Cogn Process DOI 10.1007/s10339-014-0605-5
RESEARCH REPORT
Modification of EEG power spectra and EEG connectivity in autobiographical memory: a sLORETA study Claudio Imperatori • Riccardo Brunetti • Benedetto Farina Anna Maria Speranza • Anna Losurdo • Elisa Testani • Anna Contardi • Giacomo Della Marca
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Received: 3 June 2013 / Accepted: 24 February 2014 Ó Marta Olivetti Belardinelli and Springer-Verlag Berlin Heidelberg 2014
Abstract The aim of the present study was to explore the modifications of scalp EEG power spectra and EEG connectivity during the autobiographical memory test (AM-T) and during the retrieval of an autobiographical event (the high school final examination, Task 2). Seventeen healthy volunteers were enrolled (9 women and 8 men, mean age 23.4 ± 2.8 years, range 19–30). EEG was recorded at baseline and while performing the autobiographical memory (AM) tasks, by means of 19 surface electrodes and a nasopharyngeal electrode. EEG analysis was conducted by means of the standardized LOw Resolution Electric Tomography (sLORETA) software. Power spectra and lagged EEG coherence were compared between EEG acquired during the memory tasks and baseline recording. The frequency bands considered were as follows: delta (0.5–4 Hz); theta (4.5–7.5 Hz); alpha (8–12.5 Hz); beta1 (13–17.5 Hz); beta2 (18–30 Hz); gamma (30.5–60 Hz). During AM-T, we observed a significant delta power increase in left frontal and midline cortices (T = 3.554; p \ 0.05) and increased EEG connectivity in delta band in prefrontal, temporal, parietal, and occipital areas, and for gamma bands in the left temporo-parietal regions (T = 4.154; p \ 0.05). In Task 2, we measured an increased power in the gamma band located in the left
C. Imperatori R. Brunetti B. Farina (&) A. Contardi Department of Human Science, European University of Rome, Rome, Italy e-mail:
[email protected] A. M. Speranza Department of Dynamic and Clinical Psychology, Sapienza University, Rome, Italy A. Losurdo E. Testani G. Della Marca Department of Neurosciences, Catholic University, Rome, Italy
posterior midline areas (T = 3.960; p \ 0.05) and a significant increase in delta band connectivity in the prefrontal, temporal, parietal, and occipital areas, and in the gamma band involving right temporo-parietal areas (T = 4.579; p \ 0.05). These results indicate that AM retrieval engages in a complex network which is mediated by both low- (delta) and high-frequency (gamma) EEG bands. Keywords Autobiographical memory sLORETA EEG power spectra EEG connectivity
Introduction Autobiographical memory (AM) is a complex form of explicit memory referring to the ability to remember events from one’s own life, and it is believed to be a dynamic integration between episodic memory (EM) and semantic memory (SM) (Cabeza and St Jacques 2007; Levine et al. 2004). EM is defined as a system characterized by an autonoetic consciousness, reflecting the ability to remember unique past events together with their associated contextual details (Tulving 1985, 1987). In contrast, SM is characterized by noetic consciousness, and it refers to the knowledge of facts about the world and about our life (Tulving 1985, 1987). Autobiographical memories are characterized by both elements of these memory systems such as vivid and emotional images (EM) of people during a particular event (i.e., high school final examination) with the knowledge about concepts and facts related to this special day (SM). Retrieving autobiographical information involves two different phases: event construction and elaboration (Conway et al. 2001, 2003; Daselaar et al. 2008; Holland et al. 2011).
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The first one is characterized by a dynamic and inferential process based on an initial cue, and by a subsequent continuous monitoring and evaluation of the search results; the second phase is characterized by the emerging of different aspects and details about the autobiographical recall kept in mind (Holland et al. 2011). The complexity of the AM is reflected by its neurobiological underpinnings. The study of AM neural substrates has received significant attention in the last decade, and recent neuroimaging investigations (Cabeza and St Jacques 2007; Holland et al. 2011; Rugg and Vilberg 2013; Svoboda et al. 2006) offered the possibility to map a relatively consistent cooperation between different brain areas, predominantly left-lateralized, involving in particular the prefrontal cortex (PFC) and different structures of the mesial temporal lobe (MTL). Cabeza and St Jacques (2007), in a review of functional neuroimaging studies of AM, proposed a complex brain network in which ‘memory search process,’ ‘monitoring phase,’ and ‘self-referential process’ seem to involve, respectively, left lateral PFC, medial PFC, and ventromedial PFC. Moreover, the hippocampus and the retrosplenial cortex are involved in the recollection during event elaboration process, whereas the amygdala, the occipital area, and the cuneus/precuneus region are, respectively, involved in emotional processing and in visual imagery (Cabeza and St Jacques 2007). Several electrophysiology studies confirmed the involvement of PFC and MTL in AM. EEG and eventrelated potentials (ERP) studies documented modifications in brain electrical activity during the different phases of AM retrieval. Conway et al. (Conway et al. 2001, 2003) documented changes in slow cortical potentials during construction and event elaboration phases: the first is characterized by enhanced EEG activity in the left frontal lobe, whereas the second is characterized by enhanced EEG activity in posterior temporal and occipital lobes. Furthermore, Steinvorth et al. (2010), using intracranial ERP recordings in a single case, reported an increase in gamma, theta, and delta frequency bands in the left entorhinal cortex: gamma was predominant in superficial layers (which project to the hippocampus) during the presentation of the memory cue, whereas theta and delta were prolonged and dominant in deep layers (which project to neocortices) during memory retrieval. These authors also observed that this last activation pattern was exclusive for AM and was not observed in SM tasks (Steinvorth et al. 2010). Therefore, it has been proposed that changes in lowfrequency bands could endorse a long-range cortical interaction within complex and spread brain networks (Steinvorth et al. 2010; Toth et al. 2012). It has also been proposed that the encoding and maintenance of memory traces in MTL rely on the crucial modulation of brain
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activity in theta and gamma frequencies (Duzel et al. 2010; Steinvorth et al. 2010). The aim of the present study was to explore the modifications of scalp EEG power spectra and EEG connectivity during AM tasks. For these reason, we used the AM test (AM-T) (Williams and Broadbent 1986), one of the most common and validated tests used to study AM both clinically and experimentally (Van Vreeswijk and De Wilde 2004; Williams et al. 2007). Furthermore, in order to explore the AM network organization in a more ecological situation, we also explored the EEG modifications during the retrieval of a single autobiographical event (namely, the high school final examination, Task 2), based on concrete questions (‘what,’ ‘where,’ ‘who’). In order to detect modifications of EEG frequencies, and their topographic distribution, we used the standardized LOw Resolution brain Electric Tomography (sLORETA) software, a validated method for localizing the electric activity in the brain based on multichannel surface EEG recordings (Pascual-Marqui et al. 1994). Furthermore, a nasopharyngeal (NP) electrode was introduced via the nostril and positioned with the tip touching the posterior pharyngeal wall. This electrode is known to record EEG activity originating from MTL structures (Zijlmans et al. 2008); in this way, we were able to collect the signal as close as possible to the MTL and achieve a more accurate source reconstruction for that region.
Materials and methods Seventeen healthy volunteers were enrolled for the experiment, 9 women and 8 men. Mean age was 23.4 ± 2.8 years (age range 19–30 years). The only inclusion criterion was the consent to participate. Exclusion criteria were as follows: left handedness; history of medical, psychiatric, and neurologic diseases; head trauma; assumption of central nervous system active drugs in the 3 weeks before the study; presence of EEG abnormalities at the baseline recording. The research was approved by the Catholic University’s and Universita` Europea’s ethics review boards. Both the ethics review boards issued a formal written waiver of informed consent because the research involved no more than minimal risk, and the data were analyzed anonymously. All subjects gave their written informed consent to participate. AM tasks After electrode montage, the subjects were invited to sit in a comfortable armchair, with eyes closed, in a quiet, semidark silent room for a 5-min resting EEG recording (baseline condition). Afterward, the subjects were
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instructed to perform the AM-T (Williams and Broadbent 1986): participants were asked to remain silent with their eyes closed and to recall several autobiographical episodic memories linked to 10 cue words. The cue words were read out loud by a researcher (B.F.): five positive words (happy, proud, faithful, tender, friendly) and five negative words (tired, ashamed, painful, sad, weakness) translated and adapted to Italian from Moradi et al. (2008). The AM-T lasted 5 min. Upon completing, the AM-T subjects were instructed to remain seated with their eyes closed for 5 min before starting the second task. In Task 2, participants were instructed to recall and concentrate on the autobiographical event of their high school exit examination. Subjects were asked to remember as many details as possible about that experience (e.g., people, emotions, venue, etc.). The subject was requested to perform the memory task for 5 min. At the end of this session, the subjects were asked whether they were able to successfully perform the task.
from the analysis. After artifact rejection, the remaining EEG intervals were exported into American Standard Code for Information Interchange (ASCII) files and imported into the sLORETA software. We analyzed segments of EEG recorded in baseline (BL) state and during tasks. Since the duration of both tasks (AM-T and Task 2) and BL was about 5 min, we decided to analyze at least 2 min of artifact-free recording (not necessarily consecutive) for each of the three condition (baseline, AM-T, Task 2), in all subjects. The average time analyzed was 146 ± 19 s. This procedure has been already used to investigate modifications of EEG power spectra during a working memory task (N-back) (Imperatori et al. 2013), and in order to assess EEG connectivity and EEG power spectra in dissociative disorders (Farina et al. 2013). All EEG analyses were performed by means of the sLORETA software (PascualMarqui et al. 1994).
EEG recordings
EEG frequency analysis was performed by means of fast Fourier transform algorithm, with a 2-s interval on the EEG signal, in all scalp locations. The following frequency bands were considered: delta (0.5–4 Hz); theta (4.5–7.5 Hz); alpha (8–12.5 Hz); beta1 (13–17.5 Hz); beta2 (18–30 Hz); gamma (30.5–60 Hz). For frequency analysis, monopolar EEG traces (each electrode referred to joint mastoids) were used. Topographic sources of EEG activities were determined using the sLORETA software. The sLORETA software computes the current distribution throughout the brain volume. In order to find a solution for the 3-dimensional distribution of the EEG signal, the sLORETA method assumes that neighboring neurons are simultaneously and synchronously activated. This assumption rests on evidence from single-cell recordings in the brain that shows strong synchronization of adjacent neurons (Kreiter and Singer 1992; Murphy et al. 1992). The computational task is to select the smoothest of all possible 3-dimensional current distributions, a common procedure in signal processing (Grave de Peralta-Menendez and Gonzalez-Andino 1998; Grave de Peralta Menendez et al. 2000). The result is a true 3-dimensional tomography, in which the localization of brain signals is preserved with a low amount of dispersion (Pascual-Marqui et al. 1994).
Continuous EEG recordings were performed at baseline and during the two AM tasks. EEG was recorded by means of a Micromed System Plus digital EEGraph (MicromedÓ S.p.A., Mogliano Veneto, TV, Italy). EEG montage included 19 standard scalp leads positioned according to the 10–20 system (recording sites: Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2), EOG, and EKG. Moreover, a NP electrode was used to record EEG activity in proximity of MTL structures. NP electrode was introduced via the nostril and positioned with the tip touching the posterior pharyngeal wall. The reference electrodes were placed on the linked mastoids. Impedances were kept below 5 KX before starting the recording and checked again at the end. In particular, impedances of the mastoids reference electrodes were checked to be identical. Sampling frequency was 256 Hz; A/D conversion was made at 16 bit; preamplifiers amplitude range was ±3,200 lV and low-frequency pre-filters were set at 0.15 Hz. The following band-pass filters were used: HFF = 0.2 Hz; LFF = 128 Hz. The line noise (in Italy: 50 Hz) was removed by using a 50 Hz notch filter. Offline artifact rejection (eye movements, blinks, muscular activations, or movement artifacts) was performed visually on the raw EEG trace, by posing a marker at the onset of the artifact signal and a further marker at the end of the artifact. Successively, the artifact segment (that is, the EEG signal interval included between the two markers) was deleted, and this cancellation involved all the EEG traces acquired within that interval. In this way, all the EEG intervals characterized by the presence of artifacts were excluded
Frequency analysis
Connectivity analysis The connectivity analysis was performed by the computation of lagged coherence. This approach allows to better evaluate ‘true’ connectivity. Two measures of coherence can be calculated between EEG signals: ‘lagged coherence’
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and ‘instantaneous coherence.’ These measures evaluate, respectively, the contribution of potentials generated by local neural networks and of potentials due to volume conduction. The lagged coherence is a much more appropriate measure of electrophysiological connectivity, because it removes the confounding effect of instantaneous dependence due to volume conduction and low spatial resolution (Pascual-Marqui 2007a). Furthermore, the lagged component is purely physiological and affected minimally by low spatial resolution, which affects the instantaneous component (Pascual-Marqui et al. 2011). However, other techniques such as the imaginary
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coherence have been previously developed (Nolte et al. 2004), it is important to note that when there exists a lagged connection, the imaginary part of the coherence fails to detect it by tending to zero if the instantaneous component is large. This is not the case for the lagged coherence which asymptotically tends to a nonzero value, detecting the presence of a physiological lagged connection (Pascual-Marqui et al. 2011). For these reasons in the present study, lagged coherence was calculated. The sLORETA software computes instantaneous coherence qxy(x), in the case of univariate series, by the formula (Pascual-Marqui 2007b):
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q2x:y ðxÞ ¼
½ReðSyxx Þ Syxx Sxxx
whereas the lagged coherence qx$y(x) is calculated by the formula (Pascual-Marqui 2007b): q2x$y ðxÞ ¼ 1 exp½Fx$y ðxÞ ¼1 Syyx Syxx Syyx O Sxyx Sxxx OT Sxxx Re Syyx Syxx Re Syyx O T Sxyx Sxxx O Sxxx
of the labels on the observed data points. Correction of significance for multiple testing was computed for the two comparisons between conditions for each frequency band: for the correction, we applied the nonparametric randomization procedure available in the sLORETA program package (Nichols and Holmes 2002). T-level thresholds were computed by the statistical software implemented in the sLORETA, which correspond to the statistical significance thresholds (p \ 0.05 and p \ 0.01) (for details see, Friston et al. 1990; Friston et al. 1991).
Results In this formula, ‘x’ is the discrete frequency considered, ‘Re’ indicates the real part of an element; Sxxx, Syyx, Sxyx, and Syxx denote complex valued covariance matrices. Fx$y(x) is the lagged linear dependence, ‘O’ is a matrix of zeros, and the superscript ‘T’ stands for ‘transposed.’ The EEG coherence analysis was performed on the same blocks of EEG tracings used for power spectra analysis. Coherence values were computed for each frequency band (delta, theta, alpha, beta, gamma), in the frequency range of 0.5–60 Hz. In order to evaluate the modifications of connectivity, 20 region of interests (ROIs) were defined: one in the temporal mesial region, placed on the midline, corresponding to the site of the NP electrode, and 19 for the scalp (one for each scalp electrode). In Fig. 1, we reported the sLORETA representation of the scalp electrodes. We chose the ‘single nearest voxel’ option: in this way, each ROI consisted of a single voxel, the one closest to each seed. Then, the sLORETA computed the coherence values between all these ROIs (total 20 9 20 = 400 connections). The sLORETA also computed the source reconstruction algorithm previously described (PascualMarqui and Biscay-Lirio 1993; Pascual-Marqui et al. 1994, 1995).
Statistical analysis Power spectra analysis and EEG connectivity (lagged coherence) were compared among conditions, for each frequency band. The conditions analyzed were three: baseline (BL), AM-T, and exam recall (Task 2). All comparisons were performed by using the statistical nonparametric mapping methodology supplied by the sLORETA (Nichols and Holmes 2002). This methodology is based on the Fisher’s permutation test: a subset of nonparametric statistics. In particular, this is a type of statistical significance test in which the distribution of the test statistic under the null hypothesis is obtained by calculating all possible values of the test statistic under rearrangements
EEG recordings suitable for the analysis were obtained in all cases. Visual evaluation of the EEG recordings showed no relevant modifications of the background rhythm frequency, focal abnormalities, or epileptic discharges. No subject showed evidence of drowsiness or sleep during the recordings. In a post-session interview, all subjects reported that they had no difficulties and no major distractions while performing the tasks. Despite our statistical tests were two-tailed, all our comparisons generated positive T values. Power spectra analysis In the comparison between AM-T and BL, the thresholds for significance were T = 3.554, corresponding to p \ 0.05, and T = 4.559, corresponding to p \ 0.01. Significant modifications were documented in the delta (0.5–4 Hz) frequency band: in the AM-T condition, increased power of delta activity was observed in frontal and midline cortices. sLORETA software localized these modification in the dorsolateral PFC (Brodmann areas, BA 9; T = 3.682, corresponding to p = 0,043), in anterior PFC (BA 10; T = 3.801, corresponding to p = 0.038), in orbitofrontal cortex (OFC, BA 11; T = 4.323, corresponding to p = 0.016), in ventral anterior cingulate cortex (ACC, BA 32, T = 4.204, corresponding to p = 0.021), and in anterior (dorsal and ventral) cingulate cortex (BA 24; T = 4.086, corresponding to p = 0.026) (Fig. 2a). In the comparison between Task 2 and BL, the thresholds for significance were T = 3.960, corresponding to p \ 0.05, and T = 4.872, corresponding to p \ 0.01. The only significant modification was observed in the gamma (30–60 Hz): during Task 2, an increase in gamma power was recorded in the posterior midline areas. sLORETA software localized this modification in left precuneus (BA 7; T = 4.169, corresponding to p = 0.025) (Fig. 2b). Finally, in the comparison between AM-T and Task 2, the thresholds for significance were T = 8.808,
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Fig. 2 Results of the sLORETA comparison of EEG power spectra in all frequency bands. Panel A AM-T versus Baseline; Panel B Task 2 versus Baseline. Colored spots indicate areas where statistically significant increase in delta EEG spectral power was measured. Levels of significance are represented in the center of the figure. Red colour indicates significant increase in power spectra; blue colour indicates significant reduction in power spectra. Threshold values (T) for statistical significance (corresponding to p \ 0.05) are reported in the figure center. In AM-T, significant modifications were observed in delta band in dorsolateral PFC (BA 9), in anterior PFC (BA 10), in OFC (BA 11), in ventral, and in ACC (BA 32, 24). In Task 2, significant modifications were observed in delta band in the left precuneus (BA 7). BA Brodmann areas, LH left hemisphere, A anterior, P posterior
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Fig. 3 Results of the sLORETA comparison of EEG lagged coherence in all frequency bands. Panel A AM-T versus Baseline; Panel B Task 2 versus Baseline. Threshold values (T) for statistical significance (corresponding to p \ 0.05) are reported in center of the figure; red lines indicate connections which presented significant increase in coherence; blue lines (not present) would indicate significant reduction in coherence. R right, L left, A anterior, P posterior
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corresponding to p \ 0.05, and T = 8.821, corresponding to p \ 0.01. No significant differences were observed, in all frequency bands. Lagged coherence analysis In the comparison between AM-T and BL, the thresholds for significance were T = 4.154, corresponding to p \ 0.05, and T = 4,984, corresponding to p \ 0.01. Significant modifications were observed in the delta (0.5–4 Hz; T = 4.522, corresponding to p = 0.031) as well as in the gamma (30–60 Hz; T = 4.346, corresponding to p = 0.039) frequency bands. In the delta band, the AM-T condition was associated with a widespread increase in lagged coherence, involving the prefrontal, temporal, parietal, and occipital ROIs (Fig. 3a). In the gamma band, AM-T was associated with increased coherence between mesial temporal and left parieto-occipital ROIs (Fig. 3a). In the comparison between Task 2 and BL, the thresholds for significance were T = 4.579, corresponding to p \ 0.05, and T = 5.572, corresponding to p \ 0.01. As for AM-T, significant modifications involved the delta (0.5–4 Hz; T = 5.236, corresponding to p = 0.027) as well as the gamma (30–60 Hz; T = 4.904, corresponding to p = 0.041) bands. In the delta band, the Task 2 condition was associated with a widespread increase in lagged coherence, involving the prefrontal, temporal, parietal, and occipital ROIs (Fig. 3b). In the gamma band, Task 2 was associated with increased coherence between mesial temporal and right parieto-occipital ROIs (Fig. 3b). Finally, in the comparison between AM-T and Task 2, the thresholds for significance were T = 4.096, corresponding to p \ 0.05, and T = 4,901, corresponding to p \ 0.01. No significant differences were observed, in all frequency bands.
Discussion The principal aim of this study was to explore the modifications of EEG power spectra and connectivity induced by two different AM tasks: a standardized, validated AM task (Williams and Broadbent 1986) and a more ecological retrieval of a single autobiographical event. The results indicate that the AM-T is associated with increased delta band power in left PFC and in ACC: bilateral, widespread increase in EEG connectivity also in the delta band, and increase in left temporo-parietal connections in the gamma band. Conversely, the Task 2 was characterized by an increase in gamma power in left parieto-occipital cortex, and similarly to AM-T, a widespread increase in EEG connectivity also in the delta band. Moreover, in Task 2, we observed an
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increase in right temporo-parietal connections in the gamma band. The engagement of these brain areas in AM is widely documented (Cabeza and St Jacques 2007; Maguire 2001; Svoboda et al. 2006). Different regions of frontal lobes play a crucial role in AM. In AM-T, we documented the involvement of dorsolateral PFC (BA 9), in anterior PFC (BA 10), and in OFC (BA 11). Medial frontal region (BA 9, 10) seems to be critical in AM. Indeed, it is documented that this region is involved in self-referential processing during AMs (Addis et al. 2004; Cabeza et al. 2004; Levine et al. 2004; Macrae et al. 2004). This process seems to be the key element of AM so that several authors (Conway 2005; Conway and Pleydell-Pearce 2000; Conway et al. 2001, 2003) proposed that autobiographical memory is constructed within a self-memory system (SMS), a conceptual model which consists of two main components: the working self and the autobiographical memory knowledge base. When these components interlock in acts of remembering, specific autobiographical memories can be formed. Furthermore, the increased delta power in OFC, observed in our study, could reflect the emotional information processing during the AM-T: this is consistent with previous findings which documented the engagement of OFC in emotional AM task (Maddock et al. 2001; Markowitsch et al. 2003; Piefke et al. 2003). The AM-T, as compared with BL, also provoked an increase in delta power in the anterior cingulate cortex. Although the role of posterior cingulate cortex in AM has been established by neuroimaging studies (Svoboda et al. 2006), also ACC seems to play an important role in memory reconstruction and monitoring process (Cabeza and Nyberg 2000; Duncan and Owen 2000; Fletcher and Henson 2001) of AM. BA 24 and 32 are considered responsible for the cognitive components of ACC (Devinsky et al. 1995), and the interaction with PFC could reflect the response to the erroneous information during monitoring process of AMs. According to the ‘conflictmonitoring’ theory (Carter et al. 1999, 2000), it has been suggested that ACC provides an online conflict signal, indicating the need to engage brain regions such as dorsolateral prefrontal cortex and inferior parietal cortex to implement strategic processes (Carter et al. 1999, 2000). This has already been documented in different cognitive tasks such as the Stroop Task (MacDonald et al. 2000). Our results also documented an increase in gamma band localized in the left precuneus (BA 7) in Task 2 when compared with BL. This structure is involved in self-referential processing, visuo-spatial imagery, and episodic memory retrieval (Cavanna and Trimble 2006), and it seems to be an important area in the cortical AM network (Bullmore and Sporns 2009). In the perspective of AM,
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several studies suggest the implication of precuneus in visual imagery (Addis et al. 2004; Gardini et al. 2006). It is possible to speculate that the greater activation of precuneus in this task could reflect the vividness of a very stressful event such as the high school exit examination. The results of the EEG connectivity analysis suggest that the AM tasks are associated with increased cortical connectivity, both in the low-frequency (delta) and in the high-frequency (gamma) EEG bands. The two different tasks used in this study showed similar effects in the delta frequency band: in both tasks, a spread bilateral activation of a cortical network was observed (Fig. 3a, b). Conversely, the two AM tasks produced different, localized changes in the gamma band: while the AM-T was associated with increased connectivity in the left temporo-parietal areas, the Task 2 induced a similar increase in connectivity in the right hemisphere. In this respect, the results are consistent with the findings of functional imaging and neurophysiological studies of AM described in the literature (Cabeza and St Jacques 2007; Conway et al. 2001, 2003; Maguire 2001; Steinvorth et al. 2010; Svoboda et al. 2006). In both AM tasks, we observed two similar EEG connectivity patterns: the first is associated with the delta band, and it corresponds to a wide and complex brain network, involving bilateral prefrontal, temporal, and occipital areas; the second is associated with the gamma band, and it corresponds to smaller brain networks involving posterior areas. Bilateral activation of functional connections observed in the first network could reflect the emotional valence of our AM tasks. The predominant activation of left-side areas reported in previous studies (Cabeza and St Jacques 2007; Maguire 2001; Svoboda et al. 2006) is thought to reflect the contribution of semantic information to AM neutral retrieval cues (Cabeza and St Jacques 2007). Nevertheless, AM is also characterized by emotional content and vivid sensory details (Rubin 2006), and several researches documented right-lateralized or bilateral activation patterns during emotional AM tasks (Denkova et al. 2006; Fink et al. 1996; Markowitsch et al. 2000; Vandekerckhove et al. 2005). Our results are consistent with Vandekerckhove et al. (2005) which reported bi-hemispheric activation (including MTL and PCF) during stressful, negative or positive, AM tasks. Furthermore, we showed that this complex network is supported by the delta frequency band. This is consistent with different studies, reporting the involvement of delta frequency in memory. It is proposed that delta oscillation plays a crucial role in memory consolidation during sleep (Sagar et al. 1985; Sirota et al. 2003), successful explicit memory formation (Fell et al. 2006), working memory (Harmony et al. 1996; Imperatori et al. 2013), and with AM (Steinvorth et al. 2010). Moreover, the involvement of delta band could reflect the
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neurophysiological index of the inter-neuronal exchange along the distributed brain areas that shape the AM cortical network (Steinvorth et al. 2010; Toth et al. 2012). Our results also documented an increase in EEG coherence in gamma band during both AM tasks in posterior areas. Whereas AM-T is characterized by increase in gamma coherence in the left hemisphere, Task 2 is characterized by increase in gamma coherence in the right hemisphere (Fig. 3). The difference between left and right connectivity could reflect the difference among the two AM Tasks: in AM-T, participants were instructed to recall different AMs in response to verbally presented cue words, whereas in Task 2 we only asked to remember and keep in mind details about a single autobiographical event. The crucial role of gamma bands in memory is widely documented (Duzel et al. 2010; Jutras and Buffalo 2010). In the present research, the increase in gamma coherence between parietal and temporal areas could reflect the use of information arising from correspondences between the cues and material from the long-term memory. This is in line with the results reported by Steinvorth et al. (2010), who observed an increase in gamma power in superficial layers of enthorhinal cortex beginning at 200 ms after the cue and lasting until 12,000 ms at the end of the analyzed time period. It must be specified that we did not document modifications of EEG coherence and EEG power spectra in the theta band. In a study by Corsi-Cabrera et al. (2000), power spectra from wake and sleep in healthy adults were submitted to principal component analyses to investigate which frequencies covaried together (Corsi-Cabrera et al. 2000). The results indicated that slow-wave activity can oscillate at higher frequencies, up to 8 Hz; interestingly, no theta band was independently identified: According to the authors, this suggested either that delta and theta oscillations are two rhythms under the same global influence or that the traditional division of theta band in the human cortical EEG is artificial (Corsi-Cabrera et al. 2000). Furthermore, Lega et al. (2012) recording intracranial EEG during an EM task revealed that only ‘slow-theta’ (2.5–5 Hz) oscillation was functionally linked to gamma oscillations suggesting that this EEG pattern plays an important role in cortico–hippocampal communication. Moreover, we did not find any significant modification in the beta bands, although this activity seems to play an important role in specific memory tasks such as visuospatial working memory (Roberts et al. 2013). However, the role of beta activity in AM remains controversial and not constantly documented (Conway et al. 2001, 2003; Steinvorth et al. 2010). In conclusion, our findings indicate that AM retrieval engages in a complex network which is mediated by both low- (delta) and high-frequency (gamma) EEG bands.
Study limitations The present study has main limitations. The first is the use of scalp EEG recordings, which have an intrinsic limit in space resolution, particularly in the identification of deep subcortical sources. A further limit is in the montage applied, which is the one used in standard EEG recording. It is known that spatial resolution of EEG sources increases with the number of electrodes, and therefore, high-density recordings are more reliable in the esteem of EEG rhythms source analysis. The same kind of limitation, obviously, is reflected by the sLORETA software, which is, by definition, a low-resolution electric source analysis software. Furthermore, it is possible that the modification observed in the gamma band was influenced by the superimposed cranial and ocular muscles artifacts, which is particularly evident in gamma activity (Hipp and Siegel 2013). Finally, although it is widely used, we compared the AM tasks with a resting state and this needs caution in the interpretation of results (Svoboda et al. 2006). Conflict of interest
None.
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