Science Horizons Research Summary (Lisa Lin)
(On site Mentor: Dr. Mark Stokes, Oxford Centre of Human Brain Activity, Attention
Group; Off-Site Mentor: Dr. Anjali Thapar, Department of Psychology, Bryn Mawr
College)
Introduction
Working memory (WM) has been assumed to incorporate different brain mechanisms and to involve different structures from those associated with long-term memory (LTM). Evidence for this come from neuropsychology where lesions to the hippocampus cause profound impairment in LTM but relatively spares WM. However, recent research suggests that the hippocampus also plays a role in organizing information in WM (Lisman 1995). Axmacher et al. (2010) examined patients with surgical electrodes in their hippocampi and discovered that low-frequency oscillations (4-6Hz theta frequency band) appeared to help organize information in WM and this activity correlated with the amount of items in WM. Furthermore, recent studies involving MEG found that theta activity measured at the scalp surface appeared to help organize information in WM and may be coordinating replay of the displayed items during the delay period (Fuentemilla et al., 2010). This activity has been attributed to occurring in and therefore involving the hippocampus (Poch et al. 2011).
In this current research study, we are exploring the role of low-frequency theta rhythms in maintaining visuospatial information in working memory. EEGs were used to measure the magnitude and distribution of theta activity during the delay period of a working memory task. We predicted that theta power will increase in the frontal cortex while participants are maintaining information, and that this increase will scale with working memory load. Specifically, we expect that two items will increase theta power relative to the baseline and that four items will increase theta power relative to two items.
The results provided us with a greater understanding of the involvement of the hippocampus in working memory.
Methods & Procedure
EEG data were collected in 10 healthy participants (age range: 17-30 years).
Participants performed a computer-based visuospatial working memory task illustrated in
Figure 1. Participants were presented with either 2 or 4 memory items in the form of colored shapes on cards. A 3-second delay followed where the cards were faced down and the shapes hidden. After the delay, a memory test item was presented at the centre of the screen. Participants were asked to discriminate where the memory item had previously appeared on the screen by pressing a button on a keyboard that corresponded to the previous spatial location of the probe item. Participants were encouraged to respond as quickly as possible and were provided with accuracy feedback in the form of a
‘woohoo’ or a ‘d’oh’ after each trial.
EEG: Electroencephalography (EEG) data was collected with Ag-Ag/Cl sintered surface electrodes from 61 electrode sites laid out according to the extended international
10-20 system. The anterior midline frontal electrode (AFz) was used as the ground.

Impedances were kept below 10kΩ. Data was recorded with a sampling rate of 1000 Hz and electrodes were referenced online to the right mastoid electrode.
Results
EEG: TFQ and ERP
Only correct trials that fell in the time of interest specified previously were included in all EEG analyses. The raw EEG data was smoothed with a Gaussian kernel
(40 SD) and base lined to the average voltage from 250 to 50ms pre-memory array.
All group statistics were then performed using standard cluster-based nonparametric tests ( Maris and Oostenveld 2007 ). First, we estimate the t-statistic across participants for a contrast of interest at each time point, then define observed clusters of consecutive above-threshold time points, and calculated the cluster mass (by summing all t-values in an above-threshold cluster). Next, we randomly shuffle condition labels within participant (sign-flip for contrasts against zero), and extract the largest cluster mass produced by chance. This permutation step is performed 10,000 times to estimate the null distribution. The probability of the observed group-level cluster against chance is then derived as the rank order of the observed cluster relative to the null distribution (see also
Nichols and Holmes 2002 ; Myers, Stokes et al. 2014 ).
ERP :
The cluster-based non-parametric test indicated that there were significant differences in group-level clusters for Set Size 2 compared to Set Size 4 across both the frontal (F3 F4 FC3 FC1 FCZ FC2 FC4 FZ) and posterior channels of interest (P07 PO3
POZ PO4 PO8 O2 O1 OZ); p <. 05. Specifically, we found three significant cluster differences in group-level for Set Size 2 compared to Set Size 4 from 2350ms-2572ms
(p=.0404), from 2588ms-2800ms (p=.0404), and from 3016-3240ms post-memory array
(p=.0488) in the posterior channels. Additionally, we found three significant cluster differences in group-level clusters for Set Size 2 compared to Set Size 4 from 231.6ms-
3092ms (p=.0144), from 3112ms-3515ms (p=.0339), and from 3528ms-4400ms
(p=.0056) in the frontal electrodes.
TFQ Channel-Based Cluster Permutation
The cluster-based non-parametric test compared frequency power between the set size conditions. There was no difference in group-level clusters for set size 2 compared to set size 4 across posterior electrodes in the theta frequency band (4-6Hz). There was a non-significant group-level cluster difference for the two conditions across frontal electrodes in the theta frequency band (4-6 Hz); p=.2409, from 200ms—2252 ms postmemory array. Although non-significant, the results showed a trend in theta band frequency during the delay period, which suggests that there may potentially be greater theta power for Set Size 4 compared to Set Size 2 in the frontal channels.

Discussion
Overall, the ERP results indicated that participants who saw four items as opposed to two items showed significantly different neural activity across both frontal and posterior electrode sites, thus confirming our hypothesis. The time frequency results indicated that there was a nonsignificant increase in theta activity across the frontal channels when participants saw four items as opposed to two. In summary, the results suggest that theta power may be involved in the maintenance of visual working memory information during the delay period and may scale with the amount of items being held in working memory. However, due to the non-significant increases in theta activity across the frontal cortex when participants saw four as opposed to two items, no conclusions can be made about its specific involvement in working memory.
This project greatly expanded my understanding in how to conduct EEG analyses and how to interpret the results I found. I am very grateful to Dr. Mark Stokes for supervising my summer research as well as the Howard Hughes Medical Institute for making this all possible.
References
Axmacher, N., Henseler, M.M., Jensen, O., Wenreich, I., Elger, C.E., Fell, J. (2010)
Cross-frequency coupling supports multi-item working memory in the human hippocampus, Proc Natl Acad Sci, 107(7), 3228-33.
Fuentemilla, L., Penny, W.D., Cashdollar, N., Bunzeck, N., Duzel, E. (2010). Thetacoupled periodic replay in working memory, Curr Biol., 20(7), 606-612.
Lisman, J.E., Idiart, M.A. (1995). Storage of 7+/- 2 short-term memories in oscillatory subcycles. Science, 267(5203), 1512-1515.
Maris, E. & Oostenveld, R. (2007). Nonparametric statistical testing of EEG and MEG data, Journal of Neuroscience Methods, 164(1), 177-190.
Myers, N.E., Stokes, M.G., Walther, L., & Nobre, A.C. (2014). Oscillatory brain state predicts variability in working memory, J Neurosci, 34, 7735-7743.
Nichols, T.E. & Holmes, A.P. (2002). Nonparametric permutation tests for functional neuroimaging: a primer with examples, Human Brain Mapping, 15(1), 1-25.
Poch, C., Fuentemilla, L., Barnes, G.R., Duzel, E. (2011). Hippocampal theta-phase modulation of replay correlates with configural-relational short-term memory performance. J Neurosci, 31(19), 7038-42.