Reconfiguration patterns of large-scale brain networks in motor imagery

Zhang, Tao; Wang, Fei; Li, Fali; Tan, Yuyong; Zhang, Yangsong; Yang, Hang; Biswal, Bharat B.; Yao, Dezhong; Xu, Peng

doi:10.1007/s00429-018-1786-y

Cited by 14 publications

(7 citation statements)

References 77 publications

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“…The causal interaction difference among these four regions indicated that the dMPFC subsystem may act as a mentor to influence on the MTL subsystem, indicating the influence exerted by the dMPFC subsystem on the MTL subsystem may modulate the social function of the DMN. We also found that the DMN causal connectivity network architecture is relatively dissimilar ( r = .4239) between ASD and HC compared with previous reported resting‐state functional connectivity network (Cole et al, 2014 ; Schultz & Cole, 2016 ; Zhang et al, 2019 ). This finding implied that the resting DMN inter‐regional information coupling does not have an “intrinsic” pattern between ASD and HC, whereas exhibiting a disease‐related excitation–inhibition imbalance causal network pattern.…”

Section: Discussioncontrasting

confidence: 48%

“…T A B L E 3 Classification results of the SVM using F score based on the DMN causal patterns Schultz & Cole, 2016;Zhang et al, 2019). This finding implied that the resting DMN inter-regional information coupling does not have an "intrinsic" pattern between ASD and HC, whereas exhibiting a disease-related excitation-inhibition imbalance causal network pattern.…”

Section: Altered Causal Network Architecture Of Dmn In Asdmentioning

confidence: 92%

“…Given that several features are uninformative or redundant for classification, reducing the number of features can improve performance (Dosenbach et al, 2010 ). F score method (Liu et al, 2015 ; Zhang et al, 2019 ) was used to select features to improve the classification performance. In this study, support vector machine (SVM) classifier with linear kernel was used (Chang & Lin, 2011 ).…”

Section: Methodsmentioning

confidence: 99%

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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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Section: Discussioncontrasting

confidence: 48%

Section: Altered Causal Network Architecture Of Dmn In Asdmentioning

confidence: 92%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Self Cite

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“…This finding (Guo et al 2018) indicates that an external periodic visual stimulus can induce the modification of intrinsic oscillatory activities different from the restingstate activity at the network level. The further evidence uncovers how the brain reconfigures from rest idle to task state (Li et al 2015a(Li et al , 2020dSong et al 2019) and these factors on inter-subject's reconfiguring variability (Li et al 2020c), which guarantees the brain to efficiently process the information of the specific MI tasks (Caravaglios et al 2015;Shine and Poldrack 2018;Zhang et al 2019) (e.g., right or left MI) with ERD (Li et al 2019d), or provides one stable and successful auditory control network for listening (Alavash et al 2019). Moreover, the reconfiguring phenomenon also occurs in the DMN under task (Zuo et al 2018).…”

Section: Methods Of Network Constructionmentioning

confidence: 87%

A survey of brain network analysis by electroencephalographic signals

Luo

et al. 2021

Cogn Neurodyn

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Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

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“…The first reason was that we used the dynamic large‐scale network FNC as a feature. Several previous studies have consistently shown that understanding brain cognitive processes depends on knowledge of large‐scale brain network organization (Mayberg et al, 1999; Zhang et al, 2019). Moreover, the dynamic connectivity could capture temporal variations of FNC providing more hidden interaction information in a large‐scale network.…”

Section: Discussionmentioning

confidence: 96%

Identification and analysis of autism spectrum disorder via large‐scale dynamic functional network connectivity

et al. 2023

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Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with severe cognitive impairment. Several studies have reported that brain functional network connectivity (FNC) has great potential for identifying ASD from healthy control (HC) and revealing the relationships between the brain and behaviors of ASD. However, few studies have explored dynamic large‐scale FNC as a feature to identify individuals with ASD. This study used a time‐sliding window method to study the dynamic FNC (dFNC) on the resting‐state fMRI. To avoid arbitrarily determining the window length, we set a window length range of 10–75 TRs (TR = 2 s). We constructed linear support vector machine classifiers for all window length conditions. Using a nested 10‐fold cross‐validation framework, we obtained a grand average accuracy of 94.88% across window length conditions, which is higher than those reported in previous studies. In addition, we determined the optimal window length using the highest classification accuracy of 97.77%. Based on the optimal window length, we found that the dFNCs were located mainly in dorsal and ventral attention networks (DAN and VAN) and exhibited the highest weight in classification. Specifically, we found that the dFNC between DAN and temporal orbitofrontal network (TOFN) was significantly negatively correlated with social scores of ASD. Finally, using the dFNCs with high classification weights as features, we construct a model to predict the clinical score of ASD. Overall, our findings demonstrated that the dFNC could be a potential biomarker to identify ASD and provide new perspectives to detect cognitive changes in ASD.

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Reconfiguration patterns of large-scale brain networks in motor imagery

Cited by 14 publications

References 77 publications

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

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

A survey of brain network analysis by electroencephalographic signals

Identification and analysis of autism spectrum disorder via large‐scale dynamic functional network connectivity

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