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Liu YS, Baxi M, Madan CR, Zhan K, Makris N, Rosene DL, Killiany RJ, Cetin-Karayumak S, Pasternak O, Kubicki M, Cao B. Brain age of rhesus macaques over the lifespan. Neurobiol Aging 2024; 139:73-81. [PMID: 38643691 DOI: 10.1016/j.neurobiolaging.2024.02.014] [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: 09/11/2023] [Revised: 02/27/2024] [Accepted: 02/28/2024] [Indexed: 04/23/2024]
Abstract
Through the application of machine learning algorithms to neuroimaging data the brain age methodology was shown to provide a useful individual-level biological age prediction and identify key brain regions responsible for the prediction. In this study, we present the methodology of constructing a rhesus macaque brain age model using a machine learning algorithm and discuss the key predictive brain regions in comparison to the human brain, to shed light on cross-species primate similarities and differences. Structural information of the brain (e.g., parcellated volumes) from brain magnetic resonance imaging of 43 rhesus macaques were used to develop brain atlas-based features to build a brain age model that predicts biological age. The best-performing model used 22 selected features and achieved an R2 of 0.72. We also identified interpretable predictive brain features including Right Fronto-orbital Cortex, Right Frontal Pole, Right Inferior Lateral Parietal Cortex, and Bilateral Posterior Central Operculum. Our findings provide converging evidence of the parallel and comparable brain regions responsible for both non-human primates and human biological age prediction.
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Affiliation(s)
- Yang S Liu
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
| | - Madhura Baxi
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Kevin Zhan
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
| | - Nikolaos Makris
- Department of Psychiatry, Center for Morphometric Analysis, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Douglas L Rosene
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Ronald J Killiany
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marek Kubicki
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Center for Morphometric Analysis, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Bo Cao
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada; Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
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2
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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] [MESH Headings] [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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3
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Young AL, Oxtoby NP, Garbarino S, Fox NC, Barkhof F, Schott JM, Alexander DC. Data-driven modelling of neurodegenerative disease progression: thinking outside the black box. Nat Rev Neurosci 2024; 25:111-130. [PMID: 38191721 DOI: 10.1038/s41583-023-00779-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/30/2023] [Indexed: 01/10/2024]
Abstract
Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to 'black box' machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.
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Affiliation(s)
- Alexandra L Young
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Neil P Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
| | - Sara Garbarino
- Life Science Computational Laboratory, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Frederik Barkhof
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Daniel C Alexander
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
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4
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Wang J, Lu J, He M, Song Z, Dong L, Tang H, Wang Y, Zhou Z. Linear brain measurement: a new screening method for cognitive impairment in elderly patients with cerebral small vessel disease. Front Neurol 2024; 15:1297076. [PMID: 38318441 PMCID: PMC10840835 DOI: 10.3389/fneur.2024.1297076] [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: 09/19/2023] [Accepted: 01/04/2024] [Indexed: 02/07/2024] Open
Abstract
Background The old adults have high incidence of cognitive impairment, especially in patients with cerebral small vessel disease (CSVD). Cognitive impairment is not easy to be detected in such populations. We aimed to develop clinical prediction models for different degrees of cognitive impairments in elderly CSVD patients based on conventional imaging and clinical data to determine the better indicators for assessing cognitive function in the CSVD elderly. Methods 210 CSVD patients were screened out by the evaluation of Magnetic Resonance Imaging (MRI). Then, participants were divided into the following three groups according to the cognitive assessment results: control, mild cognitive impairment (MCI), and dementia groups. Clinical data were collected from all patients, including demographic data, biochemical indicators, carotid ultrasound, transcranial Doppler (TCD) indicators, and linear measurement parameters based on MRI. Results Our results showed that the brain atrophy and vascular lesions developed progressive worsening with increased degree of cognitive impairment. Crouse score and Interuncal distance/Bitemporal distance (IUD/BTD) were independent risk factors for MCI in CSVD patients, and independent risk factors for dementia in CSVD were Crouse Score, the pulsatility index of the middle cerebral artery (MCAPI), IUD/BTD, and Sylvian fissure ratio (SFR). Overall, the parameters with high performance were the IUD/BTD (OR 2.28; 95% CI 1.26-4.10) and SFR (OR 3.28; 95% CI 1.54-6.91), and the AUC (area under the curve) in distinguishing between CSVD older adults with MCI and with dementia was 0.675 and 0.724, respectively. Linear brain measurement parameters had larger observed effect than other indexes to identify cognitive impairments in CSVD patients. Conclusion This study shows that IUD/BTD and SFR are good predictors of cognitive impairments in CSVD elderly. Linear brain measurement showed a good predictive power for identifying MCI and dementia in elderly subjects with CSVD. Linear brain measurement could be a more suitable and novel method for screening cognitive impairment in aged CSVD patients in primary healthcare facilities, and worth further promotion among the rural population.
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Affiliation(s)
- Jing Wang
- Department of Geratology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jinhua Lu
- Department of Geratology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Mingqing He
- Department of Geratology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Ziyang Song
- Department of Radiology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, China
| | - Lingyan Dong
- Department of Geratology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Haiying Tang
- Department of Geratology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yueju Wang
- Department of Geratology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Zheping Zhou
- Department of Geratology, Affiliated Changshu Hospital of Nantong University, Changshu, China
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5
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Banaj N, Vecchio D, Piras F, De Rossi P, Bustillo J, Ciufolini S, Dazzan P, Di Forti M, Dickie EW, Ford JM, Fuentes-Claramonte P, Gruber O, Guerrero-Pedraza A, Hamilton HK, Howells FM, Kraemer B, Lawrie SM, Mathalon DH, Murray R, Pomarol-Clotet E, Potkin SG, Preda A, Radua J, Richter A, Salvador R, Sawa A, Scheffler F, Sim K, Spaniel F, Stein DJ, Temmingh HS, Thomopoulos SI, Tomecek D, Uhlmann A, Voineskos A, Yang K, Jahanshad N, Thompson PM, Van Erp TGM, Turner JA, Spalletta G, Piras F. Cortical morphology in patients with the deficit and non-deficit syndrome of schizophrenia: a worldwide meta- and mega-analyses. Mol Psychiatry 2023; 28:4363-4373. [PMID: 37644174 PMCID: PMC10827665 DOI: 10.1038/s41380-023-02221-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 08/02/2023] [Accepted: 08/07/2023] [Indexed: 08/31/2023]
Abstract
Converging evidence suggests that schizophrenia (SZ) with primary, enduring negative symptoms (i.e., Deficit SZ (DSZ)) represents a distinct entity within the SZ spectrum while the neurobiological underpinnings remain undetermined. In the largest dataset of DSZ and Non-Deficit (NDSZ), we conducted a meta-analysis of data from 1560 individuals (168 DSZ, 373 NDSZ, 1019 Healthy Controls (HC)) and a mega-analysis of a subsampled data from 944 individuals (115 DSZ, 254 NDSZ, 575 HC) collected across 9 worldwide research centers of the ENIGMA SZ Working Group (8 in the mega-analysis), to clarify whether they differ in terms of cortical morphology. In the meta-analysis, sites computed effect sizes for differences in cortical thickness and surface area between SZ and control groups using a harmonized pipeline. In the mega-analysis, cortical values of individuals with schizophrenia and control participants were analyzed across sites using mixed-model ANCOVAs. The meta-analysis of cortical thickness showed a converging pattern of widespread thinner cortex in fronto-parietal regions of the left hemisphere in both DSZ and NDSZ, when compared to HC. However, DSZ have more pronounced thickness abnormalities than NDSZ, mostly involving the right fronto-parietal cortices. As for surface area, NDSZ showed differences in fronto-parietal-temporo-occipital cortices as compared to HC, and in temporo-occipital cortices as compared to DSZ. Although DSZ and NDSZ show widespread overlapping regions of thinner cortex as compared to HC, cortical thinning seems to better typify DSZ, being more extensive and bilateral, while surface area alterations are more evident in NDSZ. Our findings demonstrate for the first time that DSZ and NDSZ are characterized by different neuroimaging phenotypes, supporting a nosological distinction between DSZ and NDSZ and point toward the separate disease hypothesis.
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Affiliation(s)
- Nerisa Banaj
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy.
| | - Daniela Vecchio
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Fabrizio Piras
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Pietro De Rossi
- Child and Adolescence Neuropsychiatry Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Juan Bustillo
- Psichiatry and Neuroscience, University of New Mexico, Albuquerque, NM, USA
| | - Simone Ciufolini
- Psychosis Studies, Institute of Psychiatry, Psychology and Neurology, King's College London, London, UK
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neurology, King's College London, London, UK
| | - Marta Di Forti
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neurology, King's College London, London, UK
| | - Erin W Dickie
- Center for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Kimel Family Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Judith M Ford
- San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Paola Fuentes-Claramonte
- FIMDAG Sisters Hospitallers Research Foundation, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Baden-Wuerttemberg, Germany
| | | | - Holly K Hamilton
- San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Fleur M Howells
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
| | - Bernd Kraemer
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Baden-Wuerttemberg, Germany
| | - Stephen M Lawrie
- Division of Psychiatry, University of Edinburgh, Edinburg, EH10 5HF, UK
| | - Daniel H Mathalon
- San Francisco VA Health Care System, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Robin Murray
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neurology, King's College London, London, UK
| | - Edith Pomarol-Clotet
- FIMDAG Sisters Hospitallers Research Foundation, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Steven G Potkin
- Department of Psychiatry, University of California Irvine, Newfoundland, NJ, NJ 07435, USA
| | - Adrian Preda
- Psychiatry and Human Behavior, University of California Irvine, Orange, CA, 92868, USA
| | - Joaquim Radua
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Imaging of mood- and anxiety-related disorders (IMARD), Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, 08036, Spain
- Medicina, University of Barcelona, Barcelona, 08036, Spain
| | - Anja Richter
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Baden-Wuerttemberg, Germany
| | - Raymond Salvador
- FIMDAG Sisters Hospitallers Research Foundation, Barcelona, Spain
| | - Akira Sawa
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine Baltimore, Baltimore, MD, USA
- Department of Pharmacology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Freda Scheffler
- Department of Psychiatry and Mental Health, Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Brain Behavior Unit, Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Kang Sim
- West Region, Institute of Mental Health, National Healthcare Group, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Filip Spaniel
- CARE, National Institute of Mental Health, Klecany, Czech Republic
| | - Dan J Stein
- SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Henk S Temmingh
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, Western Cape, South Africa
- Department of Psychiatry and Mental Health, Valkenberg Psychiatric Hospital, Cape Town, Western Cape, South Africa
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - David Tomecek
- CARE, National Institute of Mental Health, Klecany, Czech Republic
| | - Anne Uhlmann
- Department of child and adolescent psychiatry, TU Dresden, Dresden, Saxony, Germany
| | - Aristotle Voineskos
- Center for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Temerty Faculty of Medicine, Toronto, ON, Canada
| | - Kun Yang
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Theo G M Van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, Wexner Medical Center, The Ohio State University, Columbus, OH, USA
| | - Gianfranco Spalletta
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Federica Piras
- Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
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Mulyadi AW, Jung W, Oh K, Yoon JS, Lee KH, Suk HI. Estimating explainable Alzheimer's disease likelihood map via clinically-guided prototype learning. Neuroimage 2023; 273:120073. [PMID: 37037063 DOI: 10.1016/j.neuroimage.2023.120073] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 03/03/2023] [Accepted: 03/30/2023] [Indexed: 04/12/2023] Open
Abstract
Identifying Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristics make AD biomarker identification from structural brain imaging (e.g., structural MRI) scans quite challenging. Using clinically-guided prototype learning, we propose a novel deep-learning approach through eXplainable AD Likelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs. Specifically, we establish a set of topologically-aware prototypes onto the clusters of latent clinical features, uncovering an AD spectrum manifold. Considering this pseudo map as an enriched reference, we employ an estimating network to approximate the AD likelihood map over a 3D sMRI scan. Additionally, we promote the explainability of such a likelihood map by revealing a comprehensible overview from clinical and morphological perspectives. During the inference, this estimated likelihood map served as a substitute for unseen sMRI scans for effectively conducting the downstream task while providing thorough explainable states.
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Affiliation(s)
- Ahmad Wisnu Mulyadi
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Wonsik Jung
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Kwanseok Oh
- Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea
| | - Jee Seok Yoon
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Kun Ho Lee
- Gwangju Alzheimer's & Related Dementia Cohort Research Center, Chosun University, Gwangju 61452, Republic of Korea; Department of Biomedical Science, Chosun University, Gwangju 61452, Republic of Korea; Korea Brain Research Institute, Daegu 41062, Republic of Korea
| | - Heung-Il Suk
- Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
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7
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Bass C, Silva MD, Sudre C, Williams LZJ, Sousa HS, Tudosiu PD, Alfaro-Almagro F, Fitzgibbon SP, Glasser MF, Smith SM, Robinson EC. ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:959-970. [PMID: 36374873 DOI: 10.1109/tmi.2022.3221890] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based on image registration, historically fail to detect variable features of disease, as they utilise population-based analyses, suited primarily to studying group-average effects. In this paper we therefore take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regression, and feature attribution (FA). Specifically, we explore the use of a VAE-GAN (variational autoencoder - general adversarial network) for translation called ICAM, to explicitly disentangle class relevant features, from background confounds, for improved interpretability and regression of neurological phenotypes. We validate our method on the tasks of Mini-Mental State Examination (MMSE) cognitive test score prediction for the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age prediction, for both neurodevelopment and neurodegeneration, using the developing Human Connectome Project (dHCP) and UK Biobank datasets. We show that the generated FA maps can be used to explain outlier predictions and demonstrate that the inclusion of a regression module improves the disentanglement of the latent space. Our code is freely available on GitHub https://github.com/CherBass/ICAM.
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8
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Pombo G, Gray R, Cardoso MJ, Ourselin S, Rees G, Ashburner J, Nachev P. Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models. Med Image Anal 2023; 84:102723. [PMID: 36542907 PMCID: PMC10591114 DOI: 10.1016/j.media.2022.102723] [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: 12/07/2021] [Revised: 11/21/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022]
Abstract
We describe CounterSynth, a conditional generative model of diffeomorphic deformations that induce label-driven, biologically plausible changes in volumetric brain images. The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations. Focusing on demographic attributes, we evaluate the quality of synthesised counterfactuals with voxel-based morphometry, classification and regression of the conditioning attributes, and the Fréchet inception distance. Examining downstream discriminative performance in the context of engineered demographic imbalance and confounding, we use UK Biobank and OASIS magnetic resonance imaging data to benchmark CounterSynth augmentation against current solutions to these problems. We achieve state-of-the-art improvements, both in overall fidelity and equity. The source code for CounterSynth is available at https://github.com/guilherme-pombo/CounterSynth.
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Affiliation(s)
- Guilherme Pombo
- UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Robert Gray
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - M Jorge Cardoso
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Geraint Rees
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - John Ashburner
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Parashkev Nachev
- UCL Queen Square Institute of Neurology, University College London, London, UK
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Zhang Y, Liu T, Lanfranchi V, Yang P. Explainable Tensor Multi-Task Ensemble Learning Based on Brain Structure Variation for Alzheimer's Disease Dynamic Prediction. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 11:1-12. [PMID: 36478772 PMCID: PMC9721355 DOI: 10.1109/jtehm.2022.3219775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 10/11/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022]
Abstract
Machine learning approaches for predicting Alzheimer's disease (AD) progression can substantially assist researchers and clinicians in developing effective AD preventive and treatment strategies. This study proposes a novel machine learning algorithm to predict the AD progression utilising a multi-task ensemble learning approach. Specifically, we present a novel tensor multi-task learning (MTL) algorithm based on similarity measurement of spatio-temporal variability of brain biomarkers to model AD progression. In this model, the prediction of each patient sample in the tensor is set as one task, where all tasks share a set of latent factors obtained through tensor decomposition. Furthermore, as subjects have continuous records of brain biomarker testing, the model is extended to ensemble the subjects' temporally continuous prediction results utilising a gradient boosting kernel to find more accurate predictions. We have conducted extensive experiments utilising data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to evaluate the performance of the proposed algorithm and model. Results demonstrate that the proposed model have superior accuracy and stability in predicting AD progression compared to benchmarks and state-of-the-art multi-task regression methods in terms of the Mini Mental State Examination (MMSE) questionnaire and The Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) cognitive scores. Brain biomarker correlation information can be utilised to identify variations in individual brain structures and the model can be utilised to effectively predict the progression of AD with magnetic resonance imaging (MRI) data and cognitive scores of AD patients at different stages.
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Affiliation(s)
- Yu Zhang
- Department of Computer ScienceThe University of Sheffield Sheffield S10 2TN U.K
| | - Tong Liu
- Department of Computer ScienceThe University of Sheffield Sheffield S10 2TN U.K
| | | | - Po Yang
- Department of Computer ScienceThe University of Sheffield Sheffield S10 2TN U.K
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10
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Ouyang J, Zhao Q, Adeli E, Zaharchuk G, Pohl KM. Disentangling Normal Aging From Severity of Disease via Weak Supervision on Longitudinal MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2558-2569. [PMID: 35404811 PMCID: PMC9578549 DOI: 10.1109/tmi.2022.3166131] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The continuous progression of neurological diseases are often categorized into conditions according to their severity. To relate the severity to changes in brain morphometry, there is a growing interest in replacing these categories with a continuous severity scale that longitudinal MRIs are mapped onto via deep learning algorithms. However, existing methods based on supervised learning require large numbers of samples and those that do not, such as self-supervised models, fail to clearly separate the disease effect from normal aging. Here, we propose to explicitly disentangle those two factors via weak-supervision. In other words, training is based on longitudinal MRIs being labelled either normal or diseased so that the training data can be augmented with samples from disease categories that are not of primary interest to the analysis. We do so by encouraging trajectories of controls to be fully encoded by the direction associated with brain aging. Furthermore, an orthogonal direction linked to disease severity captures the residual component from normal aging in the diseased cohort. Hence, the proposed method quantifies disease severity and its progression speed in individuals without knowing their condition. We apply the proposed method on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, N =632 ). We then show that the model properly disentangled normal aging from the severity of cognitive impairment by plotting the resulting disentangled factors of each subject and generating simulated MRIs for a given chronological age and condition. Moreover, our representation obtains higher balanced accuracy when used for two downstream classification tasks compared to other pre-training approaches. The code for our weak-supervised approach is available at https://github.com/ouyangjiahong/longitudinal-direction-disentangle.
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11
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Wilms M, Bannister JJ, Mouches P, MacDonald ME, Rajashekar D, Langner S, Forkert ND. Invertible Modeling of Bidirectional Relationships in Neuroimaging With Normalizing Flows: Application to Brain Aging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2331-2347. [PMID: 35324436 DOI: 10.1109/tmi.2022.3161947] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Many machine learning tasks in neuroimaging aim at modeling complex relationships between a brain's morphology as seen in structural MR images and clinical scores and variables of interest. A frequently modeled process is healthy brain aging for which many image-based brain age estimation or age-conditioned brain morphology template generation approaches exist. While age estimation is a regression task, template generation is related to generative modeling. Both tasks can be seen as inverse directions of the same relationship between brain morphology and age. However, this view is rarely exploited and most existing approaches train separate models for each direction. In this paper, we propose a novel bidirectional approach that unifies score regression and generative morphology modeling and we use it to build a bidirectional brain aging model. We achieve this by defining an invertible normalizing flow architecture that learns a probability distribution of 3D brain morphology conditioned on age. The use of full 3D brain data is achieved by deriving a manifold-constrained formulation that models morphology variations within a low-dimensional subspace of diffeomorphic transformations. This modeling idea is evaluated on a database of MR scans of more than 5000 subjects. The evaluation results show that our bidirectional brain aging model (1) accurately estimates brain age, (2) is able to visually explain its decisions through attribution maps and counterfactuals, (3) generates realistic age-specific brain morphology templates, (4) supports the analysis of morphological variations, and (5) can be utilized for subject-specific brain aging simulation.
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12
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Zhang Y, Zhou M, Liu T, Lanfranchi V, Yang P. Spatio-temporal Tensor Multi-Task Learning for Predicting Alzheimer's Disease in a Longitudinal study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:979-985. [PMID: 36086566 DOI: 10.1109/embc48229.2022.9870882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The utilisation of machine learning techniques to predict Alzheimer's Disease (AD) progression will substantially assist researchers and clinicians in establishing effective AD prevention and treatment strategies. In this research, we present a novel Multi-Task Learning (MTL) model for modelling AD progression based on tensor formation from spatio-temporal similarity measures of brain biomarkers. In this model, each patient sample's prediction in the tensor is assigned to a task, with each task sharing a set of latent factors acquired via tensor decomposition. To further improve the performance of the model, we present a novel regularisation term which utilises the convex combination of disease progression to modify longitudinal stability and ensure that two regression models have a minimal variation at successive time points. The model can be utilised to effectively predict AD progression with magnetic resonance imaging (MRI) data and cognitive scores of AD patients at various stages. We conducted extensive experiments to evaluate the performance for the proposed model and algorithm utilising data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Compared to single-task and state-of-the-art multi-task regression techniques, our proposed method has greater accuracy and stability for predicting AD progress in terms of root mean square error, with an average reduction of 2.60 compared to single-task regression methods and 1.17 compared to multi-task regression methods in the Mini-Mental State Examination (MMSE) questionnaire; with an average reduction of 5.08 compared to single-task regression methods and 2.71 compared to multi-task regression methods in the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog).
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13
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de Mélo Silva Júnior ML, Diniz PRB, de Souza Vilanova MV, Basto GPT, Valença MM. Brain ventricles, CSF and cognition: a narrative review. Psychogeriatrics 2022; 22:544-552. [PMID: 35488797 DOI: 10.1111/psyg.12839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/07/2022] [Accepted: 04/12/2022] [Indexed: 11/29/2022]
Abstract
The brain ventricles are structures that have been related to cognition since antiquity. They are essential components in the development and maintenance of brain functions. The aging process runs with the enlargement of ventricles and is related to a less selective blood-cerebrospinal fluid barrier and then a more toxic cerebrospinal fluid environment. The study of brain ventricles as a biological marker of aging is promissing because they are structures easily identified in neuroimaging studies, present good inter-rater reliability, and measures of them can identify brain atrophy earlier than cortical structures. The ventricular system also plays roles in the development of dementia, since dysfunction in the clearance of beta-amyloid protein is a key mechanism in sporadic Alzheimer's disease. The morphometric and volumetric studies of the brain ventricles can help to distinguish between healthy elderly and persons with mild cognitive impairment (MCI) and dementia. Brain ventricle data may contribute to the appropriate allocation of individuals in groups at higher risk for MCI-dementia progression in clinical trials and to measuring therapeutic responses in these studies, as well as providing differential diagnosis, such as normal pressure hydrocephalus. Here, we reviewed the pathophysiology of healthy aging and cognitive decline, focusing on the role of the choroid plexus and brain ventricles in this process.
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Affiliation(s)
- Mário Luciano de Mélo Silva Júnior
- Medical School, Universidade Federal de Pernambuco, Recife, Brazil.,Medical School, Centro Universitário Maurício de Nassau, Recife, Brazil.,Neurology Unit, Hospital da Restauração, Recife, Brazil
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14
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Fu Z, Zhao M, He Y, Wang X, Li X, Kang G, Han Y, Li S. Aberrant topological organization and age-related differences in the human connectome in subjective cognitive decline by using regional morphology from magnetic resonance imaging. Brain Struct Funct 2022; 227:2015-2033. [PMID: 35579698 DOI: 10.1007/s00429-022-02488-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 03/24/2022] [Indexed: 11/25/2022]
Abstract
Subjective cognitive decline (SCD) is characterized by self-experienced deficits in cognitive capacity with normal performance in objective cognitive tests. Previous structural covariance studies showed specific insights into understanding the structural alterations of the brain in neurodegenerative diseases. Moreover, in subjects with neurodegenerative diseases, accelerated brain degeneration with aging was shown. However, the age-related variations in coordinated topological patterns of morphological networks in individuals with SCD remain poorly understood. In this study, 77 individual morphological networks were constructed, including 42 normal controls (NCs) and 35 SCD individuals, from structural magnetic resonance imaging (sMRI). A stepwise linear regression model and partial correlation analysis were constructed to evaluate the differences in age-related alterations of the network properties in individuals with SCD compared with NCs. Compared with NC, the properties of integration and segregation in individuals with SCD were lower, and the aberrant metrics were negatively correlated with age in SCD. The rich-club connections persevered, but the paralimbic system connections were disrupted in individuals with SCD compared with NCs. In addition, age-related differences in nodal global efficiency are distributed mainly in prefrontal cortex regions. In conclusion, the age-related disruption of topological organizations in individuals with SCD may indicate that the degeneration of brain efficiency with aging was accelerated in individuals with SCD.
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Affiliation(s)
- Zhenrong Fu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
| | - Mingyan Zhao
- Department of Neurology, Tangshan Gongren Hospital, Tangshan, Hebei, China
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Yirong He
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
| | - Xuetong Wang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
| | - Xin Li
- School of Electrical Engineering, Yanshan University, Qinhuangdao, China
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, China
| | - Guixia Kang
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
- Biomedical Engineering Institute, Hainan University, Haikou, China
- National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Shuyu Li
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China.
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15
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Veitch DP, Weiner MW, Aisen PS, Beckett LA, DeCarli C, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Okonkwo O, Perrin RJ, Petersen RC, Rivera‐Mindt M, Saykin AJ, Shaw LM, Toga AW, Tosun D, Trojanowski JQ. Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease. Alzheimers Dement 2022; 18:824-857. [PMID: 34581485 PMCID: PMC9158456 DOI: 10.1002/alz.12422] [Citation(s) in RCA: 55] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 06/08/2021] [Accepted: 06/09/2021] [Indexed: 02/06/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020. METHODS We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion. RESULTS Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity. DISCUSSION ADNI has had a profound impact in improving clinical trials for AD.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of MedicineUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of PsychiatryUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Laurel A. Beckett
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | - Charles DeCarli
- Department of Neurology and Center for NeuroscienceUniversity of California DavisDavisCaliforniaUSA
| | - Robert C. Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Broad Institute, Ariadne Labsand Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | | | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Casamitjana A, Lorenzi M, Ferraris S, Peter L, Modat M, Stevens A, Fischl B, Vercauteren T, Iglesias JE. Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas. Med Image Anal 2022; 75:102265. [PMID: 34741894 PMCID: PMC8678374 DOI: 10.1016/j.media.2021.102265] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 08/07/2021] [Accepted: 10/07/2021] [Indexed: 02/04/2023]
Abstract
Joint registration of a stack of 2D histological sections to recover 3D structure ("3D histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (ℓ2 norm, which can be minimised in closed form) and Laplacian (ℓ1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest.
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Affiliation(s)
| | - Marco Lorenzi
- Universitë Côte dÁzur, Inria, Epione Team, 06902 Sophia Antipolis, France
| | | | - Loïc Peter
- Center for Medical Image Computing, University College London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Allison Stevens
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA
| | - Bruce Fischl
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA; Program in Health Sciences and Technology, Massachusetts Institute of Technology, USA
| | - Tom Vercauteren
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Juan Eugenio Iglesias
- Center for Medical Image Computing, University College London, UK; Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
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An experimental analysis of different Deep Learning based Models for Alzheimer’s Disease classification using Brain Magnetic Resonance Images. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2021. [DOI: 10.1016/j.jksuci.2021.09.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Xia T, Chartsias A, Wang C, Tsaftaris SA. Learning to synthesise the ageing brain without longitudinal data. Med Image Anal 2021; 73:102169. [PMID: 34311421 DOI: 10.1016/j.media.2021.102169] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 07/01/2021] [Accepted: 07/09/2021] [Indexed: 12/21/2022]
Abstract
How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data) a multivariate auto-regressive function which given an image and a desired target age generates an output image. While collecting data for faces may be easier, collecting longitudinal brain data is not trivial. We propose a deep learning-based method that learns to simulate subject-specific brain ageing trajectories without relying on longitudinal data. Our method synthesises images conditioned on two factors: age (a continuous variable), and status of Alzheimer's Disease (AD, an ordinal variable). With an adversarial formulation we learn the joint distribution of brain appearance, age and AD status, and define reconstruction losses to address the challenging problem of preserving subject identity. We compare with several benchmarks using two widely used datasets. We evaluate the quality and realism of synthesised images using ground-truth longitudinal data and a pre-trained age predictor. We show that, despite the use of cross-sectional data, our model learns patterns of gray matter atrophy in the middle temporal gyrus in patients with AD. To demonstrate generalisation ability, we train on one dataset and evaluate predictions on the other. In conclusion, our model shows an ability to separate age, disease influence and anatomy using only 2D cross-sectional data that should be useful in large studies into neurodegenerative disease, that aim to combine several data sources. To facilitate such future studies by the community at large our code is made available at https://github.com/xiat0616/BrainAgeing.
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Affiliation(s)
- Tian Xia
- Institute for Digital Communications, School of Engineering, University of Edinburgh, West Mains Rd, Edinburgh EH9 3FB, UK.
| | - Agisilaos Chartsias
- Institute for Digital Communications, School of Engineering, University of Edinburgh, West Mains Rd, Edinburgh EH9 3FB, UK
| | - Chengjia Wang
- The BHF Centre for Cardiovascular Science, Edinburgh EH16 4TJ, UK
| | - Sotirios A Tsaftaris
- Institute for Digital Communications, School of Engineering, University of Edinburgh, West Mains Rd, Edinburgh EH9 3FB, UK; The Alan Turing Institute, London NW1 2DB, UK
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Şeker M, Özbek Y, Yener G, Özerdem MS. Complexity of EEG Dynamics for Early Diagnosis of Alzheimer's Disease Using Permutation Entropy Neuromarker. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 206:106116. [PMID: 33957376 DOI: 10.1016/j.cmpb.2021.106116] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 04/11/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Electroencephalogram (EEG) is one of the most demanded screening tools that investigates the effects of Alzheimer's Disease (AD) on human brain. Identification of AD in early stage gives rise to efficient treatment in dementia. Mild Cognitive Impairment (MCI) is considered as a conversion stage. Reducing EEG complexity can be used as a marker to detect AD. The aim of this study is to develop a 3-way diagnostic classification using EEG complexity in the detection of MCI/AD in clinical practice. This study also investigates the effects of different eyes states, i.e. eyes-open, eyes-closed on classification performance. METHODS EEG recordings from 85 AD, 85 MCI subjects, and 85 Healthy Controls with eyes-open and eyes- closed are analyzed. Permutation Entropy (PE) values are computed from frontal, central, parietal, temporal, and occipital regions for each EEG epoch. Distribution of PE values are visualized to observe discrimination of MCI/AD with HC. Visual investigations are combined with statistical analysis using ANOVA to determine whether groups are significant or not. Multinomial Logistic Regression model is applied to feature sets in order to classify participants individually. RESULTS Distribution of measured PE shows that EEG complexity is lower in AD and higher in HC group. MCI group is observed as an intermediate form due to heterogeneous values. Results from 3-way classification indicate that F1-scores and rates of sensitivity and specificity achieve the highest overall discrimination rates reaching up to 100% for at TP8 for eyes-closed condition; and C3, C4, T8, O2 electrodes for eyes-open condition. Classification of HC from both patient groups is achieved best. Eyes-open state increases discrimination of MCI and AD. CONCLUSIONS This nonlinear EEG methodology study contributes to literature with high discrimination rates for identification of AD. PE is recommended as a practical diagnostic neuro-marker for AD studies. Resting state EEG at eyes-open condition can be more advantageous over eyes-closed EEG recordings for diagnosis of AD.
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Affiliation(s)
- Mesut Şeker
- Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey.
| | - Yağmur Özbek
- Department of Neurosciences, Health Science Institute, Dokuz Eylül University, Izmir
| | - Görsev Yener
- Department of Neurosciences, Health Science Institute, Dokuz Eylül University, Izmir; Izmir Biomedicine and Genome Center, Izmir, Turkey; Department of Neurology, Faculty of Medicine, Izmir Ekonomi University, Izmir, Turkey; Department of Neurology, Faculty of Medicine, Dokuz Eylül University, Izmir, Turkey
| | - Mehmet Siraç Özerdem
- Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey
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20
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LV YUTING, ZHAO WENSHUO, YAO XUFENG, XU SONG, TANG ZHIXIAN, FAN YIFENG, HUANG GANG. ANALYSES OF BRAIN CORTICAL CHANGES OF ALZHEIMER’S DISEASE. J MECH MED BIOL 2021. [DOI: 10.1142/s021951942140025x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Alzheimer’s disease (AD) produces complicated cortical changes in gray matter (GM) of the human brain. However, alterations in the brain cortex have not been clearly addressed. In our study, a cohort of 236 cases MR data enrolled from the ADNI database was categorized into three groups of normal controls (NCs), mild cognitive impairment (MCI) and AD. The GM morphological differences were investigated among the three groups using the magnetic resonance (MR) GM characteristics of gray matter volume (GMV), cortical thickness (CT), cortical surface area (CSA) and local gyrification index (LGI) at the three levels of whole brain, bilateral hemispheres and critical brain regions. Totally, there were six critical brain regions for GMV, 11 for CT, 2 for CSA and 59 for LGI among the three groups for the no-division groups. Also, there were 11 critical brain regions for GMV, 15 for CT, 8 for CSA, 3 for LGI for female sub-groups and 4 critical brain regions for GMV, 11 for CT, 1 for CSA, 3 for LGI for male sub-groups. The four measured cortical characteristics showed reliable capability in the morphological description of GM changes of AD. In conclusion, the cortical characteristics of GMV, CT, CSA and LGI of critical brain regions showed valuable indications for GM changes of AD, and those characteristics could be used as imaging markers for AD prediction.
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Affiliation(s)
- YUTING LV
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, P. R. China
| | - WENSHUO ZHAO
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
| | - XUFENG YAO
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, P. R. China
| | - SONG XU
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
| | - ZHIXIAN TANG
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, P. R. China
| | - YIFENG FAN
- School of Medical Imaging, Hangzhou Medical College, Hangzhou 310053, P. R. China
| | - GANG HUANG
- College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, P. R. China
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21
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Seibel R, Schneider RH, Gottlieb MGV. Effects of Spices (Saffron, Rosemary, Cinnamon, Turmeric and Ginger) in Alzheimer's Disease. Curr Alzheimer Res 2021; 18:347-357. [PMID: 34279199 DOI: 10.2174/1567205018666210716122034] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 01/14/2021] [Accepted: 03/17/2021] [Indexed: 12/06/2022]
Abstract
Alzheimer's disease (AD) is the most prevalent dementia in the elderly, causing disability, physical, psychological, social, and economic damage to the individual, their families, and caregivers. Studies have shown some spices, such as saffron, rosemary, cinnamon, turmeric, and ginger, have antioxidant and anti-inflammatory properties that act in inhibiting the aggregation of acetylcholinesterase and amyloid in AD. For this reason, spices have been studied as beneficial sources against neurodegenerative diseases, including AD. In this sense, this study aims to present a review of some spices (Saffron, Rosemary, Cinnamon, Turmeric and Ginger) and their bioactive compounds, most consumed and investigated in the world regarding AD. In this article, scientific evidence is compiled in clinical trials in adults, the elderly, animals, and in vitro, on properties considered neuroprotective, having no or negative effects on neuroprotection of these spices and their bioactive compounds. The importance of this issue is based on the pharmacological treatment for AD that is still not very effective. In addition, the recommendations and prescriptions of these spices are still permeated by questioning and lack of robust evidence of their effects on neurodegeneration. The literature search suggests all spices included in this article have bioactive compounds with anti-inflammatory and antioxidant actions associated with neuroprotection. To date, the amounts of spice ingestion in humans are not uniform, and there is no consensus on its indication and chronic consumption guarantees safety and efficacy in neuroprotection. Therefore, clinical evidence on this topic is necessary to become a formal adjuvant treatment for AD.
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Affiliation(s)
- Raquel Seibel
- School of Medicine, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre/RS, Brazil
| | - Rodolfo H Schneider
- School of Medicine, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre/RS, Brazil
| | - Maria G V Gottlieb
- School of Medicine, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre/RS, Brazil
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22
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Zhang Z, Wang Y, Gao Y, Li Z, Zhang S, Lin X, Hou Z, Yu Q, Wang X, Liu S. Morphological changes of the cerebral cortex between children with isolated growth hormone deficiency and idiopathic short stature. Brain Res 2020; 1748:147081. [PMID: 32882231 DOI: 10.1016/j.brainres.2020.147081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 08/12/2020] [Accepted: 08/23/2020] [Indexed: 10/23/2022]
Abstract
The growth hormone (GH)-insulin-like growth factor-I (IGF-I) axis plays an important role in normal brain development, and GH deficiency inevitably affects the growth of the cerebral cortex. This study was designed to analyze morphological differences in gray matter volume, cortical surface area, and gray matter thickness between children with isolated growth hormone deficiency (IGHD) and children with idiopathic short stature (ISS). Twenty-four children with IGHD (mean age 9.42 years, peak GH < 5 μg/l) and 24 controls with ISS (mean age 9.21 years, peak GH > 10 μg/l) were included. High-resolution three-dimensional T1-weighted MRIs were acquired at participants' first visit. Measurements of gray matter volume, cortical surface area and gray matter thickness were obtained using FreeSurfer. The total and regional differences between groups were statistically analyzed. Correlations between the FreeSurfer results and GH and IGF-I levels were also obtained. The gray matter volume, cortical surface area and gray matter thickness of the total brain and of the bilateral hemispheres of children with IGHD were significantly smaller than those of children with ISS (all P values < 0.05). All the measurements had similar cortical distributions between groups but varied across regions. Cortical regions with significant differences in the mean gray matter volume and surface area were mainly distributed around the bilateral central sulci and the lateral and basal parts of the temporal lobes (all P values < 0.05). There were negative correlations between gray matter volume, cortical surface area and GH levels, and the right hemispheric and total cortical surface area correlated significantly with GH levels (all P values < 0.05) in children with IGHD. There were significant positive correlations between gray matter volume, cortical surface area and IGF-I levels (all P values < 0.05) in both groups, except for in left hemispheric gray matter volume in children with ISS. Children with IGHD have significant morphological changes in the cerebral cortex, which were partially influenced by GH and IGF-I levels. These cortical changes may be related to deficits in their relatively slower development in intelligence, motor performance, and other functions.
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Affiliation(s)
- Zhonghe Zhang
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong University, Shandong, China; Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, China; Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Shandong, China.
| | - Yu Wang
- Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Shandong, China
| | - Yue Gao
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong University, Shandong, China; Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, China
| | - Zhuoran Li
- Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Shandong, China
| | - Shuhan Zhang
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong University, Shandong, China; Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, China
| | - Xiangtao Lin
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong University, Shandong, China; Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, China; Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Shandong, China
| | - Zhongyu Hou
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong University, Shandong, China; Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, China; Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Shandong, China
| | - Qiaowen Yu
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong University, Shandong, China; Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, China; Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Shandong, China
| | - Ximing Wang
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong University, Shandong, China; Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, China
| | - Shuwei Liu
- Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Shandong, China
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23
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Sivera R, Capet N, Manera V, Fabre R, Lorenzi M, Delingette H, Pennec X, Ayache N, Robert P. Voxel-based assessments of treatment effects on longitudinal brain changes in the Multidomain Alzheimer Preventive Trial cohort. Neurobiol Aging 2020; 94:50-59. [PMID: 32574818 DOI: 10.1016/j.neurobiolaging.2019.11.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 10/26/2019] [Accepted: 11/17/2019] [Indexed: 10/24/2022]
Abstract
The Multidomain Alzheimer Preventive Trial was designed to assess the effect of omega-3 supplementation and multidomain intervention on cognitive decline of subjects with subjective memory complaint. In terms of cognitive testing, no significant effect was found. In this paper, we evaluate the effect of the interventions on the brain morphological changes. Subjects with magnetic resonance imaging acquisitions at baseline and at 36 months were included (N = 376). Morphological changes were characterized by volume measurements and nonlinear deformation. The multidomain intervention was associated with a significant effect on the 3-year brain morphological changes in the deformation-based approach. Differences were mainly located in the left periventricular area next to the temporoparietal junction. These changes were associated with better cognitive performance and mood/behavior stabilization. No effect of the omega-3 supplementation was observed. This result suggests a possible effect on cognition, not yet observable after 3 years. We argue that neuroimaging could help define whether early intervention strategies are effective to delay cognitive decline and dementia.
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Affiliation(s)
- Raphaël Sivera
- Université Côte d'Azur, Inria Sophia Antipolis, Epione Research Project, Sophia Antipolis, France.
| | - Nicolas Capet
- Centre Hospitalier Universitaire (CHU) de Nice, CMRR, Nice, France
| | - Valeria Manera
- Université Côte d'Azur, CoBTeK Lab, Nice, France; Association Innovation Alzheimer, Nice, France
| | - Roxane Fabre
- Université Côte d'Azur, CoBTeK Lab, Nice, France; Centre Hospitalier Universitaire (CHU) de Nice, Département de Santé Publique, Nice, France
| | - Marco Lorenzi
- Université Côte d'Azur, Inria Sophia Antipolis, Epione Research Project, Sophia Antipolis, France
| | - Hervé Delingette
- Université Côte d'Azur, Inria Sophia Antipolis, Epione Research Project, Sophia Antipolis, France
| | - Xavier Pennec
- Université Côte d'Azur, Inria Sophia Antipolis, Epione Research Project, Sophia Antipolis, France
| | - Nicholas Ayache
- Université Côte d'Azur, Inria Sophia Antipolis, Epione Research Project, Sophia Antipolis, France
| | - Philippe Robert
- Centre Hospitalier Universitaire (CHU) de Nice, CMRR, Nice, France; Université Côte d'Azur, CoBTeK Lab, Nice, France; Association Innovation Alzheimer, Nice, France
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24
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Abi Nader C, Ayache N, Robert P, Lorenzi M. Monotonic Gaussian Process for spatio-temporal disease progression modeling in brain imaging data. Neuroimage 2019; 205:116266. [PMID: 31648001 DOI: 10.1016/j.neuroimage.2019.116266] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 10/08/2019] [Accepted: 10/10/2019] [Indexed: 01/08/2023] Open
Abstract
We introduce a probabilistic generative model for disentangling spatio-temporal disease trajectories from collections of high-dimensional brain images. The model is based on spatio-temporal matrix factorization, where inference on the sources is constrained by anatomically plausible statistical priors. To model realistic trajectories, the temporal sources are defined as monotonic and time-reparameterized Gaussian Processes. To account for the non-stationarity of brain images, we model the spatial sources as sparse codes convolved at multiple scales. The method was tested on synthetic data favourably comparing with standard blind source separation approaches. The application on large-scale imaging data from a clinical study allows to disentangle differential temporal progression patterns mapping brain regions key to neurodegeneration, while revealing a disease-specific time scale associated to the clinical diagnosis.
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Affiliation(s)
- Clément Abi Nader
- Université Côte d'Azur, Inria Sophia Antipolis, Epione Research Project, France.
| | - Nicholas Ayache
- Université Côte d'Azur, Inria Sophia Antipolis, Epione Research Project, France.
| | | | - Marco Lorenzi
- Université Côte d'Azur, Inria Sophia Antipolis, Epione Research Project, France.
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