TY - JOUR
T1 - Eeg cortical connectivity analysis of working memory reveals topological reorganization in theta and alpha bands
AU - Dai, Zhongxiang
AU - De Souza, Joshua
AU - Lim, Julian
AU - Ho, Paul M.
AU - Chen, Yu
AU - Li, Junhua
AU - Thakor, Nitish
AU - Bezerianos, Anastasios
AU - Sun, Yu
N1 - Funding Information:
The authors thank the National University of Singapore for supporting the Cognitive Engineering Group at the Singapore Institute for Neurotechnology (SINAPSE) under grant R-719-001-102-232. This work was also partially supportedby the Ministry of Education of Singapore under the grant MOE2014-T2-1-115.
Publisher Copyright:
© 2017 Dai, de Souza, Lim, Ho, Chen, Li, Thakor, Bezerianos and Sun.
PY - 2017/5/12
Y1 - 2017/5/12
N2 - Numerous studies have revealed various working memory (WM)-related brain activities that originate fromvarious cortical regions and oscillate at different frequencies. However, multi-frequency band analysis of the brain network in WM in the cortical space remains largely unexplored. In this study, we employed a graph theoretical framework to characterize the topological properties of the brain functional network in the theta and alpha frequency bands during WM tasks. Twenty-eight subjects performed visual n-back tasks at two difficulty levels, i.e., 0-back (control task) and 2-back (WM task). After preprocessing, Electroencephalogram(EEG) signals were projected into the source space and 80 cortical brain regions were selected for further analysis. Subsequently, the theta- and alpha-band networks were constructed by calculating the Pearson correlation coefficients between the power series (obtained by concatenating the power values of all epochs in each session) of all pairs of brain regions. Graph theoretical approaches were then employed to estimate the topological properties of the brain networks at different WM tasks. We found higher functional integration in the theta band and lower functional segregation in the alpha band in the WM task compared with the control task. Moreover, compared to the 0-back task, altered regional centrality was revealed in the 2-back task in various brain regions that mainly resided in the frontal, temporal and occipital lobes, with distinct presentations in the theta and alpha bands. In addition, significant negative correlations were found between the reaction time with the average path length of the theta-band network and the local clustering of the alpha-band network, which demonstrates the potential for using the brain network metrics as biomarkers for predicting the task performance during WM tasks.
AB - Numerous studies have revealed various working memory (WM)-related brain activities that originate fromvarious cortical regions and oscillate at different frequencies. However, multi-frequency band analysis of the brain network in WM in the cortical space remains largely unexplored. In this study, we employed a graph theoretical framework to characterize the topological properties of the brain functional network in the theta and alpha frequency bands during WM tasks. Twenty-eight subjects performed visual n-back tasks at two difficulty levels, i.e., 0-back (control task) and 2-back (WM task). After preprocessing, Electroencephalogram(EEG) signals were projected into the source space and 80 cortical brain regions were selected for further analysis. Subsequently, the theta- and alpha-band networks were constructed by calculating the Pearson correlation coefficients between the power series (obtained by concatenating the power values of all epochs in each session) of all pairs of brain regions. Graph theoretical approaches were then employed to estimate the topological properties of the brain networks at different WM tasks. We found higher functional integration in the theta band and lower functional segregation in the alpha band in the WM task compared with the control task. Moreover, compared to the 0-back task, altered regional centrality was revealed in the 2-back task in various brain regions that mainly resided in the frontal, temporal and occipital lobes, with distinct presentations in the theta and alpha bands. In addition, significant negative correlations were found between the reaction time with the average path length of the theta-band network and the local clustering of the alpha-band network, which demonstrates the potential for using the brain network metrics as biomarkers for predicting the task performance during WM tasks.
KW - Cortical functional connectivity
KW - EEG
KW - ELORETA
KW - Graph theory
KW - N-back
KW - Working memory
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U2 - 10.3389/fnhum.2017.00237
DO - 10.3389/fnhum.2017.00237
M3 - Article
C2 - 28553215
AN - SCOPUS:85018906326
SN - 1662-5161
VL - 11
JO - Frontiers in Human Neuroscience
JF - Frontiers in Human Neuroscience
M1 - 237
ER -