Memories, both the mundane and the significant, play an integral role in our daily lives. Scientists have long sought to establish exactly how our memories are formed; how does an experience, with its sights, sounds and feelings, become a mental representation stored within our brain?

One way to investigate this question is to look at the activity of different parts of the brain. Brain imaging techniques have helped researchers identify two key brain regions that are involved in the process of memory formation: the neocortex and the hippocampus. The neocortex forms the outer layer of the brain, and performs complex tasks such as decision-making and language comprehension. The hippocampus, which sits deeper within the brain, deals primarily with memory and navigation. Research has shown that memory formation depends on communication between the neocortex and the hippocampus. However, scientists suspected that additional structures located beneath the neocortex—among them, the anterior thalamic nuclei (ATN)—are also crucial for forming memories. This has been difficult to confirm as the small size of the ATN, and their location deep within the brain, make their activity almost impossible to monitor using standard brain imaging techniques.

One way reliable data can be recorded from the ATN is by inserting electrodes into the brain. Brain surgery of course cannot be carried out on healthy human participants, but occasionally an opportunity arises to study the brain activity of patients who have electrodes inserted for therapeutic purposes. For example, in cases where a patient's epilepsy does not respond to conventional treatments, electrodes may be implanted to electrically stimulate the ATN in an attempt to improve their symptoms.

Sweeney-Reed et al. asked eight volunteers to perform a memory task, and monitored the activity of each volunteer's ATN via electrodes that had already been implanted in their brain to treat epilepsy. Simultaneously, electrodes attached to the scalps of the volunteers recorded the activity of the neocortex. When a memory was successfully stored in the brain, the activity of the two regions became synchronized. This suggests that successful memory formation depends upon communication between the ATN and the neocortex.

While the involvement of the ATN in human memory formation has long been a topic of speculation, Sweeney-Reed et al. now provide direct biological evidence for its crucial role in the process. Consequently, future research into memory formation should focus upon the ATN in addition to the more familiar structures of the neocortex and the hippocampus.

The anterior thalamic nuclei (ATN) are thought to play an important role in an extended hippocampal network central to memory formation (encoding) and novelty processing, which coordinates synaptic changes involving widespread neocortical areas, enabling life events to be recorded and later reinstated (Knight, 1996; Aggleton et al., 2010; Nyhus and Curran, 2011; Ritchey et al., 2013; Schott et al., 2013). While corticothalamic interactions are well-known to be crucial for adaptive behavior (Saalmann et al., 2012), the functional role of the ATN in humans has resisted investigation with non-invasive techniques owing both to its depth and small size. Here we had the rare opportunity to record electrophysiological activity during memory encoding directly from the ATN and dorsomedial thalamic nuclei (DMTN) of eight epileptic human volunteers with electrodes implanted for epilepsy treatment, as well as from frontal (and in two cases, parietal) scalp electrodes, reflecting neocortical activity (Figure 1). The DMTN are thought to be involved in executive control during memory retrieval (Van der Werf et al., 2003) and were hypothesized to play a lesser role than the ATN during encoding.

Figure 1

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Indication that the ATN has a role in memory processing comes from human lesion and animal studies. Damage to the human ATN results in amnesia for new episodes (Harding et al., 2000; Van der Werf et al., 2003; Aggleton et al., 2010), and reciprocal frontal and parietal connections with the ATN (Aggleton, 2012) have led to the hypothesis that ATN-hippocampal connections play a regulatory role in encoding (Vertes et al., 2001; Aggleton et al., 2010). Notably, 75% of ATN oscillatory power in non-human animal studies is in the theta (4–8 Hz) range, the dominant hippocampal rhythm (Vertes et al., 2001), which is also implicated in ATN-hippocampal communication (Vertes et al., 2001; Aggleton et al., 2010).

Neural communication and synaptic plasticity rely on long-range phase–phase and phase–amplitude synchrony of neural oscillations (Lachaux et al., 1999; Lisman and Jensen, 2013). Fronto-hippocampal theta synchrony accompanies memory formation (Benchenane et al., 2010; Nyhus and Curran, 2011). Furthermore, theta synchrony is reported to bind medial temporal (MT) and parietal areas during associative encoding (Crespo-Garcia et al., 2010), and MT with frontal and parietal cortex during successful retrieval (Watrous et al., 2013). Neocortical and hippocampal gamma (>30 Hz) oscillations appear to reflect local processing related to activation and maintenance of neuronal object representations (Jensen et al., 2007), binding diverse perceptual and contextual information (Nyhus and Curran, 2011). Spatially separate gamma oscillations have been found to be locked to the phase of the theta oscillation, supporting binding of the coherent ensemble underlying a given memory trace (Mizuhara and Yamaguchi, 2011). Indeed such gamma-power-to-theta-phase cross-frequency coupling (CFC) has been identified within human neocortex during word recognition (Canolty et al., 2006), working memory maintenance (Axmacher et al., 2010), and in successful long-term memory encoding (Friese et al., 2012), as well as within the rat hippocampus (Colgin et al., 2009), and has been proposed as a mechanism for transiently coupling distributed cortical activity (Canolty et al., 2006; Friese et al., 2012; Lisman and Jensen, 2013).

Together, these findings suggest that neocortical-ATN communication might be related to local ATN processing during encoding. We hypothesized that neocortical-ATN theta phase synchrony, and the relationship between theta phase and local ATN gamma amplitudes, would be critical for memory encoding.

We assessed the role of the ATN in memory encoding by contrasting electrophysiological activity during successful compared with unsuccessful encoding of serially presented photographs of 200 complex indoor and outdoor scenes. Participants judged whether each photograph depicted an indoor or an outdoor scene. Successful encoding was defined as correct recognition of a scene as old on a subsequent recognition memory test combining photographs of old and similar new scenes. All eight participants were able to discriminate old from new scenes (Table 1). Mean successful encoding across participants was 55%, and mean unsuccessful encoding was 45%, resulting in means of 101 and 87 observations per category, respectively, for EEG analysis.

All significance tests reported here were two-tailed. Response times for the indoor/outdoor judgment at encoding were compared between successfully (group mean = 1.04 s) and unsuccessfully (group mean = 1.14 s) encoded scenes for the seven participants for whom the paradigm was identical (see ‘Materials and methods’), and no difference was detected (paired T-test: T = 1.81, p = 0.12, with 6° of freedom, DF). For each participant, the difference between their expected probability of sequential successful encoding, calculated according to their overall rate of successful encoding, and their observed probability of sequential successful encoding, was calculated. The mean difference between these probabilities across the group did not significantly differ from zero (one-sample T-test: T = 1.35, p = 0.22, with 7 DF). Both these behavioral findings suggest that the neural findings to be reported did not reflect simple global attentional fluctuations (see ‘Discussion’).

We contrasted key oscillatory features of the EEG (512 Hz sampling frequency) during successful compared with unsuccessful encoding. To assess long-range communication, we calculated an amplitude-independent measure of phase synchrony (Lachaux et al., 1999) of oscillations in local field potentials recorded from thalamus and neocortex. We then employed CFC to assess the relationship between long-range communication and local neural processing, and Granger causality (GC) to assess likely direction of influence.

Mean fronto-thalamic phase-locking values (PLVs) across all eight participants during encoding are plotted against time and frequency in Figure 2. Corticothalamic theta synchrony differences in a late (0.5–1.5 s) time window were predicted, given the theoretical importance of theta oscillations in memory, and the typical late timing of encoding-related differences in frontal and parietal event-related potentials (ERPs) (Schott et al., 2002) and in post-stimulus MT theta oscillatory power (Hanslmayr and Staudigl, 2014). Frontal-right-ATN (RATN) theta synchrony was indeed greater during successful than unsuccessful encoding at 5–6 Hz between 0.5 and 1.5 s after picture presentation (permutation tests, PT: p = 0.001; paired T-tests, TT: T = 9.9, p = 0.000022; Figure 2, Figure 2—figure supplement 1). Conservative false discovery rate correction (PT; 57 frequencies, 0–100 Hz; 1024 data-points, 0–2 s) yielded a threshold of p = 0.0019 (overall criterion p = 0.05). Cluster-size PT (CSPT) on binary TT outcomes (significant/nonsignificant at p = 0.05) showed the second cluster of this late theta synchrony to be significant (∼1.0–1.5 s; p = 0.016; observed contiguous cluster 437 pixels; criterial cluster 285 pixels for overall p = 0.05). Synchrony at 5.2 Hz, averaged from 0.5 to 1.5 s for each participant, was greater during successful than unsuccessful encoding in seven of eight participants (Figure 2E), yielding a group difference in median synchrony (Wilcoxon test: p = 0.038). Participant 4 (Figure 2E) also showed the difference from 1 to 1.5 s (Figure 2F, Figure 2—figure supplement 2), so that all eight participants showed a difference in this time-window. The time course of theta synchrony is shown in Figure 2—figure supplement 3. Compared with surrogate data (Theiler et al., 1992), the PLV was significant (criterion p = 0.05) for 230 ms, which is more than a complete theta cycle at 5 Hz. The timescale is of the order of that over which synchrony is commonly detected (Varela et al., 2001). Note from Figure 2F and Figure 2—supplement 2 that theta synchrony was greater during successful than unsuccessful encoding in all eight participants from 1.25–1.5 s post-stimulus, and the appearance of two separate episodes at the group level is likely to have arisen due to inter-participant differences in the timing of the synchrony episode over the 1 s time window from 0.5–1.5 s post-stimulus.

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To further test frequency-specificity, a two-way repeated measures analysis of variance was applied to frontal-RATN PLVs obtained using theta (5.2 Hz) and beta (17.5 Hz) wavelets averaged from 0.5 to 1.5 s for each participant during successful and unsuccessful encoding. The mean PLVs showed a significant interaction between frequency and encoding success (p < 0.001). This interaction remained significant when taking adjacent theta (4.9 Hz and 5.5 Hz) and beta (16.5 Hz and 18.6 Hz) wavelets, and taking theta (5.2 Hz) and alpha (11.7 Hz) wavelets (all ps < 0.002). In all cases the advantage for successful over unsuccessful encoding for theta was greater than for alpha or beta, which showed negligible differences. The interaction was not significant when taking two theta wavelets (4.9 Hz and 5.2 Hz: p > 0.30; 5.2 Hz and 5.5 Hz: p > 0.60). No significant difference was observed between delta (2–4 Hz) phase synchrony during successful compared with unsuccessful encoding.

We did not observe corresponding significant synchrony differences for DMTN or left ATN (Figure 2—figure supplement 4). Indeed, on direct comparison, the difference in theta (5.2 Hz) PLVs between successful and unsuccessful encoding in the RATN 0.5–1.5 s post-stimulus was greater than the difference in the right DMTN (Wilcoxon test: p = 0.018), supporting nucleus specificity. A direct comparison of the relevant difference in theta PLVs between RATN and LATN only approached significance (Wilcoxon test: p = 0.130). While we do not wish to make strong claims about laterality, the finding that the synchrony difference in RATN was significant, whereas that in LATN was not (Figure 2—figure supplement 4), accords well with the non-verbal scene stimuli used (Maillard et al., 2011) (see also ‘Discussion’).

There was additional early upper theta and alpha synchrony during successful compared with unsuccessful encoding (∼8–12 Hz; 0–0.2 s; PT: p = 0.002; TT: T = 5.6, p = 0.00085; CSPT: p = 0.029; observed contiguous cluster 342 pixels; Figure 2, Figure 2—figure supplement 1), which might reflect enhanced item-specific attention and perception during successful encoding (Düzel et al., 2005). All synchrony patterns were post-stimulus (Figure 2—figure supplement 5). There was a significantly greater synchrony difference between successful and unsuccessful encoding in upper theta/alpha post-stimulus (0–0.2 s) than in the same frequency range during the 1 s pre-stimulus period (Wilcoxon test: p = 0.039). Furthermore, the difference between pre-stimulus theta synchrony preceding successful compared with unsuccessful encoding was not significant (Wilcoxon test: p = 0.46), whereas post-stimulus, the synchrony difference between successful and unsuccessful encoding was significant (Wilcoxon test: p = 0.039).

Because parietal scalp signals could be recorded from only two participants due to post-operative dressing placement, these were also analyzed as individual cases (Figure 2—figure supplements 6–7). Parietal-RATN and frontal-RATN (consistent with the group data) theta synchrony 0.5–1.5 s post-stimulus were significantly enhanced individually during successful compared with unsuccessful encoding (permutation tests on individual epochs within participants: ps < 0.05).

Because the main synchrony findings occurred in theta between 0.5 and 1.5 s, further analyses focussed on this time-frequency range. CFC was greater during successful than unsuccessful encoding between frontal theta phase-troughs and RATN gamma (∼40–50 Hz) amplitude-peaks (TT, T = 4.9, p = 0.0018; CSPT: p = 0.038; observed contiguous cluster 14 pixels; criterial cluster 12 pixels for overall p = 0.05; Figure 3). The difference was absent for thalamic high gamma (up to 256 Hz) (Canolty et al., 2006).

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In contrast to the frontal-RATN findings, within the RATN, theta phase-peaks were coupled with gamma amplitude-peaks. Of note is that the gamma range was narrower (∼40–50 Hz compared with ∼40–70 Hz) during successful than during unsuccessful encoding, resulting in significantly greater within-RATN CFC involving higher gamma-frequency amplitudes during unsuccessful compared with during successful encoding (paired T-test, TT, T = 3.6, p = 0.0086; cluster-size permutation test, CSPT: p = 0.045; observed contiguous cluster 15 pixels; criterial cluster 14 pixels for overall p = 0.05; Figure 3—figure supplement 1A–C). The CFC patterns during successful and unsuccessful encoding were then compared directly. Within-RATN CFC during successful encoding was compared with a distribution of 1000 phase-scattered surrogates, and the theta-gamma (3.8–8.1 Hz, 30.5–64.6 Hz) cross-frequency points at which CFC during successful encoding exceeded a threshold with criterion p = 0.05 were identified. The same analysis was performed for unsuccessful encoding. The two resulting CFC patterns differed significantly (two-dimensional Kolmogorov–Smirnov test—2-D KS test: d = 0.81, p = 0.048), with a wider gamma range during unsuccessful encoding. Assuming that an assembly of synchronously firing neurons is associated with a particular memory trace, the narrower RATN gamma range coupled with theta phase during successful memory formation could be interpreted as reflecting firing of only relevant neural assemblies, thus reflecting neural specificity during encoding (Desimone, 1996; Düzel et al., 2005; Schott et al., 2006). We correspondingly postulate that the corticothalamic coupling may coordinate the firing of particular thalamic neural assemblies underpinning the memory to be encoded, facilitating synaptic strengthening and relevant memory formation. Indeed, ongoing CFC has been detected in the centromedian thalamic nucleus during cognitive task performance (Fitzgerald et al., 2013). Again, there was no encoding-related CFC difference apparent for thalamic high gamma (Canolty et al., 2006).

CFC findings within the frontal cortex (Figure 3—figure supplement 1D–F) were consistent with the literature. Theta-to-gamma CFC was peak-to-peak at higher theta frequencies, and occurred for both successful and unsuccessful encoding (phase-scattered surrogate-data tests for successful and for unsuccessful encoding: p = 0.001). There was no significant relationship to encoding success (TT, p > 0.05; CSPT: p > 0.05; Figure 3—figure supplement 1D–F), consistent with hippocampal CFC patterns during working memory (Axmacher et al., 2010). By contrast, theta-to-gamma CFC was trough-to-peak at lower theta frequencies, and was greater during successful than unsuccessful encoding (TT, T = 3.6, p = 0.0087; CSPT: p = 0.017; observed contiguous cluster 19 pixels; criterial cluster 11 pixels for overall p = 0.05), consistent with (Canolty et al., 2006).

Theta-gamma CFC patterns within RATN and within frontal neocortex differed from the frontal-RATN pattern (Figure 3—figure supplements 1–2). Frontal-RATN theta-gamma (3.8–8.1 Hz, 30.5–64.6 Hz) coupling during successful vs unsuccessful encoding was compared using the above paired T-tests, to provide the cross-frequency pattern, and the same was performed for within-RATN CFC. These CFC patterns differed significantly (2-D KS test: d = 0.83, p = 0.024). Frontal-RATN and within-frontal CFC patterns also differed significantly (2-D KS test: d = 0.98, p = 0.011).

GC revealed that frontal theta better predicted RATN theta than vice-versa (Figure 4). In GC analyses, the peak model order provides an indication of how far into the past one signal provides information about another, and may thus be interpreted as indicating the approximate delay in transfer of information (Staudigl et al., 2012). We found that frontal theta prediction of RATN theta peaked at a model-order of 32, which corresponds to 63 ms, or one third of a theta cycle phase-lag between frontal and RATN theta. Such a delay is broadly consistent with frontal-RATN theta-to-gamma CFC being trough-to-peak (Figure 3), and intra-RATN theta-to-gamma CFC being peak-to-peak (Figure 3—figure supplement 1A–C), illustrated in Figure 3—figure supplement 2. Together, the CFC and GC findings suggest that frontal theta modulates RATN gamma via frontal-ATN theta synchrony during successful encoding.

Figure 4

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We demonstrate increased corticothalamic synchrony during successful memory encoding, recording directly from the two most memory-relevant thalamic nuclei (ATN and DMTN). Our amplitude-independent phase synchrony measurements (Lachaux et al., 1999) show that timing of post-stimulus ATN theta activity alone (see also Figure 2—figure supplement 8), and its relation to local ATN processing as indexed by gamma amplitudes, is critical in successful memory encoding, providing the first electrophysiological evidence concerning the role of ATN in human memory formation. The absence of neocortical-DMTN synchrony differences, together with their recent detection during verbal memory retrieval (Staudigl et al., 2012), fits with evidence suggesting ATN specialization for encoding (Harding et al., 2000; Van der Werf et al., 2003), and DMTN for retrieval (Harding et al., 2000; Van der Werf et al., 2003; Aggleton et al., 2010; Aggleton, 2012). RATN involvement is moreover consistent with the non-verbal scene stimuli employed (Maillard et al., 2011), and with evidence that left thalamus lesions produce more severe memory deficits for verbal than non-verbal material (Squire et al., 1989). The late theta synchrony is in accord with the timing of differences in ERPs (Schott et al., 2002) and post-stimulus MT theta power (Hanslmayr and Staudigl, 2014) during successful compared with unsuccessful encoding of episodic memories. While oscillations in the delta frequency range have recently been found to show a memory-related difference in the hippocampus (Watrous et al., 2011; Lega et al., 2014), we did not detect a significant difference between corticothalamic delta phase synchrony during successful compared with unsuccessful encoding.

While we provide here novel evidence that the coordination of theta and gamma oscillations involving the ATN plays a critical role in trial-by-trial memory ability, it should be noted that memory encoding is recognized to include not only content processing and information storage, but also attention (reviewed by Kim, 2011). Item-specific attention indeed has a well-recognized effect on whether an item is successfully encoded (Raz and Buhle, 2006; Muzzio et al., 2009; Kim, 2011; Burke et al., 2014). Attention is not only necessary for optimal memory encoding, but multiple brain structures involved in memory formation may be subject to attentional modulation, including the hippocampus itself (Muzzio et al., 2009). There are several indicators, however, that our findings reflect memory encoding processes beyond simply global arousal or attention fluctuations.

Firstly, the timing of the key memory differences here at around 1 s post-stimulus is strongly suggestive of a memory-related difference. Neural encoding differences related to later memory have been identified using ERPs and cortical theta/gamma oscillations, generally peaking at around 1 s after stimulus presentation, continuing up to 1.5 s post-stimulus (Paller et al., 1987; Schott et al., 2002; Sederberg et al., 2003; Osipova et al., 2006; Lega et al., 2012; Hanslmayr and Staudigl, 2014; Long et al., 2014). Secondly, whereas oscillatory dynamics between the pulvinar thalamic nucleus and the parietal cortex support visual attention in macaques, consistent with attention deficits following focal pulvinar lesions (Saalmann et al., 2012), our main findings pertain to the ATN, for which lesion and animal studies suggest a role in memory. Moreover, based on current knowledge about the ATN and the DMTN (Van der Werf et al., 2003) one would rather expect attentional differences to be reflected in DMTN activity, which we did not find. Most critically, if global attentional fluctuations were responsible for our theta synchrony findings, one would expect longer response times during the encoding phase for later forgotten compared with later remembered scenes, due to lack of attention to the encoding task. However, response times did not differ. Finally, global attentional fluctuations over time would also imply a dependency between the probability of successful encoding on successive trials during the study phase of the experiment, and we found no dependency.

We note, however, that variability in factors such as attention and the emotional valence of stimuli are fundamental to the study design in subsequent memory paradigms, affecting the probability of successfully encoding each item and thus enabling comparison of successful with unsuccessful encoding. Indeed, the early upper theta and alpha synchrony immediately following stimulus presentation during successful memory encoding is likely to reflect enhanced item-specific attention and perception (Düzel et al., 2005), with the later theta synchrony timing fitting well with previous findings relating to memory encoding (Schott et al., 2002; Hanslmayr and Staudigl, 2014).

The ATN is the target of stimulation in the treatment of focal epilepsy on the basis that seizure activity starting focally is propagated through this site to widespread cortical areas (Lega et al., 2010). Regional cortical specificity in frontal-RATN synchrony would support our argument that the differences that we have identified are memory-specific. While the phase synchrony pattern we report is discernible in all three electrode placements (frontopolar, other frontal, and parietal), it is indeed not identical across regions (Figure 2—figure supplement 9). We also show the difference between fronto-RATN theta-gamma CFC involving the different frontal electrode placements (Figure 3—figure supplement 3). Again, the patterns are similar but not identical. The limited available scalp electrode coverage in our participants, however, precludes drawing strong conclusions in this regard. We note also, though, that both frontal and parietal cortices are well-recognized as being involved in memory processing (Weiss and Rappelsberger, 2000; Otten et al., 2002; Sauseng et al., 2005; Uncapher and Wagner, 2009; Friese et al., 2012; Sweeney-Reed et al., 2012).

The encoding and retrieval tasks were chosen for their simplicity, because recording was only possible at the bedside in the few days following intracranial surgery, and because we did not wish to lose data owing to failure of these rare participants to succeed at the tasks. Under these circumstances, we were able to achieve large and comparable behavioral trial numbers for successful and unsuccessful encoding, which could then be submitted to oscillatory analysis. Pilot testing revealed that one participant (not included in the current cohort) could not adequately perform an objective source-memory task under these post-operative conditions, and we thus additionally judged it unlikely that the participants could properly implement the more complex ‘remember/know/guess’ instructions (Gardiner and Richardson-Klavehn, 2000) necessary to obtain reports of subjective recollection and familiarity during recognition. Moreover, given the nature of the data ultimately obtained from our eight participants, we elected to collapse the data across the levels of response certainty to maximize trial numbers in each category for electrophysiological synchrony analysis, in a pragmatic trade-off between the power of the electrophysiological analyses and behavioral/psychological resolution.

Thus, a limitation of the study is that we cannot conclusively link our results to recollection, and therefore episodic memory formation, separately from familiarity. Our findings concerning ATN electrophysiological involvement in human memory formation are, however, consistent with the theoretical proposal that the ATN is a part of an extended hippocampal system supporting episodic recollection (Aggleton, 2012), whether during encoding or retrieval, and with the competing theoretical proposal that the ATN is specialized for encoding rather than retrieval, whether the information involved is episodic (recollection) or semantic (Van der Werf et al., 2003). The latter proposal is consistent with human lesion data, showing that sufferers from Korsakoff's syndrome with lesions to the ATN fail to acquire new semantic as well as new episodic information (for example, Harding et al., 2000) and is thus particularly consistent with our new data. We note, furthermore, that whether recollection and familiarity are separate processes (Eichenbaum et al., 2008), or reflect a single process, with familiarity and recollection reflecting different degrees of memory strength (Wixted and Squire, 2008), is an area of continuing debate (see also Gardiner and Richardson-Klavehn, 2000 and Yonelinas, 2002) for relevant information regarding humans. Despite this lack of behavioral/psychological resolution, our data nevertheless provide novel evidence concerning the real-time role of the human ATN in memory formation.

It should also be noted that memory processing may be divided into different subsystems, with different components of memory processing involving different thalamic nuclei (Mennemeier et al., 1992). For example, whereas human lesion and animal studies suggest a regulatory role for the ATN in memory encoding (Harding et al., 2000; Vertes et al., 2001; Van der Werf et al., 2003; Aggleton et al., 2010; Aggleton, 2012) and for the DMTN in retrieval (Van der Werf et al., 2003; Staudigl et al., 2012), other thalamic nuclei have also been found to be involved in different aspects of memory processing. For example, evidence supports a role for the nucleus reuniens in fear conditioning and memory generalization (Vertes et al., 2007; Xu and Südhof, 2013), as well as spatial processing (Jankowski et al., 2014), for the pulvinar nucleus in attention (Saalmann et al., 2012) and nonverbal memory processing (Johnson and Ojemann, 2000), and for the left ventro-lateral thalamus in verbal memory encoding (Johnson and Ojemann, 2000). Our focus on the ATN and DMTN is based on the rare availability of human electrophysiological data from these sites, which was determined by clinical requirements, and is consistent with extant data concerning the amnesic effects of lesions in these thalamic areas in humans (Harding et al., 2000; Van der Werf et al., 2003).

In summary, our findings shed new light on real-time interaction between the ATN and the neocortex, thus broadening understanding of the brain structures involved in memory formation from a focus on the hippocampus and neocortex to recognition of a pivotal role for the ATN. More generally, the CFC theta-gamma findings, together with the amnesic effects of lesions to the human ATN, provide evidence that the ATN plays an active role in encoding, instead of simply relaying cortical or hippocampal signals (Aggleton et al., 2010).

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Author details

1. Catherine M Sweeney-Reed

   Departments of Neurology and Stereotactic Neurosurgery, Otto von Guericke University, Magdeburg, Germany

   Contribution

   CMS-R, Conceived and designed the research, Prepared the experiment, Analyzed and interpreted the behavioral and electrophysiological data, including writing data-analysis programs, Drafted and revised the article

   For correspondence

   catherine.sweeney-reed@med.ovgu.de

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-3684-1245

2. Tino Zaehle

   Departments of Neurology and Stereotactic Neurosurgery, Otto von Guericke University, Magdeburg, Germany

   Contribution

   TZ, Acquired the behavioral and electrophysiological data, Revised the article

   Competing interests

   The authors declare that no competing interests exist.

3. Juergen Voges

   1. Departments of Neurology and Stereotactic Neurosurgery, Otto von Guericke University, Magdeburg, Germany
   2. Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany

   Contribution

   JV, Acquired data: selected and provided the technical facilities used for the human stereotactic neurosurgery and intracranial recording, recommended and supervised patient treatment with deep brain stimulation, surgically implanted the intracranial electrodes, Analyzed and interpreted data: localized the intracranial electrodes, Revised the article

   Competing interests

   The authors declare that no competing interests exist.

4. Friedhelm C Schmitt

   Departments of Neurology and Stereotactic Neurosurgery, Otto von Guericke University, Magdeburg, Germany

   Contribution

   FCS, Acquired data: diagnosed the patients, recommended and supervised patient treatment with deep brain stimulation, Revised the article

   Competing interests

   The authors declare that no competing interests exist.

5. Lars Buentjen

   Departments of Neurology and Stereotactic Neurosurgery, Otto von Guericke University, Magdeburg, Germany

   Contribution

   LB, Analyzed and interpreted data: localized the intracranial electrodes, Revised the article

   Competing interests

   The authors declare that no competing interests exist.

6. Klaus Kopitzki

   1. Departments of Neurology and Stereotactic Neurosurgery, Otto von Guericke University, Magdeburg, Germany
   2. Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany

   Contribution

   KK, Analyzed and interpreted data: localized the intracranial electrodes, Revised the article

   Competing interests

   The authors declare that no competing interests exist.

7. Christine Esslinger

   Departments of Neurology and Stereotactic Neurosurgery, Otto von Guericke University, Magdeburg, Germany

   Contribution

   CE, Analyzed and interpreted data: localized the intracranial electrodes, Revised the article

   Competing interests

   The authors declare that no competing interests exist.

8. Hermann Hinrichs

   1. Departments of Neurology and Stereotactic Neurosurgery, Otto von Guericke University, Magdeburg, Germany
   2. Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany
   3. German Centre for Neurodegenerative Diseases, Magdeburg, Germany

   Contribution

   HH, Acquired data: selected and provided the technical facilities used for the human stereotactic neurosurgery and intracranial recording, Analyzed and interpreted data: commented on the data analysis, Revised the article

   Competing interests

   The authors declare that no competing interests exist.

9. Hans-Jochen Heinze

   1. Departments of Neurology and Stereotactic Neurosurgery, Otto von Guericke University, Magdeburg, Germany
   2. Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany
   3. German Centre for Neurodegenerative Diseases, Magdeburg, Germany

   Contribution

   H-JH, Acquired data: selected and provided the technical facilities used for the human stereotactic neurosurgery and intracranial recording, Analyzed and interpreted data: commented on the data analysis, Revised the article

   Competing interests

   The authors declare that no competing interests exist.

10. Robert T Knight

    Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, Berkeley, United States

    Contribution

    RTK, Analyzed and interpreted data: advised on the data analysis, Advised on and revised the article

    Competing interests

    The authors declare that no competing interests exist.

11. Alan Richardson-Klavehn

    1. Departments of Neurology and Stereotactic Neurosurgery, Otto von Guericke University, Magdeburg, Germany
    2. Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany

    Contribution

    AR-K, Conceived and designed the research, Analyzed and interpreted the behavioral and electrophysiological data, Drafted and revised the article

    For correspondence

    alan.richardson-klavehn@med.ovgu.de

    Competing interests

    The authors declare that no competing interests exist.

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