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Baumeister H, Gellersen HM, Polk SE, Lattmann R, Wuestefeld A, Wisse LEM, Glenn T, Yakupov R, Stark M, Kleineidam L, Roeske S, Morgado BM, Esselmann H, Brosseron F, Ramirez A, Lüsebrink F, Synofzik M, Schott BH, Schmid MC, Hetzer S, Dechent P, Scheffler K, Ewers M, Hellmann-Regen J, Ersözlü E, Spruth E, Gemenetzi M, Fliessbach K, Bartels C, Rostamzadeh A, Glanz W, Incesoy EI, Janowitz D, Rauchmann BS, Kilimann I, Sodenkamp S, Coenjaerts M, Spottke A, Peters O, Priller J, Schneider A, Wiltfang J, Buerger K, Perneczky R, Teipel S, Laske C, Wagner M, Ziegler G, Jessen F, Düzel E, Berron D. Disease stage-specific atrophy markers in Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2025.03.13.25323904. [PMID: 40162264 PMCID: PMC11952614 DOI: 10.1101/2025.03.13.25323904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/02/2025]
Abstract
INTRODUCTION Structural MRI often lacks diagnostic, prognostic, and monitoring value in Alzheimer's disease (AD), particularly in early disease stages. To improve its utility, we aimed to identify optimal MRI readouts for different use cases. METHODS We included 363 older adults; healthy controls (HC) who were negative or positive for amyloidbeta (Aβ) and Aβ-positive patients with subjective cognitive decline (SCD), mild cognitive impairment, or dementia of the Alzheimer type. MRI and neuropsychological assessments were administered annually for up to three years. RESULTS Accelerated atrophy of distinct MTL subregions was evident already during preclinical AD. Symptomatic disease stages most notably differed in their hippocampal and parietal atrophy signatures. Associations of atrophy markers and cognitive inventories varied by intended use and disease stage. DISCUSSION With the appropriate readout, MRI can detect abnormal atrophy already during preclinical AD. To optimize performance, MRI readouts should be tailored to the targeted disease stage and intended use.
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Affiliation(s)
- Hannah Baumeister
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Helena M. Gellersen
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Sarah E. Polk
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - René Lattmann
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Laura E. M. Wisse
- Diagnostic Radiology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Trevor Glenn
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Melina Stark
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department for Cognitive Disorders and Old Age Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department for Cognitive Disorders and Old Age Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Sandra Roeske
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Barbara Marcos Morgado
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, Goettingen, Germany
| | - Hermann Esselmann
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, Goettingen, Germany
| | | | - Alfredo Ramirez
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department for Cognitive Disorders and Old Age Psychiatry, University Hospital Bonn, Bonn, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), Cologne, Germany
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany
- Department of Psychiatry & Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, San Antonio, USA
| | - Falk Lüsebrink
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Matthis Synofzik
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Division of Translational Genomics of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, Tübingen, Germany
- Center for Neurology, University of Tübingen, Tübingen, Germany
| | - Björn H. Schott
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, Goettingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Matthias C. Schmid
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany
| | - Stefan Hetzer
- Berlin Center for Advanced Neuroimaging, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Peter Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Goettingen, Goettingen, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Julian Hellmann-Regen
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité – Universitätsmedizin Berlin, Berlin, Germany
- ECRC Experimental and Clinical Research Center, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Ersin Ersözlü
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Neurosciences, Charité – Universitätsmedizin Berlin, Berlin, Germany
- ECRC Experimental and Clinical Research Center, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Eike Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Institute of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Maria Gemenetzi
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Institute of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department for Cognitive Disorders and Old Age Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Claudia Bartels
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, Goettingen, Germany
| | - Ayda Rostamzadeh
- Department of Psychiatry, University of Cologne, Cologne, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Enise I. Incesoy
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, University Clinic Magdeburg, Otto-von-Guericke University, Magdeburg, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
- Department of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Sebastian Sodenkamp
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Marie Coenjaerts
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University Hospital Bonn, Bonn, Germany
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Institute of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Institute of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
- University of Edinburgh and UK DRI, Edinburgh, UK
- Department of Psychiatry and Psychotherapy, School of Medicine and Health, Technical University of Munich, Munich, Germany
- German Center for Mental Health (DZPG), Munich, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department for Cognitive Disorders and Old Age Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, Goettingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany
- Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research, Tübingen, Germany
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department for Cognitive Disorders and Old Age Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Gabriel Ziegler
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), Cologne, Germany
- Department of Psychiatry, University of Cologne, Cologne, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - David Berron
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Center for Behavioral Brain Sciences (CBBS), Otto-von-Guericke University, Magdeburg, Germany
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Alford K, O'Brien C, Banerjee S, Fitzpatrick C, Vera JH. Managing cognitive impairment in people with HIV. Curr Opin Infect Dis 2025; 38:1-9. [PMID: 39602088 DOI: 10.1097/qco.0000000000001078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
PURPOSE OF REVIEW To describe patient-centred multiciplinary management and care of people with HIV presenting with cognitive disorders. RECENT FINDINGS In the era of effective antiretroviral therapy a comprehensive, multifactorial approach to assessing and managing cognitive impairment in people with HIV is required. The complexity of cognitive disorders in this population demands more than current guidelines offer, which focus primarily on HIV management, overlooking broader clinical, psychological, and social factors. Key recommendations include the integration of medical history, physical examinations, brain imaging (especially MRI), neuropsychological testing, and lumbar puncture to identify underlying causes of cognitive decline. Pharmacological treatments for HIV-related cognitive decline remain ineffective, making nonpharmacological interventions, such as cognitive training and holistic rehabilitation programs, essential for managing symptoms. Additionally, the review calls for early detection through routine screening, monitoring, and preventive care. Social and psychological support are emphasized as critical factors in addressing the mental health issues exacerbated by cognitive decline in people with HIV. Emerging models of care, such as integrated, multidisciplinary clinics, show promise in delivering comprehensive, patient-centered care that addresses both cognitive issues and broader quality of life. SUMMARY This review underscores the need for a holistic, multifaceted approach to managing cognitive impairment in people with HIV, integrating clinical, psychological, and social interventions alongside HIV treatment. Given the lack of effective pharmacological options, early detection, prevention, and nonpharmacological strategies are critical in optimizing quality of life and maintaining cognitive function in this vulnerable population.
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Affiliation(s)
- Kate Alford
- Department of Global Health and Infection, Brighton and Sussex Medical School, University of Sussex, Brighton
| | | | - Sube Banerjee
- Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | | | - Jaime H Vera
- Department of Global Health and Infection, Brighton and Sussex Medical School, University of Sussex, Brighton
- University Hospitals Sussex NHS Foundation Trust
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Aumont E, Bedard MA, Bussy A, Arias JF, Tissot C, Hall BJ, Therriault J, Rahmouni N, Stevenson J, Servaes S, Macedo AC, Vitali P, Poltronetti NM, Fliaguine O, Trudel L, Gauthier S, Chakravarty MM, Rosa-Neto P. Hippocampal atrophy over two years in relation to tau, amyloid-β and memory in older adults. Neurobiol Aging 2025; 146:48-57. [PMID: 39631245 DOI: 10.1016/j.neurobiolaging.2024.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 09/27/2024] [Accepted: 11/18/2024] [Indexed: 12/07/2024]
Abstract
In this longitudinal brain imaging study, we aimed to characterize hippocampal tau accumulation and subfield atrophy relative to cortical amyloid-β and memory performance. We measured tau-PET in regions associated with Braak stages I to VI, global amyloid-PET burden, hippocampal subfield volumes and memory assessments from 173 participants aged 55-85. Eighty-six of these participants were tested again two years later. Tau-PET change in the Braak II region, corresponding to the hippocampus and the entorhinal cortex, was significantly associated with the cornu ammonis 1 (CA1) atrophy and memory score. This CA1 atrophy did not significantly mediate the association between tau and memory, nor did global amyloid-PET burden correlate with tau-PET changes in the Braak II region. Longitudinal hippocampal tau accumulation is amyloid-β-independent and co-localized with subfield atrophy. As tau-associated memory decline seems to be independent from hippocampal atrophy, other mechanisms could contribute to the deficit.
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Affiliation(s)
- Etienne Aumont
- NeuroQAM Research Centre, Université du Québec à Montréal (UQAM), Montreal, QC H2X 3P2, Canada; McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada
| | - Marc-André Bedard
- NeuroQAM Research Centre, Université du Québec à Montréal (UQAM), Montreal, QC H2X 3P2, Canada; McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada
| | - Aurélie Bussy
- Cerebral Imaging Center, Douglas Research Center, Montreal, QC H4H 1R3, Canada; Computational Brain Anatomy (CoBrALab) Laboratory, Montreal, QC H4H 1R3, Canada
| | - Jaime Fernandez Arias
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Cecile Tissot
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada; Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States
| | - Brandon J Hall
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Joseph Therriault
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Nesrine Rahmouni
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Jenna Stevenson
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Stijn Servaes
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Arthur C Macedo
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Paolo Vitali
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | | | - Olga Fliaguine
- NeuroQAM Research Centre, Université du Québec à Montréal (UQAM), Montreal, QC H2X 3P2, Canada
| | - Lydia Trudel
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Serge Gauthier
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Mallar M Chakravarty
- Cerebral Imaging Center, Douglas Research Center, Montreal, QC H4H 1R3, Canada; Computational Brain Anatomy (CoBrALab) Laboratory, Montreal, QC H4H 1R3, Canada; Department of Psychiatry, McGill University, Montreal, QC H3A 1A1, Canada
| | - Pedro Rosa-Neto
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada.
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Katsumi Y, Eckbo R, Chapleau M, Wong B, McGinnis SM, Touroutoglou A, Dickerson BC, Putcha D. Greater baseline cortical atrophy in the dorsal attention network predicts faster clinical decline in Posterior Cortical Atrophy. Alzheimers Res Ther 2024; 16:262. [PMID: 39696378 PMCID: PMC11653806 DOI: 10.1186/s13195-024-01636-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2024] [Accepted: 12/02/2024] [Indexed: 12/20/2024]
Abstract
BACKGROUND Posterior Cortical Atrophy (PCA) is a clinical syndrome characterized by progressive visuospatial and visuoperceptual impairment. As the neurodegenerative disease progresses, patients lose independent functioning due to the worsening of initial symptoms and development of symptoms in other cognitive domains. The timeline of clinical progression is variable across patients, and the field currently lacks robust methods for prognostication. Here, evaluated the utility of MRI-based cortical atrophy as a predictor of longitudinal clinical decline in a sample of PCA patients. METHODS PCA patients were recruited through the Massachusetts General Hospital Frontotemporal Disorders Unit PCA Program. All patients had cortical thickness estimates from baseline MRI scans, which were used to predict longitudinal change in clinical impairment assessed by the CDR Sum-of-Boxes (CDR-SB) score. Multivariable linear regression was used to estimate the magnitude of cortical atrophy in PCA patients relative to a group of amyloid-negative cognitively unimpaired participants. Linear mixed-effects models were used to test hypotheses about the utility of baseline cortical atrophy for predicting longitudinal clinical decline. RESULTS Data acquired from 34 PCA patients (mean age = 65.41 ± 7.90, 71% females) and 24 controls (mean age = 67.34 ± 4.93, 50% females) were analyzed. 62% of the PCA patients were classified as having mild cognitive impairment (CDR 0.5) at baseline, with the rest having mild dementia (CDR 1). Each patient had at least one clinical follow-up, with the mean duration of 2.78 ± 1.62 years. Relative to controls, PCA patients showed prominent baseline atrophy in the posterior cortical regions, with the largest effect size observed in the visual network of the cerebral cortex. Cortical atrophy localized to the dorsal attention network, which supports higher-order visuospatial function, selectively predicted the rate of subsequent clinical decline. CONCLUSIONS These results demonstrate the utility of a snapshot measure of cortical atrophy of the dorsal attention network for predicting the rate of subsequent clinical decline in PCA. If replicated, this topographically-specific MRI-based biomarker could be useful as a clinical prognostication tool that facilitates personalized care planning.
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Affiliation(s)
- Yuta Katsumi
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02115, USA.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
- Massachusetts Alzheimer's Disease Research Center, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
| | - Ryan Eckbo
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Marianne Chapleau
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Bonnie Wong
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Scott M McGinnis
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Alexandra Touroutoglou
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Massachusetts Alzheimer's Disease Research Center, Massachusetts General Hospital, Charlestown, MA, 02129, USA
| | - Bradford C Dickerson
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Massachusetts Alzheimer's Disease Research Center, Massachusetts General Hospital, Charlestown, MA, 02129, USA
| | - Deepti Putcha
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02115, USA.
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02115, USA.
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5
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Katsumi Y, Eckbo R, Chapleau M, Wong B, McGinnis SM, Touroutoglou A, Dickerson BC, Putcha D. Greater baseline cortical atrophy in the dorsal attention network predicts faster clinical decline in Posterior Cortical Atrophy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.10.15.24315270. [PMID: 39484250 PMCID: PMC11527058 DOI: 10.1101/2024.10.15.24315270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/03/2024]
Abstract
Background and Objectives Posterior Cortical Atrophy (PCA) is a clinical syndrome characterized by progressive visuospatial and visuoperceptual impairment. As the neurodegenerative disease progresses, patients lose independent functioning due to the worsening of initial symptoms and development of symptoms in other cognitive domains. The timeline of clinical progression is variable across patients, and the field currently lacks robust methods for prognostication. Here, evaluated the utility of MRI-based cortical atrophy as a predictor of longitudinal clinical decline in a sample of PCA patients. Methods PCA patients were recruited through the Massachusetts General Hospital Frontotemporal Disorders Unit PCA Program. All patients had cortical thickness estimates from baseline MRI scans, which were used to predict longitudinal change in clinical impairment assessed by the CDR Sum-of-Boxes (CDR-SB) score. Multivariable linear regression was used to estimate the magnitude of cortical atrophy in PCA patients relative to a group of amyloid-negative cognitively unimpaired participants. Linear mixed-effects models were used to test hypotheses about the utility of baseline cortical atrophy for predicting longitudinal clinical decline. Results Data acquired from 34 PCA patients (mean age = 65.41 ± 7.90, 71% females) and 24 controls (mean age = 67.34 ± 4.93, 50% females) were analyzed. Sixty-two percent of the PCA patients were classified as having mild cognitive impairment (CDR 0.5) at baseline, with the rest having mild dementia (CDR 1). Each patient had at least one clinical follow-up, with the mean duration of 2.78 ± 1.62 years. Relative to controls, PCA patients showed prominent baseline atrophy in the posterior cortical regions, with the largest effect size observed in the visual network of the cerebral cortex. Cortical atrophy localized to the dorsal attention network, which supports higher-order visuospatial function, selectively predicted the rate of subsequent clinical decline. Discussion These results demonstrate the utility of a snapshot measure of cortical atrophy of the dorsal attention network for predicting the rate of subsequent clinical decline in PCA. If replicated, this topographically-specific MRI-based biomarker could be useful as a clinical prognostication tool that facilitates personalized care planning.
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Affiliation(s)
- Yuta Katsumi
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Ryan Eckbo
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Marianne Chapleau
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Bonnie Wong
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Scott M McGinnis
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Alexandra Touroutoglou
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Massachusetts Alzheimer's Disease Research Center, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Bradford C Dickerson
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Massachusetts Alzheimer's Disease Research Center, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Deepti Putcha
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA
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Xie L, Das SR, Wisse LEM, Ittyerah R, de Flores R, Shaw LM, Yushkevich PA, Wolk DA. Correction: Baseline structural MRI and plasma biomarkers predict longitudinal structural atrophy and cognitive decline in early Alzheimer's disease. Alzheimers Res Ther 2024; 16:11. [PMID: 38217025 PMCID: PMC10785540 DOI: 10.1186/s13195-023-01374-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6 Floor, Philadelphia, PA, 19104, USA.
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6 Floor, Philadelphia, PA, 19104, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6 Floor, Philadelphia, PA, 19104, USA
| | - Robin de Flores
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6 Floor, Philadelphia, PA, 19104, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6 Floor, Philadelphia, PA, 19104, USA
| | - David A Wolk
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
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Chen Y, Su Y, Wu J, Chen K, Atri A, Caselli RJ, Reiman EM, Wang Y. Combining Blood-Based Biomarkers and Structural MRI Measurements to Distinguish Persons with and without Significant Amyloid Plaques. J Alzheimers Dis 2024; 98:1415-1426. [PMID: 38578889 PMCID: PMC11789004 DOI: 10.3233/jad-231162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2024]
Abstract
Background Amyloid-β (Aβ) plaques play a pivotal role in Alzheimer's disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing Aβ plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of Aβ plaques. Objective To develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma Aβ42/40 ratio. Methods We extracted hippocampal multivariate morphometry statistics from MR images and together with plasma Aβ42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimer's Institute (BAI), including prediction accuracy, precision, recall rate, F1 score, and AUC score. Results Results from ADNI (mean age 72.6, Aβ+ rate 49.5%) and BAI (mean age 66.2, Aβ+ rate 36.9%) datasets revealed the integrated multimodal (IMM) model's superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma Aβ42/40 (0.73 and 0.81) predictors. CONCLUSIONS Our IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimer's disease diagnostics, leveraging diverse pathologic features related to Aβ plaques and structural MRI.
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Affiliation(s)
- Yanxi Chen
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Yi Su
- Banner Alzheimer’s Institute, Phoenix, AZ, USA
| | - Jianfeng Wu
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Kewei Chen
- College of Health Solutions, Arizona State University, Tempe, AZ, USA
| | - Alireza Atri
- Banner Alzheimer’s Institute, Phoenix, AZ, USA
- Banner Sun Health Research Institute, Sun City, AZ, USA
- Center for Brain/Mind Medicine, Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | - Yalin Wang
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
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Ghaderi S, Mohammadi S, Mohammadi M. Obstructive sleep apnea and attention deficits: A systematic review of magnetic resonance imaging biomarkers and neuropsychological assessments. Brain Behav 2023; 13:e3262. [PMID: 37743582 PMCID: PMC10636416 DOI: 10.1002/brb3.3262] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/07/2023] [Accepted: 09/12/2023] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Obstructive sleep apnea (OSA) is a common sleep disorder that causes intermittent hypoxia and sleep fragmentation, leading to attention impairment and other cognitive deficits. Magnetic resonance imaging (MRI) is a powerful modality that can reveal the structural and functional brain alterations associated with attention impairment in OSA patients. The objective of this systematic review is to identify and synthesize the evidence on MRI biomarkers and neuropsychological assessments of attention deficits in OSA patients. METHODS We searched the Scopus and PubMed databases for studies that used MRI to measure biomarkers related to attention alteration in OSA patients and reported qualitative and quantitative data on the association between MRI biomarkers and attention outcomes. We also included studies that found an association between neuropsychological assessments and MRI findings in OSA patients with attention deficits. RESULTS We included 19 studies that met our inclusion criteria and extracted the relevant data from each study. We categorized the studies into three groups based on the MRI modality and the cognitive domain they used: structural and diffusion tensor imaging MRI findings, functional, perfusion, and metabolic MRI findings, and neuropsychological assessment findings. CONCLUSIONS We found that OSA is associated with structural, functional, and metabolic brain alterations in multiple regions and networks that are involved in attention processing. Treatment with continuous positive airway pressure can partially reverse some of the brain changes and improve cognitive function in some domains and in some studies. This review suggests that MRI techniques and neuropsychological assessments can be useful tools for monitoring the progression and response to treatment of OSA patients.
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Affiliation(s)
- Sadegh Ghaderi
- Department of Neuroscience and Addiction StudiesSchool of Advanced Technologies in MedicineTehran University of Medical SciencesTehranIran
| | - Sana Mohammadi
- Department of Medical SciencesSchool of MedicineIran University of Medical SciencesTehranIran
| | - Mahdi Mohammadi
- Department of Medical Physics and Biomedical Engineering, School of MedicineTehran University of Medical SciencesTehranIran
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9
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Wang S, Liu S, Ke S, Zhou W, Pan T. APOEɛ4 Status and Plasma p-tau181 Levels May Influence Memory and Executive Function Decline in Older Adults Without Dementia. J Alzheimers Dis 2023; 95:1509-1518. [PMID: 37718807 DOI: 10.3233/jad-230437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
BACKGROUND Elevated tau phosphorylation has been linked to the Apolipoprotein E (APOE) ɛ4 allele, which is considered one of the most significant genes related to Alzheimer's disease (AD). However, it is uncertain whether the impact of increased plasma tau phosphorylated at threonine 181 (p-tau181) on memory and executive function decline would be greater among APOEɛ4 carriers. OBJECTIVE To investigate the effects of plasma p-tau181 and APOEɛ4 on memory and executive function. METHODS The longitudinal analysis included 608 older adults without dementia (aged 72±7 years; 47% female; follow-up period of 1.59±1.47 years) from the ADNI dataset, including 180 individuals with normal cognition and 429 individuals with mild cognitive impairment. Linear mixed-effects models were utilized to assess the contributions of APOEɛ4 status and plasma p-tau181 to longitudinal changes in memory composite score and executive function composite score. RESULTS At baseline, the APOEɛ4+/Tau+ group exhibited poorer performance in memory composite score and executive function composite score, and an elevated load of cerebrospinal fluid Aβ and tau pathologies. To further understand longitudinal changes, we compared groups directly based on plasma p-tau181 and APOEɛ4 status (four groups: APOEɛ4-/Tau-, APOEɛ4-/Tau+, APOEɛ4+/Tau-, APOEɛ4+/Tau+). Both the memory composite score and executive function composite score showed a significantly greater decline in the APOEɛ4+/Tau+ group than in all other groups. CONCLUSIONS Our findings indicate that there is an interaction between plasma p-tau181 levels and APOEɛ4 status, which contributes to the longitudinal changes of memory and executive function in older adults without dementia.
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Affiliation(s)
- Shanshan Wang
- Department of Neurology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China
| | - Suzhi Liu
- Department of Neurology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China
| | - Shaofa Ke
- Department of Neurology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China
| | - Wenjun Zhou
- Research and Development, Hangzhou Shansier Medical Technologies Co., Ltd., Hangzhou, China
| | - Tengwei Pan
- Department of Neurology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China
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