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Aksman LM, Oxtoby NP, Scelsi MA, Wijeratne PA, Young AL, Alves IL, Collij LE, Vogel JW, Barkhof F, Alexander DC, Altmann A. A data-driven study of Alzheimer's disease related amyloid and tau pathology progression. Brain 2023; 146:4935-4948. [PMID: 37433038 PMCID: PMC10690020 DOI: 10.1093/brain/awad232] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 06/12/2023] [Accepted: 06/29/2023] [Indexed: 07/13/2023] Open
Abstract
Amyloid-β is thought to facilitate the spread of tau throughout the neocortex in Alzheimer's disease, though how this occurs is not well understood. This is because of the spatial discordance between amyloid-β, which accumulates in the neocortex, and tau, which accumulates in the medial temporal lobe during ageing. There is evidence that in some cases amyloid-β-independent tau spreads beyond the medial temporal lobe where it may interact with neocortical amyloid-β. This suggests that there may be multiple distinct spatiotemporal subtypes of Alzheimer's-related protein aggregation, with potentially different demographic and genetic risk profiles. We investigated this hypothesis, applying data-driven disease progression subtyping models to post-mortem neuropathology and in vivo PET-based measures from two large observational studies: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Religious Orders Study and Rush Memory and Aging Project (ROSMAP). We consistently identified 'amyloid-first' and 'tau-first' subtypes using cross-sectional information from both studies. In the amyloid-first subtype, extensive neocortical amyloid-β precedes the spread of tau beyond the medial temporal lobe, while in the tau-first subtype, mild tau accumulates in medial temporal and neocortical areas prior to interacting with amyloid-β. As expected, we found a higher prevalence of the amyloid-first subtype among apolipoprotein E (APOE) ε4 allele carriers while the tau-first subtype was more common among APOE ε4 non-carriers. Within tau-first APOE ε4 carriers, we found an increased rate of amyloid-β accumulation (via longitudinal amyloid PET), suggesting that this rare group may belong within the Alzheimer's disease continuum. We also found that tau-first APOE ε4 carriers had several fewer years of education than other groups, suggesting a role for modifiable risk factors in facilitating amyloid-β-independent tau. Tau-first APOE ε4 non-carriers, in contrast, recapitulated many of the features of primary age-related tauopathy. The rate of longitudinal amyloid-β and tau accumulation (both measured via PET) within this group did not differ from normal ageing, supporting the distinction of primary age-related tauopathy from Alzheimer's disease. We also found reduced longitudinal subtype consistency within tau-first APOE ε4 non-carriers, suggesting additional heterogeneity within this group. Our findings support the idea that amyloid-β and tau may begin as independent processes in spatially disconnected regions, with widespread neocortical tau resulting from the local interaction of amyloid-β and tau. The site of this interaction may be subtype-dependent: medial temporal lobe in amyloid-first, neocortex in tau-first. These insights into the dynamics of amyloid-β and tau may inform research and clinical trials that target these pathologies.
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Affiliation(s)
- Leon M Aksman
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Marzia A Scelsi
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
| | - Peter A Wijeratne
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | | | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1007MB, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Jacob W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
- Brain Research Center, Amsterdam 1081 GN, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1007MB, The Netherlands
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
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Xiao C, Pappas I, Aksman LM, O'Bryant SE, Toga AW. Comparison of genetic and health risk factors for mild cognitive impairment and Alzheimer's disease between Hispanic and non-Hispanic white participants. Alzheimers Dement 2023; 19:5086-5094. [PMID: 37104247 DOI: 10.1002/alz.13110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 03/28/2023] [Accepted: 04/03/2023] [Indexed: 04/28/2023]
Abstract
INTRODUCTION The influence of apolipoprotein E (APOE) genotype on mild cognitive impairment (MCI) and Alzheimer's disease (AD) is well studied in the non-Hispanic white (NHW) population but not in the Hispanic population. Additionally, health risk factors such as hypertension, stroke, and depression may also differ between the two populations. METHODS We combined three data sets (National Alzheimer's Coordinating Center [NACC], Alzheimer's Disease Neuroimaging Initiative [ADNI], Health and Aging Brain Study: Health Disparities [HABS-HD]) and compared risk factors for MCI and AD between Hispanic and NHW participants, with a total of 24,268 participants (11.1% Hispanic). RESULTS APOEε4 was associated with fewer all-cause MCI cases in Hispanic participants (Hispanic odds ratio [OR]: 1.114; NHW OR: 1.453), and APOEε2 (Hispanic OR: 1.224; NHW OR: 0.592) and depression (Hispanic OR: 2.817; NHW OR: 1.847) were associated with more AD cases in Hispanic participants. DISCUSSION APOEε2 may not be protective for AD in Hispanic participants and Hispanic participants with depression may face a higher risk for AD. HIGHLIGHTS GAAIN allows for discovery of data sets to use in secondary analyses. APOEε2 was not protective for AD in Hispanic participants. APOEε4 was associated with fewer MCI cases in Hispanic participants. Depression was associated with more AD cases in Hispanic participants.
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Affiliation(s)
- Cally Xiao
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Ioannis Pappas
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Leon M Aksman
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Sid E O'Bryant
- Institute for Translational Research, University of North Texas Health Science Center, Fort Worth, Texas, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
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Lopez SM, Aksman LM, Oxtoby NP, Vos SB, Rao J, Kaestner E, Alhusaini S, Alvim M, Bender B, Bernasconi A, Bernasconi N, Bernhardt B, Bonilha L, Caciagli L, Caldairou B, Caligiuri ME, Calvet A, Cendes F, Concha L, Conde‐Blanco E, Davoodi‐Bojd E, de Bézenac C, Delanty N, Desmond PM, Devinsky O, Domin M, Duncan JS, Focke NK, Foley S, Fortunato F, Galovic M, Gambardella A, Gleichgerrcht E, Guerrini R, Hamandi K, Ives‐Deliperi V, Jackson GD, Jahanshad N, Keller SS, Kochunov P, Kotikalapudi R, Kreilkamp BAK, Labate A, Larivière S, Lenge M, Lui E, Malpas C, Martin P, Mascalchi M, Medland SE, Meletti S, Morita‐Sherman ME, Owen TW, Richardson M, Riva A, Rüber T, Sinclair B, Soltanian‐Zadeh H, Stein DJ, Striano P, Taylor P, Thomopoulos SI, Thompson PM, Tondelli M, Vaudano AE, Vivash L, Wang Y, Weber B, Whelan CD, Wiest R, Winston GP, Yasuda CL, McDonald CR, Alexander D, Sisodiya SM, Altmann A. Event-based modeling in temporal lobe epilepsy demonstrates progressive atrophy from cross-sectional data. Epilepsia 2022; 63:2081-2095. [PMID: 35656586 PMCID: PMC9540015 DOI: 10.1111/epi.17316] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 06/01/2022] [Accepted: 06/01/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross-sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE-HS) correlate with clinical features. METHODS We extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1-weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA-Epilepsy consortium, comprising 804 people with MTLE-HS and 1625 healthy controls from 25 centers. Features with a moderate case-control effect size (Cohen d ≥ .5) were used to train an event-based model (EBM), which estimates a sequence of disease-specific biomarker changes from cross-sectional data and assigns a biomarker-based fine-grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance. RESULTS In MTLE-HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ = .293, p = 7.03 × 10-16 ), age at onset (ρ = -.18, p = 9.82 × 10-7 ), and ASM resistance (area under the curve = .59, p = .043, Mann-Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE-HS with mild or nondetectable abnormality on T1W MRI. SIGNIFICANCE From cross-sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE-HS subjects in other cohorts and help establish connections between imaging-based progression staging and clinical features.
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Affiliation(s)
- Seymour M. Lopez
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Leon M. Aksman
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
- Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Neil P. Oxtoby
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
| | - Sjoerd B. Vos
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
- Neuroradiological Academic Unit, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Jun Rao
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Erik Kaestner
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Saud Alhusaini
- Department of NeurologyAlpert Medical School of Brown UniversityProvidenceRhode IslandUSA
- Department of Molecular and Cellular TherapeuticsRoyal College of Surgeons in IrelandDublinIreland
| | - Marina Alvim
- Department of Neurology and Neuroimaging LaboratoryUniversity of CampinasCampinasBrazil
| | - Benjamin Bender
- Department of Radiology, Diagnostic and Interventional NeuroradiologyUniversity Hospital TübingenTübingenGermany
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy LaboratoryMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy LaboratoryMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | - Boris Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealQuebecCanada
| | | | - Lorenzo Caciagli
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealQuebecCanada
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Benoit Caldairou
- Neuroimaging of Epilepsy LaboratoryMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | - Maria Eugenia Caligiuri
- Neuroscience Research Center, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
| | - Angels Calvet
- Magnetic Resonance Image Core FacilityAugust Pi i Sunyer Biomedical Research Institute, University of BarcelonaBarcelonaSpain
| | - Fernando Cendes
- Department of Neurology and Neuroimaging LaboratoryUniversity of CampinasCampinasBrazil
| | - Luis Concha
- Institute of NeurobiologyNational Autonomous University of MexicoQuerétaroMexico
| | - Estefania Conde‐Blanco
- Epilepsy Program, Neurology DepartmentHospital Clinic of BarcelonaBarcelonaSpain
- August Pi i Sunyer Biomedical Research InstituteBarcelonaSpain
| | | | - Christophe de Bézenac
- Department of Pharmacology and TherapeuticsInstitute of Systems, Molecular and Integrative Biology, University of LiverpoolLiverpoolUK
| | - Norman Delanty
- Department of Molecular and Cellular TherapeuticsRoyal College of Surgeons in IrelandDublinIreland
- FutureNeuro SFI Research Centre for Rare and Chronic Neurological DiseasesDublinIreland
| | - Patricia M. Desmond
- Department of Radiology, Royal Melbourne HospitalUniversity of MelbourneMelbourneVictoriaAustralia
| | - Orrin Devinsky
- New York University Grossman School of MedicineNew YorkNew YorkUSA
| | - Martin Domin
- Functional Imaging Unit, Department of Diagnostic Radiology and NeuroradiologyGreifswald University MedicineGreifswaldGermany
| | - John S. Duncan
- Department of NeurologyEmory UniversityAtlantaUSA
- Chalfont Centre for EpilepsyChalfont St PeterUK
| | - Niels K. Focke
- Department of NeurologyUniversity Medical CenterGöttingenGermany
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
| | - Francesco Fortunato
- Institute of Neurology, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
| | - Marian Galovic
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- Department of NeurologyUniversity Hospital ZurichZurichSwitzerland
| | - Antonio Gambardella
- Neuroscience Research Center, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
- Institute of Neurology, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
| | | | - Renzo Guerrini
- Neuroscience DepartmentUniversity of FlorenceFlorenceItaly
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
- Wales Epilepsy Unit, Department of NeurologyUniversity Hospital of WalesCardiffUK
| | | | - Graeme D. Jackson
- Florey Institute of Neuroscience and Mental Health, Austin CampusHeidelbergVictoriaAustralia
- University of MelbourneParkvilleVictoriaAustralia
- Department of NeurologyAustin HealthHeidelbergVictoriaAustralia
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Simon S. Keller
- Institute of Systems, Molecular and Integrative BiologyUniversity of LiverpoolLiverpoolUK
| | - Peter Kochunov
- Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Raviteja Kotikalapudi
- Department of Radiology, Diagnostic and Interventional NeuroradiologyUniversity Hospital TübingenTübingenGermany
- Department of Clinical NeurophysiologyUniversity Hospital GöttingenGöttingenGermany
- Department of Neurology and EpileptologyHertie Institute for Clinical Brain Research, University of TübingenTübingenGermany
| | - Barbara A. K. Kreilkamp
- Institute of Systems, Molecular and Integrative BiologyUniversity of LiverpoolLiverpoolUK
- Clinical NeurophysiologyUniversity Medical Center GöttingenGöttingenGermany
| | - Angelo Labate
- Neuroscience Research Center, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
- Institute of Neurology, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealQuebecCanada
| | - Matteo Lenge
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and LaboratoriesA. Meyer Children's Hospital, University of FlorenceFlorenceItaly
- Functional and Epilepsy Neurosurgery Unit, Neurosurgery DepartmentA. Meyer Children's Hospital, University of FlorenceFlorenceItaly
| | - Elaine Lui
- Department of Radiology, Royal Melbourne HospitalUniversity of MelbourneMelbourneVictoriaAustralia
| | - Charles Malpas
- Department of NeurologyRoyal Melbourne HospitalMelbourneVictoriaAustralia
- Department of Medicine, Royal Melbourne HospitalUniversity of MelbourneParkvilleVictoriaAustralia
| | - Pascal Martin
- Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Mario Mascalchi
- Mario Serio Department of Clinical and Experimental Medical SciencesUniversity of FlorenceFlorenceItaly
| | - Sarah E. Medland
- Psychiatric GeneticsQIMR Berghofer Medical Research InstituteBrisbaneQueenslandAustralia
| | - Stefano Meletti
- Department of Biomedical, Metabolic, and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
- Neurology Unit, OCB HospitalModena University HospitalModenaItaly
| | - Marcia E. Morita‐Sherman
- Department of NeurologyUniversity of CampinasCampinasBrazil
- Cleveland Clinic Neurological InstituteClevelandOhioUSA
| | - Thomas W. Owen
- School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | | | - Antonella Riva
- Giannina Gaslini Institute, Scientific Institute for Research and Health CareGenoaItaly
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child HealthUniversity of GenoaGenoaItaly
| | - Theodor Rüber
- Department of EpileptologyUniversity Hospital BonnBonnGermany
| | - Ben Sinclair
- Department of Neuroscience, Central Clinical School, Alfred HospitalMonash UniversityMelbourneVictoriaAustralia
- Departments of Medicine and Radiology, Royal Melbourne HospitalUniversity of MelbourneParkvilleVictoriaAustralia
| | - Hamid Soltanian‐Zadeh
- Radiology and Research AdministrationHenry Ford Health SystemDetroitMichiganUSA
- School of Electrical and Computer EngineeringCollege of Engineering, University of TehranTehranIran
| | - Dan J. Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Pasquale Striano
- Giannina Gaslini Institute, Scientific Institute for Research and Health CareGenoaItaly
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child HealthUniversity of GenoaGenoaItaly
| | - Peter N. Taylor
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Manuela Tondelli
- Department of Biomedical, Metabolic, and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
- Primary Care DepartmentLocal Health Authority of ModenaModenaItaly
| | - Anna Elisabetta Vaudano
- Department of Biomedical, Metabolic, and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
- Neurology Unit, OCB HospitalModena University HospitalModenaItaly
| | - Lucy Vivash
- Department of Neuroscience, Central Clinical School, Alfred HospitalMonash UniversityMelbourneVictoriaAustralia
- Departments of Medicine and Radiology, Royal Melbourne HospitalUniversity of MelbourneParkvilleVictoriaAustralia
| | - Yujiang Wang
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition ResearchUniversity of BonnBonnGermany
| | - Christopher D. Whelan
- Department of Molecular and Cellular TherapeuticsRoyal College of Surgeons in IrelandDublinIreland
| | - Roland Wiest
- Support Center for Advanced NeuroimagingUniversity Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of BernBernSwitzerland
| | - Gavin P. Winston
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- Chalfont Centre for EpilepsyChalfont St PeterUK
- Department of Medicine, Division of NeurologyQueen's UniversityKingstonOntarioCanada
| | - Clarissa Lin Yasuda
- Department of Neurology and Neuroimaging LaboratoryUniversity of CampinasCampinasBrazil
| | - Carrie R. McDonald
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Daniel C. Alexander
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
| | - Sanjay M. Sisodiya
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- Chalfont Centre for EpilepsyChalfont St PeterUK
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
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Affiliation(s)
- Mohammed Janahi
- Centre for Medical Image Computing, University College London London United Kingdom
| | | | - Jonathan M. Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
| | - Andre Altmann
- Centre for Medical Image Computing, University College London London United Kingdom
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Aksman LM, Wijeratne PA, Oxtoby NP, Eshaghi A, Shand C, Altmann A, Alexander DC, Young AL. pySuStaIn: a Python implementation of the Subtype and Stage Inference algorithm. SoftwareX 2021; 16:100811. [PMID: 34926780 PMCID: PMC8682799 DOI: 10.1016/j.softx.2021.100811] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Progressive disorders are highly heterogeneous. Symptom-based clinical classification of these disorders may not reflect the underlying pathobiology. Data-driven subtyping and staging of patients has the potential to disentangle the complex spatiotemporal patterns of disease progression. Tools that enable this are in high demand from clinical and treatment-development communities. Here we describe the pySuStaIn software package, a Python-based implementation of the Subtype and Stage Inference (SuStaIn) algorithm. SuStaIn unravels the complexity of heterogeneous diseases by inferring multiple disease progression patterns (subtypes) and individual severity (stages) from cross-sectional data. The primary aims of pySuStaIn are to enable widespread application and translation of SuStaIn via an accessible Python package that supports simple extension and generalization to novel modelling situations within a single, consistent architecture.
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Affiliation(s)
- Leon M Aksman
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California
- Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
| | - Peter A Wijeratne
- Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
| | - Arman Eshaghi
- Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London
| | - Cameron Shand
- Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
| | - Andre Altmann
- Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
| | - Daniel C Alexander
- Centre for Medical Image Computing, Departments of Computer Science and Medical Physics, University College London
| | - Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London
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Collij LE, Salvadó G, Wottschel V, Schoenmakers P, Mutti M, Aksman LM, Wink AM, van der Flier WM, Scheltens P, Visser PJ, Van Berckel BN, Barkhof F, Haller S, Gispert JD, Alves IL. Data‐driven evidence for three distinct patterns of amyloid‐β accumulation. Alzheimers Dement 2021. [DOI: 10.1002/alz.055417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Lyduine E. Collij
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam Netherlands
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation Barcelona Spain
- IMIM (Hospital del Mar Medical Research Institute) Barcelona Spain
| | - Viktor Wottschel
- Amsterdam UMC, VU University Medical Center Amsterdam Netherlands
| | | | | | - Leon M Aksman
- University of Southern California Los Angeles CA USA
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC Amsterdam Netherlands
| | | | - Pieter Jelle Visser
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience Campus, VU University Medical Center Amsterdam Netherlands
| | - Bart N.M. Van Berckel
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam Netherlands
- Institutes of Neurology and Healthcare Engineering, University College London London United Kingdom
| | - Sven Haller
- University of Geneva, Faculty of Medicine Geneva Switzerland
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation Barcelona Spain
| | - Isadora Lopes Alves
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam Netherlands
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Young AL, Vogel JW, Aksman LM, Wijeratne PA, Eshaghi A, Oxtoby NP, Williams SCR, Alexander DC. Ordinal SuStaIn: Subtype and Stage Inference for Clinical Scores, Visual Ratings, and Other Ordinal Data. Front Artif Intell 2021; 4:613261. [PMID: 34458723 PMCID: PMC8387598 DOI: 10.3389/frai.2021.613261] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 07/20/2021] [Indexed: 12/28/2022] Open
Abstract
Subtype and Stage Inference (SuStaIn) is an unsupervised learning algorithm that uniquely enables the identification of subgroups of individuals with distinct pseudo-temporal disease progression patterns from cross-sectional datasets. SuStaIn has been used to identify data-driven subgroups and perform patient stratification in neurodegenerative diseases and in lung diseases from continuous biomarker measurements predominantly obtained from imaging. However, the SuStaIn algorithm is not currently applicable to discrete ordinal data, such as visual ratings of images, neuropathological ratings, and clinical and neuropsychological test scores, restricting the applicability of SuStaIn to a narrower range of settings. Here we propose 'Ordinal SuStaIn', an ordinal version of the SuStaIn algorithm that uses a scored events model of disease progression to enable the application of SuStaIn to ordinal data. We demonstrate the validity of Ordinal SuStaIn by benchmarking the performance of the algorithm on simulated data. We further demonstrate that Ordinal SuStaIn out-performs the existing continuous version of SuStaIn (Z-score SuStaIn) on discrete scored data, providing much more accurate subtype progression patterns, better subtyping and staging of individuals, and accurate uncertainty estimates. We then apply Ordinal SuStaIn to six different sub-scales of the Clinical Dementia Rating scale (CDR) using data from the Alzheimer's disease Neuroimaging Initiative (ADNI) study to identify individuals with distinct patterns of functional decline. Using data from 819 ADNI1 participants we identified three distinct CDR subtype progression patterns, which were independently verified using data from 790 ADNI2 participants. Our results provide insight into patterns of decline in daily activities in Alzheimer's disease and a mechanism for stratifying individuals into groups with difficulties in different domains. Ordinal SuStaIn is broadly applicable across different types of ratings data, including visual ratings from imaging, neuropathological ratings and clinical or behavioural ratings data.
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Affiliation(s)
- Alexandra L. Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Computer Science, University College London, London, United Kingdom
| | - Jacob W. Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, Unites States
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, Unites States
| | - Leon M. Aksman
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, Unites States
| | - Peter A. Wijeratne
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Computer Science, University College London, London, United Kingdom
| | - Arman Eshaghi
- Department of Computer Science, University College London, London, United Kingdom
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Faculty of Brain Sciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Neil P. Oxtoby
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Computer Science, University College London, London, United Kingdom
| | - Steven C. R. Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Daniel C. Alexander
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Department of Computer Science, University College London, London, United Kingdom
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8
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Vogel JW, Young AL, Oxtoby NP, Smith R, Ossenkoppele R, Strandberg OT, La Joie R, Aksman LM, Grothe MJ, Iturria-Medina Y, Pontecorvo MJ, Devous MD, Rabinovici GD, Alexander DC, Lyoo CH, Evans AC, Hansson O. Four distinct trajectories of tau deposition identified in Alzheimer's disease. Nat Med 2021; 27:871-881. [PMID: 33927414 PMCID: PMC8686688 DOI: 10.1038/s41591-021-01309-6] [Citation(s) in RCA: 290] [Impact Index Per Article: 96.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 03/04/2021] [Indexed: 01/15/2023]
Abstract
Alzheimer's disease (AD) is characterized by the spread of tau pathology throughout the cerebral cortex. This spreading pattern was thought to be fairly consistent across individuals, although recent work has demonstrated substantial variability in the population with AD. Using tau-positron emission tomography scans from 1,612 individuals, we identified 4 distinct spatiotemporal trajectories of tau pathology, ranging in prevalence from 18 to 33%. We replicated previously described limbic-predominant and medial temporal lobe-sparing patterns, while also discovering posterior and lateral temporal patterns resembling atypical clinical variants of AD. These 'subtypes' were stable during longitudinal follow-up and were replicated in a separate sample using a different radiotracer. The subtypes presented with distinct demographic and cognitive profiles and differing longitudinal outcomes. Additionally, network diffusion models implied that pathology originates and spreads through distinct corticolimbic networks in the different subtypes. Together, our results suggest that variation in tau pathology is common and systematic, perhaps warranting a re-examination of the notion of 'typical AD' and a revisiting of tau pathological staging.
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Affiliation(s)
- Jacob W Vogel
- Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada.
| | - Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Neil P Oxtoby
- Centre for Medical Image Computing, University College London, London, UK
- Department of Computer Science, University College London, London, UK
| | - Ruben Smith
- Clinical Memory Research Unit, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | | | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Leon M Aksman
- Centre for Medical Image Computing, University College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Michel J Grothe
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/Consejo Superior de Investigaciones Científicas/Universidad de Sevilla, Seville, Spain
| | | | | | | | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London, UK
- Department of Computer Science, University College London, London, UK
| | - Chul Hyoung Lyoo
- Departments of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
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9
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Oxtoby NP, Leyland LA, Aksman LM, Thomas GEC, Bunting EL, Wijeratne PA, Young AL, Zarkali A, Tan MMX, Bremner FD, Keane PA, Morris HR, Schrag AE, Alexander DC, Weil RS. Sequence of clinical and neurodegeneration events in Parkinson's disease progression. Brain 2021; 144:975-988. [PMID: 33543247 PMCID: PMC8041043 DOI: 10.1093/brain/awaa461] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 10/05/2020] [Accepted: 10/24/2020] [Indexed: 02/07/2023] Open
Abstract
Dementia is one of the most debilitating aspects of Parkinson's disease. There are no validated biomarkers that can track Parkinson's disease progression, nor accurately identify patients who will develop dementia and when. Understanding the sequence of observable changes in Parkinson's disease in people at elevated risk for developing dementia could provide an integrated biomarker for identifying and managing individuals who will develop Parkinson's dementia. We aimed to estimate the sequence of clinical and neurodegeneration events, and variability in this sequence, using data-driven statistical modelling in two separate Parkinson's cohorts, focusing on patients at elevated risk for dementia due to their age at symptom onset. We updated a novel version of an event-based model that has only recently been extended to cope naturally with clinical data, enabling its application in Parkinson's disease for the first time. The observational cohorts included healthy control subjects and patients with Parkinson's disease, of whom those diagnosed at age 65 or older were classified as having high risk of dementia. The model estimates that Parkinson's progression in patients at elevated risk for dementia starts with classic prodromal features of Parkinson's disease (olfaction, sleep), followed by early deficits in visual cognition and increased brain iron content, followed later by a less certain ordering of neurodegeneration in the substantia nigra and cortex, neuropsychological cognitive deficits, retinal thinning in dopamine layers, and further deficits in visual cognition. Importantly, we also characterize variation in the sequence. We found consistent, cross-validated results within cohorts, and agreement between cohorts on the subset of features available in both cohorts. Our sequencing results add powerful support to the increasing body of evidence suggesting that visual processing specifically is affected early in patients with Parkinson's disease at elevated risk of dementia. This opens a route to earlier and more precise detection, as well as a more detailed understanding of the pathological mechanisms underpinning Parkinson's dementia.
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Affiliation(s)
- Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science and Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | | | - Leon M Aksman
- Centre for Medical Image Computing, Department of Computer Science and Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - George E C Thomas
- Dementia Research Centre, UCL Institute of Neurology, UCL, London, UK
| | - Emma L Bunting
- Dementia Research Centre, UCL Institute of Neurology, UCL, London, UK
| | - Peter A Wijeratne
- Centre for Medical Image Computing, Department of Computer Science and Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Alexandra L Young
- Centre for Medical Image Computing, Department of Computer Science and Department of Medical Physics and Biomedical Engineering, UCL, London, UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Angelika Zarkali
- Dementia Research Centre, UCL Institute of Neurology, UCL, London, UK
| | - Manuela M X Tan
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, UCL, London, UK
- Movement Disorders Consortium, UCL, London, UK
| | - Fion D Bremner
- Neuro-ophthalmology, National Hospital for Neurology and Neurosurgery, University College London Hospitals, London, UK
| | - Pearse A Keane
- Institute of Ophthalmology, UCL, London, UK
- Moorfields Eye Hospital, London, UK
| | - Huw R Morris
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, UCL, London, UK
- Movement Disorders Consortium, UCL, London, UK
| | - Anette E Schrag
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, UCL, London, UK
- Movement Disorders Consortium, UCL, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science and Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Rimona S Weil
- Dementia Research Centre, UCL Institute of Neurology, UCL, London, UK
- Movement Disorders Consortium, UCL, London, UK
- The Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, UCL, London, UK
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10
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Vogel JW, Young AL, Oxtoby NP, Smith R, Ossenkoppele R, Aksman LM, Strandberg O, La Joie R, Grothe M, Medina YI, Rabinovici GD, Alexander DC, Evans AC, Hansson O. Spatiotemporal imaging phenotypes of tau pathology in Alzheimer’s disease. Alzheimers Dement 2020. [DOI: 10.1002/alz.045612] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Jacob W. Vogel
- Montreal Neurological Institute McGill University Montreal QC Canada
| | - Alexandra L. Young
- Centre for Medical Image Computing University College London London United Kingdom
| | | | - Ruben Smith
- Clinical Memory Research Unit Lund University Malmö Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit Department of Clinical Sciences Mälmo Lund University Lund Sweden
| | - Leon M. Aksman
- Centre for Medical Image Computing University College London London United Kingdom
| | | | - Renaud La Joie
- Memory and Aging Center UCSF Weill Institute for Neurosciences University of California, San Francisco San Francisco CA USA
| | - Michel Grothe
- German Center for Neurodegenerative Diseases (DZNE) Rostock Germany
| | | | - Gil D. Rabinovici
- Department of Neurology Memory and Aging Center University of California San Francisco San Francisco CA USA
| | - Daniel C. Alexander
- Centre for Medical Image Computing University College London London United Kingdom
| | - Alan C. Evans
- Montreal Neurological Institute McGill University Montreal QC Canada
| | - Oskar Hansson
- Clinical Memory Research Unit Department of Clinical Sciences Mälmo Lund University Malmö Sweden
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11
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Vogel JW, Young AL, Oxtoby NP, Smith R, Ossenkoppele R, Aksman LM, Strandberg O, La Joie R, Grothe MJ, Rabinovici GD, Alexander DC, Evans AC, Hansson O. Accounting for systematic spatiotemporal variation improves connectome‐based models of tau spreading in human Alzheimer’s disease. Alzheimers Dement 2020. [DOI: 10.1002/alz.040586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Jacob W. Vogel
- Montreal Neurological Institute McGill University Montreal QC Canada
| | - Alexandra L. Young
- Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King’s College London London United Kingdom
| | | | - Ruben Smith
- Neurology Clinic Skåne University Hospital Lund Sweden
- Clinical Memory Research Unit Lund University Malmö Sweden
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam Department of Neurology Amsterdam Neuroscience Vrije Universiteit Amsterdam, Amsterdam UMC Amsterdam Netherlands
| | - Leon M Aksman
- Centre for Medical Image Computing University College London London United Kingdom
| | - Olof Strandberg
- Clinical Memory Research Unit Department of Clinical Sciences Mälmo Lund University Lund Sweden
| | - Renaud La Joie
- Memory and Aging Center UCSF Weill Institute for Neurosciences University of California, San Francisco San Francisco CA USA
| | - Michel J. Grothe
- Wallenberg Centre for Molecular and Translational Medicine University of Gothenburg Gothenburg Sweden
| | - Gil D. Rabinovici
- Memory and Aging Center UCSF Weill Institute for Neurosciences University of California, San Francisco San Francisco CA USA
| | - Daniel C. Alexander
- Centre for Medical Image Computing University College London London United Kingdom
| | - Alan C. Evans
- Montreal Neurological Institute McGill University Montreal QC Canada
| | - Oskar Hansson
- Clinical Memory Research Unit Lund University Malmö Sweden
- Clinical Memory Research Unit Department of Clinical Sciences Mälmo Lund University Malmö Sweden
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12
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Aksman LM, Oxtoby NP, Scelsi MA, Wijeratne PA, Young AL, Alves IL, Alexander DC, Barkhof F, Altmann A. Tau‐first subtype of Alzheimer’s disease progression consistently identified through PET and CSF. Alzheimers Dement 2020. [DOI: 10.1002/alz.045412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Leon M. Aksman
- Centre for Medical Image Computing University College London London United Kingdom
| | | | | | - Peter A. Wijeratne
- Centre for Medical Image Computing University College London London United Kingdom
| | - Alexandra L. Young
- Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom
| | | | - Daniel C. Alexander
- Centre for Medical Image Computing University College London London United Kingdom
| | - Frederik Barkhof
- UCL Institutes of Neurology and Healthcare Engineering London United Kingdom
| | - Andre Altmann
- Centre for Medical Image Computing University College London London United Kingdom
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13
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Altmann A, Scelsi MA, Shoai M, de Silva E, Aksman LM, Cash DM, Hardy J, Schott JM. A comprehensive analysis of methods for assessing polygenic burden on Alzheimer's disease pathology and risk beyond APOE. Brain Commun 2019; 2:fcz047. [PMID: 32226939 PMCID: PMC7100005 DOI: 10.1093/braincomms/fcz047] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Genome-wide association studies have identified dozens of loci that alter the risk to develop Alzheimer's disease. However, with the exception of the APOE-ε4 allele, most variants bear only little individual effect and have, therefore, limited diagnostic and prognostic value. Polygenic risk scores aim to collate the disease risk distributed across the genome in a single score. Recent works have demonstrated that polygenic risk scores designed for Alzheimer's disease are predictive of clinical diagnosis, pathology confirmed diagnosis and changes in imaging biomarkers. Methodological innovations in polygenic risk modelling include the polygenic hazard score, which derives effect estimates for individual single nucleotide polymorphisms from survival analysis, and methods that account for linkage disequilibrium between genomic loci. In this work, using data from the Alzheimer's disease neuroimaging initiative, we compared different approaches to quantify polygenic disease burden for Alzheimer's disease and their association (beyond the APOE locus) with a broad range of Alzheimer's disease-related traits: cross-sectional CSF biomarker levels, cross-sectional cortical amyloid burden, clinical diagnosis, clinical progression, longitudinal loss of grey matter and longitudinal decline in cognitive function. We found that polygenic scores were associated beyond APOE with clinical diagnosis, CSF-tau levels and, to a minor degree, with progressive atrophy. However, for many other tested traits such as clinical disease progression, CSF amyloid, cognitive decline and cortical amyloid load, the additional effects of polygenic burden beyond APOE were of minor nature. Overall, polygenic risk scores and the polygenic hazard score performed equally and given the ease with which polygenic risk scores can be derived; they constitute the more practical choice in comparison with polygenic hazard scores. Furthermore, our results demonstrate that incomplete adjustment for the APOE locus, i.e. only adjusting for APOE-ε4 carrier status, can lead to overestimated effects of polygenic scores due to APOE-ε4 homozygous participants. Lastly, on many of the tested traits, the major driving factor remained the APOE locus, with the exception of quantitative CSF-tau and p-tau measures.
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Affiliation(s)
- Andre Altmann
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London WC1V 6LJ, UK
| | - Marzia A Scelsi
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London WC1V 6LJ, UK
| | - Maryam Shoai
- Reta Lilla Research Laboratories, Department of Neurodegeneration, Queen Square Institute of Neurology, University College London (UCL), London WC1V 6LJ, UK.,UK Dementia Research Institute, University College London (UCL), London WC1V 6LJ, UK
| | - Eric de Silva
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London WC1V 6LJ, UK.,Institute for Health Informatics, University College London (UCL), London WC1V 6LJ, UK
| | - Leon M Aksman
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London WC1V 6LJ, UK
| | - David M Cash
- UK Dementia Research Institute, University College London (UCL), London WC1V 6LJ, UK.,Dementia Research Centre, Queen Square Institute of Neurology, University College London (UCL), London WC1V 6LJ, UK
| | - John Hardy
- Reta Lilla Research Laboratories, Department of Neurodegeneration, Queen Square Institute of Neurology, University College London (UCL), London WC1V 6LJ, UK.,UK Dementia Research Institute, University College London (UCL), London WC1V 6LJ, UK
| | - Jonathan M Schott
- UK Dementia Research Institute, University College London (UCL), London WC1V 6LJ, UK.,Dementia Research Centre, Queen Square Institute of Neurology, University College London (UCL), London WC1V 6LJ, UK
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14
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Aksman LM, Scelsi MA, Marquand AF, Alexander DC, Ourselin S, Altmann A. Modeling longitudinal imaging biomarkers with parametric Bayesian multi-task learning. Hum Brain Mapp 2019; 40:3982-4000. [PMID: 31168892 PMCID: PMC6679792 DOI: 10.1002/hbm.24682] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Revised: 05/03/2019] [Accepted: 05/19/2019] [Indexed: 01/09/2023] Open
Abstract
Longitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression and to detect disease earlier than cross-sectional biomarkers. To properly realize their potential, biomarker trajectory models must be robust to both under-sampling and measurement errors and should be able to integrate multi-modal information to improve trajectory inference and prediction. Here we present a parametric Bayesian multi-task learning based approach to modeling univariate trajectories across subjects that addresses these criteria. Our approach learns multiple subjects' trajectories within a single model that allows for different types of information sharing, that is, coupling, across subjects. It optimizes a combination of uncoupled, fully coupled and kernel coupled models. Kernel-based coupling allows linking subjects' trajectories based on one or more biomarker measures. We demonstrate this using Alzheimer's Disease Neuroimaging Initiative (ADNI) data, where we model longitudinal trajectories of MRI-derived cortical volumes in neurodegeneration, with coupling based on APOE genotype, cerebrospinal fluid (CSF) and amyloid PET-based biomarkers. In addition to detecting established disease effects, we detect disease related changes within the insula that have not received much attention within the literature. Due to its sensitivity in detecting disease effects, its competitive predictive performance and its ability to learn the optimal parameter covariance from data rather than choosing a specific set of random and fixed effects a priori, we propose that our model can be used in place of or in addition to linear mixed effects models when modeling biomarker trajectories. A software implementation of the method is publicly available.
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Affiliation(s)
- Leon M. Aksman
- Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Marzia A. Scelsi
- Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Andre F. Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | | | - Sebastien Ourselin
- Centre for Medical Image ComputingUniversity College LondonLondonUK
- School of Biomedical Engineering and Imaging SciencesSt Thomas' Hospital, King's College LondonLondonUK
| | - Andre Altmann
- Centre for Medical Image ComputingUniversity College LondonLondonUK
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15
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Aksman LM, Alexander DC, Barkhof F, Altmann A. P4-320: AMYLOID-BETA ACCUMULATION AFFECTS IN VIVO STAGING OF TAU DEPOSITION IN COGNITIVELY IMPAIRED INDIVIDUALS. Alzheimers Dement 2019. [DOI: 10.1016/j.jalz.2019.06.3990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Leon M. Aksman
- Centre for Medical Image Computing; University College London; London United Kingdom
| | - Daniel C. Alexander
- Centre for Medical Image Computing; University College London; London United Kingdom
| | - Frederik Barkhof
- Institutes of Neurology and Healthcare Engineering; University College London; London United Kingdom
- Centre for Medical Imaging Computing, Faculty of Engineering; University College London; London United Kingdom
| | - Andre Altmann
- Centre for Medical Image Computing; University College London; London United Kingdom
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16
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Aksman LM, Firth N, Scelsi MA, Schott JM, Ourselin S, Altmann A. P3‐420: AN EVENT BASED MODEL OF ALZHEIMER'S DISEASE IN APOE+ SUBJECTS USING ROBUST BIOMARKERS OF VOLUMETRIC CHANGE IN REGIONAL BRAIN STRUCTURE. Alzheimers Dement 2018. [DOI: 10.1016/j.jalz.2018.06.1783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Leon M. Aksman
- Centre for Medical Image ComputingUniversity College LondonLondonUnited Kingdom
| | - Nicholas Firth
- Centre for Medical Image ComputingUniversity College LondonLondonUnited Kingdom
| | - Marzia Antonella Scelsi
- Translational Imaging Group, Centre for Medical Image ComputingUniversity College LondonLondonUnited Kingdom
| | | | - Sebastien Ourselin
- Translational Imaging Group, Centre for Medical Image ComputingUniversity College LondonLondonUnited Kingdom
| | - Andre Altmann
- Translational Imaging Group, Centre for Medical Image ComputingUniversity College LondonLondonUnited Kingdom
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17
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Aksman LM, Lythgoe DJ, Williams SCR, Jokisch M, Mönninghoff C, Streffer J, Jöckel KH, Weimar C, Marquand AF. Making use of longitudinal information in pattern recognition. Hum Brain Mapp 2016; 37:4385-4404. [PMID: 27451934 PMCID: PMC5111621 DOI: 10.1002/hbm.23317] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Revised: 06/20/2016] [Accepted: 07/05/2016] [Indexed: 12/31/2022] Open
Abstract
Longitudinal designs are widely used in medical studies as a means of observing within-subject changes over time in groups of subjects, thereby aiming to improve sensitivity for detecting disease effects. Paralleling an increased use of such studies in neuroimaging has been the adoption of pattern recognition algorithms for making individualized predictions of disease. However, at present few pattern recognition methods exist to make full use of neuroimaging data that have been collected longitudinally, with most methods relying instead on cross-sectional style analysis. This article presents a principal component analysis-based feature construction method that uses longitudinal high-dimensional data to improve predictive performance of pattern recognition algorithms. The method can be applied to data from a wide range of longitudinal study designs and permits an arbitrary number of time-points per subject. We apply the method to two longitudinal datasets, one containing subjects with mild cognitive impairment along with healthy controls, the other with early dementia subjects and healthy controls. Across both datasets, we show improvements in predictive accuracy relative to cross-sectional classifiers for discriminating disease subjects from healthy controls on the basis of whole-brain structural magnetic resonance image-based voxels. In addition, we can transfer longitudinal information from one set of subjects to make disease predictions in another set of subjects. The proposed method is simple and, as a feature construction method, flexible with respect to the choice of classifier and image registration algorithm. Hum Brain Mapp 37:4385-4404, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Leon M Aksman
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - David J Lythgoe
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Steven C R Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Martha Jokisch
- Department of Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Christoph Mönninghoff
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Johannes Streffer
- Janssen-Pharmaceutical Companies of Johnson & Johnson, Janssen Research and Development, Beerse, Belgium
| | - Karl-Heinz Jöckel
- Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Germany
| | - Christian Weimar
- Department of Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Andre F Marquand
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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