Poster No:
2377
Submission Type:
Abstract Submission
Authors:
Lucia Z Rivera1, Andre Gómez-Lombardi2, Begoña Góngora2, Alejandra Figueroa-Vargas3, WAEL EL-DEREDY4, Pablo Billeke5
Institutions:
1Universidad de Valparaiso, Valparaíso, Chile, 2Universidad de Valparaíso, Valparaíso, Chile, 3University of Desarrollo UDD · Centro de Investigación en Complejidad Social (CICS), Santiago, Santiago, 4UNIVERSIDAD DE VALPARAISO, Valparaíso, Valparaíso, 5University of Desarrollo UDD · Centro de Investigación en Complejidad Social (CICS), Santiago, Chile
First Author:
Co-Author(s):
Alejandra Figueroa-Vargas
University of Desarrollo UDD · Centro de Investigación en Complejidad Social (CICS)
Santiago, Santiago
Pablo Billeke
University of Desarrollo UDD · Centro de Investigación en Complejidad Social (CICS)
Santiago, Chile
Introduction:
Neuroimaging studies of working memory [WM] show that it depends on a distributed functional network, primarily involving fronto-parietal association cortices (Ester et al., 2015; Christophel et al., 2017; Cabeza et al., 2008). The ability to sustain activity in these distributed networks depends on oscillatory activity in different frequency ranges (Roux et al., 2014). Specifically, it has been observed that EEG activity in the theta range (4-8 Hz) between fronto-parietal electrodes represents the maintenance of information in WM(Roux et al., 2014). Most EEG studies of WM test the modulation of the theta power by the WM load. Our study analyse WM performance under different load focusing on the individual theta frequency in a group of older adults. We show that individual theta frequency decreases with WM load, suggesting the recruitment of a more extensive cortical network, as proposed by (Lea-Carnall et al 2016).
Methods:
EEG recordings from individuals aged over 60 (n=20) were utilized for this study. All participants were right-handed and exhibited a cognitive profile consistent with mild cognitive impairment, as per Petersen's criteria (2014), with a minimum of 6 years of formal schooling. To assess working memory, a modified version of the Sternberg task (Figueroa-Vargas, A. et al., 2020) was employed. This task focused on short-term memory retrieval and was presented at three difficulty levels: patterns of 2 (low load (LL)), 4 (medium load (ML)), and 6(high load (HL)) letters presented randomly. EEG data were recorded using a Biosemi Active-Two 64+8 channels according to the 10/20 system at a sampling rate of 1 kHz. Analyses were performed using EEGLab (Delorme and Makeig 2004) under MATLAB (Version 2021b, Mathworks Inc., Natick, USA). Time-frequency (TF) analyses were calculated using continuous wavelet transform. The individual theta frequency was computed in a window of 100–500 ms after stimulus presentation during memory encoding, in the average of central electrodes (Cz, C1, C2).
Results:
A significant difference was found in participants' performance between low load (LL) and high load (HL) conditions (p < 0.004). Individual theta frequency was lower at the higher WM load, with a significant difference in theta frequency between LL and HL (p < 0.039). There was a significant correlation between individual theta frequency and accuracy, with lower frequency associated with lower accuracy and higher frequency linked to better performance.
Conclusions:
The individual theta frequency varied during the execution of a variable-load working memory (WM) task. We demonstrate that, as the task becomes more challenging, a lower theta frequency is required, indicating a more extensive cortical network. This is due to the inversely proportional correlation between frequency and network size (Lea-Carnall et al 2016).
Learning and Memory:
Working Memory 2
Lifespan Development:
Aging
Novel Imaging Acquisition Methods:
EEG 1
Keywords:
Aging
Electroencephaolography (EEG)
Other - Working Memory
1|2Indicates the priority used for review
Provide references using author date format
Ester, E. F., Sprague, T. C., & Serences, J. T. (2015). Parietal and frontal cortex encode stimulus-specific mnemonic representations during visual working memory. Neuron, 87(4), 893-905.
Christophel, T. B., Klink, P. C., Spitzer, B., Roelfsema, P. R., & Haynes, J.-D. (2017). The distributed nature of working memory. Trends in cognitive sciences, 21(2), 111-124.
Cabeza, R. (2002). Hemispheric asymmetry reduction in older adults: The HAROLD model. Psychology and Aging, 17(1), 85-100. https://doi.org/10.1037/0882-7974.17.1.85
Roux, F., & Uhlhaas, P. J. (2014). Working memory and neural oscillations: Alpha–gamma versus theta–gamma codes for distinct WM information? Trends in cognitive sciences, 18(1), 16-25.
Delorme, Arnaud, and Scott Makeig. 2004. “EEGLAB: An Open Source Toolbox for Analysis of Single-Trial EEG Dynamics Including Independent Component Analysis.” Journal of Neuroscience Methods 134 (1): 9–21.
Lea-Carnall, C. A., Montemurro, M. A., Trujillo-Barreto, N. J., Parkes, L. M., & El-Deredy, W. (2016). Cortical resonance frequencies emerge from network size and connectivity. PLoS computational biology, 12(2), e1004740.