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Kim SY, Yeh PH, Ollinger JM, Morris HD, Hood MN, Ho VB, Choi KH. Military-related mild traumatic brain injury: clinical characteristics, advanced neuroimaging, and molecular mechanisms. Transl Psychiatry 2023; 13:289. [PMID: 37652994 PMCID: PMC10471788 DOI: 10.1038/s41398-023-02569-1] [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: 10/31/2022] [Revised: 07/18/2023] [Accepted: 07/24/2023] [Indexed: 09/02/2023] Open
Abstract
Mild traumatic brain injury (mTBI) is a significant health burden among military service members. Although mTBI was once considered relatively benign compared to more severe TBIs, a growing body of evidence has demonstrated the devastating neurological consequences of mTBI, including chronic post-concussion symptoms and deficits in cognition, memory, sleep, vision, and hearing. The discovery of reliable biomarkers for mTBI has been challenging due to under-reporting and heterogeneity of military-related mTBI, unpredictability of pathological changes, and delay of post-injury clinical evaluations. Moreover, compared to more severe TBI, mTBI is especially difficult to diagnose due to the lack of overt clinical neuroimaging findings. Yet, advanced neuroimaging techniques using magnetic resonance imaging (MRI) hold promise in detecting microstructural aberrations following mTBI. Using different pulse sequences, MRI enables the evaluation of different tissue characteristics without risks associated with ionizing radiation inherent to other imaging modalities, such as X-ray-based studies or computerized tomography (CT). Accordingly, considering the high morbidity of mTBI in military populations, debilitating post-injury symptoms, and lack of robust neuroimaging biomarkers, this review (1) summarizes the nature and mechanisms of mTBI in military settings, (2) describes clinical characteristics of military-related mTBI and associated comorbidities, such as post-traumatic stress disorder (PTSD), (3) highlights advanced neuroimaging techniques used to study mTBI and the molecular mechanisms that can be inferred, and (4) discusses emerging frontiers in advanced neuroimaging for mTBI. We encourage multi-modal approaches combining neuropsychiatric, blood-based, and genetic data as well as the discovery and employment of new imaging techniques with big data analytics that enable accurate detection of post-injury pathologic aberrations related to tissue microstructure, glymphatic function, and neurodegeneration. Ultimately, this review provides a foundational overview of military-related mTBI and advanced neuroimaging techniques that merit further study for mTBI diagnosis, prognosis, and treatment monitoring.
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Affiliation(s)
- Sharon Y Kim
- School of Medicine, Uniformed Services University, Bethesda, MD, USA
- Program in Neuroscience, Uniformed Services University, Bethesda, MD, USA
| | - Ping-Hong Yeh
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - John M Ollinger
- Program in Neuroscience, Uniformed Services University, Bethesda, MD, USA
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Herman D Morris
- Department of Radiology and Radiological Sciences, Uniformed Services University, Bethesda, MD, USA
- Department of Radiology, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Maureen N Hood
- Department of Radiology and Radiological Sciences, Uniformed Services University, Bethesda, MD, USA
- Department of Radiology, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Vincent B Ho
- Department of Radiology and Radiological Sciences, Uniformed Services University, Bethesda, MD, USA
- Department of Radiology, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Kwang H Choi
- Program in Neuroscience, Uniformed Services University, Bethesda, MD, USA.
- Center for the Study of Traumatic Stress, Uniformed Services University, Bethesda, MD, USA.
- Department of Psychiatry, Uniformed Services University, Bethesda, MD, USA.
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2
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Pat N, Wang Y, Bartonicek A, Candia J, Stringaris A. Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition. Cereb Cortex 2023; 33:2682-2703. [PMID: 35697648 PMCID: PMC10016053 DOI: 10.1093/cercor/bhac235] [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: 02/04/2021] [Revised: 04/27/2022] [Accepted: 04/28/2022] [Indexed: 11/13/2022] Open
Abstract
Despite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based functional magnetic resonance imaging (fMRI). Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n = 3,989). We compared 9 predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and ordinary least squares (OLS) multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than nonlinear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects, and Friedman's H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.
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Affiliation(s)
- Narun Pat
- Department of Psychology, University of Otago, William James Building, 275 Leith Walk, Dunedin 9016, New Zealand
| | - Yue Wang
- Department of Psychology, University of Otago, William James Building, 275 Leith Walk, Dunedin 9016, New Zealand
| | - Adam Bartonicek
- Department of Psychology, University of Otago, William James Building, 275 Leith Walk, Dunedin 9016, New Zealand
| | - Julián Candia
- Longitudinal Studies Section, Translational Gerontology National Institute on Aging, National Institute of Health, Branch, 251 Bayview Boulevard, Rm 05B113A, Biomedical Research Center, Baltimore, MD 21224, USA
| | - Argyris Stringaris
- Division of Psychiatry and Department of Clinical, Educational – Health Psychology, University College London, 1-19 Torrington Pl, London WC1E 7HB, United Kingdom
- Department of Psychiatry, National and Kapodistrian University of Athens, Medical School, Mikras Asias 75, Athina 115 27, Greece
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3
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MRI data quality assessment for the RIN - Neuroimaging Network using the ACR phantoms. Phys Med 2022; 104:93-100. [PMID: 36379160 DOI: 10.1016/j.ejmp.2022.10.008] [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: 06/24/2022] [Revised: 09/20/2022] [Accepted: 10/08/2022] [Indexed: 11/13/2022] Open
Abstract
PURPOSE Generating big-data is becoming imperative with the advent of machine learning. RIN-Neuroimaging Network addresses this need by developing harmonized protocols for multisite studies to identify quantitative MRI (qMRI) biomarkers for neurological diseases. In this context, image quality control (QC) is essential. Here, we present methods and results of how the RIN performs intra- and inter-site reproducibility of geometrical and image contrast parameters, demonstrating the relevance of such QC practice. METHODS American College of Radiology (ACR) large and small phantoms were selected. Eighteen sites were equipped with a 3T scanner that differed by vendor, hardware/software versions, and receiver coils. The standard ACR protocol was optimized (in-plane voxel, post-processing filters, receiver bandwidth) and repeated monthly. Uniformity, ghosting, geometric accuracy, ellipse's ratio, slice thickness, and high-contrast detectability tests were performed using an automatic QC script. RESULTS Measures were mostly within the ACR tolerance ranges for both T1- and T2-weighted acquisitions, for all scanners, regardless of vendor, coil, and signal transmission chain type. All measurements showed good reproducibility over time. Uniformity and slice thickness failed at some sites. Scanners that upgraded the signal transmission chain showed a decrease in geometric distortion along the slice encoding direction. Inter-vendor differences were observed in uniformity and geometric measurements along the slice encoding direction (i.e. ellipse's ratio). CONCLUSIONS Use of the ACR phantoms highlighted issues that triggered interventions to correct performance at some sites and to improve the longitudinal stability of the scanners. This is relevant for establishing precision levels for future multisite studies of qMRI biomarkers.
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Sendi MSE, Salat DH, Miller RL, Calhoun VD. Two-step clustering-based pipeline for big dynamic functional network connectivity data. Front Neurosci 2022; 16:895637. [PMID: 35958983 PMCID: PMC9358255 DOI: 10.3389/fnins.2022.895637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/29/2022] [Indexed: 11/13/2022] Open
Abstract
Background Dynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic imaging (rs-fMRI) studies the temporally varying functional integration between brain networks. In a conventional dFNC pipeline, a clustering stage to summarize the connectivity patterns that are transiently but reliably realized over the course of a scanning session. However, identifying the right number of clusters (or states) through a conventional clustering criterion computed by running the algorithm repeatedly over a large range of cluster numbers is time-consuming and requires substantial computational power even for typical dFNC datasets, and the computational demands become prohibitive as datasets become larger and scans longer. Here we developed a new dFNC pipeline based on a two-step clustering approach to analyze large dFNC data without having access to huge computational power. Methods In the proposed dFNC pipeline, we implement two-step clustering. In the first step, we randomly use a sub-sample dFNC data and identify several sets of states at different model orders. In the second step, we aggregate all dFNC states estimated from all iterations in the first step and use this to identify the optimum number of clusters using the elbow criteria. Additionally, we use this new reduced dataset and estimate a final set of states by performing a second kmeans clustering on the aggregated dFNC states from the first k-means clustering. To validate the reproducibility of results in the new pipeline, we analyzed four dFNC datasets from the human connectome project (HCP). Results We found that both conventional and proposed dFNC pipelines generate similar brain dFNC states across all four sessions with more than 99% similarity. We found that the conventional dFNC pipeline evaluates the clustering order and finds the final dFNC state in 275 min, while this process takes only 11 min for the proposed dFNC pipeline. In other words, the new pipeline is 25 times faster than the traditional method in finding the optimum number of clusters and finding the final dFNC states. We also found that the new method results in better clustering quality than the conventional approach (p < 0.001). We show that the results are replicated across four different datasets from HCP. Conclusion We developed a new analytic pipeline that facilitates the analysis of large dFNC datasets without having access to a huge computational power source. We validated the reproducibility of the result across multiple datasets.
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Affiliation(s)
- Mohammad S. E. Sendi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- *Correspondence: Mohammad S. E. Sendi,
| | - David H. Salat
- Harvard Medical School, Boston, MA, United States
- Massachusetts General Hospital, Boston, MA, United States
| | - Robyn L. Miller
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
- Vince D. Calhoun,
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Marusak HA. Editorial: Growing up in a High-Stigma Context: An Unseen Driver of Neurodevelopment, Health, and Pathology. J Am Acad Child Adolesc Psychiatry 2022; 61:749-751. [PMID: 34688856 DOI: 10.1016/j.jaac.2021.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 10/14/2021] [Indexed: 11/17/2022]
Abstract
Growing research establishes that stigma can play a key role in driving population health inequalities, particularly among minority groups.1 Indeed, stigma related to gender, race/ethnicity, sexual orientation, mental illness, or disability has been linked to elevated risk of mental and physical health problems ranging from depression to cardiovascular disease. Recent neuroimaging studies point to changes in brain structure and function-particularly in stress-responsive brain networks-in mediating the link between stigma and risk of mental disorders. For example, recent neuroimaging research links racial discrimination to elevated neural response to threat in Black women in the United States.2 Importantly, stigma can occur at multiple levels, and most research to date has focused on stigma experienced at individual or interpersonal levels (eg, identity concealment, victimization). However, structural stigma (eg, laws and social norms) is increasingly recognized as a critical social determinant of health for people in marginalized groups. However, structural stigma is methodologically challenging to study at the level of neurobiology because relatively large, multisite studies are needed that span different sociopolitical contexts. An innovative study by Hatzenbuehler et al.3 in this issue of the Journal examines for the first time the impact of structural stigma on neurodevelopment in children. This study paves the way for future studies examining how contexts-particularly stigmatizing contexts-can act as unseen drivers of neurodevelopment, health, or pathology.
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Manjón JV, Romero JE, Vivo-Hernando R, Rubio G, Aparici F, de la Iglesia-Vaya M, Coupé P. vol2Brain: A New Online Pipeline for Whole Brain MRI Analysis. Front Neuroinform 2022; 16:862805. [PMID: 35685943 PMCID: PMC9171328 DOI: 10.3389/fninf.2022.862805] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 04/07/2022] [Indexed: 11/13/2022] Open
Abstract
Automatic and reliable quantitative tools for MR brain image analysis are a very valuable resource for both clinical and research environments. In the past few years, this field has experienced many advances with successful techniques based on label fusion and more recently deep learning. However, few of them have been specifically designed to provide a dense anatomical labeling at the multiscale level and to deal with brain anatomical alterations such as white matter lesions (WML). In this work, we present a fully automatic pipeline (vol2Brain) for whole brain segmentation and analysis, which densely labels (N > 100) the brain while being robust to the presence of WML. This new pipeline is an evolution of our previous volBrain pipeline that extends significantly the number of regions that can be analyzed. Our proposed method is based on a fast and multiscale multi-atlas label fusion technology with systematic error correction able to provide accurate volumetric information in a few minutes. We have deployed our new pipeline within our platform volBrain (www.volbrain.upv.es), which has been already demonstrated to be an efficient and effective way to share our technology with the users worldwide.
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Affiliation(s)
- José V. Manjón
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
- *Correspondence: José V. Manjón
| | - José E. Romero
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
| | - Roberto Vivo-Hernando
- Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain
| | - Gregorio Rubio
- Departamento de Matemática Aplicada, Universitat Politècnica de València, Valencia, Spain
| | - Fernando Aparici
- Área de Imagen Medica, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Mariam de la Iglesia-Vaya
- Unidad Mixta de Imagen Biomédica FISABIO-CIPF, Fundación Para el Fomento de la Investigación Sanitario y Biomédica de la Comunidad Valenciana, Valencia, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, ISC III, València, Spain
| | - Pierrick Coupé
- Centre National de la Recherche Scientifique, Univ. Bordeaux, Bordeaux INP, Laboratoire Bordelais de Recherche en Informatique, UMR5800, PICTURA, Talence, France
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Nigri A, Ferraro S, Gandini Wheeler-Kingshott CAM, Tosetti M, Redolfi A, Forloni G, D'Angelo E, Aquino D, Biagi L, Bosco P, Carne I, De Francesco S, Demichelis G, Gianeri R, Lagana MM, Micotti E, Napolitano A, Palesi F, Pirastru A, Savini G, Alberici E, Amato C, Arrigoni F, Baglio F, Bozzali M, Castellano A, Cavaliere C, Contarino VE, Ferrazzi G, Gaudino S, Marino S, Manzo V, Pavone L, Politi LS, Roccatagliata L, Rognone E, Rossi A, Tonon C, Lodi R, Tagliavini F, Bruzzone MG. Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN-Neuroimaging Network. Front Neurol 2022; 13:855125. [PMID: 35493836 PMCID: PMC9047871 DOI: 10.3389/fneur.2022.855125] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 03/17/2022] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures.
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Affiliation(s)
- Anna Nigri
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Stefania Ferraro
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Unità di Neuroradiologia, IRCCS Mondino Foundation, Pavia, Italy
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Michela Tosetti
- Medical Physics and MR Lab, Fondazione IRCCS Stella Maris, Pisa, Italy
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Gianluigi Forloni
- Medical Physics and MR Lab, Fondazione IRCCS Stella Maris, Pisa, Italy
| | - Egidio D'Angelo
- Unità di Neuroradiologia, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Domenico Aquino
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Laura Biagi
- Medical Physics and MR Lab, Fondazione IRCCS Stella Maris, Pisa, Italy
| | - Paolo Bosco
- Medical Physics and MR Lab, Fondazione IRCCS Stella Maris, Pisa, Italy
| | - Irene Carne
- Neuroradiology Unit, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Greta Demichelis
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Ruben Gianeri
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | - Edoardo Micotti
- Laboratory of Biology of Neurodegenerative Disorders, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Antonio Napolitano
- Medical Physics, IRCCS Istituto Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Fulvia Palesi
- Unità di Neuroradiologia, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | - Giovanni Savini
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Elisa Alberici
- Neuroradiology Unit, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Carmelo Amato
- Unit of Neuroradiology, Oasi Research Institute-IRCCS, Troina, Italy
| | - Filippo Arrigoni
- Neuroimaging Unit, Scientific Institute, IRCCS E. Medea, Bosisio Parini, Italy
| | | | - Marco Bozzali
- Neuroimaging Laboratory, Santa Lucia Foundation, IRCCS, Rome, Italy
| | | | | | - Valeria Elisa Contarino
- Unità di Neuroradiologia, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | | | - Simona Gaudino
- Istituto di Radiologia, UOC Radiologia e Neuroradiologia, IRCCS Fondazione Policlinico Universitario Agostino Gemelli, Rome, Italy
| | - Silvia Marino
- IRCCS Centro Neurolesi “Bonino-Pulejo”, Messina, Italy
| | - Vittorio Manzo
- Department of Radiology, Istituto Auxologico Italiano, IRCCS, Milan, Italy
| | | | - Letterio S. Politi
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Luca Roccatagliata
- Neuroradiologia IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Dipartimento di Scienze della Salute Università di Genova, Genoa, Italy
| | - Elisa Rognone
- Unità di Neuroradiologia, IRCCS Mondino Foundation, Pavia, Italy
| | - Andrea Rossi
- Dipartimento di Scienze della Salute Università di Genova, Genoa, Italy
- UO Neuroradiologia, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Caterina Tonon
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Raffaele Lodi
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Fabrizio Tagliavini
- Scientific Direction, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Maria Grazia Bruzzone
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
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WeBrain: A web-based brainformatics platform of computational ecosystem for EEG big data analysis. Neuroimage 2021; 245:118713. [PMID: 34798231 DOI: 10.1016/j.neuroimage.2021.118713] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 10/25/2021] [Accepted: 11/04/2021] [Indexed: 01/06/2023] Open
Abstract
The current evolution of 'cloud neuroscience' leads to more efforts with the large-scale EEG applications, by using EEG pipelines to handle the rapidly accumulating EEG data. However, there are a few specific cloud platforms that seek to address the cloud computational challenges of EEG big data analysis to benefit the EEG community. In response to the challenges, a WeBrain cloud platform (https://webrain.uestc.edu.cn/) is designed as a web-based brainformatics platform and computational ecosystem to enable large-scale EEG data storage, exploration and analysis using cloud high-performance computing (HPC) facilities. WeBrain connects researchers from different fields to EEG and multimodal tools that have become the norm in the field and the cloud processing power required to handle those large EEG datasets. This platform provides an easy-to-use system for novice users (even no computer programming skills) and provides satisfactory maintainability, sustainability and flexibility for IT administrators and tool developers. A range of resources are also available on https://webrain.uestc.edu.cn/, including documents, manuals, example datasets related to WeBrain, and collected links to open EEG datasets and tools. It is not necessary for users or administrators to install any software or system, and all that is needed is a modern web browser, which reduces the technical expertise required to use or manage WeBrain. The WeBrain platform is sponsored and driven by the China-Canada-Cuba international brain cooperation project (CCC-Axis, http://ccc-axis.org/), and we hope that WeBrain will be a promising cloud brainformatics platform for exploring brain information in large-scale EEG applications in the EEG community.
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de Brito Robalo BM, Biessels GJ, Chen C, Dewenter A, Duering M, Hilal S, Koek HL, Kopczak A, Yin Ka Lam B, Leemans A, Mok V, Onkenhout LP, van den Brink H, de Luca A. Diffusion MRI harmonization enables joint-analysis of multicentre data of patients with cerebral small vessel disease. Neuroimage Clin 2021; 32:102886. [PMID: 34911192 PMCID: PMC8609094 DOI: 10.1016/j.nicl.2021.102886] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 11/16/2021] [Indexed: 01/10/2023]
Abstract
OBJECTIVES Acquisition-related differences in diffusion magnetic resonance imaging (dMRI) hamper pooling of multicentre data to achieve large sample sizes. A promising solution is to harmonize the raw diffusion signal using rotation invariant spherical harmonic (RISH) features, but this has not been tested in elderly subjects. Here we aimed to establish if RISH harmonization effectively removes acquisition-related differences in multicentre dMRI of elderly subjects with cerebral small vessel disease (SVD), while preserving sensitivity to disease effects. METHODS Five cohorts of patients with SVD (N = 397) and elderly controls (N = 175) with 3 Tesla MRI on different systems were included. First, to establish effectiveness of harmonization, the RISH method was trained with data of 13 to 15 age and sex-matched controls from each site. Fractional anisotropy (FA) and mean diffusivity (MD) were compared in matched controls between sites using tract-based spatial statistics (TBSS) and voxel-wise analysis, before and after harmonization. Second, to assess sensitivity to disease effects, we examined whether the contrast (effect sizes of FA, MD and peak width of skeletonized MD - PSMD) between patients and controls within each site remained unaffected by harmonization. Finally, we evaluated the association between white matter hyperintensity (WMH) burden, FA, MD and PSMD using linear regression analyses both within individual cohorts as well as with pooled scans from multiple sites, before and after harmonization. RESULTS Before harmonization, significant differences in FA and MD were observed between matched controls of different sites (p < 0.05). After harmonization these site-differences were removed. Within each site, RISH harmonization did not alter the effect sizes of FA, MD and PSMD between patients and controls (relative change in Cohen's d = 4 %) nor the strength of association with WMH volume (relative change in R2 = 2.8 %). After harmonization, patient data of all sites could be aggregated in a single analysis to infer the association between WMH volume and FA (R2 = 0.62), MD (R2 = 0.64), and PSMD (R2 = 0.60). CONCLUSIONS We showed that RISH harmonization effectively removes acquisition-related differences in dMRI of elderly subjects while preserving sensitivity to SVD-related effects. This study provides proof of concept for future multicentre SVD studies with pooled datasets.
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Affiliation(s)
- Bruno M de Brito Robalo
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Christopher Chen
- Memory, Aging and Cognition Center, Department of Pharmacology, National University of Singapore, Singapore.
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany.
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany; Medical Image Analysis Center (MIAC AG) and qbig, Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
| | - Saima Hilal
- Memory, Aging and Cognition Center, Department of Pharmacology, National University of Singapore, Singapore.
| | - Huiberdina L Koek
- Department of Geriatric Medicine, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Anna Kopczak
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany.
| | - Bonnie Yin Ka Lam
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region.
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Vincent Mok
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region.
| | - Laurien P Onkenhout
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Hilde van den Brink
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Alberto de Luca
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
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10
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Fully automated quality control of rigid and affine registrations of T1w and T2w MRI in big data using machine learning. Comput Biol Med 2021; 139:104997. [PMID: 34753079 DOI: 10.1016/j.compbiomed.2021.104997] [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: 08/17/2021] [Revised: 10/06/2021] [Accepted: 10/26/2021] [Indexed: 11/23/2022]
Abstract
BACKGROUND Magnetic resonance imaging (MRI)-based morphometry and relaxometry are proven methods for the structural assessment of the human brain in several neurological disorders. These procedures are generally based on T1-weighted (T1w) and/or T2-weighted (T2w) MRI scans, and rigid and affine registrations to a standard template(s) are essential steps in such studies. Therefore, a fully automatic quality control (QC) of these registrations is necessary in big data scenarios to ensure that they are suitable for subsequent processing. METHOD A supervised machine learning (ML) framework is proposed by computing similarity metrics such as normalized cross-correlation, normalized mutual information, and correlation ratio locally. We have used these as candidate features for cross-validation and testing of different ML classifiers. For 5-fold repeated stratified grid search cross-validation, 400 correctly aligned, 2000 randomly generated misaligned images were used from the human connectome project young adult (HCP-YA) dataset. To test the cross-validated models, the datasets from autism brain imaging data exchange (ABIDE I) and information eXtraction from images (IXI) were used. RESULTS The ensemble classifiers, random forest, and AdaBoost yielded best performance with F1-scores, balanced accuracies, and Matthews correlation coefficients in the range of 0.95-1.00 during cross-validation. The predictive accuracies reached 0.99 on the Test set #1 (ABIDE I), 0.99 without and 0.96 with noise on Test set #2 (IXI, stratified w.r.t scanner vendor and field strength). CONCLUSIONS The cross-validated and tested ML models could be used for QC of both T1w and T2w rigid and affine registrations in large-scale MRI studies.
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11
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Pontoriero AD, Nordio G, Easmin R, Giacomel A, Santangelo B, Jahuar S, Bonoldi I, Rogdaki M, Turkheimer F, Howes O, Veronese M. Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106239. [PMID: 34289438 PMCID: PMC8404039 DOI: 10.1016/j.cmpb.2021.106239] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 06/10/2021] [Indexed: 06/07/2023]
Abstract
INTRODUCTION With biomedical imaging research increasingly using large datasets, it becomes critical to find operator-free methods to quality control the data collected and the associated analysis. Attempts to use artificial intelligence (AI) to perform automated quality control (QC) for both single-site and multi-site datasets have been explored in some neuroimaging techniques (e.g. EEG or MRI), although these methods struggle to find replication in other domains. The aim of this study is to test the feasibility of an automated QC pipeline for brain [18F]-FDOPA PET imaging as a biomarker for the dopamine system. METHODS Two different Convolutional Neural Networks (CNNs) were used and combined to assess spatial misalignment to a standard template and the signal-to-noise ratio (SNR) relative to 200 static [18F]-FDOPA PET images that had been manually quality controlled from three different PET/CT scanners. The scans were combined with an additional 400 scans, in which misalignment (200 scans) and low SNR (200 scans) were simulated. A cross-validation was performed, where 80% of the data were used for training and 20% for validation. Two additional datasets of [18F]-FDOPA PET images (50 and 100 scans respectively with at least 80% of good quality images) were used for out-of-sample validation. RESULTS The CNN performance was excellent in the training dataset (accuracy for motion: 0.86 ± 0.01, accuracy for SNR: 0.69 ± 0.01), leading to 100% accurate QC classification when applied to the two out-of-sample datasets. Data dimensionality reduction affected the generalizability of the CNNs, especially when the classifiers were applied to the out-of-sample data from 3D to 1D datasets. CONCLUSIONS This feasibility study shows that it is possible to perform automatic QC of [18F]-FDOPA PET imaging with CNNs. The approach has the potential to be extended to other PET tracers in both brain and non-brain applications, but it is dependent on the availability of large datasets necessary for the algorithm training.
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Affiliation(s)
- Antonella D Pontoriero
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Giovanna Nordio
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
| | - Rubaida Easmin
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Alessio Giacomel
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Barbara Santangelo
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Sameer Jahuar
- Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Ilaria Bonoldi
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Maria Rogdaki
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Federico Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Oliver Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; H. Lundbeck UK, Ottiliavej 9 2500 Valby, Denmark; Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, Du Cane Road, London W12 0NN
| | - Mattia Veronese
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; Department of Information Engineering, University of Padua, Padua, Italy
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12
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Virtual Connectomic Datasets in Alzheimer's Disease and Aging Using Whole-Brain Network Dynamics Modelling. eNeuro 2021; 8:ENEURO.0475-20.2021. [PMID: 34045210 PMCID: PMC8260273 DOI: 10.1523/eneuro.0475-20.2021] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/08/2021] [Accepted: 04/12/2021] [Indexed: 12/18/2022] Open
Abstract
Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring “virtual FC” from empirical SC or “virtual SC” from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.
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13
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Abstract
Brain scientists are now capable of collecting more data in a single experiment than researchers a generation ago might have collected over an entire career. Indeed, the brain itself seems to thirst for more and more data. Such digital information not only comprises individual studies but is also increasingly shared and made openly available for secondary, confirmatory, and/or combined analyses. Numerous web resources now exist containing data across spatiotemporal scales. Data processing workflow technologies running via cloud-enabled computing infrastructures allow for large-scale processing. Such a move toward greater openness is fundamentally changing how brain science results are communicated and linked to available raw data and processed results. Ethical, professional, and motivational issues challenge the whole-scale commitment to data-driven neuroscience. Nevertheless, fueled by government investments into primary brain data collection coupled with increased sharing and community pressure challenging the dominant publishing model, large-scale brain and data science is here to stay.
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Affiliation(s)
- John Darrell Van Horn
- Department of Psychology, University of Virginia, Charlottesville, Virginia, USA
- School of Data Science, University of Virginia, Charlottesville, Virginia, USA
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14
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Huguet J, Falcon C, Fusté D, Girona S, Vicente D, Molinuevo JL, Gispert JD, Operto G. Management and Quality Control of Large Neuroimaging Datasets: Developments From the Barcelonaβeta Brain Research Center. Front Neurosci 2021; 15:633438. [PMID: 33935631 PMCID: PMC8081968 DOI: 10.3389/fnins.2021.633438] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 03/02/2021] [Indexed: 12/02/2022] Open
Abstract
Recent decades have witnessed an increasing number of large to very large imaging studies, prominently in the field of neurodegenerative diseases. The datasets collected during these studies form essential resources for the research aiming at new biomarkers. Collecting, hosting, managing, processing, or reviewing those datasets is typically achieved through a local neuroinformatics infrastructure. In particular for organizations with their own imaging equipment, setting up such a system is still a hard task, and relying on cloud-based solutions, albeit promising, is not always possible. This paper proposes a practical model guided by core principles including user involvement, lightweight footprint, modularity, reusability, and facilitated data sharing. This model is based on the experience from an 8-year-old research center managing cohort research programs on Alzheimer’s disease. Such a model gave rise to an ecosystem of tools aiming at improved quality control through seamless automatic processes combined with a variety of code libraries, command line tools, graphical user interfaces, and instant messaging applets. The present ecosystem was shaped around XNAT and is composed of independently reusable modules that are freely available on GitLab/GitHub. This paradigm is scalable to the general community of researchers working with large neuroimaging datasets.
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Affiliation(s)
- Jordi Huguet
- Barcelonabeta Brain Research Center, Barcelona, Spain
| | - Carles Falcon
- Barcelonabeta Brain Research Center, Barcelona, Spain
| | - David Fusté
- Barcelonabeta Brain Research Center, Barcelona, Spain
| | - Sergi Girona
- Barcelona Supercomputing Center, Barcelona, Spain
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15
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Barbone GE, Bravin A, Mittone A, Grosu S, Ricke J, Cavaletti G, Djonov V, Coan P. High-Spatial-Resolution Three-dimensional Imaging of Human Spinal Cord and Column Anatomy with Postmortem X-ray Phase-Contrast Micro-CT. Radiology 2021; 298:135-146. [DOI: 10.1148/radiol.2020201622] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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16
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Chen L, Yan J, Chen J, Sheng Y, Xu Z, Mahmud M. An event based topic learning pipeline for neuroimaging literature mining. Brain Inform 2020; 7:18. [PMID: 33226547 PMCID: PMC7683633 DOI: 10.1186/s40708-020-00121-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 10/31/2020] [Indexed: 11/20/2022] Open
Abstract
Neuroimaging text mining extracts knowledge from neuroimaging texts and has received widespread attention. Topic learning is an important research focus of neuroimaging text mining. However, current neuroimaging topic learning researches mainly used traditional probability topic models to extract topics from literature and cannot obtain high-quality neuroimaging topics. The existing topic learning methods also cannot meet the requirements of topic learning oriented to full-text neuroimaging literature. In this paper, three types of neuroimaging research topic events are defined to describe the process and result of neuroimaging researches. An event based topic learning pipeline, called neuroimaging Event-BTM, is proposed to realize topic learning from full-text neuroimaging literature. The experimental results on the PLoS One data set show that the accuracy and completeness of the proposed method are significantly better than the existing main topic learning methods.
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Affiliation(s)
- Lihong Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China
| | - Jianzhuo Yan
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Engineering Research Center of Digital Community, Beijing University of Technology, Beijing, 100124, China
| | - Jianhui Chen
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing University of Technology, Beijing, 100124, China. .,Beijing Key Laboratory of MRI and Brain Informatics, Beijing University of Technology, Beijing, 100124, China.
| | - Ying Sheng
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Zhe Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Mufti Mahmud
- Department of Computing & Technology, Nottingham Trent University, Nottingham, NG11 8NS, UK
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17
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Hamamci A. Cellular Automata Tractography: Fast Geodesic Diffusion MR Tractography and Connectivity Based Segmentation on the GPU. Neuroinformatics 2020; 18:25-41. [PMID: 30997599 DOI: 10.1007/s12021-019-09425-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Geodesic based tractography on diffusion magnetic resonance data is a method to devise long distance connectivities among the brain regions. In this study, cellular automata technique is applied to the geodesic tractography problem and the algorithm is implemented on a graphics processing unit. Cellular automaton based method is preferable to current techniques due to its parallel nature and ability to solve the connectivity based segmentation problem with the same computational complexity, which has important applications in neuroimaging. An application to prior-less tracking and connectivity based segmentation of corpus callosum fibers is presented as an example. A geodesic tractography based corpus callosum atlas is provided, which reveals high projections to the cortical language areas. The developed method not only allows fast computation especially for segmentation but also provides a powerful and intuitive framework, suitable to derive new algorithms to perform connectivity calculations and allowing novel applications.
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Affiliation(s)
- Andac Hamamci
- Faculty of Engineering, Department of Biomedical Engineering, Yeditepe University, Istanbul, Turkey.
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18
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Amunts K, Mohlberg H, Bludau S, Zilles K. Julich-Brain: A 3D probabilistic atlas of the human brain’s
cytoarchitecture. Science 2020; 369:988-992. [DOI: 10.1126/science.abb4588] [Citation(s) in RCA: 117] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 06/24/2020] [Indexed: 12/13/2022]
Abstract
Cytoarchitecture is a basic principle of microstructural brain parcellation.
We introduce Julich-Brain, a three-dimensional atlas containing cytoarchitectonic
maps of cortical areas and subcortical nuclei. The atlas is probabilistic, which
enables it to account for variations between individual brains. Building such an
atlas was highly data- and labor-intensive and required the development of nested,
interdependent workflows for detecting borders between brain areas, data
processing, provenance tracking, and flexible execution of processing chains to
handle large amounts of data at different spatial scales. Full cortical coverage
was achieved by the inclusion of gap maps to complement cortical maps. The atlas
is dynamic and will be adapted as mapping progresses; it is openly available to
support neuroimaging studies as well as modeling and simulation; and it is
interoperable, enabling connection to other atlases and resources.
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Affiliation(s)
- Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- C. and O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Hartmut Mohlberg
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Sebastian Bludau
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Karl Zilles
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
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Myszczynska MA, Ojamies PN, Lacoste AMB, Neil D, Saffari A, Mead R, Hautbergue GM, Holbrook JD, Ferraiuolo L. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat Rev Neurol 2020; 16:440-456. [DOI: 10.1038/s41582-020-0377-8] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/09/2020] [Indexed: 12/11/2022]
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20
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Rittman T. Neurological update: neuroimaging in dementia. J Neurol 2020; 267:3429-3435. [PMID: 32638104 PMCID: PMC7578138 DOI: 10.1007/s00415-020-10040-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 06/26/2020] [Accepted: 06/30/2020] [Indexed: 12/18/2022]
Abstract
Neuroimaging for dementia has made remarkable progress in recent years, shedding light on diagnostic subtypes of dementia, predicting prognosis and monitoring pathology. This review covers some updates in the understanding of dementia using structural imaging, positron emission tomography (PET), structural and functional connectivity, and using big data and artificial intelligence. Progress with neuroimaging methods allows neuropathology to be examined in vivo, providing a suite of biomarkers for understanding neurodegeneration and for application in clinical trials. In addition, we highlight quantitative susceptibility imaging as an exciting new technique that may prove to be a sensitive biomarker for a range of neurodegenerative diseases. There are challenges in translating novel imaging techniques to clinical practice, particularly in developing standard methodologies and overcoming regulatory issues. It is likely that clinicians will need to lead the way if these obstacles are to be overcome. Continued efforts applying neuroimaging to understand mechanisms of neurodegeneration and translating them to clinical practice will complete a revolution in neuroimaging.
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Affiliation(s)
- Timothy Rittman
- Department of Neurosciences, University of Cambridge, Cambridge, UK.
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21
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Gurve D, Delisle-Rodriguez D, Bastos-Filho T, Krishnan S. Trends in Compressive Sensing for EEG Signal Processing Applications. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3703. [PMID: 32630685 PMCID: PMC7374282 DOI: 10.3390/s20133703] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 06/17/2020] [Accepted: 06/23/2020] [Indexed: 11/16/2022]
Abstract
The tremendous progress of big data acquisition and processing in the field of neural engineering has enabled a better understanding of the patient's brain disorders with their neural rehabilitation, restoration, detection, and diagnosis. An integration of compressive sensing (CS) and neural engineering emerges as a new research area, aiming to deal with a large volume of neurological data for fast speed, long-term, and energy-saving purposes. Furthermore, electroencephalography (EEG) signals for brain-computer interfaces (BCIs) have shown to be very promising, with diverse neuroscience applications. In this review, we focused on EEG-based approaches which have benefited from CS in achieving fast and energy-saving solutions. In particular, we examine the current practices, scientific opportunities, and challenges of CS in the growing field of BCIs. We emphasized on summarizing major CS reconstruction algorithms, the sparse basis, and the measurement matrix used in CS to process the EEG signal. This literature review suggests that the selection of a suitable reconstruction algorithm, sparse basis, and measurement matrix can help to improve the performance of current CS-based EEG studies. In this paper, we also aim at providing an overview of the reconstruction free CS approach and the related literature in the field. Finally, we discuss the opportunities and challenges that arise from pushing the integration of the CS framework for BCI applications.
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Affiliation(s)
- Dharmendra Gurve
- Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada;
| | - Denis Delisle-Rodriguez
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria 29075-910, Brazil; (D.D.-R.); (T.B.-F.)
| | - Teodiano Bastos-Filho
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria 29075-910, Brazil; (D.D.-R.); (T.B.-F.)
| | - Sridhar Krishnan
- Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada;
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Cai MB, Shvartsman M, Wu A, Zhang H, Zhu X. Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis. Neuropsychologia 2020; 144:107500. [PMID: 32433952 PMCID: PMC7387580 DOI: 10.1016/j.neuropsychologia.2020.107500] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 05/09/2020] [Accepted: 05/15/2020] [Indexed: 01/27/2023]
Abstract
With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent years. Aggregating these data to derive scientific insights often faces the challenge that fMRI data are high-dimensional, heterogeneous across people, and noisy. These challenges demand the development of computational tools that are tailored both for the neuroscience questions and for the properties of the data. We review a few recently developed algorithms in various domains of fMRI research: fMRI in naturalistic tasks, analyzing full-brain functional connectivity, pattern classification, inferring representational similarity and modeling structured residuals. These algorithms all tackle the challenges in fMRI similarly: they start by making clear statements of assumptions about neural data and existing domain knowledge, incorporate those assumptions and domain knowledge into probabilistic graphical models, and use those models to estimate properties of interest or latent structures in the data. Such approaches can avoid erroneous findings, reduce the impact of noise, better utilize known properties of the data, and better aggregate data across groups of subjects. With these successful cases, we advocate wider adoption of explicit model construction in cognitive neuroscience. Although we focus on fMRI, the principle illustrated here is generally applicable to brain data of other modalities.
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Affiliation(s)
- Ming Bo Cai
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Japan; Princeton Neuroscience Institute, Princeton University, United States.
| | | | - Anqi Wu
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, United States
| | - Hejia Zhang
- Department of Electrical Engineering, Princeton University, United States
| | - Xia Zhu
- Intel Corporation, United States
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Bashyam VM, Erus G, Doshi J, Habes M, Nasrallah IM, Truelove-Hill M, Srinivasan D, Mamourian L, Pomponio R, Fan Y, Launer LJ, Masters CL, Maruff P, Zhuo C, Völzke H, Johnson SC, Fripp J, Koutsouleris N, Satterthwaite TD, Wolf D, Gur RE, Gur RC, Morris J, Albert MS, Grabe HJ, Resnick S, Bryan RN, Wolk DA, Shou H, Davatzikos C. MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide. Brain 2020; 143:2312-2324. [PMID: 32591831 PMCID: PMC7364766 DOI: 10.1093/brain/awaa160] [Citation(s) in RCA: 135] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 03/17/2020] [Accepted: 03/31/2020] [Indexed: 01/21/2023] Open
Abstract
Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.
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Affiliation(s)
- Vishnu M Bashyam
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA,Correspondence to: Vishnu Bashyam3700 Hamilton Walk, 7th FloorCenter of Biomedical Image Computing and Analytics, University of PennsylvaniaPhiladelphia, PA 19104, USA E-mail: Website: https://www.med.upenn.edu/cbica/
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Mohamad Habes
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA,Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Ilya M Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Monica Truelove-Hill
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Liz Mamourian
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Raymond Pomponio
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Bethesda, USA
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Chuanjun Zhuo
- Tianjin Mental Health Center, Nankai University Affiliated Tianjin Anding Hospital, Tianjin, China,Department of Psychiatry, Tianjin Medical University, Tianjin, China
| | - Henry Völzke
- Institute for Community Medicine, University of Greifswald, Germany,German Centre for Cardiovascular Research, Partner Sit Greifswald, Germany
| | - Sterling C Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, USA
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Melbourne, Australia
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany
| | - Theodore D Satterthwaite
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Daniel Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Raquel E Gur
- Department of Radiology, University of Pennsylvania, Philadelphia, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Ruben C Gur
- Department of Radiology, University of Pennsylvania, Philadelphia, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - John Morris
- Department of Neurology, Washington University in St. Louis, St Louis, USA
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, Ernst-Moritz-Arndt University, Greifswald, Mecklenburg-Vorpommern, Germany
| | - Susan Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Bethesda, USA
| | - R Nick Bryan
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadephia, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, USA,Correspondence may also be addressed to: Christos DavatzikosE-mail:
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24
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Freitas LGA, Bolton TAW, Krikler BE, Jochaut D, Giraud AL, Hüppi PS, Van De Ville D. Time-resolved effective connectivity in task fMRI: Psychophysiological interactions of Co-Activation patterns. Neuroimage 2020; 212:116635. [PMID: 32105884 DOI: 10.1016/j.neuroimage.2020.116635] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 02/10/2020] [Accepted: 02/12/2020] [Indexed: 12/12/2022] Open
Abstract
Investigating context-dependent modulations of Functional Connectivity (FC) with functional magnetic resonance imaging is crucial to reveal the neurological underpinnings of cognitive processing. Most current analysis methods hypothesise sustained FC within the duration of a task, but this assumption has been shown too limiting by recent imaging studies. While several methods have been proposed to study functional dynamics during rest, task-based studies are yet to fully disentangle network modulations. Here, we propose a seed-based method to probe task-dependent modulations of brain activity by revealing Psychophysiological Interactions of Co-activation Patterns (PPI-CAPs). This point process-based approach temporally decomposes task-modulated connectivity into dynamic building blocks which cannot be captured by current methods, such as PPI or Dynamic Causal Modelling. Additionally, it identifies the occurrence of co-activation patterns at single frame resolution as opposed to window-based methods. In a naturalistic setting where participants watched a TV program, we retrieved several patterns of co-activation with a posterior cingulate cortex seed whose occurrence rates and polarity varied depending on the context; on the seed activity; or on an interaction between the two. Moreover, our method exposed the consistency in effective connectivity patterns across subjects and time, allowing us to uncover links between PPI-CAPs and specific stimuli contained in the video. Our study reveals that explicitly tracking connectivity pattern transients is paramount to advance our understanding of how different brain areas dynamically communicate when presented with a set of cues.
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Affiliation(s)
- Lorena G A Freitas
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Division of Development and Growth, Department of Pediatrics, University of Geneva, Switzerland.
| | - Thomas A W Bolton
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | | | - Delphine Jochaut
- Department of Basic Neurosciences, University of Geneva, Switzerland
| | - Anne-Lise Giraud
- Department of Basic Neurosciences, University of Geneva, Switzerland
| | - Petra S Hüppi
- Division of Development and Growth, Department of Pediatrics, University of Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland
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25
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MRI quality control for the Italian Neuroimaging Network Initiative: moving towards big data in multiple sclerosis. J Neurol 2019; 266:2848-2858. [PMID: 31422457 DOI: 10.1007/s00415-019-09509-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 08/12/2019] [Accepted: 08/13/2019] [Indexed: 01/19/2023]
Abstract
The Italian Neuroimaging Network Initiative (INNI) supports the creation of a repository, where MRI, clinical, and neuropsychological data from multiple sclerosis (MS) patients and healthy controls are collected from Italian Research Centers with internationally recognized expertise in MRI applied to MS. However, multicenter MRI data integration needs standardization and quality control (QC). This study aimed to implement quantitative measures for characterizing the standardization and quality of MRI collected within INNI. MRI scans of 423 MS patients, including 3D T1- and T2-weighted, were obtained from INNI repository (from Centers A, B, C, and D). QC measures were implemented to characterize: (1) head positioning relative to the magnet isocenter; (2) intensity inhomogeneity; (3) relative image contrast between brain tissues; and (4) image artefacts. Centers A and D showed the most accurate subject positioning within the MR scanner (median z-offsets = - 2.6 ± 1.7 cm and - 1.1 ± 2 cm). A low, but significantly different, intensity inhomogeneity on 3D T1-weighted MRI was found between all centers (p < 0.05), except for Centers A and C that showed comparable image bias fields. Center D showed the highest relative contrast between gray and normal appearing white matter (NAWM) on 3D T1-weighed MRI (0.63 ± 0.04), while Center B showed the highest relative contrast between NAWM and MS lesions on FLAIR (0.21 ± 0.06). Image artefacts were mainly due to brain movement (60%) and ghosting (35%). The implemented QC procedure ensured systematic data quality assessment within INNI, thus making available a huge amount of high-quality MRI to better investigate pathophysiological substrates and validate novel MRI biomarkers in MS.
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26
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Li X, Guo N, Li Q. Functional Neuroimaging in the New Era of Big Data. GENOMICS, PROTEOMICS & BIOINFORMATICS 2019; 17:393-401. [PMID: 31809864 PMCID: PMC6943787 DOI: 10.1016/j.gpb.2018.11.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 09/17/2018] [Accepted: 12/25/2018] [Indexed: 12/15/2022]
Abstract
The field of functional neuroimaging has substantially advanced as a big data science in the past decade, thanks to international collaborative projects and community efforts. Here we conducted a literature review on functional neuroimaging, with focus on three general challenges in big data tasks: data collection and sharing, data infrastructure construction, and data analysis methods. The review covers a wide range of literature types including perspectives, database descriptions, methodology developments, and technical details. We show how each of the challenges was proposed and addressed, and how these solutions formed the three core foundations for the functional neuroimaging as a big data science and helped to build the current data-rich and data-driven community. Furthermore, based on our review of recent literature on the upcoming challenges and opportunities toward future scientific discoveries, we envisioned that the functional neuroimaging community needs to advance from the current foundations to better data integration infrastructure, methodology development toward improved learning capability, and multi-discipline translational research framework for this new era of big data.
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Affiliation(s)
- Xiang Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Ning Guo
- Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Quanzheng Li
- Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
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27
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Makkie M, Li X, Quinn S, Lin B, Ye J, Mon G, Liu T. A Distributed Computing Platform for fMRI Big Data Analytics. IEEE TRANSACTIONS ON BIG DATA 2019; 5:109-119. [PMID: 31240237 PMCID: PMC6592627 DOI: 10.1109/tbdata.2018.2811508] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Since the BRAIN Initiative and Human Brain Project began, a few efforts have been made to address the computational challenges of neuroscience Big Data. The promises of these two projects were to model the complex interaction of brain and behavior and to understand and diagnose brain diseases by collecting and analyzing large quanitites of data. Archiving, analyzing, and sharing the growing neuroimaging datasets posed major challenges. New computational methods and technologies have emerged in the domain of Big Data but have not been fully adapted for use in neuroimaging. In this work, we introduce the current challenges of neuroimaging in a big data context. We review our efforts toward creating a data management system to organize the large-scale fMRI datasets, and present our novel algorithms/methods for the distributed fMRI data processing that employs Hadoop and Spark. Finally, we demonstrate the significant performance gains of our algorithms/methods to perform distributed dictionary learning.
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Affiliation(s)
- Milad Makkie
- Department of Computer Science, University of Georgia, Athens, GA 30602
| | - Xiang Li
- Clincial Data Science Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114
| | - Shannon Quinn
- Department of Computer Science, University of Georgia, Athens, GA 30602
| | - Binbin Lin
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Ml 48109
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Ml 48109
| | - Geoffrey Mon
- Department of Computer Science, University of Georgia, Athens, GA 30602
| | - Tianming Liu
- Department of Computer Science, University of Georgia, Athens, GA 30602
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28
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Keshavan A, Yeatman JD, Rokem A. Combining Citizen Science and Deep Learning to Amplify Expertise in Neuroimaging. Front Neuroinform 2019; 13:29. [PMID: 31139070 PMCID: PMC6517786 DOI: 10.3389/fninf.2019.00029] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 04/01/2019] [Indexed: 01/02/2023] Open
Abstract
Big Data promises to advance science through data-driven discovery. However, many standard lab protocols rely on manual examination, which is not feasible for large-scale datasets. Meanwhile, automated approaches lack the accuracy of expert examination. We propose to (1) start with expertly labeled data, (2) amplify labels through web applications that engage citizen scientists, and (3) train machine learning on amplified labels, to emulate the experts. Demonstrating this, we developed a system to quality control brain magnetic resonance images. Expert-labeled data were amplified by citizen scientists through a simple web interface. A deep learning algorithm was then trained to predict data quality, based on citizen scientist labels. Deep learning performed as well as specialized algorithms for quality control (AUC = 0.99). Combining citizen science and deep learning can generalize and scale expert decision making; this is particularly important in disciplines where specialized, automated tools do not yet exist.
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Affiliation(s)
- Anisha Keshavan
- eScience Institute, University of Washington, Seattle, WA, United States
- Institute for Neuroengineering, University of Washington, Seattle, WA, United States
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
- Department of Speech and Hearing, University of Washington, Seattle, WA, United States
| | - Jason D. Yeatman
- Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States
- Department of Speech and Hearing, University of Washington, Seattle, WA, United States
| | - Ariel Rokem
- eScience Institute, University of Washington, Seattle, WA, United States
- Institute for Neuroengineering, University of Washington, Seattle, WA, United States
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29
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Jolly E, Chang LJ. The Flatland Fallacy: Moving Beyond Low-Dimensional Thinking. Top Cogn Sci 2019; 11:433-454. [PMID: 30576066 PMCID: PMC6519046 DOI: 10.1111/tops.12404] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 06/29/2018] [Accepted: 07/13/2018] [Indexed: 01/22/2023]
Abstract
Psychology is a complicated science. It has no general axioms or mathematical proofs, is rarely directly observable, and is the only discipline in which the subject matter (i.e., human psychological phenomena) is also the tool of investigation. Like the Flatlanders in Edwin Abbot's famous short story (), we may be led to believe that the parsimony offered by our low-dimensional theories reflects the reality of a much higher-dimensional problem. Here we contend that this "Flatland fallacy" leads us to seek out simplified explanations of complex phenomena, limiting our capacity as scientists to build and communicate useful models of human psychology. We suggest that this fallacy can be overcome through (a) the use of quantitative models, which force researchers to formalize their theories to overcome this fallacy, and (b) improved quantitative training, which can build new norms for conducting psychological research.
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Affiliation(s)
- Eshin Jolly
- Computational Social Affective Neuroscience LaboratoryDepartment of Psychological and Brain SciencesDartmouth College
| | - Luke J. Chang
- Computational Social Affective Neuroscience LaboratoryDepartment of Psychological and Brain SciencesDartmouth College
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30
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Allen WJ, Gabr RE, Tefera GB, Pednekar AS, Vaughn MW, Narayana PA. Platform for Automated Real-Time High Performance Analytics on Medical Image Data. IEEE J Biomed Health Inform 2019; 22:318-324. [PMID: 29505399 DOI: 10.1109/jbhi.2017.2771299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Biomedical data are quickly growing in volume and in variety, providing clinicians an opportunity for better clinical decision support. Here, we demonstrate a robust platform that uses software automation and high performance computing (HPC) resources to achieve real-time analytics of clinical data, specifically magnetic resonance imaging (MRI) data. We used the Agave application programming interface to facilitate communication, data transfer, and job control between an MRI scanner and an off-site HPC resource. In this use case, Agave executed the graphical pipeline tool GRAphical Pipeline Environment (GRAPE) to perform automated, real-time, quantitative analysis of MRI scans. Same-session image processing will open the door for adaptive scanning and real-time quality control, potentially accelerating the discovery of pathologies and minimizing patient callbacks. We envision this platform can be adapted to other medical instruments, HPC resources, and analytics tools.
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31
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Wiseman SJ, Meijboom R, Valdés Hernández MDC, Pernet C, Sakka E, Job D, Waldman AD, Wardlaw JM. Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing. Trials 2019; 20:21. [PMID: 30616680 PMCID: PMC6323670 DOI: 10.1186/s13063-018-3113-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 12/06/2018] [Indexed: 11/10/2022] Open
Abstract
Background Research involving brain imaging is important for understanding common brain diseases. Study endpoints can include features and measures derived from imaging modalities, providing a benchmark against which other phenotypical data can be assessed. In trials, imaging data provide objective evidence of beneficial and adverse outcomes. Multi-centre studies increase generalisability and statistical power. However, there is a lack of practical guidelines for the set-up and conduct of large neuroimaging studies. Methods We address this deficit by describing aspects of study design and other essential practical considerations that will help researchers avoid common pitfalls and data loss. Results The recommendations are grouped into seven categories: (1) planning, (2) defining the imaging endpoints, developing an imaging manual and managing the workflow, (3) performing a dummy run and testing the analysis methods, (4) acquiring the scans, (5) anonymising and transferring the data, (6) monitoring quality, and (7) using structured data and sharing data. Conclusions Implementing these steps will lead to valuable and usable data and help to avoid imaging data wastage.
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Affiliation(s)
- Stewart J Wiseman
- Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK. .,UK Dementia Research Institute Edinburgh, University of Edinburgh, Edinburgh, UK. .,CCBS, Chancellor's Building, Royal Infirmary of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.
| | - Rozanna Meijboom
- Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute Edinburgh, University of Edinburgh, Edinburgh, UK
| | - Maria Del C Valdés Hernández
- Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute Edinburgh, University of Edinburgh, Edinburgh, UK
| | - Cyril Pernet
- Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Eleni Sakka
- Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Dominic Job
- Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Adam D Waldman
- Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Joanna M Wardlaw
- Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,UK Dementia Research Institute Edinburgh, University of Edinburgh, Edinburgh, UK
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32
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Russ TC, Woelbert E, Davis KAS, Hafferty JD, Ibrahim Z, Inkster B, John A, Lee W, Maxwell M, McIntosh AM, Stewart R. How data science can advance mental health research. Nat Hum Behav 2019; 3:24-32. [PMID: 30932051 DOI: 10.1038/s41562-018-0470-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 10/11/2018] [Indexed: 02/07/2023]
Abstract
Accessibility of powerful computers and availability of so-called big data from a variety of sources means that data science approaches are becoming pervasive. However, their application in mental health research is often considered to be at an earlier stage than in other areas despite the complexity of mental health and illness making such a sophisticated approach particularly suitable. In this Perspective, we discuss current and potential applications of data science in mental health research using the UK Clinical Research Collaboration classification: underpinning research; aetiology; detection and diagnosis; treatment development; treatment evaluation; disease management; and health services research. We demonstrate that data science is already being widely applied in mental health research, but there is much more to be done now and in the future. The possibilities for data science in mental health research are substantial.
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Affiliation(s)
- Tom C Russ
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
- Centre for Dementia Prevention, University of Edinburgh, Edinburgh, UK.
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK.
- Old Age Psychiatry, Royal Edinburgh Hospital, NHS Lothian, Edinburgh, UK.
| | | | - Katrina A S Davis
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Jonathan D Hafferty
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Zina Ibrahim
- Department of Biostatistics and Health Informatics, King's College London, London, UK
- The Farr Institute of Health Informatics Research, University College London, London, UK
| | - Becky Inkster
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Ann John
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - William Lee
- Community and Primary Care Research Group, Plymouth University Peninsula Schools of Medicine and Dentistry, University of Plymouth, Plymouth, UK
- Devon Partnership NHS Trust, Exeter, UK
| | | | - Andrew M McIntosh
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Rob Stewart
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
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33
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Das S, Lecours Boucher X, Rogers C, Makowski C, Chouinard-Decorte F, Oros Klein K, Beck N, Rioux P, Brown ST, Mohaddes Z, Zweber C, Foing V, Forest M, O'Donnell KJ, Clark J, Meaney MJ, Greenwood CMT, Evans AC. Integration of "omics" Data and Phenotypic Data Within a Unified Extensible Multimodal Framework. Front Neuroinform 2018; 12:91. [PMID: 30631270 PMCID: PMC6315165 DOI: 10.3389/fninf.2018.00091] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 11/16/2018] [Indexed: 12/11/2022] Open
Abstract
Analysis of “omics” data is often a long and segmented process, encompassing multiple stages from initial data collection to processing, quality control and visualization. The cross-modal nature of recent genomic analyses renders this process challenging to both automate and standardize; consequently, users often resort to manual interventions that compromise data reliability and reproducibility. This in turn can produce multiple versions of datasets across storage systems. As a result, scientists can lose significant time and resources trying to execute and monitor their analytical workflows and encounter difficulties sharing versioned data. In 2015, the Ludmer Centre for Neuroinformatics and Mental Health at McGill University brought together expertise from the Douglas Mental Health University Institute, the Lady Davis Institute and the Montreal Neurological Institute (MNI) to form a genetics/epigenetics working group. The objectives of this working group are to: (i) design an automated and seamless process for (epi)genetic data that consolidates heterogeneous datasets into the LORIS open-source data platform; (ii) streamline data analysis; (iii) integrate results with provenance information; and (iv) facilitate structured and versioned sharing of pipelines for optimized reproducibility using high-performance computing (HPC) environments via the CBRAIN processing portal. This article outlines the resulting generalizable “omics” framework and its benefits, specifically, the ability to: (i) integrate multiple types of biological and multi-modal datasets (imaging, clinical, demographics and behavioral); (ii) automate the process of launching analysis pipelines on HPC platforms; (iii) remove the bioinformatic barriers that are inherent to this process; (iv) ensure standardization and transparent sharing of processing pipelines to improve computational consistency; (v) store results in a queryable web interface; (vi) offer visualization tools to better view the data; and (vii) provide the mechanisms to ensure usability and reproducibility. This framework for workflows facilitates brain research discovery by reducing human error through automation of analysis pipelines and seamless linking of multimodal data, allowing investigators to focus on research instead of data handling.
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Affiliation(s)
- Samir Das
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Xavier Lecours Boucher
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Christine Rogers
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Carolina Makowski
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Douglas Hospital Research Centre, McGill University, Montreal, QC, Canada
| | - François Chouinard-Decorte
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Kathleen Oros Klein
- Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada.,Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - Natacha Beck
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Pierre Rioux
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Shawn T Brown
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Zia Mohaddes
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Cole Zweber
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Victoria Foing
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Marie Forest
- Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada.,Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - Kieran J O'Donnell
- Douglas Hospital Research Centre, McGill University, Montreal, QC, Canada.,Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
| | - Joanne Clark
- Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
| | - Michael J Meaney
- Douglas Hospital Research Centre, McGill University, Montreal, QC, Canada.,Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
| | - Celia M T Greenwood
- Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada.,Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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34
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Bigler ED. Structural neuroimaging in sport-related concussion. Int J Psychophysiol 2018; 132:105-123. [DOI: 10.1016/j.ijpsycho.2017.09.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Revised: 09/03/2017] [Accepted: 09/07/2017] [Indexed: 10/18/2022]
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35
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Ito KL, Kumar A, Zavaliangos-Petropulu A, Cramer SC, Liew SL. Pipeline for Analyzing Lesions After Stroke (PALS). Front Neuroinform 2018; 12:63. [PMID: 30319385 PMCID: PMC6165891 DOI: 10.3389/fninf.2018.00063] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Accepted: 09/05/2018] [Indexed: 02/04/2023] Open
Abstract
Lesion analyses are critical for drawing insights about stroke injury and recovery, and their importance is underscored by growing efforts to collect and combine stroke neuroimaging data across research sites. However, while there are numerous processing pipelines for neuroimaging data in general, few can be smoothly applied to stroke data due to complications analyzing the lesioned region. As researchers often use their own tools or manual methods for stroke MRI analysis, this could lead to greater errors and difficulty replicating findings over time and across sites. Rigorous analysis protocols and quality control pipelines are thus urgently needed for stroke neuroimaging. To this end, we created the Pipeline for Analyzing Lesions after Stroke (PALS; DOI: https://doi.org/10.5281/zenodo.1266980), a scalable and user-friendly toolbox to facilitate and ensure quality in stroke research specifically using T1-weighted MRIs. The PALS toolbox offers four modules integrated into a single pipeline, including (1) reorientation to radiological convention, (2) lesion correction for healthy white matter voxels, (3) lesion load calculation, and (4) visual quality control. In the present paper, we discuss each module and provide validation and example cases of our toolbox using multi-site data. Importantly, we also show that lesion correction with PALS significantly improves similarity between manual lesion segmentations by different tracers (z = 3.43, p = 0.0018). PALS can be found online at https://github.com/npnl/PALS. Future work will expand the PALS capabilities to include multimodal stroke imaging. We hope PALS will be a useful tool for the stroke neuroimaging community and foster new clinical insights.
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Affiliation(s)
- Kaori L Ito
- Neural Plasticity and Neurorehabilitation Laboratory, University of Southern California, Los Angeles, CA, United States
| | - Amit Kumar
- Neural Plasticity and Neurorehabilitation Laboratory, University of Southern California, Los Angeles, CA, United States
| | - Artemis Zavaliangos-Petropulu
- Neural Plasticity and Neurorehabilitation Laboratory, University of Southern California, Los Angeles, CA, United States.,Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Steven C Cramer
- Department of Neurology, University of California, Irvine, Irvine, CA, United States
| | - Sook-Lei Liew
- Neural Plasticity and Neurorehabilitation Laboratory, University of Southern California, Los Angeles, CA, United States.,Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
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36
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Fuchigami T, Shikauchi Y, Nakae K, Shikauchi M, Ogawa T, Ishii S. Zero-shot fMRI decoding with three-dimensional registration based on diffusion tensor imaging. Sci Rep 2018; 8:12342. [PMID: 30120378 PMCID: PMC6098116 DOI: 10.1038/s41598-018-30676-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 08/02/2018] [Indexed: 11/09/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) acquisitions include a great deal of individual variability. This individuality often generates obstacles to the efficient use of databanks from multiple subjects. Although recent studies have suggested that inter-regional connectivity reflects individuality, conventional three-dimensional (3D) registration methods that calibrate inter-subject variability are based on anatomical information about the gray matter shape (e.g., T1-weighted). Here, we present a new registration method focusing more on the white matter structure, which is directly related to the connectivity in the brain, and apply it to subject-transfer brain decoding. Our registration method based on diffusion tensor imaging (DTI) transferred functional maps of each individual to a common anatomical space, where a decoding analysis of multi-voxel patterns was performed. The decoder trained on functional maps from other individuals in the common space showed a transfer decoding accuracy comparable to that of an individual decoder trained on single-subject functional maps. The DTI-based registration allowed more precise transformation of gray matter boundaries than a well-established T1-based method. These results suggest that the DTI-based registration is a promising tool for standardization of the brain functions, and moreover, will allow us to perform ‘zero-shot’ learning of decoders which is profitable in brain machine interface scenes.
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Affiliation(s)
- Takuya Fuchigami
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan.,ATR Cognitive Mechanisms Laboratories, Kyoto, 619-0288, Japan.,FUJIFILM Corporation, Minato, Tokyo, Japan
| | - Yumi Shikauchi
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan.,Rhythm-based Brain Information Processing, RIKEN BSI-TOYOTA Collaboration Center, Wako, 351-0198, Japan
| | - Ken Nakae
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan
| | - Manabu Shikauchi
- ATR Cognitive Mechanisms Laboratories, Kyoto, 619-0288, Japan.,Cingulate Co. Ltd., Osaka, Japan
| | - Takeshi Ogawa
- ATR Cognitive Mechanisms Laboratories, Kyoto, 619-0288, Japan
| | - Shin Ishii
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan. .,ATR Cognitive Mechanisms Laboratories, Kyoto, 619-0288, Japan.
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37
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Waller L, Brovkin A, Dorfschmidt L, Bzdok D, Walter H, Kruschwitz JD. GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures. J Neurosci Methods 2018; 308:21-33. [PMID: 30026069 DOI: 10.1016/j.jneumeth.2018.07.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2018] [Revised: 06/30/2018] [Accepted: 07/01/2018] [Indexed: 01/06/2023]
Abstract
BACKGROUND We previously presented GraphVar as a user-friendly MATLAB toolbox for comprehensive graph analyses of functional brain connectivity. Here we introduce a comprehensive extension of the toolbox allowing users to seamlessly explore easily customizable decoding models across functional connectivity measures as well as additional features. NEW METHOD GraphVar 2.0 provides machine learning (ML) model construction, validation and exploration. Machine learning can be performed across any combination of graph measures and additional variables, allowing for a flexibility in neuroimaging applications. RESULTS In addition to previously integrated functionalities, such as network construction and graph-theoretical analyses of brain connectivity with a high-speed general linear model (GLM), users can now perform customizable ML across connectivity matrices, graph measures and additionally imported variables. The new extension also provides parametric and nonparametric testing of classifier and regressor performance, data export, figure generation and high quality export. COMPARISON WITH EXISTING METHODS Compared to other existing toolboxes, GraphVar 2.0 offers (1) comprehensive customization, (2) an all-in-one user friendly interface, (3) customizable model design and manual hyperparameter entry, (4) interactive results exploration and data export, (5) automated queue system for modelling multiple outcome variables within the same session, (6) an easy to follow introductory review. CONCLUSIONS GraphVar 2.0 allows comprehensive, user-friendly exploration of encoding (GLM) and decoding (ML) modelling approaches on functional connectivity measures making big data neuroscience readily accessible to a broader audience of neuroimaging investigators.
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Affiliation(s)
- L Waller
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Division of Mind and Brain Research, Germany
| | - A Brovkin
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Division of Mind and Brain Research, Germany; Collaborative Research Centre (SFB 940) "Volition and Cognitive Control", Technische Universität, Dresden, Germany
| | - L Dorfschmidt
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Division of Mind and Brain Research, Germany; Collaborative Research Centre (SFB 940) "Volition and Cognitive Control", Technische Universität, Dresden, Germany
| | - D Bzdok
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH, Aachen University, 52072 Aachen, Germany; JARA BRAIN, Jülich-Aachen Research Alliance, Germany; Parietal team, INRIA, Neurospin, bat 145, CEA Saclay, 91191, Gif-sur-Yvette, France
| | - H Walter
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Division of Mind and Brain Research, Germany
| | - J D Kruschwitz
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Division of Mind and Brain Research, Germany; Collaborative Research Centre (SFB 940) "Volition and Cognitive Control", Technische Universität, Dresden, Germany.
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38
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Operto G, Chupin M, Batrancourt B, Habert MO, Colliot O, Benali H, Poupon C, Champseix C, Delmaire C, Marie S, Rivière D, Pélégrini-Issac M, Perlbarg V, Trebossen R, Bottlaender M, Frouin V, Grigis A, Orfanos DP, Dary H, Fillon L, Azouani C, Bouyahia A, Fischer C, Edward L, Bouin M, Thoprakarn U, Li J, Makkaoui L, Poret S, Dufouil C, Bouteloup V, Chételat G, Dubois B, Lehéricy S, Mangin JF, Cointepas Y. CATI: A Large Distributed Infrastructure for the Neuroimaging of Cohorts. Neuroinformatics 2018; 14:253-64. [PMID: 27066973 DOI: 10.1007/s12021-016-9295-8] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
This paper provides an overview of CATI, a platform dedicated to multicenter neuroimaging. Initiated by the French Alzheimer's plan (2008-2012), CATI is a research project called on to provide service to other projects like an industrial partner. Its core mission is to support the neuroimaging of large populations, providing concrete solutions to the increasing complexity involved in such projects by bringing together a service infrastructure, the know-how of its expert academic teams and a large-scale, harmonized network of imaging facilities. CATI aims to make data sharing across studies easier and promotes sharing as much as possible. In the last 4 years, CATI has assisted the clinical community by taking charge of 35 projects so far and has emerged as a recognized actor at the national and international levels.
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Affiliation(s)
- Grégory Operto
- Centre pour l'Acquisition et le Traitement des Images, . .,NeuroSpin, I2BM, Commissariat à l'Energie Atomique, Saclay, France. .,INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France.
| | - Marie Chupin
- Centre pour l'Acquisition et le Traitement des Images.,INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France.,INRIA Paris-Rocquencourt, 75013, Paris, France
| | - Bénédicte Batrancourt
- Centre pour l'Acquisition et le Traitement des Images.,INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France.,Information Analysis and Management project of France Life Imaging
| | - Marie-Odile Habert
- Centre pour l'Acquisition et le Traitement des Images.,Nuclear Medicine Department, Pitié-Salpêtrière University Hospital, AP-HP, Paris, France.,Laboratoire d'Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm U 1146, CNRS UMR 7371, Paris, France
| | - Olivier Colliot
- Centre pour l'Acquisition et le Traitement des Images.,INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France.,INRIA Paris-Rocquencourt, 75013, Paris, France
| | - Habib Benali
- Centre pour l'Acquisition et le Traitement des Images.,Laboratoire d'Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm U 1146, CNRS UMR 7371, Paris, France
| | - Cyril Poupon
- Centre pour l'Acquisition et le Traitement des Images.,NeuroSpin, I2BM, Commissariat à l'Energie Atomique, Saclay, France
| | - Catherine Champseix
- Centre pour l'Acquisition et le Traitement des Images.,NeuroSpin, I2BM, Commissariat à l'Energie Atomique, Saclay, France.,INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France
| | - Christine Delmaire
- Centre pour l'Acquisition et le Traitement des Images.,U1171 - Département de Neuroradiologie, Centre Hospitalier Régional Universitaire de Lille, Université Lille Nord de France, F-59037, Lille, France
| | - Sullivan Marie
- Centre pour l'Acquisition et le Traitement des Images.,Nuclear Medicine Department, Pitié-Salpêtrière University Hospital, AP-HP, Paris, France.,Laboratoire d'Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm U 1146, CNRS UMR 7371, Paris, France
| | - Denis Rivière
- Centre pour l'Acquisition et le Traitement des Images.,NeuroSpin, I2BM, Commissariat à l'Energie Atomique, Saclay, France
| | - Mélanie Pélégrini-Issac
- Centre pour l'Acquisition et le Traitement des Images.,Laboratoire d'Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm U 1146, CNRS UMR 7371, Paris, France
| | - Vincent Perlbarg
- Centre pour l'Acquisition et le Traitement des Images.,Laboratoire d'Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm U 1146, CNRS UMR 7371, Paris, France.,IHU-A-ICM, Bioinformatics/Biostatistics Platform, F-75013, Paris, France
| | - Régine Trebossen
- Centre pour l'Acquisition et le Traitement des Images.,NeuroSpin, I2BM, Commissariat à l'Energie Atomique, Saclay, France.,Information Analysis and Management project of France Life Imaging.,Commissariat à l'Energie Atomique, Service Hospitalier Frédéric Joliot, I2BM, Orsay, France
| | - Michel Bottlaender
- Centre pour l'Acquisition et le Traitement des Images.,NeuroSpin, I2BM, Commissariat à l'Energie Atomique, Saclay, France.,Commissariat à l'Energie Atomique, Service Hospitalier Frédéric Joliot, I2BM, Orsay, France
| | - Vincent Frouin
- NeuroSpin, I2BM, Commissariat à l'Energie Atomique, Saclay, France
| | - Antoine Grigis
- NeuroSpin, I2BM, Commissariat à l'Energie Atomique, Saclay, France
| | | | - Hugo Dary
- Centre pour l'Acquisition et le Traitement des Images.,INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France.,INRIA Paris-Rocquencourt, 75013, Paris, France
| | - Ludovic Fillon
- Centre pour l'Acquisition et le Traitement des Images.,INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France.,INRIA Paris-Rocquencourt, 75013, Paris, France
| | - Chabha Azouani
- Centre pour l'Acquisition et le Traitement des Images.,INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France.,INRIA Paris-Rocquencourt, 75013, Paris, France
| | - Ali Bouyahia
- Centre pour l'Acquisition et le Traitement des Images.,INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France.,INRIA Paris-Rocquencourt, 75013, Paris, France
| | - Clara Fischer
- Centre pour l'Acquisition et le Traitement des Images.,NeuroSpin, I2BM, Commissariat à l'Energie Atomique, Saclay, France.,INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France
| | - Lydie Edward
- Centre pour l'Acquisition et le Traitement des Images.,INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France
| | - Mathilde Bouin
- Centre pour l'Acquisition et le Traitement des Images.,NeuroSpin, I2BM, Commissariat à l'Energie Atomique, Saclay, France.,INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France
| | - Urielle Thoprakarn
- Centre pour l'Acquisition et le Traitement des Images.,NeuroSpin, I2BM, Commissariat à l'Energie Atomique, Saclay, France.,INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France
| | - Jinpeng Li
- Centre pour l'Acquisition et le Traitement des Images.,NeuroSpin, I2BM, Commissariat à l'Energie Atomique, Saclay, France.,Information Analysis and Management project of France Life Imaging
| | - Leila Makkaoui
- Centre pour l'Acquisition et le Traitement des Images.,NeuroSpin, I2BM, Commissariat à l'Energie Atomique, Saclay, France
| | - Sylvain Poret
- Centre pour l'Acquisition et le Traitement des Images.,INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France.,Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Paris, France
| | - Carole Dufouil
- INSERM U708, Neuroepidemiology, CIC-EC7 & Bordeaux University, Bordeaux, France
| | - Vincent Bouteloup
- INSERM U897, Clinical Investigation Center-Clinical Epidemiology-CIC-1401, Epidemiology and Biostatistics Center, Bordeaux School of Public Health, Bordeaux University, Bordeaux, France
| | - Gaël Chételat
- Centre pour l'Acquisition et le Traitement des Images.,Ecole Pratique des Hautes Etudes, CHU de Caen, INSERM U1077 - Université de Caen Basse-Normandie, UMR-S1077, Caen, France
| | - Bruno Dubois
- Centre pour l'Acquisition et le Traitement des Images.,INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France.,Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Paris, France
| | - Stéphane Lehéricy
- Centre pour l'Acquisition et le Traitement des Images.,INSERM U1127, CNRS UMR 7225, Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, F-75013, Paris, France.,Centre de NeuroImagerie de Recherche - CENIR, Département de Neuroradiologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Paris, France
| | - Jean-François Mangin
- Centre pour l'Acquisition et le Traitement des Images.,NeuroSpin, I2BM, Commissariat à l'Energie Atomique, Saclay, France
| | - Yann Cointepas
- Centre pour l'Acquisition et le Traitement des Images.,NeuroSpin, I2BM, Commissariat à l'Energie Atomique, Saclay, France.,Information Analysis and Management project of France Life Imaging
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39
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Erus G, Doshi J, An Y, Verganelakis D, Resnick SM, Davatzikos C. Longitudinally and inter-site consistent multi-atlas based parcellation of brain anatomy using harmonized atlases. Neuroimage 2018; 166:71-78. [PMID: 29107121 PMCID: PMC5748021 DOI: 10.1016/j.neuroimage.2017.10.026] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 09/29/2017] [Accepted: 10/13/2017] [Indexed: 11/17/2022] Open
Abstract
As longitudinal and multi-site studies become increasingly frequent in neuroimaging, maintaining longitudinal and inter-scanner consistency of brain parcellation has become a major challenge due to variation in scanner models and/or image acquisition protocols across scanners and sites. We present a new automated segmentation method specifically designed to achieve a consistent parcellation of anatomical brain structures in such heterogeneous datasets. Our method combines a site-specific atlas creation strategy with a state-of-the-art multi-atlas anatomical label fusion framework. Site-specific atlases are computed such that they preserve image intensity characteristics of each site's scanner and acquisition protocol, while atlas pairs share anatomical labels in a way consistent with inter-scanner acquisition variations. This harmonization of atlases improves inter-study and longitudinal consistency of segmentations in the subsequent consensus labeling step. We tested this approach on a large sample of older adults from the Baltimore Longitudinal Study of Aging (BLSA) who had longitudinal scans acquired using two scanners that vary with respect to vendor and image acquisition protocol. We compared the proposed method to standard multi-atlas segmentation for both cross-sectional and longitudinal analyses. The harmonization significantly reduced scanner-related differences in the age trends of ROI volumes, improved longitudinal consistency of segmentations, and resulted in higher across-scanner intra-class correlations, particularly in the white matter.
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Affiliation(s)
- Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | | | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
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40
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Van Horn JD, Fierro L, Kamdar J, Gordon J, Stewart C, Bhattrai A, Abe S, Lei X, O'Driscoll C, Sinha A, Jain P, Burns G, Lerman K, Ambite JL. Democratizing data science through data science training. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2018; 23:292-303. [PMID: 29218890 PMCID: PMC5731238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The biomedical sciences have experienced an explosion of data which promises to overwhelm many current practitioners. Without easy access to data science training resources, biomedical researchers may find themselves unable to wrangle their own datasets. In 2014, to address the challenges posed such a data onslaught, the National Institutes of Health (NIH) launched the Big Data to Knowledge (BD2K) initiative. To this end, the BD2K Training Coordinating Center (TCC; bigdatau.org) was funded to facilitate both in-person and online learning, and open up the concepts of data science to the widest possible audience. Here, we describe the activities of the BD2K TCC and its focus on the construction of the Educational Resource Discovery Index (ERuDIte), which identifies, collects, describes, and organizes online data science materials from BD2K awardees, open online courses, and videos from scientific lectures and tutorials. ERuDIte now indexes over 9,500 resources. Given the richness of online training materials and the constant evolution of biomedical data science, computational methods applying information retrieval, natural language processing, and machine learning techniques are required - in effect, using data science to inform training in data science. In so doing, the TCC seeks to democratize novel insights and discoveries brought forth via large-scale data science training.
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Affiliation(s)
- John Darrell Van Horn
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, SHN, Los Angeles, CA 90033, USA,
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Dixit R, Rogith D, Narayana V, Salimi M, Gururaj A, Ohno-Machado L, Xu H, Johnson TR. User needs analysis and usability assessment of DataMed - a biomedical data discovery index. J Am Med Inform Assoc 2017; 25:337-344. [PMID: 29202203 PMCID: PMC7378884 DOI: 10.1093/jamia/ocx134] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 10/16/2017] [Accepted: 10/27/2017] [Indexed: 02/06/2023] Open
Abstract
Objective To present user needs and usability evaluations of DataMed, a Data Discovery Index (DDI) that allows searching for biomedical data from multiple sources. Materials and Methods We conducted 2 phases of user studies. Phase 1 was a user needs analysis conducted before the development of DataMed, consisting of interviews with researchers. Phase 2 involved iterative usability evaluations of DataMed prototypes. We analyzed data qualitatively to document researchers’ information and user interface needs. Results Biomedical researchers’ information needs in data discovery are complex, multidimensional, and shaped by their context, domain knowledge, and technical experience. User needs analyses validate the need for a DDI, while usability evaluations of DataMed show that even though aggregating metadata into a common search engine and applying traditional information retrieval tools are promising first steps, there remain challenges for DataMed due to incomplete metadata and the complexity of data discovery. Discussion Biomedical data poses distinct problems for search when compared to websites or publications. Making data available is not enough to facilitate biomedical data discovery: new retrieval techniques and user interfaces are necessary for dataset exploration. Consistent, complete, and high-quality metadata are vital to enable this process. Conclusion While available data and researchers’ information needs are complex and heterogeneous, a successful DDI must meet those needs and fit into the processes of biomedical researchers. Research directions include formalizing researchers’ information needs, standardizing overviews of data to facilitate relevance judgments, implementing user interfaces for concept-based searching, and developing evaluation methods for open-ended discovery systems such as DDIs.
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Affiliation(s)
- Ram Dixit
- University of Texas Health Science Center at Houston, School of Biomedical Informatics, Houston, TX, USA
| | - Deevakar Rogith
- University of Texas Health Science Center at Houston, School of Biomedical Informatics, Houston, TX, USA
| | - Vidya Narayana
- University of Texas Health Science Center at Houston, School of Biomedical Informatics, Houston, TX, USA
| | - Mandana Salimi
- University of Texas Health Science Center at Houston, School of Biomedical Informatics, Houston, TX, USA
| | - Anupama Gururaj
- University of Texas Health Science Center at Houston, School of Biomedical Informatics, Houston, TX, USA
| | - Lucila Ohno-Machado
- University of California San Diego Health System, Department of Biomedical Informatics, La Jolla, CA, USA
| | - Hua Xu
- University of Texas Health Science Center at Houston, School of Biomedical Informatics, Houston, TX, USA
| | - Todd R Johnson
- University of Texas Health Science Center at Houston, School of Biomedical Informatics, Houston, TX, USA
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Nyholt DR, Borsook D, Griffiths LR. Migrainomics — identifying brain and genetic markers of migraine. Nat Rev Neurol 2017; 13:725-741. [DOI: 10.1038/nrneurol.2017.151] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Yarkoni T, Westfall J. Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2017; 12:1100-1122. [PMID: 28841086 PMCID: PMC6603289 DOI: 10.1177/1745691617693393] [Citation(s) in RCA: 646] [Impact Index Per Article: 92.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology's near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research questions. We suggest that an increased focus on prediction, rather than explanation, can ultimately lead us to greater understanding of behavior.
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Abstract
OBJECTIVE Outline effects of functional neuroimaging on neuropsychology over the past 25 years. METHOD Functional neuroimaging methods and studies will be described that provide a historical context, offer examples of the utility of neuroimaging in specific domains, and discuss the limitations and future directions of neuroimaging in neuropsychology. RESULTS Tracking the history of publications on functional neuroimaging related to neuropsychology indicates early involvement of neuropsychologists in the development of these methodologies. Initial progress in neuropsychological application of functional neuroimaging has been hampered by costs and the exposure to ionizing radiation. With rapid evolution of functional methods-in particular functional MRI (fMRI)-neuroimaging has profoundly transformed our knowledge of the brain. Its current applications span the spectrum of normative development to clinical applications. The field is moving toward applying sophisticated statistical approaches that will help elucidate distinct neural activation networks associated with specific behavioral domains. The impact of functional neuroimaging on clinical neuropsychology is more circumscribed, but the prospects remain enticing. CONCLUSIONS The theoretical insights and empirical findings of functional neuroimaging have been led by many neuropsychologists and have transformed the field of behavioral neuroscience. Thus far they have had limited effects on the clinical practices of neuropsychologists. Perhaps it is time to add training in functional neuroimaging to the clinical neuropsychologist's toolkit and from there to the clinic or bedside. (PsycINFO Database Record
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Affiliation(s)
- David R. Roalf
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine Philadelphia, Philadelphia, PA, 19104
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine Philadelphia, Philadelphia, PA, 19104
- Lifespan Brain Institute (LiBI) at the University of Pennsylvania and Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
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Bzdok D. Classical Statistics and Statistical Learning in Imaging Neuroscience. Front Neurosci 2017; 11:543. [PMID: 29056896 PMCID: PMC5635056 DOI: 10.3389/fnins.2017.00543] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Accepted: 09/19/2017] [Indexed: 11/13/2022] Open
Abstract
Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques.
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Affiliation(s)
- Danilo Bzdok
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany.,Translational Brain Medicine, Jülich-Aachen Research Alliance (JARA), Aachen, Germany.,Parietal Team, Institut National de Recherche en Informatique et en Automatique (INRIA), Gif-sur-Yvette, France
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Bzdok D, Varoquaux G, Thirion B. Neuroimaging Research: From Null-Hypothesis Falsification to Out-of-Sample Generalization. EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT 2017; 77:868-880. [PMID: 29795936 PMCID: PMC5965634 DOI: 10.1177/0013164416667982] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Brain-imaging technology has boosted the quantification of neurobiological phenomena underlying human mental operations and their disturbances. Since its inception, drawing inference on neurophysiological effects hinged on classical statistical methods, especially, the general linear model. The tens of thousands of variables per brain scan were routinely tackled by independent statistical tests on each voxel. This circumvented the curse of dimensionality in exchange for neurobiologically imperfect observation units, a challenging multiple comparisons problem, and limited scaling to currently growing data repositories. Yet, the always bigger information granularity of neuroimaging data repositories has lunched a rapidly increasing adoption of statistical learning algorithms. These scale naturally to high-dimensional data, extract models from data rather than prespecifying them, and are empirically evaluated for extrapolation to unseen data. The present article portrays commonalities and differences between long-standing classical inference and upcoming generalization inference relevant for conducting neuroimaging research.
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Affiliation(s)
- Danilo Bzdok
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- JARA, Translational Brain Medicine, Aachen, Germany
- INRIA, Neurospin, Gif-sur-Yvette, Paris, France
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Shi Y, Toga AW. Connectome imaging for mapping human brain pathways. Mol Psychiatry 2017; 22:1230-1240. [PMID: 28461700 PMCID: PMC5568931 DOI: 10.1038/mp.2017.92] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Revised: 02/06/2017] [Accepted: 02/24/2017] [Indexed: 01/23/2023]
Abstract
With the fast advance of connectome imaging techniques, we have the opportunity of mapping the human brain pathways in vivo at unprecedented resolution. In this article we review the current developments of diffusion magnetic resonance imaging (MRI) for the reconstruction of anatomical pathways in connectome studies. We first introduce the background of diffusion MRI with an emphasis on the technical advances and challenges in state-of-the-art multi-shell acquisition schemes used in the Human Connectome Project. Characterization of the microstructural environment in the human brain is discussed from the tensor model to the general fiber orientation distribution (FOD) models that can resolve crossing fibers in each voxel of the image. Using FOD-based tractography, we describe novel methods for fiber bundle reconstruction and graph-based connectivity analysis. Building upon these novel developments, there have already been successful applications of connectome imaging techniques in reconstructing challenging brain pathways. Examples including retinofugal and brainstem pathways will be reviewed. Finally, we discuss future directions in connectome imaging and its interaction with other aspects of brain imaging research.
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Affiliation(s)
- Y Shi
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - A W Toga
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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Hinojosa-Rodríguez M, Harmony T, Carrillo-Prado C, Van Horn JD, Irimia A, Torgerson C, Jacokes Z. Clinical neuroimaging in the preterm infant: Diagnosis and prognosis. Neuroimage Clin 2017; 16:355-368. [PMID: 28861337 PMCID: PMC5568883 DOI: 10.1016/j.nicl.2017.08.015] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Revised: 08/11/2017] [Accepted: 08/12/2017] [Indexed: 01/30/2023]
Abstract
Perinatal care advances emerging over the past twenty years have helped to diminish the mortality and severe neurological morbidity of extremely and very preterm neonates (e.g., cystic Periventricular Leukomalacia [c-PVL] and Germinal Matrix Hemorrhage - Intraventricular Hemorrhage [GMH-IVH grade 3-4/4]; 22 to < 32 weeks of gestational age, GA). However, motor and/or cognitive disabilities associated with mild-to-moderate white and gray matter injury are frequently present in this population (e.g., non-cystic Periventricular Leukomalacia [non-cystic PVL], neuronal-axonal injury and GMH-IVH grade 1-2/4). Brain research studies using magnetic resonance imaging (MRI) report that 50% to 80% of extremely and very preterm neonates have diffuse white matter abnormalities (WMA) which correspond to only the minimum grade of severity. Nevertheless, mild-to-moderate diffuse WMA has also been associated with significant affectations of motor and cognitive activities. Due to increased neonatal survival and the intrinsic characteristics of diffuse WMA, there is a growing need to study the brain of the premature infant using non-invasive neuroimaging techniques sensitive to microscopic and/or diffuse lesions. This emerging need has led the scientific community to try to bridge the gap between concepts or ideas from different methodologies and approaches; for instance, neuropathology, neuroimaging and clinical findings. This is evident from the combination of intense pre-clinical and clinicopathologic research along with neonatal neurology and quantitative neuroimaging research. In the following review, we explore literature relating the most frequently observed neuropathological patterns with the recent neuroimaging findings in preterm newborns and infants with perinatal brain injury. Specifically, we focus our discussions on the use of neuroimaging to aid diagnosis, measure morphometric brain damage, and track long-term neurodevelopmental outcomes.
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Affiliation(s)
- Manuel Hinojosa-Rodríguez
- Unidad de Investigación en Neurodesarrollo, Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México (UNAM), Campus Juriquilla, Mexico
| | - Thalía Harmony
- Unidad de Investigación en Neurodesarrollo, Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México (UNAM), Campus Juriquilla, Mexico
| | - Cristina Carrillo-Prado
- Unidad de Investigación en Neurodesarrollo, Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México (UNAM), Campus Juriquilla, Mexico
| | - John Darrell Van Horn
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, 2025 Zonal Avenue, SHN, Los Angeles, California 90033, USA
| | - Andrei Irimia
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, 2025 Zonal Avenue, SHN, Los Angeles, California 90033, USA
| | - Carinna Torgerson
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, 2025 Zonal Avenue, SHN, Los Angeles, California 90033, USA
| | - Zachary Jacokes
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, 2025 Zonal Avenue, SHN, Los Angeles, California 90033, USA
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Winter A, Takabayashi K, Jahn F, Kimura E, Engelbrecht R, Haux R, Honda M, Hübner UH, Inoue S, Kohl CD, Matsumoto T, Matsumura Y, Miyo K, Nakashima N, Prokosch HU, Staemmler M. Quality Requirements for Electronic Health Record Systems*. A Japanese-German Information Management Perspective. Methods Inf Med 2017; 56:e92-e104. [PMID: 28925415 PMCID: PMC6291988 DOI: 10.3414/me17-05-0002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 06/13/2017] [Indexed: 12/16/2022]
Abstract
BACKGROUND For more than 30 years, there has been close cooperation between Japanese and German scientists with regard to information systems in health care. Collaboration has been formalized by an agreement between the respective scientific associations. Following this agreement, two joint workshops took place to explore the similarities and differences of electronic health record systems (EHRS) against the background of the two national healthcare systems that share many commonalities. OBJECTIVES To establish a framework and requirements for the quality of EHRS that may also serve as a basis for comparing different EHRS. METHODS Donabedian's three dimensions of quality of medical care were adapted to the outcome, process, and structural quality of EHRS and their management. These quality dimensions were proposed before the first workshop of EHRS experts and enriched during the discussions. RESULTS The Quality Requirements Framework of EHRS (QRF-EHRS) was defined and complemented by requirements for high quality EHRS. The framework integrates three quality dimensions (outcome, process, and structural quality), three layers of information systems (processes and data, applications, and physical tools) and three dimensions of information management (strategic, tactical, and operational information management). CONCLUSIONS Describing and comparing the quality of EHRS is in fact a multidimensional problem as given by the QRF-EHRS framework. This framework will be utilized to compare Japanese and German EHRS, notably those that were presented at the second workshop.
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Affiliation(s)
- Alfred Winter
- Prof. Alfred Winter, University of Leipzig, Institute for Medical Informatics, Statistics and Epidemiology, Haertelstr. 16 -18, 04107 Leipzig, Germany, E-mail:
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Madan CR. Advances in Studying Brain Morphology: The Benefits of Open-Access Data. Front Hum Neurosci 2017; 11:405. [PMID: 28824407 PMCID: PMC5543094 DOI: 10.3389/fnhum.2017.00405] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 07/21/2017] [Indexed: 12/20/2022] Open
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