Accurately Modeling the Resting Brain Functional Correlations Using Wave Equation With Spatiotemporal Varying Hypergraph Laplacian

Wang, Yanjiang; Jian, Ma; Liu, Baodi

doi:10.1109/tmi.2022.3196007

Cited by 4 publications

(6 citation statements)

References 42 publications

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“…We hypothesized that the functional networks generated at different time scales would all exhibit a certain correspondence to the empirical FC. In other words, we assumed that the configuration at the critical time scale is not the only one to match the empirical FC, which is different from taking only one key time point, as in [19][20][21]28]. Given a discrete increasing time series: t ∈ {t 1 , t 2 , .…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Previous studies using the HDM to predict functional networks have focused solely on predicting the functional network at a single time point in the heat kernel equation [19][20][21]28]. This means that during the process of heat transfer from the initial distribution to thermal equilibrium in the network, there is a critical time point at which the predicted functional network has the highest correlation with the empirical functional network.…”

Section: Feasibility Of the Proposed Methodsmentioning

confidence: 99%

“…This implies that analysing the relationship between the two requires the use of higherorder information interaction models [13]. Following the classification outlined in [14], the existing methods include statistical methods [15,16], biophysical models [11,17,18], spectral methods [19][20][21][22][23], network analysis methods [24][25][26], and deep learning methods [14,22,27].…”

Section: Introductionmentioning

confidence: 99%

“…Among the spectral methods, some works have focused on modelling the signal diffusion process with the SC, such as the heat diffusion model (HDM) [19,21,28] and hypergraph Laplacian model [19,20]. The HDM computes the signal diffusion process according to SC to predict FC, which is mathematically represented by the heat kernel equation.…”

Section: Introductionmentioning

confidence: 99%

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Predicting resting-state brain functional connectivity from the structural connectome using the heat diffusion model: a multiple-timescale fusion method

Lv,

Li,

Yao

et al. 2024

J. Neural Eng.

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Objective. Understanding the intricate relationship between structural connectivity (SC) and functional connectivity (FC) is pivotal for understanding the complexities of the human brain. To explore this relationship, the heat diffusion model (HDM) was utilized to predict FC from SC. However, previous studies using the HDM have typically predicted FC at a critical time scale in the heat kernel equation, overlooking the dynamic nature of the diffusion process and providing an incomplete representation of the predicted FC. Approach. In this study, we propose an alternative approach based on the HDM. First, we introduced a multiple-timescale fusion method to capture the dynamic features of the diffusion process. Additionally, to enhance the smoothness of the predicted FC values, we employed the Wavelet reconstruction method to maintain local consistency and remove noise. Moreover, to provide a more accurate representation of the relationship between SC and FC, we calculated the linear transformation between the smoothed FC and the empirical FC. Main results. We conducted extensive experiments in two independent datasets. By fusing different time scales in the diffusion process for predicting FC, the proposed method demonstrated higher predictive correlation compared with method considering only critical time points (SingleScale). Furthermore, compared with other existing methods, the proposed method achieved the highest predictive correlations of 0.6939±0.0079 and 0.7302±0.0117 on the two datasets respectively. We observed that the visual network at the network level and the parietal lobe at the lobe level exhibited the highest predictive correlations, indicating that the functional activity in these regions may be closely related to the direct diffusion of information between brain regions. Significance. The multiple-timescale fusion method proposed in this study provides insights into the dynamic aspects of the diffusion process, contributing to a deeper understanding of how brain structure gives rise to brain function.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Feasibility Of the Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting resting-state brain functional connectivity from the structural connectome using the heat diffusion model: a multiple-timescale fusion method

Lv,

Li,

Yao

et al. 2024

J. Neural Eng.

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show abstract

“…Free behavior produces resting waves anywhere in the cerebral cortex due to internal, spontaneous activity of the brain. People are unaware of the spontaneous waves (Livne et al, 2022;Tran et al, 2006;Wang et al, 2022). There are two components to rest waves, which work together (Broday-Dvir & Malach, 2020).…”

Section: Rest Waves In the Brain Reflect The Cdpmentioning

confidence: 99%

Free Will as Defined by the Constrained Disorder Principle: a Restricted, Mandatory, Personalized, Regulated Process for Decision-Making

Ilan

2024

Integr. psych. behav.

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The concept of free will has challenged physicists, biologists, philosophers, and other professionals for decades. The constrained disorder principle (CDP) is a fundamental law that defines systems according to their inherent variability. It provides mechanisms for adapting to dynamic environments. This work examines the CDP's perspective of free will concerning various free will theories. Per the CDP, systems lack intentions, and the "freedom" to select and act is built into their design. The "freedom" is embedded within the response range determined by the boundaries of the systems' variability. This built-in and self-generating mechanism enables systems to cope with perturbations. According to the CDP, neither dualism nor an unknown metaphysical apparatus dictates choices. Brain variability facilitates cognitive adaptation to complex, unpredictable situations across various environments. Human behaviors and decisions reflect an underlying physical variability in the brain and other organs for dealing with unpredictable noises. Choices are not predetermined but reflect the ongoing adaptation processes to dynamic prssu½res. Malfunctions and disease states are characterized by inappropriate variability, reflecting an inability to respond adequately to perturbations. Incorporating CDP-based interventions can overcome malfunctions and disease states and improve decision processes. CDP-based second-generation artificial intelligence platforms improve interventions and are being evaluated to augment personal development, wellness, and health.

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A comprehensive survey of complex brain network representation

Tang,

Ma,

Zhang

et al. 2023

Meta-Radiology

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Accurately Modeling the Resting Brain Functional Correlations Using Wave Equation With Spatiotemporal Varying Hypergraph Laplacian

Cited by 4 publications

References 42 publications

Predicting resting-state brain functional connectivity from the structural connectome using the heat diffusion model: a multiple-timescale fusion method

Predicting resting-state brain functional connectivity from the structural connectome using the heat diffusion model: a multiple-timescale fusion method

Free Will as Defined by the Constrained Disorder Principle: a Restricted, Mandatory, Personalized, Regulated Process for Decision-Making

A comprehensive survey of complex brain network representation

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