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Batouli SAH, Sisakhti M, Haghshenas S, Dehghani H, Sachdev P, Ekhtiari H, Kochan N, Wen W, Leemans A, Kohanpour M, Oghabian MA. Iranian Brain Imaging Database: A Neuropsychiatric Database of Healthy Brain. Basic Clin Neurosci 2021; 12:115-132. [PMID: 33995934 PMCID: PMC8114860 DOI: 10.32598/bcn.12.1.1774.2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 06/19/2019] [Indexed: 12/11/2022] Open
Abstract
INTRODUCTION The Iranian Brain Imaging Database (IBID) was initiated in 2017, with 5 major goals: provide researchers easy access to a neuroimaging database, provide normative quantitative measures of the brain for clinical research purposes, study the aging profile of the brain, examine the association of brain structure and function, and join the ENIGMA consortium. Many prestigious databases with similar goals are available. However, they were not done on an Iranian population, and the battery of their tests (e.g. cognitive tests) is selected based on their specific questions and needs. METHODS The IBID will include 300 participants (50% female) in the age range of 20 to 70 years old, with an equal number of participants (#60) in each age decade. It comprises a battery of cognitive, lifestyle, medical, and mental health tests, in addition to several Magnetic Resonance Imaging (MRI) protocols. Each participant completes the assessments on two referral days. RESULTS The study currently has a cross-sectional design, but longitudinal assessments are considered for the future phases of the study. Here, details of the methodology and the initial results of assessing the first 152 participants of the study are provided. CONCLUSION IBID is established to enable research into human brain function, to aid clinicians in disease diagnosis research, and also to unite the Iranian researchers with interests in the brain.
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Affiliation(s)
- Seyed Amir Hossein Batouli
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Departmen of Neuroimaging and Analysis, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Minoo Sisakhti
- Departmen of Neuroimaging and Analysis, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
- Institute for Cognitive Sciences Studies, Tehran, Iran
| | - Shirin Haghshenas
- Departmen of Neuroimaging and Analysis, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamed Dehghani
- Departmen of Neuroimaging and Analysis, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Perminder Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | | | - Nicole Kochan
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mohsen Kohanpour
- Departmen of Neuroimaging and Analysis, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Oghabian
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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Rangaprakash D, Odemuyiwa T, Narayana Dutt D, Deshpande G. Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment. Brain Inform 2020; 7:19. [PMID: 33242116 PMCID: PMC7691406 DOI: 10.1186/s40708-020-00120-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 10/29/2020] [Indexed: 11/29/2022] Open
Abstract
Various machine-learning classification techniques have been employed previously to classify brain states in healthy and disease populations using functional magnetic resonance imaging (fMRI). These methods generally use supervised classifiers that are sensitive to outliers and require labeling of training data to generate a predictive model. Density-based clustering, which overcomes these issues, is a popular unsupervised learning approach whose utility for high-dimensional neuroimaging data has not been previously evaluated. Its advantages include insensitivity to outliers and ability to work with unlabeled data. Unlike the popular k-means clustering, the number of clusters need not be specified. In this study, we compare the performance of two popular density-based clustering methods, DBSCAN and OPTICS, in accurately identifying individuals with three stages of cognitive impairment, including Alzheimer’s disease. We used static and dynamic functional connectivity features for clustering, which captures the strength and temporal variation of brain connectivity respectively. To assess the robustness of clustering to noise/outliers, we propose a novel method called recursive-clustering using additive-noise (R-CLAN). Results demonstrated that both clustering algorithms were effective, although OPTICS with dynamic connectivity features outperformed in terms of cluster purity (95.46%) and robustness to noise/outliers. This study demonstrates that density-based clustering can accurately and robustly identify diagnostic classes in an unsupervised way using brain connectivity.
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Affiliation(s)
- D Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA.,Division of Health Sciences and Technology, Harvard University and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Toluwanimi Odemuyiwa
- Division of Engineering Science, Faculty of Applied Science & Engineering, University of Toronto, Toronto, ON, Canada
| | - D Narayana Dutt
- Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore, India
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA. .,Department of Psychological Sciences, Auburn University, Auburn, AL, USA. .,Alabama Advanced Imaging Consortium, University of Alabama Birmingham, Alabama, USA. .,Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA. .,Center for Neuroscience, Auburn University, Auburn, AL, USA. .,School of Psychology, Capital Normal University, Beijing, China. .,Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China. .,Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India.
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Mechelli A, Vieira S. From models to tools: clinical translation of machine learning studies in psychosis. NPJ SCHIZOPHRENIA 2020; 6:4. [PMID: 32060287 PMCID: PMC7021680 DOI: 10.1038/s41537-020-0094-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 01/24/2020] [Indexed: 11/16/2022]
Affiliation(s)
- Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
| | - Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
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Vieira S, Gong QY, Pinaya WHL, Scarpazza C, Tognin S, Crespo-Facorro B, Tordesillas-Gutierrez D, Ortiz-García V, Setien-Suero E, Scheepers FE, Van Haren NEM, Marques TR, Murray RM, David A, Dazzan P, McGuire P, Mechelli A. Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence. Schizophr Bull 2020; 46:17-26. [PMID: 30809667 PMCID: PMC6942152 DOI: 10.1093/schbul/sby189] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing literature. This study aimed to elucidate the extent to which the application of ML to neuroanatomical data allows detection of first episode psychosis (FEP), while putting in place methodological precautions to avoid overoptimistic results. We tested both traditional ML and an emerging approach known as deep learning (DL) using 3 feature sets of interest: (1) surface-based regional volumes and cortical thickness, (2) voxel-based gray matter volume (GMV) and (3) voxel-based cortical thickness (VBCT). To assess the reliability of the findings, we repeated all analyses in 5 independent datasets, totaling 956 participants (514 FEP and 444 within-site matched controls). The performance was assessed via nested cross-validation (CV) and cross-site CV. Accuracies ranged from 50% to 70% for surfaced-based features; from 50% to 63% for GMV; and from 51% to 68% for VBCT. The best accuracies (70%) were achieved when DL was applied to surface-based features; however, these models generalized poorly to other sites. Findings from this study suggest that, when methodological precautions are adopted to avoid overoptimistic results, detection of individuals in the early stages of psychosis is more challenging than originally thought. In light of this, we argue that the current evidence for the diagnostic value of ML and structural neuroimaging should be reconsidered toward a more cautious interpretation.
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Affiliation(s)
- Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Qi-yong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, China
| | - Walter H L Pinaya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
- Centre of Mathematics, Computation, and Cognition, Universidade Federal do ABC, São Paulo, Brazil
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
- Department of General Psychology, University of Padova, Padova, Italy
| | - Stefania Tognin
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Benedicto Crespo-Facorro
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
- Department of Psychiatry, University Hospital Marqués de Valdecilla, School of Medicine, University of Cantabria-IDIVAL, Santander, Spain
| | - Diana Tordesillas-Gutierrez
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
- Neuroimaging Unit, Technological Facilities, Valdecilla Biomedical Research Institute IDIVAL, Santander, Cantabria, Spain
| | - Victor Ortiz-García
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
- Department of Psychiatry, University Hospital Marqués de Valdecilla, School of Medicine, University of Cantabria-IDIVAL, Santander, Spain
| | - Esther Setien-Suero
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
- Department of Psychiatry, University Hospital Marqués de Valdecilla, School of Medicine, University of Cantabria-IDIVAL, Santander, Spain
| | - Floortje E Scheepers
- Department of Psychiatry, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Neeltje E M Van Haren
- Brain Centre Rudolf Magnus, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Tiago R Marques
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Robin M Murray
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Anthony David
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom
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Pinaya WHL, Mechelli A, Sato JR. Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study. Hum Brain Mapp 2018; 40:944-954. [PMID: 30311316 PMCID: PMC6492107 DOI: 10.1002/hbm.24423] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2018] [Revised: 09/25/2018] [Accepted: 10/02/2018] [Indexed: 11/11/2022] Open
Abstract
Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a "black box" that provides little or no insight into the nature of the data. In this article, we propose an alternative conceptual and practical approach for investigating brain-based disorders which aim to overcome these limitations. We used an artificial neural network known as "deep autoencoder" to create a normative model using structural magnetic resonance imaging data from 1,113 healthy people. We then used this model to estimate total and regional neuroanatomical deviation in individual patients with schizophrenia and autism spectrum disorder using two independent data sets (n = 263). We report that the model was able to generate different values of total neuroanatomical deviation for each disease under investigation relative to their control group (p < .005). Furthermore, the model revealed distinct patterns of neuroanatomical deviations for the two diseases, consistent with the existing neuroimaging literature. We conclude that the deep autoencoder provides a flexible and promising framework for assessing total and regional neuroanatomical deviations in neuropsychiatric populations.
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Affiliation(s)
- Walter H L Pinaya
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil.,Center for Engineering, Modeling and Applied Social Sciences, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil.,Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - João R Sato
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
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Maudsley S, Devanarayan V, Martin B, Geerts H. Intelligent and effective informatic deconvolution of “Big Data” and its future impact on the quantitative nature of neurodegenerative disease therapy. Alzheimers Dement 2018; 14:961-975. [DOI: 10.1016/j.jalz.2018.01.014] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Revised: 10/03/2017] [Accepted: 01/18/2018] [Indexed: 12/31/2022]
Affiliation(s)
- Stuart Maudsley
- Department of Biomedical ResearchUniversity of AntwerpAntwerpBelgium
- VIB Center for Molecular NeurologyAntwerpBelgium
| | | | - Bronwen Martin
- Department of Biomedical ResearchUniversity of AntwerpAntwerpBelgium
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Sato JR, Biazoli CE, Salum GA, Gadelha A, Crossley N, Vieira G, Zugman A, Picon FA, Pan PM, Hoexter MQ, Amaro E, Anés M, Moura LM, Del'Aquilla MAG, Mcguire P, Rohde LA, Miguel EC, Jackowski AP, Bressan RA. Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning. World J Biol Psychiatry 2018. [PMID: 28635541 DOI: 10.1080/15622975.2016.1274050] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
OBJECTIVES One of the major challenges facing psychiatry is how to incorporate biological measures in the classification of mental health disorders. Many of these disorders affect brain development and its connectivity. In this study, we propose a novel method for assessing brain networks based on the combination of a graph theory measure (eigenvector centrality) and a one-class support vector machine (OC-SVM). METHODS We applied this approach to resting-state fMRI data from 622 children and adolescents. Eigenvector centrality (EVC) of nodes from positive- and negative-task networks were extracted from each subject and used as input to an OC-SVM to label individual brain networks as typical or atypical. We hypothesised that classification of these subjects regarding the pattern of brain connectivity would predict the level of psychopathology. RESULTS Subjects with atypical brain network organisation had higher levels of psychopathology (p < 0.001). There was a greater EVC in the typical group at the bilateral posterior cingulate and bilateral posterior temporal cortices; and significant decreases in EVC at left temporal pole. CONCLUSIONS The combination of graph theory methods and an OC-SVM is a promising method to characterise neurodevelopment, and may be useful to understand the deviations leading to mental disorders.
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Affiliation(s)
- João Ricardo Sato
- a Center of Mathematics, Computation and Cognition, Universidade Federal do ABC , Santo André , Brazil.,b Department of Psychiatry, Interdisciplinary Lab for Clinical Neurosciences (LiNC) , Universidade Federal de Sao Paulo (UNIFESP) , Sao Paulo , Brazil.,c Department of Radiology , School of Medicine, University of Sao Paulo , Brazil.,d National Institute of Developmental Psychiatry for Children and Adolescents , CNPq , Brazil
| | - Claudinei Eduardo Biazoli
- a Center of Mathematics, Computation and Cognition, Universidade Federal do ABC , Santo André , Brazil.,c Department of Radiology , School of Medicine, University of Sao Paulo , Brazil
| | - Giovanni Abrahão Salum
- d National Institute of Developmental Psychiatry for Children and Adolescents , CNPq , Brazil.,e Hospital de Clinicas de Porto Alegre and Department of Psychiatry , Federal University of Rio Grande do Sul , Porto Alegre , Brazil
| | - Ary Gadelha
- b Department of Psychiatry, Interdisciplinary Lab for Clinical Neurosciences (LiNC) , Universidade Federal de Sao Paulo (UNIFESP) , Sao Paulo , Brazil.,d National Institute of Developmental Psychiatry for Children and Adolescents , CNPq , Brazil
| | - Nicolas Crossley
- f Department of Psychosis Studies, Institute of Psychiatry, King's College London , United Kingdom
| | - Gilson Vieira
- c Department of Radiology , School of Medicine, University of Sao Paulo , Brazil.,g Bioinformatics Program , Institute of Mathematics and Statistics, University of Sao Paulo , Brazil
| | - André Zugman
- b Department of Psychiatry, Interdisciplinary Lab for Clinical Neurosciences (LiNC) , Universidade Federal de Sao Paulo (UNIFESP) , Sao Paulo , Brazil.,d National Institute of Developmental Psychiatry for Children and Adolescents , CNPq , Brazil
| | - Felipe Almeida Picon
- d National Institute of Developmental Psychiatry for Children and Adolescents , CNPq , Brazil.,e Hospital de Clinicas de Porto Alegre and Department of Psychiatry , Federal University of Rio Grande do Sul , Porto Alegre , Brazil
| | - Pedro Mario Pan
- b Department of Psychiatry, Interdisciplinary Lab for Clinical Neurosciences (LiNC) , Universidade Federal de Sao Paulo (UNIFESP) , Sao Paulo , Brazil.,d National Institute of Developmental Psychiatry for Children and Adolescents , CNPq , Brazil
| | - Marcelo Queiroz Hoexter
- b Department of Psychiatry, Interdisciplinary Lab for Clinical Neurosciences (LiNC) , Universidade Federal de Sao Paulo (UNIFESP) , Sao Paulo , Brazil.,d National Institute of Developmental Psychiatry for Children and Adolescents , CNPq , Brazil.,h Department of Psychiatry , School of Medicine, University of Sao Paulo , Brazil
| | - Edson Amaro
- i Institute of Radiology (InRad), Faculdade de Medicina , Universidade de Sao Paulo , Brazil
| | - Mauricio Anés
- d National Institute of Developmental Psychiatry for Children and Adolescents , CNPq , Brazil.,e Hospital de Clinicas de Porto Alegre and Department of Psychiatry , Federal University of Rio Grande do Sul , Porto Alegre , Brazil
| | - Luciana Monteiro Moura
- b Department of Psychiatry, Interdisciplinary Lab for Clinical Neurosciences (LiNC) , Universidade Federal de Sao Paulo (UNIFESP) , Sao Paulo , Brazil.,d National Institute of Developmental Psychiatry for Children and Adolescents , CNPq , Brazil
| | - Marco Antonio Gomes Del'Aquilla
- b Department of Psychiatry, Interdisciplinary Lab for Clinical Neurosciences (LiNC) , Universidade Federal de Sao Paulo (UNIFESP) , Sao Paulo , Brazil.,d National Institute of Developmental Psychiatry for Children and Adolescents , CNPq , Brazil
| | - Philip Mcguire
- f Department of Psychosis Studies, Institute of Psychiatry, King's College London , United Kingdom
| | - Luis Augusto Rohde
- d National Institute of Developmental Psychiatry for Children and Adolescents , CNPq , Brazil.,e Hospital de Clinicas de Porto Alegre and Department of Psychiatry , Federal University of Rio Grande do Sul , Porto Alegre , Brazil
| | - Euripedes Constantino Miguel
- d National Institute of Developmental Psychiatry for Children and Adolescents , CNPq , Brazil.,h Department of Psychiatry , School of Medicine, University of Sao Paulo , Brazil
| | - Andrea Parolin Jackowski
- b Department of Psychiatry, Interdisciplinary Lab for Clinical Neurosciences (LiNC) , Universidade Federal de Sao Paulo (UNIFESP) , Sao Paulo , Brazil.,d National Institute of Developmental Psychiatry for Children and Adolescents , CNPq , Brazil
| | - Rodrigo Affonseca Bressan
- b Department of Psychiatry, Interdisciplinary Lab for Clinical Neurosciences (LiNC) , Universidade Federal de Sao Paulo (UNIFESP) , Sao Paulo , Brazil.,d National Institute of Developmental Psychiatry for Children and Adolescents , CNPq , Brazil
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Ngo Ho AK, Eglin V, Ragot N, Ramel JY. A multi-one-class dynamic classifier for adaptive digitization of document streams. INT J DOC ANAL RECOG 2017. [DOI: 10.1007/s10032-017-0286-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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El Azami M, Hammers A, Jung J, Costes N, Bouet R, Lartizien C. Detection of Lesions Underlying Intractable Epilepsy on T1-Weighted MRI as an Outlier Detection Problem. PLoS One 2016; 11:e0161498. [PMID: 27603778 PMCID: PMC5015774 DOI: 10.1371/journal.pone.0161498] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Accepted: 08/05/2016] [Indexed: 11/19/2022] Open
Abstract
Pattern recognition methods, such as computer aided diagnosis (CAD) systems, can help clinicians in their diagnosis by marking abnormal regions in an image. We propose a machine learning system based on a one-class support vector machine (OC-SVM) classifier for the detection of abnormalities in magnetic resonance images (MRI) applied to patients with intractable epilepsy. The system learns the features associated with healthy control subjects, allowing a voxelwise assessment of the deviation of a test subject pattern from the learned patterns. While any number of various features can be chosen and learned, here we focus on two texture parameters capturing image patterns associated with epileptogenic lesions on T1-weighted brain MRI e.g. heterotopia and blurred junction between the grey and white matter. The CAD output consists of patient specific 3D maps locating clusters of suspicious voxels ranked by size and degree of deviation from control patterns. System performance was evaluated using realistic simulations of challenging detection tasks as well as clinical data of 77 healthy control subjects and of eleven patients (13 lesions). It was compared to that of a mass univariate statistical parametric mapping (SPM) single subject analysis based on the same set of features. For all simulations, OC-SVM yielded significantly higher values of the area under the ROC curve (AUC) and higher sensitivity at low false positive rate. For the clinical data, both OC-SVM and SPM successfully detected 100% of the lesions in the MRI positive cases (3/13). For the MRI negative cases (10/13), OC-SVM detected 7/10 lesions and SPM analysis detected 5/10 lesions. In all experiments, OC-SVM produced fewer false positive detections than SPM. OC-SVM may be a versatile system for unbiased lesion detection.
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Affiliation(s)
- Meriem El Azami
- Université de Lyon, CREATIS; CNRS UMR5220; INSERM U1206; INSA-Lyon; Univ. Lyon 1, France
| | - Alexander Hammers
- Neurodis Foundation, Lyon, France
- PET Centre, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
| | - Julien Jung
- INSERM U1028/CNRS UMR5292, Lyon Neuroscience Research Center, Lyon, France
| | | | - Romain Bouet
- INSERM U1028/CNRS UMR5292, Lyon Neuroscience Research Center, Lyon, France
| | - Carole Lartizien
- Université de Lyon, CREATIS; CNRS UMR5220; INSERM U1206; INSA-Lyon; Univ. Lyon 1, France
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Retico A, Gori I, Giuliano A, Muratori F, Calderoni S. One-Class Support Vector Machines Identify the Language and Default Mode Regions As Common Patterns of Structural Alterations in Young Children with Autism Spectrum Disorders. Front Neurosci 2016; 10:306. [PMID: 27445675 PMCID: PMC4925658 DOI: 10.3389/fnins.2016.00306] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Accepted: 06/16/2016] [Indexed: 01/05/2023] Open
Abstract
The identification of reliable brain endophenotypes of autism spectrum disorders (ASD) has been hampered to date by the heterogeneity in the neuroanatomical abnormalities detected in this condition. To handle the complexity of neuroimaging data and to convert brain images in informative biomarkers of pathology, multivariate analysis techniques based on Support Vector Machines (SVM) have been widely used in several disease conditions. They are usually trained to distinguish patients from healthy control subjects by making a binary classification. Here, we propose the use of the One-Class Classification (OCC) or Data Description method that, in contrast to two-class classification, is based on a description of one class of objects only. This approach, by defining a multivariate normative rule on one class of subjects, allows recognizing examples from a different category as outliers. We applied the OCC to 314 regional features extracted from brain structural Magnetic Resonance Imaging (MRI) scans of young children with ASD (21 males and 20 females) and control subjects (20 males and 20 females), matched on age [range: 22-72 months of age; mean = 49 months] and non-verbal intelligence quotient (NVIQ) [range: 31-123; mean = 73]. We demonstrated that a common pattern of features characterize the ASD population. The OCC SVM trained on the group of ASD subjects showed the following performances in the ASD vs. controls separation: the area under the receiver operating characteristic curve (AUC) was 0.74 for the male and 0.68 for the female population, respectively. Notably, the ASD vs. controls discrimination results were maximized when evaluated on the subsamples of subjects with NVIQ ≥ 70, leading to AUC = 0.81 for the male and AUC = 0.72 for the female populations, respectively. Language regions and regions from the default mode network-posterior cingulate cortex, pars opercularis and pars triangularis of the inferior frontal gyrus, and transverse temporal gyrus-contributed most to distinguishing individuals with ASD from controls, arguing for the crucial role of these areas in the ASD pathophysiology. The observed brain patterns associate preschoolers with ASD independently of their age, gender and NVIQ and therefore they are expected to constitute part of the ASD brain endophenotype.
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Affiliation(s)
| | - Ilaria Gori
- Pisa Division, National Institute for Nuclear PhysicsPisa, Italy
- Department of Chemistry and Pharmacy, University of SassariSassari, Italy
| | - Alessia Giuliano
- Pisa Division, National Institute for Nuclear PhysicsPisa, Italy
- Department of Physics, University of PisaPisa, Italy
| | - Filippo Muratori
- Department of Developmental Neuroscience, IRCCS Stella Maris FoundationPisa, Italy
- Department of Clinical and Experimental Medicine, University of PisaPisa, Italy
| | - Sara Calderoni
- Department of Developmental Neuroscience, IRCCS Stella Maris FoundationPisa, Italy
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Retico A, Giuliano A, Tancredi R, Cosenza A, Apicella F, Narzisi A, Biagi L, Tosetti M, Muratori F, Calderoni S. The effect of gender on the neuroanatomy of children with autism spectrum disorders: a support vector machine case-control study. Mol Autism 2016; 7:5. [PMID: 26788282 PMCID: PMC4717545 DOI: 10.1186/s13229-015-0067-3] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Accepted: 12/30/2015] [Indexed: 01/07/2023] Open
Abstract
Background Genetic, hormonal, and environmental factors contribute since infancy to sexual dimorphism in regional brain structures of subjects with typical development. However, the neuroanatomical differences between male and female children with autism spectrum disorders (ASD) are an intriguing and still poorly investigated issue. This study aims to evaluate whether the brain of young children with ASD exhibits sex-related structural differences and if a correlation exists between clinical ASD features and neuroanatomical underpinnings. Methods A total of 152 structural MRI scans were analysed. Specifically, 76 young children with ASD (38 males and 38 females; 2–7 years of age; mean = 53 months, standard deviation = 17 months) were evaluated employing a support vector machine (SVM)-based analysis of the grey matter (GM). Group comparisons consisted of 76 age-, gender- and non-verbal-intelligence quotient-matched children with typical development or idiopathic developmental delay without autism. Results For both genders combined, SVM showed a significantly increased GM volume in young children with ASD with respect to control subjects, predominantly in the bilateral superior frontal gyrus (Brodmann area –BA– 10), bilateral precuneus (BA 31), bilateral superior temporal gyrus (BA 20/22), whereas less GM in patients with ASD was found in right inferior temporal gyrus (BA 37). For the within gender comparisons (i.e., females with ASD vs. controls and males with ASD vs. controls), two overlapping regions in bilateral precuneus (BA 31) and left superior frontal gyrus (BA 9/10) were detected. Sex-by-group analyses revealed in males with ASD compared to matched controls two male-specific regions of increased GM volume (left middle occipital gyrus—BA 19—and right superior temporal gyrus—BA 22). Comparisons in females with and without ASD demonstrated increased GM volumes predominantly in the bilateral frontal regions. Additional regions of significantly increased GM volume in the right anterior cingulate cortex (BA 32) and right cerebellum were typical only of females with ASD. Conclusions Despite the specific behavioural correlates of sex-dimorphism in ASD, brain morphology as yet remains unclear and requires future dedicated investigations. This study provides evidence of structural brain gender differences in young children with ASD that possibly contribute to the different phenotypic disease manifestations in males and females. Electronic supplementary material The online version of this article (doi:10.1186/s13229-015-0067-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alessandra Retico
- Istituto Nazionale di Fisica Nucleare, Pisa Division, Largo B. Pontecorvo 3, 56127 Pisa, Italy
| | - Alessia Giuliano
- Istituto Nazionale di Fisica Nucleare, Pisa Division, Largo B. Pontecorvo 3, 56127 Pisa, Italy ; University of Pisa, Department of Physics, Largo B. Pontecorvo 3, 56127 Pisa, Italy
| | | | - Angela Cosenza
- IRCCS Stella Maris Foundation, viale del Tirreno 331, 56018 Pisa, Italy
| | - Fabio Apicella
- IRCCS Stella Maris Foundation, viale del Tirreno 331, 56018 Pisa, Italy
| | - Antonio Narzisi
- IRCCS Stella Maris Foundation, viale del Tirreno 331, 56018 Pisa, Italy
| | - Laura Biagi
- IRCCS Stella Maris Foundation, viale del Tirreno 331, 56018 Pisa, Italy
| | - Michela Tosetti
- IRCCS Stella Maris Foundation, viale del Tirreno 331, 56018 Pisa, Italy
| | - Filippo Muratori
- IRCCS Stella Maris Foundation, viale del Tirreno 331, 56018 Pisa, Italy ; University of Pisa, Department of Clinical and Experimental Medicine, Via Savi 10, 56126 Pisa, Italy
| | - Sara Calderoni
- IRCCS Stella Maris Foundation, viale del Tirreno 331, 56018 Pisa, Italy
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PrediQt-Cx: post treatment health related quality of life prediction model for cervical cancer patients. PLoS One 2014; 9:e89851. [PMID: 24587074 PMCID: PMC3935936 DOI: 10.1371/journal.pone.0089851] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2013] [Accepted: 01/26/2014] [Indexed: 12/22/2022] Open
Abstract
Background Cervical cancer is the third largest cause of cancer mortality in India. The objectives of the study were to compare the pre and the post treatment quality of life in cervical cancer patients and to develop a prediction model to provide an insight into the possibilities in the treatment modules. Methodology/Principal Findings A total of 198 patients were assessed with two structured questionnaires of Health Related Quality of Life (The European Organisation for Research and Treatment of Cancer, EORTC QLQ C-30 and CX-24). The baseline observations were recorded when the patients first reported (T1) and second evaluation was done at 6 months post treatment (T2). The mean age of detection was 50.9 years with the literacy level being non-educated or less than high school. Majority of them were married/cohabiting 179 (90.4%). On histopathological examination (HPE) squamous cell carcinoma was found to be the most common cell type carcinoma 147 (74.2%) followed by Adenocarcinoma 31 (15.7%). Radical hysterectomy was the most common treatment modality 76 (38.4%), followed by Wertheims Hysterectomy 46 (23.2%) and Radiochemotherapy 59 (29.8%). The mean score of global health of cervical cancer patients post treatment was 77.90, which was significantly higher than the pre - treatment score (54.32). Mean “symptoms score” post treatment was 21.69 with an aggravation of 7.32 compared to pre treatment scores. Patients experienced substantial decrease in sexual activity post treatment. Conclusions/Significance The prediction model(PrediQt-Cx), based on Support Vector Machine(SVM) for predicting post treatment HRQoL in cervical cancer patients was developed and internally cross validated. After external validation PrediQt-Cx can be easily employed to support decision making by clinicians and patients from north India region, through openly made available for access at http://prediqt.org.
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