Mild traumatic brain injury: Graph-model characterization of brain networks for episodic memory

Tsirka, Vasso; Simos, Panagiotis G.; Vakis, Antonios; Kanatsouli, Kassiani; Vourkas, Michael; Erimaki, Sophia; Pachou, Ellie; Stam, Cornelis J.; Micheloyannis, Sifis

doi:10.1016/j.ijpsycho.2010.09.006

Cited by 27 publications

(24 citation statements)

References 69 publications

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“…Altered small world networks have been observed in clinical populations including patients with CNS tumors (Bartolomei et al 2006), epilepsy (Ponten et al 2007; van Dellen et al 2009), schizophrenia (Rubinov et al 2009), and Alzheimer’s disease (Stam et al 2007a, b). As would be anticipated alterations in networks are associated with traumatic brain injury (Cao and Slobounov 2010; Nakamura et al 2009; Tsirka et al 2011; Zouridakis et al 2011; Catsellanos et al 2011a, b). The calculations presented in this paper and in Madulara et al (2012) suggest that when calculated using an adaptive partition of the joint probability distribution, mutual information, lagged mutual information and transfer entropy can provide computationally efficient, noise-robust metrics for the analysis of CNS small world networks.…”

Section: Discussionmentioning

confidence: 93%

Time domain measures of inter-channel EEG correlations: a comparison of linear, nonparametric and nonlinear measures

et al. 2013

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Correlations between ten-channel EEGs obtained from thirteen healthy adult participants were investigated. Signals were obtained in two behavioral states: eyes open no task and eyes closed no task. Four time domain measures were compared: Pearson product moment correlation, Spearman rank order correlation, Kendall rank order correlation and mutual information. The psychophysiological utility of each measure was assessed by determining its ability to discriminate between conditions. The sensitivity to epoch length was assessed by repeating calculations with 1, 2, 3, …, 8 s epochs. The robustness to noise was assessed by performing calculations with noise corrupted versions of the original signals (SNRs of 0, 5 and 10 dB). Three results were obtained in these calculations. First, mutual information effectively discriminated between states with less data. Pearson, Spearman and Kendall failed to discriminate between states with a 1 s epoch, while a statistically significant separation was obtained with mutual information. Second, at all epoch durations tested, the measure of between-state discrimination was greater for mutual information. Third, discrimination based on mutual information was more robust to noise. The limitations of this study are discussed. Further comparisons should be made with frequency domain measures, with measures constructed with embedded data and with the maximal information coefficient.

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Section: Discussionmentioning

confidence: 93%

Time domain measures of inter-channel EEG correlations: a comparison of linear, nonparametric and nonlinear measures

et al. 2013

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“…EEG research has also indicated that reduced electrophysiological interactions among brain areas may contribute to cognitive and behavioural problems associated with PCS. Reduced EEG coherence, for example, has been observed during visuospatial working memory in mTBI [26] and disrupted organization of network synchronization during episodic memory processing has also been reported [27]. Such reports of altered task dependent connectivity are congruent with reports of atypical electrophysiological and hemodynamic responses during cognitive processing following mTBI [28].…”

Section: Discussionmentioning

confidence: 96%

Detecting Mild Traumatic Brain Injury Using Resting State Magnetoencephalographic Connectivity

et al. 2016

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Accurate means to detect mild traumatic brain injury (mTBI) using objective and quantitative measures remain elusive. Conventional imaging typically detects no abnormalities despite post-concussive symptoms. In the present study, we recorded resting state magnetoencephalograms (MEG) from adults with mTBI and controls. Atlas-guided reconstruction of resting state activity was performed for 90 cortical and subcortical regions, and calculation of inter-regional oscillatory phase synchrony at various frequencies was performed. We demonstrate that mTBI is associated with reduced network connectivity in the delta and gamma frequency range (>30 Hz), together with increased connectivity in the slower alpha band (8–12 Hz). A similar temporal pattern was associated with correlations between network connectivity and the length of time between the injury and the MEG scan. Using such resting state MEG network synchrony we were able to detect mTBI with 88% accuracy. Classification confidence was also correlated with clinical symptom severity scores. These results provide the first evidence that imaging of MEG network connectivity, in combination with machine learning, has the potential to accurately detect and determine the severity of mTBI.

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“…A recent study of his group found that patients with subacute stroke have significantly lower small worldness of the affected hand when compared with the unaffected hand during the motor imagery [46]. And Tsirka demonstrated that patients with brain injury have sub-optimal network organization, as reflected by a decrease in small worldness value [47]. Therefore, small worldness is a useful index to evaluate brain function.…”

Section: Discussionmentioning

confidence: 99%

“…It has been reported that small world structure is one of the most important features of the human brain, which has been shown to pursue a balance between local processing and global integration with information transfer at a minimal energy cost [47], [50], [51]. In mammalian brain networks, it has been shown that energy consumption is proportional to the physical distance between brain regions in information transfer [52].…”

Section: Discussionmentioning

confidence: 99%

Brain Network Evolution after Stroke Based on Computational Experiments

Huang

et al. 2013

PLoS ONE

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Stroke is a frequently-occurring disease threatening the human nervous system. As a serious debilitation affecting a large-scale, hierarchical, and vastly complex electrochemical system, stroke remains relatively misunderstood. Rehabilitation mechanisms and means have suffered from this lack of systematic understanding. Here we propose an evolution model to simulate the dynamic actual evolvement process of functional brain networks computationally in an effort to address current shortcomings in the state of the field. According to simulation results, we conclude that the brain networks of patients following acute stroke were characterized by lower small worldness and lower quantity of long-distance connections compared with the healthy condition. Moreover, distance penalization may be used to describe the general mechanism of brain network evolution in the acute period after stroke.

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Mild traumatic brain injury: Graph-model characterization of brain networks for episodic memory

Cited by 27 publications

References 69 publications

Time domain measures of inter-channel EEG correlations: a comparison of linear, nonparametric and nonlinear measures

Time domain measures of inter-channel EEG correlations: a comparison of linear, nonparametric and nonlinear measures

Detecting Mild Traumatic Brain Injury Using Resting State Magnetoencephalographic Connectivity

Brain Network Evolution after Stroke Based on Computational Experiments

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