scholarly journals Graph‐theoretical analysis of EEG functional connectivity during balance perturbation in traumatic brain injury: A pilot study

2021 ◽  
Author(s):  
Vikram Shenoy Handiru ◽  
Alaleh Alivar ◽  
Armand Hoxha ◽  
Soha Saleh ◽  
Easter S. Suviseshamuthu ◽  
...  
Author(s):  
Vikram Shenoy Handiru ◽  
Alaleh Alivar ◽  
Armand Hoxha ◽  
Soha Saleh ◽  
Easter S. Suviseshamuthu ◽  
...  

AbstractTraumatic Brain Injury (TBI) often results in balance impairment, increasing the risk of falls, and the chances of further injuries. However, the underlying neurophysiological mechanisms of postural control after TBI are not well understood. To this end, we conducted a pilot study with a multimodal approach of EEG, MRI, and Diffusion Tensor Imaging (DTI) to explore the neural mechanisms of unpredictable balance perturbations in 17 chronic TBI participants and 15 matched Healthy Controls (HC). As quantitative measures of the functional integration and segregation of the brain networks during the postural task, we computed the global graph-theoretic network measures (global efficiency and modularity) of brain functional connectivity derived from source-space EEG in different frequency bands. We observed that the TBI group showed a lower balance performance as measured by the Center of Pressure (COP) displacement during the task, and the Berg Balance Scale. They also showed altered brain activation and connectivity during the balance task. In particular, the task modulation of brain network segregation in alpha-band was reduced in TBI. Moreover, the DTI findings revealed that the structural damage is associated with reduced network connectivity and integration. In terms of the neural correlates, we observed a distinct role played by different frequency bands; greater theta-band modularity during the task was strongly correlated with the BBS in TBI group; alpha-band and beta-band graph-theoretic measures were associated with the measures of white matter structural integrity. Our future studies will focus on how postural training will modulate the functional brain networks in TBI.


2012 ◽  
Vol 1483 ◽  
pp. 71-81 ◽  
Author(s):  
Junjie Wu ◽  
Junsong Zhang ◽  
Chu Liu ◽  
Dongwei Liu ◽  
Xiaojun Ding ◽  
...  

2008 ◽  
Vol 29 (12) ◽  
pp. 1368-1378 ◽  
Author(s):  
Dirk J. A. Smit ◽  
Cornelis J. Stam ◽  
Danielle Posthuma ◽  
Dorret I. Boomsma ◽  
Eco J. C. de Geus

2020 ◽  
Vol 75 ◽  
pp. 149-156
Author(s):  
John K. Yue ◽  
Ryan R.L. Phelps ◽  
Ethan A. Winkler ◽  
Hansen Deng ◽  
Pavan S. Upadhyayula ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gregory Simchick ◽  
Kelly M. Scheulin ◽  
Wenwu Sun ◽  
Sydney E. Sneed ◽  
Madison M. Fagan ◽  
...  

AbstractFunctional magnetic resonance imaging (fMRI) has significant potential to evaluate changes in brain network activity after traumatic brain injury (TBI) and enable early prognosis of potential functional (e.g., motor, cognitive, behavior) deficits. In this study, resting-state and task-based fMRI (rs- and tb-fMRI) were utilized to examine network changes in a pediatric porcine TBI model that has increased predictive potential in the development of novel therapies. rs- and tb-fMRI were performed one day post-TBI in piglets. Activation maps were generated using group independent component analysis (ICA) and sparse dictionary learning (sDL). Activation maps were compared to pig reference functional connectivity atlases and evaluated using Pearson spatial correlation coefficients and mean ratios. Nonparametric permutation analyses were used to determine significantly different activation areas between the TBI and healthy control groups. Significantly lower Pearson values and mean ratios were observed in the visual, executive control, and sensorimotor networks for TBI piglets compared to controls. Significant differences were also observed within several specific individual anatomical structures within each network. In conclusion, both rs- and tb-fMRI demonstrate the ability to detect functional connectivity disruptions in a translational TBI piglet model, and these disruptions can be traced to specific affected anatomical structures.


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