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Mehdipour Ghazi M, Selnes P, Timón-Reina S, Tecelão S, Ingala S, Bjørnerud A, Kirsebom BE, Fladby T, Nielsen M. Comparative analysis of multimodal biomarkers for amyloid-beta positivity detection in Alzheimer's disease cohorts. Front Aging Neurosci 2024; 16:1345417. [PMID: 38469163 PMCID: PMC10925621 DOI: 10.3389/fnagi.2024.1345417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 02/12/2024] [Indexed: 03/13/2024] Open
Abstract
Introduction Efforts to develop cost-effective approaches for detecting amyloid pathology in Alzheimer's disease (AD) have gained significant momentum with a focus on biomarker classification. Recent research has explored non-invasive and readily accessible biomarkers, including magnetic resonance imaging (MRI) biomarkers and some AD risk factors. Methods In this comprehensive study, we leveraged a diverse dataset, encompassing participants with varying cognitive statuses from multiple sources, including cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and our in-house Dementia Disease Initiation (DDI) cohort. As brain amyloid plaques have been proposed as sufficient for AD diagnosis, our primary aim was to assess the effectiveness of multimodal biomarkers in identifying amyloid plaques, using deep machine learning methodologies. Results Our findings underscore the robustness of the utilized methods in detecting amyloid beta positivity across multiple cohorts. Additionally, we investigated the potential of demographic data to enhance MRI-based amyloid detection. Notably, the inclusion of demographic risk factors significantly improved our models' ability to detect amyloid-beta positivity, particularly in early-stage cases, exemplified by an average area under the ROC curve of 0.836 in the unimpaired DDI cohort. Discussion These promising, non-invasive, and cost-effective predictors of MRI biomarkers and demographic variables hold the potential for further refinement through considerations like APOE genotype and plasma markers.
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Affiliation(s)
- Mostafa Mehdipour Ghazi
- Department of Computer Science, Pioneer Centre for Artificial Intelligence, University of Copenhagen, Copenhagen, Denmark
| | - Per Selnes
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
| | | | - Sandra Tecelão
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | - Silvia Ingala
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Atle Bjørnerud
- Department of Physics, University of Oslo, Oslo, Norway
- Unit for Computational Radiology and Artificial Intelligence, Oslo University Hospital, Oslo, Norway
| | - Bjørn-Eivind Kirsebom
- Department of Neurology, University Hospital of North Norway, Tromsø, Norway
- Department of Psychology, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway
| | - Tormod Fladby
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
| | - Mads Nielsen
- Department of Computer Science, Pioneer Centre for Artificial Intelligence, University of Copenhagen, Copenhagen, Denmark
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Bakhtiari A, Petersen J, Urdanibia-Centelles O, Ghazi MM, Fagerlund B, Mortensen EL, Osler M, Lauritzen M, Benedek K. Power and distribution of evoked gamma oscillations in brain aging and cognitive performance. GeroScience 2023:10.1007/s11357-023-00749-x. [PMID: 36763241 PMCID: PMC10400513 DOI: 10.1007/s11357-023-00749-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 01/31/2023] [Indexed: 02/11/2023] Open
Abstract
AIMS Gamma oscillations (≈25-100 Hz) are believed to play an essential role in cognition, and aberrant gamma oscillations occur in brain aging and neurodegeneration. This study examined age-related changes in visually evoked gamma oscillations at two different time points 5 years apart and tested the hypothesis that the power of gamma oscillations correlated to cognitive skills. METHODS The cohort consists of elderly males belonging to the Metropolit 1953 Danish Male Birth Cohort (first visit, N=124; second visit, N=88) over a 5-year period from 63 to 68 years of age. Cognitive functions were assessed using a neuropsychological test battery measuring global cognition, intelligence, memory, and processing speed. The power of steady-state visual evoked potentials (SSVEP) was measured at 8 Hz (alpha) and 36 Hz (gamma) frequencies using EEG scalp electrodes. RESULTS Over the 5-year period cognitive performance remained relatively stable while the power of visually evoked gamma oscillations shifted from posterior to anterior brain regions with increasing age. A higher-than-average cognitive score was correlated with higher gamma power in parieto-occipital areas and lower in frontocentral areas, i.e., preserved distribution of the evoked activity. CONCLUSIONS Our data reveal that the distribution of visually evoked gamma activity becomes distributed with age. Preserved posterior-occipital gamma power in participants with a high level of cognitive performance is consistent with a close association between the ability to produce gamma oscillations and cognition. The data may contribute to our understanding of the mechanisms that link evoked gamma activity and cognition in the aging brain.
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Affiliation(s)
- Aftab Bakhtiari
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. .,Center for Healthy Aging, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. .,Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Jacob Petersen
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Olalla Urdanibia-Centelles
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Mostafa Mehdipour Ghazi
- Pioneer Centre for Artificial Intelligence, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Birgitte Fagerlund
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark.,Child and Adolescent Mental Health Center, Copenhagen University Hospital - Mental Health Services CPH, Copenhagen, Denmark
| | | | - Merete Osler
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark.,Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Martin Lauritzen
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.,Center for Healthy Aging, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Krisztina Benedek
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
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Jimenez-Solem E, Petersen TS, Hansen C, Hansen C, Lioma C, Igel C, Boomsma W, Krause O, Lorenzen S, Selvan R, Petersen J, Nyeland ME, Ankarfeldt MZ, Virenfeldt GM, Winther-Jensen M, Linneberg A, Ghazi MM, Detlefsen N, Lauritzen AD, Smith AG, de Bruijne M, Ibragimov B, Petersen J, Lillholm M, Middleton J, Mogensen SH, Thorsen-Meyer HC, Perner A, Helleberg M, Kaas-Hansen BS, Bonde M, Bonde A, Pai A, Nielsen M, Sillesen M. Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients. Sci Rep 2021; 11:3246. [PMID: 33547335 PMCID: PMC7864944 DOI: 10.1038/s41598-021-81844-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 01/12/2021] [Indexed: 12/15/2022] Open
Abstract
Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics—Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.
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Affiliation(s)
- Espen Jimenez-Solem
- Department of Clinical Pharmacology, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Copenhagen Phase IV Unit (Phase4CPH), Department of Clinical Pharmacology and Center for Clinical Research and Prevention, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Tonny S Petersen
- Department of Clinical Pharmacology, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Casper Hansen
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Christian Hansen
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Christina Lioma
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Christian Igel
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Wouter Boomsma
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Oswin Krause
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Stephan Lorenzen
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Raghavendra Selvan
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Janne Petersen
- Center for Clinical Research and Prevention, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Copenhagen, Denmark.,Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.,Copenhagen Phase IV Unit (Phase4CPH), Department of Clinical Pharmacology and Center for Clinical Research and Prevention, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Martin Erik Nyeland
- Department of Clinical Pharmacology, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Mikkel Zöllner Ankarfeldt
- Center for Clinical Research and Prevention, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Copenhagen, Denmark.,Copenhagen Phase IV Unit (Phase4CPH), Department of Clinical Pharmacology and Center for Clinical Research and Prevention, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Gert Mehl Virenfeldt
- Center for Clinical Research and Prevention, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Matilde Winther-Jensen
- Center for Clinical Research and Prevention, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | | | - Nicki Detlefsen
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.,DTU Compute, Denmarks Technical University, Lyngby, Denmark
| | | | | | - Marleen de Bruijne
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.,Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Bulat Ibragimov
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Jens Petersen
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Martin Lillholm
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Jon Middleton
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Anders Perner
- Department of Intensive Care Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Marie Helleberg
- Department of Infectious Diseases, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | - Mikkel Bonde
- Center for Surgical Translational and Artificial Intelligence Research (CSTAR), Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Alexander Bonde
- Department of Surgical Gastroenterology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen Ø, Denmark.,Center for Surgical Translational and Artificial Intelligence Research (CSTAR), Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Akshay Pai
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.,Cerebriu A/S, Copenhagen, Denmark
| | - Mads Nielsen
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Martin Sillesen
- Department of Surgical Gastroenterology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen Ø, Denmark. .,Center for Surgical Translational and Artificial Intelligence Research (CSTAR), Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark. .,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
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Mehdipour Ghazi M, Nielsen M, Pai A, Modat M, Jorge Cardoso M, Ourselin S, Sørensen L. Robust parametric modeling of Alzheimer's disease progression. Neuroimage 2021; 225:117460. [PMID: 33075562 PMCID: PMC9068750 DOI: 10.1016/j.neuroimage.2020.117460] [Citation(s) in RCA: 3] [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: 06/20/2020] [Revised: 10/11/2020] [Accepted: 10/12/2020] [Indexed: 11/30/2022] Open
Abstract
Quantitative characterization of disease progression using longitudinal data can provide long-term predictions for the pathological stages of individuals. This work studies the robust modeling of Alzheimer's disease progression using parametric methods. The proposed method linearly maps the individual's age to a disease progression score (DPS) and jointly fits constrained generalized logistic functions to the longitudinal dynamics of biomarkers as functions of the DPS using M-estimation. Robustness of the estimates is quantified using bootstrapping via Monte Carlo resampling, and the estimated inflection points of the fitted functions are used to temporally order the modeled biomarkers in the disease course. Kernel density estimation is applied to the obtained DPSs for clinical status classification using a Bayesian classifier. Different M-estimators and logistic functions, including a novel type proposed in this study, called modified Stannard, are evaluated on the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for robust modeling of volumetric magnetic resonance imaging (MRI) and positron emission tomography (PET) biomarkers, cerebrospinal fluid (CSF) measurements, as well as cognitive tests. The results show that the modified Stannard function fitted using the logistic loss achieves the best modeling performance with an average normalized mean absolute error (NMAE) of 0.991 across all biomarkers and bootstraps. Applied to the ADNI test set, this model achieves a multiclass area under the ROC curve (AUC) of 0.934 in clinical status classification. The obtained results for the proposed model outperform almost all state-of-the-art results in predicting biomarker values and classifying clinical status. Finally, the experiments show that the proposed model, trained using abundant ADNI data, generalizes well to data from the National Alzheimer's Coordinating Center (NACC) with an average NMAE of 1.182 and a multiclass AUC of 0.929.
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Affiliation(s)
- Mostafa Mehdipour Ghazi
- Biomediq A/S, Copenhagen, DK; Cerebriu A/S, Copenhagen, DK; Department of Computer Science, University of Copenhagen, Copenhagen, DK; Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
| | - Mads Nielsen
- Biomediq A/S, Copenhagen, DK; Cerebriu A/S, Copenhagen, DK; Department of Computer Science, University of Copenhagen, Copenhagen, DK
| | - Akshay Pai
- Biomediq A/S, Copenhagen, DK; Cerebriu A/S, Copenhagen, DK; Department of Computer Science, University of Copenhagen, Copenhagen, DK
| | - Marc Modat
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - M Jorge Cardoso
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sébastien Ourselin
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Lauge Sørensen
- Biomediq A/S, Copenhagen, DK; Cerebriu A/S, Copenhagen, DK; Department of Computer Science, University of Copenhagen, Copenhagen, DK
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