A Survey of Macroscopic Brain Network Visualization Technology

Chen, Weifeng; Shi, Lei; Chen, Wei

doi:10.1049/cje.2018.04.007

Cited by 8 publications

(5 citation statements)

References 45 publications

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“…Based on the scope of this survey, we provide an account of the micro ‐ and nano ‐ scale modalities used for brain data acquisition. For macroscale modalities that capture Neural fibers and their connectivity information at centimeter and millimeter resolutions, we refer the reader to detailed surveys by Pfister et al [PKB * 12], and Chen et al [CSC18].…”

Section: Data Acquisitionmentioning

confidence: 99%

“…The most relevant previous surveys in the area of visualization include a micro‐, meso‐, and macro‐scale connectomics report [PKB * 12], a survey on large‐scale GPU‐based volume visualization [BHP15], surveys on modern (scientific) visualization on the web [MKRE16, FH20], and a survey on macroscopic brain circuit visualization [CSC18]. Other relevant surveys include biomedical image registration [VMK * 16], segmentation [MBK * 19,LRL * 21], motif discovery in graphs [YFZ * 20] and subgraph counting [RPS * 19].…”

Section: Introductionmentioning

confidence: 99%

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A Survey of Visualization and Analysis in High‐Resolution Connectomics

Beyer

Troidl

Boorboor³

et al. 2022

Computer Graphics Forum

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The field of connectomics aims to reconstruct the wiring diagram of Neurons and synapses to enable new insights into the workings of the brain. Reconstructing and analyzing the Neuronal connectivity, however, relies on many individual steps, starting from high‐resolution data acquisition to automated segmentation, proofreading, interactive data exploration, and circuit analysis. All of these steps have to handle large and complex datasets and rely on or benefit from integrated visualization methods. In this state‐of‐the‐art report, we describe visualization methods that can be applied throughout the connectomics pipeline, from data acquisition to circuit analysis. We first define the different steps of the pipeline and focus on how visualization is currently integrated into these steps. We also survey open science initiatives in connectomics, including usable open‐source tools and publicly available datasets. Finally, we discuss open challenges and possible future directions of this exciting research field.

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Section: Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey of Visualization and Analysis in High‐Resolution Connectomics

Beyer

Troidl

Boorboor³

et al. 2022

Computer Graphics Forum

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“…ey were particularly focused on techniques for visualizing brain networks at the macroscopic level. Compared to neuronal connections at the microscopic level, it reveals the structural and functional connectivity of the entire brain [1]. e sonic market segment is a neighbourhood-wide system for approximating the probability of pitch in a frame of sound.…”

Section: Related Workmentioning

confidence: 99%

Multi-Tone Piano Transcription Based on Visualization and Knowledge Recognition Algorithm

Fan

2022

Mathematical Problems in Engineering

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Scientific computing visualization is also known as visualization. In an academic sense, visualization is a way of computing. It can convert data into intuitive geometric images, making it easier to understand. Visualization is the use of computer graphics and image processing techniques. It is a technical means to convert piles of seemingly disorganized data into graphics or images through special computer methods. Recording is the process of recording sound signals on a medium. Specifically, it is the process of converting sound into electrical signals through microphones and amplifiers and recording them with different materials and techniques. Piano tuning is the adjustment of the strings so that they reach a certain high pitch. This paper aims to study the analysis and evaluation of multi-tone piano transcriptions based on scientific computing visualization and knowledge recognition algorithms. It expects to improve the quality of piano recording with the support of scientific visualization technology and knowledge recognition algorithm. This paper briefly explains the big data, and on this basis, the visual data mining technology is mentioned, and the current situation in the visual data mining is explained. A matrix calculation of the characteristic matrix of piano timbres was carried out to achieve the resynthesis of the 25th harmony in piano timbres electronically. The results of the trials in this article demonstrate that when the Gaussian number changes to 5 during the recording process, the difference that exists of the acoustic voltage intensity and the noise level phase is clearly diminished. When the Gauss figure is 10, there is an inversion of the value of the sound pressure phase.

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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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A Survey of Macroscopic Brain Network Visualization Technology

Cited by 8 publications

References 45 publications

A Survey of Visualization and Analysis in High‐Resolution Connectomics

A Survey of Visualization and Analysis in High‐Resolution Connectomics

Multi-Tone Piano Transcription Based on Visualization and Knowledge Recognition Algorithm

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

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