scholarly journals Modulation of Brain Functional Connectivity and Efficiency During an Endurance Cycling Task: A Source-Level EEG and Graph Theory Approach

Author(s):  
Gabriella Tamburro ◽  
Selenia di Fronso ◽  
Claudio Robazza ◽  
Maurizio Bertollo ◽  
Silvia Comani
2017 ◽  
Vol 19 (2) ◽  
pp. 119-129 ◽  
Author(s):  
João Ricardo Sato ◽  
Claudinei Eduardo Biazoli ◽  
Giovanni Abrahão Salum ◽  
Ary Gadelha ◽  
Nicolas Crossley ◽  
...  

2015 ◽  
Vol 39 (3) ◽  
pp. 374 ◽  
Author(s):  
Yu-Sun Min ◽  
Yongmin Chang ◽  
Jang Woo Park ◽  
Jong-Min Lee ◽  
Jungho Cha ◽  
...  

2020 ◽  
Vol 91 (8) ◽  
pp. e17.1-e17
Author(s):  
M Arbabi ◽  
S Amiri ◽  
F Badragheh ◽  
MM Mirbagheri ◽  
AA Asadi-Pooya

ObjectiveDespite being the subject of many studies over the past two decades, mechanisms underlying psychogenic non-epileptic seizures (PNES) are still poorly understood. We tried to address this issue by utilizing brain functional connectivity analysis to identify brain regions with abnormal activities in patients with PNES. In a case-control study, we performed graph based network analysis, a robust technique that determines the organization of brain connectivity and characterizes topological properties of the brain networks.MethodsTwelve individuals with PNES and twenty-one healthy control subjects were examined. Resting state functional magnetic resonance imaging (rsfMRI) was acquired. All subjects were asked to keep their eyes open during the scanning process. The rsfMRI analysis consisted of pre-processing, extracting the functional connectivity matrix (FCM) based on the AAL atlas, threshold for binary FCM, constructing a graph network from FCM and extracting graph features, and finally statistical analysis. For all cortical and subcortical regions of the AAL atlas, we calculated measures of ‘degree,’ which is one of the features of the graph theory. Results: Our results revealed that, as compared to the healthy control subjects, patients with PNES had a significantly lower degree in some brain regions including their left and right insula (INS), right Putamen (PUT), left and right Supramarginal gyrus (SMG), right Middle occipital gyrus (MOG), and left and right Rolandic operculum (ROL). In contrast, degree was significantly greater in two regions [i.e., right Caudate (CAU) and left Inferior frontal gyrus orbital part (ORBinf)] in patients with PNES compared to that in controls.ConclusionOur findings suggest that functional connectivity of several major brain regions are different in patients with PNES compared with that in healthy individuals. While there is hypoactivity in regions important in perception, motor control, self- awareness, and cognitive functioning (e.g., insula) and also movement regulation (e.g., putamen), there is hyperactivity in areas involved in feedback processing (i.e., using information from past experiences to influence future actions and decisions) (e.g., caudate) in patients with PNES. The observation that individuals with PNES suffer from a wide range of abnormal activities in functional connectivity of their brain networks is consistent with the fact that PNES occur in a heterogeneous patient population; no single mechanism or contributing factor could explain PNES in all patients.


2020 ◽  
pp. 107565
Author(s):  
Saba Amiri ◽  
Mehdi M. Mirbagheri ◽  
Ali A. Asadi-Pooya ◽  
Fatemeh Badragheh ◽  
Hamideh Ajam Zibadi ◽  
...  

2020 ◽  
Vol 30 (03) ◽  
pp. 2050007 ◽  
Author(s):  
Yongjie Zhu ◽  
Jia Liu ◽  
Tapani Ristaniemi ◽  
Fengyu Cong

Recent continuous task studies, such as narrative speech comprehension, show that fluctuations in brain functional connectivity (FC) are altered and enhanced compared to the resting state. Here, we characterized the fluctuations in FC during comprehension of speech and time-reversed speech conditions. The correlations of Hilbert envelope of source-level EEG data were used to quantify FC between spatially separate brain regions. A symmetric multivariate leakage correction was applied to address the signal leakage issue before calculating FC. The dynamic FC was estimated based on a sliding time window. Then, principal component analysis (PCA) was performed on individually concatenated and temporally concatenated FC matrices to identify FC patterns. We observed that the mode of FC induced by speech comprehension can be characterized with a single principal component. The condition-specific FC demonstrated decreased correlations between frontal and parietal brain regions and increased correlations between frontal and temporal brain regions. The fluctuations of the condition-specific FC characterized by a shorter time demonstrated that dynamic FC also exhibited condition specificity over time. The FC is dynamically reorganized and FC dynamic pattern varies along a single mode of variation during speech comprehension. The proposed analysis framework seems valuable for studying the reorganization of brain networks during continuous task experiments.


2020 ◽  
Vol 11 ◽  
Author(s):  
Adellah Sariah ◽  
Shuixia Guo ◽  
Jing Zuo ◽  
Weidan Pu ◽  
Haihong Liu ◽  
...  

Author(s):  
Haitao Chen ◽  
Janelle Liu ◽  
Yuanyuan Chen ◽  
Andrew Salzwedel ◽  
Emil Cornea ◽  
...  

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