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

Angulo, Diego A.; Schneider, Cyril; Oliver, James H.; Charpak, Nathalie; Hernández, José Tiberio

doi:10.3389/fninf.2016.00036

Cited by 21 publications

(21 citation statements)

References 35 publications

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“…For an integrated view of individual and grouped multiple outcomes, we developed software that allows visualization of neuroimaging results with outcome according to potential independent variables. 25 …”

Section: Figurementioning

confidence: 99%

Twenty-year Follow-up of Kangaroo Mother Care Versus Traditional Care

Charpak¹,

et al. 2017

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BACKGROUND AND OBJECTIVES: Kangaroo mother care (KMC) is a multifaceted intervention for preterm and low birth weight infants and their parents. Short-and mid-term benefits of KMC on survival, neurodevelopment, breastfeeding, and the quality of mother-infant bonding were documented in a randomized controlled trial (RCT) conducted in Colombia from 1993 to 1996. The aim of the present study was to evaluate the persistence of these results in young adulthood.

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Section: Figurementioning

confidence: 99%

Twenty-year Follow-up of Kangaroo Mother Care Versus Traditional Care

Charpak¹,

et al. 2017

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“…Similar to our approach, Angulo et al . [ASO*16] developed an exploratory tool integrating selected visualization components for MRI‐derived measurements and clinical data while Furcilia et al . [FGT*19] focus on tabular neuroscience data.…”

Section: Related Workmentioning

confidence: 99%

VisualNeuro: A Hypothesis Formation and Reasoning Application for Multi‐Variate Brain Cohort Study Data

Jonsson

Bergström

Forsell

et al. 2020

Computer Graphics Forum

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We present an application, and its development process, for interactive visual analysis of brain imaging data and clinical measurements. The application targets neuroscientists interested in understanding the correlations between active brain regions and physiological or psychological factors. The application has been developed in a participatory design process and has subsequently been released as the free software ‘VisualNeuro’. From initial observations of the neuroscientists' workflow, we concluded that while existing tools provide powerful analysis options, they lack effective interactive exploration requiring the use of many tools side by side. Consequently, our application has been designed to simplify the workflow combining statistical analysis with interactive visual exploration. The resulting environment comprises parallel coordinates for effective overview and selection, Welch's t‐test to filter out brain regions with statistically significant differences and multiple visualizations for comparison between brain regions and clinical parameters. These exploration concepts enable neuroscientists to interactively explore the complex bidirectional interplay between clinical and brain measurements and easily compare different patient groups. A qualitative user study has been performed with three neuroscientists from different domains. The study shows that the developed environment supports simultaneous analysis of more parameters, provides rapid pathways to insights and is an effective tool for hypothesis formation.

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“…Other approaches, e.g. [ 90 , 91 ], use a windowed approach which allows the user to present multiple graphs about related information from the fMRI and from other data sources, such as diffusion tensor imaging (DTI) or electroencephalography (EEG) readings, which can provide more information about the subject and add to the interpretation. Such tools can be categorised into: Windowed approaches , which present data in multiple movable and resizable windows.…”

Section: Existing Analysis Processes Visualisations and Visual Analymentioning

confidence: 99%

“…EEG. A notable example is BraViz [ 90 ] (Fig. 11 a) in which users can view fMRI data alongside DTI, underlying temporal and second-order statistics.…”

Section: Existing Analysis Processes Visualisations and Visual Analymentioning

confidence: 99%

“…

Fig. 11 Examples of multimodality and data source windowed approaches: a three windows created in BraViz [ 90 ] showing an fMRI timeline above two windows of population demographic statistics; b Brain Modulyzer [ 92 ] displaying derived statistics alongside fMRI connectivity data Linked views similarly enable users to view fMRI data from multiple angles. However, the visual elements are fixed in size and position.…”

Section: Existing Analysis Processes Visualisations and Visual Analymentioning

confidence: 99%

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A review and outlook on visual analytics for uncertainties in functional magnetic resonance imaging

2018

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Analysis of functional magnetic resonance imaging (fMRI) plays a pivotal role in uncovering an understanding of the brain. fMRI data contain both spatial volume and temporal signal information, which provide a depiction of brain activity. The analysis pipeline, however, is hampered by numerous uncertainties in many of the steps; often seen as one of the last hurdles for the domain. In this review, we categorise fMRI research into three pipeline phases: (i) image acquisition and processing; (ii) image analysis; and (iii) visualisation and human interpretation, to explore the uncertainties that arise in each phase, including the compound effects due to the inter-dependence of steps. Attempts at mitigating uncertainties rely on providing interactive visual analytics that aid users in understanding the effects of the uncertainties and adjusting their analyses. This impetus for visual analytics comes in light of considerable research investigating uncertainty throughout the pipeline. However, to the best of our knowledge, there is yet to be a comprehensive review on the importance and utility of uncertainty visual analytics (UVA) in addressing fMRI concerns, which we term fMRI-UVA. Such techniques have been broadly implemented in related biomedical fields, and its potential for fMRI has recently been explored; however, these attempts are limited in their scope and utility, primarily focussing on addressing small parts of single pipeline phases. Our comprehensive review of the fMRI uncertainties from the perspective of visual analytics addresses the three identified phases in the pipeline. We also discuss the two interrelated approaches for future research opportunities for fMRI-UVA.

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

Cited by 21 publications

References 35 publications

Twenty-year Follow-up of Kangaroo Mother Care Versus Traditional Care

Twenty-year Follow-up of Kangaroo Mother Care Versus Traditional Care

VisualNeuro: A Hypothesis Formation and Reasoning Application for Multi‐Variate Brain Cohort Study Data

A review and outlook on visual analytics for uncertainties in functional magnetic resonance imaging

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