abstract

Visual analytics of brain effective connectivity using convergent cross mapping

Authors:

Hiroaki Natsukawa,

Koji KoyamadaAuthors Info & Claims

SA '17: SIGGRAPH Asia 2017 Symposium on Visualization

Article No.: 6, Pages 1 - 9

https://doi.org/10.1145/3139295.3139303

Published: 27 November 2017 Publication History

Abstract

To elucidate the dynamics of information processing in the brain, it is necessary to identify the direction of neural information transmission in the neuronal network and clarify the effects (i.e., the causal relationship) of neuronal activity in one area on neuronal activity in another area. Convergent cross mapping (CCM) has been employed in the neuroscience field to examine the effective connectivity of brain functions. CCM can detect causality from time series data created from deterministic and nonlinear systems. Because CCM includes complicated processes such as the determination of advance parameters, the confirmation of nonlinearity, and the interpretation of results, which results in a lowering of the usability of CCM, there is a strong need for an effective visual interface. In this paper, we propose a visual analytic system that increases the usability of CCM and contributes to new discoveries in effective connectivity. The usability was evaluated using a domain expert questionnaire. It was confirmed that the usability was improved by comparing the proposed system to the original character user interface from the viewpoint of the results and process comprehensibility. In addition, with the proposed system, new findings in human brain connectivity have been obtained from actual magnetoencephalography data during visual cognitive task and resting-state task.

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https://doi.org/10.1109/TNSRE.2024.3398662
Takahashi KIchikawa KPao G(2024)Toward Scalable Empirical Dynamic ModelingSustained Simulation Performance 202210.1007/978-3-031-41073-4_5(61-69)Online publication date: 15-Mar-2024
https://doi.org/10.1007/978-3-031-41073-4_5
Ji JZou ALiu JYang CZhang XSong Y(2023)A Survey on Brain Effective Connectivity Network LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.310629934:4(1879-1899)Online publication date: Apr-2023
https://doi.org/10.1109/TNNLS.2021.3106299
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Index Terms

Visual analytics of brain effective connectivity using convergent cross mapping
1. Applied computing
  1. Life and medical sciences
    1. Computational biology
      1. Biological networks
2. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Visual analytics

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Published In

cover image ACM Conferences

SA '17: SIGGRAPH Asia 2017 Symposium on Visualization

November 2017

154 pages

ISBN:9781450354110

DOI:10.1145/3139295

Conference Chairs:
Koji Koyamada
Kyoto University, Japan
,
Puripant Ruchikachorn
Chulalongkorn University, Thailand

Copyright © 2017 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

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Publication History

Published: 27 November 2017

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The Keihanshin Consortium for Fostering the Next Generation of Global Leaders in Research (K-CONNEX)

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SA '17

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SIGGRAPH

SA '17: SIGGRAPH Asia 2017

November 27 - 30, 2017

Bangkok, Thailand

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Overall Acceptance Rate 178 of 869 submissions, 20%

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Cited By

Zhu RShe QLi RTan TZhang Y(2024)Neurovascular Coupling Analysis Based on Multivariate Variational Gaussian Process Convergent Cross-MappingIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2024.339866232(1873-1883)Online publication date: 2024
https://doi.org/10.1109/TNSRE.2024.3398662
Takahashi KIchikawa KPao G(2024)Toward Scalable Empirical Dynamic ModelingSustained Simulation Performance 202210.1007/978-3-031-41073-4_5(61-69)Online publication date: 15-Mar-2024
https://doi.org/10.1007/978-3-031-41073-4_5
Ji JZou ALiu JYang CZhang XSong Y(2023)A Survey on Brain Effective Connectivity Network LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.310629934:4(1879-1899)Online publication date: Apr-2023
https://doi.org/10.1109/TNNLS.2021.3106299
De Castro Martins CChaminade TCavazza M(2022)Causal Analysis of Activity in Social Brain Areas During Human-Agent ConversationFrontiers in Neuroergonomics10.3389/fnrgo.2022.8430053Online publication date: 17-May-2022
https://doi.org/10.3389/fnrgo.2022.843005
Takahashi KWatanakeesuntorn WIchikawa KPark JTakano RHaga JSugihara GPao G(2021)kEDM: A Performance-portable Implementation of Empirical Dynamic Modeling using KokkosPractice and Experience in Advanced Research Computing 2021: Evolution Across All Dimensions10.1145/3437359.3465571(1-8)Online publication date: 17-Jul-2021
https://dl.acm.org/doi/10.1145/3437359.3465571
Natsukawa HDeyle EPao GKoyamada KSugihara G(2021)A Visual Analytics Approach for Ecosystem Dynamics based on Empirical Dynamic ModelingIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.302895627:2(506-516)Online publication date: Feb-2021
https://doi.org/10.1109/TVCG.2020.3028956
Watanakeesuntorn WTakahashi KIchikawa KPark JSugihara GTakano RHaga JPao G(2020)Massively Parallel Causal Inference of Whole Brain Dynamics at Single Neuron Resolution2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS51040.2020.00035(196-205)Online publication date: Dec-2020
https://doi.org/10.1109/ICPADS51040.2020.00035

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