Improving data availability for brain image biobanking in healthy subjects: Practice-based suggestions from an international multidisciplinary working group

Considerations for an integrated population health databank in Africa: lessons from global best practices

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.

Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain

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.

Management and Quality Control of Large Neuroimaging Datasets: Developments From the Barcelonaβeta Brain Research Center

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.

Impact of preterm birth on brain development and long-term outcome: protocol for a cohort study in Scotland

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.

The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services

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.

Responsible data sharing in international health research: a systematic review of principles and norms

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.

Normal Aging Brain Collection Amsterdam (NABCA): A comprehensive collection of postmortem high-field imaging, neuropathological and morphometric datasets of non-neurological controls

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

Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing

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.

Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review

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.

Predicting age from cortical structure across the lifespan

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.

Human brain atlasing: past, present and future

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.