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Bouwman MMA, Frigerio I, Lin CP, Reijner N, van de Berg WDJ, Jonkman LE. Hippocampal subfields: volume, neuropathological vulnerability and cognitive decline in Alzheimer's and Parkinson's disease. Alzheimers Res Ther 2025; 17:121. [PMID: 40448161 PMCID: PMC12124080 DOI: 10.1186/s13195-025-01768-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2025] [Accepted: 05/18/2025] [Indexed: 06/02/2025]
Abstract
BACKGROUND The hippocampus is highly affected in neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD). The relationship between neuropathology and atrophy in hippocampal subfields is complex due to differences in the selective neuronal vulnerability to distinct protein aggregates that underlie cognitive impairment. The aim of the current study was to investigate the relation between hippocampal subfield volumes, neuropathological burden (amyloid-β, p-tau and α-synuclein) and cognitive performance in AD, PD and control brain donors, using a cross-disease and within-subject post-mortem in situ MRI and neuropathology approach. METHODS A total of 60 brain donors, including 14 non-neurological controls, 27 AD and 19 PD, underwent post-mortem in situ MRI. From 3D-T1 images hippocampal subfield and entorhinal cortex volumes were derived using FreeSurfer-based subfield segmentation. Hippocampal tissue was obtained at subsequent autopsy, fixed and immunostained for amyloid-β, p-tau and pSer129-αSyn. Immunoreactivity in hippocampal subfields was quantified as area% load using QuPath. Clinical Dementia Rating scores were extracted from the clinical files when available. RESULTS AD showed atrophy and increased p-tau, but not amyloid-β, burden in the CA1, subiculum and entorhinal cortex compared to controls, however MRI and neuropathology did not correlate. Controls and PD had similar hippocampal subfield volumes and pathology load. In PD, p-tau pathology, rather than pSer129-αSyn, was associated with lower total hippocampal volume (r=-0.68, p = 0.045), predominantly in PD with dementia (PDD) (r=-0.99, p = 0.013). Cross-disease, volume loss of the subiculum (r=-0.68, p = 0.001) and entorhinal cortex (r=-0.73, p = 0.004) strongly associated with cognitive impairment. Moreover, p-tau pathology had the strongest effect on subfield atrophy, most pronounced in the subiculum (β=-0.570, p < 0.001), but could only explain 22-44% of the volumetric variance. CONCLUSIONS Even though p-tau was the strongest predictor of hippocampal subfield atrophy, AD-pathology (p-tau and amyloid-β) only partially accounted for volumetric differences in hippocampal subfields, highlighting the significance of other pathologies or mechanisms. The increased sensitivity of subicular and entorhinal cortical atrophy compared to total hippocampal atrophy highlights the potential clinical value of incorporating hippocampal subfield atrophy in monitoring disease progression.
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Affiliation(s)
- Maud M A Bouwman
- Department of Anatomy and Neurosciences, Section Clinical Neuroanatomy and Biobanking, Amsterdam UMC, location VUmc, De Boelelaan 1118, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Brain imaging, Amsterdam, The Netherlands.
| | - Irene Frigerio
- Department of Anatomy and Neurosciences, Section Clinical Neuroanatomy and Biobanking, Amsterdam UMC, location VUmc, De Boelelaan 1118, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain imaging, Amsterdam, The Netherlands
| | - Chen-Pei Lin
- Department of Anatomy and Neurosciences, Section Clinical Neuroanatomy and Biobanking, Amsterdam UMC, location VUmc, De Boelelaan 1118, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain imaging, Amsterdam, The Netherlands
| | - Niels Reijner
- Department of Anatomy and Neurosciences, Section Clinical Neuroanatomy and Biobanking, Amsterdam UMC, location VUmc, De Boelelaan 1118, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain imaging, Amsterdam, The Netherlands
| | - Wilma D J van de Berg
- Department of Anatomy and Neurosciences, Section Clinical Neuroanatomy and Biobanking, Amsterdam UMC, location VUmc, De Boelelaan 1118, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Laura E Jonkman
- Department of Anatomy and Neurosciences, Section Clinical Neuroanatomy and Biobanking, Amsterdam UMC, location VUmc, De Boelelaan 1118, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain imaging, Amsterdam, The Netherlands
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Delhaye E, Besson G, Bahri MA, Bastin C. Object fine-grained discrimination as a sensitive cognitive marker of transentorhinal integrity. Commun Biol 2025; 8:800. [PMID: 40415135 DOI: 10.1038/s42003-025-08201-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Accepted: 05/08/2025] [Indexed: 05/27/2025] Open
Abstract
The transentorhinal cortex (tErC) is one of the first regions affected by Alzheimer's disease (AD), often showing changes before clinical symptoms appear. Understanding its role in cognition is key to detecting early cognitive impairments in AD. This study tested the hypothesis that the tErC supports fine-grained representations of unique individual objects, sensitively to the granularity of the demanded discrimination, influencing both perceptual and mnemonic functions. We examined the tErC's role in object versus scene discrimination, using objective (based on a pretrained convolutional neural network, CNN) and subjective (human-rated) measures of visual similarity. Our results show that the structural integrity of the tErC is specifically related to the sensitivity to visual similarity for objects, but not for scenes. Importantly, this relationship depends on how visual similarity is measured: it appears only when using CNN visual similarity measures in perceptual discrimination, and solely when using subjective similarity ratings in mnemonic discrimination. Furthermore, in mnemonic discrimination, object sensitivity to visual similarity was specifically associated with the integrity of tErC-BA36 connectivity, only when similarity was computed from subjective ratings. Altogether, these findings suggest that discrimination sensitivity to object visual similarity may represent a specific marker of tErC integrity after accounting for the type of similarity measured.
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Affiliation(s)
- Emma Delhaye
- GIGA Research, CRC Human Imaging, University of Liège, Liège, Belgium.
- PsyNCog Research Unit, Faculty of Psychology, University of Liège, Liège, Belgium.
- CICPSI, Faculty of Psychology, University of Lisbon, Lisbon, Portugal.
| | - Gabriel Besson
- Proaction Laboratory, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal
- CINEICC, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal
| | - Mohamed Ali Bahri
- GIGA Research, CRC Human Imaging, University of Liège, Liège, Belgium
| | - Christine Bastin
- GIGA Research, CRC Human Imaging, University of Liège, Liège, Belgium
- PsyNCog Research Unit, Faculty of Psychology, University of Liège, Liège, Belgium
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Duong MT, Das SR, Khandelwal P, Lyu X, Xie L, McGrew E, Dehghani N, McMillan CT, Lee EB, Shaw LM, Yushkevich PA, Wolk DA, Nasrallah IM. Hypometabolic mismatch with atrophy and tau pathology in mixed Alzheimer's and Lewy body disease. Brain 2025; 148:1577-1587. [PMID: 39573823 PMCID: PMC12073973 DOI: 10.1093/brain/awae352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 08/27/2024] [Accepted: 10/10/2024] [Indexed: 01/19/2025] Open
Abstract
Polypathology is a major driver of heterogeneity in the clinical presentation and extent of neurodegeneration (N) in patients with Alzheimer's disease (AD). Beyond amyloid (A) and tau (T) pathologies, over half of patients with AD have concomitant pathology such as α-synuclein (S) in mixed AD with Lewy body disease (LBD). Patients with multiple aetiology dementia such as AD + LBD have faster progression and potentially differential responses to targeted treatments, although the diagnosis of AD + LBD can be challenging given the overlapping clinical and imaging features. Development and validation of improved in vivo biomarkers are required to study relationships between N and S and identify imaging patterns reflecting mixed AD + LBD pathologies. We hypothesized that individual proteinopathies, such as T and S, are associated with commensurate levels of N. Thus, we assessed biomarkers of A, T, N and S with PET, MRI and CSF seeding amplification assay (SAA) data to determine molecular presentations of mixed A+S+ versus A+S- cognitively impaired patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Strikingly, A+S+ patients had parieto-occipital 18F-fluorodeoxyglucose hypometabolism (a measure of N) disproportionate to the degree of regional atrophy or T burden, highlighting worse hypometabolism associated with S+ status on SAA. Following up on this hypometabolic mismatch with CSF metabolite and proteome analyses, we found that A+S+ patients exhibited lower CSF levels of dopamine metabolites and synaptic markers like neuronal pentraxin-2 (NPTX2), suggesting that altered neurotransmission and neuron integrity contribute to this dissociation between metabolic PET and MRI. Potential confounders exist when studying relations between N, AD and LBD pathologies, including neuroinflammation and other non-Alzheimer's pathologies in mixed dementia, although our findings imply posterior hypometabolic mismatch is related more to S than vascular or TDP-43 pathology. Overall, A+S+ patients had posterior mismatch with excessive 18F-fluorodeoxyglucose hypometabolism relative to atrophy or T load, possibly reflecting impaired neuronal integrity. Further research must disentangle the impact of multiple proteinopathies and clinicopathologic factors on hypometabolism and atrophy. Cumulatively, patients with mixed AD + LBD aetiologies harbour a unique metabolic PET mismatch signature.
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Affiliation(s)
- Michael Tran Duong
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sandhitsu R Das
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Alzheimer’s Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Pulkit Khandelwal
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Xueying Lyu
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Long Xie
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Emily McGrew
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nadia Dehghani
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Corey T McMillan
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Alzheimer’s Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Edward B Lee
- Alzheimer’s Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Leslie M Shaw
- Alzheimer’s Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Paul A Yushkevich
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Alzheimer’s Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David A Wolk
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Alzheimer’s Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ilya M Nasrallah
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Alzheimer’s Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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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, for the DELCODE study group. 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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Wisse LEM, Wuestefeld A, Murray ME, Jagust W, La Joie R. Role of tau versus TDP-43 pathology on medial temporal lobe atrophy in aging and Alzheimer's disease. Alzheimers Dement 2025; 21:e14582. [PMID: 39985478 PMCID: PMC11846482 DOI: 10.1002/alz.14582] [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: 10/01/2024] [Revised: 12/16/2024] [Accepted: 01/12/2025] [Indexed: 02/24/2025]
Abstract
Hippocampal atrophy on magnetic resonance imaging is an important biomarker in Alzheimer's disease (AD). While hippocampal atrophy was thought to result from tau tangles in AD, different neuropathologies can lead to hippocampal atrophy, especially TAR DNA-binding protein 43 (TDP-43) pathology. In this narrative review, we evaluate existing studies on the relative contribution of tau and TDP-43 pathology to medial temporal lobe (MTL) atrophy. We report a clear association of both tau and TDP-43 neuropathology with MTL atrophy, even after correcting for other neuropathologies. Next, we discuss a potential synergism between tau and TDP-43 and the relative timing of the effects of both neuropathologies. Finally, avenues for future research will be discussed. A better understanding of the interplay between tau and TDP-43 neuropathologies and their effect on atrophy will help with the development of more specific biomarkers for limbic-predominant age-related TDP-43 encephalopathy and pinpointing of the optimal timing for testing anti-tau and anti-TDP-43 treatments in trials. HIGHLIGHTS: Both tau and TAR DNA-binding protein 43 (TDP-43) pathology contribute to medial temporal lobe atrophy. There is a positive association between tau and TDP-43 and potentially a synergism. It is unclear if tau and TDP-43 have an additive or synergistic effect on atrophy. The relative timing of the tau and TDP-43 effects on atrophy remains unclear. Clarifying the interplay between tau and TDP-43 will help improve magnetic resonance imaging biomarkers.
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Affiliation(s)
| | - Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences MalmöLund UniversityLundSweden
| | - Melissa E. Murray
- Department of NeuroscienceMayo Clinic FloridaJacksonvilleFloridaUSA
- Department of Laboratory Medicine and PathologyMayo Clinic FloridaJacksonvilleFloridaUSA
| | - William Jagust
- Department of NeuroscienceUniversity of California BerkeleyBerkeleyCaliforniaUSA
- Molecular Biophysics and Integrated BioimagingLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for NeurosciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
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6
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Xie L, Das SR, Li Y, Wisse LEM, McGrew E, Lyu X, DiCalogero M, Shah U, Ilesanmi A, Denning AE, Brown CA, Cohen J, Sreepada L, Dong M, Sadeghpour N, Khandelwal P, Ittyerah R, Ravikumar S, Sadaghiani S, Hrybouski S, de Flores R, Gibson E, Yushkevich PA, Wolk DA, for the Alzheimer's Disease Neuroimaging Initiative. A multi-cohort study of longitudinal and cross-sectional Alzheimer's disease biomarkers in cognitively unimpaired older adults. Alzheimers Dement 2025; 21:e14492. [PMID: 39868491 PMCID: PMC11848397 DOI: 10.1002/alz.14492] [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: 05/03/2024] [Revised: 11/26/2024] [Accepted: 11/27/2024] [Indexed: 01/28/2025]
Abstract
INTRODUCTION The generalizability of neuroimaging and cognitive biomarkers in their sensitivity to detect preclinical Alzheimer's disease (AD) and power to predict progression in large, multisite cohorts remains unclear. METHOD Longitudinal demographics, T1-weighted magnetic resonance imaging (MRI), and cognitive scores of 3036 cognitively unimpaired (CU) older adults (amyloid beta [Aβ]-negative/positive [A-/A+]: 1270/1558) were included. Cross-sectional and longitudinal cognition and medial temporal lobe (MTL) structural measures were extracted. Cross-sectional MTL tau burden (T) was computed from tau positron emission tomography (N = 1095). RESULTS We found cross-sectional tau and longitudinal structural biomarkers best separated A+ CU from A- CU. A-T+ CU had significantly faster neurodegeneration rate compared to A-T- CU. MTL tau was significantly correlated with MRI and cognitive biomarkers regardless of Aβ status. MTL tau, MRI, and cognition provided complementary information about disease progression. DISCUSSION This large multisite study replicates prior findings in CU older adults, supporting the utility of neuroimaging and cognitive biomarkers in preclinical AD clinical trials and normal aging studies. HIGHLIGHTS We investigated neuroimaging and cognitive biomarkers in 3036 cognitively unimpaired (CU) participants. Medial temporal lobe (MTL) tau and longitudinal MTL atrophy best separate amyloid beta positive (A+) CU from amyloid beta negative (A-) CU. A- tau positive (T+) CU had a significantly faster neurodegeneration rate compared to A-T- CU. MTL tau correlated with structural magnetic resonance imaging (MRI) and cognition regardless of amyloid beta status. Combined baseline MTL tau, MRI, and cognition best predict Alzheimer's disease progression.
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Affiliation(s)
- Long Xie
- Department of Digital Technology and InnovationSiemens HealthineersPrincetonNew JerseyUSA
| | - Sandhitsu R. Das
- Penn Image Computing and Science Laboratory (PICSL)Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Yue Li
- Penn Image Computing and Science Laboratory (PICSL)Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Emily McGrew
- Penn Image Computing and Science Laboratory (PICSL)Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Xueying Lyu
- Penn Image Computing and Science Laboratory (PICSL)Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Ujashi Shah
- Penn Memory CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ademola Ilesanmi
- Penn Image Computing and Science Laboratory (PICSL)Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Amanda E. Denning
- Penn Image Computing and Science Laboratory (PICSL)Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Chris A. Brown
- Penn Memory CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jesse Cohen
- Penn Image Computing and Science Laboratory (PICSL)Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Lasya Sreepada
- Penn Image Computing and Science Laboratory (PICSL)Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Mengjin Dong
- Penn Image Computing and Science Laboratory (PICSL)Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Niyousha Sadeghpour
- Penn Image Computing and Science Laboratory (PICSL)Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Pulkit Khandelwal
- Penn Image Computing and Science Laboratory (PICSL)Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory (PICSL)Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sadhana Ravikumar
- Penn Image Computing and Science Laboratory (PICSL)Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Shokufeh Sadaghiani
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Stanislau Hrybouski
- Penn Image Computing and Science Laboratory (PICSL)Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Eli Gibson
- Department of Digital Technology and InnovationSiemens HealthineersPrincetonNew JerseyUSA
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL)Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David A. Wolk
- Penn Memory CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Ortega-Cruz D, Rabano A, Strange BA. Neuropathological contributions to grey matter atrophy and white matter hyperintensities in amnestic dementia. Alzheimers Res Ther 2025; 17:16. [PMID: 39789603 PMCID: PMC11714914 DOI: 10.1186/s13195-024-01633-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2024] [Accepted: 11/29/2024] [Indexed: 01/12/2025]
Abstract
BACKGROUND Dementia patients commonly present multiple neuropathologies, worsening cognitive function, yet structural neuroimaging signatures of dementia have not been positioned in the context of combined pathology. In this study, we implemented an MRI voxel-based approach to explore combined and independent effects of dementia pathologies on grey and white matter structural changes. METHODS In 91 amnestic dementia patients with post-mortem brain donation, grey matter density and white matter hyperintensity (WMH) burdens were obtained from pre-mortem MRI and analyzed in relation to Alzheimer's, vascular, Lewy body, TDP-43, and hippocampal sclerosis (HS) pathologies. After exploring co-occurrence profiles of these pathologies, voxel-based morphometry was implemented to determine their joint and independent effects on grey matter loss. The impact of these pathologies on WMH burden was then evaluated both in spatial and quantitative combined analyses, using voxel-based and generalized linear models respectively. RESULTS 86.8% of patients in this cohort presented more than one pathology. The combined structural effect of these pathologies was a focal impact on hippocampal grey matter atrophy, primarily driven by HS and Alzheimer's pathology (family-wise error corrected, p < 0.05), which also exhibited the strongest individual effects (uncorrected, p < 0.001). WMHs, predominant in middle and anterior cerebral portions, were most strongly associated with vascular (T = 2.47, p = 0.017) and tau pathologies (T = 2.09, p = 0.041). CONCLUSIONS The mixed associations of these dementia neuroimaging hallmarks are relevant for the fine-tuning of diagnostic protocols and underscore the need for comprehensive pathology evaluations in the study of dementia phenotypes.
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Affiliation(s)
- Diana Ortega-Cruz
- Laboratory for Clinical Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, IdISSC, Crta M40, km38, Madrid, 28223, Spain
- Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, Madrid, 28031, Spain
| | - Alberto Rabano
- Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, Madrid, 28031, Spain
| | - Bryan A Strange
- Laboratory for Clinical Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, IdISSC, Crta M40, km38, Madrid, 28223, Spain.
- Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, Madrid, 28031, Spain.
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8
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Tang G, Lu JY, Li XY, Yao RX, Yang YJ, Jiao FY, Chen MJ, Liang XN, Ju ZZ, Ge JJ, Zhao YX, Shen B, Wu P, Sun YM, Wu JJ, Yen TC, Zuo C, Wang J, Zhao QH, Zhang HW, Liu FT. 18F-Florzolotau PET Imaging Unveils Tau Pathology in Dementia with Lewy Bodies. Mov Disord 2025; 40:108-120. [PMID: 39555939 DOI: 10.1002/mds.30055] [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: 07/05/2024] [Revised: 10/16/2024] [Accepted: 10/21/2024] [Indexed: 11/19/2024] Open
Abstract
BACKGROUND Dementia with Lewy bodies (DLB) commonly exhibits a complex neuropathology, sharing characteristics with Alzheimer's disease (AD), including tau aggregates. However, studies using the 18F-AV-1451 tau tracer have shown inconsistent findings regarding both the extent and topographical distribution of tau pathology in DLB. OBJECTIVES Our aim was to elucidate the topographical patterns of tau deposition in DLB and to investigate the in vivo pathological distinction between DLB and AD in virtue of the 18F-Florzolotau positron emission tomography (PET) imaging. METHODS This cross-sectional study enrolled patients with DLB (n = 24), AD (n = 43), and cognitively healthy controls (n = 18). Clinical assessments and 18F-Florzolotau PET imaging were performed. 18F-Florzolotau binding was quantitatively assessed on PET images using standardized uptake value ratios and voxel-wise analysis. RESULTS 18F-Florzolotau PET imaging revealed widespread tau deposition across various cortical regions in DLB, uncovering heterogeneous topographical patterns. Among patients, 54.17% showed patterns similar to AD, whereas 16.67% exhibited distinct patterns. Compared to AD, DLB exhibited a unique in vivo neuropathological profile, characterized by a lower tau protein burden, heterogeneous topographical distributions, and a specific role of the medial temporal lobe in tau pathology. CONCLUSIONS 18F-Florzolotau PET imaging elucidated tau pathology patterns in DLB, providing valuable insights for future in vivo pathological differentiation and potential disease-modifying therapies. © 2024 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Gan Tang
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jia-Ying Lu
- Department of Nuclear Medicine and PET Center, National Center for Neurological Disorders, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xin-Yi Li
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Rui-Xin Yao
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yu-Jie Yang
- Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
| | - Fang-Yang Jiao
- Department of Nuclear Medicine and PET Center, National Center for Neurological Disorders, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Ming-Jia Chen
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiao-Niu Liang
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Neurology, Fudan University, Shanghai, China
| | - Zi-Zhao Ju
- Department of Nuclear Medicine and PET Center, National Center for Neurological Disorders, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jing-Jie Ge
- Department of Nuclear Medicine and PET Center, National Center for Neurological Disorders, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yi-Xin Zhao
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Bo Shen
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Ping Wu
- Department of Nuclear Medicine and PET Center, National Center for Neurological Disorders, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yi-Min Sun
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jian-Jun Wu
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | | | - Chuantao Zuo
- Department of Nuclear Medicine and PET Center, National Center for Neurological Disorders, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jian Wang
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Qian-Hua Zhao
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Neurology, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Hui-Wei Zhang
- Department of Nuclear Medicine and PET Center, National Center for Neurological Disorders, and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Feng-Tao Liu
- Department of Neurology, National Research Center for Aging and Medicine, National Center for Neurological Disorders, and State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
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9
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Wolk DA, Nelson PT, Apostolova L, Arfanakis K, Boyle PA, Carlsson CM, Corriveau‐Lecavalier N, Dacks P, Dickerson BC, Domoto‐Reilly K, Dugger BN, Edelmayer R, Fardo DW, Grothe MJ, Hohman TJ, Irwin DJ, Jicha GA, Jones DT, Kawas CH, Lee EB, Lincoln K, Maestre GE, Mormino EC, Onyike CU, Petersen RC, Rabinovici GD, Rademakers R, Raman R, Rascovsky K, Rissman RA, Rogalski E, Scheltens P, Sperling RA, Yang H, Yu L, Zetterberg H, Schneider JA. Clinical criteria for limbic-predominant age-related TDP-43 encephalopathy. Alzheimers Dement 2025; 21:e14202. [PMID: 39807681 PMCID: PMC11772733 DOI: 10.1002/alz.14202] [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: 04/01/2024] [Revised: 05/21/2024] [Accepted: 05/22/2024] [Indexed: 01/16/2025]
Abstract
Limbic predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) is highly prevalent in late life and a common co-pathology with Alzheimer's disease neuropathologic change (ADNC). LATE-NC is a slowly progressive, amnestic clinical syndrome. Alternatively, when present with ADNC, LATE-NC is associated with a more rapid course. With the emergence of anti-amyloid therapeutics, discrimination of LATE-NC from ADNC is critical and will lead to greater clinical recognition of amnestic patients without ADNC. Furthermore, co-pathology with LATE-NC may influence outcomes of these therapeutics. Thus there is a need to identify patients during life with likely LATE-NC. We propose criteria for clinical diagnosis of LATE as an initial framework for further validation. In the context of progressive memory loss and substantial hippocampal atrophy, criteria are laid out for probable (amyloid negative) or possible LATE (amyloid biomarkers are unavailable or when amyloid is present, but hippocampal neurodegeneration is out of proportion to expected pure ADNC). HIGHLIGHTS: Limbic-predominant age-related TDP-43 encephalopathy (LATE) is a highly prevalent driver of neuropathologic memory loss in late life. LATE neuropathologic change (LATE-NC) is a common co-pathology with Alzheimer's disease neuropathologic change (ADNC) and may influence outcomes with emerging disease-modifying medicines. We provide initial clinical criteria for diagnosing LATE during life either when LATE-NC is the likely primary driver of symptoms or when observed in conjunction with AD. Definitions of possible and probable LATE are provided.
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Affiliation(s)
- David A. Wolk
- University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | | | - Konstantinos Arfanakis
- Rush University Medical CenterChicagoIllinoisUSA
- Illinois Institute of TechnologyChicagoIllinoisUSA
| | | | | | | | - Penny Dacks
- The Association for Frontotemporal DegenerationKing of PrussiaPennsylvaniaUSA
| | | | - Kimiko Domoto‐Reilly
- UW Alzheimer's Disease Research CenterUniversity of WashingtonSeattleWashingtonUSA
| | | | | | | | | | | | | | | | | | | | - Edward B. Lee
- University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | | | | | | | | | | | - Rosa Rademakers
- VIB Center for Molecular NeurologyAntwerpBelgium
- University of AntwerpAntwerpBelgium
| | - Rema Raman
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | | | | | - Emily Rogalski
- Healthy Aging & Alzheimer's Care (HAARC) Center, Department of NeurologyUniversity of ChicagoChicagoIllinoisUSA
| | | | - Reisa A. Sperling
- Harvard Medical SchoolBrigham and Women's Hospital, Massachusetts General HospitalBostonMassachusettsUSA
| | - Hyun‐Sik Yang
- Harvard Medical SchoolBrigham and Women's Hospital, Massachusetts General HospitalBostonMassachusettsUSA
| | - Lei Yu
- Rush University Medical CenterChicagoIllinoisUSA
| | - Henrik Zetterberg
- University of WisconsinMadisonWisconsinUSA
- University of GothenburgGothenburgSweden
- Institute of NeurologyUniversity College LondonLondonUK
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10
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Mormino EC, Biber SA, Rahman‐Filipiak A, Arfanakis K, Clark L, Dage JL, Detre JA, Dickerson BC, Donohue MC, Kecskemeti S, Hohman TJ, Jagust WJ, Keene DC, Kukull W, Levendovszky SR, Rosen H, Thompson PM, Villemagne VL, Wolk DA, Okonkwo OC, Rabinvovici GD, Rivera‐Mindt M, Foroud T, Johnson SC. The Consortium for Clarity in ADRD Research Through Imaging (CLARiTI). Alzheimers Dement 2025; 21:e14383. [PMID: 39588767 PMCID: PMC11772703 DOI: 10.1002/alz.14383] [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: 06/07/2024] [Revised: 10/10/2024] [Accepted: 10/11/2024] [Indexed: 11/27/2024]
Abstract
The presence of multiple pathologies is the largest predictor of dementia. A major gap in the field is the in vivo detection of mixed pathologies and their antecedents. The Alzheimer's Disease Research Centers (ADRCs) are uniquely positioned to address this gap. The ADRCs longitudinally follow ≈ 17,000 participants, ranging from cognitively unimpaired to dementia, arising from Alzheimer's disease (AD) and related dementias (ADRD; e.g., AD, Lewy body disorders, vascular). Motivated by the Alzheimer's Disease Neuroimaging Initiative's (ADNI) impact, the ADRC Consortium for Clarity in ADRD Research Through Imaging (CLARiTI) was formed. Leveraging existing ADRC infrastructure, CLARiTI will integrate standardized imaging and plasma collection to characterize mixed pathologies and use community-engaged research methods to ensure that ≥ 25% of the sample is from underrepresented populations (e.g., ethnoculturally minoritized, low education). The resulting ADRD profiles, within a more diverse sample, will provide key resources for ADRCs and an unprecedented, more generalizable publicly available imaging-plasma dataset. HIGHLIGHTS: In vivo detection of mixed pathologies is critical for Alzheimer's disease and related dementias research. The Alzheimer's Disease Research Centers (ADRCs) are uniquely positioned to address gaps related to mixed pathologies. The ADRC Consortium for Clarity in ADRD Research Through Imaging (CLARiTI) will enhance this national program by adding standardized imaging and plasma collection to existing ADRC infrastructure. This effort will provide key resources for ADRCs and an unprecedented publicly available imaging-plasma-neuropath dataset.
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Affiliation(s)
- Elizabeth C. Mormino
- Department of Neurology and Neurological SciencesStanford University School of MedicinePalo AltoCaliforniaUSA
- Wu Tsai Neurosciences InstituteStanford University School of Medicine, Cogen FacilityStanfordCaliforniaUSA
| | - Sarah A. Biber
- National Alzheimer's Coordinating CenterUniversity of WashingtonSeattleWashingtonUSA
| | | | | | - Lindsay Clark
- Department of MedicineUniversity of Wisconsin School of Medicine and Public Health, Health Sciences Learning CenterMadisonWisconsinUSA
| | - Jeffrey L. Dage
- Department of NeurologyIndiana University School of MedicineIndianapolisIndianaUSA
- Stark Neurosciences Research InstituteIndiana University School of MedicineIndianapolisIndianaUSA
| | - John A. Detre
- Department of NeurologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Bradford C. Dickerson
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Michael C. Donohue
- Department of NeurologyKeck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
- Alzheimer's Therapeutic Research Institute (ATRI), University of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Steven Kecskemeti
- Department of MedicineUniversity of Wisconsin School of Medicine and Public Health, Health Sciences Learning CenterMadisonWisconsinUSA
| | - Timothy J. Hohman
- Department of NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - William J. Jagust
- Department of EpidemiologySchool of Public HealthUniversity of CaliforniaBerkeleyCaliforniaUSA
- Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - Dirk C. Keene
- Department of Laboratory Medicine and PathologyUniversity of WashingtonSeattleWashingtonUSA
| | - Walter Kukull
- National Alzheimer's Coordinating CenterUniversity of WashingtonSeattleWashingtonUSA
- Department of EpidemiologyUniversity of WashingtonSeattleWashingtonUSA
| | | | - Howie Rosen
- Department of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Paul M. Thompson
- Department of Ophthalmology, Psychiatry and the Behavioral Sciences, RadiologyPsychiatry, and Engineering, Keck School of Medicine, University of Southern CaliforniaLos AngelesCaliforniaUSA
| | | | - David A. Wolk
- Department of NeurologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ozioma C. Okonkwo
- Department of MedicineUniversity of Wisconsin School of Medicine and Public Health, Health Sciences Learning CenterMadisonWisconsinUSA
| | - Gil D. Rabinvovici
- Department of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Monica Rivera‐Mindt
- Department of PsychologyFordham UniversityBronxNew YorkUSA
- Department of NeurologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Tatiana Foroud
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Sterling C. Johnson
- Department of MedicineUniversity of Wisconsin School of Medicine and Public Health, Health Sciences Learning CenterMadisonWisconsinUSA
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11
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Yushkevich PA, Ittyerah R, Li Y, Denning AE, Sadeghpour N, Lim S, McGrew E, Xie L, DeFlores R, Brown CA, Wisse LEM, Wolk DA, Das SR, for the Alzheimer's Disease Neuroimaging Initiative. Morphometry of medial temporal lobe subregions using high-resolution T2-weighted MRI in ADNI3: Why, how, and what's next? Alzheimers Dement 2024; 20:8113-8128. [PMID: 39279366 PMCID: PMC11567830 DOI: 10.1002/alz.14161] [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: 05/02/2024] [Revised: 06/19/2024] [Accepted: 06/20/2024] [Indexed: 09/18/2024]
Abstract
This paper for the 20th anniversary of the Alzheimer's Disease Neuroimaging Initiative (ADNI) provides an overview of magnetic resonance imaging (MRI) of medial temporal lobe (MTL) subregions in ADNI using a dedicated high-resolution T2-weighted sequence. A review of the work that supported the inclusion of this imaging modality into ADNI Phase 3 is followed by a brief description of the ADNI MTL imaging and analysis protocols and a summary of studies that have used these data. This review is supplemented by a new study that uses novel surface-based tools to characterize MTL neurodegeneration across biomarker-defined AD stages. This analysis reveals a pattern of spreading cortical thinning associated with increasing levels of tau pathology in the presence of elevated amyloid beta, with apparent epicenters in the transentorhinal region and inferior hippocampal subfields. The paper concludes with an outlook for high-resolution imaging of the MTL in ADNI Phase 4. HIGHLIGHTS: As of Phase 3, the Alzheimer's Disease Neuroimaging Initiative (ADNI) magnetic resonance imaging (MRI) protocol includes a high-resolution T2-weighted MRI scan optimized for imaging hippocampal subfields and medial temporal lobe (MTL) subregions. These scans are processed by the ADNI core to obtain automatic segmentations of MTL subregions and to derive morphologic measurements. More detailed granular examination of MTL neurodegeneration in response to disease progression is achieved by applying surface-based modeling techniques. Surface-based analysis of gray matter loss in MTL subregions reveals increasing and spatially expanding patterns of neurodegeneration with advancing stages of Alzheimer's disease (AD), as defined based on amyloid and tau positron emission tomography biomarkers in accordance with recently proposed criteria. These patterns closely align with post mortem literature on spread of pathological tau in AD, supporting the role of tau pathology in the presence of elevated levels of amyloid beta as the driver of neurodegeneration.
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Affiliation(s)
- Paul A. Yushkevich
- Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Ranjit Ittyerah
- Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Yue Li
- Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Amanda E. Denning
- Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Niyousha Sadeghpour
- Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Sydney Lim
- Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Emily McGrew
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Long Xie
- Department of Digital Technology and InnovationSiemens HealthineersMalvernPennsylvaniaUSA
| | - Robin DeFlores
- UMR‐S U1237PhIND “Physiopathology and Imaging of Neurological Disorders”INSERMCaenFrance
| | - Christopher A. Brown
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | | | - David A. Wolk
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Sandhitsu R. Das
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
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12
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Andrade-Guerrero J, Martínez-Orozco H, Villegas-Rojas MM, Santiago-Balmaseda A, Delgado-Minjares KM, Pérez-Segura I, Baéz-Cortés MT, Del Toro-Colin MA, Guerra-Crespo M, Arias-Carrión O, Diaz-Cintra S, Soto-Rojas LO. Alzheimer's Disease: Understanding Motor Impairments. Brain Sci 2024; 14:1054. [PMID: 39595817 PMCID: PMC11592238 DOI: 10.3390/brainsci14111054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2024] [Revised: 10/22/2024] [Accepted: 10/22/2024] [Indexed: 11/28/2024] Open
Abstract
Alzheimer's disease (AD), the most prevalent neurodegenerative disorder and the leading cause of dementia worldwide, profoundly impacts health and quality of life. While cognitive impairments-such as memory loss, attention deficits, and disorientation-predominate in AD, motor symptoms, though common, remain underexplored. These motor symptoms, including gait disturbances, reduced cardiorespiratory fitness, muscle weakness, sarcopenia, and impaired balance, are often associated with advanced stages of AD and contribute to increased mortality. Emerging evidence, however, suggests that motor symptoms may be present in earlier stages and can serve as predictive markers for AD in older adults. Despite a limited understanding of the underlying mechanisms driving these motor symptoms, several key pathways have been identified, offering avenues for further investigation. This review provides an in-depth analysis of motor symptoms in AD, discussing its progression, potential mechanisms, and therapeutic strategies. Addressing motor symptoms alongside cognitive decline may enhance patient functionality, improve quality of life, and support more comprehensive disease management strategies.
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Affiliation(s)
- Jesús Andrade-Guerrero
- Laboratorio de Patogénesis Molecular, Laboratorio 4, Edificio A4, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Mexico; (J.A.-G.); (M.M.V.-R.); (A.S.-B.); (K.M.D.-M.); (I.P.-S.); (M.T.B.-C.); (M.A.D.T.-C.)
- Departamento de Neurobiología del Desarrollo y Neurofisiología, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro 76230, Mexico;
| | - Humberto Martínez-Orozco
- Departamento de Neurobiología del Desarrollo y Neurofisiología, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro 76230, Mexico;
| | - Marcos M. Villegas-Rojas
- Laboratorio de Patogénesis Molecular, Laboratorio 4, Edificio A4, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Mexico; (J.A.-G.); (M.M.V.-R.); (A.S.-B.); (K.M.D.-M.); (I.P.-S.); (M.T.B.-C.); (M.A.D.T.-C.)
- Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Ciudad de México 11340, Mexico
| | - Alberto Santiago-Balmaseda
- Laboratorio de Patogénesis Molecular, Laboratorio 4, Edificio A4, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Mexico; (J.A.-G.); (M.M.V.-R.); (A.S.-B.); (K.M.D.-M.); (I.P.-S.); (M.T.B.-C.); (M.A.D.T.-C.)
| | - Karen M. Delgado-Minjares
- Laboratorio de Patogénesis Molecular, Laboratorio 4, Edificio A4, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Mexico; (J.A.-G.); (M.M.V.-R.); (A.S.-B.); (K.M.D.-M.); (I.P.-S.); (M.T.B.-C.); (M.A.D.T.-C.)
- Departamento de Fisiología, Biofísica y Neurociencias, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Ciudad de México 07360, Mexico
| | - Isaac Pérez-Segura
- Laboratorio de Patogénesis Molecular, Laboratorio 4, Edificio A4, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Mexico; (J.A.-G.); (M.M.V.-R.); (A.S.-B.); (K.M.D.-M.); (I.P.-S.); (M.T.B.-C.); (M.A.D.T.-C.)
| | - Mauricio T. Baéz-Cortés
- Laboratorio de Patogénesis Molecular, Laboratorio 4, Edificio A4, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Mexico; (J.A.-G.); (M.M.V.-R.); (A.S.-B.); (K.M.D.-M.); (I.P.-S.); (M.T.B.-C.); (M.A.D.T.-C.)
| | - Miguel A. Del Toro-Colin
- Laboratorio de Patogénesis Molecular, Laboratorio 4, Edificio A4, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Mexico; (J.A.-G.); (M.M.V.-R.); (A.S.-B.); (K.M.D.-M.); (I.P.-S.); (M.T.B.-C.); (M.A.D.T.-C.)
| | - Magdalena Guerra-Crespo
- Laboratorio de Medicina Regenerativa, Departamento de Fisiología, Facultad de Medicina, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico;
| | - Oscar Arias-Carrión
- Unidad de Trastornos del Movimiento y Sueño, Hospital General Dr. Manuel Gea González, Ciudad de México 14080, Mexico;
| | - Sofía Diaz-Cintra
- Departamento de Neurobiología del Desarrollo y Neurofisiología, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro 76230, Mexico;
| | - Luis O. Soto-Rojas
- Laboratorio de Patogénesis Molecular, Laboratorio 4, Edificio A4, Carrera Médico Cirujano, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Mexico; (J.A.-G.); (M.M.V.-R.); (A.S.-B.); (K.M.D.-M.); (I.P.-S.); (M.T.B.-C.); (M.A.D.T.-C.)
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13
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Mazloum‐Farzaghi N, Barense M, Ryan J, Stark C, Olsen R. The Effect of Segmentation Method on Medial Temporal Lobe Subregion Volumes in Aging. Hum Brain Mapp 2024; 45:e70054. [PMID: 39450487 PMCID: PMC11502966 DOI: 10.1002/hbm.70054] [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: 05/21/2024] [Revised: 09/04/2024] [Accepted: 10/06/2024] [Indexed: 10/26/2024] Open
Abstract
Early stages of Alzheimer's disease (AD) are associated with volume reductions in specific subregions of the medial temporal lobe (MTL). Using a manual segmentation method-the Olsen-Amaral-Palombo (OAP) protocol-previous work in healthy older adults showed that reductions in grey matter volumes in MTL subregions were associated with lower scores on the Montreal Cognitive Assessment (MoCA), suggesting atrophy may occur prior to diagnosis of mild cognitive impairment, a condition that often progresses to AD. However, current "gold standard" manual segmentation methods are labour intensive and time consuming. Here, we examined the utility of Automatic Segmentation of Hippocampal Subfields (ASHS) to detect volumetric differences in MTL subregions of healthy older adults who varied in cognitive status as determined by the MoCA. We trained ASHS on the OAP protocol to create the ASHS-OAP atlas and then examined how well automated segmentation replicated manual segmentation. Volumetric measures obtained from the ASHS-OAP atlas were also contrasted against those from the ASHS-PMC atlas, a widely used atlas provided by the ASHS team. The pattern of volumetric results was similar between the ASHS-OAP atlas and manual segmentation for anterolateral entorhinal cortex and perirhinal cortex, suggesting that ASHS-OAP is a viable alternative to current manual segmentation methods for detecting group differences based on cognitive status. Although ASHS-OAP and ASHS-PMC produced varying volumes for most regions of interest, they both identified early signs of neurodegeneration in CA2/CA3/DG and identified marginal differences in entorhinal cortex. Our findings highlight the utility of automated segmentation methods but still underscore the need for a unified and harmonized MTL segmentation atlas.
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Affiliation(s)
- Negar Mazloum‐Farzaghi
- Department of PsychologyUniversity of TorontoTorontoOntarioCanada
- Rotman Research InstituteBaycrest Academy for Research and EducationTorontoOntarioCanada
| | - Morgan D. Barense
- Department of PsychologyUniversity of TorontoTorontoOntarioCanada
- Rotman Research InstituteBaycrest Academy for Research and EducationTorontoOntarioCanada
| | - Jennifer D. Ryan
- Department of PsychologyUniversity of TorontoTorontoOntarioCanada
- Rotman Research InstituteBaycrest Academy for Research and EducationTorontoOntarioCanada
- Department of PsychiatryUniversity of TorontoTorontoOntarioCanada
| | - Craig E. L. Stark
- Department of Neurobiology and BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Rosanna K. Olsen
- Department of PsychologyUniversity of TorontoTorontoOntarioCanada
- Rotman Research InstituteBaycrest Academy for Research and EducationTorontoOntarioCanada
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14
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Salman Y, Gérard T, Huyghe L, Colmant L, Quenon L, Malotaux V, Ivanoiu A, Lhommel R, Dricot L, Hanseeuw BJ, for the Alzheimer's Disease Neuroimaging Initiative. Amygdala atrophies in specific subnuclei in preclinical Alzheimer's disease. Alzheimers Dement 2024; 20:7205-7219. [PMID: 39254209 PMCID: PMC11485073 DOI: 10.1002/alz.14235] [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: 04/29/2024] [Revised: 07/18/2024] [Accepted: 07/27/2024] [Indexed: 09/11/2024]
Abstract
INTRODUCTION Magnetic resonance imaging (MRI) segmentation algorithms make it possible to study detailed medial temporal lobe (MTL) substructures as hippocampal subfields and amygdala subnuclei, offering opportunities to develop biomarkers for preclinical Alzheimer's disease (AD). METHODS We identified the MTL substructures significantly associated with tau-positron emission tomography (PET) signal in 581 non-demented individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI-3). We confirmed our results in our UCLouvain cohort including 110 non-demented individuals by comparing volumes between individuals with different visual Braak's stages and clinical diagnosis. RESULTS Four amygdala subnuclei (cortical, central, medial, and accessory basal) were associated with tau in amyloid beta-positive (Aβ+) clinically normal (CN) individuals, while the global amygdala and hippocampal volumes were not. Using UCLouvain data, we observed that both Braak I-II and Aβ+ CN individuals had smaller volumes in these subnuclei, while no significant difference was observed in the global structure volumes or other subfields. CONCLUSION Measuring specific amygdala subnuclei, early atrophy may serve as a marker of temporal tauopathy in preclinical AD, identifying individuals at risk of progression. HIGHLIGHTS Amygdala atrophy is not homogeneous in preclinical Alzheimer's disease (AD). Tau pathology is associated with atrophy of specific amygdala subnuclei, specifically, the central, medial, cortical, and accessory basal subnuclei. Hippocampal and amygdala volume is not associated with tau in preclinical AD. Hippocampus and CA1-3 volume is reduced in preclinical AD, regardless of tau.
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Affiliation(s)
- Yasmine Salman
- Louvain Aging Brain LabInstitute of NeuroscienceUCLouvainBrusselsBelgium
| | - Thomas Gérard
- Louvain Aging Brain LabInstitute of NeuroscienceUCLouvainBrusselsBelgium
- Nuclear Medicine DepartmentSaint‐Luc University HospitalBrusselsBelgium
| | - Lara Huyghe
- Louvain Aging Brain LabInstitute of NeuroscienceUCLouvainBrusselsBelgium
| | - Lise Colmant
- Louvain Aging Brain LabInstitute of NeuroscienceUCLouvainBrusselsBelgium
- Neurology DepartmentSaint‐Luc University HospitalBrusselsBelgium
| | - Lisa Quenon
- Louvain Aging Brain LabInstitute of NeuroscienceUCLouvainBrusselsBelgium
- Neurology DepartmentSaint‐Luc University HospitalBrusselsBelgium
| | - Vincent Malotaux
- Louvain Aging Brain LabInstitute of NeuroscienceUCLouvainBrusselsBelgium
- Department of PsychiatryMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Adrian Ivanoiu
- Neurology DepartmentSaint‐Luc University HospitalBrusselsBelgium
| | - Renaud Lhommel
- Nuclear Medicine DepartmentSaint‐Luc University HospitalBrusselsBelgium
| | - Laurence Dricot
- Louvain Aging Brain LabInstitute of NeuroscienceUCLouvainBrusselsBelgium
| | - Bernard J. Hanseeuw
- Louvain Aging Brain LabInstitute of NeuroscienceUCLouvainBrusselsBelgium
- Neurology DepartmentSaint‐Luc University HospitalBrusselsBelgium
- WELBIO DepartmentWEL Research InstituteWavreBelgium
- Department of RadiologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
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15
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Wuestefeld A, Pichet Binette A, van Westen D, Strandberg O, Stomrud E, Mattsson-Carlgren N, Janelidze S, Smith R, Palmqvist S, Baumeister H, Berron D, Yushkevich PA, Hansson O, Spotorno N, Wisse LEM. Medial temporal lobe atrophy patterns in early-versus late-onset amnestic Alzheimer's disease. Alzheimers Res Ther 2024; 16:204. [PMID: 39285454 PMCID: PMC11403779 DOI: 10.1186/s13195-024-01571-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Accepted: 09/04/2024] [Indexed: 09/20/2024]
Abstract
BACKGROUND The medial temporal lobe (MTL) is hypothesized to be relatively spared in early-onset Alzheimer's disease (EOAD). Yet, detailed examination of MTL subfields and drivers of atrophy in amnestic EOAD is lacking. METHODS BioFINDER-2 participants with memory impairment, abnormal amyloid-β and tau-PET were included. Forty-one amnestic EOAD individuals ≤65 years and, as comparison, late-onset AD (aLOAD, ≥70 years, n = 154) and amyloid-β-negative cognitively unimpaired controls were included. MTL subregions and biomarkers of (co-)pathologies were measured. RESULTS AD groups showed smaller MTL subregions compared to controls. Atrophy patterns were similar across AD groups: aLOAD showed thinner entorhinal cortices than aEOAD; aEOAD showed thinner parietal regions than aLOAD. aEOAD showed lower white matter hyperintensities than aLOAD. No differences in MTL tau-PET or transactive response DNA binding protein 43-proxy positivity were found. CONCLUSIONS We found evidence for MTL atrophy in amnestic EOAD and overall similar levels to aLOAD of MTL tau pathology and co-pathologies.
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Affiliation(s)
- Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Klinikgatan 28, Room C1103b, Lund, SE-22242, Sweden.
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Klinikgatan 28, Room C1103b, Lund, SE-22242, Sweden
| | - Danielle van Westen
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, Klinikgatan 13B, Lund, SE-22242, Sweden
- Image and Function, Skåne University Hospital, Lund, 22242, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Klinikgatan 28, Room C1103b, Lund, SE-22242, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Klinikgatan 28, Room C1103b, Lund, SE-22242, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, 20502, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Klinikgatan 28, Room C1103b, Lund, SE-22242, Sweden
- Department of Neurology, Skåne University Hospital, Lund, 22242, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, 22184, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Klinikgatan 28, Room C1103b, Lund, SE-22242, Sweden
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Klinikgatan 28, Room C1103b, Lund, SE-22242, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, 20502, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Klinikgatan 28, Room C1103b, Lund, SE-22242, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, 20502, Sweden
| | - Hannah Baumeister
- German Center for Neurodegenerative Diseases (DZNE), 39120, Magdeburg, Germany
| | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Klinikgatan 28, Room C1103b, Lund, SE-22242, Sweden
- German Center for Neurodegenerative Diseases (DZNE), 39120, Magdeburg, Germany
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, 19104, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Klinikgatan 28, Room C1103b, Lund, SE-22242, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, 20502, Sweden
| | - Nicola Spotorno
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Klinikgatan 28, Room C1103b, Lund, SE-22242, Sweden
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, Klinikgatan 13B, Lund, SE-22242, Sweden.
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16
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Denning AE, Ittyerah R, Levorse LM, Sadeghpour N, Athalye C, Chung E, Ravikumar S, Dong M, Duong MT, Li Y, Ilesanmi A, Sreepada LP, Sabatini P, Lowe M, Bahena A, Zablah J, Spencer BE, Watanabe R, Kim B, Sørensen MH, Khandelwal P, Brown C, Hrybouski S, Xie SX, de Flores R, Robinson JL, Schuck T, Ohm DT, Arezoumandan S, Porta S, Detre JA, Insausti R, Wisse LEM, Das SR, Irwin DJ, Lee EB, Wolk DA, Yushkevich PA. Association of quantitative histopathology measurements with antemortem medial temporal lobe cortical thickness in the Alzheimer's disease continuum. Acta Neuropathol 2024; 148:37. [PMID: 39227502 PMCID: PMC11371872 DOI: 10.1007/s00401-024-02789-9] [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: 05/08/2024] [Revised: 08/07/2024] [Accepted: 08/15/2024] [Indexed: 09/05/2024]
Abstract
The medial temporal lobe (MTL) is a hotspot for neuropathology, and measurements of MTL atrophy are often used as a biomarker for cognitive decline associated with neurodegenerative disease. Due to the aggregation of multiple proteinopathies in this region, the specific relationship of MTL atrophy to distinct neuropathologies is not well understood. Here, we develop two quantitative algorithms using deep learning to measure phosphorylated tau (p-tau) and TDP-43 (pTDP-43) pathology, which are both known to accumulate in the MTL and are associated with MTL neurodegeneration. We focus on these pathologies in the context of Alzheimer's disease (AD) and limbic predominant age-related TDP-43 encephalopathy (LATE) and apply our deep learning algorithms to distinct histology sections, on which MTL subregions were digitally annotated. We demonstrate that both quantitative pathology measures show high agreement with expert visual ratings of pathology and discriminate well between pathology stages. In 140 cases with antemortem MR imaging, we compare the association of semi-quantitative and quantitative postmortem measures of these pathologies in the hippocampus with in vivo structural measures of the MTL and its subregions. We find widespread associations of p-tau pathology with MTL subregional structural measures, whereas pTDP-43 pathology had more limited associations with the hippocampus and entorhinal cortex. Quantitative measurements of p-tau pathology resulted in a significantly better model of antemortem structural measures than semi-quantitative ratings and showed strong associations with cortical thickness and volume. By providing a more granular measure of pathology, the quantitative p-tau measures also showed a significant negative association with structure in a severe AD subgroup where semi-quantitative ratings displayed a ceiling effect. Our findings demonstrate the advantages of using quantitative neuropathology to understand the relationship of pathology to structure, particularly for p-tau, and motivate the use of quantitative pathology measurements in future studies.
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Affiliation(s)
- Amanda E Denning
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
| | - Ranjit Ittyerah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Lisa M Levorse
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Chinmayee Athalye
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Eunice Chung
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sadhana Ravikumar
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Mengjin Dong
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Tran Duong
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Yue Li
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ademola Ilesanmi
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Lasya P Sreepada
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Philip Sabatini
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - MaKayla Lowe
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Alejandra Bahena
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jamila Zablah
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Barbara E Spencer
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ryohei Watanabe
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neurodegenerative Disease Research, Institute On Aging, University of Pennsylvania, Philadelphia, PA, USA
| | - Boram Kim
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neurodegenerative Disease Research, Institute On Aging, University of Pennsylvania, Philadelphia, PA, USA
| | - Maja Højvang Sørensen
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neurodegenerative Disease Research, Institute On Aging, University of Pennsylvania, Philadelphia, PA, USA
| | - Pulkit Khandelwal
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher Brown
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Sharon X Xie
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Robin de Flores
- UMR-S U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, INSERM, Caen-Normandie University, GIP Cyceron, Caen, France
| | - John L Robinson
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neurodegenerative Disease Research, Institute On Aging, University of Pennsylvania, Philadelphia, PA, USA
| | - Theresa Schuck
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neurodegenerative Disease Research, Institute On Aging, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel T Ohm
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sanaz Arezoumandan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sílvia Porta
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neurodegenerative Disease Research, Institute On Aging, University of Pennsylvania, Philadelphia, PA, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ricardo Insausti
- Human Neuroanatomy Lab, University of Castilla La Mancha, Albacete, Spain
| | - Laura E M Wisse
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Sandhitsu R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - David J Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neurodegenerative Disease Research, Institute On Aging, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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17
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Tazwar M, Evia AM, Ridwan AR, Leurgans SE, Bennett DA, Schneider JA, Arfanakis K. Limbic-predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC) is associated with abnormalities in white matter structural integrity and connectivity: An ex-vivo diffusion MRI and pathology investigation. Neurobiol Aging 2024; 140:81-92. [PMID: 38744041 PMCID: PMC11182335 DOI: 10.1016/j.neurobiolaging.2024.04.002] [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: 10/04/2023] [Revised: 04/04/2024] [Accepted: 04/04/2024] [Indexed: 05/16/2024]
Abstract
Limbic predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC) is common in older adults and is associated with neurodegeneration, cognitive decline and dementia. In this MRI and pathology investigation we tested the hypothesis that LATE-NC is associated with abnormalities in white matter structural integrity and connectivity of a network of brain regions typically harboring TDP-43 inclusions in LATE, referred to here as the "LATE-NC network". Ex-vivo diffusion MRI and detailed neuropathological data were collected on 184 community-based older adults. Linear regression revealed an independent association of higher LATE-NC stage with lower diffusion anisotropy in a set of white matter connections forming a pattern of connectivity that is consistent with the stereotypical spread of this pathology in the brain. Graph theory analysis revealed an association of higher LATE-NC stage with weaker integration and segregation in the LATE-NC network. Abnormalities were significant in stage 3, suggesting that they are detectable in later stages of the disease. Finally, LATE-NC network abnormalities were associated with faster cognitive decline, specifically in episodic and semantic memory.
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Affiliation(s)
- Mahir Tazwar
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Arnold M Evia
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Abdur Raquib Ridwan
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Sue E Leurgans
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA; Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA; Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA; Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA; Department of Pathology, Rush University Medical Center, Chicago, IL, USA
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA; Department of Diagnostic Radiology, Rush University Medical Center, Chicago, IL, USA.
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18
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Yu X, Przybelski SA, Reid RI, Lesnick TG, Raghavan S, Graff‐Radford J, Lowe VJ, Kantarci K, Knopman DS, Petersen RC, Jack CR, Vemuri P. NODDI in gray matter is a sensitive marker of aging and early AD changes. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12627. [PMID: 39077685 PMCID: PMC11284641 DOI: 10.1002/dad2.12627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 06/17/2024] [Accepted: 07/02/2024] [Indexed: 07/31/2024]
Abstract
INTRODUCTION Age-related and Alzheimer's disease (AD) dementia-related neurodegeneration impact brain health. While morphometric measures from T1-weighted scans are established biomarkers, they may be less sensitive to earlier changes. Neurite orientation dispersion and density imaging (NODDI), offering biologically meaningful interpretation of tissue microstructure, may be an advanced brain health biomarker. METHODS We contrasted regional gray matter NODDI and morphometric evaluations concerning their correlation with (1) age, (2) clinical diagnosis stage, and (3) tau pathology as assessed by AV1451 positron emission tomography. RESULTS Our study hypothesizes that NODDI measures are more sensitive to aging and early AD changes than morphometric measures. One NODDI output, free water fraction (FWF), showed higher sensitivity to age-related changes, generally better effect sizes in separating mild cognitively impaired from cognitively unimpaired participants, and stronger associations with regional tau deposition than morphometric measures. DISCUSSION These findings underscore NODDI's utility in capturing early neurodegenerative changes and enhancing our understanding of aging and AD. Highlights Neurite orientation dispersion and density imaging can serve as an effective brain health biomarker for aging and early Alzheimer's disease (AD).Free water fraction has higher sensitivity to normal brain aging.Free water fraction has stronger associations with early AD and regional tau deposition.
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Affiliation(s)
- Xi Yu
- Department of Physiology and Biomedical EngineeringMayo ClinicRochesterMinnesotaUSA
| | - Scott A. Przybelski
- Department of Health Sciences ResearchDivision of Biomedical Statistics and InformaticsMayo Clinic‐RochesterRochesterMinnesotaUSA
| | - Robert I. Reid
- Department of Health Sciences ResearchDivision of Biomedical Statistics and InformaticsMayo Clinic‐RochesterRochesterMinnesotaUSA
- Department of RadiologyMayo Clinic‐RochesterRochesterMinnesotaUSA
| | - Timothy G. Lesnick
- Department of Health Sciences ResearchDivision of Biomedical Statistics and InformaticsMayo Clinic‐RochesterRochesterMinnesotaUSA
| | | | | | - Val J. Lowe
- Department of RadiologyMayo Clinic‐RochesterRochesterMinnesotaUSA
| | - Kejal Kantarci
- Department of RadiologyMayo Clinic‐RochesterRochesterMinnesotaUSA
| | - David S. Knopman
- Department of NeurologyMayo Clinic‐RochesterRochesterMinnesotaUSA
| | | | - Clifford R. Jack
- Department of RadiologyMayo Clinic‐RochesterRochesterMinnesotaUSA
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19
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Strobel J, Yousefzadeh-Nowshahr E, Deininger K, Bohn KP, von Arnim CAF, Otto M, Solbach C, Anderl-Straub S, Polivka D, Fissler P, Glatting G, Riepe MW, Higuchi M, Beer AJ, Ludolph A, Winter G. Exploratory Tau PET/CT with [11C]PBB3 in Patients with Suspected Alzheimer's Disease and Frontotemporal Lobar Degeneration: A Pilot Study on Correlation with PET Imaging and Cerebrospinal Fluid Biomarkers. Biomedicines 2024; 12:1460. [PMID: 39062033 PMCID: PMC11274645 DOI: 10.3390/biomedicines12071460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 06/13/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024] Open
Abstract
Accurately diagnosing Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) is challenging due to overlapping symptoms and limitations of current imaging methods. This study investigates the use of [11C]PBB3 PET/CT imaging to visualize tau pathology and improve diagnostic accuracy. Given diagnostic challenges with symptoms and conventional imaging, [11C]PBB3 PET/CT's potential to enhance accuracy was investigated by correlating tau pathology with cerebrospinal fluid (CSF) biomarkers, positron emission tomography (PET), computed tomography (CT), amyloid-beta, and Mini-Mental State Examination (MMSE). We conducted [11C]PBB3 PET/CT imaging on 24 patients with suspected AD or FTLD, alongside [11C]PiB PET/CT (13 patients) and [18F]FDG PET/CT (15 patients). Visual and quantitative assessments of [11C]PBB3 uptake using standardized uptake value ratios (SUV-Rs) and correlation analyses with clinical assessments were performed. The scans revealed distinct tau accumulation patterns; 13 patients had no or faint uptake (PBB3-negative) and 11 had moderate to pronounced uptake (PBB3-positive). Significant inverse correlations were found between [11C]PBB3 SUV-Rs and MMSE scores, but not with CSF-tau or CSF-amyloid-beta levels. Here, we show that [11C]PBB3 PET/CT imaging can reveal distinct tau accumulation patterns and correlate these with cognitive impairment in neurodegenerative diseases. Our study demonstrates the potential of [11C]PBB3-PET imaging for visualizing tau pathology and assessing disease severity, offering a promising tool for enhancing diagnostic accuracy in AD and FTLD. Further research is essential to validate these findings and refine the use of tau-specific PET imaging in clinical practice, ultimately improving patient care and treatment outcomes.
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Affiliation(s)
- Joachim Strobel
- Department of Nuclear Medicine, Ulm University Medical Center, 89081 Ulm, Germany
| | | | - Katharina Deininger
- Department of Nuclear Medicine, Ulm University Medical Center, 89081 Ulm, Germany
| | - Karl Peter Bohn
- Department of Nuclear Medicine, Ulm University Medical Center, 89081 Ulm, Germany
| | | | - Markus Otto
- Department of Neurology, Halle University, 06120 Halle, Germany
| | - Christoph Solbach
- Department of Nuclear Medicine, Ulm University Medical Center, 89081 Ulm, Germany
| | | | - Dörte Polivka
- Department of Neurology, Ulm University Medical Center, 89081 Ulm, Germany
| | - Patrick Fissler
- Psychiatric Services Thurgau (Academic Teaching Hospital of the University of Konstanz), 8596 Münsterlingen, Switzerland
| | - Gerhard Glatting
- Department of Nuclear Medicine, Ulm University Medical Center, 89081 Ulm, Germany
| | - Matthias W. Riepe
- Department of Psychiatry and Psychotherapy II, Ulm University, 89075 Ulm, Germany
| | - Makoto Higuchi
- National Institute of Radiological Sciences, Chiba 263-8555, Japan
| | - Ambros J. Beer
- Department of Nuclear Medicine, Ulm University Medical Center, 89081 Ulm, Germany
| | - Albert Ludolph
- Department of Neurology, Ulm University Medical Center, 89081 Ulm, Germany
| | - Gordon Winter
- Department of Nuclear Medicine, Ulm University Medical Center, 89081 Ulm, Germany
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20
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Ravikumar S, Denning AE, Lim S, Chung E, Sadeghpour N, Ittyerah R, Wisse LEM, Das SR, Xie L, Robinson JL, Schuck T, Lee EB, Detre JA, Tisdall MD, Prabhakaran K, Mizsei G, de Onzono Martin MMI, Arroyo Jiménez MDM, Mũnoz M, Marcos Rabal MDP, Cebada Sánchez S, Delgado González JC, de la Rosa Prieto C, Irwin DJ, Wolk DA, Insausti R, Yushkevich PA. Postmortem imaging reveals patterns of medial temporal lobe vulnerability to tau pathology in Alzheimer's disease. Nat Commun 2024; 15:4803. [PMID: 38839876 PMCID: PMC11153494 DOI: 10.1038/s41467-024-49205-0] [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: 10/05/2023] [Accepted: 05/28/2024] [Indexed: 06/07/2024] Open
Abstract
Our current understanding of the spread and neurodegenerative effects of tau neurofibrillary tangles (NFTs) within the medial temporal lobe (MTL) during the early stages of Alzheimer's Disease (AD) is limited by the presence of confounding non-AD pathologies and the two-dimensional (2-D) nature of conventional histology studies. Here, we combine ex vivo MRI and serial histological imaging from 25 human MTL specimens to present a detailed, 3-D characterization of quantitative NFT burden measures in the space of a high-resolution, ex vivo atlas with cytoarchitecturally-defined subregion labels, that can be used to inform future in vivo neuroimaging studies. Average maps show a clear anterior to poster gradient in NFT distribution and a precise, spatial pattern with highest levels of NFTs found not just within the transentorhinal region but also the cornu ammonis (CA1) subfield. Additionally, we identify granular MTL regions where measures of neurodegeneration are likely to be linked to NFTs specifically, and thus potentially more sensitive as early AD biomarkers.
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Affiliation(s)
- Sadhana Ravikumar
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
| | - Amanda E Denning
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Lim
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Eunice Chung
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Ranjit Ittyerah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura E M Wisse
- Institute for Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Sandhitsu R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Long Xie
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - John L Robinson
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Theresa Schuck
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - M Dylan Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Gabor Mizsei
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Monica Mũnoz
- Human Neuroanatomy Laboratory, University of Castilla La Mancha, Albacete, Spain
| | | | | | | | | | - David J Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, University of Castilla La Mancha, Albacete, Spain
| | - Paul A Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
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21
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Wang J, Huang Q, He K, Li J, Guo T, Yang Y, Lin Z, Li S, Vanderlinden G, Huang Y, Van Laere K, Guan Y, Guo Q, Ni R, Li B, Xie F. Presynaptic density determined by SV2A PET is closely associated with postsynaptic metabotropic glutamate receptor 5 availability and independent of amyloid pathology in early cognitive impairment. Alzheimers Dement 2024; 20:3876-3888. [PMID: 38634334 PMCID: PMC11180932 DOI: 10.1002/alz.13817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 02/28/2024] [Accepted: 03/06/2024] [Indexed: 04/19/2024]
Abstract
INTRODUCTION Metabotropic glutamate receptor 5 (mGluR5) is involved in regulating integrative brain function and synaptic transmission. Aberrant mGluR5 signaling and relevant synaptic failure play a key role in the pathophysiological mechanism of Alzheimer's disease (AD). METHODS Ten cognitively impaired (CI) individuals and 10 healthy controls (HCs) underwent [18F]SynVesT-1 and [18F]PSS232 positron emission tomography (PET)/magnetic resonance to assess synaptic density and mGluR5 availability. The associations between mGluR5 availability and synaptic density were examined. A mediation analysis was performed to investigate the possible mediating effects of mGluR5 availability and synaptic loss on the relationship between amyloid deposition and cognition. RESULTS CI patients exhibited lower mGluR5 availability and synaptic density in the medial temporal lobe than HCs. Regional synaptic density was closely associated with regional mGluR5 availability. mGluR5 availability and synaptic loss partially mediated the relationship between amyloid deposition and cognition. CONCLUSIONS Reductions in mGluR5 availability and synaptic density exhibit similar spatial patterns in AD and are closely linked. HIGHLIGHTS Cognitively impaired patients exhibited lower mGluR5 availability and synaptic density in the medial temporal lobe than HCs. Reductions in mGluR5 availability and synaptic density exhibit similar spatial patterns in AD. Regional synaptic density was closely associated with regional mGluR5 availability. mGluR5 availability and synaptic loss partially mediated the relationship between amyloid deposition and global cognition. With further research, modulating mGluR5 availability might be a potential therapeutic strategy for improving synaptic function in AD.
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Affiliation(s)
- Jie Wang
- Department of Nuclear Medicine & PET CenterHuashan Hospital, Fudan UniversityShanghaiChina
| | - Qi Huang
- Department of Nuclear Medicine & PET CenterHuashan Hospital, Fudan UniversityShanghaiChina
| | - Kun He
- Department of Nuclear Medicine & PET CenterHuashan Hospital, Fudan UniversityShanghaiChina
| | - Junpeng Li
- Department of Nuclear Medicine & PET CenterHuashan Hospital, Fudan UniversityShanghaiChina
| | - Tengfei Guo
- Institute of Biomedical Engineering, Shenzhen Bay LaboratoryShenzhenChina
| | - Yang Yang
- Beijing United Imaging Research Institute of Intelligent ImagingBeijingChina
| | - Zengping Lin
- Central Research Institute, United Imaging Healthcare Group Co., LtdShanghaiChina
| | - Songye Li
- Department of Radiology and Biomedical ImagingPET CenterYale University School of MedicineNew HavenConnecticutUSA
| | - Greet Vanderlinden
- Department of Imaging and PathologyNuclear Medicine and Molecular Imaging, KU LeuvenLeuvenBelgium
| | - Yiyun Huang
- Department of Radiology and Biomedical ImagingPET CenterYale University School of MedicineNew HavenConnecticutUSA
| | - Koen Van Laere
- Department of Imaging and PathologyNuclear Medicine and Molecular Imaging, KU LeuvenLeuvenBelgium
| | - Yihui Guan
- Department of Nuclear Medicine & PET CenterHuashan Hospital, Fudan UniversityShanghaiChina
| | - Qihao Guo
- Department of GerontologyShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
| | - Ruiqing Ni
- Institute for Biomedical Engineering, University of Zurich & ETH ZurichZurichSwitzerland
- Institute for Regenerative MedicineUniversity of ZurichZurichSwitzerland
| | - Binying Li
- Department of Neurology and Institute of NeurologyRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Fang Xie
- Department of Nuclear Medicine & PET CenterHuashan Hospital, Fudan UniversityShanghaiChina
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan UniversityShanghaiChina
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22
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Morais-Ribeiro R, Almeida FC, Coelho A, Oliveira TG. Differential atrophy along the longitudinal hippocampal axis in Alzheimer's disease. Eur J Neurosci 2024; 59:3376-3388. [PMID: 38654447 DOI: 10.1111/ejn.16361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 04/03/2024] [Indexed: 04/26/2024]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects the hippocampus. Since hippocampal studies have highlighted a differential subregional regulation along its longitudinal axis, a more detailed analysis addressing subregional changes along the longitudinal hippocampal axis has the potential to provide new relevant biomarkers. This study included structural brain MRI data of 583 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cognitively normal (CN) subjects, mild cognitively impaired (MCI) subjects and AD patients were conveniently selected considering the age and sex match between clinical groups. Structural MRI acquisitions were pre-processed and analysed with a new longitudinal axis segmentation method, dividing the hippocampus in three subdivisions (anterior, intermediate, and posterior). When normalizing the volume of hippocampal sub-divisions to total hippocampus, the posterior hippocampus negatively correlates with age only in CN subjects (r = -.31). The longitudinal ratio of hippocampal atrophy (anterior sub-division divided by the posterior one) shows a significant increase with age only in CN (r = .25). Overall, in AD, the posterior hippocampus is predominantly atrophied early on. Consequently, the anterior/posterior hippocampal ratio is an AD differentiating metric at early disease stages with potential for diagnostic and prognostic applications.
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Affiliation(s)
- Rafaela Morais-Ribeiro
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, Braga, Portugal
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | - Francisco C Almeida
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, Braga, Portugal
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Department of Neuroradiology, Centro Hospitalar Universitário do Porto, Porto, Portugal
| | - Ana Coelho
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, Braga, Portugal
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | - Tiago Gil Oliveira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, Braga, Portugal
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
- Division of Neuroradiology, Hospital de Braga, Braga, Portugal
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23
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Wuestefeld A, Binette AP, van Westen D, Strandberg O, Stomrud E, Mattsson-Carlgren N, Janelidze S, Smith R, Palmqvist S, Baumeister H, Berron D, Yushkevich PA, Hansson O, Spotorno N, Wisse LEM. Medial temporal lobe atrophy patterns in early- versus late-onset amnestic Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.21.594976. [PMID: 38826333 PMCID: PMC11142072 DOI: 10.1101/2024.05.21.594976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Background The medial temporal lobe (MTL) is hypothesized to be relatively spared in early-onset Alzheimer's disease (EOAD). Yet, detailed examination of MTL subfield volumes and drivers of atrophy in amnestic EOAD is lacking. Methods BioFINDER-2 participants with memory impairment, abnormal amyloid-β status and tau-PET were included. Forty-one EOAD individuals aged ≤65 years and, as comparison, late-onset AD (LOAD, ≥70 years, n=154) and Aβ-negative cognitively unimpaired controls were included. MTL subregions and biomarkers of (co-)pathologies were measured. Results AD groups showed smaller MTL subregions compared to controls. Atrophy patterns were similar across AD groups, although LOAD showed thinner entorhinal cortices compared to EOAD. EOAD showed lower WMH compared to LOAD. No differences in MTL tau-PET or transactive response DNA binding protein 43-proxy positivity was found. Conclusions We found in vivo evidence for MTL atrophy in amnestic EOAD and overall similar levels to LOAD of MTL tau pathology and co-pathologies.
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Affiliation(s)
- Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Danielle van Westen
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, 22242 Lund, Sweden
- Image and Function, Skåne University Hospital, 22242 Lund Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 20502 Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Department of Neurology, Skåne University Hospital, 22242 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, 22242 Lund, Sweden
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 20502 Malmö, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 20502 Malmö, Sweden
| | - Hannah Baumeister
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia 19104, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 20502 Malmö, Sweden
| | - Nicola Spotorno
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, 22242 Lund, Sweden
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24
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Khandelwal P, Duong MT, Sadaghiani S, Lim S, Denning AE, Chung E, Ravikumar S, Arezoumandan S, Peterson C, Bedard M, Capp N, Ittyerah R, Migdal E, Choi G, Kopp E, Loja B, Hasan E, Li J, Bahena A, Prabhakaran K, Mizsei G, Gabrielyan M, Schuck T, Trotman W, Robinson J, Ohm DT, Lee EB, Trojanowski JQ, McMillan C, Grossman M, Irwin DJ, Detre JA, Tisdall MD, Das SR, Wisse LEM, Wolk DA, Yushkevich PA. Automated deep learning segmentation of high-resolution 7 Tesla postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2024; 2:1-30. [PMID: 39301426 PMCID: PMC11409836 DOI: 10.1162/imag_a_00171] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/01/2024] [Accepted: 04/15/2024] [Indexed: 09/22/2024]
Abstract
Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high-resolution dataset of 135 postmortem human brain tissue specimens imaged at 0.3 mm3 isotropic using a T2w sequence on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures, followed by post-hoc topological correction. We evaluate the reliability of this pipeline via overlap metrics with manual segmentation in 6 specimens, and intra-class correlation between cortical thickness measures extracted from the automatic segmentation and expert-generated reference measures in 36 specimens. We also segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter, providing a limited evaluation of accuracy. We show generalizing capabilities across whole-brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm3 and 0.16 mm3 isotropic T2*w fast low angle shot (FLASH) sequence at 7T. We report associations between localized cortical thickness and volumetric measurements across key regions, and semi-quantitative neuropathological ratings in a subset of 82 individuals with Alzheimer's disease (AD) continuum diagnoses. Our code, Jupyter notebooks, and the containerized executables are publicly available at the project webpage (https://pulkit-khandelwal.github.io/exvivo-brain-upenn/).
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Affiliation(s)
- Pulkit Khandelwal
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
| | - Michael Tran Duong
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Shokufeh Sadaghiani
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Sydney Lim
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Amanda E. Denning
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Eunice Chung
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Sadhana Ravikumar
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Sanaz Arezoumandan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Claire Peterson
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Madigan Bedard
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Noah Capp
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Elyse Migdal
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Grace Choi
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Emily Kopp
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Bridget Loja
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Eusha Hasan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Jiacheng Li
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Alejandra Bahena
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Karthik Prabhakaran
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Gabor Mizsei
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Marianna Gabrielyan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Theresa Schuck
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Winifred Trotman
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - John Robinson
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Daniel T. Ohm
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Edward B. Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - John Q. Trojanowski
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Corey McMillan
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Murray Grossman
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - David J. Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - John A. Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - M. Dylan Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Sandhitsu R. Das
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | | | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
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25
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Momota Y, Bun S, Hirano J, Kamiya K, Ueda R, Iwabuchi Y, Takahata K, Yamamoto Y, Tezuka T, Kubota M, Seki M, Shikimoto R, Mimura Y, Kishimoto T, Tabuchi H, Jinzaki M, Ito D, Mimura M. Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders. Sci Rep 2024; 14:7633. [PMID: 38561395 PMCID: PMC10984960 DOI: 10.1038/s41598-024-58223-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 03/26/2024] [Indexed: 04/04/2024] Open
Abstract
Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer's disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.
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Affiliation(s)
- Yuki Momota
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
- Department of Functional Brain Imaging Research, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage-Ku, Chiba-Shi, Chiba, 263-8555, Japan
| | - Shogyoku Bun
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
| | - Jinichi Hirano
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan.
| | - Kei Kamiya
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Ryo Ueda
- Office of Radiation Technology, Keio University Hospital, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Yu Iwabuchi
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Keisuke Takahata
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
- Department of Functional Brain Imaging Research, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage-Ku, Chiba-Shi, Chiba, 263-8555, Japan
| | - Yasuharu Yamamoto
- Department of Functional Brain Imaging Research, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage-Ku, Chiba-Shi, Chiba, 263-8555, Japan
| | - Toshiki Tezuka
- Department of Neurology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Masahito Kubota
- Department of Neurology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Morinobu Seki
- Department of Neurology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Ryo Shikimoto
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Yu Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Taishiro Kishimoto
- Psychiatry Department, Donald and Barbara Zucker School of Medicine, Hempstead, NY, 11549, USA
- Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine, Mori JP Tower F7, 1-3-1 Azabudai, Minato-ku, Tokyo, 106-0041, Japan
| | - Hajime Tabuchi
- Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Masahiro Jinzaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Daisuke Ito
- Department of Physiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
- Memory Center, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-Ku, Tokyo, 160-8582, Japan
| | - Masaru Mimura
- Center for Preventive Medicine, Keio University, Mori JP Tower 7th Floor, 1-3-1 Azabudai, Minato-ku, Tokyo, 106-0041, Japan
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Maldonado-Díaz C, Hiya S, Yokoda RT, Farrell K, Marx GA, Kauffman J, Daoud EV, Gonzales MM, Parker AS, Canbeldek L, Kulumani Mahadevan LS, Crary JF, White CL, Walker JM, Richardson TE. Disentangling and quantifying the relative cognitive impact of concurrent mixed neurodegenerative pathologies. Acta Neuropathol 2024; 147:58. [PMID: 38520489 PMCID: PMC10960766 DOI: 10.1007/s00401-024-02716-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/25/2024]
Abstract
Neurodegenerative pathologies such as Alzheimer disease neuropathologic change (ADNC), Lewy body disease (LBD), limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC), and cerebrovascular disease (CVD) frequently coexist, but little is known about the exact contribution of each pathology to cognitive decline and dementia in subjects with mixed pathologies. We explored the relative cognitive impact of concurrent common and rare neurodegenerative pathologies employing multivariate logistic regression analysis adjusted for age, gender, and level of education. We analyzed a cohort of 6,262 subjects from the National Alzheimer's Coordinating Center database, ranging from 0 to 6 comorbid neuropathologic findings per individual, where 95.7% of individuals had at least 1 neurodegenerative finding at autopsy and 75.5% had at least 2 neurodegenerative findings. We identified which neuropathologic entities correlate most frequently with one another and demonstrated that the total number of pathologies per individual was directly correlated with cognitive performance as assessed by Clinical Dementia Rating (CDR®) and Mini-Mental State Examination (MMSE). We show that ADNC, LBD, LATE-NC, CVD, hippocampal sclerosis, Pick disease, and FTLD-TDP significantly impact overall cognition as independent variables. More specifically, ADNC significantly affected all assessed cognitive domains, LBD affected attention, processing speed, and language, LATE-NC primarily affected tests related to logical memory and language, while CVD and other less common pathologies (including Pick disease, progressive supranuclear palsy, and corticobasal degeneration) had more variable neurocognitive effects. Additionally, ADNC, LBD, and higher numbers of comorbid neuropathologies were associated with the presence of at least one APOE ε4 allele, and ADNC and higher numbers of neuropathologies were inversely correlated with APOE ε2 alleles. Understanding the mechanisms by which individual and concomitant neuropathologies affect cognition and the degree to which each contributes is an imperative step in the development of biomarkers and disease-modifying therapeutics, particularly as these medical interventions become more targeted and personalized.
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Affiliation(s)
- Carolina Maldonado-Díaz
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, Annenberg Building, 15.238, 1468 Madison Avenue, New York, NY, 10029, USA
| | - Satomi Hiya
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, Annenberg Building, 15.238, 1468 Madison Avenue, New York, NY, 10029, USA
| | - Raquel T Yokoda
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, Annenberg Building, 15.238, 1468 Madison Avenue, New York, NY, 10029, USA
| | - Kurt Farrell
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, Annenberg Building, 15.238, 1468 Madison Avenue, New York, NY, 10029, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Ronal M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Gabriel A Marx
- Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Ronal M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Justin Kauffman
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, Annenberg Building, 15.238, 1468 Madison Avenue, New York, NY, 10029, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Ronal M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Elena V Daoud
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Mitzi M Gonzales
- Department of Neurology, Cedars Sinai Medical Center, Los Angeles, CA, 90048, USA
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA
| | - Alicia S Parker
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA
| | - Leyla Canbeldek
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, Annenberg Building, 15.238, 1468 Madison Avenue, New York, NY, 10029, USA
| | - Lakshmi Shree Kulumani Mahadevan
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, Annenberg Building, 15.238, 1468 Madison Avenue, New York, NY, 10029, USA
| | - John F Crary
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, Annenberg Building, 15.238, 1468 Madison Avenue, New York, NY, 10029, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Ronal M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Charles L White
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Jamie M Walker
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, Annenberg Building, 15.238, 1468 Madison Avenue, New York, NY, 10029, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA
| | - Timothy E Richardson
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, Annenberg Building, 15.238, 1468 Madison Avenue, New York, NY, 10029, USA.
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Lyu X, Duong MT, Xie L, de Flores R, Richardson H, Hwang G, Wisse LEM, DiCalogero M, McMillan CT, Robinson JL, Xie SX, Lee EB, Irwin DJ, Dickerson BC, Davatzikos C, Nasrallah IM, Yushkevich PA, Wolk DA, Das SR, for the Alzheimer's Disease Neuroimaging Initiative. Tau-neurodegeneration mismatch reveals vulnerability and resilience to comorbidities in Alzheimer's continuum. Alzheimers Dement 2024; 20:1586-1600. [PMID: 38050662 PMCID: PMC10984442 DOI: 10.1002/alz.13559] [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: 06/13/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 12/06/2023]
Abstract
INTRODUCTION Variability in relationship of tau-based neurofibrillary tangles (T) and neurodegeneration (N) in Alzheimer's disease (AD) arises from non-specific nature of N, modulated by non-AD co-pathologies, age-related changes, and resilience factors. METHODS We used regional T-N residual patterns to partition 184 patients within the Alzheimer's continuum into data-driven groups. These were compared with groups from 159 non-AD (amyloid "negative") patients partitioned using cortical thickness, and groups in 98 patients with ante mortem MRI and post mortem tissue for measuring N and T, respectively. We applied the initial T-N residual model to classify 71 patients in an independent cohort into predefined groups. RESULTS AD groups displayed spatial T-N mismatch patterns resembling neurodegeneration patterns in non-AD groups, similarly associated with non-AD factors and diverging cognitive outcomes. In the autopsy cohort, limbic T-N mismatch correlated with TDP-43 co-pathology. DISCUSSION T-N mismatch may provide a personalized approach for determining non-AD factors associated with resilience/vulnerability in AD.
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Affiliation(s)
- Xueying Lyu
- Departments of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Michael Tran Duong
- Departments of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Long Xie
- Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Hayley Richardson
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Gyujoon Hwang
- Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Michael DiCalogero
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Corey T. McMillan
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - John L. Robinson
- Departments of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sharon X. Xie
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Edward B. Lee
- Departments of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David J. Irwin
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Christos Davatzikos
- Departments of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ilya M. Nasrallah
- Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Paul A. Yushkevich
- Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David A. Wolk
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sandhitsu R. Das
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Herrejon IA, Jackson TB, Hicks TH, Bernard JA, Alzheimer’s Disease Neuroimaging Initiative. Functional Connectivity Differences in Distinct Dentato-Cortical Networks in Alzheimer's Disease and Mild Cognitive Impairment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.02.578249. [PMID: 38352603 PMCID: PMC10862898 DOI: 10.1101/2024.02.02.578249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Recent research has implicated the cerebellum in Alzheimer's disease (AD), and cerebrocerebellar network connectivity is emerging as a possible contributor to symptom severity. The cerebellar dentate nucleus (DN) has parallel motor and non-motor sub-regions that project to motor and frontal regions of the cerebral cortex, respectively. These distinct dentato-cortical networks have been delineated in the non-human primate and human brain. Importantly, cerebellar regions prone to atrophy in AD are functionally connected to atrophied regions of the cerebral cortex, suggesting that dysfunction perhaps occurs at a network level. Investigating functional connectivity (FC) alterations of the DN is a crucial step in understanding the cerebellum in AD and in mild cognitive impairment (MCI). Inclusion of this latter group stands to provide insights into cerebellar contributions prior to diagnosis of AD. The present study investigated FC differences in dorsal (dDN) and ventral (vDN) DN networks in MCI and AD relative to cognitively normal participants (CN) and relationships between FC and behavior. Our results showed patterns indicating both higher and lower functional connectivity in both dDN and vDN in AD compared to CN. However, connectivity in the AD group was lower when compared to MCI. We argue that these findings suggest that the patterns of higher FC in AD may act as a compensatory mechanism. Additionally, we found associations between the individual networks and behavior. There were significant interactions between dDN connectivity and motor symptoms. However, both DN seeds were associated with cognitive task performance. Together, these results indicate that cerebellar DN networks are impacted in AD, and this may impact behavior. In concert with the growing body of literature implicating the cerebellum in AD, our work further underscores the importance of investigations of this region. We speculate that much like in psychiatric diseases such as schizophrenia, cerebellar dysfunction results in negative impacts on thought and the organization therein. Further, this is consistent with recent arguments that the cerebellum provides crucial scaffolding for cognitive function in aging. Together, our findings stand to inform future clinical work in the diagnosis and understanding of this disease.
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Affiliation(s)
- Ivan A. Herrejon
- Department of Psychological and Brain Sciences Texas A&M University
| | - T. Bryan Jackson
- Department of Psychological and Brain Sciences Texas A&M University
- Vanderbilt Memory and Alzheimer’s Center Vanderbilt University Medical Center
| | - Tracey H. Hicks
- Department of Psychological and Brain Sciences Texas A&M University
| | - Jessica A. Bernard
- Department of Psychological and Brain Sciences Texas A&M University
- Texas A&M Institute for Neuroscience Texas A&M University
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29
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Petkus AJ, Salminen LE, Wang X, Driscoll I, Millstein J, Beavers DP, Espeland MA, Braskie MN, Thompson PM, Casanova R, Gatz M, Chui HC, Resnick SM, Kaufman JD, Rapp SR, Shumaker S, Younan D, Chen JC. Alzheimer's Related Neurodegeneration Mediates Air Pollution Effects on Medial Temporal Lobe Atrophy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.29.23299144. [PMID: 38076972 PMCID: PMC10705654 DOI: 10.1101/2023.11.29.23299144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Exposure to ambient air pollution, especially particulate matter with aerodynamic diameter <2.5 μm (PM2.5) and nitrogen dioxide (NO2), are environmental risk factors for Alzheimer's disease and related dementia. The medial temporal lobe (MTL) is an important brain region subserving episodic memory that atrophies with age, during the Alzheimer's disease continuum, and is vulnerable to the effects of cerebrovascular disease. Despite the importance of air pollution it is unclear whether exposure leads to atrophy of the MTL and by what pathways. Here we conducted a longitudinal study examining associations between ambient air pollution exposure and MTL atrophy and whether putative air pollution exposure effects resembled Alzheimer's disease-related neurodegeneration or cerebrovascular disease-related neurodegeneration. Participants included older women (n = 627; aged 71-87) who underwent two structural brain MRI scans (MRI-1: 2005-6; MRI-2: 2009-10) as part of the Women's Health Initiative Memory Study of Magnetic Resonance Imaging. Regionalized universal kriging was used to estimate annual concentrations of PM2.5 and NO2 at residential locations aggregated to 3-year averages prior to MRI-1. The outcome was 5-year standardized change in MTL volumes. Mediators included voxel-based MRI measures of the spatial pattern of neurodegeneration of Alzheimer's disease (Alzheimer's disease pattern similarity scores [AD-PS]) and whole-brain white matter small-vessel ischemic disease (WM-SVID) volume as a proxy of global cerebrovascular damage. Structural equation models were constructed to examine whether the associations between exposures with MTL atrophy were mediated by the initial level or concurrent change in AD-PS score or WM-SVID while adjusting for sociodemographic, lifestyle, clinical characteristics, and intracranial volume. Living in locations with higher PM2.5 (per interquartile range [IQR]=3.17μg/m3) or NO2 (per IQR=6.63ppb) was associated with greater MTL atrophy (βPM2.5 = -0.29, 95% confidence interval [CI]=[-0.41,-0.18]; βNO2 =-0.12, 95%CI=[-0.23,-0.02]). Greater PM2.5 was associated with larger increases in AD-PS (βPM2.5 = 0.23, 95%CI=[0.12,0.33]) over time, which partially mediated associations with MTL atrophy (indirect effect= -0.10; 95%CI=[-0.15, -0.05]), explaining approximately 32% of the total effect. NO2 was positively associated with AD-PS at MRI-1 (βNO2=0.13, 95%CI=[0.03,0.24]), which partially mediated the association with MTL atrophy (indirect effect= -0.01, 95% CI=[-0.03,-0.001]). Global WM-SVID at MRI-1 or concurrent change were not significant mediators between exposures and MTL atrophy. Findings support the mediating role of Alzheimer's disease-related neurodegeneration contributing to MTL atrophy associated with late-life exposures to air pollutants. Alzheimer's disease-related neurodegeneration only partially explained associations between exposure and MTL atrophy suggesting the role of multiple neuropathological processes underlying air pollution neurotoxicity on brain aging.
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Affiliation(s)
- Andrew J. Petkus
- Department of Neurology, University of Southern California, Los Angeles, California, 90033, United States
| | - Lauren E. Salminen
- Department of Neurology, University of Southern California, Los Angeles, California, 90033, United States
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, 90033, United States
| | - Xinhui Wang
- Department of Neurology, University of Southern California, Los Angeles, California, 90033, United States
| | - Ira Driscoll
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, 53792, United States
| | - Joshua Millstein
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, 90033, United States
| | - Daniel P. Beavers
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, 27101, United States
| | - Mark A. Espeland
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, 27101, United States
| | - Meredith N. Braskie
- Department of Neurology, University of Southern California, Los Angeles, California, 90033, United States
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, 90033, United States
| | - Paul M. Thompson
- Department of Neurology, University of Southern California, Los Angeles, California, 90033, United States
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, 90033, United States
| | - Ramon Casanova
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, 27101, United States
| | - Margaret Gatz
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, 90089, United States
| | - Helena C. Chui
- Department of Neurology, University of Southern California, Los Angeles, California, 90033, United States
| | - Susan M Resnick
- The Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, 20898, United States
| | - Joel D. Kaufman
- Departments of Environmental & Occupational Health Sciences, Medicine (General Internal Medicine), and Epidemiology, University of Washington, Seattle, Washington, 98195, United States
| | - Stephen R. Rapp
- Departments of Psychiatry and Behavioral Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina , 27101, United States
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, North Carolina, 27101, United States
| | - Sally Shumaker
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, North Carolina, 27101, United States
| | - Diana Younan
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, 90033, United States
| | - Jiu-Chiuan Chen
- Department of Neurology, University of Southern California, Los Angeles, California, 90033, United States
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, 90033, United States
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Balajoo SM, Eickhoff SB, Masouleh SK, Plachti A, Waite L, Saberi A, Bahri MA, Bastin C, Salmon E, Hoffstaedter F, Palomero-Gallagher N, Genon S. Hippocampal metabolic subregions and networks: Behavioral, molecular, and pathological aging profiles. Alzheimers Dement 2023; 19:4787-4804. [PMID: 37014937 PMCID: PMC10698199 DOI: 10.1002/alz.13056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 04/06/2023]
Abstract
INTRODUCTION Hippocampal local and network dysfunction is the hallmark of Alzheimer's disease (AD). METHODS We characterized the spatial patterns of hippocampus differentiation based on brain co-metabolism in healthy elderly participants and demonstrated their relevance to study local metabolic changes and associated dysfunction in pathological aging. RESULTS The hippocampus can be differentiated into anterior/posterior and dorsal cornu ammonis (CA)/ventral (subiculum) subregions. While anterior/posterior CA show co-metabolism with different regions of the subcortical limbic networks, the anterior/posterior subiculum are parts of cortical networks supporting object-centered memory and higher cognitive demands, respectively. Both networks show relationships with the spatial patterns of gene expression pertaining to cell energy metabolism and AD's process. Finally, while local metabolism is generally lower in posterior regions, the anterior-posterior imbalance is maximal in late mild cognitive impairment with the anterior subiculum being relatively preserved. DISCUSSION Future studies should consider bidimensional hippocampal differentiation and in particular the posterior subicular region to better understand pathological aging.
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Affiliation(s)
- Somayeh Maleki Balajoo
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Shahrzad Kharabian Masouleh
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Anna Plachti
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Laura Waite
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Amin Saberi
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
- Otto Hahn Research Group for Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Mohamed Ali Bahri
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Christine Bastin
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Eric Salmon
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
- Department of Neurology, University Hospital of Liège, Liège, Belgium
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM‑1), Research Centre Juelich, Juelich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
- Cécile and Oskar Vogt Institute for Brain Research, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
| | - Sarah Genon
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
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31
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Vadukul D, Papp M, Thrush RJ, Wang J, Jin Y, Arosio P, Aprile FA. α-Synuclein Aggregation Is Triggered by Oligomeric Amyloid-β 42 via Heterogeneous Primary Nucleation. J Am Chem Soc 2023; 145:18276-18285. [PMID: 37556728 PMCID: PMC10450681 DOI: 10.1021/jacs.3c03212] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Indexed: 08/11/2023]
Abstract
An increasing number of cases where amyloids of different proteins are found in the same patient are being reported. This observation complicates diagnosis and clinical intervention. Amyloids of the amyloid-β peptide or the protein α-synuclein are traditionally considered hallmarks of Alzheimer's and Parkinson's diseases, respectively. However, the co-occurrence of amyloids of these proteins has also been reported in patients diagnosed with either disease. Here, we show that soluble species containing amyloid-β can induce the aggregation of α-synuclein. Fibrils formed under these conditions are solely composed of α-synuclein to which amyloid-β can be found associated but not as part of the core of the fibrils. Importantly, by global kinetic analysis, we found that the aggregation of α-synuclein under these conditions occurs via heterogeneous primary nucleation, triggered by soluble aggregates containing amyloid-β.
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Affiliation(s)
- Devkee
M. Vadukul
- Department
of Chemistry, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, U.K.
| | - Marcell Papp
- Department
of Chemistry and Applied Biosciences, Institute
for Chemical and Bioengineering, Swiss Federal Institute of Technology, 8093 Zurich, Switzerland
| | - Rebecca J. Thrush
- Department
of Chemistry, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, U.K.
- Institute
of Chemical Biology, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, U.K.
| | - Jielei Wang
- Department
of Chemistry, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, U.K.
| | - Yiyun Jin
- Department
of Chemistry, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, U.K.
| | - Paolo Arosio
- Department
of Chemistry and Applied Biosciences, Institute
for Chemical and Bioengineering, Swiss Federal Institute of Technology, 8093 Zurich, Switzerland
| | - Francesco A. Aprile
- Department
of Chemistry, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, U.K.
- Institute
of Chemical Biology, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, U.K.
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Wuestefeld A, Pichet Binette A, Berron D, Spotorno N, van Westen D, Stomrud E, Mattsson-Carlgren N, Strandberg O, Smith R, Palmqvist S, Glenn T, Moes S, Honer M, Arfanakis K, Barnes LL, Bennett DA, Schneider JA, Wisse LEM, Hansson O. Age-related and amyloid-beta-independent tau deposition and its downstream effects. Brain 2023; 146:3192-3205. [PMID: 37082959 PMCID: PMC10393402 DOI: 10.1093/brain/awad135] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 03/20/2023] [Accepted: 04/06/2023] [Indexed: 04/22/2023] Open
Abstract
Amyloid-β (Aβ) is hypothesized to facilitate the spread of tau pathology beyond the medial temporal lobe. However, there is evidence that, independently of Aβ, age-related tau pathology might be present outside of the medial temporal lobe. We therefore aimed to study age-related Aβ-independent tau deposition outside the medial temporal lobe in two large cohorts and to investigate potential downstream effects of this on cognition and structural measures. We included 545 cognitively unimpaired adults (40-92 years) from the BioFINDER-2 study (in vivo) and 639 (64-108 years) from the Rush Alzheimer's Disease Center cohorts (ex vivo). 18F-RO948- and 18F-flutemetamol-PET standardized uptake value ratios were calculated for regional tau and global/regional Aβ in vivo. Immunohistochemistry was used to estimate Aβ load and tangle density ex vivo. In vivo medial temporal lobe volumes (subiculum, cornu ammonis 1) and cortical thickness (entorhinal cortex, Brodmann area 35) were obtained using Automated Segmentation for Hippocampal Subfields packages. Thickness of early and late neocortical Alzheimer's disease regions was determined using FreeSurfer. Global cognition and episodic memory were estimated to quantify cognitive functioning. In vivo age-related tau deposition was observed in the medial temporal lobe and in frontal and parietal cortical regions, which was statistically significant when adjusting for Aβ. This was also observed in individuals with low Aβ load. Tau deposition was negatively associated with cortical volumes and thickness in temporal and parietal regions independently of Aβ. The associations between age and cortical volume or thickness were partially mediated via tau in regions with early Alzheimer's disease pathology, i.e. early tau and/or Aβ pathology (subiculum/Brodmann area 35/precuneus/posterior cingulate). Finally, the associations between age and cognition were partially mediated via tau in Brodmann area 35, even when including Aβ-PET as covariate. Results were validated in the ex vivo cohort showing age-related and Aβ-independent increases in tau aggregates in and outside the medial temporal lobe. Ex vivo age-cognition associations were mediated by medial and inferior temporal tau tangle density, while correcting for Aβ density. Taken together, our study provides support for primary age-related tauopathy even outside the medial temporal lobe in vivo and ex vivo, with downstream effects on structure and cognition. These results have implications for our understanding of the spreading of tau outside the medial temporal lobe, also in the context of Alzheimer's disease. Moreover, this study suggests the potential utility of tau-targeting treatments in primary age-related tauopathy, likely already in preclinical stages in individuals with low Aβ pathology.
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Affiliation(s)
- Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
| | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Nicola Spotorno
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
| | - Danielle van Westen
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, SE-222 42 Lund, Sweden
- Image and Function, Skåne University Hospital, SE-205 02 Malmö, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, SE-205 02 Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Department of Neurology, Skåne University Hospital, SE-205 02 Malmö, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, 221 84 Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Department of Neurology, Skåne University Hospital, SE-205 02 Malmö, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, SE-205 02 Malmö, Sweden
| | - Trevor Glenn
- Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Svenja Moes
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, CH-4070 Basel, Switzerland
| | - Michael Honer
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, CH-4070 Basel, Switzerland
| | - Konstantinos Arfanakis
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Lisa L Barnes
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - David A Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Julie A Schneider
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, SE-222 42 Lund, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, SE-205 02 Malmö, Sweden
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Nelson PT, Schneider JA, Jicha GA, Duong MT, Wolk DA. When Alzheimer's is LATE: Why Does it Matter? Ann Neurol 2023; 94:211-222. [PMID: 37245084 PMCID: PMC10516307 DOI: 10.1002/ana.26711] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 05/07/2023] [Accepted: 05/18/2023] [Indexed: 05/29/2023]
Abstract
Recent therapeutic advances provide heightened motivation for accurate diagnosis of the underlying biologic causes of dementia. This review focuses on the importance of clinical recognition of limbic-predominant age-related TDP-43 encephalopathy (LATE). LATE affects approximately one-quarter of older adults and produces an amnestic syndrome that is commonly mistaken for Alzheimer's disease (AD). Although AD and LATE often co-occur in the same patients, these diseases differ in the protein aggregates driving neuropathology (Aβ amyloid/tau vs TDP-43). This review discusses signs and symptoms, relevant diagnostic testing, and potential treatment implications for LATE that may be helpful for physicians, patients, and families. ANN NEUROL 2023;94:211-222.
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Affiliation(s)
| | | | | | | | - David A. Wolk
- University of Pennsylvania Alzheimer’s Disease Research Center
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Zhang S, Wang A, Liu S, Liu H, Zhu W, Zhang Z. Glycemic variability correlates with medial temporal lobe atrophy and decreased cognitive performance in patients with memory deficits. Front Aging Neurosci 2023; 15:1156908. [PMID: 37533764 PMCID: PMC10390778 DOI: 10.3389/fnagi.2023.1156908] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 06/28/2023] [Indexed: 08/04/2023] Open
Abstract
Background In the past, researchers have observed a significant link between glycemia and dementia. Medial temporal atrophy (MTA) is regarded as a common marker of dementia. The correlation between glycemic variability and MTA is unclear, and it has not been determined whether glycemic variability can be utilized as a biomarker of MTA and cognitive performance. Methods The patients in a memory clinic who underwent brain MRI scans and cognitive assessments within the first week of their hospital visit, were enrolled. All participants underwent three fasting blood glucose and one HBA1c assessments on three self-selected days within 1 week of their first visit. The variability independent of the mean (VIM) was employed. Validated visual scales were used to rate the MTA results. The mini-mental state examination (MMSE) and Montreal Cognitive Assessment (MoCA) scales were employed to assess the cognitive functions of the participants. Spearman's correlation and regression models were used to examine the relationship between the MMSE and MoCA scales, and also determine the link between the MRI characteristics and cognitive status, where vascular risk factors, educational status, age, gender, and mean glucose parameters served as covariates. Results Four hundred sixty-one subjects completed the MMSE scale, while 447 participants completed the MoCA scale. Data analysis revealed that 47.72% of the participants were men (220/461), and the median age of the patients was 69.87 ± 5.37 years. The findings of Spearman's correlation analysis exhibited a strong negative relationship between the VIM and MMSE score (r = -0.729, P < 0.01), and the MoCA score (r = -0.710, P < 0.01). The VIM was regarded as an independent risk factor for determining cognitive impairment in both the MMSE and MoCA assessments. The results were unaffected by sensitivity analysis. In addition, a non-linear relationship was observed between the VIM and MTA scores. Conclusion The variability in the blood glucose levels, which was presented as VIM, was related to the reduced cognitive function, which was reflected by MMSE and MoCA scales. The relationship between the VIM and the MTA score was non-linear. The VIM was positively related to the MTA score when the VIM was less than 2.42.
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Affiliation(s)
- Shuangmei Zhang
- Department of Pain Rehabilitation, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, Hangzhou, China
| | - Anrong Wang
- The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Shen Liu
- The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, China
- Department of Neurology of Traditional Chinese Medicine, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
- Institute for Brain Disorders, Beijing University of Chinese Medicine, Beijing, China
| | - Hongyu Liu
- Affiliated Hospital of Traditional Chinese Medicine of Guangzhou Medical University, Guangzhou, China
| | - Weifeng Zhu
- Affiliated Hospital of Traditional Chinese Medicine of Guangzhou Medical University, Guangzhou, China
| | - Zhaoxu Zhang
- Department of Neurology, Peking University People's Hospital, Beijing, China
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Hu WT, Nayyar A, Kaluzova M. Charting the Next Road Map for CSF Biomarkers in Alzheimer's Disease and Related Dementias. Neurotherapeutics 2023; 20:955-974. [PMID: 37378862 PMCID: PMC10457281 DOI: 10.1007/s13311-023-01370-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2023] [Indexed: 06/29/2023] Open
Abstract
Clinical prediction of underlying pathologic substrates in people with Alzheimer's disease (AD) dementia or related dementia syndromes (ADRD) has limited accuracy. Etiologic biomarkers - including cerebrospinal fluid (CSF) levels of AD proteins and cerebral amyloid PET imaging - have greatly modernized disease-modifying clinical trials in AD, but their integration into medical practice has been slow. Beyond core CSF AD biomarkers (including beta-amyloid 1-42, total tau, and tau phosphorylated at threonine 181), novel biomarkers have been interrogated in single- and multi-centered studies with uneven rigor. Here, we review early expectations for ideal AD/ADRD biomarkers, assess these goals' future applicability, and propose study designs and performance thresholds for meeting these ideals with a focus on CSF biomarkers. We further propose three new characteristics: equity (oversampling of diverse populations in the design and testing of biomarkers), access (reasonable availability to 80% of people at risk for disease, along with pre- and post-biomarker processes), and reliability (thorough evaluation of pre-analytical and analytical factors influencing measurements and performance). Finally, we urge biomarker scientists to balance the desire and evidence for a biomarker to reflect its namesake function, indulge data- as well as theory-driven associations, re-visit the subset of rigorously measured CSF biomarkers in large datasets (such as Alzheimer's disease neuroimaging initiative), and resist the temptation to favor ease over fail-safe in the development phase. This shift from discovery to application, and from suspended disbelief to cogent ingenuity, should allow the AD/ADRD biomarker field to live up to its billing during the next phase of neurodegenerative disease research.
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Affiliation(s)
- William T Hu
- Department of Neurology, Rutgers Biomedical and Health Sciences, Rutgers-Robert Wood Johnson Medical School, 125 Paterson Street, Suite 6200, New Brunswick, NJ, 08901, USA.
- Center for Innovation in Health and Aging Research, Institute for Health, Health Care Policy, and Aging Research, Rutgers Biomedical and Health Sciences, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, 08901, USA.
| | - Ashima Nayyar
- Department of Neurology, Rutgers Biomedical and Health Sciences, Rutgers-Robert Wood Johnson Medical School, 125 Paterson Street, Suite 6200, New Brunswick, NJ, 08901, USA
| | - Milota Kaluzova
- Department of Neurology, Rutgers Biomedical and Health Sciences, Rutgers-Robert Wood Johnson Medical School, 125 Paterson Street, Suite 6200, New Brunswick, NJ, 08901, USA
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36
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Du Y, Yu J, Liu M, Qiu Q, Fang Y, Zhao L, Wei W, Wang J, Lin X, Yan F, Li X. The relationship between depressive symptoms and cognitive function in Alzheimer's disease: The mediating effect of amygdala functional connectivity and radiomic features. J Affect Disord 2023; 330:101-109. [PMID: 36863470 DOI: 10.1016/j.jad.2023.02.129] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 03/04/2023]
Abstract
BACKGROUND Depressive symptoms are common in Alzheimer's disease (AD) and are associated with cognitive function. Amygdala functional connectivity (FC) and radiomic features related to depression and cognition. However, studies have yet to explore the neural mechanisms underlying these associations. METHODS We enrolled eighty-two AD patients with depressive symptoms (ADD) and 85 healthy controls (HCs) in this study. We compared amygdala FC using the seed-based approach between ADD patients and HCs. The least absolute shrinkage and selection operator (LASSO) was used to select amygdala radiomic features. A support vector machine (SVM) model was constructed based on the identified radiomic features to distinguish ADD from HCs. We used mediation analyses to explore the mediating effects of amygdala radiomic features and amygdala FC on cognition. RESULTS We found that ADD patients showed decreased amygdala FC with posterior cingulate cortex, middle frontal gyrus (MFG), and parahippocampal gyrus involved in the default mode network compared to HCs. The area under the receiver operating characteristic curve (AUC) of the amygdala radiomic model was 0.95 for ADD patients and HCs. Notably, the mediation model demonstrated that amygdala FC with the MFG and amygdala-based radiomic features mediated the relationship between depressive symptoms and cognitive function in AD. LIMITATIONS This study is a cross-sectional study and lacks longitudinal data. CONCLUSION Our findings may not only expand existing biological knowledge of the relationship between cognition and depressive symptoms in AD from the perspective of brain function and structure but also may ultimately provide potential targets for personalized treatment strategies.
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Affiliation(s)
- Yang Du
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jie Yu
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Manhua Liu
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qi Qiu
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Yuan Fang
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Lu Zhao
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Wenjing Wei
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jinghua Wang
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Xiang Lin
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Feng Yan
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Xia Li
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China.
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37
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Lyu X, Duong MT, Xie L, de Flores R, Richardson H, Hwang G, Wisse LEM, DiCalogero M, McMillan CT, Robinson JL, Xie SX, Grossman M, Lee EB, Irwin DJ, Dickerson BC, Davatzikos C, Nasrallah IM, Yushkevich PA, Wolk DA, Das SR. Tau-Neurodegeneration mismatch reveals vulnerability and resilience to comorbidities in Alzheimer's continuum. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.12.23285594. [PMID: 36824762 PMCID: PMC9949174 DOI: 10.1101/2023.02.12.23285594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Variability in the relationship of tau-based neurofibrillary tangles (T) and degree of neurodegeneration (N) in Alzheimer's Disease (AD) is likely attributable to the non-specific nature of N, which is also modulated by such factors as other co-pathologies, age-related changes, and developmental differences. We studied this variability by partitioning patients within the Alzheimer's continuum into data-driven groups based on their regional T-N dissociation, which reflects the residuals after the effect of tau pathology is "removed". We found six groups displaying distinct spatial T-N mismatch and thickness patterns despite similar tau burden. Their T-N patterns resembled the neurodegeneration patterns of non-AD groups partitioned on the basis of z-scores of cortical thickness alone and were similarly associated with surrogates of non-AD factors. In an additional sample of individuals with antemortem imaging and autopsy, T-N mismatch was associated with TDP-43 co-pathology. Finally, T-N mismatch training was then applied to a separate cohort to determine the ability to classify individual patients within these groups. These findings suggest that T-N mismatch may provide a personalized approach for determining non-AD factors associated with resilience/vulnerability to Alzheimer's disease.
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Walker JM, Richardson TE. Cognitive resistance to and resilience against multiple comorbid neurodegenerative pathologies and the impact of APOE status. J Neuropathol Exp Neurol 2023; 82:110-119. [PMID: 36458951 PMCID: PMC9852945 DOI: 10.1093/jnen/nlac115] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Alzheimer disease (AD) is currently the leading cause of cognitive decline and dementia worldwide. Recently, studies have suggested that other neurodegenerative comorbidities such as limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC), Lewy body disease (LBD), and cerebrovascular disease frequently co-occur with Alzheimer disease neuropathologic change (ADNC) and may have significant cognitive effects both in isolation and synergistically with ADNC. Herein, we study the relative clinical impact of these multiple neurodegenerative pathologies in 704 subjects. Each of these pathologies is relatively common in the cognitively impaired population, while cerebrovascular pathology and ADNC are the most common in cognitively normal individuals. Moreover, while the number of concurrent neuropathologic entities rises with age and has a progressively deleterious effect on cognition, 44.3% of cognitively intact individuals are resistant to having any neurodegenerative proteinopathy (compared to 15.2% of cognitively impaired individuals) and 83.5% are resistant to having multiple concurrent proteinopathies (compared to 64.6% of cognitively impaired individuals). The presence of at least 1 APOE ε4 allele was associated with impaired cognition and the presence of multiple proteinopathies, while APOE ε2 was protective against cumulative proteinopathies. These results indicate that maintenance of normal cognition may depend on resistance to the development of multiple concurrent proteinopathies.
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Affiliation(s)
- Jamie M Walker
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Timothy E Richardson
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Wang S, Zheng K, Kong W, Huang R, Liu L, Wen G, Yu Y. Multimodal data fusion based on IGERNNC algorithm for detecting pathogenic brain regions and genes in Alzheimer's disease. Brief Bioinform 2023; 24:6887308. [PMID: 36502428 DOI: 10.1093/bib/bbac515] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 09/28/2022] [Accepted: 10/30/2022] [Indexed: 12/14/2022] Open
Abstract
At present, the study on the pathogenesis of Alzheimer's disease (AD) by multimodal data fusion analysis has been attracted wide attention. It often has the problems of small sample size and high dimension with the multimodal medical data. In view of the characteristics of multimodal medical data, the existing genetic evolution random neural network cluster (GERNNC) model combine genetic evolution algorithm and neural network for the classification of AD patients and the extraction of pathogenic factors. However, the model does not take into account the non-linear relationship between brain regions and genes and the problem that the genetic evolution algorithm can fall into local optimal solutions, which leads to the overall performance of the model is not satisfactory. In order to solve the above two problems, this paper made some improvements on the construction of fusion features and genetic evolution algorithm in GERNNC model, and proposed an improved genetic evolution random neural network cluster (IGERNNC) model. The IGERNNC model uses mutual information correlation analysis method to combine resting-state functional magnetic resonance imaging data with single nucleotide polymorphism data for the construction of fusion features. Based on the traditional genetic evolution algorithm, elite retention strategy and large variation genetic algorithm are added to avoid the model falling into the local optimal solution. Through multiple independent experimental comparisons, the IGERNNC model can more effectively identify AD patients and extract relevant pathogenic factors, which is expected to become an effective tool in the field of AD research.
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Affiliation(s)
- Shuaiqun Wang
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Kai Zheng
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Wei Kong
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Ruiwen Huang
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Lulu Liu
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Gen Wen
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Yaling Yu
- School of Information Engineering, Shanghai Maritime University, Shanghai, China
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Duong MT, Wolk DA. Limbic-Predominant Age-Related TDP-43 Encephalopathy: LATE-Breaking Updates in Clinicopathologic Features and Biomarkers. Curr Neurol Neurosci Rep 2022; 22:689-698. [PMID: 36190653 PMCID: PMC9633415 DOI: 10.1007/s11910-022-01232-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/21/2022] [Indexed: 01/27/2023]
Abstract
PURPOSE OF REVIEW Limbic-predominant age-related TDP-43 encephalopathy (LATE) is a recently defined neurodegenerative disease characterized by amnestic phenotype and pathological inclusions of TAR DNA-binding protein 43 (TDP-43). LATE is distinct from rarer forms of TDP-43 diseases such as frontotemporal lobar degeneration with TDP-43 but is also a common copathology with Alzheimer's disease (AD) and cerebrovascular disease and accelerates cognitive decline. LATE contributes to clinicopathologic heterogeneity in neurodegenerative diseases, so it is imperative to distinguish LATE from other etiologies. RECENT FINDINGS Novel biomarkers for LATE are being developed with magnetic resonance imaging (MRI) and positron emission tomography (PET). When cooccurring with AD, LATE exhibits identifiable patterns of limbic-predominant atrophy on MRI and hypometabolism on 18F-fluorodeoxyglucose PET that are greater than expected relative to levels of local AD pathology. Efforts are being made to develop TDP-43-specific radiotracers, molecularly specific biofluid measures, and genomic predictors of TDP-43. LATE is a highly prevalent neurodegenerative disease distinct from previously characterized cognitive disorders.
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Affiliation(s)
- Michael Tran Duong
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Alzheimer's Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Institute On Aging, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA, 19104, USA.
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Mohanty R, Ferreira D, Frerich S, Muehlboeck JS, Grothe MJ, Westman E. Neuropathologic Features of Antemortem Atrophy-Based Subtypes of Alzheimer Disease. Neurology 2022; 99:e323-e333. [PMID: 35609990 PMCID: PMC9421777 DOI: 10.1212/wnl.0000000000200573] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 03/04/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND AND OBJECTIVES To investigate whether antemortem MRI-based atrophy subtypes of Alzheimer disease (AD) differ in neuropathologic features and comorbid non-AD pathologies at postmortem. METHODS From the Alzheimer's Disease Neuroimaging Initiative cohort, we included individuals with antemortem MRI evaluating brain atrophy within 2 years before death, antemortem diagnosis of AD dementia/mild cognitive impairment, and postmortem-confirmed AD neuropathologic change. Antemortem atrophy subtypes were modeled as continuous phenomena based on a recent conceptual framework: typicality (spanning limbic-predominant AD to hippocampal-sparing AD) and severity (spanning typical AD to minimal atrophy AD). Postmortem neuropathologic evaluation included AD hallmarks, β-amyloid, and tau as well as non-AD pathologies, alpha-synuclein and TAR DNA-binding protein 43 (TDP-43). We also investigated the overall concomitance across these pathologies. Partial correlations assessed the associations between antemortem atrophy subtypes and postmortem neuropathologic outcomes. RESULTS In 31 individuals (26 AD dementia/5 mild cognitive impairment, mean age = 80 years, 26% females), antemortem typicality was significantly negatively associated with neuropathologic features, including β-amyloid (rho = -0.39 overall), tau (rho = -0.38 regionally), alpha-synuclein (rho = -0.39 regionally), TDP-43 (rho = -0.49 overall), and concomitance of pathologies (rho = -0.59 regionally). Limbic-predominant AD was associated with higher Thal phase, neuritic plaque density, and presence of TDP-43 compared with hippocampal-sparing AD. Regionally, limbic-predominant AD showed a higher presence of tau and alpha-synuclein pathologies in medial temporal structures, a higher presence of TDP-43, and concomitance of pathologies subcortically/cortically compared with hippocampal-sparing AD. Antemortem severity was significantly negatively associated with concomitance of pathologies (rho = -0.43 regionally), such that typical AD showed higher concomitance of pathologies than minimal atrophy AD. DISCUSSION We provide a direct antemortem-to-postmortem validation, highlighting the importance of understanding atrophy-based heterogeneity in AD relative to AD and non-AD pathologies. We suggest that (1) typicality and severity in atrophy reflect differential aspects of susceptibility of the brain to AD and non-AD pathologies; and (2) limbic-predominant AD and typical AD subtypes share similar biological pathways, making them more vulnerable to AD and non-AD pathologies compared with hippocampal-sparing AD, which may follow a different biological pathway. Our findings provide a deeper understanding of associations of atrophy subtypes in AD with different pathologies, enhancing the prevailing knowledge of biological heterogeneity in AD and could contribute toward tracking disease progression and designing clinical trials in the future.
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Affiliation(s)
- Rosaleena Mohanty
- From the Division of Clinical Geriatrics (R.M., D.F., S.F., J.S.M., E.W.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Radiology (D.F.), Mayo Clinic, Rochester, MN; Institute for Stroke and Dementia Research (E.W.), University Hospital, Ludwig-Maximilian-University (LMU) Munich, Germany; Unidad de Trastornos del Movimiento (M.J.G.), Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Spain; Clinical Dementia Research Section (M.J.G.), German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; and Department of Neuroimaging (E.W.), Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - Daniel Ferreira
- From the Division of Clinical Geriatrics (R.M., D.F., S.F., J.S.M., E.W.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Radiology (D.F.), Mayo Clinic, Rochester, MN; Institute for Stroke and Dementia Research (E.W.), University Hospital, Ludwig-Maximilian-University (LMU) Munich, Germany; Unidad de Trastornos del Movimiento (M.J.G.), Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Spain; Clinical Dementia Research Section (M.J.G.), German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; and Department of Neuroimaging (E.W.), Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Simon Frerich
- From the Division of Clinical Geriatrics (R.M., D.F., S.F., J.S.M., E.W.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Radiology (D.F.), Mayo Clinic, Rochester, MN; Institute for Stroke and Dementia Research (E.W.), University Hospital, Ludwig-Maximilian-University (LMU) Munich, Germany; Unidad de Trastornos del Movimiento (M.J.G.), Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Spain; Clinical Dementia Research Section (M.J.G.), German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; and Department of Neuroimaging (E.W.), Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - J-Sebastian Muehlboeck
- From the Division of Clinical Geriatrics (R.M., D.F., S.F., J.S.M., E.W.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Radiology (D.F.), Mayo Clinic, Rochester, MN; Institute for Stroke and Dementia Research (E.W.), University Hospital, Ludwig-Maximilian-University (LMU) Munich, Germany; Unidad de Trastornos del Movimiento (M.J.G.), Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Spain; Clinical Dementia Research Section (M.J.G.), German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; and Department of Neuroimaging (E.W.), Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Michel J Grothe
- From the Division of Clinical Geriatrics (R.M., D.F., S.F., J.S.M., E.W.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Radiology (D.F.), Mayo Clinic, Rochester, MN; Institute for Stroke and Dementia Research (E.W.), University Hospital, Ludwig-Maximilian-University (LMU) Munich, Germany; Unidad de Trastornos del Movimiento (M.J.G.), Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Spain; Clinical Dementia Research Section (M.J.G.), German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; and Department of Neuroimaging (E.W.), Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Eric Westman
- From the Division of Clinical Geriatrics (R.M., D.F., S.F., J.S.M., E.W.), Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Radiology (D.F.), Mayo Clinic, Rochester, MN; Institute for Stroke and Dementia Research (E.W.), University Hospital, Ludwig-Maximilian-University (LMU) Munich, Germany; Unidad de Trastornos del Movimiento (M.J.G.), Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Spain; Clinical Dementia Research Section (M.J.G.), German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; and Department of Neuroimaging (E.W.), Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
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Sun W, Huang L, Cheng Y, Qin R, Xu H, Shao P, Ma J, Yao Z, Shi L, Xu Y. Medial Temporal Atrophy Contributes to Cognitive Impairment in Cerebral Small Vessel Disease. Front Neurol 2022; 13:858171. [PMID: 35665031 PMCID: PMC9159509 DOI: 10.3389/fneur.2022.858171] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 04/14/2022] [Indexed: 11/18/2022] Open
Abstract
Background The role of brain atrophy in cognitive decline related to cerebral small vessel disease (CSVD) remains unclear. This study used AccuBrain™ to identify major CSVD-related brain changes and verified the relationship between brain atrophy and different cognition domains in CSVD patients. Methods All enrolled 242 CSVD patients and 76 healthy participants underwent magnetic resonance imaging examinations and detailed neuropsychological scale assessments were collected at the same time. The AccuBrain™ technology was applied to fully automated image segmentation, measurement, and calculation of the acquired imaging results to obtain the volumes of different brain partitions and the volume of WMH for quantitative analysis. Correlation analyses were used to estimate the relationship between MRI features and different cognitive domains. Multifactor linear regression models were performed to analyze independent predictors of MTA and cognitive decline. Results CSVD patients exhibited multiple gray matter nucleus volume decreases in the basal ganglia regions and brain lobes, including the temporal lobe (P = 0.019), especially in the medial temporal lobe (p < 0.001), parietal lobe (p = 0.013), and cingulate lobe (p = 0.036) compare to HC. The volume of PWMH was an independent predictor of MTA for CSVD patients. Both medial temporal atrophy (MTA) and PWMH were associated with cognition impairment in CSVD-CI patients. MTA mediated the effect of PWMH on executive function in CSVD-CI patients. Conclusions Our results showed that MTA was related to cognition impairment in CSVD patients, which might become a potential imaging marker for CSVD-CI.
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Affiliation(s)
- Wenshan Sun
- Department of Neurology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
- Department of Neurology, Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, China
| | - Lili Huang
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
| | - Yue Cheng
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
| | - Hengheng Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
| | - Pengfei Shao
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
| | - Junyi Ma
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
| | - Zhelv Yao
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
| | - Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
- BrainNow Research Institute, Hong Kong Science and Technology Park, Hong Kong, Hong Kong SAR, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China
- Department of Neurology, Nanjing Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Jiangsu Key Laboratory for Molecular Medicine, Nanjing University, Nanjing, China
- *Correspondence: Yun Xu
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Duong MT, Das SR, Lyu X, Xie L, Richardson H, Xie SX, Yushkevich PA, Wolk DA, Nasrallah IM. Dissociation of tau pathology and neuronal hypometabolism within the ATN framework of Alzheimer's disease. Nat Commun 2022; 13:1495. [PMID: 35314672 PMCID: PMC8938426 DOI: 10.1038/s41467-022-28941-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 02/11/2022] [Indexed: 11/08/2022] Open
Abstract
Alzheimer's disease (AD) is defined by amyloid (A) and tau (T) pathologies, with T better correlated to neurodegeneration (N). However, T and N have complex regional relationships in part related to non-AD factors that influence N. With machine learning, we assessed heterogeneity in 18F-flortaucipir vs. 18F-fluorodeoxyglucose positron emission tomography as markers of T and neuronal hypometabolism (NM) in 289 symptomatic patients from the Alzheimer's Disease Neuroimaging Initiative. We identified six T/NM clusters with differing limbic and cortical patterns. The canonical group was defined as the T/NM pattern with lowest regression residuals. Groups resilient to T had less hypometabolism than expected relative to T and displayed better cognition than the canonical group. Groups susceptible to T had more hypometabolism than expected given T and exhibited worse cognitive decline, with imaging and clinical measures concordant with non-AD copathologies. Together, T/NM mismatch reveals distinct imaging signatures with pathobiological and prognostic implications for AD.
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Affiliation(s)
- Michael Tran Duong
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandhitsu R Das
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Alzheimer's Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Xueying Lyu
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Long Xie
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hayley Richardson
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sharon X Xie
- Alzheimer's Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A Yushkevich
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Alzheimer's Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Alzheimer's Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Ilya M Nasrallah
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA.
- Alzheimer's Disease Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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de Flores R, Das SR, Xie L, Wisse LEM, Lyu X, Shah P, Yushkevich PA, Wolk DA. Medial Temporal Lobe Networks in Alzheimer's Disease: Structural and Molecular Vulnerabilities. J Neurosci 2022; 42:2131-2141. [PMID: 35086906 PMCID: PMC8916768 DOI: 10.1523/jneurosci.0949-21.2021] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 11/30/2021] [Accepted: 12/04/2021] [Indexed: 11/21/2022] Open
Abstract
The medial temporal lobe (MTL) is connected to the rest of the brain through two main networks: the anterior-temporal (AT) and the posterior-medial (PM) systems. Given the crucial role of the MTL and networks in the physiopathology of Alzheimer's disease (AD), the present study aimed at (1) investigating whether MTL atrophy propagates specifically within the AT and PM networks, and (2) evaluating the vulnerability of these networks to AD proteinopathies. To do that, we used neuroimaging data acquired in human male and female in three distinct cohorts: (1) resting-state functional MRI (rs-fMRI) from the aging brain cohort (ABC) to define the AT and PM networks (n = 68); (2) longitudinal structural MRI from Alzheimer's disease neuroimaging initiative (ADNI)GO/2 to highlight structural covariance patterns (n = 349); and (3) positron emission tomography (PET) data from ADNI3 to evaluate the networks' vulnerability to amyloid and tau (n = 186). Our results suggest that the atrophy of distinct MTL subregions propagates within the AT and PM networks in a dissociable manner. Brodmann area (BA)35 structurally covaried within the AT network while the parahippocampal cortex (PHC) covaried within the PM network. In addition, these networks are differentially associated with relative tau and amyloid burden, with higher tau levels in AT than in PM and higher amyloid levels in PM than in AT. Our results also suggest differences in the relative burden of tau species. The current results provide further support for the notion that two distinct MTL networks display differential alterations in the context of AD. These findings have important implications for disease spread and the cognitive manifestations of AD.SIGNIFICANCE STATEMENT The current study provides further support for the notion that two distinct medial temporal lobe (MTL) networks, i.e., anterior-temporal (AT) and the posterior-medial (PM), display differential alterations in the context of Alzheimer's disease (AD). Importantly, neurodegeneration appears to occur within these networks in a dissociable manner marked by their covariance patterns. In addition, the AT and PM networks are also differentially associated with relative tau and amyloid burden, and perhaps differences in the relative burden of tau species [e.g., neurofibriliary tangles (NFTs) vs tau in neuritic plaques]. These findings, in the context of a growing literature consistent with the present results, have important implications for disease spread and the cognitive manifestations of AD in light of the differential cognitive processes ascribed to them.
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Affiliation(s)
- Robin de Flores
- Department of Neurology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Université de Caen Normandie, Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche Scientifique (UMRS) Unité 1237, Caen 14000, France
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Department of Radiology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Department of Diagnostic Radiology, Lund University, Lund 22185, Sweden
| | - Xueying Lyu
- Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Preya Shah
- Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
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Raghavan S, Przybelski SA, Reid RI, Lesnick TG, Ramanan VK, Botha H, Matchett BJ, Murray ME, Reichard RR, Knopman DS, Graff-Radford J, Jones DT, Lowe VJ, Mielke MM, Machulda MM, Petersen RC, Kantarci K, Whitwell JL, Josephs KA, Jack CR, Vemuri P. White matter damage due to vascular, tau, and TDP-43 pathologies and its relevance to cognition. Acta Neuropathol Commun 2022; 10:16. [PMID: 35123591 PMCID: PMC8817561 DOI: 10.1186/s40478-022-01319-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 01/19/2022] [Indexed: 12/27/2022] Open
Abstract
Multi-compartment modelling of white matter microstructure using Neurite Orientation Dispersion and Density Imaging (NODDI) can provide information on white matter health through neurite density index and free water measures. We hypothesized that cerebrovascular disease, Alzheimer's disease, and TDP-43 proteinopathy would be associated with distinct NODDI readouts of white matter damage which would be informative for identifying the substrate for cognitive impairment. We identified two independent cohorts with multi-shell diffusion MRI, amyloid and tau PET, and cognitive assessments: specifically, a population-based cohort of 347 elderly randomly sampled from the Olmsted county, Minnesota, population and a clinical research-based cohort of 61 amyloid positive Alzheimer's dementia participants. We observed an increase in free water and decrease in neurite density using NODDI measures in the genu of the corpus callosum associated with vascular risk factors, which we refer to as the vascular white matter component. Tau PET signal reflective of 3R/4R tau deposition was associated with worsening neurite density index in the temporal white matter where we measured parahippocampal cingulum and inferior temporal white matter bundles. Worsening temporal white matter neurite density was associated with (antemortem confirmed) FDG TDP-43 signature. Post-mortem neuropathologic data on a small subset of this sample lend support to our findings. In the community-dwelling cohort where vascular disease was more prevalent, the NODDI vascular white matter component explained variability in global cognition (partial R2 of free water and neurite density = 8.3%) and MMSE performance (8.2%) which was comparable to amyloid PET (7.4% for global cognition and 6.6% for memory). In the AD dementia cohort, tau deposition was the greatest contributor to cognitive performance (9.6%), but there was also a non-trivial contribution of the temporal white matter component (8.5%) to cognitive performance. The differences observed between the two cohorts were reflective of their distinct clinical composition. White matter microstructural damage assessed using advanced diffusion models may add significant value for distinguishing the underlying substrate (whether cerebrovascular disease versus neurodegenerative disease caused by tau deposition or TDP-43 pathology) for cognitive impairment in older adults.
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Affiliation(s)
| | - Scott A. Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905 USA
| | - Robert I. Reid
- Department of Information Technology, Mayo Clinic, Rochester, MN 55905 USA
| | - Timothy G. Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905 USA
| | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN 55905 USA
| | | | | | - R. Ross Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905 USA
| | | | | | - David T. Jones
- Department of Neurology, Mayo Clinic, Rochester, MN 55905 USA
| | - Val J. Lowe
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA
| | - Michelle M. Mielke
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905 USA
- Department of Neurology, Mayo Clinic, Rochester, MN 55905 USA
| | - Mary M. Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN 55905 USA
| | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA
| | - Jennifer L. Whitwell
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA
| | | | - Clifford R. Jack
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 USA
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Jacobs HIL, O'Donnell A, Satizabal CL, Lois C, Kojis D, Hanseeuw BJ, Thibault E, Sanchez JS, Buckley RF, Yang Q, DeCarli C, Killiany R, Sargurupremraj M, Sperling RA, Johnson KA, Beiser AS, Seshadri S. Associations Between Brainstem Volume and Alzheimer's Disease Pathology in Middle-Aged Individuals of the Framingham Heart Study. J Alzheimers Dis 2022; 86:1603-1609. [PMID: 35213372 PMCID: PMC9038711 DOI: 10.3233/jad-215372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The brainstem is among the first regions to accumulate Alzheimer's disease (AD)-related hyperphosphorylated tau pathology during aging. We aimed to examine associations between brainstem volume and neocortical amyloid-β or tau pathology in 271 middle-aged clinically normal individuals of the Framingham Heart Study who underwent MRI and PET imaging. Lower volume of the medulla, pons, or midbrain was associated with greater neocortical amyloid burden. No associations were detected between brainstem volumes and tau deposition. Our results support the hypothesis that lower brainstem volumes are associated with initial AD-related processes and may signal preclinical AD pathology.
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Affiliation(s)
- Heidi I L Jacobs
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, Netherlands
- Gordon Center for Medical Imaging, Boston, MA, USA
| | - Adrienne O'Donnell
- Boston University School of Public Health, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Claudia L Satizabal
- The Framingham Heart Study, Framingham, MA, USA
- Boston University School of Medicine, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Cristina Lois
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Gordon Center for Medical Imaging, Boston, MA, USA
| | - Daniel Kojis
- Boston University School of Public Health, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Bernard J Hanseeuw
- Massachusetts General Hospital, Boston, MA, USA
- Gordon Center for Medical Imaging, Boston, MA, USA
- Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Emma Thibault
- Massachusetts General Hospital, Boston, MA, USA
- Gordon Center for Medical Imaging, Boston, MA, USA
| | - Justin S Sanchez
- Massachusetts General Hospital, Boston, MA, USA
- Gordon Center for Medical Imaging, Boston, MA, USA
| | - Rachel F Buckley
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Melbourne School of Psychological Sciences, University of Melbourne, Victoria, Australia
| | - Qiong Yang
- Boston University School of Public Health, Boston, MA, USA
| | | | - Ron Killiany
- Boston University School of Medicine, Boston, MA, USA
| | - Muralidharan Sargurupremraj
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Reisa A Sperling
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | - Keith A Johnson
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Gordon Center for Medical Imaging, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | - Alexa S Beiser
- Boston University School of Public Health, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Sudha Seshadri
- The Framingham Heart Study, Framingham, MA, USA
- Boston University School of Medicine, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
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Llamas-Rodríguez J, Oltmer J, Greve DN, Williams E, Slepneva N, Wang R, Champion S, Lang-Orsini M, Fischl B, Frosch MP, van der Kouwe AJ, Augustinack JC. Entorhinal Subfield Vulnerability to Neurofibrillary Tangles in Aging and the Preclinical Stage of Alzheimer's Disease. J Alzheimers Dis 2022; 87:1379-1399. [PMID: 35491780 PMCID: PMC9198759 DOI: 10.3233/jad-215567] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/25/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Neurofibrillary tangle (NFT) accumulation in the entorhinal cortex (EC) precedes the transformation from cognitive controls to mild cognitive impairment and Alzheimer's disease (AD). While tauopathy has been described in the EC before, the order and degree to which the individual subfields within the EC are engulfed by NFTs in aging and the preclinical AD stage is unknown. OBJECTIVE We aimed to investigate substructures within the EC to map the populations of cortical neurons most vulnerable to tau pathology in aging and the preclinical AD stage. METHODS We characterized phosphorylated tau (CP13) in 10 cases at eight well-defined anterior-posterior levels and assessed NFT density within the eight entorhinal subfields (described by Insausti and colleagues) at the preclinical stages of AD. We validated with immunohistochemistry and labeled the NFT density ratings on ex vivo MRIs. We measured subfield cortical thickness and reconstructed the labels as three-dimensional isosurfaces, resulting in anatomically comprehensive, histopathologically validated tau "heat maps." RESULTS We found the lateral EC subfields ELc, ECL, and ECs (lateral portion) to have the highest tau density in semi-quantitative scores and quantitative measurements. We observed significant stepwise higher tau from anterior to posterior levels (p < 0.001). We report an age-dependent anatomically-specific vulnerability, with all cases showing posterior tau pathology, yet older individuals displaying an additional anterior tau burden. Finally, cortical thickness of each subfield negatively correlated with respective tau scores (p < 0.05). CONCLUSION Our findings indicate that posterior-lateral subfields within the EC are the most vulnerable to early NFTs and atrophy in aging and preclinical AD.
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Affiliation(s)
- Josué Llamas-Rodríguez
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jan Oltmer
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Douglas N. Greve
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Emily Williams
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Natalya Slepneva
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Ruopeng Wang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Samantha Champion
- Department of Neuropathology, Massachusetts General Hospital, Boston, MA, USA
| | - Melanie Lang-Orsini
- Department of Neuropathology, Massachusetts General Hospital, Boston, MA, USA
| | - Bruce Fischl
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- CSAIL/HST, MIT, Cambridge, MA, USA
| | - Matthew P. Frosch
- Department of Neuropathology, Massachusetts General Hospital, Boston, MA, USA
| | - André J.W. van der Kouwe
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jean C. Augustinack
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
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Uemura MT, Robinson JL, Cousins KAQ, Tropea TF, Kargilis DC, McBride JD, Suh E, Xie SX, Xu Y, Porta S, Uemura N, Van Deerlin VM, Wolk DA, Irwin DJ, Brunden KR, Lee VMY, Lee EB, Trojanowski JQ. Distinct characteristics of limbic-predominant age-related TDP-43 encephalopathy in Lewy body disease. Acta Neuropathol 2022; 143:15-31. [PMID: 34854996 PMCID: PMC9136643 DOI: 10.1007/s00401-021-02383-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/10/2021] [Accepted: 11/11/2021] [Indexed: 12/13/2022]
Abstract
Limbic-predominant age-related TDP-43 encephalopathy (LATE) is characterized by the accumulation of TAR-DNA-binding protein 43 (TDP-43) aggregates in older adults. LATE coexists with Lewy body disease (LBD) as well as other neuropathological changes including Alzheimer's disease (AD). We aimed to identify the pathological, clinical, and genetic characteristics of LATE in LBD (LATE-LBD) by comparing it with LATE in AD (LATE-AD), LATE with mixed pathology of LBD and AD (LATE-LBD + AD), and LATE alone (Pure LATE). We analyzed four cohorts of autopsy-confirmed LBD (n = 313), AD (n = 282), LBD + AD (n = 355), and aging (n = 111). We assessed the association of LATE with patient profiles including LBD subtype and AD neuropathologic change (ADNC). We studied the morphological and distributional differences between LATE-LBD and LATE-AD. By frequency analysis, we staged LATE-LBD and examined the association with cognitive impairment and genetic risk factors. Demographic analysis showed LATE associated with age in all four cohorts and the frequency of LATE was the highest in LBD + AD followed by AD, LBD, and Aging. LBD subtype and ADNC associated with LATE in LBD or AD but not in LBD + AD. Pathological analysis revealed that the hippocampal distribution of LATE was different between LATE-LBD and LATE-AD: neuronal cytoplasmic inclusions were more frequent in cornu ammonis 3 (CA3) in LATE-LBD compared to LATE-AD and abundant fine neurites composed of C-terminal truncated TDP-43 were found mainly in CA2 to subiculum in LATE-LBD, which were not as numerous in LATE-AD. Some of these fine neurites colocalized with phosphorylated α-synuclein. LATE-LBD staging showed LATE neuropathological changes spread in the dentate gyrus and brainstem earlier than in LATE-AD. The presence and prevalence of LATE in LBD associated with cognitive impairment independent of either LBD subtype or ADNC; LATE-LBD stage also associated with the genetic risk variants of TMEM106B rs1990622 and GRN rs5848. These data highlight clinicopathological and genetic features of LATE-LBD.
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Affiliation(s)
- Maiko T Uemura
- Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Maloney Building, 3rd Floor, 3600 Spruce Street, Philadelphia, PA, 19104-2676, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
| | - John L Robinson
- Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Maloney Building, 3rd Floor, 3600 Spruce Street, Philadelphia, PA, 19104-2676, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Katheryn A Q Cousins
- Department of Neurology, Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Thomas F Tropea
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel C Kargilis
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer D McBride
- Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Maloney Building, 3rd Floor, 3600 Spruce Street, Philadelphia, PA, 19104-2676, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - EunRan Suh
- Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Maloney Building, 3rd Floor, 3600 Spruce Street, Philadelphia, PA, 19104-2676, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Sharon X Xie
- Alzheimer's Disease Research Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Yan Xu
- Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Maloney Building, 3rd Floor, 3600 Spruce Street, Philadelphia, PA, 19104-2676, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Sílvia Porta
- Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Maloney Building, 3rd Floor, 3600 Spruce Street, Philadelphia, PA, 19104-2676, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Norihito Uemura
- Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Maloney Building, 3rd Floor, 3600 Spruce Street, Philadelphia, PA, 19104-2676, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Vivianna M Van Deerlin
- Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Maloney Building, 3rd Floor, 3600 Spruce Street, Philadelphia, PA, 19104-2676, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Alzheimer's Disease Research Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Alzheimer's Disease Research Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Institute on Aging, Perelman School of Medicine at the University of Pennsylvania, Pennsylvania, PA, USA
- Penn Memory Center at the Penn Neuroscience Center, Perelman Center for Advanced Medicine, Philadelphia, USA
| | - David J Irwin
- Department of Neurology, Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, USA
- Penn Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104-4283, USA
- Institute on Aging, Perelman School of Medicine at the University of Pennsylvania, Pennsylvania, PA, USA
- Penn Lewy Body Dementia Association Research Center of Excellence, Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104-4283, USA
| | - Kurt R Brunden
- Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Maloney Building, 3rd Floor, 3600 Spruce Street, Philadelphia, PA, 19104-2676, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Virginia M-Y Lee
- Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Maloney Building, 3rd Floor, 3600 Spruce Street, Philadelphia, PA, 19104-2676, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Alzheimer's Disease Research Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Alzheimer's Disease Research Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Pennsylvania, PA, USA
| | - John Q Trojanowski
- Center for Neurodegenerative Disease Research, Perelman School of Medicine at the University of Pennsylvania, Maloney Building, 3rd Floor, 3600 Spruce Street, Philadelphia, PA, 19104-2676, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
- Alzheimer's Disease Research Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
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Chauveau L, Kuhn E, Palix C, Felisatti F, Ourry V, de La Sayette V, Chételat G, de Flores R. Medial Temporal Lobe Subregional Atrophy in Aging and Alzheimer's Disease: A Longitudinal Study. Front Aging Neurosci 2021; 13:750154. [PMID: 34720998 PMCID: PMC8554299 DOI: 10.3389/fnagi.2021.750154] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 09/13/2021] [Indexed: 11/13/2022] Open
Abstract
Medial temporal lobe (MTL) atrophy is a key feature of Alzheimer's disease (AD), however, it also occurs in typical aging. To enhance the clinical utility of this biomarker, we need to better understand the differential effects of age and AD by encompassing the full AD-continuum from cognitively unimpaired (CU) to dementia, including all MTL subregions with up-to-date approaches and using longitudinal designs to assess atrophy more sensitively. Age-related trajectories were estimated using the best-fitted polynomials in 209 CU adults (aged 19–85). Changes related to AD were investigated among amyloid-negative (Aβ−) (n = 46) and amyloid-positive (Aβ+) (n = 14) CU, Aβ+ patients with mild cognitive impairment (MCI) (n = 33) and AD (n = 31). Nineteen MCI-to-AD converters were also compared with 34 non-converters. Relationships with cognitive functioning were evaluated in 63 Aβ+ MCI and AD patients. All participants were followed up to 47 months. MTL subregions, namely, the anterior and posterior hippocampus (aHPC/pHPC), entorhinal cortex (ERC), Brodmann areas (BA) 35 and 36 [as perirhinal cortex (PRC) substructures], and parahippocampal cortex (PHC), were segmented from a T1-weighted MRI using a new longitudinal pipeline (LASHiS). Statistical analyses were performed using mixed models. Adult lifespan models highlighted both linear (PRC, BA35, BA36, PHC) and nonlinear (HPC, aHPC, pHPC, ERC) trajectories. Group comparisons showed reduced baseline volumes and steeper volume declines over time for most of the MTL subregions in Aβ+ MCI and AD patients compared to Aβ− CU, but no differences between Aβ− and Aβ+ CU or between Aβ+ MCI and AD patients (except in ERC). Over time, MCI-to-AD converters exhibited a greater volume decline than non-converters in HPC, aHPC, and pHPC. Most of the MTL subregions were related to episodic memory performances but not to executive functioning or speed processing. Overall, these results emphasize the benefits of studying MTL subregions to distinguish age-related changes from AD. Interestingly, MTL subregions are unequally vulnerable to aging, and those displaying non-linear age-trajectories, while not damaged in preclinical AD (Aβ+ CU), were particularly affected from the prodromal stage (Aβ+ MCI). This volume decline in hippocampal substructures might also provide information regarding the conversion from MCI to AD-dementia. All together, these findings provide new insights into MTL alterations, which are crucial for AD-biomarkers definition.
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Affiliation(s)
- Léa Chauveau
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Elizabeth Kuhn
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Cassandre Palix
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
| | | | - Valentin Ourry
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France.,U1077 NIMH, Inserm, Caen-Normandie University, École Pratique des Hautes Études, Caen, France
| | - Vincent de La Sayette
- U1077 NIMH, Inserm, Caen-Normandie University, École Pratique des Hautes Études, Caen, France
| | - Gaël Chételat
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Robin de Flores
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
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50
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Das SR, Lyu X, Duong MT, Xie L, McCollum L, de Flores R, DiCalogero M, Irwin DJ, Dickerson BC, Nasrallah IM, Yushkevich PA, Wolk DA. Tau-Atrophy Variability Reveals Phenotypic Heterogeneity in Alzheimer's Disease. Ann Neurol 2021; 90:751-762. [PMID: 34617306 PMCID: PMC8841129 DOI: 10.1002/ana.26233] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 09/27/2021] [Accepted: 09/27/2021] [Indexed: 01/02/2023]
Abstract
OBJECTIVE Tau neurofibrillary tangles (T) are the primary driver of downstream neurodegeneration (N) and subsequent cognitive impairment in Alzheimer's disease (AD). However, there is substantial variability in the T-N relationship - manifested in higher or lower atrophy than expected for level of tau in a given brain region. The goal of this study was to determine if region-based quantitation of this variability allows for identification of underlying modulatory factors, including polypathology. METHODS Cortical thickness (N) and 18 F-Flortaucipir SUVR (T) were computed in 104 gray matter regions from a cohort of cognitively-impaired, amyloid-positive (A+) individuals. Region-specific residuals from a robust linear fit between SUVR and cortical thickness were computed as a surrogate for T-N mismatch. A summary T-N mismatch metric defined using residuals were correlated with demographic and imaging-based modulatory factors, and to partition the cohort into data-driven subgroups. RESULTS The summary T-N mismatch metric correlated with underlying factors such as age and burden of white matter hyperintensity lesions. Data-driven subgroups based on clustering of residuals appear to represent different biologically relevant phenotypes, with groups showing distinct spatial patterns of higher or lower atrophy than expected. INTERPRETATION These data support the notion that a measure of deviation from a normative relationship between tau burden and neurodegeneration across brain regions in individuals on the AD continuum captures variability due to multiple underlying factors, and can reveal phenotypes, which if validated, may help identify possible contributors to neurodegeneration in addition to tau, which may ultimately be useful for cohort selection in clinical trials. ANN NEUROL 2021;90:751-762.
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Affiliation(s)
- Sandhitsu R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Xueying Lyu
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Tran Duong
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Long Xie
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Lauren McCollum
- Department of Medicine, University of Tennessee, Knoxville, TN, USA
| | - Robin de Flores
- Université de Caen Normandie, INSERM UMRS U1237, Caen, France
| | - Michael DiCalogero
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - David J Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Ilya M Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
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