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

Hristopulos, Dionissios T.; Babul, Arif; Babul, Shazia; Brucar, Leyla R.; Virji‐Babul, Naznin

doi:10.1101/608299

Cited by 4 publications

(12 citation statements)

References 80 publications

(144 reference statements)

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“…Based on the ensemble means of the normalized information flow rate, our analysis revealed acute changes in effective connectivity compared with age-matched controls. We find that the information flow in adolescent athletes with no previous history of concussion is characterized by a predominantly left (L) lateralized pattern, with bidirectional information exchange between frontal regions, between frontal and central/temporal regions, and between parietal and occipital regions (see also Hristopulos et al, 2019). In contrast, adolescents with subacute concussion show four distinct changes in the pattern of information flow during resting state.…”

Section: Discussionmentioning

confidence: 69%

“…Composite source activity in each brain region is modelled as a single regional current source dipole (c.f. Hristopulos et al 2019 for details) and source waveforms correspond to time series of the fifteen current dipoles. The resulting data was exported to MATLAB where the calculation and analysis of the information flow rates was performed.…”

Section: Eeg Analysismentioning

confidence: 99%

“…We investigate patterns and statistics of effective connectivity by means of the Liang-Kleeman information flow rate. A full description of the information flow rate for application in the analysis of EEG sourcereconstructed signals is provided in Hristopulos et al (2019). Below we give a brief description of the measure, including the key concepts and equations.…”

Section: Effective Connectivitymentioning

confidence: 99%

“…In order to focus on the most important connections, we follow (Hristopulos et al, 2019) and classify a connection as active if the respective |τ i→j | exceeds the threshold value, τ c = 0.05. Note that τ c = 0.05 is well above the median of the distributions of the |τ i→j | (c.f.…”

Section: Connectivity Patterns Based On Information Flow Ratementioning

confidence: 99%

“…However, to date, little is known about how information flow between brain regions is actually disrupted following concussion, particularly during the dynamic period of adolescent development. In this paper we investigate this issue using a data-driven effective brain connectivity measure based on the concept of information flow rate applied to EEG signals (Hristopulos et al, 2019). The information flow rate was developed by Liang using the concept of information entropy and the theory of dynamical systems (Liang, 2008(Liang, , 2013(Liang, , 2014(Liang, , 2015, and is based on earlier work with Kleeman (Liang and Kleeman, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Disrupted information flow in resting-state in adolescents with sports related concussion

Hristopulos

Babul

et al. 2019

Preprint

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Children and youth are at a greater risk of concussions than adults, and once injured, take longer to recover. A key feature of concussion is a diffuse increase in functional connectivity; yet it remains unclear how changes in functional connectivity relate to the patterns of information flow within resting state networks following concussion and how these relate to brain function. We applied a data-driven measure of directed effective brain connectivity to compare the patterns of information flow in healthy adolescents and adolescents with subacute concussion during the resting state condition. Data from 32 healthy adolescents (mean age =16 years) and 24 concussed adolescents (mean age = 13.8 years) with subacute concussion (< 3 months post injury) took part in the study. Five minutes of resting state data EEG were collected while participants sat quietly with their eyes closed. We applied the Kleeman-Liang information flow rate to measure the transfer of information between the EEG time series of each individual at different source locations, and therefore between different brain regions. Based on the ensemble means of the magnitude of normalized information flow rate, our analysis shows that information flow in the healthy adolescents is characterized by a predominantly (L) lateralized pattern with bidirectional information flow between frontal regions, between frontal and central/temporal regions and between parietal and occipital regions. In contrast, adolescents with concussion show distinct differences in information flow marked by a more symmetrical pattern with connections evenly distributed across the entire brain, increased information flow in the posterior regions of the brain and the emergence of bidirectional, inter-hemispheric connections between the left and right temporal regions of the brain. We also find that the statistical distribution of the normalized information flow rates in each group (control and concussed) is significantly different. Our results are the first to describe altered patterns of information flow in adolescents with concussion as well as differences in the statistical distribution of information flow rate. We hypothesize that the 1 Hristopulos et al. Disrupted information flow in concussed adolescentsobserved changes in information flow in the concussed group are a consequence of the brain injury and indicate functional reorganization of resting state networks.

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Section: Discussionmentioning

confidence: 69%

Section: Eeg Analysismentioning

confidence: 99%

Section: Effective Connectivitymentioning

confidence: 99%

Section: Connectivity Patterns Based On Information Flow Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Disrupted information flow in resting-state in adolescents with sports related concussion

Hristopulos

Babul

et al. 2019

Preprint

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Menstrual cycle phase modulates causal connectivity in the resting-state brain of healthy females

Mcleod,

Sattari,

Chavan

et al. 2024

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BackgroundOvarian hormones exert direct and indirect influences on the brain; however, little is known about how these hormones impact causal brain connectivity. Studying the female brain at a single time point may be confounded by distinct hormone phases. Despite this, the menstrual cycle is often overlooked. The primary objective of this pilot study was to evaluate resting-state causal connectivity during the early follicular and mid-luteal menstrual phases corresponding to lowvshigh estradiol and progesterone, respectively.MethodsFourteen healthy control females (M= 20.36 years,SD =2.02) participated in this study. Participants were scheduled for two resting-state electroencephalography (EEG) scans during their monthly menstrual cycle. A saliva sample was also collected at each EEG session for hormone analyses. Causal connectivity was quantified using information flow rate of EEG source data. Demographic information, emotional empathy, and sleep quality were obtained from self-report questionnaires.ResultsProgesterone levels were significantly higher in the mid-luteal phase compared to the early follicular phase (p= .041). We observed distinct patterns of causal connectivity along the menstrual cycle. Connectivity in the early follicular phase was centralized and shifted posteriorly during the mid-luteal phase. During the early follicular phase, the primary regions driving activity were the right central and left/right parietal regions, with the left central region being the predominant receiver of activity. During the mid-luteal phase, connections were primarily transmitted from the right side and the main receiver region was the left occipital region. Network topology during the mid-luteal phase was found to be significantly more assortative compared to the early follicular phase.ConclusionsThe observed difference in causal connectivity demonstrates how network dynamics reorganize as a function of menstrual phase and level of progesterone. In the mid-luteal phase, there was a strong shift for information flow to be directed at visual spatial processing and visual attention areas, whereas in the follicular phase, there was strong information flow primarily within the sensory-motor regions. The mid-luteal phase was significantly more assortative, suggesting greater network efficiency and resilience. These results contribute to the emerging literature on brain-hormone interactions.

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Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy

Harmah

et al. 2020

Front. Comput. Neurosci.

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People living with schizophrenia (SCZ) experience severe brain network deterioration. The brain is constantly fizzling with non-linear causal activities measured by electroencephalogram (EEG) and despite the variety of effective connectivity methods, only few approaches can quantify the direct non-linear causal interactions. To circumvent this problem, we are motivated to quantitatively measure the effective connectivity by multivariate transfer entropy (MTE) which has been demonstrated to be able to capture both linear and non-linear causal relationships effectively. In this work, we propose to construct the EEG effective network by MTE and further compare its performance with the Granger causal analysis (GCA) and Bivariate transfer entropy (BVTE). The simulation results quantitatively show that MTE outperformed GCA and BVTE under varied signal-to-noise conditions, edges recovered, sensitivity, and specificity. Moreover, its applications to the P300 task EEG of healthy controls (HC) and SCZ patients further clearly show the deteriorated network interactions of SCZ, compared to that of the HC. The MTE provides a novel tool to potentially deepen our knowledge of the brain network deterioration of the SCZ.

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

Cited by 4 publications

References 80 publications

Disrupted information flow in resting-state in adolescents with sports related concussion

Disrupted information flow in resting-state in adolescents with sports related concussion

Menstrual cycle phase modulates causal connectivity in the resting-state brain of healthy females

Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy

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