Estimating brain effective connectivity from EEG signals of patients with autism disorder and healthy individuals by reducing volume conduction effect

Fatemeh, Salehi; Jaloli, Mehrad; Coben, Robert; Nasrabadi, Ali Motie

doi:10.1007/s11571-021-09730-w

Cited by 6 publications

(3 citation statements)

References 55 publications

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“…GCA is a method that relies on models of the time series data for causal inference, that is, multivariate autoregressive (MVAR). However, several previous studies have indicated that the MVAR‐based causal model only considered the lag relation between time series, whereas the instantaneous effect is ignored (Faes et al, 2013 ; Salehi et al, 2022 ). In addition, Granger himself also pointed out that GCA may not necessarily a true causal relation (Granger, 1963 ).…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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show abstract

“…However, in reality, the different characteristics of the physiological structures of the skull and scalp cause the conductivity of the brain to be nonuniform. The discharge behavior of the cerebral cortex is not only caught by the electrodes right above the cerebral cortex but also affects the signal collection by electrodes in surrounding areas [38,39]. The volume conduction effect refers to the high degree of correlation between electrode signals from the same source, resulting in time series misalignment and amplitude interference.…”

Section: Phase Lag Functional Brain Networkmentioning

confidence: 99%

Research on Brain Networks of Human Balance Based on Phase Estimation Synchronization

Qiu,

Luo

2024

Brain Sciences

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Phase synchronization serves as an effective method for analyzing the synchronization of electroencephalogram (EEG) signals among brain regions and the dynamic changes of the brain. The purpose of this paper is to study the construction of the functional brain network (FBN) based on phase synchronization, with a special focus on neural processes related to human balance regulation. This paper designed four balance paradigms of different difficulty by blocking vision or proprioception and collected 19-channel EEG signals. Firstly, the EEG sequences are segmented by sliding windows. The phase-locking value (PLV) of core node pairs serves as the phase-screening index to extract the valid data segments, which are recombined into new EEG sequences. Subsequently, the multichannel weighted phase lag index (wPLI) is calculated based on the new EEG sequences to construct the FBN. The experimental results show that due to the randomness of the time points of body balance adjustment, the degree of phase synchronization of the datasets screened by PLV is more obvious, improving the effective information expression of the subsequent EEG data segments. The FBN topological structures of the wPLI show that the connectivity of various brain regions changes structurally as the difficulty of human balance tasks increases. The frontal lobe area is the core brain region for information integration. When vision or proprioception is obstructed, the EEG synchronization level of the corresponding occipital lobe area or central area decreases. The synchronization level of the frontal lobe area increases, which strengthens the synergistic effect among the brain regions and compensates for the imbalanced response caused by the lack of sensory information. These results show the brain regional characteristics of the process of human balance regulation under different balance paradigms, providing new insights into endogenous neural mechanisms of standing balance and methods of constructing brain networks.

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“…In addition, patients with schizophrenia display alterations in brain effective connectivity during the detection of mismatched auditory stimuli [24]. Effective connectivity is a measure of directed connectivity that represents the causal influence of one brain region on other brain regions (i.e., information flow from one region to another) [25,26]. Based on these findings, we hypothesized that a common neural network may be involved in both gesture and word production in the initial stage, such as conceptualization, but in a later stage, different neural networks with different information flows would be generated for gesture and word production planning.…”

Section: Introductionmentioning

confidence: 99%

Electroencephalographic Effective Connectivity Analysis of the Neural Networks during Gesture and Speech Production Planning in Young Adults

et al. 2023

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Gestures and speech, as linked communicative expressions, form an integrated system. Previous functional magnetic resonance imaging studies have suggested that neural networks for gesture and spoken word production share similar brain regions consisting of fronto-temporo-parietal brain regions. However, information flow within the neural network may dynamically change during the planning of two communicative expressions and also differ between them. To investigate dynamic information flow in the neural network during the planning of gesture and spoken word generation in this study, participants were presented with spatial images and were required to plan the generation of gestures or spoken words to represent the same spatial situations. The evoked potentials in response to spatial images were recorded to analyze the effective connectivity within the neural network. An independent component analysis of the evoked potentials indicated 12 clusters of independent components, the dipoles of which were located in the bilateral fronto-temporo-parietal brain regions and on the medial wall of the frontal and parietal lobes. Comparison of effective connectivity indicated that information flow from the right middle cingulate gyrus (MCG) to the left supplementary motor area (SMA) and from the left SMA to the left precentral area increased during gesture planning compared with that of word planning. Furthermore, information flow from the right MCG to the left superior frontal gyrus also increased during gesture planning compared with that of word planning. These results suggest that information flow to the brain regions for hand praxis is more strongly activated during gesture planning than during word planning.

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Estimating brain effective connectivity from EEG signals of patients with autism disorder and healthy individuals by reducing volume conduction effect

Cited by 6 publications

References 55 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

Research on Brain Networks of Human Balance Based on Phase Estimation Synchronization

Electroencephalographic Effective Connectivity Analysis of the Neural Networks during Gesture and Speech Production Planning in Young Adults

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