COINS Data Exchange: An open platform for compiling, curating, and disseminating neuroimaging data

Landis, Drew; Courtney, William; Dieringer, Christopher; Kelly, Ross; King, Margaret D.; Miller, Brittny; Wang, Runtang; Wood, Dylan; Turner, Jessica A.; Calhoun, Vince D.

doi:10.1016/j.neuroimage.2015.05.049

Cited by 60 publications

(46 citation statements)

References 5 publications

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“…The need for such approaches to general data computation is realized by some researchers (Bai et al, 2005) but not yet fully appreciated by the neuroimaging field. The field is currently in the state of establishing central repositories of anonymized raw data (Bockholt et al, 2009; Buccigrossi et al, 2007; Di Martino et al, 2014; Jack et al, 2008; Keator et al, 2008; Landis et al, 2015; Marcus et al, 2007; Poldrack et al, 2013; Scott et al, 2011; Turner, 2014; Van Essen et al, 2013). In the past 10 years, release of multi-site neuroimaging datasets such as: FBIRN, MCIC for schizophrenia (Ford et al, 2009; Gollub et al, 2013), ADNI for Alzheimer's disease (Jack et al, 2008), ABIDE for ASD (Di Martino et al, 2014), ADHD-200 for children with ADHD (Consortium and others, 2012) and Functional Connectomes project for healthy subjects (Biswal et al, 2010) have been started.…”

Section: Machine Learning In Neuroimaging: Shortcomings and Emergimentioning

confidence: 99%

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

et al. 2017

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Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there are extensive evidences showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.

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Section: Machine Learning In Neuroimaging: Shortcomings and Emergimentioning

confidence: 99%

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

et al. 2017

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“…We considered five software tools that create sharable, searchable databases and offer maximum flexibility: COINS (http://coins.mrn.org/) (Landis et al, 2016); LORIS (http://mcin-cnim.ca/neuroimagingtechnologies/loris/) (Das et al 2012); NiDB (https://github.com/gbook/nidb); Scitran (http://scitran.github.io/); and XNAT (http://www.xnat.org/) (Marcus et al, 2007a). The ability to link and search make those applications different from repositories; however each has strengths and limitations, some significant.…”

Section: Database Infrastructurementioning

confidence: 99%

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

et al. 2017

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Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e.g. defining ‘normality’); legal, ethical and technological issues (e.g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range of individual image banks, and provide practical recommendations to enhance creation, use and reuse of neuroimaging data. Our aim is to maximise the benefit of the image data, provided voluntarily by research participants and funded by many organisations, for human health. Our ultimate vision is of a federated network of brain image biobanks accessible for large studies of brain structure and function.

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“…Given this risk, it is becoming increasingly important to design data-driven studies with replication in mind. One avenue for addressing this is to design analyses around extant datasets that have large numbers of subjects and rigorous quality control processes, such as the Human Connectome Project (Van Essen, Smith et al 2013), or through open sharing of data on platforms such as the OASIS database (www.oasis-brains.org), the COINS platform (Landis, Courtney et al 2016) (http://coins.mrn.org), or the OpenfMRI project (Poldrack, Barch et al 2013) (https://openfmri.org/). Further, given the large numbers of subjects in some of these databases, studies can be designed with built-in replication through a variety of methods.…”

Section: ) Introductionmentioning

confidence: 99%

Multimodal neural correlates of cognitive control in the Human Connectome Project

et al. 2017

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Cognitive control is a construct that refers to the set of functions that enable decision-making and task performance through the representation of task states, goals, and rules. The neural correlates of cognitive control have been studied in humans using a wide variety of neuroimaging modalities, including structural MRI, resting-state fMRI, and task-based fMRI. The results from each of these modalities independently have implicated the involvement of a number of brain regions in cognitive control, including dorsal prefrontal cortex, and frontal parietal and cingulo-opercular brain networks. However, it is not clear how the results from a single modality relate to results in other modalities. Recent developments in multimodal image analysis methods provide an avenue for answering such questions and could yield more integrated models of the neural correlates of cognitive control. In this study, we used multiset canonical correlation analysis with joint independent component analysis (mCCA+jICA) to identify multimodal patterns of variation related to cognitive control. We used two independent cohorts of participants from the Human Connectome Project, each of which had data from four imaging modalities. We replicated the findings from the first cohort in the second cohort using both independent and predictive analyses. The independent analyses identified a component in each cohort that was highly similar to the other and significantly correlated with cognitive control performance. The replication by prediction analyses identified two independent components that were significantly correlated with cognitive control performance in the first cohort and significantly predictive of performance in the second cohort. These components identified positive relationships across the modalities in neural regions related to both dynamic and stable aspects of task control, including regions in both the frontal-parietal and cingulo-opercular networks, as well as regions hypothesized to be modulated by cognitive control signaling, such as visual cortex. Taken together, these results illustrate the potential utility of multi-modal analyses in identifying the neural correlates of cognitive control across different indicators of brain structure and function.

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COINS Data Exchange: An open platform for compiling, curating, and disseminating neuroimaging data

Cited by 60 publications

References 5 publications

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

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

Multimodal neural correlates of cognitive control in the Human Connectome Project

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