The involvement of alpha oscillations in voluntary attention directed towards encoding episodic memories

NeuroImage 166 (2018) 307–316

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The involvement of alpha oscillations in voluntary attention directed towards encoding episodic memories Tamas Minarik *, Barbara Berger, Paul Sauseng Department of Psychology, Ludwig-Maximilians University, Germany

A R T I C L E I N F O

A B S T R A C T

Keywords: Beta oscillations Episodic long-term memory Item-context binding EEG

Forming episodic memories is often driven by top-down processes of allocating attention towards voluntarily remembering the details of an episode. This attention orientation is needed to make sure that information is encoded for later remembering. Here we designed an episodic long-term memory (LTM) EEG experiment where we examined brain oscillatory activity associated with attention allocation towards the temporal link between an item and its context. The remembering of this temporal conjunction is crucial for item-context binding and hence for the formation of episodic memories. Participants saw a background picture and a word in a central position on a computer screen and were instructed to memorise (a) the picture only, (b) the word, (c) both individually (i.e. ignoring their co-occurrence) and (d) both with them being presented together. Attention allocation towards itemcontext binding was associated with oscillatory alpha desynchronization in the upper alpha band (10–13 Hz) over dominantly left posterior brain areas. The results highlight the role of alpha desynchronization in voluntary attention allocation towards the temporal conjunction of item and its context in episodic binding and the involvement of posterior brain areas. The pattern of results suggest that they most likely reflect additional visual processes recruited by attentional mechanisms and do not tap into neural processes of item-context binding per se. Moreover, it indicates that the involvement of alpha oscillations in cognitive processes may be more complex.

Introduction Directing attention to an item and its context and also to the temporal relation between the two is key for voluntarily learning new episodic information. That is in order to remember events and episodic information when we want to as opposed to when our attention is captured without voluntary involvement. Dysfunctional encoding of such relational information has been implicated in memory deficits associated € with various neurological conditions, such as schizophrenia (Ongür et al., 2006) autism spectrum disorder (Vermeulen, 2015), Alzheimer's disease (Finke et al., 2013; Perry et al., 2000; Romberg et al., 2013) as well as in healthy ageing (Craik and Byrd, 1982; Kilb et al., 2015). The neural components of this top-down controlled process that is the interaction between executive functioning (attention) and episodic long-term memory encoding (binding) is not well understood. Episodic long-term memory (LTM) encoding has been shown to involve a large network of brain areas, in particular the hippocampus (Vargha-Khadem et al., 1997) and the medial temporal lobe (MTL), but also parietal (Uncapher and Wagner, 2009) and prefrontal cortical areas (Ranganath, 2010). Neural oscillatory mechanisms across these brain

areas in slow frequencies (i.e. theta, 4–7 Hz) and also in the gamma frequency band (30–80 Hz) seem to have an important role especially in binding. Theta power change in the MTL has been associated with episodic LTM formation in intracranial EEG (Lega et al., 2012) and magnetoencephalography (MEG; Staudigl and Hanslmayr, 2013) studies. In addition, MTL gamma (as well as theta) power increase was found relevant in intracranial (Sederberg et al., 2007) and MEG studies (Osipova et al., 2006; Staudigl and Hanslmayr, 2013) and was shown to extend beyond the MTL to parietal and prefrontal areas (Sederberg et al., 2003). Besides these frequencies, alpha (8–13 Hz) and beta (14-29HZ) band oscillatory activities have also been associated with successful episodic LTM formation however typically in the form of power decrease (see Hanslmayr et al., 2012). In particular, successful encoding is characterized by alpha and beta desynchronization, presumably reflecting the semantic aspect of the encoded information (Hanslmayr et al., 2009). However, opposite findings have also been reported, e.g. a complicated pattern of synchronization and desynchronization in various areas of the brain from theta to gamma frequencies reflecting the encoding of episodic information (Sederberg et al., 2007). The encoding of episodic memories takes place quite often via largely

* Corresponding author. Department of Psychology, Ludwig-Maximilians University, Leopoldstr. 13, 80802 Munich, Germany. E-mail addresses: [email protected], [email protected] (T. Minarik). https://doi.org/10.1016/j.neuroimage.2017.10.064 Received 13 June 2017; Received in revised form 2 October 2017; Accepted 29 October 2017 Available online 5 November 2017 1053-8119/© 2017 Elsevier Inc. All rights reserved.

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Materials and methods

bottom-up processes driven for instance by stimulus saliency or emotional saliency; other times the encoding is under top-down control as attention is directed selectively to make sure that an important event is memorized with binding details and various pieces of information together, such as the temporal conjunction of the item and its context. Whilst little is known about such attention allocation towards episodic encoding into LTM, there has been extensive research dedicated to uncover the oscillatory mechanisms of selective attention allocation in the perceptual and working memory domains. Such selective attention allocation has been associated with neural synchronization/desynchronization in particular in alpha, but also theta and gamma frequencies in perceptual and working memory tasks (e.g. Bonnefond and Jensen, 2012; Glennon et al., 2016; Green and McDonald, 2008; Jokisch and Jensen, 2007; Sauseng et al., 2005b; Thut et al., 2006; Zumer et al., 2014). It is thought that alpha activity is a key mechanism in the neural implementation of top-down control by the suppression of task-irrelevant neural processes modulating cortical excitability of the affected neuronal population (Jensen and Mazaheri, 2010; Klimesch et al., 2007). This has been demonstrated clearly for instance in lateralized perceptual selective attention tasks (e.g. Capilla et al., 2014; H€andel et al., 2011; Sauseng, Klimesch, Stadler et al., 2005b; Worden et al., 2000). In contrast, regions involved in the processing of attended stimuli typically show synchronization in the gamma band (Womelsdorf and Fries, 2007). The increase in gamma power over relevant brain areas has been shown among others in lateralized and crossmodal attention tasks (Gruber et al., 1999; Kaiser et al., 2005; Siegel et al., 2008) and has been suggested to play a crucial role in various stages of active information processing (Jensen et al., 2007). Yet voluntary attention allocation towards the formation of episodic long-term memories is a somewhat special case, lying at the crossroad of episodic LTM encoding and top-down control of selective attention. There have been studies that reported posterior alpha power changes in association with allocation of attention versus information blocking in long-term memory encoding (Jiang et al., 2015; Park et al., 2014). However these studies did not assess episodic memory, but memory for items only. To date it is unclear which oscillatory mechanisms are responsible for the attention allocation towards episodic binding in particular. Here we report findings from an EEG study which specifically addressed this question. The aim was to directly investigate the oscillatory correlates of the process of encoding an item and its context versus encoding them individually i.e. without the temporal connection. Key here was the participants’ attention paid to the temporal link between item and its context for forming memories.

Participants 26 participants were recruited at the Ludwig-Maximilians University, Germany. One dataset was excluded because of poor EEG and one for technical problems (triggers were sent incorrectly). In addition, one participant was a non-native German speaker and therefore his dataset was also excluded from analysis. Mean age of the remaining 23 participants (19 female) was 23.7 (SEM ¼ 0.81, range is 18–34). All volunteers were native German speakers, had normal or corrected to normal vision, and received €20 or lab token for participation. All participants gave written informed consent; the study was approved by the local Ethics Board and was conducted according to the Declaration of Helsinki. Stimuli and design Participants’ task included a learning task, followed by a filler task and finally a recognition task. The learning (encoding) task was comprised of eight blocks of 20 trials each (Fig. 1). During each trial a word was presented in front of a background picture for 3000 ms. The inter-trial interval was 2000 ms. Each background picture and word was only presented once during the learning task. The 160 word stimuli were German nouns drawn from the CELEX database (Baayen et al., 1996) and the 160 background pictures collected from the internet were color mountain landscape photos. Each block was preceded by one out of four encoding instructions: (I1) memorize the word only (Word only; W) (I2) memorize the background picture only (Background only; B) (I3) memorize the background picture and the word individually (Non-Linking; NL) (I4) memorize the background picture and the word and that they were presented together (Linking; L) The instructions preceding the blocks were semi-randomized (each instruction presented once in the first four blocks and the last four blocks). The background picture and word combinations were randomized across participants. The subsequent filler task was a 100 trial delayed-match-to-sample visual working memory task in which the orientation of briefly presented Gabor gratings had to be retained for 2000 ms in each trial. The task lasted approximately for 10 min.

Fig. 1. (A) Study instructions, design and blocks. (B) Two sample trials of the learning task. 308

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This filler task was followed by a 6 blocks long recognition task of 40 trials each. Half of the word background picture pairs were presented again during the recognition task (match trials), and half the stimuli were paired with another earlier presented item (mismatch trials; i.e. both the background picture and the word had been presented during the leaning phase but had each been paired with another item). Additionally, 80 previously unseen background picture-word pairs were presented during the task. Each pair was presented once for 3000 ms. Then participants had to indicate with left and right mouse key press whether the background picture was presented during the learning task (old) or was new. Next, the same decision had to be made over the presented word. If the answer was ‘old’ to both the background picture and the word, participants had to indicate with similar button press if they were presented together or not. Each task was preceded by a number of practice blocks. The experiment was run by Presentation 0.71 software.

of cycles of each time window was set to 8. In addition, the frequency smoothing was defined as 20% of the examined frequency and so increased as the frequency increased (i.e.6 Hz smoothing at 30 Hz and 16 Hz at 80 Hz). Prior to group-level analysis the power estimates were log-transformed. To test if there was a statistically significant difference between the two conditions, group-level analysis was conducted with cluster-based permutation test (Maris and Oostenveld, 2007). At each electrode a t-statistic was calculated for each time-frequency point (0–3000ms and 2–80 Hz) by comparing the L and the NL conditions. Time-frequency points that had a t-value higher than the 95th percentile were kept. Adjacent channel-time-frequency points were clustered and the cluster statistics was calculated as the maximum sum of each cluster. Then a permutation test was conducted by random partitioning the trials with regards to the two conditions and calculating the cluster-statistics as above. This was repeated 5000 times and the Monte-Carlo significance probability was derived by calculating the number of random partitions that resulted in higher cluster-statistics than that of the actual data.

EEG acquisition and preprocessing Source level analysis To examine the power difference between the two conditions at source level, brain activity in source space was calculated by exact low resolution brain electromagnetic tomography (eLORETA; Pascual-Marqui et al., 2011) as implemented in Fieldtrip. The time-window was selected based on the results of the scalp-level analysis. The data were re-referenced to the average of all electrodes (excluding the eye channels vEOG and hEOG) and baseline corrected to the mean of each trial. Fast Fourier Transform was performed with Hanning taper to the alpha frequency band (10–13 Hz) to obtain the cross spectral density matrix of each condition and the concatenated dataset separately. The volume conduction model was calculated by applying Boundary Element Model (Oostendorp and Van Oosterom, 1989; Oostenveld et al., 2003) on an MNI template brain (Colin27) and the volume was discretized into 0.5 cm 3D grid. The electrode positions were projected on the skin surface and the lead field matrix was calculated for each grid point according to the forward model. To reconstruct alpha activity in 3D source space the eLORETA algorithm was used. eLORETA is a weighted minimum norm inverse solution with exact localization, but with low spatial resolution as it assumes strongly correlated neighboring neuronal activity. The resulting alpha power estimates of each grid point were log-transformed prior to further statistical testing. As group level statistical analysis a cluster-based permutation test with 5000 iterations was performed with calculating the cluster-statistic of adjacent grid points to see if there is a statistically significant difference in alpha power between the two conditions (L vs NL). For further visualization purposes the MNI coordinates of the site of maximum significant statistical difference between the two conditions of the above cluster-based permutation result within each sub-region of the AAL atlas (Tzourio-Mazoyer et al., 2002) were extracted.

EEG was recorded during all three tasks with 64-channel electrode caps (EASYCAP GmbH, Germany) according to the international 10-10 system with Ag/AgCl ring electrodes using a BrainVision BrainAmp DC amplifier (Brain Products GmbH, Germany). The reference electrode was set on the tip of the nose and to detect eye-movements horizontal and vertical EOG electrodes were applied. Data were recorded at 1000 Hz sampling rate, and online filtered with 0.016 Hz high-pass and 200 Hz low-pass filters. Impedances were kept below 10 kΩ. EEG preprocessing was done with BrainVision Analyzer 2.0. First, EEG data was offline bandpass filtered between 1 Hz and 80 Hz (Butterworth zero-phase filter, 48 dB/oct) and further a 50 Hz notch filter was applied. Large artifacts were manually rejected prior to occularcorrection independent component analysis as implemented in Brain Vision Analyzer (Jung et al., 2000), during which blink and eye-movement artifacts were removed. Subsequently, independent component analysis (ICA) and inverse ICA were performed to identify and remove further artifacts (e.g. muscle noise). Finally, manual artifact rejection was performed to remove any remaining artifact. On average 35.22 (SEM ¼ 0.99) trials were left in the L, 35.70 (SEM ¼ 0.66) in the NL condition, 35.65 (SEM ¼ 0.71) in the B and 33.91 in the W conditions. The performed repeated-measures ANOVA revealed no main effect of instruction and the subsequent set of paired-sampled t-tests showed no statistical difference between any pair of the four conditions, indicating no significant difference in trial numbers across conditions. EEG data analysis EEG data analysis was performed in the MATLAB (The MathWorks, Inc., Natick, MA, US) toolbox Fieldtrip (Oostenveld et al., 2011) and in-house MATLAB scripts. For the following analysis the main focus will be on the comparison between the Linking (L) and Non-Linking (NL) encoding task trials. Nevertheless, as part of the follow-up and control analysis results of various combinations of comparisons including the Background only (B) and the Word only (W) conditions are also reported. Scalp level analysis. The full time-window of stimulus presentation in each trial (0–3000ms) of the Linking and the Non-linking conditions was included in the scalp-level analysis. The data of each trial was baseline corrected with the mean of the trial (‘demean’), for each electrode current source density was calculated as second spatial derivative (surface Laplacian) with spherical spline interpolation method. The power spectra at each electrode were calculated in 1 Hz steps between 2 and 30 Hz and in 5 Hz steps between 30 and 80 Hz. This was done at lower frequencies (2–29 Hz) with a 6 cycle length sliding window with a single Hanning taper in 50 ms steps. This gave a decreasing time-window length as frequency increases (e.g. 1000 ms at 6 Hz, but only 500 ms at 12 Hz). At higher frequencies (30–80 Hz), multitaper method was employed again with a variable length sliding window moved in 50 ms steps. The number

Behavioral data analysis For assessing the recognition performance of background picture and word pairs that were presented together during the encoding and also recognition phase (i.e. match trials only), a performance score was calculated separately for the four encoding conditions. One-sample t-test was used to see if performance was higher than chance level and a series of paired-samples t-tests if there was a significant difference between conditions. The p-values were Bonferroni-corrected to account for multiple testing. Repeated-measures ANOVA was conducted with the factor Instruction (4 levels: L, NL, B, W) to test if there was a significant difference in the recognition of the word-picture pairs. In addition, as follow-up analysis a series of paired-samples t –tests were employed and Bonferroni correction was used to adjust for multiple comparisons. Moreover, to test if the instructions had any influence on recognition rate of the words and also of the background pictures themselves, we first ran a repeated-measures ANOVA with the factors Material (2 levels: word, 309

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picture) and Instruction (4 levels: L, NL, B, W). Greenhouse-Geisser correction was applied when appropriate. This was followed by a series of paired-samples t-tests comparing the effect of each possible instruction pairs on the recognition of words and pictures separately. To account for the multiple testing Bonferroni correction was applied. The analysis was conducted in IBM SPSS 24.

that the recognition performance differed across conditions in comparison of the background pictures and the word. Furthermore, the subsequent follow-up analysis showed that when the pictures had to be remembered recognition performance was significantly higher in the B than in the W condition, whereas the opposite was true when only the word needed to be retained (Table 1.). These results again indicated that the experimental manipulation had the expected effect. Importantly though, the recognition of pictures was significantly higher in the L than in any other condition, including the Background only condition whereby participants could focus exclusively on the background pictures. This may suggest that the Linking instruction facilitated the picture recognition in particular. In contrast, word recognition was aided less by the Linking instruction, as only the B instruction showed significantly poorer word recognition performance. The NL condition on the one hand showed lower recognition performance for pictures compared to the L condition and also for words compared to the W condition. On the other hand, performance was higher for words than in the B condition.

Results Behavioral results The recognition rate of whether a word was presented together with a particular picture was significantly modulated by the instruction participants received prior to each block, as the main effect of Instruction, F(2.19,54.83) ¼ 28.36, p < 0.001, in the conducted repeated-measures ANOVA demonstrated. Importantly, further paired samples t-tests also indicated that recognition of Linking was significantly higher for items that were studied in the L condition than in any other (t(22)L-NL ¼ 4.71, p < .001, t(22)L-B ¼ 6.64, p < .001, t(22)L-W ¼ 5.83, p < .001). These results suggest that the experimental manipulation of Linking resulted in the expected effect. It is also important to mention that the recognition rate was higher than chance level in all conditions (p < 0.05; Fig. 2.). Recognition of new items (39.7%(SEM ¼ 3.96)) was also significantly above chance level (25%), t(22) ¼ 3.66, p ¼ 0.001. Moreover, we also examined if the recognition performance for words and also for the background pictures differed across conditions. The performed repeated-measures ANOVA with the factors Material (picture, word) and Instruction (L, NL, B, W) showed a main effect of Instruction, F(3,66) ¼ 22.62, p < 0.001, but no main effect of Material (p ¼ 0.470). These results showed that overall the recognition of background pictures and words did not differ significantly, but the instructions had a significant impact on the item recognition. In addition the significant Material*Instruction interaction effect, F(3,66) ¼ 33.94, p < 0.001, indicated

Scalp level analysis The permutation results suggested only five frequency bins that showed a significant difference between the L and the NL conditions at the lower frequencies (2–30 Hz): 10 Hz (1.1–3s), 11, 12 and 13 Hz (1.2–3s) and finally 14 Hz (1.5–2s and 2.2–3s). The latter frequency (14 Hz) had the lowest cluster-statistic value amongst the above frequencies and resulted in temporally non-contiguous clusters as well as significant electrode-sites that were highly similar to the 13 Hz electrode sites, therefore it is unlikely that this effect found in the lower-beta band carries any additional information to the observed upper-alpha (10–13 Hz) effect. For this reason further statistical analysis was carried out focusing on the upper-alpha frequencies (10–13 Hz). As the L/ NL oscillatory activity difference in these frequencies showed a highly

Fig. 2. (A) Mean recognition performance with error bars of SEM of the presentation of background pictures and words together across the four instruction conditions. Dashed line indicates chance level (25%). (B) Mean recognition performance with error bars of SEM of the ‘old’ background pictures and the words separately across the four conditions.

Table 1 Paired-sample t-test results comparing the recognition performance for background pictures and words separately contrasting all four instruction conditions. Asterisk indicates significant pvalue at Bonferroni-corrected significance level (p < 0.08). Picture recognition

Linking vs Non-Linking Linking vs Backgr. Only Linking vs Word only Non-Linking vs Backgr. only Non-Linking vs Word only Backgr. only vs Word only

Word recognition t-value

df

p-value

5.167 3.369 6.649 2.072 2.374 4.380

22 22 22 22 22 22

0.000* 0.003* 0.000* 0.050 0.027 0.000*

Linking vs Non-Linking Linking vs Backgr. Only Linking vs Word only Non-Linking vs Backgr. only Non-Linking vs Word only Backgr only vs Word only

310

2.744 8.779 0.934 5.989 3.534 6.169

t-value

df

22 22 22 22 22 22

0.012 0.000* 0.361 0.000* 0.002* 0.000*

p-value

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Fig. 3. (A) Difference between L and NL conditions at 10–13 Hz (average) over time at scalp level. Asterisks indicate electrodes that showed a significant difference according to the cluster-based permutation test. (B) Mean power difference over time and frequency across selected channels that showed a significant difference between the L and NL conditions. (C) Source reconstruction of the power difference in the L/NL contrast in the upper alpha band (10–13 Hz, averaged) between 1.3 and 3s following stimulus onset. (D) The voxel with the highest statistical difference within AAL regions in the L/NL contrast in the upper alpha band as in (C).

this alpha power difference between the L and NL conditions. To do so we selected the time-window between 1.3s and 3s at the upper alpha frequency band from 10 to 13 Hz and applied eLORETA algorithm for source-localization. The cluster-based permutation test results revealed significantly (p < 0.05, two-tailed) higher power in the alpha band in the NL condition than in the L condition over posterior, but also central areas (Fig. 3C). The difference in alpha power was much more prominent in the left hemisphere than in the right hemisphere. In the latter it seemed largely restricted to occipital areas. Whereas in the former the difference between the two conditions was particularly strong in a large region including occipital and occipitotemporal areas as well as parietal areas. For further source-level analysis the results obtained from the above cluster-based permutation test were mapped onto the AAL atlas and the MNI coordinates of the site of the maximum significant statistical difference within each AAL region was extracted (Fig. 3D). The highest statistical difference between the L and NL conditions was localized at a superior part of the left middle temporal gyrus (MNI coordinates: 55, 65, 15). Moreover, left hemispheric large occipital areas (‘Occipital_Mid_L’, ‘Calcarine_L’) also showed a highly significant difference, but the voxel with the highest statistical difference was relatively close to the left middle temporal region (MNI coordinates: 40, 60, 0; 25, 65, 5). Although, given the potential for localization error and the low spatial resolution inherent to our methods (we only report MNI coordinates for the sake of transparency), these localization results should be interpreted with caution, however they suggest that the area with the strongest statistical difference between the two conditions is away from superficial occipital cortical regions and is located in the left occipitotemporal region.

similar temporal profile, over similar posterior electrode sites, their power estimates were averaged and subjected to a secondary clusterbased permutation test (Staudigl and Hanslmayr, 2013). Given the similar temporal and spatial profile of the effect, it is likely that the results reflect on the same oscillatory activity difference between the conditions across these frequencies, hence taking the mean power estimates over these frequencies for further analysis simplifies the analysis without loss of significant information. This secondary analysis was performed purely to see if these frequencies can indeed be pooled together for further analysis and if that is the case then to provide the temporal and spatial characteristics of any emerging significant cluster with power averaged across these frequencies. This secondary permutation test revealed a bilateral posterior electrode cluster which showed higher alpha power in the NL than in the L condition between 1.3s and 3s following stimulus onset (Fig. 3A). In addition, the time-frequency map of the log-transformed power averaged across the pool of electrodes which had shown a significant difference in the previous analysis ('T7', 'C5', 'C3', 'C1', 'CP5', 'CP3', 'CP1', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'PO7', 'PO3', 'POz', 'PO4', 'O1', 'Oz', 'O2) showed that this power difference between the L and NL conditions over this time window (1.3–3s) in the upper alpha band is a sustained effect (Fig. 3B). No significant cluster emerged at higher frequencies (30–80 Hz). The subsequent source-level analysis focused on the upper-alpha (10–13 Hz) effect to establish the sources of the scalp level alpha-band oscillatory activity difference in the L and NL conditions. Source level analysis Based on the significant difference between the two conditions at scalp-level over posterior electrodes, we set out to localize the sources of 311

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Picture encoding One could also argue that the relative alpha desynchronization in the NL/B comparison could indicate the differential encoding of the pictures as the B condition required processing the background pictures only, whilst the NL condition both the pictures and the words. In addition, the similarity between the L and B alpha power compared to the NL condition raises the possibility that the alpha effect in the L/NL comparison is related to picture encoding too. Importantly, permutation tests performed on the L/B conditions revealed no significant difference at alpha or any other frequency. However the above mentioned cluster-based permutation test results (null effects) performed on the NL/W condition suggests otherwise, as one would have expected to find a posterior alpha difference as the picture was encoded in the NL but not in the W condition. In fact, findings from the beta band may provide a much more straightforward candidate for picture encoding. Each contrast of the Word only condition (L/W, NL/W and B/W) indicated posterior electrode clusters in the beta range (14–24 Hz, 14–21 Hz and 14–23 Hz, respectively) which were significantly higher in the W condition in all contrasts (Supplementary Figure 1C, 2B and 3C). Both the time course and the electrode clusters of the beta power difference showed a very similar profile across the three comparisons. The common denominator in all three conditions (L, NL, B) versus the W condition is the processing of the pictures. Therefore with most likelihood this beta effect indicates visual processing of the background pictures or the suppression of picture processing in the Word only condition.

Follow-up analyses The above results indicate that voluntary attention allocation towards item-context binding is associated with upper alpha decrease in posterior brain regions. However, these findings could tap into various attention allocation sub-processes and at this point it is difficult to interpret these findings. To disentangle various alternative explanations and to understand the results better, we ran a number of further tests. Cognitive load One could argue that the L condition requires the encoding of quantitatively more information (item, context and the temporal link) than the NL condition (only item and context), which in turn leads to higher cognitive load. Indeed changes in posterior alpha activity have been implicated as correlates of cognitive load increase (Gevins et al., 1997; Sauseng et al., 2005a) and decrease (Jensen et al., 2002) in working memory tasks. Thus if the observed posterior alpha effect reflects cognitive load differences in the L/NL conditions then examining other condition comparisons with a clear load difference (i.e. NL/B, NL/W) should reveal similar alpha power differences. To examine this issue we conducted an additional series of scalp-level cluster-based permutation tests contrasting the NL with the B and the W conditions. The analysis revealed a significantly higher alpha power in the NL than in the B condition from 9 Hz up to 15 Hz over posterior channels. When in a subsequent test we restricted the analysis to the averaged power between 10 Hz and 13 Hz (i.e. the frequencies of the effect we found between L and NL conditions), the permutation test results indicated again significantly higher alpha power over a large posterior channel cluster between 0.9 s and 2.6s after stimulus onset in the NL condition (Fig. 4A). Note, these results are the exact opposite of what one would expect based on the findings of the L/NL comparison. Moreover, the analysis of the NL/W contrast revealed no significant posterior effect in any of the alpha frequencies and did not even show a trend of posterior synchronization or desynchronization. Only a dominantly right-central electrode cluster at 12 Hz showed higher alpha power in the NL condition, significant (p < 0.05) at a one-tailed level (Fig. 4B). Repeating the analysis on the average power between 10 and 13 Hz provided no significant cluster. Hence, unlike in working memory tasks the L/NL posterior alpha desynchronization in our task does not seem to reflect on increased cognitive load. That is because comparisons with clear load difference either revealed no alpha power difference or the exact opposite pattern (i.e. alpha synchronization).

Posterior alpha in the L/NL and in the B/NL contrasts To further understand the nature of the relative posterior alpha desynchronization in the L compared to the NL condition it might be crucial to see how similar the sources of this are compared to the posterior alpha desynchronization in the B versus NL contrast. Hence, we first source localized the B/NL alpha power difference at frequencies and time-window of the L/NL effect (i.e. between 10 and 13 Hz and over 1.3 and 3s). This was done as to contrast the sources of the alpha activity differences at comparable frequencies and time-windows in the B/NL (Fig. 5A) and L/NL (Supplementary Fig. 6A) conditions. Then we compared the sources of significantly different alpha activity in the L/NL and B/NL comparisons. That is we overlaid the regions showing significantly different alpha activity in the two contrasts to see the extent of the overlap and also areas which are uniquely involved in the L/NL and B/ NL contrasts. The source localization provided highly overlapping sources in the two comparisons (Fig. 5B), so much so that the L/NL contrast showed only few unique sources (Fig. 6A). Please note that these unique sources

Fig. 4. (A) Power difference between B and NL conditions over 10–13 Hz (average) over time at scalp level. Asterisks indicate electrodes that showed significant difference according to cluster-based permutation test. (B) Power difference between NL and W conditions at 12 Hz over time at scalp level. Electrodes marked with X showed significantly higher alpha power, but only at one-tailed level. 312

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Fig. 5. (A) Source reconstruction of the power difference in the B/NL contrast in the upper alpha band (10–13 Hz, averaged) between 1.3 and 3s following stimulus onset. (B) Sources common in the L/NL and the B/NL contrasts at 10–13 Hz (averaged) and in the 1.3–3s time-window. Masked areas showed significantly lower power in the L and B conditions than in the NL condition.

effect in the L/NL contrast (Fig. 6B). In particular, primarily righthemispheric posterior and central regions were identified as unique to the B/NL comparison, including the parietal cortex, large temporoparietal areas and also deep central structures. Please note that we also reprocessed the B/NL data by setting the time-window to what the scalplevel statistics indicated i.e. to 0.9–2.6s and repeated the above analysis. The conclusions drawn however remained virtually unchanged. We also looked at the site of the largest statistical difference in the B/ NL contrast and importantly it differed rather considerably from the site of the maximum L/NL contrast. As it was mentioned, in the latter case it was mainly around the left occipito-parietal-temporal junction, while in the B/NL contrast, this was at a right occipitotemporal region (MNI coordinates: 40, 65, 30) and secondarily in the right parietal region (MNI coordinates: 45, 70, 30). That is whilst there is a rather large overlap

are unlikely to be the results of smearing arising from generally higher alpha power difference in the L/NL contrast than in the B/NL contrast. If anything the power difference was higher in the B/NL contrast at most sub-regions and the site of the maximum statistical difference between two conditions showed also higher value in the B/NL contrast. Possibly the most notable and largest unique sources were clustering around the junction of the left occipital, parietal and temporal cortices (AAL atlas labels: Temporal_Mid_L, Temporal_Inf_L, Fusiform_L, Angular_L; with the largest statistical value at the MNI coordinate: 55, 65, 15), and the right occipitotemporal areas (AAL atlas labels: Temporal_Inf_R, Temporal_Mid_R, Fusiform_R). However, considering the limitations of the source localization the results should be interpreted with caution. In contrast, the posterior alpha effect in the B/NL contrast had a number of large areas that showed no overlap with the posterior alpha

Fig. 6. (A) Sources unique to the L/NL and (B) to the B/NL contrasts at 10–13 Hz (averaged) and in the 1.3–3s time-window. Masked areas showed significantly lower power in the L and B conditions respectively than in the NL condition. 313

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between the posterior alpha effect in the L/NL and the B/NL contrasts, the site of the maximum difference suggests that they are the results of overlapping but considerably different processes. It must also be noted that alpha power from 8 Hz to 13 Hz showed significant relative desynchronization in both the L and the B (but not the NL) conditions as compared to the W condition over posterior electrodes between 1.3s and 3s after stimulus onset (Supplementary materials, Figs. 1B and 3B). These findings also point towards visual or related to visual processes which are however absent in the NL condition.

by then, target stimuli identified and even semantic LTM representations activated. Therefore, we argue that this late alpha effect likely suggests that the effect in both cases is due to a strategic, effortful process employed to memorize the relevant stimuli and/or the temporal contiguity of the stimuli in long-term memory. Put differently, it is likely the neural indicator of an active attempt to memorize certain information. Furthermore, the pattern of results points towards mostly additional visual processes carried out both in the L, but also in the B conditions; or less likely more verbal processes in the NL and W conditions. This argument is based on the very posterior nature of the effect including large areas of the visual cortex. Assuming that the alpha desynchronization here indeed reflects on the release of inhibition of neural activity, the involvement of visual cortical areas should mean the employment of additional visual processes. Here we argue that this is most likely a mnemonic visual process. If it was additional verbal process in the NL and W conditions, one would expect to see the neural sources other than the visual cortex. In sum, we argue that the voluntary allocation of attention to the temporal link between item and its context during episodic encoding led to the engaging of strategic visual mnemonic processes. Whilst it may be that this posterior alpha effect in the L/NL conditions is strategic visual mnemonic in nature and that it is due to the orientation of attention to the to-be encoded information, there are several possible interpretations as to what these processes could also be. One could argue that ignoring irrelevant stimuli could have played a role in our results. However, we think that in the case of the observed posterior alpha power difference in the B/NL this does not apply. Not the least because in the NL condition the picture does not have to be ignored. In particular, if it had reflected on ignoring the words in the B condition, than one would have expected increased alpha power in the B condition than in the NL condition, whereas the results showed the opposite pattern. It could also be that the effect is more to do with mental imagery or the instruction leading to better encoding of perceptual features and subsequently better performance in the L condition. Thus, the effect is indicative of successful encoding of the pictures into episodic LTM (see Hanslmayr et al., 2012). Arguably there are other, however less likely alternative explanations (e.g. global versus feature-based encoding, deeper versus shallower encoding, additional visual semantic processes etc.). Thus, there are clearly a number of potential cognitive functions underlying the observed alpha difference across the conditions. It also needs to be mentioned that the pattern of results cannot be fully explained by simply assuming more or less inhibition imposed by alpha oscillations in the service of modulating visual mnemonic processes during episodic encoding. This is because any such explanation would run into the problem of accounting for the complete lack of alpha power difference in the NL/W conditions. Clearly the NL condition requires more visual processes than the W condition, as the former required the processing of the pictures and the words, whereas the latter the words only. Therefore, one would fully expect some alpha power difference between the two conditions if the posterior alpha effects in the L/NL, B/ NL, L/W, and B/W comparisons had anything to do with the inhibition/ release of inhibition of visual or verbal processes. This absence of any significant alpha power difference in the NL/W condition cannot be resolved here. A further point which needs mentioning is that our arguments are in line with currently dominant views on the functional role of alpha synchronization, i.e. inhibition of neural processes (Jensen and Mazaheri, 2010; Klimesch et al., 2007); even though empirical evidence for that mostly comes from perception and working memory research. However, the question remains whether alpha synchronization is indeed purely an inhibitory mechanism across all cognitive and memory domains and brain areas. This assumption has been called into question arguing for facilitation as well as inhibition in cognitive processing by alpha synchronization (Palva and Palva, 2007) and sporadic empirical findings seems to support this claim (Johnson et al., 2011; Nenert et al., 2012). Importantly, animal research also suggests that alpha synchronization

Discussion The ability to remember the temporal relationship between an item and its context is essential for forming new episodic memories. In addition, such encoding is often initiated voluntarily and kept under conscious control to make sure that certain episodes are remembered. In the above reported study we show that voluntary attention allocation towards memorizing the temporal contiguity between an item and its context (i.e. towards LTM encoding) is associated with upper alpha desynchronization in predominantly left posterior brain regions. This rather late decreased alpha activity is likely an indicator of additional visual processes employed for consciously directed mnemonic processes and not so much item-context binding per se. Source localization suggests that this effect is particularly strong over a region at the occipitotemporo-parietal junction. In addition, we also found that picture encoding is associated with decreased power not in the alpha, but in the beta band over bilateral posterior electrode clusters. Posterior upper alpha band power Our main finding revealed that the allocation of attention towards the temporal conjunction of an item with its context during encoding was associated with posterior upper alpha desynchronization. That is when we compared the condition where the temporal link also had to be memorized compared to when only the item and context stimuli without this temporal link, we found a robust alpha desynchronization over posterior areas. The effect is also to some extent in line with alpha power changes due to attentional shifts in the perceptual (Thut et al., 2006), working memory (Bonnefond and Jensen, 2012) and LTM encoding domains (Jiang et al., 2015), most often indicating the release of inhibition. However, results of the follow-up analyses and also some of the characteristics of the main finding suggests a more complicated picture. To further understand our main finding - that is the 10–13 Hz posterior effect between the L and NL conditions - we conducted a series of scalp- and source-level analyses, which indicate that alpha desynchronization does not reflect the episodic linking per se. This is because a similar effect was found on scalp level when comparing the NL with the B condition which does not require episodic processing. In addition, when we further tested if the underlying sources were shared between the L/ NL and B/NL comparisons, we found that indeed despite some notable differences the alpha activity difference originated from largely common sources in both comparisons. Thus, even though on task level the critical episodic encoding L/NL comparison is clearly associated with posterior alpha desynchronization, the alpha effect is not unique to it. Meaning that even though alpha desynchronization does seem to support the formation of episodic memories by releasing inhibition over posterior areas, it is not exclusive to episodic memory encoding. In addition, further findings from the follow-up analyses showed that our main findings are not simply reflecting cognitive load or picture encoding. However, some of the characteristics (e.g. time-course) of the main alpha desynchronization effect gave important insight into the cognitive processes most likely associated with it. An important point to emphasize is that the alpha effect in both of these comparisons (L/NL and B/NL) appeared rather late during the trial sometime between 1 and 1.5 s after stimulus onset. That is the effects occur only very late during the encoding stage. Early perceptual processes should have been completed 314

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can be associated with increased neuronal firing and thus with the facilitation of visual processes in certain circumstances (Mo et al., 2011). Arguably most findings in the attention domain are in line with the alpha inhibition assumption (for review see e.g. Frey et al., 2015), but it is far from being clear if alpha synchronization is purely an inhibitory mechanism. A key question regarding our study is how the obtained relative alpha power differences relate to baseline alpha power. Put differently, whether there is synchronization or desynchronization in the L and NL conditions during encoding compared to the resting alpha level. This could show if for instance there is a release of alpha inhibition in one or both conditions, making the interpretation of the results easier in terms of inhibition/release of inhibition. In the current experiment this cannot be established as the inter-trial interval is too short and potentially contaminated with memory processes (as the experiment had a block design). The resting interval between blocks is also very noisy making it impossible to derive a reliable baseline alpha level. Finally, an important finding of our study is that a region that was unique to the linking of item and its context, as seen in the L/NL comparison, is at the intersection of the left occipital, parietal and temporal cortices in particular. This could indicate more neural activity in this area processing the linking of words with the background pictures than processing the two items separately, i.e. the binding of two items. This may suggest that this area is important in episodic long-term memory encoding and in particular in the formation of associations between an item and its context, i.e. the binding. Alpha desynchronization in other posterior areas may not have been unique to the L/NL comparison, but should also be relevant for stimulus encoding and processing. In retrieval of episodic long-term memories alpha/beta desynchronization in sensory areas (i.e. visual areas) have been shown to be especially important (Waldhauser et al., 2016) indicating sensory reactivation of memories in early visual areas. Thus the alpha desynchronization of large posterior areas including sensory areas during encoding may be critical in storing of perceptual details of the episode.

subsequent memory effect (SME). To examine SME in our study is simply not possible due to the limitation of trial numbers. When designing the experiment we opted for examining also the B and W conditions to potentially understand the effect of attention orientation in more detail, as opposed to trying to examine SME. It is also an outstanding question how well our methods can access activity in and around the MTL. With relation to the number of trials per condition, it is also impossible to tell if the reported posterior alpha effect correlates with the recognition of pictures, words or the linking. Clearly, further research is needed to resolve these questions. Finally, we detected no gamma band activity difference between the L and NL conditions, despite the fact the gamma activity is commonly thought to indicate increased local neuronal activity. Again, it is not possible to tell whether the effect is not there or we just could not detect it. With small amplitude high frequency activity it is very much possible that the signal-to-noise ratio was just too low and with a higher number of trials per condition it should be possible to answer empirically.

Posterior lower beta band power

Appendix A. Supplementary data

An incidental finding i.e. the higher beta power in posterior areas during W trials than in other condition highlights the role of posterior beta activity in encoding. The low beta frequency range (14–20 Hz), where this effect fell into, is again usually interpreted as inhibition of neural processes, just like the alpha band. Considering that the beta power difference was over posterior areas it is likely an indicator of the inhibitory processes in the W condition and/or the release of inhibition in the other conditions (L, NL, B) to facilitate picture encoding and memorizing. Again, this would need further testing to establish the nature of this effect. Beta power changes over posterior areas have been reported previously in combination with alpha inhibitory processes during WM processes (Medendorp et al., 2007). In our study, a posterior beta but not alpha power difference was found in the NL/W trials comparison and so beta oscillatory activity may indicate neural processes independent from alpha oscillations. Importantly, beta synchronization in posterior (and frontal) areas has been shown to be relevant in the maintenance of verbal information in a WM task (Hwang et al., 2005; Shahin et al., 2009) and in sentence processing (Bastiaansen et al., 2010), hence our interpretation favors beta power change in association with verbal processes.

Supplementary data related to this article can be found at https://doi. org/10.1016/j.neuroimage.2017.10.064.

Conclusions In sum, our results indicate that voluntary attention allocation to item-context binding is associated with posterior alpha desynchronization. These correlates of directed attention are likely indicative of increased visual processing during episodic encoding. However, our results also suggest that the relative alpha desynchronization in our memory encoding task is a very complex mechanism and its role in LTM encoding is far from fully understood. Finally, inhibition or facilitation of verbal encoding in our task is associated not with alpha but with posterior beta oscillations. Acknowledgement This study was supported by a grant from the German Research Foundation (DFG; SA1872/2-1).

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