Evaluating the Effect of Increasing Working Memory Load on EEG-Based Functional Brain Networks
Abstract
Purpose: Working Memory (WM) plays a crucial role in many cognitive functions of the human brain. Examining how the inter-regional connectivity and characteristics of functional brain networks modulate with increasing WM load could lead to a more in-depth understanding of the WM system.
Materials and Methods: To investigate the effect of WM load alterations on the inter-regional synchronization and functional network characteristics, we used Electroencephalogram (EEG) data recorded from 21 healthy participants during an n-back task with three load levels (0-back, 2-back, and 3-back). The networks were constructed based on the weighted Phase Lag Index (wPLI) in the theta, alpha, beta, low-gamma, and high-gamma frequency bands. After constructing the fully connected, weighted, and undirected networks, the node-to-node connections, graph-theory metrics consisting of mean Clustering coefficient (C), characteristic path Length (L), and node strength were analyzed by statistical tests.
Results: It was revealed that in the presence of WM load (2- and 3-back tasks) compared with the WM-free condition (0-back task) within the alpha range, the Inter-Regional Functional Connectivity (IRFC), functional integration, functional segregation, and node strength in channels located at the frontal, parietal and occipital regions were significantly reduced. In the high-gamma band, IRFC was significantly higher in the difficult task (3-back) compared to the easy and moderate tasks (0- and 2-back). Besides, locally clustered connections were significantly increased in 3-back relative to the 2-back task.
Conclusion: Inter-regional alpha synchronization and alpha-band network metrics can distinguish between the WM and WM-free tasks. In contrast, phase synchronization of high-gamma oscillations can differentiate between the levels of WM load, which demonstrates the potential of the phase-based functional connectivity and brain network metrics for predicting the WM load level.
2- S. A. Schapkin, G. Freude, P. D. Gajewski, N. Wild-Wall, and M. Falkenstein, “Effects of working memory load on performance and cardiovascular activity in younger and older workers.” Int. J. Behav. Med., vol. 19, no. 3, pp. 359–371, 2012.
3- A. M. Owen, K. M. McMillan, A. R. Laird, and E. Bullmore, “N‐back working memory paradigm: A meta‐analysis of normative functional neuroimaging studies.” Hum. Brain Mapp., vol. 25, no. 1, pp. 46–59, (2005).
4- D. J. Veltman, S. A. R. B. Rombouts, and R. J. Dolan, “Maintenance versus manipulation in verbal working memory revisited: an fMRI study.” Neuroimage, vol. 18, no. 2, pp. 247–256, (2003).
5- D. E. Nee et al., “A meta-analysis of executive components of working memory.” Cereb. cortex, vol. 23, no. 2, pp. 264–282, (2013).
6- M. K. Yeung et al., “Reduced frontal activations at high working memory load in mild cognitive impairment: near-infrared spectroscopy.” Dement. Geriatr. Cogn. Disord., vol. 42, no. 5–6, pp. 278–296, (2016).
7- F. A. Fishburn, M. E. Norr, A. V Medvedev, and C. J. Vaidya, “Sensitivity of fNIRS to cognitive state and load.” Front. Hum. Neurosci., vol. 8, p. 76, (2014).
8- M. P. Van Den Heuvel and H. E. H. Pol, “Exploring the brain network: a review on resting-state fMRI functional connectivity.” Eur. Neuropsychopharmacol., vol. 20, no. 8, pp. 519–534, (2010).
9- M. X. Cohen, Analyzing neural time series data: theory and practice. MIT press, (2014).
10- E. T. Bullmore and D. S. Bassett, “Brain graphs: graphical models of the human brain connectome.” Annu. Rev. Clin. Psychol., vol. 7, pp. 113–140, (2011).
11- M. Rubinov and O. Sporns, “Complex network measures of brain connectivity: uses and interpretations.” Neuroimage, vol. 52, no. 3, pp. 1059–1069, (2010).
12- Z. Dai et al., “EEG cortical connectivity analysis of working memory reveals topological reorganization in theta and alpha bands.” Front. Hum. Neurosci., vol. 11, p. 237, (2017).
13- J. Sun et al., “Connectivity properties in the prefrontal cortex during working memory: a near-infrared spectroscopy study.” J. Biomed. Opt., vol. 24, no. 5, p. 51410, (2019).
14- F. W. Carver, D. Y. Rubinstein, A. H. Gerlich, S. I. Fradkin, T. Holroyd, and R. Coppola, “Prefrontal high gamma during a magnetoencephalographic working memory task.” Hum. Brain Mapp., vol. 40, no. 6, pp. 1774–1785, (2019).
15- I. Alekseichuk, Z. Turi, G. A. de Lara, A. Antal, and W. Paulus, “Spatial working memory in humans depends on theta and high gamma synchronization in the prefrontal cortex.” Curr. Biol., vol. 26, no. 12, pp. 1513–1521, (2016).
16- J. Yamamoto, J. Suh, D. Takeuchi, and S. Tonegawa, “Successful execution of working memory linked to synchronized high-frequency gamma oscillations.” Cell, vol. 157, no. 4, pp. 845–857, (2014).
17- J. Shin, A. Von Lühmann, D.-W. Kim, J. Mehnert, H.-J. Hwang, and K.-R. Müller, “Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset.” Sci. data, vol. 5, p. 180003, (2018).
18- A. Delorme and S. Makeig, “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.” J. Neurosci. Methods, vol. 134, no. 1, pp. 9–21, (2004).
19- A. Widmann, E. Schröger, and B. Maess, “Digital filter design for electrophysiological data–a practical approach.” J. Neurosci. Methods, vol. 250, pp. 34–46, (2015).
20- J. A. Palmer, K. Kreutz-Delgado, and S. Makeig, “AMICA: An adaptive mixture of independent component analyzers with shared components.” Swart. Cent. Comput. Neursoscience, Univ. Calif. San Diego, Tech. Rep, (2012).
21- L. Pion-Tonachini, K. Kreutz-Delgado, and S. Makeig, “ICLabel: An automated electroencephalographic independent component classifier, dataset, and website.” Neuroimage, vol. 198, pp. 181–197, (2019).
22- M. Vinck, R. Oostenveld, M. Van Wingerden, F. Battaglia, and C. M. A. Pennartz, “An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias.” Neuroimage, vol. 55, no. 4, pp. 1548–1565, (2011).
23- M. X. Cohen, “A better way to define and describe Morlet wavelets for time-frequency analysis.” Neuroimage, vol. 199, pp. 81–86, (2019).
24- I. Zakharov, T. Adamovich, A. Tabueva, V. Ismatullina, and S. Malykh, “The effect of density thresholding on the EEG network construction.” in Journal of Physics: Conference Series, vol. 1727, no. 1, p. 12009, (2021).
25- M. Jalili, “Functional brain networks: does the choice of dependency estimator and binarization method matter?.” Sci. Rep., vol. 6, no. 1, pp. 1–12, (2016).
26- Y. Benjamini and Y. Hochberg, “Controlling the false discovery rate: a practical and powerful approach to multiple testing.” J. R. Stat. Soc. Ser. B, vol. 57, no. 1, pp. 289–300, (1995).
27- M. Xia, J. Wang, and Y. He, “BrainNet Viewer: a network visualization tool for human brain connectomics.” PLoS One, vol. 8, no. 7, p. e68910, (2013).
28- A. Gevins, M. E. Smith, L. McEvoy, and D. Yu, “High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice.” Cereb. cortex (New York, NY 1991), vol. 7, no. 4, pp. 374–385, (1997).
29- F. Roux and P. J. Uhlhaas, “Working memory and neural oscillations: alpha–gamma versus theta–gamma codes for distinct WM information?.” Trends Cogn. Sci., vol. 18, no. 1, pp. 16–25, (2014).
30- K. Fukuda, I. Mance, and E. K. Vogel, “α power modulation and event-related slow wave provide dissociable correlates of visual working memory.” J. Neurosci., vol. 35, no. 41, pp. 14009–14016, (2015).
31- E. Wianda and B. Ross, “The roles of alpha oscillation in working memory retention.” Brain Behav., vol. 9, no. 4, p. e01263, (2019).
32- F. Roux, M. Wibral, H. M. Mohr, W. Singer, and P. J. Uhlhaas, “Gamma-band activity in human prefrontal cortex codes for the number of relevant items maintained in working memory.” J. Neurosci., vol. 32, no. 36, pp. 12411–12420, (2012).
33- O. W. Murphy, K. E. Hoy, D. Wong, N. W. Bailey, P. B. Fitzgerald, and R. A. Segrave, “Individuals with depression display abnormal modulation of neural oscillatory activity during working memory encoding and maintenance.” Biol. Psychol., vol. 148, p. 107766, (2019).
34- R. T. Canolty and R. T. Knight, “The functional role of cross-frequency coupling.” Trends Cogn. Sci., vol. 14, no. 11, pp. 506–515, (2010).
35- T. Li, Q. Luo, and H. Gong, “Gender-specific hemodynamics in prefrontal cortex during a verbal working memory task by near-infrared spectroscopy.” Behav. Brain Res., vol. 209, no. 1, pp. 148–153, (2010).
36- C. Gao, J. Sun, X. Yang, and H. Gong, “Gender differences in brain networks during verbal Sternberg tasks: A simultaneous near‐infrared spectroscopy and electro‐encephalography study.” J. Biophotonics, vol. 11, no. 3, p. e201700120, (2018).
| Files | ||
| Issue | Vol 9 No 3 (2022) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v9i3.9641 | |
| Keywords | ||
| Electroencephalogram Working Memory Functional Connectivity Weighted Phase Lag Index Graph Theory | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
