A Dynamic Multi-Reduction Algorithm for Brain Functional Connection Pathways Analysis

Dai, Guangyao; Yang, Chuanfang; Liu, Yingjie; Jiang, Tong-Bang; Mgaya, Gervas Batister

doi:10.3390/sym11050701

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…Bosnia and Herzegovina-Lithuania-Serbia-Malaysia [6], Lithuania-Bosnia and Herzegovina-Serbia [7], Iran-Lithuania [8], China-Lithuania [9], Malaysia-Lithuania [10], Serbia-South Africa-Lithuania-Bosnia and Herzegovina [11] and Chile-Iran-Lithuania-Australia [12]. Authors from China contributed in total eight papers, four without international collaboration [13][14][15][16] and four more in international cooperation: [9], China-USA [17], China-Pakistan [18] and Russia-China-Serbia [19]. Authors from Poland contributed four papers, but only to a national cooperation [20][21][22][23].…”

Section: Contributionsmentioning

confidence: 99%

Multi-Criteria Decision-Making Techniques for Improvement Sustainability Engineering Processes

Zavadskas¹,

Pamučar²,

Stević³

et al. 2020

Symmetry

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The success of any activity and process depends fundamentally on the possibility of balancing (symmetry) needs and their satisfaction. That is, the ability to properly define a set of success indicators. The application of the developed new multi-criteria decision-making (MCDM) methods can be eliminated or decreased by decision-makers’ subjectivity, which leads to consistency or symmetry in the weight values of the criteria. In this Special Issue, 40 research papers and one review study co-authored by 137 researchers from 23 different countries explore aspects of multi-criteria modeling and optimization in crisp or uncertain environments. The papers proposing new approaches and elaborate case studies in the following areas of applications: MCDM optimization in sustainable engineering, environmental sustainability in engineering processes, sustainable multi-criteria production and logistics processes planning, integrated approach for modeling processes in engineering, new trends in the multi-criteria evaluation of sustainable processes, multi-criteria decision-making in strategic management based on sustainable criteria.

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Section: Contributionsmentioning

confidence: 99%

Multi-Criteria Decision-Making Techniques for Improvement Sustainability Engineering Processes

Zavadskas¹,

Pamučar²,

Stević³

et al. 2020

Symmetry

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“…Brain functional connectivity is a metric used to measure the relationships between different regions of the brain, facilitating a better understanding of the coupling relationships among these areas [ 19 ]. Common indices of functional connectivity include the Pearson correlation coefficient (PCC) [ 20 ], the phase lag index (PLI) [ 21 ], and the phase-locking value (PLV) [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Visually Induced Motion Sickness Based on Phase-Locked Value Functional Connectivity Matrix and CNN-LSTM

Shen,

Liu,

et al. 2024

Sensors

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To effectively detect motion sickness induced by virtual reality environments, we developed a classification model specifically designed for visually induced motion sickness, employing a phase-locked value (PLV) functional connectivity matrix and a CNN-LSTM architecture. This model addresses the shortcomings of traditional machine learning algorithms, particularly their limited capability in handling nonlinear data. We constructed PLV-based functional connectivity matrices and network topology maps across six different frequency bands using EEG data from 25 participants. Our analysis indicated that visually induced motion sickness significantly alters the synchronization patterns in the EEG, especially affecting the frontal and temporal lobes. The functional connectivity matrix served as the input for our CNN-LSTM model, which was used to classify states of visually induced motion sickness. The model demonstrated superior performance over other methods, achieving the highest classification accuracy in the gamma frequency band. Specifically, it reached a maximum average accuracy of 99.56% in binary classification and 86.94% in ternary classification. These results underscore the model’s enhanced classification effectiveness and stability, making it a valuable tool for aiding in the diagnosis of motion sickness.

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“…In the past five years, Dai et al [ 21 ] analyzed changes in the brain functional network of patients with depression through topological property analysis. They also examined the network cost function at different thresholds when analyzing network properties and obtained the optimal threshold for the evolution model of the brain network in patients with depression.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Topological Properties of Brain Functional Networks Using Multi-Threshold Derivative for End-Stage Renal Disease with Mild Cognitive Impairment

Zhang

Song

et al. 2023

Brain Sciences

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Patients with end-stage renal disease (ESRD) experience changes in both the structure and function of their brain networks. In the past, cognitive impairment was often classified based on connectivity features, which only reflected the characteristics of the binary brain network or weighted brain network. It exhibited limited interpretability and stability. This study aims to quantitatively characterize the topological properties of brain functional networks (BFNs) using multi-threshold derivative (MTD), and to establish a new classification framework for end-stage renal disease with mild cognitive impairment (ESRDaMCI). The dynamic BFNs (DBFNs) were constructed and binarized with multiple thresholds, and then their topological properties were extracted from each binary brain network. These properties were then quantified by calculating their derivative curves and expressing them as multi-threshold derivative (MTD) features. The classification results of MTD features were compared with several commonly used DBFN features, and the effectiveness of MTD features in the classification of ESRDaMCI was evaluated based on the classification performance test. The results indicated that the linear fusion of MTD features improved classification performance and outperformed individual MTD features. Its accuracy, sensitivity, and specificity were 85.98 ± 2.92%, 86.10 ± 4.11%, and 81.54 ± 4.27%, respectively. Finally, the feature weights of MTD were analyzed, and MTD-cc had the highest weight percentage of 28.32% in the fused features. The MTD features effectively supplemented traditional feature quantification by addressing the issue of indistinct classification differentiation. It improved the quantification of topological properties and provided more detailed features for diagnosing cognitive disorders.

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A Dynamic Multi-Reduction Algorithm for Brain Functional Connection Pathways Analysis

Cited by 3 publications

References 31 publications

Multi-Criteria Decision-Making Techniques for Improvement Sustainability Engineering Processes

Multi-Criteria Decision-Making Techniques for Improvement Sustainability Engineering Processes

Classification of Visually Induced Motion Sickness Based on Phase-Locked Value Functional Connectivity Matrix and CNN-LSTM

Characterizing Topological Properties of Brain Functional Networks Using Multi-Threshold Derivative for End-Stage Renal Disease with Mild Cognitive Impairment

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