Statistical parametric network analysis of functional connectivity dynamics during a working memory task

Ginestet, Cedric E.; Simmons, Andrew

doi:10.1016/j.neuroimage.2010.11.030

Cited by 99 publications

(108 citation statements)

References 69 publications

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“…Overall, Matrix is about 20% more accurate than Node-Link. The RM-ANOVA also revealed a significant effect of Task (F (2,20) = 28.67, p < .0001) with large effect size (η 2 p = .74), and a significant effect of the interaction Task We also found a significant effect of Size (F (1,10) = 18.69, p < .01) with a large effect size (η 2 p = .65) and Density (F (1,10) = 61.00, p < .0001) with a large effect size (η 2 p = .86). As expected, accuracy decreases for Large or Dense networks.…”

Section: Resultsmentioning

confidence: 85%

“…Matrices of functional connectivity as an alternative representation are also popular [1,19,34] and are occasionally used in the form of small multiples to illustrate trends across different connectivity datasets [5,20,28]. To support direct comparisons, correlation coefficients from multiple scan states can be shown within nested quadrants of a matrix cell [39].…”

Section: Use Of Visualizations In Brain Connectivity Analysismentioning

confidence: 99%

“…Recent research in neuroscience successfully began to analyze the connectivity data using graph theoretical methods [20] and statistics [43]. However, visualization systems can provide significant insights for the discovery of unforeseen structural correlation patterns, in particular across several datasets.…”

Section: Introductionmentioning

confidence: 99%

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Weighted graph comparison techniques for brain connectivity analysis

Alper

Bach

Riche

et al. 2013

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

156

153

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The analysis of brain connectivity is a vast field in neuroscience with a frequent use of visual representations and an increasing need for visual analysis tools. Based on an in-depth literature review and interviews with neuroscientists, we explore high-level brain connectivity analysis tasks that need to be supported by dedicated visual analysis tools. A significant example of such a task is the comparison of different connectivity data in the form of weighted graphs. Several approaches have been suggested for graph comparison within information visualization, but the comparison of weighted graphs has not been addressed. We explored the design space of applicable visual representations and present augmented adjacency matrix and node-link visualizations. To assess which representation best support weighted graph comparison tasks, we performed a controlled experiment. Our findings suggest that matrices support these tasks well, outperforming node-link diagrams. These results have significant implications for the design of brain connectivity analysis tools that require weighted graph comparisons. They can also inform the design of visual analysis tools in other domains, e.g. comparison of weighted social networks or biological pathways.

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Section: Resultsmentioning

confidence: 85%

Section: Use Of Visualizations In Brain Connectivity Analysismentioning

confidence: 99%

See 1 more Smart Citation

Weighted graph comparison techniques for brain connectivity analysis

Alper

Bach

Riche

et al. 2013

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

156

153

View full text Add to dashboard Cite

show abstract

“…Similar procedures have been proposed and used in previous studies, in which the functional brain network was the result of a statistical comparison between conditions (Ginestet and Simmons, 2011;Zalesky et al, 2010) or populations (De Vico Fallani et al, 2010). For an analysis of resting states, in which no contrasting procedure is available, the general procedure consisted of repeating the network analysis for a series of increasing threshold values (Rubinov and Sporns, 2010).…”

Section: Functional Connectivity Estimationmentioning

confidence: 99%

Multiscale topological properties of functional brain networks during motor imagery after stroke

et al. 2013

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“…A study of graph characteristics using bioelectrical signals during visual working memory maintenance found that α and β bands showed a memory-load-dependent scale-free SWN behavior [43] . Returning to graph theoretical tools and fMRI, it was found that during working memory, connectivity strength decreased as working memory load increased [44] . Intelligence is related to brain network or- A: Node degree corresponds to the number of edges attached to a given node, which are shown here for a highly connected node (left) and a peripheral node (right); B: The clustering coefficient is shown here for a central node and its six neighbors.…”

Section: Normal Brain Network Organizationmentioning

confidence: 99%

Graph-based network analysis in schizophrenia

Micheloyannis¹

2012

WJP

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Over the last few years, many studies have been published using modern network analysis of the brain. Researchers and practical doctors alike should understand this method and its results on the brain evaluation at rest, during activation and in brain disease. The studies are noninvasive and usually performed with elecroencephalographic, magnetoencephalographic, magnetic resonance imaging and diffusion tensor imaging brain recordings. Different tools for analysis have been developed, although the methods are in their early stages. The results of these analyses are of special value. Studies of these tools in schizophrenia are important because widespread and local network disturbances can be evaluated by assessing integration, segregation and several structural and functional properties. With the help of network analyses, the main findings in schizophrenia are lower optimum network organization, less efficiently wired networks, less local clustering, less hierarchical organization and signs of disconnection. There are only about twenty five relevant papers on the subject today. Only a few years of study of these methods have produced interesting results and it appears promising that the development of these methods will present important knowledge for both the preclinical signs of schizophrenia and the methods' therapeutic effects.

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Statistical parametric network analysis of functional connectivity dynamics during a working memory task

Cited by 99 publications

References 69 publications

Weighted graph comparison techniques for brain connectivity analysis

Weighted graph comparison techniques for brain connectivity analysis

Multiscale topological properties of functional brain networks during motor imagery after stroke

Graph-based network analysis in schizophrenia

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