorvo MJ, Iaccarino L, Stomrud E, Ossenkoppele R, Smith R, Hansson O. Comparison of Group-Level and Individualized Brain Regions for Measuring Change in Longitudinal Tau Positron Emission Tomography in Alzheimer Disease. JAMA Neurol 2023; 80:614-623. [PMID: 37155176 PMCID: PMC10167602 DOI: 10.1001/jamaneurol.2023.1067] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/15/2023] [Indexed: 05/10/2023]
Abstract
Importance Longitudinal tau positron emission tomography (PET) is a relevant outcome in clinical trials evaluating disease-modifying therapies in Alzheimer disease (AD). A key unanswered question is whether the use of participant-specific (individualized) regions of interest (ROIs) is superior to conventional approaches where the same ROI (group-level) is used for each participant. Objective To compare group- and participant-level ROIs in participants at different stages of the AD clinical continuum in terms of annual percentage change in tau-PET standardized uptake value ratio (SUVR) and sample size requirements. Design, Setting, and Participants This was a longitudinal cohort study with consecutive participant enrollment between September 18, 2017, and November 15, 2021. Included in the analysis were participants with mild cognitive impairment and AD dementia from the prospective and longitudinal Swedish Biomarkers For Identifying Neurodegenerative Disorders Early and Reliably 2 (BioFINDER-2) study; in addition, a validation sample (the AVID 05e, Expedition-3, Alzheimer's Disease Neuroimaging Initiative [ADNI], and BioFINDER-1 study cohorts) was also included. Exposures Tau PET (BioFINDER-2, [18F]RO948; validation sample, [18F]flortaucipir), 7 group-level (5 data-driven stages, meta-temporal, whole brain), and 5 individualized ROIs. Main Outcomes and Measures Annual percentage change in tau-PET SUVR across ROIs. Sample size requirements in simulated clinical trials using tau PET as an outcome were also calculated. Results A total of 215 participants (mean [SD] age, 71.4 (7.5) years; 111 male [51.6%]) from the BioFINDER-2 study were included in this analysis: 97 amyloid-β (Aβ)-positive cognitively unimpaired (CU) individuals, 77 with Aβ-positive mild cognitive impairment (MCI), and 41 with AD dementia. In the validation sample were 137 Aβ-positive CU participants, 144 with Aβ-positive MCI, and 125 with AD dementia. Mean (SD) follow-up time was 1.8 (0.3) years. Using group-level ROIs, the largest annual percentage increase in tau-PET SUVR in Aβ-positive CU individuals was seen in a composite ROI combining the entorhinal cortex, hippocampus, and amygdala (4.29%; 95% CI, 3.42%-5.16%). In individuals with Aβ-positive MCI, the greatest change was seen in the temporal cortical regions (5.82%; 95% CI, 4.67%-6.97%), whereas in those with AD dementia, the greatest change was seen in the parietal regions (5.22%; 95% CI, 3.95%-6.49%). Significantly higher estimates of annual percentage change were found using several of the participant-specific ROIs. Importantly, the simplest participant-specific approach, where change in tau PET was calculated in an ROI that best matched the participant's data-driven disease stage, performed best in all 3 subgroups. For the power analysis, sample size reductions for the participant-specific ROIs ranged from 15.94% (95% CI, 8.14%-23.74%) to 72.10% (95% CI, 67.10%-77.20%) compared with the best-performing group-level ROIs. Findings were replicated using [18F]flortaucipir. Conclusions and Relevance Finding suggest that certain individualized ROIs carry an advantage over group-level ROIs for assessing longitudinal tau changes and increase the power to detect treatment effects in AD clinical trials using longitudinal tau PET as an outcome.
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Affiliation(s)
- Antoine Leuzy
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Jacob W. Vogel
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia
- Department of Psychiatry, University of Pennsylvania, Philadelphia
| | | | | | | | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
- Wallenberg Centre for Molecular Medicine, Lund University, Lund, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Lund, Sweden
| | - Michael J. Pontecorvo
- Avid Radiopharmaceuticals, Philadelphia, Pennsylvania
- Eli Lilly and Company, Indianapolis, Indiana
| | - Leonardo Iaccarino
- Avid Radiopharmaceuticals, Philadelphia, Pennsylvania
- Eli Lilly and Company, Indianapolis, Indiana
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Lund, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, the Netherlands
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Lund, Sweden
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14
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Young AL, Vogel JW, Robinson JL, McMillan CT, Ossenkoppele R, Wolk DA, Irwin DJ, Elman L, Grossman M, Lee VMY, Lee EB, Hansson O. Data-driven neuropathological staging and subtyping of TDP-43 proteinopathies. medRxiv 2023:2023.01.31.23285242. [PMID: 36778217 PMCID: PMC9915837 DOI: 10.1101/2023.01.31.23285242] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
TAR DNA-binding protein-43 (TDP-43) accumulation is the primary pathology underlying several neurodegenerative diseases. Charting the progression and heterogeneity of TDP-43 accumulation is necessary to better characterise TDP-43 proteinopathies, but current TDP-43 staging systems are heuristic and assume each syndrome is homogeneous. Here, we use data-driven disease progression modelling to derive a fine-grained empirical staging system for the classification and differentiation of frontotemporal lobar degeneration due to TDP-43 (FTLD-TDP, n=126), amyotrophic lateral sclerosis (ALS, n=141) and limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) with and without Alzheimer’s disease (n=304). The data-driven staging of ALS and FTLD-TDP complement and extend previously described human-defined staging schema for ALS and behavioural variant frontotemporal dementia. In LATE-NC individuals, progression along data-driven stages was positively associated with age, but negatively associated with age in individuals with FTLD-TDP. Using only regional TDP-43 severity, our data driven model distinguished individuals diagnosed with ALS, FTLD-TDP or LATE-NC with a cross-validated accuracy of 85.9%, with misclassifications associated with mixed pathological diagnosis, age and genetic mutations. Adding age and SuStaIn stage to this model increased accuracy to 92.3%. Our model differentiates LATE-NC from FTLD-TDP, though some overlap was observed between late-stage LATE-NC and early-stage FTLD-TDP. We further tested for the presence of subtypes with distinct regional TDP-43 progression patterns within each diagnostic group, identifying two distinct cortical-predominant and brainstem-predominant subtypes within FTLD-TDP and a further two subcortical-predominant and corticolimbic-predominant subtypes within ALS. The FTLD-TDP subtypes exhibited differing proportions of TDP-43 type, while there was a trend for age differing between ALS subtypes. Interestingly, a negative relationship between age and SuStaIn stage was seen in the brainstem/subcortical-predominant subtype of each proteinopathy. No subtypes were observed for the LATE-NC group, despite aggregating AD+ and AD-individuals and a larger sample size for this group. Overall, we provide an empirical pathological TDP-43 staging system for ALS, FTLD-TDP and LATE-NC, which yielded accurate classification. We further demonstrate that there is substantial heterogeneity amongst ALS and FTLD-TDP progression patterns that warrants further investigation in larger cross-cohort studies.
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Affiliation(s)
- Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, Lund, Sweden
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - John L Robinson
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Corey T McMillan
- Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - David A Wolk
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - David J Irwin
- Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lauren Elman
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Murray Grossman
- Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Virginia M-Y Lee
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Edward B Lee
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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15
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Cui Z, Pines AR, Larsen B, Sydnor VJ, Li H, Adebimpe A, Alexander-Bloch AF, Bassett DS, Bertolero M, Calkins ME, Davatzikos C, Fair DA, Gur RC, Gur RE, Moore TM, Shanmugan S, Shinohara RT, Vogel JW, Xia CH, Fan Y, Satterthwaite TD. Linking Individual Differences in Personalized Functional Network Topography to Psychopathology in Youth. Biol Psychiatry 2022; 92:973-983. [PMID: 35927072 PMCID: PMC10040299 DOI: 10.1016/j.biopsych.2022.05.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 03/30/2022] [Accepted: 05/04/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND The spatial layout of large-scale functional brain networks differs between individuals and is particularly variable in the association cortex, implicated in a broad range of psychiatric disorders. However, it remains unknown whether this variation in functional topography is related to major dimensions of psychopathology in youth. METHODS The authors studied 790 youths ages 8 to 23 years who had 27 minutes of high-quality functional magnetic resonance imaging data as part of the Philadelphia Neurodevelopmental Cohort. Four correlated dimensions were estimated using a confirmatory correlated traits factor analysis on 112 item-level clinical symptoms, and one overall psychopathology factor with 4 orthogonal dimensions were extracted using a confirmatory factor analysis. Spatially regularized nonnegative matrix factorization was used to identify 17 individual-specific functional networks for each participant. Partial least square regression with split-half cross-validation was conducted to evaluate to what extent the topography of personalized functional networks encodes major dimensions of psychopathology. RESULTS Personalized functional network topography significantly predicted unseen individuals' major dimensions of psychopathology, including fear, psychosis, externalizing, and anxious-misery. Reduced representation of association networks was among the most important features for the prediction of all 4 dimensions. Further analysis revealed that personalized functional network topography predicted overall psychopathology (r = 0.16, permutation testing p < .001), which drove prediction of the 4 correlated dimensions. CONCLUSIONS These results suggest that individual differences in functional network topography in association networks is related to overall psychopathology in youth. Such results underscore the importance of considering functional neuroanatomy for personalized diagnostics and therapeutics in psychiatry.
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Affiliation(s)
- Zaixu Cui
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Chinese Institute for Brain Research, Beijing, China.
| | - Adam R Pines
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Hongming Li
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Azeez Adebimpe
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Dani S Bassett
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania; Santa Fe Institute, Santa Fe, New Mexico
| | - Max Bertolero
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Monica E Calkins
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, Minnesota
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Tyler M Moore
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sheila Shanmugan
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Russell T Shinohara
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jacob W Vogel
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Cedric H Xia
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Yong Fan
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania.
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16
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Vogel JW, Strandberg O, Gaiteri C, Cieslak M, Covitz S, Wolk DA, Davatzikos C, Hansson O, Satterthwaite T. An autopsy‐validated, easily deployable MRI predictor of Alzheimer’s disease tau pathology. Alzheimers Dement 2022. [DOI: 10.1002/alz.065959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Jacob W Vogel
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania Philadelphia PA USA
- Department of Psychiatry, University of Pennsylvania Philadelphia PA USA
| | | | | | - Matthew Cieslak
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania Philadelphia PA USA
- Department of Psychiatry, University of Pennsylvania Philadelphia PA USA
| | - Sydney Covitz
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania Philadelphia PA USA
- Department of Psychiatry, University of Pennsylvania Philadelphia PA USA
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania School of Medicine Philadelphia PA USA
- Department of Pathology and Laboratory Medicine, Alzheimer’s Disease Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania Philadelphia PA USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University Malmö Sweden
- Memory Clinic, Skåne University Hospital Malmö Sweden
| | - Theodore Satterthwaite
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania Philadelphia PA USA
- Department of Psychiatry, University of Pennsylvania Philadelphia PA USA
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17
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Young AL, Vogel JW, Robinson J, McMillan CT, Ossenkoppele R, Wolk DA, Irwin DJ, Elman L, Grossman M, Lee VM, Lee EB, Trojanowski JQ, Hansson O. Empirical pathological staging and subtyping of TDP‐43 proteinopathies. Alzheimers Dement 2022. [DOI: 10.1002/alz.063390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Alexandra L. Young
- Centre for Medical Image Computing, University College London London United Kingdom
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London London United Kingdom
| | | | | | - Corey T McMillan
- Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Rik Ossenkoppele
- VU University Medical Center, Amsterdam UMC Amsterdam Netherlands
- Clinical Memory Research Unit, Lund University, Sweden Lund Sweden
| | | | | | | | | | | | - Eddie B Lee
- University of Pennsylvania Philadelphia PA USA
| | | | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Malmö Sweden
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18
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Salvadó G, Vogel JW, Beach TG, Serrano GE, Schneider JA, Ossenkoppele R, Hansson O. Neuropathological evidence that the association between APOE‐e4 genotype and higher medial temporal lobe tau burden is mediated by higher local amyloid‐β levels. Alzheimers Dement 2022. [DOI: 10.1002/alz.061630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Gemma Salvadó
- Clinical Memory Research Unit, Lund University Malmö Sweden
| | - Jacob W Vogel
- Montreal Neurological Institute, McGill University Montreal PA USA
| | | | | | - Julie A Schneider
- Department of Pathology, Rush University Medical Center Chicago IL USA
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Lund University, Sweden Lund Sweden
- Amsterdam University Medical Center Amsterdam Netherlands
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University Malmö Sweden
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19
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Leuzy A, Binette AP, Vogel JW, Klein G, Borroni E, Tonietto M, Strandberg O, Mattsson‐Carlgren N, Palmqvist S, Pontecorvo MJ, Stomrud E, Ossenkoppele R, Hansson O. Comparison of group‐level and individualized ROIs for predicting change in longitudinal tau‐PET in preclinical and prodromal AD. Alzheimers Dement 2022. [DOI: 10.1002/alz.063057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Antoine Leuzy
- Clinical Memory Research Unit Lund University Malmö Sweden
| | | | - Jacob W Vogel
- Penn/CHOP Lifespan Brain Institute University of Pennsylvania Philadelphia PA USA
- Department of Psychiatry University of Pennsylvania Philadelphia PA USA
| | - Gregory Klein
- Pharma Research and Early Development, F. Hoffmann‐La Roche Ltd. Basel Switzerland
| | - Edilio Borroni
- Pharma Research and Early Development, F. Hoffmann‐La Roche Ltd. Basel Switzerland
| | - Matteo Tonietto
- Roche Pharma Research and Early Development, FHoffmann‐La RocheLtd Basel Switzerland
| | | | - Niklas Mattsson‐Carlgren
- Clinical Memory Research Unit Lund University Malmö Sweden
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University Malmö Sweden
- Wallenberg Center for Molecular Medicine, Lund University Lund Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit Lund University Malmö Sweden
- Memory Clinic, Skåne University Hospital Malmö Sweden
| | | | - Erik Stomrud
- Memory Clinic, Skåne University Hospital Malmö Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit Lund University Malmö Sweden
- VU University Medical Center, Neuroscience Campus Amsterdam Amsterdam Netherlands
| | - Oskar Hansson
- Clinical Memory Research Unit Lund University Malmö Sweden
- Memory Clinic, Skåne University Hospital Malmö Sweden
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20
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Covitz S, Tapera TM, Adebimpe A, Alexander-Bloch AF, Bertolero MA, Feczko E, Franco AR, Gur RE, Gur RC, Hendrickson T, Houghton A, Mehta K, Murtha K, Perrone AJ, Robert-Fitzgerald T, Schabdach JM, Shinohara RT, Vogel JW, Zhao C, Fair DA, Milham MP, Cieslak M, Satterthwaite TD. Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets. Neuroimage 2022; 263:119609. [PMID: 36064140 PMCID: PMC9981813 DOI: 10.1016/j.neuroimage.2022.119609] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 08/19/2022] [Accepted: 09/02/2022] [Indexed: 11/21/2022] Open
Abstract
The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled "Curation of BIDS" (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad--a version control software package for data--as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images' metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.
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Affiliation(s)
- Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tinashe M Tapera
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Azeez Adebimpe
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron F Alexander-Bloch
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Maxwell A Bertolero
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, United States
| | - Alexandre R Franco
- Child Mind Institute, 101 E 56th St, New York, NY 10022,; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA; Department of Psychiatry, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Raquel E Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy Hendrickson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, United States; University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, United States
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, United States
| | - Kahini Mehta
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kristin Murtha
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Anders J Perrone
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, United States
| | - Tim Robert-Fitzgerald
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jenna M Schabdach
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Russell T Shinohara
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jacob W Vogel
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Chenying Zhao
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, United States
| | | | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
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21
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Hansen JY, Shafiei G, Vogel JW, Smart K, Bearden CE, Hoogman M, Franke B, van Rooij D, Buitelaar J, McDonald CR, Sisodiya SM, Schmaal L, Veltman DJ, van den Heuvel OA, Stein DJ, van Erp TGM, Ching CRK, Andreassen OA, Hajek T, Opel N, Modinos G, Aleman A, van der Werf Y, Jahanshad N, Thomopoulos SI, Thompson PM, Carson RE, Dagher A, Misic B. Local molecular and global connectomic contributions to cross-disorder cortical abnormalities. Nat Commun 2022; 13:4682. [PMID: 35948562 PMCID: PMC9365855 DOI: 10.1038/s41467-022-32420-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 07/28/2022] [Indexed: 12/21/2022] Open
Abstract
Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21,000 participants and N = 26,000 controls, collected using a harmonised processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find a relationship between molecular vulnerability and white-matter architecture that drives cortical disorder profiles. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, inferior temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local molecular attributes and global connectivity jointly shape cross-disorder cortical abnormalities.
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Affiliation(s)
- Justine Y Hansen
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Jacob W Vogel
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kelly Smart
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Carrie E Bearden
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Martine Hoogman
- Departments of Psychiatry and Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Barbara Franke
- Departments of Psychiatry and Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Daan van Rooij
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Jan Buitelaar
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Carrie R McDonald
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Sanjay M Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - Lianne Schmaal
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Odile A van den Heuvel
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Anatomy & Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Dan J Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Dept of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, & Center for the Neurobiology of Leaning and Memory, University of California Irvine, 309 Qureshey Research Lab, Irvine, CA, USA
| | - Christopher R K Ching
- Keck School of Medicine, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Tomas Hajek
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Nils Opel
- Institute of Translational Psychiatry, University of Münster, Münster, Germany & Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, Jena, Germany
| | - Gemma Modinos
- Department of Psychosis Studies & MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - André Aleman
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, Groningen, The Netherlands
| | - Ysbrand van der Werf
- Department of Anatomy & Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Neda Jahanshad
- Keck School of Medicine, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Sophia I Thomopoulos
- Keck School of Medicine, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Paul M Thompson
- Keck School of Medicine, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada.
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22
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Elman JA, Vogel JW, Bocancea DI, Ossenkoppele R, van Loenhoud AC, Tu XM, Kremen WS. Issues and recommendations for the residual approach to quantifying cognitive resilience and reserve. Alzheimers Res Ther 2022; 14:102. [PMID: 35879736 PMCID: PMC9310423 DOI: 10.1186/s13195-022-01049-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 07/14/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND Cognitive reserve and resilience are terms used to explain interindividual variability in maintenance of cognitive health in response to adverse factors, such as brain pathology in the context of aging or neurodegenerative disorders. There is substantial interest in identifying tractable substrates of resilience to potentially leverage this phenomenon into intervention strategies. One way of operationalizing cognitive resilience that has gained popularity is the residual method: regressing cognition on an adverse factor and using the residual as a measure of resilience. This method is attractive because it provides a statistical approach that is an intuitive match to the reserve/resilience conceptual framework. However, due to statistical properties of the regression equation, the residual approach has qualities that complicate its interpretation as an index of resilience and make it statistically inappropriate in certain circumstances. METHODS AND RESULTS We describe statistical properties of the regression equation to illustrate why the residual is highly correlated with the cognitive score from which it was derived. Using both simulations and real data, we model common applications of the approach by creating a residual score (global cognition residualized for hippocampal volume) in individuals along the AD spectrum. We demonstrate that in most real-life scenarios, the residual measure of cognitive resilience is highly correlated with cognition, and the degree of this correlation depends on the initial relationship between the adverse factor and cognition. Subsequently, any association between this resilience metric and an external variable may actually be driven by cognition, rather than by an operationalized measure of resilience. We then assess several strategies proposed as potential solutions to this problem, such as including both the residual and original cognitive measure in a model. However, we conclude these solutions may be insufficient, and we instead recommend against "pre-regression" strategies altogether in favor of using statistical moderation (e.g., interactions) to quantify resilience. CONCLUSIONS Caution should be taken in the use and interpretation of the residual-based method of cognitive resilience. Rather than identifying resilient individuals, we encourage building more complete models of cognition to better identify the specific adverse and protective factors that influence cognitive decline.
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Affiliation(s)
- Jeremy A. Elman
- grid.266100.30000 0001 2107 4242Department of Psychiatry, University of California San Diego, 9500 Gilman Dr. (MC0738), La Jolla, CA 92093 USA ,grid.266100.30000 0001 2107 4242Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA USA
| | - Jacob W. Vogel
- grid.25879.310000 0004 1936 8972Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA USA ,grid.25879.310000 0004 1936 8972Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Diana I. Bocancea
- grid.12380.380000 0004 1754 9227Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Rik Ossenkoppele
- grid.12380.380000 0004 1754 9227Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands ,grid.16872.3a0000 0004 0435 165XVU University Medical Center, Amsterdam, the Netherlands ,grid.4514.40000 0001 0930 2361Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Anna C. van Loenhoud
- grid.12380.380000 0004 1754 9227Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands ,grid.16872.3a0000 0004 0435 165XVU University Medical Center, Amsterdam, the Netherlands
| | - Xin M. Tu
- grid.266100.30000 0001 2107 4242Family Medicine and Public Health, University of California San Diego, La Jolla, CA USA
| | - William S. Kremen
- grid.266100.30000 0001 2107 4242Department of Psychiatry, University of California San Diego, 9500 Gilman Dr. (MC0738), La Jolla, CA 92093 USA ,grid.266100.30000 0001 2107 4242Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA USA
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23
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Levitis E, Vogel JW, Funck T, Hachinski V, Gauthier S, Vöglein J, Levin J, Gordon BA, Benzinger T, Iturria-Medina Y, Evans AC. Differentiating amyloid beta spread in autosomal dominant and sporadic Alzheimer's disease. Brain Commun 2022; 4:fcac085. [PMID: 35602652 PMCID: PMC9116976 DOI: 10.1093/braincomms/fcac085] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 12/05/2021] [Accepted: 04/08/2022] [Indexed: 11/12/2022] Open
Abstract
Amyloid-beta deposition is one of the hallmark pathologies in both sporadic Alzheimer's disease and autosomal-dominant Alzheimer's disease, the latter of which is caused by mutations in genes involved in amyloid-beta processing. Despite amyloid-beta deposition being a centrepiece to both sporadic Alzheimer's disease and autosomal-dominant Alzheimer's disease, some differences between these Alzheimer's disease subtypes have been observed with respect to the spatial pattern of amyloid-beta. Previous work has shown that the spatial pattern of amyloid-beta in individuals spanning the sporadic Alzheimer's disease spectrum can be reproduced with high accuracy using an epidemic spreading model which simulates the diffusion of amyloid-beta across neuronal connections and is constrained by individual rates of amyloid-beta production and clearance. However, it has not been investigated whether amyloid-beta deposition in the rarer autosomal-dominant Alzheimer's disease can be modelled in the same way, and if so, how congruent the spreading patterns of amyloid-beta across sporadic Alzheimer's disease and autosomal-dominant Alzheimer's disease are. We leverage the epidemic spreading model as a data-driven approach to probe individual-level variation in the spreading patterns of amyloid-beta across three different large-scale imaging datasets (2 sporadic Alzheimer's disease, 1 autosomal-dominant Alzheimer's disease). We applied the epidemic spreading model separately to the Alzheimer's Disease Neuroimaging initiative (n = 737), the Open Access Series of Imaging Studies (n = 510) and the Dominantly Inherited Alzheimer's Network (n = 249), the latter two of which were processed using an identical pipeline. We assessed inter- and intra-individual model performance in each dataset separately and further identified the most likely subject-specific epicentre of amyloid-beta spread. Using epicentres defined in previous work in sporadic Alzheimer's disease, the epidemic spreading model provided moderate prediction of the regional pattern of amyloid-beta deposition across all three datasets. We further find that, whilst the most likely epicentre for most amyloid-beta-positive subjects overlaps with the default mode network, 13% of autosomal-dominant Alzheimer's disease individuals were best characterized by a striatal origin of amyloid-beta spread. These subjects were also distinguished by being younger than autosomal-dominant Alzheimer's disease subjects with a default mode network amyloid-beta origin, despite having a similar estimated age of symptom onset. Together, our results suggest that most autosomal-dominant Alzheimer's disease patients express amyloid-beta spreading patterns similar to those of sporadic Alzheimer's disease, but that there may be a subset of autosomal-dominant Alzheimer's disease patients with a separate, striatal phenotype.
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Affiliation(s)
- Elizabeth Levitis
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada,Correspondence to: Elizabeth Levitis Magnuson Clinical Center Room 4N244, MSC 1367 Bethesda, MD 20814, USA E-mail:
| | - Jacob W Vogel
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Thomas Funck
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | | | - Serge Gauthier
- McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Jonathan Vöglein
- German Center for Neurodegenerative Diseases, Munich, Germany,Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Johannes Levin
- German Center for Neurodegenerative Diseases, Munich, Germany
| | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine in Saint Louis, St Louis, Missouri, USA
| | - Tammie Benzinger
- Department of Radiology, Washington University School of Medicine in Saint Louis, St Louis, Missouri, USA
| | - Yasser Iturria-Medina
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada,Correspondence may also be addressed to: Alan C. Evans Montreal Neurological Institute Montreal, H3A 2B4, Quebec Canada E-mail:
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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24
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Bethlehem RAI, Seidlitz J, White SR, Vogel JW, Anderson KM, Adamson C, Adler S, Alexopoulos GS, Anagnostou E, Areces-Gonzalez A, Astle DE, Auyeung B, Ayub M, Bae J, Ball G, Baron-Cohen S, Beare R, Bedford SA, Benegal V, Beyer F, Blangero J, Blesa Cábez M, Boardman JP, Borzage M, Bosch-Bayard JF, Bourke N, Calhoun VD, Chakravarty MM, Chen C, Chertavian C, Chetelat G, Chong YS, Cole JH, Corvin A, Costantino M, Courchesne E, Crivello F, Cropley VL, Crosbie J, Crossley N, Delarue M, Delorme R, Desrivieres S, Devenyi GA, Di Biase MA, Dolan R, Donald KA, Donohoe G, Dunlop K, Edwards AD, Elison JT, Ellis CT, Elman JA, Eyler L, Fair DA, Feczko E, Fletcher PC, Fonagy P, Franz CE, Galan-Garcia L, Gholipour A, Giedd J, Gilmore JH, Glahn DC, Goodyer IM, Grant PE, Groenewold NA, Gunning FM, Gur RE, Gur RC, Hammill CF, Hansson O, Hedden T, Heinz A, Henson RN, Heuer K, Hoare J, Holla B, Holmes AJ, Holt R, Huang H, Im K, Ipser J, Jack CR, Jackowski AP, Jia T, Johnson KA, Jones PB, Jones DT, Kahn RS, Karlsson H, Karlsson L, Kawashima R, Kelley EA, Kern S, Kim KW, Kitzbichler MG, Kremen WS, Lalonde F, Landeau B, Lee S, Lerch J, Lewis JD, Li J, Liao W, Liston C, Lombardo MV, Lv J, Lynch C, Mallard TT, Marcelis M, Markello RD, Mathias SR, Mazoyer B, McGuire P, Meaney MJ, Mechelli A, Medic N, Misic B, Morgan SE, Mothersill D, Nigg J, Ong MQW, Ortinau C, Ossenkoppele R, Ouyang M, Palaniyappan L, Paly L, Pan PM, Pantelis C, Park MM, Paus T, Pausova Z, Paz-Linares D, Pichet Binette A, Pierce K, Qian X, Qiu J, Qiu A, Raznahan A, Rittman T, Rodrigue A, Rollins CK, Romero-Garcia R, Ronan L, Rosenberg MD, Rowitch DH, Salum GA, Satterthwaite TD, Schaare HL, Schachar RJ, Schultz AP, Schumann G, Schöll M, Sharp D, Shinohara RT, Skoog I, Smyser CD, Sperling RA, Stein DJ, Stolicyn A, Suckling J, Sullivan G, Taki Y, Thyreau B, Toro R, Traut N, Tsvetanov KA, Turk-Browne NB, Tuulari JJ, Tzourio C, Vachon-Presseau É, Valdes-Sosa MJ, Valdes-Sosa PA, Valk SL, van Amelsvoort T, Vandekar SN, Vasung L, Victoria LW, Villeneuve S, Villringer A, Vértes PE, Wagstyl K, Wang YS, Warfield SK, Warrier V, Westman E, Westwater ML, Whalley HC, Witte AV, Yang N, Yeo B, Yun H, Zalesky A, Zar HJ, Zettergren A, Zhou JH, Ziauddeen H, Zugman A, Zuo XN, Bullmore ET, Alexander-Bloch AF. Brain charts for the human lifespan. Nature 2022; 604:525-533. [PMID: 35388223 PMCID: PMC9021021 DOI: 10.1038/s41586-022-04554-y] [Citation(s) in RCA: 372] [Impact Index Per Article: 186.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 02/16/2022] [Indexed: 02/02/2023]
Abstract
Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.
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Affiliation(s)
- R A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | - J Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA.
| | - S R White
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - J W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Informatics & Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - K M Anderson
- Department of Psychology, Yale University, New Haven, CT, USA
| | - C Adamson
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Medicine, Monash University, Melbourne, Victoria, Australia
| | - S Adler
- UCL Great Ormond Street Institute for Child Health, London, UK
| | - G S Alexopoulos
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, USA
| | - E Anagnostou
- Department of Pediatrics University of Toronto, Toronto, Canada
- Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada
| | - A Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
- University of Pinar del Río "Hermanos Saiz Montes de Oca", Pinar del Río, Cuba
| | - D E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - B Auyeung
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - M Ayub
- Queen's University, Department of Psychiatry, Centre for Neuroscience Studies, Kingston, Ontario, Canada
- University College London, Mental Health Neuroscience Research Department, Division of Psychiatry, London, UK
| | - J Bae
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - G Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - S Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridge Lifetime Asperger Syndrome Service (CLASS), Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - R Beare
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Medicine, Monash University, Melbourne, Victoria, Australia
| | - S A Bedford
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - V Benegal
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
| | - F Beyer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - J Blangero
- Department of Human Genetics, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Edinburg, TX, USA
| | - M Blesa Cábez
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - J P Boardman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - M Borzage
- Fetal and Neonatal Institute, Division of Neonatology, Children's Hospital Los Angeles, Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - J F Bosch-Bayard
- McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Montreal, Quebec, Canada
- McGill University, Montreal, Quebec, Canada
| | - N Bourke
- Department of Brain Sciences, Imperial College London, London, UK
- Care Research and Technology Centre, Dementia Research Institute, London, UK
| | - V D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - M M Chakravarty
- McGill University, Montreal, Quebec, Canada
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - C Chen
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - C Chertavian
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - G Chetelat
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - Y S Chong
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - J H Cole
- Centre for Medical Image Computing (CMIC), University College London, London, UK
- Dementia Research Centre (DRC), University College London, London, UK
| | - A Corvin
- Department of Psychiatry, Trinity College, Dublin, Ireland
| | - M Costantino
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada
- Undergraduate program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - E Courchesne
- Department of Neuroscience, University of California, San Diego, San Diego, CA, USA
- Autism Center of Excellence, University of California, San Diego, San Diego, CA, USA
| | - F Crivello
- Institute of Neurodegenerative Disorders, CNRS UMR5293, CEA, University of Bordeaux, Bordeaux, France
| | - V L Cropley
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - J Crosbie
- The Hospital for Sick Children, Toronto, Ontario, Canada
| | - N Crossley
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Instituto Milenio Intelligent Healthcare Engineering, Santiago, Chile
| | - M Delarue
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - R Delorme
- Child and Adolescent Psychiatry Department, Robert Debré University Hospital, AP-HP, Paris, France
- Human Genetics and Cognitive Functions, Institut Pasteur, Paris, France
| | - S Desrivieres
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - G A Devenyi
- Cerebral Imaging Centre, McGill Department of Psychiatry, Douglas Mental Health University Institute, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - M A Di Biase
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - R Dolan
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, London, UK
| | - K A Donald
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - G Donohoe
- Center for Neuroimaging, Cognition & Genomics (NICOG), School of Psychology, National University of Ireland Galway, Galway, Ireland
| | - K Dunlop
- Weil Family Brain and Mind Research Institute, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - A D Edwards
- Centre for the Developing Brain, King's College London, London, UK
- Evelina London Children's Hospital, London, UK
- MRC Centre for Neurodevelopmental Disorders, London, UK
| | - J T Elison
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - C T Ellis
- Department of Psychology, Yale University, New Haven, CT, USA
- Haskins Laboratories, New Haven, CT, USA
| | - J A Elman
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - L Eyler
- Desert-Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, Los Angeles, CA, USA
| | - D A Fair
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - E Feczko
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - P C Fletcher
- Department of Psychiatry, University of Cambridge, and Wellcome Trust MRC Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - P Fonagy
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
- Anna Freud National Centre for Children and Families, London, UK
| | - C E Franz
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | | | - A Gholipour
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
| | - J Giedd
- Department of Child and Adolescent Psychiatry, University of California, San Diego, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - J H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - D C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - I M Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - P E Grant
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - N A Groenewold
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, SA-MRC Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
| | - F M Gunning
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - R E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - R C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - C F Hammill
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Mouse Imaging Centre, Toronto, Ontario, Canada
| | - O Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - T Hedden
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - A Heinz
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Berlin, Germany
| | - R N Henson
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - K Heuer
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Université de Paris, Paris, France
| | - J Hoare
- Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - B Holla
- Department of Integrative Medicine, NIMHANS, Bengaluru, India
- Accelerator Program for Discovery in Brain disorders using Stem cells (ADBS), Department of Psychiatry, NIMHANS, Bengaluru, India
| | - A J Holmes
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - R Holt
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - H Huang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- The Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - K Im
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - J Ipser
- Department of Psychiatry and Mental Health, Clinical Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - C R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - A P Jackowski
- Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil
- National Institute of Developmental Psychiatry, Beijing, China
| | - T Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and BrainInspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology and Neuroscience, SGDP Centre, King's College London, London, UK
| | - K A Johnson
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - P B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - D T Jones
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - R S Kahn
- Department of Psychiatry, Icahn School of Medicine, Mount Sinai, NY, USA
| | - H Karlsson
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
| | - L Karlsson
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
| | - R Kawashima
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - E A Kelley
- Queen's University, Departments of Psychology and Psychiatry, Centre for Neuroscience Studies, Kingston, Ontario, Canada
| | - S Kern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - K W Kim
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, South Korea
| | - M G Kitzbichler
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - W S Kremen
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - F Lalonde
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - B Landeau
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - S Lee
- Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
| | - J Lerch
- Mouse Imaging Centre, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - J D Lewis
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - J Li
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - W Liao
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - C Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - M V Lombardo
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - J Lv
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- School of Biomedical Engineering and Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - C Lynch
- Weil Family Brain and Mind Research Institute, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - T T Mallard
- Department of Psychology, University of Texas, Austin, TX, USA
| | - M Marcelis
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, Maastricht, The Netherlands
- Institute for Mental Health Care Eindhoven (GGzE), Eindhoven, The Netherlands
| | - R D Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - S R Mathias
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - B Mazoyer
- Institute of Neurodegenerative Disorders, CNRS UMR5293, CEA, University of Bordeaux, Bordeaux, France
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - P McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - M J Meaney
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Singapore Institute for Clinical Sciences, Singapore, Singapore
| | - A Mechelli
- Bordeaux University Hospital, Bordeaux, France
| | - N Medic
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - B Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - S E Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - D Mothersill
- Department of Psychology, School of Business, National College of Ireland, Dublin, Ireland
- School of Psychology and Center for Neuroimaging and Cognitive Genomics, National University of Ireland Galway, Galway, Ireland
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - J Nigg
- Department of Psychiatry, School of Medicine, Oregon Health and Science University, Portland, OR, USA
| | - M Q W Ong
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - C Ortinau
- Department of Pediatrics, Washington University in St Louis, St Louis, MO, USA
| | - R Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Lund University, Clinical Memory Research Unit, Lund, Sweden
| | - M Ouyang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - L Palaniyappan
- Robarts Research Institute and The Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada
| | - L Paly
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - P M Pan
- Department of Psychiatry, Federal University of Sao Poalo (UNIFESP), Sao Poalo, Brazil
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), Sao Poalo, Brazil
| | - C Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
- Melbourne School of Engineering, The University of Melbourne, Parkville, Victoria, Australia
- Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - M M Park
- Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - T Paus
- Department of Psychiatry, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Z Pausova
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
| | - D Paz-Linares
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
- Cuban Neuroscience Center, Havana, Cuba
| | - A Pichet Binette
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - K Pierce
- Department of Neuroscience, University of California, San Diego, San Diego, CA, USA
| | - X Qian
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - J Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - A Qiu
- Department of Biomedical Engineering, The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - A Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - T Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - A Rodrigue
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - C K Rollins
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | - R Romero-Garcia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla, Dpto. de Fisiología Médica y Biofísica, Seville, Spain
| | - L Ronan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - M D Rosenberg
- Department of Psychology and Neuroscience Institute, University of Chicago, Chicago, IL, USA
| | - D H Rowitch
- Department of Paediatrics and Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - G A Salum
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul (UFRGS), Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil
- National Institute of Developmental Psychiatry (INPD), São Paulo, Brazil
| | - T D Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Informatics & Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - H L Schaare
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Juelich, Juelich, Germany
| | - R J Schachar
- The Hospital for Sick Children, Toronto, Ontario, Canada
| | - A P Schultz
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - G Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Institute for Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- PONS-Centre, Charite Mental Health, Dept of Psychiatry and Psychotherapy, Charite Campus Mitte, Berlin, Germany
| | - M Schöll
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
- Dementia Research Centre, Queen's Square Institute of Neurology, University College London, London, UK
| | - D Sharp
- Department of Brain Sciences, Imperial College London, London, UK
- Care Research and Technology Centre, UK Dementia Research Institute, London, UK
| | - R T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - I Skoog
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - C D Smyser
- Departments of Neurology, Pediatrics, and Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - R A Sperling
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - D J Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Dept of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - A Stolicyn
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - J Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - G Sullivan
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - Y Taki
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - B Thyreau
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - R Toro
- Université de Paris, Paris, France
- Department of Neuroscience, Institut Pasteur, Paris, France
| | - N Traut
- Department of Neuroscience, Institut Pasteur, Paris, France
- Center for Research and Interdisciplinarity (CRI), Université Paris Descartes, Paris, France
| | - K A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - N B Turk-Browne
- Department of Psychology, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - J J Tuulari
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Department of Clinical Medicine, University of Turku, Turku, Finland
- Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
| | - C Tzourio
- Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France
| | - É Vachon-Presseau
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Quebec, Canada
| | | | - P A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Alan Edwards Centre for Research on Pain (AECRP), McGill University, Montreal, Quebec, Canada
| | - S L Valk
- Institute for Neuroscience and Medicine 7, Forschungszentrum Jülich, Jülich, Germany
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - T van Amelsvoort
- Department of Psychiatry and Neurosychology, Maastricht University, Maastricht, The Netherlands
| | - S N Vandekar
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - L Vasung
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - L W Victoria
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - S Villeneuve
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - A Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
| | - P E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - K Wagstyl
- Wellcome Centre for Human Neuroimaging, London, UK
| | - Y S Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - S K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
| | - V Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - E Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - M L Westwater
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - H C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - A V Witte
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
- Faculty of Medicine, CRC 1052 'Obesity Mechanisms', University of Leipzig, Leipzig, Germany
| | - N Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - B Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition and Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - H Yun
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - A Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - H J Zar
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, SA-MRC Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
| | - A Zettergren
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
| | - J H Zhou
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Center for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - H Ziauddeen
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - A Zugman
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), Sao Poalo, Brazil
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, MD, USA
- Department of Psychiatry, Escola Paulista de Medicina, São Paulo, Brazil
| | - X N Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning, China
| | - E T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - A F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
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Vogel JW, Tosun D. Multiple Cortical to Striatal Accumulation Trajectories of β-Amyloid: Do All Roads Lead to Rome? Neurology 2022; 98:695-696. [PMID: 35338076 DOI: 10.1212/wnl.0000000000200191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Jacob W Vogel
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, USA.,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.,Veterans Affairs San Francisco, CA, USA
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26
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Vogel JW, Hansson O. Subtypes of Alzheimer’s disease: questions, controversy, and meaning. Trends Neurosci 2022; 45:342-345. [DOI: 10.1016/j.tins.2022.02.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 01/22/2022] [Accepted: 02/01/2022] [Indexed: 12/24/2022]
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27
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Jung NY, Shin JH, Kim HJ, Jang H, Moon SH, Kim SJ, Kim Y, Cho SH, Kim KW, Kim JP, Jung YH, Kim ST, Kim EJ, Na DL, Vogel JW, Lee S, Seong JK, Seo SW. Distinctive Mediating Effects of Subcortical Structure Changes on the Relationships Between Amyloid or Vascular Changes and Cognitive Decline. Front Neurol 2021; 12:762251. [PMID: 34950100 PMCID: PMC8688398 DOI: 10.3389/fneur.2021.762251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 11/04/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: We investigated the mediation effects of subcortical volume change in the relationship of amyloid beta (Aβ) and lacune with cognitive function in patients with mild cognitive impairment (MCI). Methods: We prospectively recruited 101 patients with MCI who were followed up with neuropsychological tests, MRI, or Pittsburgh compound B (PiB) PET for 3 years. The mediation effect of subcortical structure on the association of PiB or lacunes with cognitive function was analyzed using mixed effects models. Results: Volume changes in the amygdala and hippocampus partially mediated the effect of PiB changes on memory function (direct effect = -0.168/-0.175, indirect effect = -0.081/-0.077 for amygdala/hippocampus) and completely mediated the effect of PiB changes on clinical dementia rating scale sum of the box (CDR-SOB) (indirect effect = 0.082/0.116 for amygdala/hippocampus). Volume changes in the thalamus completely mediated the effect of lacune on memory, frontal executive functions, and CDR-SOB (indirect effect = -0.037, -0.056, and 0.047, respectively). Conclusions: Our findings provide a better understanding of the distinct role of subcortical structures in the mediation of the relationships of amyloid or vascular changes with a decline in specific cognitive domains.
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Affiliation(s)
- Na-Yeon Jung
- Department of Neurology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine and Research Institute for Convergence of Biomedical Science and Technology, Yangsan, South Korea
| | - Jeong-Hyeon Shin
- School of Biomedical Engineering, Korea University, Seoul, South Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Seoul, South Korea
| | - Seung Joo Kim
- Department of Neurology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Changwon, South Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University College of Medicine, Chuncheon-si, South Korea
| | - Soo Hyun Cho
- Department of Neurology, Chonnam National University Medical School and Hospital, Gwangju, South Korea
| | - Ko Woon Kim
- Department of Neurology, Chonbuk National University Medical School and Hospital, Jeonju, South Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Young Hee Jung
- Department of Neurology, Myongji Hospital, College of Medicine, Hanyang University, Goyang, South Korea
| | - Sung Tae Kim
- Department of Radiology, Samsung Medical Center, Seoul, South Korea
| | - Eun-Joo Kim
- Department of Neurology, Pusan National University Hospital, Pusan National University School of Medicine, Pusan, South Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Jacob W Vogel
- Montreal Neurological Institute, McGill University, Montrèal, QC, Canada
| | - Sangjin Lee
- Graduate School, Department of Statistics, Pusan National University, Busan, South Korea
| | - Joon-Kyung Seong
- School of Biomedical Engineering, Korea University, Seoul, South Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
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28
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Leuzy A, Smith R, Cullen NC, Strandberg O, Vogel JW, Binette AP, Borroni E, Janelidze S, Ohlsson T, Jögi J, Ossenkoppele R, Palmqvist S, Mattsson-Carlgren N, Klein G, Stomrud E, Hansson O. Biomarker-Based Prediction of Longitudinal Tau Positron Emission Tomography in Alzheimer Disease. JAMA Neurol 2021; 79:149-158. [PMID: 34928318 PMCID: PMC8689441 DOI: 10.1001/jamaneurol.2021.4654] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Question Which biomarkers best predict longitudinal tau accumulation at different clinical stages of Alzheimer disease? Findings In this cohort study of 343 participants including amyloid-β–positive individuals who were cognitively unimpaired or had mild cognitive impairment, the largest annual increase in [18F]RO948 tau positron emission tomography (PET) was seen across the entorhinal cortex, hippocampus, and amygdala and in temporal cortical regions, respectively. In a power analysis, plasma phosphorylated tau217 with tau PET at baseline in stage I and II, respectively, resulted in sample size reductions. Meaning In trials using tau PET as a main outcome, plasma phosphorylated tau217 with tau PET may prove optimal for enrichment in both preclinical and prodromal Alzheimer disease. Importance There is currently no consensus as to which biomarkers best predict longitudinal tau accumulation at different clinical stages of Alzheimer disease (AD). Objective To describe longitudinal [18F]RO948 tau positron emission tomography (PET) findings across the clinical continuum of AD and determine which biomarker combinations showed the strongest associations with longitudinal tau PET and best optimized clinical trial enrichment. Design, Setting, and Participants This longitudinal cohort study consecutively enrolled amyloid-β (Aβ)–negative cognitively unimpaired (CU) participants, Aβ-positive CU individuals, Aβ-positive individuals with mild cognitive impairment (MCI), and individuals with AD dementia between September 2017 and November 2020 from the Swedish BioFINDER-2 (discovery cohort) and BioFINDER-1 (validation cohort) studies. Exposures Baseline plasma and cerebrospinal fluid Aβ42/Aβ40, tau phosphorylated at threonine-217 (p-tau217), p-tau181 and neurofilament light, magnetic resonance imaging, amyloid PET ([18F]flutemetamol), and tau PET ([18F]RO948 in the BioFINDER-2 study; [18F]flortaucipir in the BioFINDER-1 study). Main Outcomes and Measures Baseline tau PET standardized uptake value ratio (SUVR) and annual percent change in tau PET SUVR across regions of interest derived using a data-driven approach combining clustering and event-based modeling. Regression models were used to examine associations between individual biomarkers and longitudinal tau PET and to identify which combinations best predicted longitudinal tau PET. These combinations were then entered in a power analysis to examine how their use as an enrichment strategy would affect sample size in a simulated clinical trial. Results Of 343 participants, the mean (SD) age was 72.56 (7.24) years, and 157 (51.1%) were female. The clustering/event-based modeling–based approach identified 5 regions of interest (stages). In Aβ-positive CU individuals, the largest annual increase in tau PET SUVR was seen in stage I (entorhinal cortex, hippocampus, and amygdala; 4.04% [95% CI, 2.67%-5.32%]). In Aβ-positive individuals with MCI and with AD dementia, the greatest increases were seen in stages II (temporal cortical regions; 4.45% [95% CI, 3.41%-5.49%]) and IV (certain frontal regions; 5.22% [95% CI, 3.95%-6.49%]), respectively. In Aβ-negative CU individuals and those with MCI, modest change was seen in stage I (1.38% [95% CI, 0.78%-1.99%] and 1.80% [95% CI, 0.76%-2.84%], respectively). When looking at individual predictors and longitudinal tau PET in the stages that showed most change, plasma p-tau217 (R2 = 0.27, P < .005), tau PET (stage I baseline SUVR; R2 = 0.13, P < .05) and amyloid PET (R2 = 0.10, P < .05) were significantly associated with longitudinal tau PET in stage I in Aβ-positive CU individuals. In Aβ-positive individuals with MCI, plasma p-tau217 (R2 = 0.24, P < .005) and tau PET (stage II baseline SUVR; R2 = 0.44, P < .001) were significantly associated with longitudinal tau PET in stage II. Findings were replicated in BioFINDER-1 using longitudinal [18F]flortaucipir. For the power analysis component, plasma p-tau217 with tau PET resulted in sample size reductions of 43% (95% CI, 34%-46%; P < .005) in Aβ-positive CU individuals and of 68% (95% CI, 61%-73%; P < .001) in Aβ-positive individuals with MCI. Conclusions and Relevance In trials using tau PET as the outcome, plasma p-tau217 with tau PET may prove optimal for enrichment in preclinical and prodromal AD. However, plasma p-tau217 was most important in preclinical AD, while tau PET was more important in prodromal AD.
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Affiliation(s)
- Antoine Leuzy
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.,Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Nicholas C Cullen
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Jacob W Vogel
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia.,Department of Psychiatry, University of Pennsylvania, Philadelphia
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | | | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Tomas Ohlsson
- Department of Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Jonas Jögi
- Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Lund, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.,VU University Medical Center, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.,Memory Clinic, Skåne University Hospital, Lund, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.,Department of Neurology, Skåne University Hospital, Lund, Sweden.,Wallenberg Centre for Molecular Medicine, Lund University, Lund, Sweden
| | | | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.,VU University Medical Center, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.,Memory Clinic, Skåne University Hospital, Lund, Sweden
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29
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Berron D, Vogel JW, Insel P, Pereira JB, Xie L, Wisse L, Yushkevich PA, Palmqvist S, Mattsson‐Carlgren N, Stomrud E, Smith R, Strandberg O, Hansson O. Early stages of tau pathology and its associations with functional connectivity, atrophy and memory. Alzheimers Dement 2021. [DOI: 10.1002/alz.053787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University Lund Sweden
- German Center for Neurodegenerative Diseases (DZNE) Magdeburg Germany
| | - Jacob W. Vogel
- Department of Psychiatry, University of Pennsylvania Philadelphia PA USA
| | - Philip Insel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University Malmö Sweden
| | - Joana B. Pereira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden Stockholm Sweden
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
| | | | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania Philadelphia PA USA
- Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | | | | | | | | | | | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University Malmö Sweden
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30
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Ahmadi K, Pereira JB, Berron D, Vogel JW, Ingala S, Strandberg O, Janelidze S, Barkhof F, Knutsson L, van Westen D, Mutsaerts HJ, Palmqvist S, Hansson O. Tau and synaptic biomarkers but not amyloid‐β are associated with cerebral perfusion in the Alzheimer’s disease spectrum. Alzheimers Dement 2021. [DOI: 10.1002/alz.052552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Khazar Ahmadi
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University Lund Sweden
| | - Joana B. Pereira
- Clinical Memory Research Unit, Lund University Malmö Sweden
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute Stockholm Sweden
| | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University Lund Sweden
| | - Jacob W. Vogel
- Department of Psychiatry, University of Pennsylvania Philadelphia PA USA
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam Netherlands
| | | | | | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam Netherlands
- Institutes of Neurology and Healthcare Engineering, University College London, London United Kingdom
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University Lund Sweden
- Department of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine Baltimore MD USA
| | - Danielle van Westen
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University Lund Sweden
- Diagnostic Radiology, Lund University Lund Sweden
| | - Henri J.M.M. Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam Netherlands
| | - Sebastian Palmqvist
- Lund University Lund Sweden
- Department of Neurology, Skåne University Hospital Lund Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University Lund Sweden
- Memory Clinic, Skåne University Hospital Malmö Sweden
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31
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Hansen JY, Markello RD, Vogel JW, Seidlitz J, Bzdok D, Misic B. Mapping gene transcription and neurocognition across human neocortex. Nat Hum Behav 2021; 5:1240-1250. [PMID: 33767429 DOI: 10.1038/s41562-021-01082-z] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 02/18/2021] [Indexed: 01/31/2023]
Abstract
Regulation of gene expression drives protein interactions that govern synaptic wiring and neuronal activity. The resulting coordinated activity among neuronal populations supports complex psychological processes, yet how gene expression shapes cognition and emotion remains unknown. Here, we directly bridge the microscale and macroscale by mapping gene expression patterns to functional activation patterns across the cortical sheet. Applying unsupervised learning to the Allen Human Brain Atlas and Neurosynth databases, we identify a ventromedial-dorsolateral gradient of gene assemblies that separate affective and perceptual domains. This topographic molecular-psychological signature reflects the hierarchical organization of the neocortex, including systematic variations in cell type, myeloarchitecture, laminar differentiation and intrinsic network affiliation. In addition, this molecular-psychological signature strengthens over neurodevelopment and can be replicated in two independent repositories. Collectively, our results reveal spatially covarying transcriptomic and cognitive architectures, highlighting the influence that molecular mechanisms exert on psychological processes.
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Affiliation(s)
- Justine Y Hansen
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Ross D Markello
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Jacob W Vogel
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jakob Seidlitz
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Danilo Bzdok
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Québec, Canada.,Biological and Biomedical Engineering, McGill University, Montréal, Québec, Canada.,Mila, Quebec Artificial Intelligence Institute, Montréal, Québec, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Québec, Canada.
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32
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Young AL, Vogel JW, Aksman LM, Wijeratne PA, Eshaghi A, Oxtoby NP, Williams SCR, Alexander DC. Ordinal SuStaIn: Subtype and Stage Inference for Clinical Scores, Visual Ratings, and Other Ordinal Data. Front Artif Intell 2021; 4:613261. [PMID: 34458723 PMCID: PMC8387598 DOI: 10.3389/frai.2021.613261] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 07/20/2021] [Indexed: 12/28/2022] Open
Abstract
Subtype and Stage Inference (SuStaIn) is an unsupervised learning algorithm that uniquely enables the identification of subgroups of individuals with distinct pseudo-temporal disease progression patterns from cross-sectional datasets. SuStaIn has been used to identify data-driven subgroups and perform patient stratification in neurodegenerative diseases and in lung diseases from continuous biomarker measurements predominantly obtained from imaging. However, the SuStaIn algorithm is not currently applicable to discrete ordinal data, such as visual ratings of images, neuropathological ratings, and clinical and neuropsychological test scores, restricting the applicability of SuStaIn to a narrower range of settings. Here we propose 'Ordinal SuStaIn', an ordinal version of the SuStaIn algorithm that uses a scored events model of disease progression to enable the application of SuStaIn to ordinal data. We demonstrate the validity of Ordinal SuStaIn by benchmarking the performance of the algorithm on simulated data. We further demonstrate that Ordinal SuStaIn out-performs the existing continuous version of SuStaIn (Z-score SuStaIn) on discrete scored data, providing much more accurate subtype progression patterns, better subtyping and staging of individuals, and accurate uncertainty estimates. We then apply Ordinal SuStaIn to six different sub-scales of the Clinical Dementia Rating scale (CDR) using data from the Alzheimer's disease Neuroimaging Initiative (ADNI) study to identify individuals with distinct patterns of functional decline. Using data from 819 ADNI1 participants we identified three distinct CDR subtype progression patterns, which were independently verified using data from 790 ADNI2 participants. Our results provide insight into patterns of decline in daily activities in Alzheimer's disease and a mechanism for stratifying individuals into groups with difficulties in different domains. Ordinal SuStaIn is broadly applicable across different types of ratings data, including visual ratings from imaging, neuropathological ratings and clinical or behavioural ratings data.
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Affiliation(s)
- Alexandra L. Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Computer Science, University College London, London, United Kingdom
| | - Jacob W. Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, Unites States
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, Unites States
| | - Leon M. Aksman
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, Unites States
| | - Peter A. Wijeratne
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Computer Science, University College London, London, United Kingdom
| | - Arman Eshaghi
- Department of Computer Science, University College London, London, United Kingdom
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Faculty of Brain Sciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Neil P. Oxtoby
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Computer Science, University College London, London, United Kingdom
| | - Steven C. R. Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Daniel C. Alexander
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Computer Science, University College London, London, United Kingdom
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33
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Vogel JW, Iturria-Medina Y, Strandberg OT, Smith R, Levitis E, Evans AC, Hansson O. Author Correction: Spread of pathological tau proteins through communicating neurons in human Alzheimer's disease. Nat Commun 2021; 12:4862. [PMID: 34354079 PMCID: PMC8342451 DOI: 10.1038/s41467-021-25193-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Affiliation(s)
- Jacob W Vogel
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
| | | | | | - Ruben Smith
- Clinical Memory Research Unit, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Lund, Sweden
| | - Elizabeth Levitis
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Lund, Sweden.
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34
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Vogel JW, Young AL, Oxtoby NP, Smith R, Ossenkoppele R, Strandberg OT, La Joie R, Aksman LM, Grothe MJ, Iturria-Medina Y, Pontecorvo MJ, Devous MD, Rabinovici GD, Alexander DC, Lyoo CH, Evans AC, Hansson O. Four distinct trajectories of tau deposition identified in Alzheimer's disease. Nat Med 2021; 27:871-881. [PMID: 33927414 PMCID: PMC8686688 DOI: 10.1038/s41591-021-01309-6] [Citation(s) in RCA: 290] [Impact Index Per Article: 96.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 03/04/2021] [Indexed: 01/15/2023]
Abstract
Alzheimer's disease (AD) is characterized by the spread of tau pathology throughout the cerebral cortex. This spreading pattern was thought to be fairly consistent across individuals, although recent work has demonstrated substantial variability in the population with AD. Using tau-positron emission tomography scans from 1,612 individuals, we identified 4 distinct spatiotemporal trajectories of tau pathology, ranging in prevalence from 18 to 33%. We replicated previously described limbic-predominant and medial temporal lobe-sparing patterns, while also discovering posterior and lateral temporal patterns resembling atypical clinical variants of AD. These 'subtypes' were stable during longitudinal follow-up and were replicated in a separate sample using a different radiotracer. The subtypes presented with distinct demographic and cognitive profiles and differing longitudinal outcomes. Additionally, network diffusion models implied that pathology originates and spreads through distinct corticolimbic networks in the different subtypes. Together, our results suggest that variation in tau pathology is common and systematic, perhaps warranting a re-examination of the notion of 'typical AD' and a revisiting of tau pathological staging.
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Affiliation(s)
- Jacob W Vogel
- Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada.
| | - Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Neil P Oxtoby
- Centre for Medical Image Computing, University College London, London, UK
- Department of Computer Science, University College London, London, UK
| | - Ruben Smith
- Clinical Memory Research Unit, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | | | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Leon M Aksman
- Centre for Medical Image Computing, University College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Michel J Grothe
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/Consejo Superior de Investigaciones Científicas/Universidad de Sevilla, Seville, Spain
| | | | | | | | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London, UK
- Department of Computer Science, University College London, London, UK
| | - Chul Hyoung Lyoo
- Departments of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
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35
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Berron D, Vogel JW, Insel PS, Pereira JB, Xie L, Wisse LEM, Yushkevich PA, Palmqvist S, Mattsson-Carlgren N, Stomrud E, Smith R, Strandberg O, Hansson O. Early stages of tau pathology and its associations with functional connectivity, atrophy and memory. Brain 2021; 144:2771-2783. [PMID: 33725124 PMCID: PMC8557349 DOI: 10.1093/brain/awab114] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 01/15/2021] [Accepted: 03/04/2021] [Indexed: 11/12/2022] Open
Abstract
In Alzheimer's disease, postmortem studies have shown that the first cortical site where neurofibrillary tangles appear is the transentorhinal region, a subregion within the medial temporal lobe that largely overlaps with area 35, and the entorhinal cortex. Here we used tau-PET imaging to investigate the sequence of tau pathology progression within the human medial temporal lobe and across regions in the posterior-medial system. Our objective was to study how medial temporal tau is related to functional connectivity, regional atrophy, and memory performance. We included 215 β-amyloid negative cognitively unimpaired, 81 β-amyloid positive cognitively unimpaired and 87 β-amyloid positive individuals with mild cognitive impairment, who each underwent [18]F-RO948 tau and [18]F-flutemetamol amyloid PET imaging, structural T1-MRI and memory assessments as part of the Swedish BioFINDER-2 study. First, event-based modelling revealed that the entorhinal cortex and area 35 show the earliest signs of tau accumulation followed by the anterior and posterior hippocampus, area 36 and the parahippocampal cortex. In later stages, tau accumulation became abnormal in neocortical temporal and finally parietal brain regions. Second, in cognitively unimpaired individuals, increased tau load was related to local atrophy in the entorhinal cortex, area 35 and the anterior hippocampus and tau load in several anterior medial temporal lobe subregions was associated with distant atrophy of the posterior hippocampus. Tau load, but not atrophy, in these regions was associated with lower memory performance. Further, tau-related reductions in functional connectivity in critical networks between the medial temporal lobe and regions in the posterior-medial system were associated with this early memory impairment. Finally, in patients with mild cognitive impairment, the association of tau load in the hippocampus with memory performance was partially mediated by posterior hippocampal atrophy. In summary, our findings highlight the progression of tau pathology across medial temporal lobe subregions and its disease-stage specific association with memory performance. While tau pathology might affect memory performance in cognitively unimpaired individuals via reduced functional connectivity in critical medial temporal lobe-cortical networks, memory impairment in mild cognitively impaired patients is associated with posterior hippocampal atrophy.
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Affiliation(s)
- David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden
| | - Jacob W Vogel
- Department of Psychiatry, University of Pennsylvania, 19104 Philadelphia, USA
| | - Philip S Insel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Department of Psychiatry and Behavioral Sciences, University of California, 94143 San Francisco, USA
| | - Joana B Pereira
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, 171 77 Stockholm, Sweden
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, 19104, Philadelphia, Pennsylvania, USA.,Department of Radiology, University of Pennsylvania, 19104 Philadelphia, Pennsylvania, USA
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, 19104, Philadelphia, Pennsylvania, USA.,Department of Radiology, University of Pennsylvania, 19104 Philadelphia, Pennsylvania, USA.,Department of Diagnostic Radiology, Lund University, 221 00 Lund, Sweden
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, 19104, Philadelphia, Pennsylvania, USA.,Department of Radiology, University of Pennsylvania, 19104 Philadelphia, Pennsylvania, USA
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Memory Clinic, Skåne University Hospital, 205 02 Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Department of Neurology, Skåne University Hospital, 221 00 Lund, Sweden.,Wallenberg Center for Molecular Medicine, Lund University, 221 00 Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Memory Clinic, Skåne University Hospital, 205 02 Malmö, Sweden
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Department of Neurology, Skåne University Hospital, 221 00 Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Department of Psychiatry, University of Pennsylvania, 19104 Philadelphia, USA
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36
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Köbe T, Binette AP, Vogel JW, Meyer PF, Breitner JCS, Poirier J, Villeneuve S. Vascular risk factors are associated with a decline in resting-state functional connectivity in cognitively unimpaired individuals at risk for Alzheimer's disease: Vascular risk factors and functional connectivity changes. Neuroimage 2021; 231:117832. [PMID: 33549747 DOI: 10.1016/j.neuroimage.2021.117832] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 01/25/2021] [Accepted: 01/27/2021] [Indexed: 12/12/2022] Open
Abstract
Resting-state functional connectivity is suggested to be cross-sectionally associated with both vascular burden and Alzheimer's disease (AD) pathology. However, evidence is lacking regarding longitudinal changes in functional connectivity. This study includes 247 cognitively unimpaired individuals with a family history of sporadic AD (185 women/ 62 men; mean [SD] age of 63 [5.3] years). Plasma total-, HDL-, and LDL-cholesterol and systolic and diastolic blood pressure were measured at baseline. Global (whole-brain) brain functional connectivity and connectivity from canonical functional networks were computed from resting-state functional MRI obtained at baseline and ~3.5 years of annual follow-ups, using a predefined functional parcellation. A subsample underwent Aβ- and tau-PET (n=91). Linear mixed-effects models demonstrated that global functional connectivity increased over time across the entire sample. In contrast, higher total-cholesterol and LDL-cholesterol levels were associated with greater reduction of functional connectivity in the default-mode network over time. In addition, higher diastolic blood pressure was associated with global functional connectivity reduction. The associations were similar when the analyses were repeated using two other functional brain parcellations. Aβ and tau deposition in the brain were not associated with changes in functional connectivity over time in the subsample. These findings provide evidence that vascular burden is associated with a decrease in functional connectivity over time in older adults with elevated risk for AD. Future studies are needed to determine if the impact of vascular risk factors on functional brain changes precede the impact of AD pathology on functional brain changes.
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Affiliation(s)
- Theresa Köbe
- Department of Psychiatry, McGill University, H3A 1A1, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Studies on Prevention of Alzheimer's Disease (StoP-AD) Centre, H4H 1R3, Montreal, Quebec, Canada; German Center for Neurodegenerative Diseases (DZNE), 01307, Dresden, Germany.
| | - Alexa Pichet Binette
- Department of Psychiatry, McGill University, H3A 1A1, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Studies on Prevention of Alzheimer's Disease (StoP-AD) Centre, H4H 1R3, Montreal, Quebec, Canada
| | - Jacob W Vogel
- Montreal Neurological Institute, McGill University, H3A 2B4, Montreal, QC, Canada
| | - Pierre-François Meyer
- Department of Psychiatry, McGill University, H3A 1A1, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Studies on Prevention of Alzheimer's Disease (StoP-AD) Centre, H4H 1R3, Montreal, Quebec, Canada
| | - John C S Breitner
- Department of Psychiatry, McGill University, H3A 1A1, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Studies on Prevention of Alzheimer's Disease (StoP-AD) Centre, H4H 1R3, Montreal, Quebec, Canada
| | - Judes Poirier
- Department of Psychiatry, McGill University, H3A 1A1, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Studies on Prevention of Alzheimer's Disease (StoP-AD) Centre, H4H 1R3, Montreal, Quebec, Canada
| | - Sylvia Villeneuve
- Department of Psychiatry, McGill University, H3A 1A1, Montreal, Quebec, Canada; Douglas Mental Health University Institute, Studies on Prevention of Alzheimer's Disease (StoP-AD) Centre, H4H 1R3, Montreal, Quebec, Canada; Department of Neurology and Neurosurgery, McGill University, H3A 2B4, Montreal, Quebec, Canada.
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37
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Vogel JW, Young AL, Oxtoby NP, Smith R, Ossenkoppele R, Aksman LM, Strandberg O, La Joie R, Grothe M, Medina YI, Rabinovici GD, Alexander DC, Evans AC, Hansson O. Spatiotemporal imaging phenotypes of tau pathology in Alzheimer’s disease. Alzheimers Dement 2020. [DOI: 10.1002/alz.045612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Jacob W. Vogel
- Montreal Neurological Institute McGill University Montreal QC Canada
| | - Alexandra L. Young
- Centre for Medical Image Computing University College London London United Kingdom
| | | | - Ruben Smith
- Clinical Memory Research Unit Lund University Malmö Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit Department of Clinical Sciences Mälmo Lund University Lund Sweden
| | - Leon M. Aksman
- Centre for Medical Image Computing University College London London United Kingdom
| | | | - Renaud La Joie
- Memory and Aging Center UCSF Weill Institute for Neurosciences University of California, San Francisco San Francisco CA USA
| | - Michel Grothe
- German Center for Neurodegenerative Diseases (DZNE) Rostock Germany
| | | | - Gil D. Rabinovici
- Department of Neurology Memory and Aging Center University of California San Francisco San Francisco CA USA
| | - Daniel C. Alexander
- Centre for Medical Image Computing University College London London United Kingdom
| | - Alan C. Evans
- Montreal Neurological Institute McGill University Montreal QC Canada
| | - Oskar Hansson
- Clinical Memory Research Unit Department of Clinical Sciences Mälmo Lund University Malmö Sweden
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38
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Vogel JW, Young AL, Oxtoby NP, Smith R, Ossenkoppele R, Aksman LM, Strandberg O, La Joie R, Grothe MJ, Rabinovici GD, Alexander DC, Evans AC, Hansson O. Accounting for systematic spatiotemporal variation improves connectome‐based models of tau spreading in human Alzheimer’s disease. Alzheimers Dement 2020. [DOI: 10.1002/alz.040586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Jacob W. Vogel
- Montreal Neurological Institute McGill University Montreal QC Canada
| | - Alexandra L. Young
- Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King’s College London London United Kingdom
| | | | - Ruben Smith
- Neurology Clinic Skåne University Hospital Lund Sweden
- Clinical Memory Research Unit Lund University Malmö Sweden
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam Department of Neurology Amsterdam Neuroscience Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam Netherlands
| | - Leon M Aksman
- Centre for Medical Image Computing University College London London United Kingdom
| | - Olof Strandberg
- Clinical Memory Research Unit Department of Clinical Sciences Mälmo Lund University Lund Sweden
| | - Renaud La Joie
- Memory and Aging Center UCSF Weill Institute for Neurosciences University of California, San Francisco San Francisco CA USA
| | - Michel J. Grothe
- Wallenberg Centre for Molecular and Translational Medicine University of Gothenburg Gothenburg Sweden
| | - Gil D. Rabinovici
- Memory and Aging Center UCSF Weill Institute for Neurosciences University of California, San Francisco San Francisco CA USA
| | - Daniel C. Alexander
- Centre for Medical Image Computing University College London London United Kingdom
| | - Alan C. Evans
- Montreal Neurological Institute McGill University Montreal QC Canada
| | - Oskar Hansson
- Clinical Memory Research Unit Lund University Malmö Sweden
- Clinical Memory Research Unit Department of Clinical Sciences Mälmo Lund University Malmö Sweden
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39
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Groot C, Yeo BTT, Vogel JW, Zhang X, Sun N, Mormino EC, Pijnenburg YAL, Miller BL, Rosen HJ, La Joie R, Barkhof F, Scheltens P, van der Flier WM, Rabinovici GD, Ossenkoppele R. Latent atrophy factors related to phenotypical variants of posterior cortical atrophy. Neurology 2020; 95:e1672-e1685. [PMID: 32675078 PMCID: PMC7713727 DOI: 10.1212/wnl.0000000000010362] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 04/06/2020] [Indexed: 01/30/2023] Open
Abstract
OBJECTIVE To determine whether atrophy relates to phenotypical variants of posterior cortical atrophy (PCA) recently proposed in clinical criteria (i.e., dorsal, ventral, dominant-parietal, and caudal) we assessed associations between latent atrophy factors and cognition. METHODS We employed a data-driven Bayesian modeling framework based on latent Dirichlet allocation to identify latent atrophy factors in a multicenter cohort of 119 individuals with PCA (age 64 ± 7 years, 38% male, Mini-Mental State Examination 21 ± 5, 71% β-amyloid positive, 29% β-amyloid status unknown). The model uses standardized gray matter density images as input (adjusted for age, sex, intracranial volume, MRI scanner field strength, and whole-brain gray matter volume) and provides voxelwise probabilistic maps for a predetermined number of atrophy factors, allowing every individual to express each factor to a degree without a priori classification. Individual factor expressions were correlated to 4 PCA-specific cognitive domains (object perception, space perception, nonvisual/parietal functions, and primary visual processing) using general linear models. RESULTS The model revealed 4 distinct yet partially overlapping atrophy factors: right-dorsal, right-ventral, left-ventral, and limbic. We found that object perception and primary visual processing were associated with atrophy that predominantly reflects the right-ventral factor. Furthermore, space perception was associated with atrophy that predominantly represents the right-dorsal and right-ventral factors. However, individual participant profiles revealed that the large majority expressed multiple atrophy factors and had mixed clinical profiles with impairments across multiple domains, rather than displaying a discrete clinical-radiologic phenotype. CONCLUSION Our results indicate that specific brain behavior networks are vulnerable in PCA, but most individuals display a constellation of affected brain regions and symptoms, indicating that classification into 4 mutually exclusive variants is unlikely to be clinically useful.
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Affiliation(s)
- Colin Groot
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden.
| | - B T Thomas Yeo
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Jacob W Vogel
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Xiuming Zhang
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Nanbo Sun
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Elizabeth C Mormino
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Yolande A L Pijnenburg
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Bruce L Miller
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Howard J Rosen
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Renaud La Joie
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Frederik Barkhof
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Philip Scheltens
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Wiesje M van der Flier
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Gil D Rabinovici
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Rik Ossenkoppele
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
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40
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Vogel JW, Iturria-Medina Y, Strandberg OT, Smith R, Levitis E, Evans AC, Hansson O. Spread of pathological tau proteins through communicating neurons in human Alzheimer's disease. Nat Commun 2020; 11:2612. [PMID: 32457389 PMCID: PMC7251068 DOI: 10.1038/s41467-020-15701-2] [Citation(s) in RCA: 231] [Impact Index Per Article: 57.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 03/06/2020] [Indexed: 02/07/2023] Open
Abstract
Tau is a hallmark pathology of Alzheimer's disease, and animal models have suggested that tau spreads from cell to cell through neuronal connections, facilitated by β-amyloid (Aβ). We test this hypothesis in humans using an epidemic spreading model (ESM) to simulate tau spread, and compare these simulations to observed patterns measured using tau-PET in 312 individuals along Alzheimer's disease continuum. Up to 70% of the variance in the overall spatial pattern of tau can be explained by our model. Surprisingly, the ESM predicts the spatial patterns of tau irrespective of whether brain Aβ is present, but regions with greater Aβ burden show greater tau than predicted by connectivity patterns, suggesting a role of Aβ in accelerating tau spread. Altogether, our results provide evidence in humans that tau spreads through neuronal communication pathways even in normal aging, and that this process is accelerated by the presence of brain Aβ.
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Affiliation(s)
- Jacob W Vogel
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
| | | | | | - Ruben Smith
- Clinical Memory Research Unit, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Lund, Sweden
| | - Elizabeth Levitis
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Lund, Sweden.
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41
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Vogel JW, La Joie R, Grothe MJ, Diaz-Papkovich A, Doyle A, Vachon-Presseau E, Lepage C, Vos de Wael R, Thomas RA, Iturria-Medina Y, Bernhardt B, Rabinovici GD, Evans AC. A molecular gradient along the longitudinal axis of the human hippocampus informs large-scale behavioral systems. Nat Commun 2020; 11:960. [PMID: 32075960 PMCID: PMC7031290 DOI: 10.1038/s41467-020-14518-3] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 12/09/2019] [Indexed: 12/20/2022] Open
Abstract
The functional organization of the hippocampus is distributed as a gradient along its longitudinal axis that explains its differential interaction with diverse brain systems. We show that the location of human tissue samples extracted along the longitudinal axis of the adult human hippocampus can be predicted within 2mm using the expression pattern of less than 100 genes. Futhermore, this model generalizes to an external set of tissue samples from prenatal human hippocampi. We examine variation in this specific gene expression pattern across the whole brain, finding a distinct anterioventral-posteriodorsal gradient. We find frontal and anterior temporal regions involved in social and motivational behaviors, and more functionally connected to the anterior hippocampus, to be clearly differentiated from posterior parieto-occipital regions involved in visuospatial cognition and more functionally connected to the posterior hippocampus. These findings place the human hippocampus at the interface of two major brain systems defined by a single molecular gradient.
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Affiliation(s)
- Jacob W Vogel
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
| | - Renaud La Joie
- Memory and Aging Center, University of California, San Francisco, CA, USA
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Alexandr Diaz-Papkovich
- McGill University and Genome Quebec Innovation Centre, Montréal, QC, Canada
- Quantitative Life Sciences, McGill University, Montreal, QC, H3A 0G1, Canada
| | - Andrew Doyle
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Etienne Vachon-Presseau
- Faculty of Dentistry, Department of Anesthesia, McGill University, Montréal, QC, Canada
- Alan Edwards Centre for Research on Pain (AECRP), McGill University, Montréal, QC, Canada
| | - Claude Lepage
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | | | - Rhalena A Thomas
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | | | - Boris Bernhardt
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Gil D Rabinovici
- Memory and Aging Center, University of California, San Francisco, CA, USA
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
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42
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Pichet Binette A, Gonneaud J, Vogel JW, La Joie R, Rosa-Neto P, Collins DL, Poirier J, Breitner JCS, Villeneuve S, Vachon-Presseau E. Morphometric network differences in ageing versus Alzheimer's disease dementia. Brain 2020; 143:635-649. [PMID: 32040564 PMCID: PMC7009528 DOI: 10.1093/brain/awz414] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 10/21/2019] [Accepted: 11/15/2019] [Indexed: 12/21/2022] Open
Abstract
Age being the main risk factor for Alzheimer's disease, it is particularly challenging to disentangle structural changes related to normal brain ageing from those specific to Alzheimer's disease. Most studies aiming to make this distinction focused on older adults only and on a priori anatomical regions. Drawing on a large, multi-cohort dataset ranging from young adults (n = 468; age range 18-35 years), to older adults with intact cognition (n = 431; age range 55-90 years) and with Alzheimer's disease (n = 50 with late mild cognitive impairment and 71 with Alzheimer's dementia, age range 56-88 years), we investigated grey matter organization and volume differences in ageing and Alzheimer's disease. Using independent component analysis on all participants' structural MRI, we first derived morphometric networks and extracted grey matter volume in each network. We also derived a measure of whole-brain grey matter pattern organization by correlating grey matter volume in all networks across all participants from the same cohort. We used logistic regressions and receiver operating characteristic analyses to evaluate how well grey matter volume in each network and whole-brain pattern could discriminate between ageing and Alzheimer's disease. Because increased heterogeneity is often reported as one of the main features characterizing brain ageing, we also evaluated interindividual heterogeneity within morphometric networks and across the whole-brain organization in ageing and Alzheimer's disease. Finally, to investigate the clinical validity of the different grey matter features, we evaluated whether grey matter volume or whole-brain pattern was related to clinical progression in cognitively normal older adults. Ageing and Alzheimer's disease contributed additive effects on grey matter volume in nearly all networks, except frontal lobe networks, where differences in grey matter were more specific to ageing. While no networks specifically discriminated Alzheimer's disease from ageing, heterogeneity in grey matter volumes across morphometric networks and in the whole-brain grey matter pattern characterized individuals with cognitive impairments. Preservation of the whole-brain grey matter pattern was also related to lower risk of developing cognitive impairment, more so than grey matter volume. These results suggest both ageing and Alzheimer's disease involve widespread atrophy, but that the clinical expression of Alzheimer's disease is uniquely associated with disruption of morphometric organization.
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Affiliation(s)
- Alexa Pichet Binette
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Qc, H3A 1Y2, Canada
- Douglas Mental Health University Institute, Montreal, Qc, H4H 1R3, Canada
| | - Julie Gonneaud
- Douglas Mental Health University Institute, Montreal, Qc, H4H 1R3, Canada
| | - Jacob W Vogel
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Qc, H3A 2B4, Canada
| | - Renaud La Joie
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Pedro Rosa-Neto
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Qc, H3A 1Y2, Canada
- Douglas Mental Health University Institute, Montreal, Qc, H4H 1R3, Canada
| | - D Louis Collins
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Qc, H3A 2B4, Canada
| | - Judes Poirier
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Qc, H3A 1Y2, Canada
- Douglas Mental Health University Institute, Montreal, Qc, H4H 1R3, Canada
| | - John C S Breitner
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Qc, H3A 1Y2, Canada
- Douglas Mental Health University Institute, Montreal, Qc, H4H 1R3, Canada
| | - Sylvia Villeneuve
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Qc, H3A 1Y2, Canada
- Douglas Mental Health University Institute, Montreal, Qc, H4H 1R3, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Qc, H3A 2B4, Canada
| | - Etienne Vachon-Presseau
- Department of Anesthesia, Faculty of Medicine, McGill University, Montreal, Qc, H3A 1G1, Canada
- Faculty of Dentistry, McGill University, Montreal, Qc, H3A 1G1, Canada
- Alan Edwards Centre for Research on Pain (AECRP), McGill University, Montreal, Qc, H3A 1G1, Canada
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43
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Villeneuve S, Vogel JW, Gonneaud J, Pichet Binette A, Rosa-Neto P, Gauthier S, Bateman RJ, Fagan AM, Morris JC, Benzinger TLS, Johnson SC, Breitner JCS, Poirier J. Proximity to Parental Symptom Onset and Amyloid-β Burden in Sporadic Alzheimer Disease. JAMA Neurol 2019; 75:608-619. [PMID: 29482212 DOI: 10.1001/jamaneurol.2017.5135] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Importance Alzheimer disease (AD) develops during several decades. Presymptomatic individuals might be the best candidates for clinical trials, but their identification is challenging because they have no symptoms. Objective To assess whether a sporadic parental estimated years to symptom onset calculation could be used to identify information about amyloid-β (Aβ) levels in asymptomatic individuals with a parental history of AD dementia. Design, Setting, and Participants This cohort study analyzed Aβ1-42 in cerebrospinal fluid (CSF) specimens from 101 cognitively normal individuals who had a lumbar puncture as part of the Presymptomatic Evaluation of Novel or Experimental Treatments for Alzheimer Disease (PREVENT-AD) cohort from September 1, 2011, through November 30, 2016 (374 participants were enrolled in the cohort during this period). The study estimated each participant's proximity to his/her parent's symptom onset by subtracting the index relative's onset age from his/her current age. The association between proximity to parental symptom onset and Aβ levels was then assessed using apolipoprotein E ε4 (APOE4) status and sex as interactive terms. These analyses were performed again in 2 independent cohorts using CSF and Pittsburgh compound B carbon 11-labeled positron emission tomography (PIB-PET) Aβ biomarkers: the Adult Children Study (ACS) and the Wisconsin Registry for Alzheimer Prevention (WRAP) cohorts. Main Outcomes and Measures The association between proximity to parental symptom onset and Aβ burden in asymptomatic individuals with a parental history of sporadic AD. Results The present analysis included a subset of 101 PREVENT-AD individuals (mean [SD] age, 61.8 [5.1] years; 30 [29.7%] male), 128 ACS participants (112 participants underwent CSF measurement: mean [SD] age, 63.4 [5.1] years; 31 [27.7%] male; and 107 underwent PIB-PET: mean [SD] age, 64.6 [5.3] years; 27 [25.2%] male), and 135 WRAP participants (85 participants underwent CSF measurement: mean [SD] age, 59.9 [6.0] years; 27 [31.8%] male; and 135 underwent PIB-PET: mean [SD] age, 59.6 [6.1] years; 43 [31.9%] male). In the PREVENT-AD cohort, individuals approaching their parent's onset age had lower CSF Aβ1-42 levels (range, 402-1597; B = -9.09, P = .04). This association was stronger in APOE4 carriers (B = -17.9, P = .03) and women (B = -19.8, P = .02). In the ACS cohort, the main association was replicated using PIB-PET data, and the sex interaction was replicated using CSF and PIB-PET data. In the WRAP cohort, the results were not replicated using cross-sectional data, but the main association and the APOE interaction were replicated using PIB-PET longitudinal data. Conclusions and Relevance These results suggest that proximity to parental symptom onset may help estimate Aβ biomarker changes in women or APOE4 carrier asymptomatic individuals with a parental history of sporadic AD.
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Affiliation(s)
- Sylvia Villeneuve
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Douglas Mental Health University Institute, Studies on Prevention of Alzheimer's Disease (StOP-AD) Centre, Montreal, Quebec, Canada.,McGill Centre for Integrative Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Jacob W Vogel
- Douglas Mental Health University Institute, Studies on Prevention of Alzheimer's Disease (StOP-AD) Centre, Montreal, Quebec, Canada.,McGill Centre for Integrative Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Julie Gonneaud
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada.,Douglas Mental Health University Institute, Studies on Prevention of Alzheimer's Disease (StOP-AD) Centre, Montreal, Quebec, Canada
| | - Alexa Pichet Binette
- Douglas Mental Health University Institute, Studies on Prevention of Alzheimer's Disease (StOP-AD) Centre, Montreal, Quebec, Canada.,McGill Centre for Integrative Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Pedro Rosa-Neto
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Douglas Mental Health University Institute, Studies on Prevention of Alzheimer's Disease (StOP-AD) Centre, Montreal, Quebec, Canada.,McGill Centre for Integrative Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Serge Gauthier
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Douglas Mental Health University Institute, Studies on Prevention of Alzheimer's Disease (StOP-AD) Centre, Montreal, Quebec, Canada
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St Louis, Missouri.,Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, Missouri
| | - Anne M Fagan
- Department of Neurology, Washington University School of Medicine, St Louis, Missouri.,Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, Missouri
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St Louis, Missouri.,Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, Missouri
| | - Tammie L S Benzinger
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, Missouri.,Department of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Sterling C Johnson
- Wisconsin Alzheimer's Institute, University of Wisconsin-Madison School of Medicine and Public Health, Madison.,Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison
| | - John C S Breitner
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada.,Douglas Mental Health University Institute, Studies on Prevention of Alzheimer's Disease (StOP-AD) Centre, Montreal, Quebec, Canada.,McGill Centre for Integrative Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Judes Poirier
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada.,Douglas Mental Health University Institute, Studies on Prevention of Alzheimer's Disease (StOP-AD) Centre, Montreal, Quebec, Canada
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Vogel JW, Medina YI, Strandberg O, Smith R, Evans AC, Hansson O. P2-355: EPIDEMIC SPREADING OF TAU THROUGH HUMAN FUNCTIONAL BRAIN CONNECTIONS. Alzheimers Dement 2019. [DOI: 10.1016/j.jalz.2019.06.2762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Jacob W. Vogel
- McGill Centre for Integrative Neuroscience; McGill University; Montreal QC Canada
- Montreal Neurological Institute; McGill University; Montreal QC Canada
| | | | - Olof Strandberg
- Clinical Memory Research Unit; Lund University; Malmö Sweden
| | - Ruben Smith
- Clinical Memory Research Unit; Lund University; Malmö Sweden
| | - Alan C. Evans
- McGill Centre for Integrative Neuroscience; McGill University; Montreal QC Canada
- Montreal Neurological Institute; McGill University; Montreal QC Canada
| | - Oskar Hansson
- Lund University; Lund Sweden
- Memory Clinic; Skåne University Hospital; Lund Sweden
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45
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Kim YJ, Cho SK, Kim HJ, Lee JS, Lee J, Jang YK, Vogel JW, Na DL, Kim C, Seo SW. Data-driven prognostic features of cognitive trajectories in patients with amnestic mild cognitive impairments. Alzheimers Res Ther 2019; 11:10. [PMID: 30670089 PMCID: PMC6343354 DOI: 10.1186/s13195-018-0462-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 12/19/2018] [Indexed: 11/10/2022]
Abstract
BACKGROUND Although amnestic mild cognitive impairment (aMCI) is generally considered to be a prodromal stage of Alzheimer's disease, patients with aMCI show heterogeneous patterns of progression. Moreover, there are few studies investigating data-driven cognitive trajectory in aMCI. We therefore classified patients with aMCI based on their cognitive trajectory, measured by clinical dementia rating sum of boxes (CDR-SOB). Then, we compared the clinical and neuroimaging features among groups classified by cognitive trajectory. METHODS We retrospectively recruited 278 patients with aMCI who underwent three or more timepoints of neuropsychological testing. They also had magnetic resonance imaging (MRI) including structured three-dimensional volume images. Cortical thickness was measured using surface-based methods. We performed trajectory analyses to classify our aMCI patients according to their progression and investigate their cognitive trajectory using CDR-SOB. RESULTS Trajectory analyses showed that patients with aMCI were divided into three groups: stable (61.8%), slow decliner (31.7%), and fast decliner (6.5%). Changes throughout a mean follow-up duration of 3.7 years in the CDR-SOB for the subgroups of stable/slow/fast decliners were 1.3-, 6.4-, and 12-point increases, respectively. Decliners were older and carried apolipoprotein E4 (APOE4) genotypes more frequently than stable patients. Compared with the stable group, decliners showed a higher frequency of aMCI patients with both visual and verbal memory dysfunction, late stage aMCI, and multiple domain dysfunction. In addition, compared with the stable group, the slow decliners showed cortical thinning predominantly in bilateral parietotemporal areas, while the fast decliners showed cortical thinning predominantly in bilateral frontotemporal areas. Both decliner groups showed worse cognitive function in attention, language, visuospatial, memory, and frontal/executive domains than the stable group. CONCLUSIONS Our data-driven trajectory analysis provides new insights into heterogeneous cognitive trajectories of aMCI and further suggests that baseline clinical and neuroimaging profiles might predict aMCI patients with poor prognosis.
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Affiliation(s)
- Yeo Jin Kim
- Department of Neurology, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Korea.,Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Ilwon-dong, Kangnam-ku, Seoul, 135-710, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Seong-Kyoung Cho
- Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Ilwon-dong, Kangnam-ku, Seoul, 135-710, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jin San Lee
- Department of Neurology, Kyung Hee University Hospital, Seoul, Korea
| | - Juyoun Lee
- Department of Neurology, Chungnam National University Hospital, Daejeon, Korea
| | - Young Kyoung Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Ilwon-dong, Kangnam-ku, Seoul, 135-710, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jacob W Vogel
- Montreal Neurological Institute, McGill University, Montrèal, Quebec, Canada
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Ilwon-dong, Kangnam-ku, Seoul, 135-710, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Changsoo Kim
- Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Korea. .,Department of Preventive Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Republic of Korea.
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Ilwon-dong, Kangnam-ku, Seoul, 135-710, Republic of Korea. .,Neuroscience Center, Samsung Medical Center, Seoul, Korea. .,Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea.
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46
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Vogel JW, Mattsson N, Iturria-Medina Y, Strandberg OT, Schöll M, Dansereau C, Villeneuve S, van der Flier WM, Scheltens P, Bellec P, Evans AC, Hansson O, Ossenkoppele R. Data-driven approaches for tau-PET imaging biomarkers in Alzheimer's disease. Hum Brain Mapp 2018; 40:638-651. [PMID: 30368979 DOI: 10.1002/hbm.24401] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2018] [Revised: 08/09/2018] [Accepted: 09/04/2018] [Indexed: 12/14/2022] Open
Abstract
Previous positron emission tomography (PET) studies have quantified filamentous tau pathology using regions-of-interest (ROIs) based on observations of the topographical distribution of neurofibrillary tangles in post-mortem tissue. However, such approaches may not take full advantage of information contained in neuroimaging data. The present study employs an unsupervised data-driven method to identify spatial patterns of tau-PET distribution, and to compare these patterns to previously published "pathology-driven" ROIs. Tau-PET patterns were identified from a discovery sample comprised of 123 normal controls and patients with mild cognitive impairment or Alzheimer's disease (AD) dementia from the Swedish BioFINDER cohort, who underwent [18 F]AV1451 PET scanning. Associations with cognition were tested in a separate sample of 90 individuals from ADNI. BioFINDER [18 F]AV1451 images were entered into a robust voxelwise stable clustering algorithm, which resulted in five clusters. Mean [18 F]AV1451 uptake in the data-driven clusters, and in 35 previously published pathology-driven ROIs, was extracted from ADNI [18 F]AV1451 scans. We performed linear models comparing [18 F]AV1451 signal across all 40 ROIs to tests of global cognition and episodic memory, adjusting for age, sex, and education. Two data-driven ROIs consistently demonstrated the strongest or near-strongest effect sizes across all cognitive tests. Inputting all regions plus demographics into a feature selection routine resulted in selection of two ROIs (one data-driven, one pathology-driven) and education, which together explained 28% of the variance of a global cognitive composite score. Our findings suggest that [18 F]AV1451-PET data naturally clusters into spatial patterns that are biologically meaningful and that may offer advantages as clinical tools.
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Affiliation(s)
- Jacob W Vogel
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Niklas Mattsson
- Clinical Memory Research Unit, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Lund, Sweden.,Department of Neurology, Skåne University Hospital, Lund, Sweden
| | | | | | - Michael Schöll
- Clinical Memory Research Unit, Lund University, Lund, Sweden.,Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Christian Dansereau
- Department of Computer Science and Operations Research, Université de Montréal, Montreal, Quebec, Canada.,Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, University of Montreal, Montreal, Quebec, Canada
| | - Sylvia Villeneuve
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Wiesje M van der Flier
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands.,Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, Netherlands
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Pierre Bellec
- Department of Computer Science and Operations Research, Université de Montréal, Montreal, Quebec, Canada.,Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, University of Montreal, Montreal, Quebec, Canada
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Lund, Sweden
| | - Rik Ossenkoppele
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands.,Clinical Memory Research Unit, Lund University, Lund, Sweden
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47
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Vogel JW, Ossenkoppele R, Kiar G, Medina YI, Baker SL, Hansson O, Evans AC. IC‐P‐224: HETEROGENEOUS TAU‐PET SIGNAL IN THE HIPPOCAMPUS HELPS RESOLVE DISCREPANCIES BETWEEN IMAGING AND PATHOLOGY. Alzheimers Dement 2018. [DOI: 10.1016/j.jalz.2018.06.2291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Jacob W. Vogel
- Montreal Neurological InstituteMcGill UniversityMontrealQCCanada
- McGill Centre for Integrative NeuroscienceMcGill UniversityMontrealQCCanada
| | - Rik Ossenkoppele
- Lund UniversityLundSweden
- VU University Medical CenterAmsterdamNetherlands
| | - Gregory Kiar
- McGill Centre for Integrative NeuroscienceMcGill UniversityMontrealQCCanada
| | - Yasser Iturria Medina
- Montreal Neurological InstituteMcGill UniversityMontrealQCCanada
- McGill Centre for Integrative NeuroscienceMcGill UniversityMontrealQCCanada
| | | | - Oskar Hansson
- Lund UniversityLundSweden
- Skåne University HospitalMalmöSweden
| | - Alan C. Evans
- Montreal Neurological InstituteMcGill UniversityMontrealQCCanada
- McGill Centre for Integrative NeuroscienceMcGill UniversityMontrealQCCanada
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48
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Groot C, Yeo T, Vogel JW, Zhang EX, Suna N, Crane PK, Rabinovici GD, Mormino BC, Pijnenburg YA, Barkhof F, Scheltens P, Flier WM, Ossenkoppele R. P2‐363: LATENT ATROPHY FACTORS IN POSTERIOR CORTICAL ATROPHY RELATE TO SPECIFIC COGNITIVE IMPAIRMENTS. Alzheimers Dement 2018. [DOI: 10.1016/j.jalz.2018.06.1054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Colin Groot
- Alzheimer Center and Department of Neurology, Amsterdam NeuroscienceVU University Medical CenterAmsterdamNetherlands
| | - Thomas Yeo
- National University of SingaporeSingaporeSingapore
| | - Jacob W. Vogel
- Montreal Neurological InstituteMcGill UniversityMontrealQCCanada
| | | | - Nanbo Suna
- National University of SingaporeSingaporeSingapore
| | | | | | | | - Yolande A.L. Pijnenburg
- Alzheimer Center and Department of Neurology, Amsterdam NeuroscienceVU University Medical CenterAmsterdamNetherlands
| | - Frederik Barkhof
- Dementia Research Centre and Department of Neurodegenerative Disease, UCL Institute of NeurologyUniversity College LondonLondonUnited Kingdom
| | - Philip Scheltens
- VU University Medical CenterAmsterdam NeuroscienceAmsterdamNetherlands
| | - Wiesje M. Flier
- VU University Medical CenterAmsterdam NeuroscienceAmsterdamNetherlands
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49
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Vogel JW, Vachon-Presseau E, Pichet Binette A, Tam A, Orban P, La Joie R, Savard M, Picard C, Poirier J, Bellec P, Breitner JCS, Villeneuve S. Brain properties predict proximity to symptom onset in sporadic Alzheimer's disease. Brain 2018; 141:1871-1883. [PMID: 29688388 PMCID: PMC5972641 DOI: 10.1093/brain/awy093] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2017] [Revised: 02/20/2018] [Accepted: 02/25/2018] [Indexed: 11/16/2022] Open
Abstract
See Tijms and Visser (doi:10.1093/brain/awy113) for a scientific commentary on this article.Alzheimer's disease is preceded by a lengthy 'preclinical' stage spanning many years, during which subtle brain changes occur in the absence of overt cognitive symptoms. Predicting when the onset of disease symptoms will occur is an unsolved challenge in individuals with sporadic Alzheimer's disease. In individuals with autosomal dominant genetic Alzheimer's disease, the age of symptom onset is similar across generations, allowing the prediction of individual onset times with some accuracy. We extend this concept to persons with a parental history of sporadic Alzheimer's disease to test whether an individual's symptom onset age can be informed by the onset age of their affected parent, and whether this estimated onset age can be predicted using only MRI. Structural and functional MRIs were acquired from 255 ageing cognitively healthy subjects with a parental history of sporadic Alzheimer's disease from the PREVENT-AD cohort. Years to estimated symptom onset was calculated as participant age minus age of parental symptom onset. Grey matter volume was extracted from T1-weighted images and whole-brain resting state functional connectivity was evaluated using degree count. Both modalities were summarized using a 444-region cortical-subcortical atlas. The entire sample was divided into training (n = 138) and testing (n = 68) sets. Within the training set, individuals closer to or beyond their parent's symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer's disease. Machine learning was used to identify a weighted set of imaging features trained to predict years to estimated symptom onset. This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data. This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting. We next tested if these brain properties could be generalized to predict time to clinical progression in a subgroup of 26 individuals from the Alzheimer's Disease Neuroimaging Initiative, who eventually converted either to mild cognitive impairment or to Alzheimer's dementia. The feature set trained on years to estimated symptom onset in the PREVENT-AD predicted variance in time to clinical conversion in this separate longitudinal dataset. Adjusting for participant age did not impact any of the results. These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals.
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Affiliation(s)
- Jacob W Vogel
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
| | | | - Alexa Pichet Binette
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
| | - Angela Tam
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, Quebec, Canada
| | - Pierre Orban
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, Quebec, Canada
- Department of Psychiatry, University of Montreal, Montreal, Quebec, Canada
| | - Renaud La Joie
- Memory and Aging Center, University of California, San Francisco, California, USA
| | - Mélissa Savard
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
| | - Cynthia Picard
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Judes Poirier
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
- McGill University and Genome Quebec Innovation Centre, Quebec, Canada
| | - Pierre Bellec
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, Quebec, Canada
- Department of Computer Science and Operations Research, University of Montreal, Montreal, QC, Canada
| | - John C S Breitner
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Sylvia Villeneuve
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Centre for the Studies on Prevention of Alzheimer’s Disease, Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
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50
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Vogel JW, Varga Doležalová M, La Joie R, Marks SM, Schwimmer HD, Landau SM, Jagust WJ. Subjective cognitive decline and β-amyloid burden predict cognitive change in healthy elderly. Neurology 2017; 89:2002-2009. [PMID: 28986416 PMCID: PMC5679415 DOI: 10.1212/wnl.0000000000004627] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Accepted: 08/23/2017] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To assess in a longitudinal study whether subjective cognitive decline (SCD) and brain β-amyloid (Aβ) contribute unique information to cognitive decline. METHODS One hundred thirty-six healthy elderly from the Berkeley Aging Cohort Study were followed up for a mean of 4 years. SCD and affective measures were generated from the Geriatric Depression Scale (GDS) with factor analysis on data from a larger set of 347 healthy, nondepressed (GDS <11) elderly individuals. Cognition was summarized with previously validated factor scores. Pittsburgh compound B (PiB)-PET scans were acquired to determine the presence (PiB+) or absence (PiB-) of Aβ pathology. Mixed models were used to assess the independent and interactive effects of SCD, affective features, PiB status, and time on cognition, with adjustment for demographic variables. RESULTS SCD score demonstrated good construct validity compared to an existing measure of subjective memory and was partially explained by several lower-order measurements. Mixed models revealed that SCD interacted with PiB status to predict change in episodic memory and global cognition over time, with adjustment for affective features. PiB+ individuals with more severe SCD demonstrated the steepest cognitive decline. Worse SCD predicted faster decline in working memory independently of PiB status. No such effects were seen for affective scores when adjusted for SCD. CONCLUSIONS PiB+ individuals with SCD are at greatest risk of cognitive decline. Evidence for amyloid alone is not sufficient to indicate risk of rapid cognitive decline in healthy elderly. Effects of GDS on cognitive decline in nondepressed cohorts may be driven by SCD rather than subsyndromal depression.
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Affiliation(s)
- Jacob W Vogel
- From Helen Wills Neuroscience Institute (J.W.V., M.V.D., R.L.J., S.M.M., H.D.S., S.M.L., W.J.J.), University of California, Berkeley; and Memory and Aging Center (R.L.J.), University of California, San Francisco.
| | - Monika Varga Doležalová
- From Helen Wills Neuroscience Institute (J.W.V., M.V.D., R.L.J., S.M.M., H.D.S., S.M.L., W.J.J.), University of California, Berkeley; and Memory and Aging Center (R.L.J.), University of California, San Francisco
| | - Renaud La Joie
- From Helen Wills Neuroscience Institute (J.W.V., M.V.D., R.L.J., S.M.M., H.D.S., S.M.L., W.J.J.), University of California, Berkeley; and Memory and Aging Center (R.L.J.), University of California, San Francisco
| | - Shawn M Marks
- From Helen Wills Neuroscience Institute (J.W.V., M.V.D., R.L.J., S.M.M., H.D.S., S.M.L., W.J.J.), University of California, Berkeley; and Memory and Aging Center (R.L.J.), University of California, San Francisco
| | - Henry D Schwimmer
- From Helen Wills Neuroscience Institute (J.W.V., M.V.D., R.L.J., S.M.M., H.D.S., S.M.L., W.J.J.), University of California, Berkeley; and Memory and Aging Center (R.L.J.), University of California, San Francisco
| | - Susan M Landau
- From Helen Wills Neuroscience Institute (J.W.V., M.V.D., R.L.J., S.M.M., H.D.S., S.M.L., W.J.J.), University of California, Berkeley; and Memory and Aging Center (R.L.J.), University of California, San Francisco
| | - William J Jagust
- From Helen Wills Neuroscience Institute (J.W.V., M.V.D., R.L.J., S.M.M., H.D.S., S.M.L., W.J.J.), University of California, Berkeley; and Memory and Aging Center (R.L.J.), University of California, San Francisco
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