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Ruiz Tornero AM, García Carpintero EE, Rodríguez Ortiz de Salazar B. Effectiveness of brain magnetic resonance imaging in the early diagnosis and characterization of dementias; a systematic review. Med Clin (Barc) 2024:S0025-7753(24)00405-6. [PMID: 39245624 DOI: 10.1016/j.medcli.2024.05.028] [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: 03/17/2024] [Revised: 05/29/2024] [Accepted: 05/30/2024] [Indexed: 09/10/2024]
Abstract
INTRODUCTION Magnetic resonance imaging (MRI) is a frequently used test in the diagnosis of dementia. The objective was to evaluate its effectiveness for the early diagnosis of dementia in patients with mild cognitive impairment (MCI). MATERIAL AND METHODS Original studies were selected from systematic reviews between 2011 and 2021, according to PRISMA 2020 criteria. QUADAS-2 and GRADE tools were used, and a meta-analysis was performed. RESULTS Final selection of 23 articles. Patient selection and index test had a high probability of bias. The certainty of the evidence was very low. In the hippocampus, sensitivity was 0.62 (95%CI 0.48-0.79) and specificity 0.70 (95%CI 0.55-0.80). In the temporal lobe, sensitivity was 0.65 (range 0.45) and specificity 0.69 (range 0.32). CONCLUSIONS There is insufficient evidence to recommend routine brain MRI for the early diagnosis of dementia in patients with MCI.
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Affiliation(s)
- Ana María Ruiz Tornero
- Servicio de Medicina Preventiva y Gestión de Calidad, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria, Madrid, España.
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2
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Cotta Ramusino M, Massa F, Festari C, Gandolfo F, Nicolosi V, Orini S, Nobili F, Frisoni GB, Morbelli S, Garibotto V. Diagnostic performance of molecular imaging methods in predicting the progression from mild cognitive impairment to dementia: an updated systematic review. Eur J Nucl Med Mol Imaging 2024; 51:1876-1890. [PMID: 38355740 DOI: 10.1007/s00259-024-06631-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 01/27/2024] [Indexed: 02/16/2024]
Abstract
PURPOSE Epidemiological and logistical reasons are slowing the clinical validation of the molecular imaging biomarkers in the initial stages of neurocognitive disorders. We provide an updated systematic review of the recent advances (2017-2022), highlighting methodological shortcomings. METHODS Studies reporting the diagnostic accuracy values of the molecular imaging techniques (i.e., amyloid-, tau-, [18F]FDG-PETs, DaT-SPECT, and cardiac [123I]-MIBG scintigraphy) in predicting progression from mild cognitive impairment (MCI) to dementia were selected according to the Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) method and evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Main eligibility criteria were as follows: (1) ≥ 50 subjects with MCI, (2) follow-up ≥ 3 years, (3) gold standard: progression to dementia or diagnosis on pathology, and (4) measures of prospective accuracy. RESULTS Sensitivity (SE) and specificity (SP) in predicting progression to dementia, mainly to Alzheimer's dementia were 43-100% and 63-94% for [18F]FDG-PET and 64-94% and 48-93% for amyloid-PET. Longitudinal studies were lacking for less common disorders (Dementia with Lewy bodies-DLB and Frontotemporal lobe degeneration-FTLD) and for tau-PET, DaT-SPECT, and [123I]-MIBG scintigraphy. Therefore, the accuracy values from cross-sectional studies in a smaller sample of subjects (n > 20, also including mild dementia stage) were chosen as surrogate outcomes. DaT-SPECT showed 47-100% SE and 71-100% SP in differentiating Lewy body disease (LBD) from non-LBD conditions; tau-PET: 88% SE and 100% SP in differentiating DLB from Posterior Cortical Atrophy. [123I]-MIBG scintigraphy differentiated LBD from non-LBD conditions with 47-100% SE and 71-100% SP. CONCLUSION Molecular imaging has a moderate-to-good accuracy in predicting the progression of MCI to Alzheimer's dementia. Longitudinal studies are sparse in non-AD conditions, requiring additional efforts in these settings.
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Affiliation(s)
- Matteo Cotta Ramusino
- Unit of Behavior Neurology and Dementia Research Center, IRCCS Mondino Foundation, via Mondino 2, 27100, Pavia, Italy.
| | - Federico Massa
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Festari
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni Di Dio Fatebenefratelli, Brescia, Italy
| | - Federica Gandolfo
- Department of Geriatric Care, Orthogeriatrics and Rehabilitation, E.O. Galliera Hospital, Genoa, Italy
| | - Valentina Nicolosi
- UOC Neurologia Ospedale Magalini Di Villafranca Di Verona (VR) ULSS 9, Verona, Italy
| | - Stefania Orini
- Alzheimer's Unit-Memory Clinic, IRCCS Istituto Centro San Giovanni Di Dio Fatebenefratelli, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Flavio Nobili
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University and University Hospitals, Geneva, Switzerland
| | - Silvia Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, University Hospitals of Geneva, Geneva, Switzerland
- NIMTLab, Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Geneva, Switzerland
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Wood DA, Townend M, Guilhem E, Kafiabadi S, Hammam A, Wei Y, Al Busaidi A, Mazumder A, Sasieni P, Barker GJ, Ourselin S, Cole JH, Booth TC. Optimising brain age estimation through transfer learning: A suite of pre-trained foundation models for improved performance and generalisability in a clinical setting. Hum Brain Mapp 2024; 45:e26625. [PMID: 38433665 PMCID: PMC10910262 DOI: 10.1002/hbm.26625] [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: 08/14/2023] [Revised: 12/27/2023] [Accepted: 01/30/2024] [Indexed: 03/05/2024] Open
Abstract
Estimated age from brain MRI data has emerged as a promising biomarker of neurological health. However, the absence of large, diverse, and clinically representative training datasets, along with the complexity of managing heterogeneous MRI data, presents significant barriers to the development of accurate and generalisable models appropriate for clinical use. Here, we present a deep learning framework trained on routine clinical data (N up to 18,890, age range 18-96 years). We trained five separate models for accurate brain age prediction (all with mean absolute error ≤4.0 years, R2 ≥ .86) across five different MRI sequences (T2 -weighted, T2 -FLAIR, T1 -weighted, diffusion-weighted, and gradient-recalled echo T2 *-weighted). Our trained models offer dual functionality. First, they have the potential to be directly employed on clinical data. Second, they can be used as foundation models for further refinement to accommodate a range of other MRI sequences (and therefore a range of clinical scenarios which employ such sequences). This adaptation process, enabled by transfer learning, proved effective in our study across a range of MRI sequences and scan orientations, including those which differed considerably from the original training datasets. Crucially, our findings suggest that this approach remains viable even with limited data availability (as low as N = 25 for fine-tuning), thus broadening the application of brain age estimation to more diverse clinical contexts and patient populations. By making these models publicly available, we aim to provide the scientific community with a versatile toolkit, promoting further research in brain age prediction and related areas.
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Affiliation(s)
- David A. Wood
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
| | - Matthew Townend
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
| | - Emily Guilhem
- King's College Hospital NHS Foundation TrustLondonUK
| | | | - Ahmed Hammam
- King's College Hospital NHS Foundation TrustLondonUK
| | - Yiran Wei
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
| | | | | | - Peter Sasieni
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
| | - Gareth J. Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology, and NeuroscienceKing's College LondonLondonUK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
| | - James H. Cole
- Dementia Research Centre, Institute of NeurologyUniversity College LondonLondonUK
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
| | - Thomas C. Booth
- School of Biomedical Engineering and Imaging Sciences, Rayne InstituteKing's College LondonLondonUK
- King's College Hospital NHS Foundation TrustLondonUK
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Martínez‐Cañada P, Perez‐Valero E, Minguillon J, Pelayo F, López‐Gordo MA, Morillas C. Combining aperiodic 1/f slopes and brain simulation: An EEG/MEG proxy marker of excitation/inhibition imbalance in Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12477. [PMID: 37662693 PMCID: PMC10474329 DOI: 10.1002/dad2.12477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 07/27/2023] [Accepted: 08/08/2023] [Indexed: 09/05/2023]
Abstract
INTRODUCTION Accumulation and interaction of amyloid-beta (Aβ) and tau proteins during progression of Alzheimer's disease (AD) are shown to tilt neuronal circuits away from balanced excitation/inhibition (E/I). Current available techniques for noninvasive interrogation of E/I in the intact human brain, for example, magnetic resonance spectroscopy (MRS), are highly restrictive (i.e., limited spatial extent), have low temporal and spatial resolution and suffer from the limited ability to distinguish accurately between different neurotransmitters complicating its interpretation. As such, these methods alone offer an incomplete explanation of E/I. Recently, the aperiodic component of neural power spectrum, often referred to in the literature as the '1/f slope', has been described as a promising and scalable biomarker that can track disruptions in E/I potentially underlying a spectrum of clinical conditions, such as autism, schizophrenia, or epilepsy, as well as developmental E/I changes as seen in aging. METHODS Using 1/f slopes from resting-state spectral data and computational modeling, we developed a new method for inferring E/I alterations in AD. RESULTS We tested our method on recent freely and publicly available electroencephalography (EEG) and magnetoencephalography (MEG) datasets of patients with AD or prodromal disease and demonstrated the method's potential for uncovering regional patterns of abnormal excitatory and inhibitory parameters. DISCUSSION Our results provide a general framework for investigating circuit-level disorders in AD and developing therapeutic interventions that aim to restore the balance between excitation and inhibition.
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Affiliation(s)
- Pablo Martínez‐Cañada
- Department of Computer EngineeringAutomation and RoboticsUniversity of GranadaGranadaSpain
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
| | - Eduardo Perez‐Valero
- Department of Computer EngineeringAutomation and RoboticsUniversity of GranadaGranadaSpain
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
| | - Jesus Minguillon
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
- Department of Signal TheoryTelematics and CommunicationsUniversity of GranadaGranadaSpain
| | - Francisco Pelayo
- Department of Computer EngineeringAutomation and RoboticsUniversity of GranadaGranadaSpain
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
| | - Miguel A. López‐Gordo
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
- Department of Signal TheoryTelematics and CommunicationsUniversity of GranadaGranadaSpain
| | - Christian Morillas
- Department of Computer EngineeringAutomation and RoboticsUniversity of GranadaGranadaSpain
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
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Verdi S, Kia SM, Yong KXX, Tosun D, Schott JM, Marquand AF, Cole JH. Revealing Individual Neuroanatomical Heterogeneity in Alzheimer Disease Using Neuroanatomical Normative Modeling. Neurology 2023; 100:e2442-e2453. [PMID: 37127353 PMCID: PMC10264044 DOI: 10.1212/wnl.0000000000207298] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 03/02/2023] [Indexed: 05/03/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Alzheimer disease (AD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology. To explore this, we used neuroanatomical normative modeling to index regional patterns of variability in cortical thickness. We aimed to characterize individual differences and outliers in cortical thickness in patients with AD, people with mild cognitive impairment (MCI), and controls. Furthermore, we assessed the relationships between cortical thickness heterogeneity and cognitive function, β-amyloid, phosphorylated-tau, and ApoE genotype. Finally, we examined whether cortical thickness heterogeneity was predictive of conversion from MCI to AD. METHODS Cortical thickness measurements across 148 brain regions were obtained from T1-weighted MRI scans from 62 sites of the Alzheimer's Disease Neuroimaging Initiative. AD was determined by clinical and neuropsychological examination with no comorbidities present. Participants with MCI had reported memory complaints, and controls were cognitively normal. A neuroanatomical normative model indexed cortical thickness distributions using a separate healthy reference data set (n = 33,072), which used hierarchical Bayesian regression to predict cortical thickness per region using age and sex, while adjusting for site noise. Z-scores per region were calculated, resulting in a Z-score brain map per participant. Regions with Z-scores <-1.96 were classified as outliers. RESULTS Patients with AD (n = 206) had a median of 12 outlier regions (out of a possible 148), with the highest proportion of outliers (47%) in the parahippocampal gyrus. For 62 regions, over 90% of these patients had cortical thicknesses within the normal range. Patients with AD had more outlier regions than people with MCI (n = 662) or controls (n = 159) (F(2, 1,022) = 95.39, p = 2.0 × 10-16). They were also more dissimilar to each other than people with MCI or controls (F(2, 1,024) = 209.42, p = 2.2 × 10-16). A greater number of outlier regions were associated with worse cognitive function, CSF protein concentrations, and an increased risk of converting from MCI to AD within 3 years (hazard ratio 1.028, 95% CI 1.016-1.039, p = 1.8 × 10-16). DISCUSSION Individualized normative maps of cortical thickness highlight the heterogeneous effect of AD on the brain. Regional outlier estimates have the potential to be a marker of disease and could be used to track an individual's disease progression or treatment response in clinical trials.
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Affiliation(s)
- Serena Verdi
- From the Centre for Medical Image Computing (S.V., J.H.C.), Medical Physics and Biomedical Engineering, University College London; Dementia Research Centre (S.V., K.X.X.Y., J.M.S., J.H.C.), UCL Queen Square Institute of Neurology, London, United Kingdom; Donders Centre for Cognitive Neuroimaging (S.M.K., A.F.M.), Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen; Department of Psychiatry (S.M.K.), University Medical Centre Utrecht, the Netherlands; Department of Radiology and Biomedical Imaging (D.T.), University of California, San Francisco; and Department of Cognitive Neuroscience (A.F.M.), Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Seyed Mostafa Kia
- From the Centre for Medical Image Computing (S.V., J.H.C.), Medical Physics and Biomedical Engineering, University College London; Dementia Research Centre (S.V., K.X.X.Y., J.M.S., J.H.C.), UCL Queen Square Institute of Neurology, London, United Kingdom; Donders Centre for Cognitive Neuroimaging (S.M.K., A.F.M.), Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen; Department of Psychiatry (S.M.K.), University Medical Centre Utrecht, the Netherlands; Department of Radiology and Biomedical Imaging (D.T.), University of California, San Francisco; and Department of Cognitive Neuroscience (A.F.M.), Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Keir X X Yong
- From the Centre for Medical Image Computing (S.V., J.H.C.), Medical Physics and Biomedical Engineering, University College London; Dementia Research Centre (S.V., K.X.X.Y., J.M.S., J.H.C.), UCL Queen Square Institute of Neurology, London, United Kingdom; Donders Centre for Cognitive Neuroimaging (S.M.K., A.F.M.), Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen; Department of Psychiatry (S.M.K.), University Medical Centre Utrecht, the Netherlands; Department of Radiology and Biomedical Imaging (D.T.), University of California, San Francisco; and Department of Cognitive Neuroscience (A.F.M.), Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Duygu Tosun
- From the Centre for Medical Image Computing (S.V., J.H.C.), Medical Physics and Biomedical Engineering, University College London; Dementia Research Centre (S.V., K.X.X.Y., J.M.S., J.H.C.), UCL Queen Square Institute of Neurology, London, United Kingdom; Donders Centre for Cognitive Neuroimaging (S.M.K., A.F.M.), Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen; Department of Psychiatry (S.M.K.), University Medical Centre Utrecht, the Netherlands; Department of Radiology and Biomedical Imaging (D.T.), University of California, San Francisco; and Department of Cognitive Neuroscience (A.F.M.), Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Jonathan M Schott
- From the Centre for Medical Image Computing (S.V., J.H.C.), Medical Physics and Biomedical Engineering, University College London; Dementia Research Centre (S.V., K.X.X.Y., J.M.S., J.H.C.), UCL Queen Square Institute of Neurology, London, United Kingdom; Donders Centre for Cognitive Neuroimaging (S.M.K., A.F.M.), Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen; Department of Psychiatry (S.M.K.), University Medical Centre Utrecht, the Netherlands; Department of Radiology and Biomedical Imaging (D.T.), University of California, San Francisco; and Department of Cognitive Neuroscience (A.F.M.), Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Andre F Marquand
- From the Centre for Medical Image Computing (S.V., J.H.C.), Medical Physics and Biomedical Engineering, University College London; Dementia Research Centre (S.V., K.X.X.Y., J.M.S., J.H.C.), UCL Queen Square Institute of Neurology, London, United Kingdom; Donders Centre for Cognitive Neuroimaging (S.M.K., A.F.M.), Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen; Department of Psychiatry (S.M.K.), University Medical Centre Utrecht, the Netherlands; Department of Radiology and Biomedical Imaging (D.T.), University of California, San Francisco; and Department of Cognitive Neuroscience (A.F.M.), Radboud University Medical Centre, Nijmegen, the Netherlands
| | - James H Cole
- From the Centre for Medical Image Computing (S.V., J.H.C.), Medical Physics and Biomedical Engineering, University College London; Dementia Research Centre (S.V., K.X.X.Y., J.M.S., J.H.C.), UCL Queen Square Institute of Neurology, London, United Kingdom; Donders Centre for Cognitive Neuroimaging (S.M.K., A.F.M.), Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen; Department of Psychiatry (S.M.K.), University Medical Centre Utrecht, the Netherlands; Department of Radiology and Biomedical Imaging (D.T.), University of California, San Francisco; and Department of Cognitive Neuroscience (A.F.M.), Radboud University Medical Centre, Nijmegen, the Netherlands.
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Vasquez-Rios G, Oh W, Lee S, Bhatraju P, Mansour SG, Moledina DG, Gulamali FF, Siew ED, Garg AX, Sarder P, Chinchilli VM, Kaufman JS, Hsu CY, Liu KD, Kimmel PL, Go AS, Wurfel MM, Himmelfarb J, Parikh CR, Coca SG, Nadkarni GN. Joint Modeling of Clinical and Biomarker Data in Acute Kidney Injury Defines Unique Subphenotypes with Differing Outcomes. Clin J Am Soc Nephrol 2023; 18:716-726. [PMID: 36975209 PMCID: PMC10278836 DOI: 10.2215/cjn.0000000000000156] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 03/13/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND AKI is a heterogeneous syndrome. Current subphenotyping approaches have only used limited laboratory data to understand a much more complex condition. METHODS We focused on patients with AKI from the Assessment, Serial Evaluation, and Subsequent Sequelae in AKI (ASSESS-AKI). We used hierarchical clustering with Ward linkage on biomarkers of inflammation, injury, and repair/health. We then evaluated clinical differences between subphenotypes and examined their associations with cardiorenal events and death using Cox proportional hazard models. RESULTS We included 748 patients with AKI: 543 (73%) of them had AKI stage 1, 112 (15%) had AKI stage 2, and 93 (12%) had AKI stage 3. The mean age (±SD) was 64 (13) years; 508 (68%) were men; and the median follow-up was 4.7 (Q1: 2.9, Q3: 5.7) years. Patients with AKI subphenotype 1 ( N =181) had the highest kidney injury molecule (KIM-1) and troponin T levels. Subphenotype 2 ( N =250) had the highest levels of uromodulin. AKI subphenotype 3 ( N =159) comprised patients with markedly high pro-brain natriuretic peptide and plasma tumor necrosis factor receptor-1 and -2 and low concentrations of KIM-1 and neutrophil gelatinase-associated lipocalin. Finally, patients with subphenotype 4 ( N =158) predominantly had sepsis-AKI and the highest levels of vascular/kidney inflammation (YKL-40, MCP-1) and injury (neutrophil gelatinase-associated lipocalin, KIM-1). AKI subphenotypes 3 and 4 were independently associated with a higher risk of death compared with subphenotype 2 and had adjusted hazard ratios of 2.9 (95% confidence interval, 1.8 to 4.6) and 1.6 (95% confidence interval, 1.01 to 2.6, P = 0.04), respectively. Subphenotype 3 was also independently associated with a three-fold risk of CKD and cardiovascular events. CONCLUSIONS We discovered four AKI subphenotypes with differing clinical features and biomarker profiles that are associated with longitudinal clinical outcomes.
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Affiliation(s)
- George Vasquez-Rios
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Wonsuk Oh
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Samuel Lee
- Icahn School of Medicine at Mount Sinai, New York, New York
| | - Pavan Bhatraju
- Division of Nephrology, Department of Medicine, Kidney Research Institute, University of Washington, Seattle, Washington
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington
| | - Sherry G. Mansour
- Section of Nephrology, Yale University School of Medicine, New Haven, Connecticut
| | - Dennis G. Moledina
- Section of Nephrology, Yale University School of Medicine, New Haven, Connecticut
| | - Faris F. Gulamali
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Edward D. Siew
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Amit X. Garg
- Division of Nephrology, Department of Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Pinaki Sarder
- Department of Biomedical Engineering, SUNY Buffalo, Buffalo, New York
| | - Vernon M. Chinchilli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania
| | - James S. Kaufman
- Division of Nephrology, Veterans Affairs New York Harbor Healthcare System and New York University School of Medicine, New York, New York
| | - Chi-yuan Hsu
- Division of Nephrology, Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Kathleen D. Liu
- Division of Nephrology, Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Paul L. Kimmel
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Alan S. Go
- Kaiser Permanente Northern California, Oakland, California
| | - Mark M. Wurfel
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington
| | - Jonathan Himmelfarb
- Division of Nephrology, Department of Medicine, Kidney Research Institute, University of Washington, Seattle, Washington
| | - Chirag R. Parikh
- Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Steven G. Coca
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Girish N. Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
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7
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Cumplido-Mayoral I, García-Prat M, Operto G, Falcon C, Shekari M, Cacciaglia R, Milà-Alomà M, Lorenzini L, Ingala S, Meije Wink A, Mutsaerts HJMM, Minguillón C, Fauria K, Molinuevo JL, Haller S, Chetelat G, Waldman A, Schwarz AJ, Barkhof F, Suridjan I, Kollmorgen G, Bayfield A, Zetterberg H, Blennow K, Suárez-Calvet M, Vilaplana V, Gispert JD. Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer's disease and neurodegeneration stratified by sex. eLife 2023; 12:e81067. [PMID: 37067031 PMCID: PMC10181824 DOI: 10.7554/elife.81067] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 04/10/2023] [Indexed: 04/18/2023] Open
Abstract
Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer's disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD, and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging in non-demented individuals with abnormal levels of biomarkers of AD and axonal injury.
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Affiliation(s)
- Irene Cumplido-Mayoral
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
| | - Marina García-Prat
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
| | - Grégory Operto
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- CIBER Fragilidad y Envejecimiento Saludable (CIBERFES)MadridFrance
| | - Carles Falcon
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN)MadridSpain
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
| | - Raffaele Cacciaglia
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- CIBER Fragilidad y Envejecimiento Saludable (CIBERFES)MadridFrance
| | - Marta Milà-Alomà
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- Universitat Pompeu FabraBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- CIBER Fragilidad y Envejecimiento Saludable (CIBERFES)MadridFrance
| | - Luigi Lorenzini
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Henk JMM Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Carolina Minguillón
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- CIBER Fragilidad y Envejecimiento Saludable (CIBERFES)MadridFrance
| | - Karine Fauria
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- CIBER Fragilidad y Envejecimiento Saludable (CIBERFES)MadridFrance
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
| | - Sven Haller
- CIRD Centre d'Imagerie Rive DroiteGenevaSwitzerland
| | - Gael Chetelat
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and BrainCyceronFrance
| | - Adam Waldman
- Centre for Dementia Prevention, Edinburgh Imaging, and UK Dementia Research Institute at The University of EdinburghEdinburghUnited Kingdom
| | | | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
- Institutes of Neurology and Healthcare Engineering, University College LondonLondonUnited Kingdom
| | | | | | | | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, University of GothenburgMölndalSweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University HospitalMölndalSweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
- Hong Kong Center for Neurodegenerative DiseasesHong KongChina
- UK Dementia Research Institute at UCLLondonUnited Kingdom
| | - Kaj Blennow
- Institute of Neuroscience and Physiology, University of GothenburgMölndalSweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University HospitalMölndalSweden
| | - Marc Suárez-Calvet
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- CIBER Fragilidad y Envejecimiento Saludable (CIBERFES)MadridFrance
- Servei de Neurologia, Hospital del MarBarcelonaSpain
| | - Verónica Vilaplana
- Department of Signal Theory and Communications, Universitat Politècnica de CatalunyaBarcelonaSpain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center, Pasqual Maragall FoundationBarcelonaSpain
- IMIM (Hospital del Mar Medical Research Institute)BarcelonaSpain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN)MadridSpain
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8
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Fu J, Tzortzakakis A, Barroso J, Westman E, Ferreira D, Moreno R. Fast three-dimensional image generation for healthy brain aging using diffeomorphic registration. Hum Brain Mapp 2023; 44:1289-1308. [PMID: 36468536 PMCID: PMC9921328 DOI: 10.1002/hbm.26165] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 11/15/2022] [Accepted: 11/16/2022] [Indexed: 12/12/2022] Open
Abstract
Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R2 ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning-based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points.
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Affiliation(s)
- Jingru Fu
- Division of Biomedical ImagingDepartment of Biomedical Engineering and Health Systems, KTH Royal Institute of TechnologyStockholmSweden
| | - Antonios Tzortzakakis
- Division of RadiologyDepartment for Clinical Science, Intervention and Technology (CLINTEC), Karolinska InstitutetStockholmSweden
- Medical Radiation Physics and Nuclear MedicineFunctional Unit of Nuclear Medicine, Karolinska University HospitalHuddingeStockholmSweden
| | - José Barroso
- Department of PsychologyFaculty of Health Sciences, University Fernando Pessoa CanariasLas PalmasSpain
| | - Eric Westman
- Division of Clinical GeriatricsCentre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society (NVS), Karolinska InstitutetStockholmSweden
- Department of NeuroimagingCentre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
| | - Daniel Ferreira
- Division of Clinical GeriatricsCentre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society (NVS), Karolinska InstitutetStockholmSweden
| | - Rodrigo Moreno
- Division of Biomedical ImagingDepartment of Biomedical Engineering and Health Systems, KTH Royal Institute of TechnologyStockholmSweden
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9
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Huang Y, Zhang D, Zhang X, Cheng M, Yang Z, Gao J, Tang M, Ai K, Lei X, Zhang X. Altered functional hubs and connectivity in type 2 diabetes mellitus with and without mild cognitive impairment. Front Neurol 2022; 13:1062816. [PMID: 36578308 PMCID: PMC9792165 DOI: 10.3389/fneur.2022.1062816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 11/21/2022] [Indexed: 12/14/2022] Open
Abstract
Cognitive impairment in type 2 diabetes mellitus (T2DM) is associated with functional and structural abnormalities of brain networks, especially the damage to hub nodes in networks. This study explored the abnormal hub nodes of brain functional networks in patients with T2DM under different cognitive states. Sixty-five patients with T2DM and 34 healthy controls (HCs) underwent neuropsychological assessment. Then, degree centrality (DC) analysis and seed-based functional connectivity (FC) analysis were performed to identify the abnormal hub nodes and the FC patterns of these hubs in T2DM patients with mild cognitive impairment (MCI) (DMCI group, N = 31) and without MCI (DMCN group, N = 34). Correlation analyzes examined the relationship between abnormal DC and FC and clinical/cognitive variables. Compared with HCs, both T2DM groups showed decreased DC values in the visual cortex, and the T2DM patients with MCI (DMCI) showed more extensive alterations in the right parahippocampal gyrus (PHG), bilateral posterior cingulate cortex (PCC), and left superior frontal gyrus (SFG) regions than T2DM patients with normal cognitive function. Seed-based FC analysis of PHG and PCC nodes showed that functional disconnection mainly occurred in visual and memory connectivity in patients with DMCI. Multiple abnormal DC values correlated with neuropsychological tests in patients with T2DM. In conclusion, this study found that the DMCI group displayed more extensive alterations in hub nodes and FC in vision and memory-related brain regions, suggesting that visual-related regions dysfunctions and disconnection may be involved in the neuropathology of visuospatial function impairment in patients with DMCI.
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Affiliation(s)
- Yang Huang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Dongsheng Zhang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Xin Zhang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Miao Cheng
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Zhen Yang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Jie Gao
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Min Tang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Kai Ai
- Department of Clinical and Technical Support, Philips Healthcare, Xi'an, China
| | - Xiaoyan Lei
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China,Xiaoyan Lei
| | - Xiaoling Zhang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China,*Correspondence: Xiaoling Zhang
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10
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Wagen AZ, Coath W, Keshavan A, James SN, Parker TD, Lane CA, Buchanan SM, Keuss SE, Storey M, Lu K, Macdougall A, Murray-Smith H, Freiberger T, Cash DM, Malone IB, Barnes J, Sudre CH, Wong A, Pavisic IM, Street R, Crutch SJ, Escott-Price V, Leonenko G, Zetterberg H, Wellington H, Heslegrave A, Barkhof F, Richards M, Fox NC, Cole JH, Schott JM. Life course, genetic, and neuropathological associations with brain age in the 1946 British Birth Cohort: a population-based study. THE LANCET. HEALTHY LONGEVITY 2022; 3:e607-e616. [PMID: 36102775 PMCID: PMC10499760 DOI: 10.1016/s2666-7568(22)00167-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 06/29/2022] [Accepted: 06/30/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND A neuroimaging-based biomarker termed the brain age is thought to reflect variability in the brain's ageing process and predict longevity. Using Insight 46, a unique narrow-age birth cohort, we aimed to examine potential drivers and correlates of brain age. METHODS Participants, born in a single week in 1946 in mainland Britain, have had 24 prospective waves of data collection to date, including MRI and amyloid PET imaging at approximately 70 years old. Using MRI data from a previously defined selection of this cohort, we derived brain-predicted age from an established machine-learning model (trained on 2001 healthy adults aged 18-90 years); subtracting this from chronological age (at time of assessment) gave the brain-predicted age difference (brain-PAD). We tested associations with data from early life, midlife, and late life, as well as rates of MRI-derived brain atrophy. FINDINGS Between May 28, 2015, and Jan 10, 2018, 502 individuals were assessed as part of Insight 46. We included 456 participants (225 female), with a mean chronological age of 70·7 years (SD 0·7; range 69·2 to 71·9). The mean brain-predicted age was 67·9 years (8·2, 46·3 to 94·3). Female sex was associated with a 5·4-year (95% CI 4·1 to 6·8) younger brain-PAD than male sex. An increase in brain-PAD was associated with increased cardiovascular risk at age 36 years (β=2·3 [95% CI 1·5 to 3·0]) and 69 years (β=2·6 [1·9 to 3·3]); increased cerebrovascular disease burden (1·9 [1·3 to 2·6]); lower cognitive performance (-1·3 [-2·4 to -0·2]); and increased serum neurofilament light concentration (1·2 [0·6 to 1·9]). Higher brain-PAD was associated with future hippocampal atrophy over the subsequent 2 years (0·003 mL/year [0·000 to 0·006] per 5-year increment in brain-PAD). Early-life factors did not relate to brain-PAD. Combining 12 metrics in a hierarchical partitioning model explained 33% of the variance in brain-PAD. INTERPRETATION Brain-PAD was associated with cardiovascular risk, and imaging and biochemical markers of neurodegeneration. These findings support brain-PAD as an integrative summary metric of brain health, reflecting multiple contributions to pathological brain ageing, and which might have prognostic utility. FUNDING Alzheimer's Research UK, Medical Research Council Dementia Platforms UK, Selfridges Group Foundation, Wolfson Foundation, Wellcome Trust, Brain Research UK, Alzheimer's Association.
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Affiliation(s)
- Aaron Z Wagen
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK; Neurodegeneration Biology Laboratory, The Francis Crick Institute, London, UK
| | - William Coath
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Ashvini Keshavan
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Sarah-Naomi James
- Medical Research Council Unit for Lifelong Health and Ageing, University College London, London, UK
| | - Thomas D Parker
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Department of Brain Sciences, Imperial College London, London, UK; UK Dementia Research Institute Centre for Care Research and Technology, Imperial College London, London, UK
| | - Christopher A Lane
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Sarah M Buchanan
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Sarah E Keuss
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Mathew Storey
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Kirsty Lu
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Amy Macdougall
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Tamar Freiberger
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - David M Cash
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Dementia Research Institute, University College London Queen Square Institute of Neurology, London, UK
| | - Ian B Malone
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Josephine Barnes
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Carole H Sudre
- Medical Research Council Unit for Lifelong Health and Ageing, University College London, London, UK; Department of Computer Science, Centre for Medical Imaging Computing, University College London, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andrew Wong
- Medical Research Council Unit for Lifelong Health and Ageing, University College London, London, UK
| | - Ivanna M Pavisic
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Rebecca Street
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | - Sebastian J Crutch
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK
| | | | - Ganna Leonenko
- Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Henrik Zetterberg
- Dementia Research Institute, University College London Queen Square Institute of Neurology, London, UK; Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK; Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrietta Wellington
- Dementia Research Institute, University College London Queen Square Institute of Neurology, London, UK; Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK
| | - Amanda Heslegrave
- Dementia Research Institute, University College London Queen Square Institute of Neurology, London, UK; Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK
| | - Frederik Barkhof
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK; Department of Computer Science, Centre for Medical Imaging Computing, University College London, London, UK; Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, Netherlands
| | - Marcus Richards
- Medical Research Council Unit for Lifelong Health and Ageing, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Dementia Research Institute, University College London Queen Square Institute of Neurology, London, UK
| | - James H Cole
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Department of Computer Science, Centre for Medical Imaging Computing, University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, University College London Queen Square Institute of Neurology, London, UK; Dementia Research Institute, University College London Queen Square Institute of Neurology, London, UK.
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11
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Biondo F, Jewell A, Pritchard M, Aarsland D, Steves CJ, Mueller C, Cole JH. Brain-age is associated with progression to dementia in memory clinic patients. Neuroimage Clin 2022; 36:103175. [PMID: 36087560 PMCID: PMC9467894 DOI: 10.1016/j.nicl.2022.103175] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 07/30/2022] [Accepted: 08/27/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Biomarkers for the early detection of dementia risk hold promise for better disease monitoring and targeted interventions. However, most biomarker studies, particularly in neuroimaging, have analysed artificially 'clean' research groups, free from comorbidities, erroneous referrals, contraindications and from a narrow sociodemographic pool. Such biases mean that neuroimaging samples are often unrepresentative of the target population for dementia risk (e.g., people referred to a memory clinic), limiting the generalisation of these studies to real-world clinical settings. To facilitate better translation from research to the clinic, datasets that are more representative of dementia patient groups are warranted. METHODS We analysed T1-weighted MRI scans from a real-world setting of patients referred to UK memory clinic services (n = 1140; 60.2 % female and mean [SD] age of 70.0[10.8] years) to derive 'brain-age'. Brain-age is an index of age-related brain health based on quantitative analysis of structural neuroimaging, largely reflecting brain atrophy. Brain-predicted age difference (brain-PAD) was calculated as brain-age minus chronological age. We determined which patients went on to develop dementia between three months and 7.8 years after neuroimaging assessment (n = 476) using linkage to electronic health records. RESULTS Survival analysis, using Cox regression, indicated a 3 % increased risk of dementia per brain-PAD year (hazard ratio [95 % CI] = 1.03 [1.02,1.04], p < 0.0001), adjusted for baseline age, age2, sex, Mini Mental State Examination (MMSE) score and normalised brain volume. In sensitivity analyses, brain-PAD remained significant when time-to-dementia was at least 3 years (hazard ratio [95 % CI] = 1.06 [1.02, 1.09], p = 0.0006), or when baseline MMSE score ≥ 27 (hazard ratio [95 % CI] = 1.03 [1.01, 1.05], p = 0.0006). CONCLUSIONS Memory clinic patients with older-appearing brains are more likely to receive a subsequent dementia diagnosis. Potentially, brain-age could aid decision-making during initial memory clinic assessment to improve early detection of dementia. Even when neuroimaging assessment was more than 3 years prior to diagnosis and when cognitive functioning was not clearly impaired, brain-age still proved informative. These real-world results support the use of quantitative neuroimaging biomarkers like brain-age in memory clinics.
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Affiliation(s)
- Francesca Biondo
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AF, UK; South London and Maudsley NHS Foundation Trust, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, WC1V 6LJ, UK.
| | | | | | - Dag Aarsland
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AF, UK; Centre for Age-Related Research, Stavanger University Hospital, Stavanger, Norway
| | - Claire J Steves
- Department of Ageing and Health, Guy's and St Thomas' NHS Foundation Trust, SE1 7EH, UK; Department of Twin Research and Genetic Epidemiology, King's College London, SE1 7EH, UK
| | - Christoph Mueller
- South London and Maudsley NHS Foundation Trust, UK; Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AF, UK
| | - James H Cole
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AF, UK; South London and Maudsley NHS Foundation Trust, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, WC1V 6LJ, UK; Dementia Research Centre, Institute of Neurology, University College London, WC1N 3AR, UK.
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12
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Feng X, Provenzano FA, Small SA. A deep learning MRI approach outperforms other biomarkers of prodromal Alzheimer's disease. Alzheimers Res Ther 2022; 14:45. [PMID: 35351193 PMCID: PMC8966329 DOI: 10.1186/s13195-022-00985-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 03/07/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND The three core pathologies of Alzheimer's disease (AD) are amyloid pathology, tau pathology, and neurodegeneration. Biomarkers exist for each. Neurodegeneration is often detected by neuroimaging, and we hypothesized that a voxel-based deep learning approach using structural MRI might outperform other neuroimaging methods. METHODS First, we implement an MRI-based deep learning model, trained with a data augmentation strategy, which classifies Alzheimer's dementia and generates class activation maps. Next, we tested the model in prodromal AD and compared its performance to other biomarkers of amyloid pathology, tau pathology, and neuroimaging biomarkers of neurodegeneration. RESULTS The model distinguished between controls and AD with high accuracy (AUROC = 0.973) with class activation maps that localized to the hippocampal formation. As hypothesized, the model also outperformed other neuroimaging biomarkers of neurodegeneration in prodromal AD (AUROC = 0.788) but also outperformed biomarkers of amyloid (CSF Aβ = 0.702) or tau pathology (CSF tau = 0.682), and the findings are interpreted in the context of AD's known anatomical biology. CONCLUSIONS The advantages of using deep learning to extract biomarker information from conventional MRIs extend practically, potentially reducing patient burden, risk, and cost.
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Affiliation(s)
- Xinyang Feng
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
- Current address: Meta Platforms, Inc., Menlo Park, CA, USA
| | - Frank A Provenzano
- Department of Neurology, Columbia University, 630 West 168th Street, P&S Box 16, New York, NY, 10032, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, 10032, USA
| | - Scott A Small
- Department of Neurology, Columbia University, 630 West 168th Street, P&S Box 16, New York, NY, 10032, USA.
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, 10032, USA.
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13
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Sadiq MU, Kwak K, Dayan E. Model-based stratification of progression along the Alzheimer disease continuum highlights the centrality of biomarker synergies. Alzheimers Res Ther 2022; 14:16. [PMID: 35073974 PMCID: PMC8787915 DOI: 10.1186/s13195-021-00941-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 11/23/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND The progression rates of Alzheimer's disease (AD) are variable and dynamic, yet the mechanisms that contribute to heterogeneity in progression rates remain ill-understood. Particularly, the role of synergies in pathological processes reflected by biomarkers for amyloid-beta ('A'), tau ('T'), and neurodegeneration ('N') in progression along the AD continuum is not fully understood. METHODS Here, we used a combination of model and data-driven approaches to address this question. Working with a large dataset (N = 321 across the training and testing cohorts), we first applied unsupervised clustering on longitudinal cognitive assessments to divide individuals on the AD continuum into those showing fast vs. moderate decline. Next, we developed a deep learning model that differentiated fast vs. moderate decline using baseline AT(N) biomarkers. RESULTS Training the model with AT(N) biomarker combination revealed more prognostic utility than any individual biomarkers alone. We additionally found little overlap between the model-driven progression phenotypes and established atrophy-based AD subtypes. Our model showed that the combination of all AT(N) biomarkers had the most prognostic utility in predicting progression along the AD continuum. A comprehensive AT(N) model showed better predictive performance than biomarker pairs (A(N) and T(N)) and individual biomarkers (A, T, or N). CONCLUSIONS This study combined data and model-driven methods to uncover the role of AT(N) biomarker synergies in the progression of cognitive decline along the AD continuum. The results suggest a synergistic relationship between AT(N) biomarkers in determining this progression, extending previous evidence of A-T synergistic mechanisms.
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Affiliation(s)
- Muhammad Usman Sadiq
- Biomedical Research Imaging Center (BRIC), UNC-Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Kichang Kwak
- Biomedical Research Imaging Center (BRIC), UNC-Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Eran Dayan
- Biomedical Research Imaging Center (BRIC), UNC-Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Radiology, UNC-Chapel Hill, Chapel Hill, NC, 27599, USA.
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14
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Wang M, Ren Q, Shi Y, Shu H, Liu D, Gu L, Xie C, Zhang Z, Wu T, Wang Z. The effect of Alzheimer's disease risk factors on brain aging in normal Chineses: Cognitive aging and cognitive reserve. Neurosci Lett 2021; 771:136398. [PMID: 34923042 DOI: 10.1016/j.neulet.2021.136398] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 11/22/2021] [Accepted: 12/12/2021] [Indexed: 11/28/2022]
Abstract
Aging has been recognized as a major driving force of the Alzheimer's disease's (AD) progression, however, the relationship between brain aging and AD is still unclear. There is also a lack of studies investigating the influence of AD risk factors on brain aging in cognitively normal people. Here, the "Brain Age Gap Estimation" (BrainAGE) framework was applied to investigate the effects of AD risk factors on individual brain aging. Across a total of 165 cognitively normal elderly subjects, although no significant difference was observed in the BrainAGE scores among the three groups, AD risk dose (i.e., the number of AD risk factors) is tend to associated with an increased BrainAGE scores (high-risk > middle risk > low risk). Female exhibited more advanced brain aging (P = 0.004), and higher education years were associated with preserved brain aging (P < 0.001). APOE-ɛ4 (P = 0.846) and family history (FH) of dementia (P = 0.209) did not increase BrainAGE scores. When comparing 52 aMCI patients with 38 cognitively normal controls from ADNI dataset, aMCI patients showed significantly increased BrainAGE scores. BrainAGE scores were negatively correlated with CSF Aβ42 levels in the aMCI group (r = -0.275, P = 0.048). With an accuracy of 68.9%, BrainAGE outperformed APOE-ɛ4 and hippocampus gray matter volume (GMV) in predicting aMCI. In conclusion, AD is independently associated with structural changes in the brain that reflect advanced aging. Potentially, BrainAGE combined with APOE-ɛ4 and hippocampus GMV could be used as a pre-screening tool in early-stage AD.
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Affiliation(s)
- Mengxue Wang
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Qingguo Ren
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China.
| | - Yachen Shi
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Hao Shu
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China
| | - Duan Liu
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Lihua Gu
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China
| | - Chunming Xie
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China
| | - Zhijun Zhang
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China
| | - Tiange Wu
- School of Medicine, Southeast University, Nanjing 210009, China
| | - Zan Wang
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China.
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15
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Popescu SG, Glocker B, Sharp DJ, Cole JH. Local Brain-Age: A U-Net Model. Front Aging Neurosci 2021; 13:761954. [PMID: 34966266 PMCID: PMC8710767 DOI: 10.3389/fnagi.2021.761954] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 11/03/2021] [Indexed: 11/13/2022] Open
Abstract
We propose a new framework for estimating neuroimaging-derived "brain-age" at a local level within the brain, using deep learning. The local approach, contrary to existing global methods, provides spatial information on anatomical patterns of brain ageing. We trained a U-Net model using brain MRI scans from n = 3,463 healthy people (aged 18-90 years) to produce individualised 3D maps of brain-predicted age. When testing on n = 692 healthy people, we found a median (across participant) mean absolute error (within participant) of 9.5 years. Performance was more accurate (MAE around 7 years) in the prefrontal cortex and periventricular areas. We also introduce a new voxelwise method to reduce the age-bias when predicting local brain-age "gaps." To validate local brain-age predictions, we tested the model in people with mild cognitive impairment or dementia using data from OASIS3 (n = 267). Different local brain-age patterns were evident between healthy controls and people with mild cognitive impairment or dementia, particularly in subcortical regions such as the accumbens, putamen, pallidum, hippocampus, and amygdala. Comparing groups based on mean local brain-age over regions-of-interest resulted in large effects sizes, with Cohen's d values >1.5, for example when comparing people with stable and progressive mild cognitive impairment. Our local brain-age framework has the potential to provide spatial information leading to a more mechanistic understanding of individual differences in patterns of brain ageing in health and disease.
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Affiliation(s)
- Sebastian G. Popescu
- Biomedical Image Analysis Group, Imperial College London, London, United Kingdom
- Computational, Cognitive & Clinical Neuroimaging Laboratory, Imperial College London, London, United Kingdom
| | - Ben Glocker
- Biomedical Image Analysis Group, Imperial College London, London, United Kingdom
| | - David J. Sharp
- Computational, Cognitive & Clinical Neuroimaging Laboratory, Imperial College London, London, United Kingdom
- Care Research & Technology Centre, UK Dementia Research Institute, London, United Kingdom
| | - James H. Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
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16
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Minhas S, Khanum A, Alvi A, Riaz F, Khan SA, Alsolami F, A Khan M. Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:6628036. [PMID: 34608385 PMCID: PMC8487363 DOI: 10.1155/2021/6628036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 07/27/2021] [Accepted: 08/12/2021] [Indexed: 11/18/2022]
Abstract
In Alzheimer's disease (AD) progression, it is imperative to identify the subjects with mild cognitive impairment before clinical symptoms of AD appear. This work proposes a technique for decision support in identifying subjects who will show transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD) in the future. We used robust predictors from multivariate MRI-derived biomarkers and neuropsychological measures and tracked their longitudinal trajectories to predict signs of AD in the MCI population. Assuming piecewise linear progression of the disease, we designed a novel weighted gradient offset-based technique to forecast the future marker value using readings from at least two previous follow-up visits. Later, the complete predictor trajectories are used as features for a standard support vector machine classifier to identify MCI-to-AD progressors amongst the MCI patients enrolled in the Alzheimer's disease neuroimaging initiative (ADNI) cohort. We explored the performance of both unimodal and multimodal models in a 5-fold cross-validation setup. The proposed technique resulted in a high classification AUC of 91.2% and 95.7% for 6-month- and 1-year-ahead AD prediction, respectively, using multimodal markers. In the end, we discuss the efficacy of MRI markers as compared to NM for MCI-to-AD conversion prediction.
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Affiliation(s)
- Sidra Minhas
- Department of Computer Science, Forman Christian College University, Lahore, Pakistan
| | - Aasia Khanum
- Department of Computer Science, Forman Christian College University, Lahore, Pakistan
| | - Atif Alvi
- Department of Computer Science, University of Management and Technology, Lahore, Pakistan
| | - Farhan Riaz
- Department of Computer Engineering, National University of Sciences & Technology, EME College, Rawalpindi, Pakistan
| | - Shoab A Khan
- Department of Computer Engineering, National University of Sciences & Technology, EME College, Rawalpindi, Pakistan
| | - Fawaz Alsolami
- Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Muazzam A Khan
- Department of Computer Sciences, Quaid I Azam University, Islamabad, Pakistan
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17
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Fabrizio C, Termine A, Caltagirone C, Sancesario G. Artificial Intelligence for Alzheimer's Disease: Promise or Challenge? Diagnostics (Basel) 2021; 11:1473. [PMID: 34441407 PMCID: PMC8391160 DOI: 10.3390/diagnostics11081473] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 08/12/2021] [Accepted: 08/12/2021] [Indexed: 01/23/2023] Open
Abstract
Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer's disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.
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Affiliation(s)
- Carlo Fabrizio
- Laboratory of Experimental and Behavioral Neurophysiology, IRCCS Santa Lucia Foundation, 00143 Rome, Italy; (C.F.); (A.T.)
| | - Andrea Termine
- Laboratory of Experimental and Behavioral Neurophysiology, IRCCS Santa Lucia Foundation, 00143 Rome, Italy; (C.F.); (A.T.)
| | - Carlo Caltagirone
- Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy;
| | - Giulia Sancesario
- Biobank, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
- European Center for Brain Research, Experimental Neuroscience, 00143 Rome, Italy
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18
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Liang CS, Tsai CL, Lin GY, Lee JT, Lin YK, Chu CS, Sung YF, Tsai CK, Yeh TC, Chu HT, Su MW, Yang FC. Better Identification of Cognitive Decline With Interleukin-2 Than With Amyloid and Tau Protein Biomarkers in Amnestic Mild Cognitive Impairment. Front Aging Neurosci 2021; 13:670115. [PMID: 34122046 PMCID: PMC8193360 DOI: 10.3389/fnagi.2021.670115] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Accepted: 05/03/2021] [Indexed: 12/01/2022] Open
Abstract
The rate of cognitive decline among patients with amnestic mild cognitive impairment (aMCI) varies, and it is thus crucial to accurately predict the probability of cognitive deterioration in patients with MCI. We compared the potential of cytokines with amyloid beta (Aβ) and tau biomarkers for predicting cognitive decline in patients with aMCI or Alzheimer’s disease (AD). All participants (controls, aMCI, and AD patients) underwent plasma biomarker examinations for Aβ1–40, Aβ1–42, total tau (t-tau), tau phosphorylated at threonine 181 [p-Tau181]), and 29 cytokines and baseline cognitive tests, including Mini-Mental State Examination (MMSE). The correlation between biomarker levels and annual MMSE change during the follow-up was examined. Receiver operating characteristic (ROC) curve analysis was performed to determine whether the statistically significant plasma biomarkers could identify cognitive decline. Higher baseline levels of IL-2, sCD40L, IL-8, and VEGF were associated with a lower annual cognitive decline in the aMCI group, and higher baseline levels of Aβ1–40, IFNγ, IL-5, IL-17A, IL-25, and FGF were associated with a rapid annual cognitive decline in the AD group. IL-2 had a high discriminatory capacity for identifying cognitive decline, with an area under curve (AUC) of 85.7% in the aMCI group, and the AUC was slightly increased when combining IL-2 with Aβ or tau biomarkers. However, none of the biomarkers had a satisfactory discriminatory capacity in the AD group. IL-2 may have a better discriminatory capacity for identifying cognitive decline than Aβ and tau biomarkers in patients with aMCI.
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Affiliation(s)
- Chih-Sung Liang
- Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.,Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan
| | - Chia-Lin Tsai
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Guan-Yu Lin
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Jiunn-Tay Lee
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Yu-Kai Lin
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan.,Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Che-Sheng Chu
- Department of Psychiatry, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.,Center for Geriatric and Gerontology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Yueh-Feng Sung
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chia-Kuang Tsai
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Ta-Chuan Yeh
- Department of Psychiatry, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Hsuan-Te Chu
- Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Ming-Wei Su
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Fu-Chi Yang
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan.,Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
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19
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Predicting Alzheimer's disease progression using deep recurrent neural networks. Neuroimage 2020; 222:117203. [PMID: 32763427 PMCID: PMC7797176 DOI: 10.1016/j.neuroimage.2020.117203] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 07/22/2020] [Accepted: 07/23/2020] [Indexed: 01/12/2023] Open
Abstract
Early identification of individuals at risk of developing Alzheimer’s disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more timepoints, we seek to predict the clinical diagnosis, cognition and ventricular volume of the individual for every month (indefinitely) into the future. We proposed and applied a minimal recurrent neural network (minimalRNN) model to data from The Alzheimer’s Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants (Marinescu et al., 2018) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We compared the performance of the minimalRNN model and four baseline algorithms up to 6 years into the future. Most previous work on predicting AD progression ignore the issue of missing data, which is a prevalent issue in longitudinal data. Here, we explored three different strategies to handle missing data. Two of the strategies treated the missing data as a “preprocessing” issue, by imputing the missing data using the previous timepoint (“forward filling”) or linear interpolation (“linear filling). The third strategy utilized the minimalRNN model itself to fill in the missing data both during training and testing (“model filling”). Our analyses suggest that the minimalRNN with “model filling” compared favorably with baseline algorithms, including support vector machine/regression, linear state space (LSS) model, and long short-term memory (LSTM) model. Importantly, although the training procedure utilized longitudinal data, we found that the trained minimalRNN model exhibited similar performance, when using only 1 input timepoint or 4 input timepoints, suggesting that our approach might work well with just cross-sectional data. An earlier version of our approach was ranked 5th (out of 53 entries) in the TADPOLE challenge in 2019. The current approach is ranked 2nd out of 63 entries as of June 3rd, 2020.
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