Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal

Introduction: Ensuring an adequate Depth of Anesthesia (DOA) during surgery is essential for anesthesiologists. Since the effect of anesthetic drugs is on the central nervous system, brain signals such as Electroencephalogram (EEG) can be used for DOA estimation. Anesthesia can interfere among brain regions, so the relationship among different areas can be a key factor in the anesthetic process. Methods: In this paper, by combining the Wiener causality concept and the conditional mutual information, a nonlinear effective connectivity measure called Transfer Entropy (TE) is presented to describe the relationship between EEG signals at frontal and temporal regions from eight volunteers in three anesthetic states (awake, unconscious and recovery). This index is also compared with Granger causality and partial directional coherence methods as common effective connectivity indexes. Results: Based on a statistical analysis of the probability predictive value and Kruskal-Wallis statistical method, TE can effectively fallow the effect-site concentration of propofol and distinguish the anesthetic states well, and perform better than the other effective connectivity indexes. This index is also better than Bispectral Index (BIS) as commercial DOA monitor because of the faster response and higher correlation with the drug concentration effect-site, less irregularity in the unconscious state and better ability to distinguish three states of anesthestesia. Conclusion: TE index is a confident indicator for designing a new monitoring system of the two EEG channels for DOA estimation.

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Asymmetric high-order anatomical brain connectivity sculpts effective connectivity

Network Neuroscience ◽

10.1162/netn_a_00150 ◽

2020 ◽

Vol 4 (3) ◽

pp. 871-890

Author(s):

Arseny A. Sokolov ◽

Peter Zeidman ◽

Adeel Razi ◽

Michael Erb ◽

Philippe Ryvlin ◽

...

Keyword(s):

White Matter ◽

Dynamic Range ◽

Diffusion Processes ◽

Brain Connectivity ◽

Effective Connectivity ◽

Structural Connectivity ◽

Laplacian Matrix ◽

Graph Laplacian ◽

Brain Regions ◽

High Order

Bridging the gap between symmetric, direct white matter brain connectivity and neural dynamics that are often asymmetric and polysynaptic may offer insights into brain architecture, but this remains an unresolved challenge in neuroscience. Here, we used the graph Laplacian matrix to simulate symmetric and asymmetric high-order diffusion processes akin to particles spreading through white matter pathways. The simulated indirect structural connectivity outperformed direct as well as absent anatomical information in sculpting effective connectivity, a measure of causal and directed brain dynamics. Crucially, an asymmetric diffusion process determined by the sensitivity of the network nodes to their afferents best predicted effective connectivity. The outcome is consistent with brain regions adapting to maintain their sensitivity to inputs within a dynamic range. Asymmetric network communication models offer a promising perspective for understanding the relationship between structural and functional brain connectomes, both in normalcy and neuropsychiatric conditions.

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Effective brain connectivity estimation between active brain regions in autism using the dual Kalman-based method

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2019-0062 ◽

2020 ◽

Vol 65 (1) ◽

pp. 23-32

Author(s):

Mehdi Rajabioun ◽

Ali Motie Nasrabadi ◽

Mohammad Bagher Shamsollahi ◽

Robert Coben

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Effective Connectivity ◽

Estimation Error ◽

Brain Regions ◽

Estimation Methods ◽

Estimation Errors ◽

Normal Children ◽

Brain Functions ◽

The Brain

AbstractBrain connectivity estimation is a useful method to study brain functions and diagnose neuroscience disorders. Effective connectivity is a subdivision of brain connectivity which discusses the causal relationship between different parts of the brain. In this study, a dual Kalman-based method is used for effective connectivity estimation. Because of connectivity changes in autism, the method is applied to autistic signals for effective connectivity estimation. For method validation, the dual Kalman based method is compared with other connectivity estimation methods by estimation error and the dual Kalman-based method gives acceptable results with less estimation errors. Then, connectivities between active brain regions of autistic and normal children in the resting state are estimated and compared. In this simulation, the brain is divided into eight regions and the connectivity between regions and within them is calculated. It can be concluded from the results that in the resting state condition the effective connectivity of active regions is decreased between regions and is increased within each region in autistic children. In another result, by averaging the connectivity between the extracted active sources of each region, the connectivity between the left and right of the central part is more than that in other regions and the connectivity in the occipital part is less than that in others.

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The relationship between spatial configuration and functional connectivity of brain regions

10.1101/210195 ◽

2017 ◽

Author(s):

Janine D. Bijsterbosch ◽

Mark W. Woolrich ◽

Matthew F. Glasser ◽

Emma C. Robinson ◽

Christian F. Beckmann ◽

...

Keyword(s):

Functional Connectivity ◽

Spatial Configuration ◽

Brain Connectivity ◽

Brain Activity ◽

Spatial Arrangement ◽

Brain Regions ◽

Imaging Data ◽

Functional Regions ◽

Cortical Regions ◽

The Relationship

AbstractBrain connectivity is often considered in terms of the communication between functionally distinct brain regions. Many studies have investigated the extent to which patterns of coupling strength between multiple neural populations relates to behavior. For example, studies have used "functional connectivity fingerprints" to characterise individuals' brain activity. Here, we investigate the extent to which the exact spatial arrangement of cortical regions interacts with measures of brain connectivity. We find that the shape and exact location of brain regions interact strongly with the modelling of brain connectivity, and present evidence that the spatial arrangement of functional regions is strongly predictive of non-imaging measures of behaviour and lifestyle. We believe that, in many cases, cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Therefore, a better understanding of these effects is important when interpreting the relationship between functional imaging data and cognitive traits.

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The relationship between spatial configuration and functional connectivity of brain regions

eLife ◽

10.7554/elife.32992 ◽

2018 ◽

Vol 7 ◽

Cited By ~ 64

Author(s):

Janine Diane Bijsterbosch ◽

Mark W Woolrich ◽

Matthew F Glasser ◽

Emma C Robinson ◽

Christian F Beckmann ◽

...

Keyword(s):

Functional Connectivity ◽

Spatial Configuration ◽

Brain Connectivity ◽

Brain Activity ◽

Spatial Arrangement ◽

Brain Regions ◽

Imaging Data ◽

Functional Regions ◽

Cortical Regions ◽

The Relationship

Brain connectivity is often considered in terms of the communication between functionally distinct brain regions. Many studies have investigated the extent to which patterns of coupling strength between multiple neural populations relates to behaviour. For example, studies have used ‘functional connectivity fingerprints’ to characterise individuals' brain activity. Here, we investigate the extent to which the exact spatial arrangement of cortical regions interacts with measures of brain connectivity. We find that the shape and exact location of brain regions interact strongly with the modelling of brain connectivity, and present evidence that the spatial arrangement of functional regions is strongly predictive of non-imaging measures of behaviour and lifestyle. We believe that, in many cases, cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Therefore, a better understanding of these effects is important when interpreting the relationship between functional imaging data and cognitive traits.

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Altered effective connectivity from the posterior insula to the amygdala mediates the relationship between psychopathic traits and concern with the Harm foundation

10.1101/2021.06.21.449236 ◽

2021 ◽

Author(s):

Shuer Ye ◽

Wei Li ◽

Bing Zhu ◽

Yangting Lv ◽

Qun Yang ◽

...

Keyword(s):

Resting State ◽

Effective Connectivity ◽

Resting State Fmri ◽

Brain Regions ◽

Psychopathic Traits ◽

Self Report ◽

Moral Foundations ◽

Moral Foundation ◽

Posterior Insula ◽

The Relationship

Psychopathic traits have been demonstrated to be associated with different types of morality; however, the neuropsychological mechanism underlying the relationship between psychopathic traits and morality remains unclear. Our study examined the effective connectivity (EC) of psychopathic traits-related brain regions and its association to concern with different moral foundations by combining behavioral measures with resting-state fMRI. We administered the Levenson Self-Report Psychopathy Scale (LSRP) and Moral Foundation Questionnaire (MFQ) to 78 college students after resting-state fMRI scanning. Our results showed that total and primary psychopathic traits score predicted concern with the Harm foundation. The EC from the posterior insula to the amygdala was negatively correlated with psychopathic traits and positively with concern with the Harm foundation. Altered posterior insula-amygdala EC partially mediated the relationship between psychopathic traits and concern with the Harm foundation. Our findings indicated that individuals with elevated psychopathic traits may have atypical processes in recognizing and integrating bodily state information into emotional responses, leading to less concern for harm-related morality. The study deepened our understanding of the neuropsychological mechanism underlying the relationship between psychopathic traits and morality and may have implications for the prevention of higher psychopathic traits individuals from committing serious transgressions.

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Resting-state directed brain connectivity patterns in adolescents from source-reconstructed EEG signals based on information flow rate

10.1101/608299 ◽

2019 ◽

Cited By ~ 3

Author(s):

Dionissios Hristopulos ◽

Arif Babul ◽

Shazia Babul ◽

Leyla R Brucar ◽

Naznin Virji-Babul

Keyword(s):

Information Flow ◽

Flow Rate ◽

Resting State ◽

Brain Connectivity ◽

Effective Connectivity ◽

Brain Regions ◽

Brain Maturation ◽

Eeg Signals ◽

Stochastic Dynamical Systems ◽

Flow Rate Coefficient

Quantifying the brain's effective connectivity offers a unique window onto the causal architecture coupling the different regions of the brain. Here, we advocate a new, data-driven measure of directed (or effective) brain connectivity based on the recently developed information flow rate coefficient. The concept of the information flow rate is founded in the theory of stochastic dynamical systems and its derivation is based on first principles; unlike various commonly used linear and nonlinear correlations and empirical directional coefficients, the information flow rate can measure causal relations between time series with minimal assumptions. We apply the information flow rate to electroencephalography (EEG) signals in adolescent males to map out the directed, causal, spatial interactions between brain regions during resting-state conditions. To our knowledge, this is the first study of effective connectivity in the adolescent brain. Our analysis reveals that adolescents show a pattern of information flow that is strongly left lateralized, and consists of short and medium ranged bidirectional interactions across the frontal-central-temporal regions. These results suggest an intermediate state of brain maturation in adolescence.

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The relationship between spatial configuration and functional connectivity of brain regions revisited

10.1101/520502 ◽

2019 ◽

Author(s):

Janine D. Bijsterbosch ◽

Christian F. Beckmann ◽

Mark W. Woolrich ◽

Stephen M. Smith ◽

Samuel J. Harrison

Keyword(s):

Functional Connectivity ◽

Network Structure ◽

Resting State ◽

Spatial Configuration ◽

Brain Connectivity ◽

Brain Regions ◽

Spatial Effects ◽

Functional Brain ◽

Dual Regression ◽

The Relationship

AbstractIn our previous paper (Bijsterbosch et al., 2018), we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.Impact statementWe show that functional connectivity network matrices as estimated from resting state functional MRI are biased by spatially overlapping network structure.

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Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals

Frontiers in Biomedical Technologies ◽

10.18502/fbt.v8i2.6515 ◽

2021 ◽

Author(s):

Ali Ekhlasi ◽

Ali Motie Nasrabadi ◽

Mohammadreza Mohammadi

Keyword(s):

Frequency Band ◽

Phase Transfer ◽

Effective Connectivity ◽

Brain Regions ◽

Transfer Entropy ◽

Healthy Children ◽

Eeg Signals ◽

Children With Adhd ◽

Frequency Bands ◽

Artificial Neural Network Ann

Purpose: The present study was conducted to investigate and classify two groups of healthy children and children with Attention Deficit Hyperactivity Disorder (ADHD) by Effective Connectivity (EC) measure. Since early detection of ADHD can make the treatment process more effective, it is important to diagnose it using new methods. Materials and Methods: For this purpose, Effective Connectivity Matrices (ECMs) were constructed based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children of the same age. ECMs of each individual were obtained by the directed Phase Transfer Entropy (dPTE) between each pair of electrodes. ECMs were calculated in five frequency bands including, delta, theta, alpha, beta, and gamma. Based on ECM, an Effective Connectivity Vector (ECV) was constructed as a feature vector for the classification process. Furthermore, ECV of different frequency bands was pooled in one global ECV (gECV). Multilayer Artificial Neural Network (ANN) was used in the steps of classification and feature selection by the Genetic Algorithm (GA). Results: The highest classification accuracy with the selected features of ECV was related to theta frequency band with 89.7%. After that, the delta frequency band had the highest accuracy with 89.2%. The results of ANN classification and GA on the gECV reported 89.1% of accuracy. Conclusion: Our findings show that the dPTE measure, which determines effective connectivity between the brain regions, can be used to classify between ADHD and healthy groups. The results of the classification have improved compared to some studies that used the functional connectivity measures.

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The relationship between dynamic functional network connectivity and spatial orientation in healthy young adults

10.1101/2021.02.14.431143 ◽

2021 ◽

Author(s):

Mohammad S. E. Sendi ◽

Charles A. Ellis ◽

Robyn L. Miller ◽

David H. Salat ◽

Vince D. Calhoun

Keyword(s):

Young Adults ◽

Spatial Orientation ◽

Brain Connectivity ◽

Network Connectivity ◽

Brain Regions ◽

Functional Network ◽

Sensorimotor Network ◽

Functional Network Connectivity ◽

The Relationship ◽

Dynamic Functional Network Connectivity

ABSTRACTSpatial orientation is essential to interacting with a physical environment, and better understanding it could contribute to a better understanding of a variety of diseases and disorders that are characterized by deficits in spatial orientation. Many previous studies have focused on the relationship between spatial orientation and individual brain regions, though in recent years studies have begun to examine spatial orientation from a network perspective. This study analyzes dynamic functional network connectivity (dFNC) values extracted from over 800 resting-state fMRI recordings of healthy young adults (age 22-37 years) and applies unsupervised machine learning methods to identify neural brain states that occur across all subjects. We estimated the occupancy rate (OCR) for each subject, which was proportional to the amount of time that they spent in each state, and investigated the link between the OCR and spatial orientation and the state-specific FNC values and spatial orientation controlling for age and sex. Our findings showed that the amount of time subjects spent in a state characterized by increased connectivity within and between visual, auditory, and sensorimotor networks and within the default mode network while at rest corresponded to their performance on tests of spatial orientation. We also found that increased sensorimotor network connectivity in two of the identified states negatively correlated with decreased spatial orientation, further highlighting the relationship between the sensorimotor network and spatial orientation. This study provides insight into how the temporal properties of the functional brain connectivity within and between key brain networks may influence spatial orientation.

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The relationship between spatial configuration and functional connectivity of brain regions revisited

eLife ◽

10.7554/elife.44890 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 23

Author(s):

Janine Diane Bijsterbosch ◽

Christian F Beckmann ◽

Mark W Woolrich ◽

Stephen M Smith ◽

Samuel J Harrison

Keyword(s):

Functional Connectivity ◽

Spatial Configuration ◽

Brain Connectivity ◽

Brain Regions ◽

Spatial Effects ◽

Functional Brain ◽

Spatial Overlap ◽

Exact Location ◽

Dual Regression ◽

The Relationship

Previously we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity (Bijsterbosch et al., 2018). Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.

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