Offering to share: How to put heads together in autism neuroimaging

Department of Human Development, Cornell University, Martha Van Rensselaer Hall, Ithaca, NY 14853-4401, USA.
Journal of Autism and Developmental Disorders (Impact Factor: 3.34). 02/2008; 38(1):2-13. DOI: 10.1007/s10803-006-0352-2
Source: PubMed

ABSTRACT Data sharing in autism neuroimaging presents scientific, technical, and social obstacles. We outline the desiderata for a data-sharing scheme that combines imaging with other measures of phenotype and with genetics, defines requirements for comparability of derived data and recommendations for raw data, outlines a core protocol including multispectral structural and diffusion-tensor imaging and optional extensions, provides for the collection of prospective, confound-free normative data, and extends sharing and collaborative development not only to data but to the analytical tools and methods applied to these data. A theme in these requirements is the need to preserve creative approaches and risk-taking within individual laboratories at the same time as common standards are provided for these laboratories to build on.

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Available from: Robert T Schultz, Aug 25, 2015
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    • "Hence, data sharing in imaging informatics is a fundamental issue. However, image data sharing has scientific, technical and social obstacles (Belmonte et al., 2008). For example, human imaging data sharing should meet the need for protection of patient privacy, and compliance with applicable regulations and guidelines including those of the Institutional Review Board, Health Insurance Portability and Accountability Act (HIPAA), and other institutional, local, state and federal government and community bodies. "
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    ABSTRACT: Imaging informatics has emerged as a major research theme in biomedicine in the last few decades. Currently, personalised, predictive and preventive patient care is believed to be one of the top priorities in biomedical research and practice. Imaging informatics plays a major role in biomedicine studies. This paper reviews main applications and challenges of imaging informatics in biomedicine.
    International Journal of Functional Informatics and Personalised Medicine 01/2009; 2(2):125-135. DOI:10.1504/IJFIPM.2009.027587
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    • "Given the growth of multicenter imaging studies that seek to identify quantitative measures of brain structure or function that relate to disease (Jack et al., 2003; Mueller et al., 2005; Murphy et al., 2006; Belmonte et al., 2007), it is surprising that there has been fairly little study of the influence of MRI instrument-related factors on the reliability of such putative imaging biomarkers (Han et al., 2006; Jovicich et al., 2006). Since measures of cortical thinning are sensitive (Lerch et al., 2006) and reasonably specific (Du et al., 2007), at least in the context of particular neurodegenerative diseases, these measures are a promising candidate MRI imaging biomarker. "
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    ABSTRACT: In normal humans, relationships between cognitive test performance and cortical structure have received little study, in part, because of the paucity of tools for measuring cortical structure. Computational morphometric methods have recently been developed that enable the measurement of cortical thickness from MRI data, but little data exist on their reliability. We undertook this study to evaluate the reliability of an automated cortical thickness measurement method to detect correlates of interest between thickness and cognitive task performance. Fifteen healthy older participants were scanned four times at 2-week intervals on three different scanner platforms. The four MRI data sets were initially treated independently to investigate the reliability of the spatial localization of findings from exploratory whole-cortex analyses of cortical thickness-cognitive performance correlates. Next, the first data set was used to define cortical ROIs based on the exploratory results that were then applied to the remaining three data sets to determine whether the relationships between cognitive performance and regional cortical thickness were comparable across different scanner platforms and field strengths. Verbal memory performance was associated with medial temporal cortical thickness, while visuomotor speed/set shifting was associated with lateral parietal cortical thickness. These effects were highly reliable - in terms of both spatial localization and magnitude of absolute cortical thickness measurements - across the four scan sessions. Brain-behavior relationships between regional cortical thickness and cognitive task performance can be reliably identified using an automated data analysis system, suggesting that these measures may be useful as imaging biomarkers of disease or performance ability in multicenter studies in which MRI data are pooled.
    NeuroImage 02/2008; 39(1):10-8. DOI:10.1016/j.neuroimage.2007.08.042 · 6.36 Impact Factor
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    ABSTRACT: This is a report from a JISC commissioned project. It is also available at: It is becoming increasingly clear that effective and efficient management and reuse of research data will be a key component in the UK knowledge economy in years to come, essential for the efficient conduct of research and its dissemination and use. In recognition of this, there have been many calls for access to science data at national and international levels. JISC and other UK funding bodies have developed a number of initiatives concerned with the management and curation of research data. The report by Lyon (2007) was pivotal in delineating the issues that need to be addressed and this project aims to take forward Recommendation 30: JISC should work in partnership with the research funding bodies and jointly commission a cost-benefit study of data curation and preservation infrastructure. The project’s objectives are to: • Identify the benefits of curating and sharing research data; • Identify a methodology by which to estimate the benefits to UK Higher Education and the UK more generally of curating and openly sharing research data produced by researchers in UK HE; • Use the methodology, as far as possible, to derive an estimate, expressed in financial terms where possible, for the identified benefits; • Document case studies and examples of data re-use, where that re-use led to tangible benefits. Potential benefits of the open sharing and re-use of research data include: maximised investment in data collection; broader access where costs would be prohibitive for individual researchers/institutions; potential for new discoveries from existing data, especially where data are aggregated and integrated; reduced duplication of data collection costs and increased transparency of the scientific record; increased research impact and reduced time-lag in realising those impacts; new collaborations and new knowledge-based industries ... Joint Information Systems Committee (JISC) Not specified
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