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Blevins AS, Bassett DS, Scott EK, Vanwalleghem GC. From calcium imaging to graph topology. Netw Neurosci 2022; 6:1125-1147. [PMID: 38800465 PMCID: PMC11117109 DOI: 10.1162/netn_a_00262] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/13/2022] [Indexed: 05/29/2024] Open
Abstract
Systems neuroscience is facing an ever-growing mountain of data. Recent advances in protein engineering and microscopy have together led to a paradigm shift in neuroscience; using fluorescence, we can now image the activity of every neuron through the whole brain of behaving animals. Even in larger organisms, the number of neurons that we can record simultaneously is increasing exponentially with time. This increase in the dimensionality of the data is being met with an explosion of computational and mathematical methods, each using disparate terminology, distinct approaches, and diverse mathematical concepts. Here we collect, organize, and explain multiple data analysis techniques that have been, or could be, applied to whole-brain imaging, using larval zebrafish as an example model. We begin with methods such as linear regression that are designed to detect relations between two variables. Next, we progress through network science and applied topological methods, which focus on the patterns of relations among many variables. Finally, we highlight the potential of generative models that could provide testable hypotheses on wiring rules and network progression through time, or disease progression. While we use examples of imaging from larval zebrafish, these approaches are suitable for any population-scale neural network modeling, and indeed, to applications beyond systems neuroscience. Computational approaches from network science and applied topology are not limited to larval zebrafish, or even to systems neuroscience, and we therefore conclude with a discussion of how such methods can be applied to diverse problems across the biological sciences.
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Affiliation(s)
- Ann S. Blevins
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S. Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Ethan K. Scott
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
- Department of Anatomy and Physiology, School of Biomedical Sciences, University of Melbourne, Parkville, Australia
| | - Gilles C. Vanwalleghem
- Danish Research Institute of Translational Neuroscience (DANDRITE), Nordic EMBL Partnership for Molecular Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
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2
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Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain. Neuroinformatics 2022; 20:109-137. [PMID: 33974213 PMCID: PMC8111663 DOI: 10.1007/s12021-021-09519-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2021] [Indexed: 02/06/2023]
Abstract
We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.
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3
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Igumbor JO, Bosire EN, Vicente-Crespo M, Igumbor EU, Olalekan UA, Chirwa TF, Kinyanjui SM, Kyobutungi C, Fonn S. Considerations for an integrated population health databank in Africa: lessons from global best practices. Wellcome Open Res 2021; 6:214. [PMID: 35224211 PMCID: PMC8844538 DOI: 10.12688/wellcomeopenres.17000.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/12/2021] [Indexed: 12/17/2022] Open
Abstract
Background: The rising digitisation and proliferation of data sources and repositories cannot be ignored. This trend expands opportunities to integrate and share population health data. Such platforms have many benefits, including the potential to efficiently translate information arising from such data to evidence needed to address complex global health challenges. There are pockets of quality data on the continent that may benefit from greater integration. Integration of data sources is however under-explored in Africa. The aim of this article is to identify the requirements and provide practical recommendations for developing a multi-consortia public and population health data-sharing framework for Africa. Methods: We conducted a narrative review of global best practices and policies on data sharing and its optimisation. We searched eight databases for publications and undertook an iterative snowballing search of articles cited in the identified publications. The Leximancer software © enabled content analysis and selection of a sample of the most relevant articles for detailed review. Themes were developed through immersion in the extracts of selected articles using inductive thematic analysis. We also performed interviews with public and population health stakeholders in Africa to gather their experiences, perceptions, and expectations of data sharing. Results: Our findings described global stakeholder experiences on research data sharing. We identified some challenges and measures to harness available resources and incentivise data sharing. We further highlight progress made by the different groups in Africa and identified the infrastructural requirements and considerations when implementing data sharing platforms. Furthermore, the review suggests key reforms required, particularly in the areas of consenting, privacy protection, data ownership, governance, and data access. Conclusions: The findings underscore the critical role of inclusion, social justice, public good, data security, accountability, legislation, reciprocity, and mutual respect in developing a responsive, ethical, durable, and integrated research data sharing ecosystem.
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Affiliation(s)
- Jude O. Igumbor
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
| | - Edna N. Bosire
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
| | - Marta Vicente-Crespo
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
- African Population and Health Research Centre, Nairobi, Kenya
| | - Ehimario U. Igumbor
- Nigeria Centre for Disease Control, Abuja, Nigeria
- School of Public Health, University of the Western Cape, Cape Town, Western Cape, South Africa
| | - Uthman A. Olalekan
- Warwick-Centre for Applied Health Research and Delivery (WCAHRD), Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Tobias F. Chirwa
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
| | | | | | - Sharon Fonn
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
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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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5
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Boardman JP, Hall J, Thrippleton MJ, Reynolds RM, Bogaert D, Davidson DJ, Schwarze J, Drake AJ, Chandran S, Bastin ME, Fletcher-Watson S. Impact of preterm birth on brain development and long-term outcome: protocol for a cohort study in Scotland. BMJ Open 2020; 10:e035854. [PMID: 32139495 PMCID: PMC7059503 DOI: 10.1136/bmjopen-2019-035854] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
INTRODUCTION Preterm birth is closely associated with altered brain development and is a leading cause of neurodevelopmental, cognitive and behavioural impairments across the life course. We aimed to investigate neuroanatomic variation and adverse outcomes associated with preterm birth by studying a cohort of preterm infants and controls born at term using brain MRI linked to biosamples and clinical, environmental and neuropsychological data. METHODS AND ANALYSIS Theirworld Edinburgh Birth Cohort is a prospective longitudinal cohort study at the University of Edinburgh. We plan to recruit 300 infants born at <33 weeks of gestational age (GA) and 100 healthy control infants born after 37 weeks of GA. Multiple domains are assessed: maternal and infant clinical and demographic information; placental histology; immunoregulatory and trophic proteins in umbilical cord and neonatal blood; brain macrostructure and microstructure from structural and diffusion MRI (dMRI); DNA methylation; hypothalamic-pituitary-adrenal axis activity; social cognition, attention and processing speed from eye tracking during infancy and childhood; neurodevelopment; gut and respiratory microbiota; susceptibility to viral infections; and participant experience. Main analyses include creation of novel methods for extracting information from neonatal structural and dMRI, regression analyses of predictors of brain maldevelopment and neurocognitive outcome associated with preterm birth, and determination of the quantitative predictive performance of MRI and other early life factors for childhood outcome. ETHICS AND DISSEMINATION Ethical approval has been obtained from the National Research Ethics Service (NRES), South East Scotland Research Ethics Committee (NRES numbers 11/55/0061 and 13/SS/0143 (phase I) and 16/SS/0154 (phase II)), and NHS Lothian Research and Development (2016/0255). Results are disseminated through open access journals, scientific meetings, social media, newsletters anda study website (www.tebc.ed.ac.uk), and we engage with the University of Edinburgh public relations and media office to ensure maximum publicity and benefit.
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Affiliation(s)
- James P Boardman
- MRC Centre for Reproductive Health, The University of Edinburgh, Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Jill Hall
- MRC Centre for Reproductive Health, The University of Edinburgh, Edinburgh, UK
| | | | - Rebecca M Reynolds
- Centre for Cardiovascular Science, The University of Edinburgh, Edinburgh, UK
| | - Debby Bogaert
- Centre for Inflammation Research, The University of Edinburgh, Edinburgh, UK
| | - Donald J Davidson
- Centre for Inflammation Research, The University of Edinburgh, Edinburgh, UK
| | - Jurgen Schwarze
- Centre for Inflammation Research, The University of Edinburgh, Edinburgh, UK
| | - Amanda J Drake
- Centre for Cardiovascular Science, The University of Edinburgh, Edinburgh, UK
| | | | - Mark E Bastin
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Sue Fletcher-Watson
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
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6
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Avesani P, McPherson B, Hayashi S, Caiafa CF, Henschel R, Garyfallidis E, Kitchell L, Bullock D, Patterson A, Olivetti E, Sporns O, Saykin AJ, Wang L, Dinov I, Hancock D, Caron B, Qian Y, Pestilli F. The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. Sci Data 2019; 6:69. [PMID: 31123325 PMCID: PMC6533280 DOI: 10.1038/s41597-019-0073-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 04/11/2019] [Indexed: 12/31/2022] Open
Abstract
We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.
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Affiliation(s)
- Paolo Avesani
- Neuroinformatics Laboratory, Center for Information Technology, Fondazione Bruno Kessler, via Sommarive 18, 38123, Trento, Italy
- Center for Mind/Brain Sciences (CIMeC), University of Trento, via Delle Regole 101, 38123, Trento, Italy
| | - Brent McPherson
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Cognitive Science, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Soichi Hayashi
- Department of Psychological and Brain Sciences and Pervasive Technology Institute, University Information Technology Services, Indiana University, 1101 E 10th Street, Bloomington, IN, 47405, USA
| | - Cesar F Caiafa
- Pestilli Lab. Department of Psychological and Brain Sciences, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
- Instituto Argentino de Radioastronomía (CCT-La Plata, CONICET; CICPBA), CC5 V, Elisa, 1894, Argentina
- Facultad de Ingeniería, Universidad de Buenos Aires, Buenos Aires, C1063ACV, Argentina
| | - Robert Henschel
- Pervasive Technology Institute, Indiana University Bloomington, 2709 E 10th Street, Bloomington, IN, 47408, USA
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Programs in Neuroscience and Cognitive Science, Indiana University Bloomington, 700N Woodlawn Ave, Bloomington, Indiana, 47408, USA
| | - Lindsey Kitchell
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Cognitive Science, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Daniel Bullock
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Andrew Patterson
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Emanuele Olivetti
- Neuroinformatics Laboratory, Center for Information Technology, Fondazione Bruno Kessler, via Sommarive 18, 38123, Trento, Italy
- Center for Mind/Brain Sciences (CIMeC), University of Trento, via Delle Regole 101, 38123, Trento, Italy
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Programs in Neuroscience and Cognitive Science, and Indiana Network Science Institute, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Andrew J Saykin
- Indiana University School of Medicine, Departments of Radiology and Imaging Sciences and Medical and Molecular Genetics, and the Indiana Alzheimer Disease Center, Indiana University, 355 W 16th St., Indianapolis, Indiana, 46202, USA
| | - Lei Wang
- Departments of Psychiatry and Behavioral Sciences and Radiology, Northwestern University Feinberg School of Medicine, 710N. Lake Shore Drive, Abbott Hall 1322, Chicago, IL, 60611, USA
| | - Ivo Dinov
- Statistics Online Computational Resource (SOCR), Center for Complexity of Self-Management in Chronic Disease (CSCD), Health Behavior and Biological Sciences, Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 49109, USA
| | - David Hancock
- Pervasive Technology Institute, Indiana University Bloomington, 2709 E 10th Street, Bloomington, IN, 47408, USA
| | - Bradley Caron
- Pestilli Lab. Indiana University School of Optometry and Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, USA
| | - Yiming Qian
- Pestilli Lab. Department of Psychological and Brain Sciences, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Franco Pestilli
- Pestilli Lab. Department of Psychological and Brain Sciences, Engineering, Computer Science, Programs in Neuroscience and Cognitive Science, School of Optometry, and Indiana Network Science Institute, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA.
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7
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Kalkman S, Mostert M, Gerlinger C, van Delden JJM, van Thiel GJMW. Responsible data sharing in international health research: a systematic review of principles and norms. BMC Med Ethics 2019; 20:21. [PMID: 30922290 PMCID: PMC6437875 DOI: 10.1186/s12910-019-0359-9] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Accepted: 03/12/2019] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Large-scale linkage of international clinical datasets could lead to unique insights into disease aetiology and facilitate treatment evaluation and drug development. Hereto, multi-stakeholder consortia are currently designing several disease-specific translational research platforms to enable international health data sharing. Despite the recent adoption of the EU General Data Protection Regulation (GDPR), the procedures for how to govern responsible data sharing in such projects are not at all spelled out yet. In search of a first, basic outline of an ethical governance framework, we set out to explore relevant ethical principles and norms. METHODS We performed a systematic review of literature and ethical guidelines for principles and norms pertaining to data sharing for international health research. RESULTS We observed an abundance of principles and norms with considerable convergence at the aggregate level of four overarching themes: societal benefits and value; distribution of risks, benefits and burdens; respect for individuals and groups; and public trust and engagement. However, at the level of principles and norms we identified substantial variation in the phrasing and level of detail, the number and content of norms considered necessary to protect a principle, and the contextual approaches in which principles and norms are used. CONCLUSIONS While providing some helpful leads for further work on a coherent governance framework for data sharing, the current collection of principles and norms prompts important questions about how to streamline terminology regarding de-identification and how to harmonise the identified principles and norms into a coherent governance framework that promotes data sharing while securing public trust.
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Affiliation(s)
- Shona Kalkman
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584, CG, Utrecht, the Netherlands.
| | - Menno Mostert
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584, CG, Utrecht, the Netherlands
| | - Christoph Gerlinger
- Statistics and Data Insights, Bayer AG, Berlin, Germany
- Clinic for Gynecology, Obstetrics and Reproductive Medicine, Saarland University Medical Center, Homburg, Saarland, Germany
| | - Johannes J M van Delden
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584, CG, Utrecht, the Netherlands
| | - Ghislaine J M W van Thiel
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584, CG, Utrecht, the Netherlands
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8
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Jonkman LE, Graaf YGD, Bulk M, Kaaij E, Pouwels PJW, Barkhof F, Rozemuller AJM, van der Weerd L, Geurts JJG, van de Berg WDJ. Normal Aging Brain Collection Amsterdam (NABCA): A comprehensive collection of postmortem high-field imaging, neuropathological and morphometric datasets of non-neurological controls. NEUROIMAGE-CLINICAL 2019; 22:101698. [PMID: 30711684 PMCID: PMC6360607 DOI: 10.1016/j.nicl.2019.101698] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 01/21/2019] [Accepted: 01/27/2019] [Indexed: 12/18/2022]
Abstract
Well-characterized, high-quality brain tissue of non-neurological control subjects is a prerequisite to study the healthy aging brain, and can serve as a control for the study of neurological disorders. The Normal Aging Brain Collection Amsterdam (NABCA) provides a comprehensive collection of post-mortem (ultra-)high-field MRI (3Tesla and 7 Tesla) and neuropathological datasets of non-neurological controls. By providing MRI within the pipeline, NABCA uniquely stimulates translational neurosciences; from molecular and morphometric tissue studies to the clinical setting. We describe our pipeline, including a description of our on-call autopsy team, donor selection, in situ and ex vivo post-mortem MRI protocols, brain dissection and neuropathological diagnosis. A demographic, radiological and pathological overview of five selected cases on all these aspects is provided. Additionally, information is given on data management, data and tissue application procedures, including review by a scientific advisory board, and setting up a material transfer agreement before distribution of tissue. Finally, we focus on future prospects, which includes laying the foundation for a unique platform for neuroanatomical, histopathological and neuro-radiological education, of professionals, students and the general (lay) audience. NABCA provides a collection of correlative post-mortem MRI and pathological datasets. Non-neurological control brains for studies on aging and neurological disorders. Stimulating micro- to macroscale structural exploration within same patient Post-mortem MRI data and tissue available for integrated advanced data analytics
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Affiliation(s)
- Laura E Jonkman
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
| | - Yvon Galis-de Graaf
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Marjolein Bulk
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Department of Human Genetics, Leiden University Medical Centre, Leiden, the Netherlands
| | - Eliane Kaaij
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Petra J W Pouwels
- Department of radiology and nuclear medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of radiology and nuclear medicine, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Institutes of neurology and healthcare engineering, University College London, London, United Kingdom
| | - Annemieke J M Rozemuller
- Department of pathology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Louise van der Weerd
- Department of Radiology, Leiden University Medical Centre, Leiden, the Netherlands; Department of Human Genetics, Leiden University Medical Centre, Leiden, the Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Wilma D J van de Berg
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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9
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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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10
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Pellegrini E, Ballerini L, Hernandez MDCV, Chappell FM, González-Castro V, Anblagan D, Danso S, Muñoz-Maniega S, Job D, Pernet C, Mair G, MacGillivray TJ, Trucco E, Wardlaw JM. Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2018; 10:519-535. [PMID: 30364671 PMCID: PMC6197752 DOI: 10.1016/j.dadm.2018.07.004] [Citation(s) in RCA: 108] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
INTRODUCTION Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear. METHODS We systematically reviewed the literature, 2006 to late 2016, for machine learning studies differentiating healthy aging from dementia of various types, assessing study quality, and comparing accuracy at different disease boundaries. RESULTS Of 111 relevant studies, most assessed Alzheimer's disease versus healthy controls, using AD Neuroimaging Initiative data, support vector machines, and only T1-weighted sequences. Accuracy was highest for differentiating Alzheimer's disease from healthy controls and poor for differentiating healthy controls versus mild cognitive impairment versus Alzheimer's disease or mild cognitive impairment converters versus nonconverters. Accuracy increased using combined data types, but not by data source, sample size, or machine learning method. DISCUSSION Machine learning does not differentiate clinically relevant disease categories yet. More diverse data sets, combinations of different types of data, and close clinical integration of machine learning would help to advance the field.
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Affiliation(s)
- Enrico Pellegrini
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Lucia Ballerini
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Maria del C. Valdes Hernandez
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Francesca M. Chappell
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Victor González-Castro
- Department of Electrical, Systems and Automatics Engineering, Universidad de León, León, Spain
| | - Devasuda Anblagan
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Samuel Danso
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Susana Muñoz-Maniega
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Dominic Job
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Cyril Pernet
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Grant Mair
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
| | - Tom J. MacGillivray
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
- VAMPIRE project, University of Edinburgh, Scotland, UK
| | - Emanuele Trucco
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, UK
| | - Joanna M. Wardlaw
- Division of Neuroimaging, Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Scotland, UK
- UK Dementia Institute, University of Edinburgh, Scotland, UK
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Madan CR, Kensinger EA. Predicting age from cortical structure across the lifespan. Eur J Neurosci 2018; 47:399-416. [PMID: 29359873 PMCID: PMC5835209 DOI: 10.1111/ejn.13835] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Revised: 01/12/2018] [Accepted: 01/15/2018] [Indexed: 01/22/2023]
Abstract
Despite interindividual differences in cortical structure, cross-sectional and longitudinal studies have demonstrated a large degree of population-level consistency in age-related differences in brain morphology. This study assessed how accurately an individual's age could be predicted by estimates of cortical morphology, comparing a variety of structural measures, including thickness, gyrification and fractal dimensionality. Structural measures were calculated across up to seven different parcellation approaches, ranging from one region to 1000 regions. The age prediction framework was trained using morphological measures obtained from T1-weighted MRI volumes collected from multiple sites, yielding a training dataset of 1056 healthy adults, aged 18-97. Age predictions were calculated using a machine-learning approach that incorporated nonlinear differences over the lifespan. In two independent, held-out test samples, age predictions had a median error of 6-7 years. Age predictions were best when using a combination of cortical metrics, both thickness and fractal dimensionality. Overall, the results reveal that age-related differences in brain structure are systematic enough to enable reliable age prediction based on metrics of cortical morphology.
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Affiliation(s)
- Christopher R. Madan
- School of Psychology, University of Nottingham, Nottingham, UK
- Department of Psychology, Boston College, Chestnut Hill, MA, USA
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Abstract
We have recently witnessed an explosion of large-scale initiatives and projects addressing mapping, modeling, simulation and atlasing of the human brain, including the BRAIN Initiative, the Human Brain Project, the Human Connectome Project (HCP), the Big Brain, the Blue Brain Project, the Allen Brain Atlas, the Brainnetome, among others. Besides these large and international initiatives, there are numerous mid-size and small brain atlas-related projects. My contribution to these global efforts has been to create adult human brain atlases in health and disease, and to develop atlas-based applications. For over two decades with my R&D lab I developed 35 brain atlases, licensed to 67 companies and made available in about 100 countries. This paper has two objectives. First, it provides an overview of the state of the art in brain atlasing. Second, as it is already 20 years from the release of our first brain atlas, I summarise my past and present efforts, share my experience in atlas creation, validation and commercialisation, compare with the state of the art, and propose future directions.
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Affiliation(s)
- Wieslaw L Nowinski
- John Paull II Center for Virtual Anatomy and Surgical Simulation, University of Cardinal Stefan Wyszynski in Warsaw, Poland
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