Causal Network Connectivity Patterns in Autism Spectrum Disorder Based on the Liang-Kleeman Information Flow Theory

Zhang, Tao; Cong, Jing; Li, Fali; Yao, Dezhong; Xu, Peng

doi:10.1016/j.ijpsycho.2021.07.592

Cited by 2 publications

(2 citation statements)

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“…This approach yields a high accuracy rate for landslide debris flow hazard prediction [7]. Meanwhile, subsequent studies [8] have established a causal linkage model for autism spectrum disorders based on a Liang-Kleeman information flow theory. Also, multi-scale Liang-Kleeman information flow has detected a causal relationship from solar radiation to ocean heat content [9,10].…”

Section: Related Workmentioning

confidence: 99%

Causal Analysis of Wood Performance Based on Liang-Kleeman Information Flow

Huang,

Kuang,

et al. 2023

Preprint

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To accurately identify high-quality functional wood with superior mechanical properties and enhance its durability, it is imperative to understand the relationships between the physical and mechanical attributes of diverse wood types. Presently, there exist issues related to accuracy, efficiency, and differentiation in studying these performance correlations. The Liang Kleeman (L-K) information flow theory, a causative analytical approach rooted in fundamental physics principles, offers swift computational capabilities. Furthermore, the coefficient of variation in L-K information flow quantitatively gauges the intensity of causal effects. Therefore, this article adopts a causal artificial intelligence perspective and introduces a wood performance analysis method based on L-K information flow.This study focuses on four Chinese fir varieties, including fast-growing Chinese fir (YKS), red heart Chinese fir (CSH, XXH), and iron heart Chinese fir (XXT). By computing and comparing variation coefficients of L-K information flow for various physical properties and their impact on mechanical properties, causal relationships between these properties are elucidated. These findings are then applied to predict wood mechanical properties, achieving a remarkable maximum accuracy of 90%.In the future, these findings will prove invaluable for the selection of high-quality wood and for improving the manufacturing process to prolong the service life of functionalized wood.

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Section: Related Workmentioning

confidence: 99%

Causal Analysis of Wood Performance Based on Liang-Kleeman Information Flow

Huang,

Kuang,

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Liang ( 2014 ) defined the calculation method of Liang information flow through a rigorously derived formalism and gave a specific calculation expression. The Liang information flow method have been shown to be effective in capturing the causal relation between time series (Liang, 2021 ; Zhang et al, 2021 ), and has been widely applied to a variety of fields in different disciplines, such as quantum mechanics (Yi & Bose, 2022 ), climate science (Cheng & Redfern, 2022 ; Docquier et al, 2022 ; Liang et al, 2021 ), brain electroencephalography (EEG) network (Hristopulos et al, 2019 ). Its advantages include its very effective performance in computing, as well as its accuracy, and, most of all, its universal applicability because of its firm physical ground, and hence many intrinsic properties that make accurate causal discovery possible.…”

Section: Introductionmentioning

confidence: 99%

Altered default mode network causal connectivity patterns in autism spectrum disorder revealed by Liang information flow analysis

Cong

Zhuang

Liu

et al. 2023

Human Brain Mapping

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Autism spectrum disorder (ASD) is a pervasive developmental disorder with severe cognitive impairment in social communication and interaction. Previous studies have reported that abnormal functional connectivity patterns within the default mode network (DMN) were associated with social dysfunction in ASD. However, how the altered causal connectivity pattern within the DMN affects the social functioning in ASD remains largely unclear. Here, we introduced the Liang information flow method, widely applied to climate science and quantum mechanics, to uncover the brain causal network patterns in ASD. Compared with the healthy controls (HC), we observed that the interactions among the dorsal medial prefrontal cortex (dMPFC), ventral medial prefrontal cortex (vMPFC), hippocampal formation, and temporo‐parietal junction showed more inter‐regional causal connectivity differences in ASD. For the topological property analysis, we also found the clustering coefficient of DMN and the In‐Out degree of anterior medial prefrontal cortex were significantly decreased in ASD. Furthermore, we found that the causal connectivity from dMPFC to vMPFC was correlated with the clinical symptoms of ASD. These altered causal connectivity patterns indicated that the DMN inter‐regions information processing was perturbed in ASD. In particular, we found that the dMPFC acts as a causal source in the DMN in HC, whereas it plays a causal target in ASD. Overall, our findings indicated that the Liang information flow method could serve as an important way to explore the DMN causal connectivity patterns, and it also can provide novel insights into the nueromechanisms underlying DMN dysfunction in ASD.

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Causal Network Connectivity Patterns in Autism Spectrum Disorder Based on the Liang-Kleeman Information Flow Theory

Cited by 2 publications

References 0 publications

Causal Analysis of Wood Performance Based on Liang-Kleeman Information Flow

Causal Analysis of Wood Performance Based on Liang-Kleeman Information Flow

Altered default mode network causal connectivity patterns in autism spectrum disorder revealed by Liang information flow analysis

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