Dataset Information

A Multi-facetted Visual Analytics Tool for Exploratory Analysis of Human Brain and Function Datasets.

ABSTRACT: Brain research typically requires large amounts of data from different sources, and often of different nature. The use of different software tools adapted to the nature of each data source can make research work cumbersome and time consuming. It follows that data is not often used to its fullest potential thus limiting exploratory analysis. This paper presents an ancillary software tool called BRAVIZ that integrates interactive visualization with real-time statistical analyses, facilitating access to multi-facetted neuroscience data and automating many cumbersome and error-prone tasks required to explore such data. Rather than relying on abstract numerical indicators, BRAVIZ emphasizes brain images as the main object of the analysis process of individuals or groups. BRAVIZ facilitates exploration of trends or relationships to gain an integrated view of the phenomena studied, thus motivating discovery of new hypotheses. A case study is presented that incorporates brain structure and function outcomes together with different types of clinical data.

SUBMITTER: Angulo DA

PROVIDER: S-EPMC4993811 | biostudies-other | 2016

REPOSITORIES: biostudies-other

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A Multi-facetted Visual Analytics Tool for Exploratory Analysis of Human Brain and Function Datasets.

Angulo Diego A DA Schneider Cyril C Oliver James H JH Charpak Nathalie N Hernandez Jose T JT

Frontiers in neuroinformatics 20160823

Brain research typically requires large amounts of data from different sources, and often of different nature. The use of different software tools adapted to the nature of each data source can make research work cumbersome and time consuming. It follows that data is not often used to its fullest potential thus limiting exploratory analysis. This paper presents an ancillary software tool called BRAVIZ that integrates interactive visualization with real-time statistical analyses, facilitating acce ...[more]

PMID: 27601990

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Project description:BACKGROUND:Health experts including planners and policy-makers face complex decisions in diverse and constantly changing healthcare systems. Visual analytics may play a critical role in supporting analysis of complex healthcare data and decision-making. The purpose of this study was to examine the real-world experience that experts in mental healthcare planning have with visual analytics tools, investigate how well current visualisation techniques meet their needs, and suggest priorities for the future development of visual analytics tools of practical benefit to mental healthcare policy and decision-making. METHODS:Health expert experience was assessed by an online exploratory survey consisting of a mix of multiple choice and open-ended questions. Health experts were sampled from an international pool of policy-makers, health agency directors, and researchers with extensive and direct experience of using visual analytics tools for complex mental healthcare systems planning. We invited them to the survey, and the experts' responses were analysed using statistical and text mining approaches. RESULTS:The forty respondents who took part in the study recognised the complexity of healthcare systems data, but had most experience with and preference for relatively simple and familiar visualisations such as bar charts, scatter plots, and geographical maps. Sixty-five percent rated visual analytics as important to their field for evidence-informed decision-making processes. Fifty-five percent indicated that more advanced visual analytics tools were needed for their data analysis, and 67.5% stated their willingness to learn new tools. This was reflected in text mining and qualitative synthesis of open-ended responses. CONCLUSIONS:This exploratory research provides readers with the first self-report insight into expert experience with visual analytics in mental healthcare systems research and policy. In spite of the awareness of their importance for complex healthcare planning, the majority of experts use simple, readily available visualisation tools. We conclude that co-creation and co-development strategies will be required to support advanced visual analytics tools and skills, which will become essential in the future of healthcare.

Project description:Background: There is limited research on brain lesions in children with cerebral visual impairment (CVI) of heterogeneous etiologies and according to associated subtyping and vision dysfunctions. This study was part of a larger project establishing data-driven subtypes of childhood CVI according to visual dysfunctions. Currently there is no consensus in relation to assessment, diagnosis and classification of CVI and more information about brain lesions may be of potential diagnostic value. Aim: This study aimed to investigate overall patterns of brain lesions and associations with level of visual dysfunction and to compare the patterns between the classification subgroups in children with CVI. Methods: School-aged children with CVI received ophthalmological and neuro-psychological/developmental assessments to establish CVI-related subtyping. Other pediatric information was collected from medical records. MRI scans were coded according to a semi-quantitative template including brain regions (right hemisphere, left hemisphere, visual pathways) and summed for total scores. Non-parametric analyses were conducted. Results: 28 children had clinical brain MRI scans available [44% of total sample, Group A (lower severity of visual dysfunctions) n = 16, Group B (higher severity) n = 12]. Total brain scores ranged between 0 and 18 (Group A mdn = 7, IQR = 0.8-10.0, Group B mdn = 10, IQR = 6.5-11.8) and were widespread across regions. 71 per cent had post-geniculate visual pathway damage. The median total brain and hemisphere scores of Group B were higher than subgroup A but differences did not reach statistical significance. No statistically significant associations were found between brain scores and vision variables (acuity, contrast sensitivity). Conclusion: This study found a spread of lesions across all regions on the brain scans in children with congenital CVI. The majority had damage in the postgeniculate visual pathways and visual cortex region suggesting this is an area of interest and potentially informative for diagnosis. However the subtyping classification did not show differences in number or region of lesions though the trend was higher toward Group B. This study confirms the complex diffuse and variable nature of brain lesions in children with congenital CVI, many of whom have other neurological impairments.

Dataset Information

A Multi-facetted Visual Analytics Tool for Exploratory Analysis of Human Brain and Function Datasets.

Publications

A Multi-facetted Visual Analytics Tool for Exploratory Analysis of Human Brain and Function Datasets.

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