Change Detection and Visualization of Functional Brain Networks using EEG Data

Ramasamy, Vijayalakshmi; Dasari, Naga; Nandagopal, Nanda; R.S.Subhiksha,; Cocks, Bernadine; Dahal, Nabaraj; Thilaga, M.

doi:10.1016/j.procs.2014.05.060

Cited by 10 publications

(4 citation statements)

References 19 publications

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“…SCIMAT excels in its robust preprocessing module designed to cleanse raw document data. Ucinet, an increasingly popular social network analysis software, currently integrates various visualization software such as Pajek, Netdraw, and Mage [42]. It is necessary to import the data into this visualization software to analyze and display the relationship between knowledge.…”

Section: Bibliometric Analysismentioning

confidence: 99%

Using Bibliometrics and Grounded Theory in Investigating Factors Influencing Profit Distribution in Integrated Project Delivery Projects

Guo,

Xiong,

Chen

et al. 2024

Buildings

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Integrated project delivery (IPD) has gained significant attention as an effective alternative to traditional project delivery models. Profit distribution is a crucial aspect of IPD projects, influencing their overall success. This study aims to investigate the key factors impacting profit distribution to offer strategic guidance for project management practices. The study employs a comprehensive bibliometric analysis to establish an updated research framework in this domain. Through this analysis, 24 articles with highly relevant and extensively cited sources are identified for further examination. Grounded theory is subsequently applied to distill the findings. This process yields a foundational theoretical framework that delineates the factors influencing profit distribution in IPD projects, namely, contribution, resource-based input, effort level, and risk sharing. Additionally, a textual analysis of ten burst words is conducted to discern research trends and identify future areas of study. This research contributes to the existing literature by addressing gaps and providing a roadmap for future IPD investigations. A theoretical framework of influencing factors in profit distribution in IPD projects is developed based on the literature. The findings not only enhance understanding of profit distribution dynamics in IPD projects but also guide the implementation and optimization of IPD practices.

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Section: Bibliometric Analysismentioning

confidence: 99%

Using Bibliometrics and Grounded Theory in Investigating Factors Influencing Profit Distribution in Integrated Project Delivery Projects

Guo,

Xiong,

Chen

et al. 2024

Buildings

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“…Data‐driven visualization approaches have been developed to visualize multichannel EEG coherence networks (Ji et al, 2018; Ten Caat et al, 2008). Visual analytics systems have been developed (Al‐Awami et al, 2014; Arsiwalla et al, 2015; Bach et al, 2015; Fujiwara et al, 2017; Ganglberger et al, 2020; Vijayalakshmi et al, 2014) and studied (Beck et al, 2017; Chen et al, 2018; Trotts et al, 2007) that enable researchers to explore human brain activity for improved structural understanding of the data.…”

Section: Visualization Approachesmentioning

confidence: 99%

V‐NeuroStack: Open‐source 3D time stack software for identifying patterns in neuronal data

Naik

Kenyon

Taheri

et al. 2022

J of Neuroscience Research

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Understanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large‐scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant visualization challenges. We developed V‐NeuroStack, a novel network visualization tool to visualize data obtained using calcium imaging of spontaneous activity of neurons in a mouse brain slice as well as in vivo using two‐photon imaging. V‐NeuroStack creates 3D time stacks by stacking 2D time frames for a time‐series dataset. It provides a web interface to explore and analyze data using both 3D and 2D visualization techniques. Previous attempts to analyze such data have been limited by the tools available to visualize large numbers of correlated activity traces. V‐NeuroStack's 3D view is used to explore patterns in dynamic large‐scale correlations between neurons over time. The 2D view is used to examine any timestep of interest in greater detail. Furthermore, a dual‐line graph provides the ability to explore the raw and first‐derivative values of activity from an individual or a functional cluster of neurons. V‐NeuroStack can scale to datasets with at least a few thousand temporal snapshots. It can potentially support future advancements in in vitro and in vivo data capturing techniques to bring forth novel hypotheses by allowing unambiguous visualization of massive patterns in neuronal activity data.

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“…BrainNet Viewer (Xia, et al, 2013), DynamicBC (Liao, et al, 2014), EEGNET (Mheich, et al, 2015), Connectome visualization utility (CVU) (LaPlante, et al, 2014) and eConnectome (He, et al, 2011) are visualization utilities implemented as open source toolboxes to visualize large brain networks. Visual analytics systems have been developed (Bach, et al, 2015) (Fujiwara, et al, 2017) (Arsiwalla, et al, 2015, (Betella, et al, 2014), (Ramasamy, et al, 2014), (Ali K. Al-Awami, 2014) and studied (Chen, et al, 2018) that enable researchers to explore human brain activity for improved structural understanding of the data.…”

Section: Visualization Approachesmentioning

confidence: 99%

V-NeuroStack: 3D Time Stacks for Identifying Patterns in Calcium Imaging Data

Naik

Kenyon

Taheri

et al. 2020

Preprint

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BackgroundUnderstanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large-scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant visualization challenges. We developed V-NeuroStack, a novel network visualization tool to visualize data obtained using calcium imaging of spontaneous activity of cortical neurons in a mouse brain slice.New MethodV-NeuroStack creates 3D time stacks by stacking 2D time frames for a period of 600 seconds. It provides a web interface that enables exploration and analysis of data using a combination of 3D and 2D visualization techniques.Comparison with existing MethodsPrevious attempts to analyze such data have been limited by the tools available to visualize large numbers of correlated activity traces. V-NeuroStack can scale data sets with at least a few thousand temporal snapshots.ResultsV-NeuroStack’s 3D view is used to explore patterns in the dynamic large-scale correlations between neurons over time. The 2D view is used to examine any timestep of interest in greater detail. Furthermore, a dual-line graph provides the ability to explore the raw and first-derivative values of a single neuron or a functional cluster of neurons.ConclusionsV-NeuroStack enables easy exploration and analysis of large spatio-temporal datasets using two visualization paradigms: (a) Space-Time cube (b)Two-dimensional networks, via web interface. It will support future advancements in in vitro and in vivo data capturing techniques and can bring forth novel hypotheses by permitting unambiguous visualization of large-scale patterns in the neuronal activity data.

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Change Detection and Visualization of Functional Brain Networks using EEG Data

Cited by 10 publications

References 19 publications

Using Bibliometrics and Grounded Theory in Investigating Factors Influencing Profit Distribution in Integrated Project Delivery Projects

Using Bibliometrics and Grounded Theory in Investigating Factors Influencing Profit Distribution in Integrated Project Delivery Projects

V‐NeuroStack: Open‐source 3D time stack software for identifying patterns in neuronal data

V-NeuroStack: 3D Time Stacks for Identifying Patterns in Calcium Imaging Data

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