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Zarkali A, Thomas GEC, Zetterberg H, Weil RS. Neuroimaging and fluid biomarkers in Parkinson's disease in an era of targeted interventions. Nat Commun 2024; 15:5661. [PMID: 38969680 PMCID: PMC11226684 DOI: 10.1038/s41467-024-49949-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 06/19/2024] [Indexed: 07/07/2024] Open
Abstract
A major challenge in Parkinson's disease is the variability in symptoms and rates of progression, underpinned by heterogeneity of pathological processes. Biomarkers are urgently needed for accurate diagnosis, patient stratification, monitoring disease progression and precise treatment. These were previously lacking, but recently, novel imaging and fluid biomarkers have been developed. Here, we consider new imaging approaches showing sensitivity to brain tissue composition, and examine novel fluid biomarkers showing specificity for pathological processes, including seed amplification assays and extracellular vesicles. We reflect on these biomarkers in the context of new biological staging systems, and on emerging techniques currently in development.
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Affiliation(s)
- Angeliki Zarkali
- Dementia Research Centre, Institute of Neurology, UCL, London, UK.
| | | | - Henrik Zetterberg
- 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
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Rimona S Weil
- Dementia Research Centre, Institute of Neurology, UCL, London, UK
- Department of Advanced Neuroimaging, UCL, London, UK
- Movement Disorders Centre, UCL, London, UK
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2
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Kumar S, Earnest T, Yang B, Kothapalli D, Aschenbrenner AJ, Hassenstab J, Xiong C, Ances B, Morris J, Benzinger TLS, Gordon BA, Payne P, Sotiras A. Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers. ARXIV 2024:arXiv:2404.05748v2. [PMID: 39010871 PMCID: PMC11247918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
INTRODUCTION Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers. METHODS We selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET. Normative modeling estimated individual-level abnormal deviations in amyloid-positive individuals compared to amyloid-negative controls. Regional abnormality patterns were mapped at different clinical group levels to assess intra-group heterogeneity. An individual-level disease severity index (DSI) was calculated using both the spatial extent and magnitude of abnormal deviations across ATN. RESULTS Greater intra-group heterogeneity in ATN abnormality patterns was observed in more severe clinical stages of AD. Higher DSI was associated with worse cognitive function and increased risk of disease progression. DISCUSSION Subject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain.
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Affiliation(s)
- Sayantan Kumar
- Department of Computer Science and Engineering, Washington University in St Louis; 1 Brookings Drive, Saint Louis, MO 63130
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St Louis; 660 S. Euclid Ave, Campus Box 8132, Saint Louis, MO 63110
| | - Tom Earnest
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110
| | - Braden Yang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110
| | - Deydeep Kothapalli
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110
| | - Andrew J. Aschenbrenner
- Department of Neurology, Washington University School of Medicine, 660 S Euclid Ave, Campus Box 8111, St louis, MO 63110
| | - Jason Hassenstab
- Department of Neurology, Washington University School of Medicine, 660 S Euclid Ave, Campus Box 8111, St louis, MO 63110
| | - Chengie Xiong
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St Louis; 660 S. Euclid Ave, Campus Box 8132, Saint Louis, MO 63110
| | - Beau Ances
- Department of Neurology, Washington University School of Medicine, 660 S Euclid Ave, Campus Box 8111, St louis, MO 63110
| | - John Morris
- Department of Neurology, Washington University School of Medicine, 660 S Euclid Ave, Campus Box 8111, St louis, MO 63110
| | - Tammie L. S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110
| | - Brian A. Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110
| | - Philip Payne
- Department of Computer Science and Engineering, Washington University in St Louis; 1 Brookings Drive, Saint Louis, MO 63130
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St Louis; 660 S. Euclid Ave, Campus Box 8132, Saint Louis, MO 63110
| | - Aristeidis Sotiras
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St Louis; 660 S. Euclid Ave, Campus Box 8132, Saint Louis, MO 63110
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis; 4525 Scott Ave, Saint Louis, MO 63110
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Lawn T, Giacomel A, Martins D, Veronese M, Howard M, Turkheimer FE, Dipasquale O. Normative modelling of molecular-based functional circuits captures clinical heterogeneity transdiagnostically in psychiatric patients. Commun Biol 2024; 7:689. [PMID: 38839931 PMCID: PMC11153627 DOI: 10.1038/s42003-024-06391-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 05/27/2024] [Indexed: 06/07/2024] Open
Abstract
Advanced methods such as REACT have allowed the integration of fMRI with the brain's receptor landscape, providing novel insights transcending the multiscale organisation of the brain. Similarly, normative modelling has allowed translational neuroscience to move beyond group-average differences and characterise deviations from health at an individual level. Here, we bring these methods together for the first time. We used REACT to create functional networks enriched with the main modulatory, inhibitory, and excitatory neurotransmitter systems and generated normative models of these networks to capture functional connectivity deviations in patients with schizophrenia, bipolar disorder (BPD), and ADHD. Substantial overlap was seen in symptomatology and deviations from normality across groups, but these could be mapped into a common space linking constellations of symptoms through to underlying neurobiology transdiagnostically. This work provides impetus for developing novel biomarkers that characterise molecular- and systems-level dysfunction at the individual level, facilitating the transition towards mechanistically targeted treatments.
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Affiliation(s)
- Timothy Lawn
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Alessio Giacomel
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Daniel Martins
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Mattia Veronese
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Information Engineering, University of Padua, Padua, Italy
| | - Matthew Howard
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Federico E Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- Department of Research & Development Advanced Applications, Olea Medical, La Ciotat, France.
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Romascano D, Rebsamen M, Radojewski P, Blattner T, McKinley R, Wiest R, Rummel C. Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods. Neuroimage Clin 2024; 43:103624. [PMID: 38823248 PMCID: PMC11168488 DOI: 10.1016/j.nicl.2024.103624] [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: 02/19/2024] [Revised: 05/21/2024] [Accepted: 05/25/2024] [Indexed: 06/03/2024]
Abstract
Over the past decades, morphometric analysis of brain MRI has contributed substantially to the understanding of healthy brain structure, development and aging as well as to improved characterisation of disease related pathologies. Certified commercial tools based on normative modeling of these metrics are meanwhile available for diagnostic purposes, but they are cost intensive and their clinical evaluation is still in its infancy. Here we have compared the performance of "ScanOMetrics", an open-source research-level tool for detection of statistical anomalies in individual MRI scans, depending on whether it is operated on the output of FreeSurfer or of the deep learning based brain morphometry tool DL + DiReCT. When applied to the public OASIS3 dataset, containing patients with Alzheimer's disease (AD) and healthy controls (HC), cortical thickness anomalies in patient scans were mainly detected in regions that are known as predilection areas of cortical atrophy in AD, regardless of the software used for extraction of the metrics. By contrast, anomaly detections in HCs were up to twenty-fold reduced and spatially unspecific using both DL + DiReCT and FreeSurfer. Progression of the atrophy pattern with clinical dementia rating (CDR) was clearly observable with both methods. DL + DiReCT provided results in less than 25 min, more than 15 times faster than FreeSurfer. This difference in computation time might be relevant when considering application of this or similar methodology as diagnostic decision support for neuroradiologists.
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Affiliation(s)
- David Romascano
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland; Danish Research Center for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
| | - Michael Rebsamen
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, sitem-insel, Bern, Switzerland
| | - Timo Blattner
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland
| | - Richard McKinley
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, sitem-insel, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland; European Campus Rottal-Inn, Technische Hochschule Deggendorf, Max-Breiherr-Straße 32, D-84347 Pfarrkirchen, Germany.
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Bhome R, Verdi S, Martin SA, Hannaway N, Dobreva I, Oxtoby NP, Castro Leal G, Rutherford S, Marquand AF, Weil RS, Cole JH. A neuroimaging measure to capture heterogeneous patterns of atrophy in Parkinson's disease and dementia with Lewy bodies. Neuroimage Clin 2024; 42:103596. [PMID: 38554485 PMCID: PMC10995913 DOI: 10.1016/j.nicl.2024.103596] [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: 11/09/2023] [Revised: 02/27/2024] [Accepted: 03/19/2024] [Indexed: 04/01/2024]
Abstract
INTRODUCTION Parkinson's disease (PD) and Dementia with Lewy bodies (DLB) show heterogeneous brain atrophy patterns which group-average analyses fail to capture. Neuroanatomical normative modelling overcomes this by comparing individuals to a large reference cohort. Patient-specific atrophy patterns are measured objectively and summarised to index overall neurodegeneration (the 'total outlier count'). We aimed to quantify patterns of neurodegenerative dissimilarity in participants with PD and DLB and evaluate the potential clinical relevance of total outlier count by testing its association with key clinical measures in PD and DLB. MATERIALS AND METHODS We included 108 participants with PD and 61 with DLB. PD participants were subclassified into high and low visual performers as this has previously been shown to stratify those at increased dementia risk. We generated z-scores from T1w-MRI scans for each participant relative to normative regional cortical thickness and subcortical volumes, modelled in a reference cohort (n = 58,836). Outliers (z < -1.96) were aggregated across 169 brain regions per participant. To measure dissimilarity, individuals' Hamming distance scores were calculated. We also examined total outlier counts between high versus low visual performance in PD; and PD versus DLB; and tested associations between these and cognition. RESULTS There was significantly greater inter-individual dissimilarity in brain-outlier patterns in PD poor compared to high visual performers (W = 522.5; p < 0.01) and in DLB compared to PD (W = 5649; p < 0.01). PD poor visual performers had significantly greater total outlier counts compared to high (β = -4.73 (SE = 1.30); t = -3.64; p < 0.01) whereas a conventional group-level GLM failed to identify differences. Higher total outlier counts were associated with poorer MoCA (β = -0.55 (SE = 0.27), t = -2.04, p = 0.05) and composite cognitive scores (β = -2.01 (SE = 0.79); t = -2.54; p = 0.02) in DLB, and visuoperception (β = -0.67 (SE = 0.19); t = -3.59; p < 0.01), in PD. CONCLUSIONS Neuroanatomical normative modelling shows promise as a clinically informative technique in PD and DLB, where patterns of atrophy are variable.
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Affiliation(s)
- R Bhome
- Dementia Research Centre, University College London, 8-11 Queen Square, London WC1N 3AR, United Kingdom; UCL Centre for Medical Image Computing, Department of Computer Science, University College London, 90 High Holborn, London WC1V 6LJ, United Kingdom.
| | - S Verdi
- Dementia Research Centre, University College London, 8-11 Queen Square, London WC1N 3AR, United Kingdom; UCL Centre for Medical Image Computing, Department of Computer Science, University College London, 90 High Holborn, London WC1V 6LJ, United Kingdom
| | - S A Martin
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, 90 High Holborn, London WC1V 6LJ, United Kingdom
| | - N Hannaway
- Dementia Research Centre, University College London, 8-11 Queen Square, London WC1N 3AR, United Kingdom
| | - I Dobreva
- Dementia Research Centre, University College London, 8-11 Queen Square, London WC1N 3AR, United Kingdom
| | - N P Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, 90 High Holborn, London WC1V 6LJ, United Kingdom
| | - G Castro Leal
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, 90 High Holborn, London WC1V 6LJ, United Kingdom
| | - S Rutherford
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Thomas van Aquinostraat 4, 6525 GD Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, the Netherlands; Department of Psychiatry, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI 48109, USA
| | - A F Marquand
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Thomas van Aquinostraat 4, 6525 GD Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Radboud University Medical Center, Kapittelweg 29, 6525 EN Nijmegen, the Netherlands
| | - R S Weil
- Dementia Research Centre, University College London, 8-11 Queen Square, London WC1N 3AR, United Kingdom; Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N 3AR, United Kingdom; Movement Disorders Consortium, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, United Kingdom
| | - J H Cole
- Dementia Research Centre, University College London, 8-11 Queen Square, London WC1N 3AR, United Kingdom; UCL Centre for Medical Image Computing, Department of Computer Science, University College London, 90 High Holborn, London WC1V 6LJ, United Kingdom
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Lin X, Huo Y, Wang Q, Liu G, Shi J, Fan Y, Lu L, Jing R, Li P. Using normative modeling to assess pharmacological treatment effect on brain state in patients with schizophrenia. Cereb Cortex 2024; 34:bhae003. [PMID: 38252996 DOI: 10.1093/cercor/bhae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/28/2023] [Accepted: 12/30/2023] [Indexed: 01/24/2024] Open
Abstract
Quantifying individual differences in neuroimaging metrics is attracting interest in clinical studies with mental disorders. Schizophrenia is diagnosed exclusively based on symptoms, and the biological heterogeneity makes it difficult to accurately assess pharmacological treatment effects on the brain state. Using the Cambridge Centre for Ageing and Neuroscience data set, we built normative models of brain states and mapped the deviations of the brain characteristics of each patient, to test whether deviations were related to symptoms, and further investigated the pharmacological treatment effect on deviation distributions. Specifically, we found that the patients can be divided into 2 groups: the normalized group had a normalization trend and milder symptoms at baseline, and the other group showed a more severe deviation trend. The baseline severity of the depression as well as the overall symptoms could predict the deviation of the static characteristics for the dorsal and ventral attention networks after treatment. In contrast, the positive symptoms could predict the deviations of the dynamic fluctuations for the default mode and dorsal attention networks after treatment. This work evaluates the effect of pharmacological treatment on static and dynamic brain states using an individualized approach, which may assist in understanding the heterogeneity of the illness pathology as well as the treatment response.
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Affiliation(s)
- Xiao Lin
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), Peking University, Beijing 100191, China
| | - Yanxi Huo
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China
| | - Qiandong Wang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Guozhong Liu
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China
| | - Jie Shi
- National Institute on Drug Dependence and Beijing Key Laboratory on Drug Dependence Research, Peking University, Beijing 100191, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, United States
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), Peking University, Beijing 100191, China
| | - Rixing Jing
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 100192, China
| | - Peng Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), Peking University, Beijing 100191, China
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Longobardi A, Catania M, Geviti A, Salvi E, Vecchi ER, Bellini S, Saraceno C, Nicsanu R, Squitti R, Binetti G, Di Fede G, Ghidoni R. Autophagy Markers Are Altered in Alzheimer's Disease, Dementia with Lewy Bodies and Frontotemporal Dementia. Int J Mol Sci 2024; 25:1125. [PMID: 38256197 PMCID: PMC10816165 DOI: 10.3390/ijms25021125] [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: 12/12/2023] [Revised: 01/12/2024] [Accepted: 01/15/2024] [Indexed: 01/24/2024] Open
Abstract
The accumulation of protein aggregates defines distinct, yet overlapping pathologies such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD). In this study, we investigated ATG5, UBQLN2, ULK1, and LC3 concentrations in 66 brain specimens and 120 plasma samples from AD, DLB, FTD, and control subjects (CTRL). Protein concentration was measured with ELISA kits in temporal, frontal, and occipital cortex specimens of 32 AD, 10 DLB, 10 FTD, and 14 CTRL, and in plasma samples of 30 AD, 30 DLB, 30 FTD, and 30 CTRL. We found alterations in ATG5, UBQLN2, ULK1, and LC3 levels in patients; ATG5 and UBQLN2 levels were decreased in both brain specimens and plasma samples of patients compared to those of the CTRL, while LC3 levels were increased in the frontal cortex of DLB and FTD patients. In this study, we demonstrate alterations in different steps related to ATG5, UBQLN2, and LC3 autophagy pathways in DLB and FTD patients. Molecular alterations in the autophagic processes could play a role in a shared pathway involved in the pathogenesis of neurodegeneration, supporting the hypothesis of a common molecular mechanism underlying major neurodegenerative dementias and suggesting different potential therapeutic targets in the autophagy pathway for these disorders.
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Affiliation(s)
- Antonio Longobardi
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125 Brescia, Italy; (S.B.); (C.S.); (R.N.); (R.S.); (R.G.)
| | - Marcella Catania
- Neurology 5/Neuropathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (M.C.); (E.R.V.); (G.D.F.)
| | - Andrea Geviti
- Service of Statistics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125 Brescia, Italy;
| | - Erika Salvi
- Neuroalgology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy;
- Data Science Center, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy
| | - Elena Rita Vecchi
- Neurology 5/Neuropathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (M.C.); (E.R.V.); (G.D.F.)
| | - Sonia Bellini
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125 Brescia, Italy; (S.B.); (C.S.); (R.N.); (R.S.); (R.G.)
| | - Claudia Saraceno
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125 Brescia, Italy; (S.B.); (C.S.); (R.N.); (R.S.); (R.G.)
| | - Roland Nicsanu
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125 Brescia, Italy; (S.B.); (C.S.); (R.N.); (R.S.); (R.G.)
| | - Rosanna Squitti
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125 Brescia, Italy; (S.B.); (C.S.); (R.N.); (R.S.); (R.G.)
- Dipartimento di Scienze di Laboratorio, Ospedale Isola Tiberina-Gemelli Isola, 00186 Rome, Italy
| | - Giuliano Binetti
- MAC-Memory Clinic and Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125 Brescia, Italy;
| | - Giuseppe Di Fede
- Neurology 5/Neuropathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (M.C.); (E.R.V.); (G.D.F.)
| | - Roberta Ghidoni
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, 25125 Brescia, Italy; (S.B.); (C.S.); (R.N.); (R.S.); (R.G.)
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Loreto F, Verdi S, Kia SM, Duvnjak A, Hakeem H, Fitzgerald A, Patel N, Lilja J, Win Z, Perry R, Marquand AF, Cole JH, Malhotra P. Alzheimer's disease heterogeneity revealed by neuroanatomical normative modeling. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12559. [PMID: 38487076 PMCID: PMC10937817 DOI: 10.1002/dad2.12559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 10/11/2023] [Accepted: 01/30/2024] [Indexed: 03/17/2024]
Abstract
INTRODUCTION Overlooking the heterogeneity in Alzheimer's disease (AD) may lead to diagnostic delays and failures. Neuroanatomical normative modeling captures individual brain variation and may inform our understanding of individual differences in AD-related atrophy. METHODS We applied neuroanatomical normative modeling to magnetic resonance imaging from a real-world clinical cohort with confirmed AD (n = 86). Regional cortical thickness was compared to a healthy reference cohort (n = 33,072) and the number of outlying regions was summed (total outlier count) and mapped at individual- and group-levels. RESULTS The superior temporal sulcus contained the highest proportion of outliers (60%). Elsewhere, overlap between patient atrophy patterns was low. Mean total outlier count was higher in patients who were non-amnestic, at more advanced disease stages, and without depressive symptoms. Amyloid burden was negatively associated with outlier count. DISCUSSION Brain atrophy in AD is highly heterogeneous and neuroanatomical normative modeling can be used to explore anatomo-clinical correlations in individual patients.
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Affiliation(s)
- Flavia Loreto
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
| | - Serena Verdi
- Centre for Medical Image ComputingMedical Physics and Biomedical EngineeringUniversity College LondonLondonUK
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Seyed Mostafa Kia
- Donders Centre for Cognitive NeuroimagingDonders Institute for BrainCognition and BehaviourRadboud UniversityNijmegenThe Netherlands
- Department of Cognitive NeuroscienceRadboud University Medical CentreNijmegenThe Netherlands
- Department of PsychiatryUtrecht University Medical CenterUtrechtThe Netherlands
| | - Aleksandar Duvnjak
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
| | - Haneen Hakeem
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
| | - Anna Fitzgerald
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
| | - Neva Patel
- Department of Nuclear MedicineImperial College Healthcare NHS TrustLondonUK
| | | | - Zarni Win
- Department of Nuclear MedicineImperial College Healthcare NHS TrustLondonUK
| | - Richard Perry
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
- Department of NeurologyImperial College Healthcare NHS TrustLondonUK
| | - Andre F. Marquand
- Donders Centre for Cognitive NeuroimagingDonders Institute for BrainCognition and BehaviourRadboud UniversityNijmegenThe Netherlands
- Department of Cognitive NeuroscienceRadboud University Medical CentreNijmegenThe Netherlands
| | - James H. Cole
- Centre for Medical Image ComputingMedical Physics and Biomedical EngineeringUniversity College LondonLondonUK
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Paresh Malhotra
- Department of Brain SciencesFaculty of MedicineImperial College LondonLondonUK
- Department of NeurologyImperial College Healthcare NHS TrustLondonUK
- UK Dementia Research Institute Care Research and Technology CentreImperial College London and the University of SurreyLondonUK
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Barkema P, Rutherford S, Lee HC, Kia SM, Savage H, Beckmann C, Marquand A. Predictive Clinical Neuroscience Portal (PCNportal): instant online access to research-grade normative models for clinical neuroscientists. Wellcome Open Res 2023; 8:326. [PMID: 37663797 PMCID: PMC10474337 DOI: 10.12688/wellcomeopenres.19591.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2023] [Indexed: 09/05/2023] Open
Abstract
Background The neurobiology of mental disorders remains poorly understood despite substantial scientific efforts, due to large clinical heterogeneity and to a lack of tools suitable to map individual variability. Normative modeling is one recently successful framework that can address these problems by comparing individuals to a reference population. The methodological underpinnings of normative modelling are, however, relatively complex and computationally expensive. Our research group has developed the python-based normative modelling package Predictive Clinical Neuroscience toolkit (PCNtoolkit) which provides access to many validated algorithms for normative modelling. PCNtoolkit has since proven to be a strong foundation for large scale normative modelling, but still requires significant computation power, time and technical expertise to develop. Methods To address these problems, we introduce PCNportal. PCNportal is an online platform integrated with PCNtoolkit that offers access to pre-trained research-grade normative models estimated on tens of thousands of participants, without the need for computation power or programming abilities. PCNportal is an easy-to-use web interface that is highly scalable to large user bases as necessary. Finally, we demonstrate how the resulting normalized deviation scores can be used in a clinical application through a schizophrenia classification task applied to cortical thickness and volumetric data from the longitudinal Northwestern University Schizophrenia Data and Software Tool (NUSDAST) dataset. Results At each longitudinal timepoint, the transferred normative models achieved a mean[std. dev.] explained variance of 9.4[8.8]%, 9.2[9.2]%, 5.6[7.4]% respectively in the control group and 4.7[5.5]%, 6.0[6.2]%, 4.2[6.9]% in the schizophrenia group. Diagnostic classifiers achieved AUC of 0.78, 0.76 and 0.71 respectively. Conclusions This replicates the utility of normative models for diagnostic classification of schizophrenia and showcases the use of PCNportal for clinical neuroimaging. By facilitating and speeding up research with high-quality normative models, this work contributes to research in inter-individual variability, clinical heterogeneity and precision medicine.
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Affiliation(s)
- Pieter Barkema
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Saige Rutherford
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
- Department of Psychiatry, University of Michigan, Ann Arbor, USA
| | - Hurng-Chun Lee
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Seyed Mostafa Kia
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
- Department of Psychiatry, Utrecht University Medical Center, Utrecht, The Netherlands
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, The Netherlands
| | - Hannah Savage
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Christian Beckmann
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
- Centre for functional MRI of the Brain, University of Oxford, Oxford, England, UK
| | - Andre Marquand
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
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Verdi S, Rutherford S, Fraza C, Tosun D, Altmann A, Raket LL, Schott JM, Marquand AF, Cole JH. Personalising Alzheimer's Disease progression using brain atrophy markers. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.15.23291418. [PMID: 37398392 PMCID: PMC10312850 DOI: 10.1101/2023.06.15.23291418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
INTRODUCTION Neuroanatomical normative modelling can capture individual variability in Alzheimer's Disease (AD). We used neuroanatomical normative modelling to track individuals' disease progression in people with mild cognitive impairment (MCI) and patients with AD. METHODS Cortical thickness and subcortical volume neuroanatomical normative models were generated using healthy controls (n~58k). These models were used to calculate regional Z-scores in 4361 T1-weighted MRI time-series scans. Regions with Z-scores <-1.96 were classified as outliers and mapped on the brain, and also summarised by total outlier count (tOC). RESULTS Rate of change in tOC increased in AD and in people with MCI who converted to AD and correlated with multiple non-imaging markers. Moreover, a higher annual rate of change in tOC increased the risk of MCI progression to AD. Brain Z-score maps showed that the hippocampus had the highest rate of atrophy change. CONCLUSIONS Individual-level atrophy rates can be tracked by using regional outlier maps and tOC.
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Affiliation(s)
- Serena Verdi
- Centre for Medical Image Computing, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Saige Rutherford
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, 6525EN, the Netherlands
| | - Charlotte Fraza
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, 6525EN, the Netherlands
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Andre Altmann
- Centre for Medical Image Computing, University College London, London, UK
| | - Lars Lau Raket
- Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, 6525EN, the Netherlands
| | - James H Cole
- Centre for Medical Image Computing, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
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