Formulation of Mindfulness States as a Network Optimization Problem and an Attempt to Identify Key Brain Pathways Using Digital Annealer

Haruka NAKAMURA; Yoshimasa TAWATSUJI; Tatsunori MATSUI; Makoto NAKAMURA; Koichi KIMURA; Hisanori FUJISAWA

doi:10.1587/transinf.2021EDP7228

Formulation of Mindfulness States as a Network Optimization Problem and an Attempt to Identify Key Brain Pathways Using Digital Annealer

Haruka NAKAMURA, Yoshimasa TAWATSUJI, Tatsunori MATSUI, Makoto NAKAMURA, Koichi KIMURA, Hisanori FUJISAWA

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Although intervention practices like mindfulness meditation have proven effective in treating psychosis, there is no clarity on the mechanism of information propagation in the brain. In this study, we formulated a network optimization problem and searched for the optimal solution using Digital Annealer developed by Fujitsu Ltd. This is inspired by quantum computing and is effective in solving large-scale combinatorial optimization problems to find the information propagation pathway in the brain that contributes to the realization of mindfulness. Specifically, we defined the optimal network state as the state of the brain network that is considered to be associated with the mindfulness state. We formulated the problem into two network optimization problems — the minimum vertex-cover problem and the maximum-flow problem — to search for the information propagation pathway that is important for realizing the state. In the minimum vertex-cover problem, we aimed to identify brain regions that are important for the realization of the mindfulness state, and identified eight regions, including four that were suggested to be consistent with previous studies. We formulated the problem as a maximum-flow problem to identify the information propagation pathways in the brain that contribute to the activation of these four identified regions. As a result, approximately 30% of the connections in the brain network structure of this study were identified, and the pathway with the highest flow rate was considered to characterize the bottom-up emotion regulation during mindfulness. The findings of this study could be useful for more direct interventions in the context of mindfulness, which are being investigated by neurofeedback and other methods. This is because existing studies have not clarified the information propagation pathways that contribute to the realization of the brain network states that characterize mindfulness states. In addition, this approach may be useful as a methodology to identify information propagation pathways in the brain that contribute to the realization of higher-order human cognitive activities, such as mindfulness, within large-scale brain networks.

Publication: IEICE TRANSACTIONS on Information Vol.E105-D No.11 pp.1969-1983

Publication Date: 2022/11/01

Publicized: 2022/08/08

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2021EDP7228

Type of Manuscript: PAPER

Category: Biocybernetics, Neurocomputing

Authors

Haruka NAKAMURA
  Waseda University
Yoshimasa TAWATSUJI
  Waseda University
Tatsunori MATSUI
  Waseda University
Makoto NAKAMURA
  FUJITSU LABORATORIES LTD.
Koichi KIMURA
  FUJITSU LABORATORIES LTD.
Hisanori FUJISAWA
  FUJITSU LABORATORIES LTD.

Keyword

mindfulness states, minimum vertex-cover problems, maximum-flow problems, digital annealer

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Haruka NAKAMURA, Yoshimasa TAWATSUJI, Tatsunori MATSUI, Makoto NAKAMURA, Koichi KIMURA, Hisanori FUJISAWA, "Formulation of Mindfulness States as a Network Optimization Problem and an Attempt to Identify Key Brain Pathways Using Digital Annealer" in IEICE TRANSACTIONS on Information, vol. E105-D, no. 11, pp. 1969-1983, November 2022, doi: 10.1587/transinf.2021EDP7228.
Abstract: Although intervention practices like mindfulness meditation have proven effective in treating psychosis, there is no clarity on the mechanism of information propagation in the brain. In this study, we formulated a network optimization problem and searched for the optimal solution using Digital Annealer developed by Fujitsu Ltd. This is inspired by quantum computing and is effective in solving large-scale combinatorial optimization problems to find the information propagation pathway in the brain that contributes to the realization of mindfulness. Specifically, we defined the optimal network state as the state of the brain network that is considered to be associated with the mindfulness state. We formulated the problem into two network optimization problems — the minimum vertex-cover problem and the maximum-flow problem — to search for the information propagation pathway that is important for realizing the state. In the minimum vertex-cover problem, we aimed to identify brain regions that are important for the realization of the mindfulness state, and identified eight regions, including four that were suggested to be consistent with previous studies. We formulated the problem as a maximum-flow problem to identify the information propagation pathways in the brain that contribute to the activation of these four identified regions. As a result, approximately 30% of the connections in the brain network structure of this study were identified, and the pathway with the highest flow rate was considered to characterize the bottom-up emotion regulation during mindfulness. The findings of this study could be useful for more direct interventions in the context of mindfulness, which are being investigated by neurofeedback and other methods. This is because existing studies have not clarified the information propagation pathways that contribute to the realization of the brain network states that characterize mindfulness states. In addition, this approach may be useful as a methodology to identify information propagation pathways in the brain that contribute to the realization of higher-order human cognitive activities, such as mindfulness, within large-scale brain networks.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7228/_p

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@ARTICLE{e105-d_11_1969,
author={Haruka NAKAMURA, Yoshimasa TAWATSUJI, Tatsunori MATSUI, Makoto NAKAMURA, Koichi KIMURA, Hisanori FUJISAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Formulation of Mindfulness States as a Network Optimization Problem and an Attempt to Identify Key Brain Pathways Using Digital Annealer},
year={2022},
volume={E105-D},
number={11},
pages={1969-1983},
abstract={Although intervention practices like mindfulness meditation have proven effective in treating psychosis, there is no clarity on the mechanism of information propagation in the brain. In this study, we formulated a network optimization problem and searched for the optimal solution using Digital Annealer developed by Fujitsu Ltd. This is inspired by quantum computing and is effective in solving large-scale combinatorial optimization problems to find the information propagation pathway in the brain that contributes to the realization of mindfulness. Specifically, we defined the optimal network state as the state of the brain network that is considered to be associated with the mindfulness state. We formulated the problem into two network optimization problems — the minimum vertex-cover problem and the maximum-flow problem — to search for the information propagation pathway that is important for realizing the state. In the minimum vertex-cover problem, we aimed to identify brain regions that are important for the realization of the mindfulness state, and identified eight regions, including four that were suggested to be consistent with previous studies. We formulated the problem as a maximum-flow problem to identify the information propagation pathways in the brain that contribute to the activation of these four identified regions. As a result, approximately 30% of the connections in the brain network structure of this study were identified, and the pathway with the highest flow rate was considered to characterize the bottom-up emotion regulation during mindfulness. The findings of this study could be useful for more direct interventions in the context of mindfulness, which are being investigated by neurofeedback and other methods. This is because existing studies have not clarified the information propagation pathways that contribute to the realization of the brain network states that characterize mindfulness states. In addition, this approach may be useful as a methodology to identify information propagation pathways in the brain that contribute to the realization of higher-order human cognitive activities, such as mindfulness, within large-scale brain networks.},
keywords={},
doi={10.1587/transinf.2021EDP7228},
ISSN={1745-1361},
month={November},}

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TY - JOUR
TI - Formulation of Mindfulness States as a Network Optimization Problem and an Attempt to Identify Key Brain Pathways Using Digital Annealer
T2 - IEICE TRANSACTIONS on Information
SP - 1969
EP - 1983
AU - Haruka NAKAMURA
AU - Yoshimasa TAWATSUJI
AU - Tatsunori MATSUI
AU - Makoto NAKAMURA
AU - Koichi KIMURA
AU - Hisanori FUJISAWA
PY - 2022
DO - 10.1587/transinf.2021EDP7228
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2022
AB - Although intervention practices like mindfulness meditation have proven effective in treating psychosis, there is no clarity on the mechanism of information propagation in the brain. In this study, we formulated a network optimization problem and searched for the optimal solution using Digital Annealer developed by Fujitsu Ltd. This is inspired by quantum computing and is effective in solving large-scale combinatorial optimization problems to find the information propagation pathway in the brain that contributes to the realization of mindfulness. Specifically, we defined the optimal network state as the state of the brain network that is considered to be associated with the mindfulness state. We formulated the problem into two network optimization problems — the minimum vertex-cover problem and the maximum-flow problem — to search for the information propagation pathway that is important for realizing the state. In the minimum vertex-cover problem, we aimed to identify brain regions that are important for the realization of the mindfulness state, and identified eight regions, including four that were suggested to be consistent with previous studies. We formulated the problem as a maximum-flow problem to identify the information propagation pathways in the brain that contribute to the activation of these four identified regions. As a result, approximately 30% of the connections in the brain network structure of this study were identified, and the pathway with the highest flow rate was considered to characterize the bottom-up emotion regulation during mindfulness. The findings of this study could be useful for more direct interventions in the context of mindfulness, which are being investigated by neurofeedback and other methods. This is because existing studies have not clarified the information propagation pathways that contribute to the realization of the brain network states that characterize mindfulness states. In addition, this approach may be useful as a methodology to identify information propagation pathways in the brain that contribute to the realization of higher-order human cognitive activities, such as mindfulness, within large-scale brain networks.
ER -