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Alpha phase coupling reflects object recognition
NeuroImage 42 (2008) 928–935
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NeuroImage j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y n i m g
Alpha phase coupling reflects object recognition Roman Freunberger, Wolfgang Klimesch ⁎, Birgit Griesmayr, Paul Sauseng, Walter Gruber Department of Physiological Psychology, University of Salzburg, Austria
a r t i c l e
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Article history: Received 29 February 2008 Revised 25 April 2008 Accepted 11 May 2008 Available online 21 May 2008
a b s t r a c t In the present study, we investigate the role of upper alpha oscillations for semantic access and retrieval processes. In each of a series of trials, subjects were presented trains of distorted pictures (with decreasing levels of degradation), and were asked to respond as quickly as possible when they recognize the meaning of the picture. The results show that during the time-window of picture recognition, upper alpha power decreased but inter-areal phase synchronization increased as compared to meaningless control pictures. We assume that synchronous alpha oscillations – particularly during a decrease in alpha power – reflect topographically specific neural network activity that is related to the access of semantic information in LTM. © 2008 Elsevier Inc. All rights reserved.
Introduction In contrast to most other frequency domains in the human EEG, alpha oscillations exhibit event-related decreases as well as increases in power (event-related desynchronization/synchronization or ERD/ERS; cf. Pfurtscheller and Klimesch,1991). A variety of studies indicate that ERS is elicited in situations, where subjects withhold or control the execution of a response and is obtained over sites that probably are under, or exert top-down control (Klimesch et al., 1999; Jensen et al., 2002; Schack and Klimesch, 2002; Busch and Herrmann, 2003; Cooper et al., 2003; Herrmann et al., 2004; Sauseng et al., 2005a,b). Most interestingly, ERD – considered for a long time ‘the typical alpha response’ – may not simply be interpreted as an obligatory response to stimulation as the following example documents. In a perceptually demanding target detection task with letters (presented for only 67 ms and followed by a pattern mask), no ERD was observed for good performers (Hanslmayr et al., 2005). Klimesch et al. (2007) reviewed a variety of studies, indicating that the onset of ERD always is associated with the onset of a retrieval process not only in cognitive but motor memory tasks (e.g., Neuper et al., 1999) as well. During retrieval a pronounced ERD can be observed, whose magnitude appears to be related to the extent of semantic integration/activation. As an example, in a ‘selective retrieval’ task by Sauseng et al. (2002, 2005b) subjects had first to learn verbal
⁎ Corresponding author. University of Salzburg, Department of Physiological Psychology, Institute of Psychology, Hellbrunnerstr. 34, A-5020 Salzburg, Austria. Fax: +43 662 8044 5126. E-mail address: [email protected] (W. Klimesch). 1053-8119/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2008.05.020
labels for each of 8 visual patterns. Then, the verbal labels were presented and subjects had to retrieve the respective visual pattern. The results showed ERS during retrieval attempts but ERD when information is actually retrieved. In addition, it was found that the onset of ERD was significantly correlated with subsequent retrieval performance. Finally, it should be emphasized that these findings – relating alpha to semantic retrieval – are highly frequency specific and can be observed only (or at least primarily) in the upper alpha frequency range (for reviews see e.g., Klimesch, 1999 and Klimesch et al., 2007). In the present study we want to investigate the question, whether during access and/or retrieval of semantic information, upper alpha phase coupling can be observed over task relevant brain areas. In an attempt to prolong the time period of access and retrieval processes, we used trains of pictures with different levels of distortions. In each trial, subjects viewed a train of 4 pictures (with the highest level of distortion for the first picture and nearly no distortion for the fourth picture). To vary the extent of semantic information we used meaningless pictures as controls. Trials with pictures of objects were intermixed with control trials where no object could be recognized. We assume that in every trial subjects build up expectancies about the forthcoming pictures in a top-down manner. As proposed by Klimesch et al. (2007) large amplitude alpha serves as a guiding mechanism enabling the access to a long-term memory trace. Thereby, frontal sites are thought to exhibit top-down control on the access to long-term memory located in more posterior brain sites (Sauseng et al., 2005a,b; Schack et al., 2005) that are also relevant for conscious perception (Babiloni et al., 2006, 2007). Thus, we expect selective effects of long-range phase-coherence between long-
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term-memory-related posterior sites and frontal sites involved in top-down processes and, hence, we selectively focus on long-range frontal–posterior electrode pairs for the analysis of phase-coherence. Because theta oscillations (4–6 Hz) have been associated with context updating (Makeig et al., 2004; Sauseng et al., 2007) and considered as relevant for episodic and semantic encoding (Klimesch, 1999) we would expect higher theta phase coupling in the object condition compared to controls. Materials and methods Participants 24 subjects participated in this experiment (14 females, 10 males) with a mean age of 25.0 years (SD = 4.7). All except one subject were right-handed and they did not report any neurological or mental diseases. Subjects participated in the experiment after giving written informed consent. After examination of eye and movement artifacts 9 subjects had to be excluded which resulted in a sample of 15 subjects for data analyses.
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Task A pool of 200 pictures was used for the task involving 150 real objects (vehicles, buildings, plants, animals) and 50 control objects that were made by smearing of real objects. Stimuli covered a visual angle of 6.22° × 4.15°. We only used pictures that were dominated by one clearly visible object centered in the middle of the picture (Fig. 1). One trial consisted of a series of 4 images of one picture varying in its degree of distortion from level 4 (maximum distortion) to level 1 (minimum distortion). Pre-testing ensured that objects could be reliably recognized within level 2. For each level, stimuli were presented for 1.0 s immediately followed by the next level image. Subjects had to respond as quickly as they could recognize the object by pressing a button in the real objects’ series. In the control series or when they were not able to recognize an object they pressed a second button, respectively. Since it has been demonstrated that physical stimulus parameters such as the spatial frequency of the stimuli can modulate alpha activity (e.g. Fründ et al., 2007), we tried to keep these parameters as constant across conditions as possible.
Fig. 1. The experimental procedure is shown. Each image was presented for 1 s and the subjects had to indicate – by pressing a respective button – whether they recognized an object or not. The trains of images were presented from high to low levels of distortion. It was assured that subjects can recognize an object within level 2.
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EEG recording EEG-signals were recorded using a 32-channel biosignal amplifier system (SynAmps, Neuroscan Inc.) and data were sampled at 1000 Hz. Signals were referenced to linked-earlobe electrodes and acquired within a bandwidth of 0.15–70 Hz. To prevent for influences of net current a notch filter was set at 50 Hz. 31 Ag– AgCl-electrodes were mounted using an EasyCap on the following positions: Fp1, Fp2, F7, F3, Fz, F4, F8, Fc3, Fcz, Fc4, T3, C3, Cz, C4, T4, Cp5, Cp1, Cp2, Cp6, T5, P3, Pz, P4, T6, Po7, Po3, Po4, Po8, O1, Oz, O2. Impedances were kept below 8 kΩ. To control for vertical eye artifacts a bipolar EOG-channel was used. Epochs containing eye or muscle artifacts were rejected. Data were epoched for controls and objects separately from −0.5 to 4.0 s to the onset of level 4. For data analysis, only epochs where subjects responded correctly within level 2 were included. To overcome possible timing artifacts due to the presentation software, every single trial was inspected. Trials including a jitter between the presented pictures were rejected. On average we obtained 41.8 (SD = 14.5) trials for objects and 31.6 (SD = 7.5) trials for controls. Total power analyses For power analysis, a complex Morlet wavelet transformation as implemented in BrainVision Analyzer 1.05 (Megis Software,
Munich) was applied to the selected epochs. The obtained power estimates were averaged over epochs. In order to establish a good time-frequency resolution we used a Morlet parameter c = 10 for frequencies from 2 to 20 Hz and c = 7 for frequencies from 30 to 70 Hz. The obtained data were then averaged for eight topographical regions: left frontal (Fp1, F7, F3, Fc3), right frontal (Fp2, F8, F4, Fc4), midline central (Fz, Fcz, Cz), left centro-temporal (T3, C3, Cp5, Cp1), right centro-temporal (T4, C4, Cp6, Cp2), left parieto-occipital (T5, P3, Po3, Po7, O1), right parieto-occipital (T6, P4, Po4, Po8, O2) and midline parieto-occipital (Pz, Oz). Five frequency bands were defined for statistical analyses: theta (3.67–6.50 Hz), lower alpha (6.42– 9.75 Hz), upper alpha (9.17–13.00 Hz), lower gamma (25.71– 54.83 Hz) and higher gamma (58.29–78.33 Hz). Analysis of phase-coherence For single-trial analyses of phase-coherence (or phase coupling) between electrodes we used the PLV measure (Lachaux et al., 1999) as implemented in BESA 5.1.8 (Megis Software, Munich). PLV values were calculated for frequencies from 2 to 20 Hz (1 Hz steps) with a time resolution of 50 ms. Values were Fisher-z transformed to establish normalization of coherence coefficients and averaged for the upper alpha (10–12 Hz) and theta (4–6 Hz) frequency bands. We selected
Fig. 2. Plots for alpha and theta total power. A) The line graphs show the power curves for upper alpha (10–12 Hz), for the right centro-temporal (RCT) region-of-interest and for theta (4–6 Hz) for the midline central region (MC). The x-axis represents the time course starting from −0.5 to 4.0 s averaged over 500 ms time-windows, respectively (t1 = −0.5–0, t2 = 0–0.5, t3 = 0.5–1.0, t4 = 1.0–1.5, t5 = 1.5–2.0, t6 = 2.0–2.5, t7 = 2.5–3.0, t8 = 3.0–3.5, t9 = 3.5–4.0 s). Black lines represent objects and grey lines controls. The shaded areas indicate significant differences between objects and controls. For upper alpha significant differences were found within 2.5–3.0 s and for theta within 2.0–2.5 and 3.0–4.0 s. B) Time-frequency images for electrode Cp2 for objects (left) and controls (right) are shown, separately. ‘L4–L1’ at the top of the TF-plots indicates the presentation of the different levels from level 4 to level 1. Note the stronger upper alpha suppression for objects within level 2. The reaction-time histograms show responses from all trials and all subjects. For objects only trials with responses within level 2 were selected to establish an equal number of trials in both conditions.
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these bands because power effects were found only within these two frequency bands (see Results section). Statistical analyses For power estimates three-way ANOVAs with factors CONDITION (Objects, Controls), TIME (9 subsequent 500 ms timewindows from −0.5 to 4.5 s) and TOPOGRAPHY (LF, RF, LCP, RCP, MC, LCT, RCT, LPO, RPO, MPO) were calculated for each of the five frequency bands (see above). For estimation of Fisher-z transformed PLVs we defined 50 pairs-of-interest (POI, see Fig. 3A) including long-range connections between frontal and posterior electrodes only. The remaining 250 pairs were defined as non-task-relevant (NonPOI) and were used for control analyses. For a similar approach see Gerloff et al. (1998). POIs were selected by two criteria: (1) the electrode pair must involve a frontal electrode and a site from parietal, parieto-occipital or occipital recording regions and (2) the electrode pair must be considered as “long-range”, thus, no neighboring electrodes are selected for POIs. This was also done to control for possible influences of volume conduction (see Nunez et al., 1997). We calculated a three-way ANOVA including factors CONDITION (objects, controls), PAIRS (POIs, NonPOIs) and TIME (t1 = 1100–1250 ms, t2 = 1300–1450 ms, t3 = 1500–1650 ms, t4 = 2100–2250 ms, t5 = 2300–2450 ms, t6 = 2500–2650 ms). These time-windows were selected for the following reasons: (1) we expected PLV differences within level 2 (2.0–3.0 s) because within this level, object recognition should take place, (2) total power analyses revealed differences within level 2 and (3) visual inspection of the peaks of PLV curves suggests differences within these narrow time-windows. (see Fig. 3C). For post-hoc comparisons of significant interactions Scheffé tests were applied to the data (p b .01). To test for PLV differences between conditions across all electrode pairs in an additional analysis we selected 500 ms time-windows starting from −0.5 to 4.0 s relative to the onset of level 4 and calculated one-way t-tests of significant differences between objects and controls (test value = 0) for all possible electrode combinations (nPAIRS = 300).
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were made in level 3 and 4. See Fig. 3B for visualization of response accuracy. In Fig. 2B reaction-time histograms are plotted. Mean reaction-time for objects within level 2 was 2.6 s (SD=0.22 s, relative to the onset of level 4) and for controls 3.4 s (SD=0.38 s). Total power For power estimates, the three-way interaction CONDITION × TIME× TOPOGRAPHY reached significance for theta (F56/784 = 2.38, p b .05) and upper alpha (F56/784 = 2.46, p b .05). Post-hoc Scheffé tests (p b .05) showed that objects elicited significantly pronounced upper alpha amplitudes in the late time-window between 3.5 and 4.0 s in regions LT, RT, POL, POR and POC. In region RCT upper alpha amplitudes were decreased for objects as compared to controls in the time-window of 2.5–3.0 s (cf. Figs. 2A,B).
Motor activity To control for motor-related effects we calculated ERD/ERS values according to the formula: ERD/ERS = Test − Reference/ Reference ⁎ 100, where the reference-interval was set between − 400 and − 200 ms to stimulus-onset and the testinterval involved the whole post-stimulus train of 4.0 s. Motor-related effects would be characterized by a stronger ERD contralateral and ERS ipsilateral to hand movements topographically located in motor areas (cf. Pfurtscheller, 1992). Results Behavioral data On the average, overall performance was 91.3% (SD = 5.2) in the object condition. More precisely, subjects correctly identified 48.8% (SD = 14.5) in level 1, 37.87% (SD = 12.7) in level 2, 4.5% (SD = 5.1) in level 3 and 0.1% (0.2) in level 4 as real pictures. For controls overall performance was 84.6% (SD = 8.4). The percentage of correctly recognized controls in level 1 and level 2 was 76.1% (SD = 14.7) and 8.6% (SD = 10.9), respectively. No decisions
Fig. 3. In A) the selected electrodes – encircled by black lines – that were used for subsequent phase-locking (PLV) analyses are depicted. In the PLV analyses frontal-toposterior long-range connections were included only. B) Mean percentage hit rate is depicted from level 4 to level 1 (L4–L1). Vertical lines represent the standard error of mean. C) The time-courses of upper alpha and theta PLVs (Fisher-z transformed) are plotted. Black lines represent objects and dotted black lines controls. The marked arrays represent the six time-windows used for statistical analyses (t1–t6). Note that there is a clear difference in phase-locking between objects and controls within level 2, apparent in both frequency bands.
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Fig. 4. Fisher-z transformed PLVs are plotted for theta and upper alpha and for POIs and NonPOIs, separately. The x-coordinate represents the time-intervals selected for statistical analyses (see also Fig. 3C, t1 = 1100–1250 ms, t2 = 1300–1450 ms, t3 = 1500–1650 ms, t4 = 2100–2250 ms, t5 = 2300–2450 ms, t6 = 2500–2650 ms). Significant differences (p b .01) between objects and controls are represented by “ ⁎ ”. There are no differences in PLVs for NonPOIs.
For theta, control pictures are characterized by enhanced amplitudes within 3.0 to 4.0 s in all topographical regions. In the midline central region objects showed higher amplitudes compared to controls between 2.0 and 2.5 s (Fig. 2A).
PLV In Fig. 4 (see also Fig. 3C) the results of the three-way ANOVAs are depicted for upper alpha and theta. For theta the ANOVA
Fig. 5. In additional analyses all pairs (n = 300) were included and one-sample t-tests were calculated between objects and controls. Two time-windows are plotted (2.0–2.5 and 2.5– 3.0 s). The results are shown for theta and upper alpha, red lines indicate that objects show significant (p b .01) stronger phase-locking compared to controls, blue lines indicate that controls show increased phase-locking compared to objects.
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Fig. 6. Topographical ERD/ERS (event-related desynchronization/synchronization) maps for objects and controls for the upper alpha range. Red indicates ERS and blue ERD. Strongest ERD was found in occipital regions.
yielded significant main effects for CONDITION (F1/14 = 6.17, p b .05) as well as significant interactions for TIME × CONDITION (F5/70 = 2.97, p b .05), CONDITION × PAIR (F1/14 = 21.49, p b .001) and TIME× CONDITION × PAIR (F5/70 = 4.23, p b .05). For upper alpha we found a significant main effect for PAIR (F1/14 = 15.15, p b .01) and significant interactions between factors CONDITION × PAIR (F3/42 = 11.65, p b .01) and TIME × CONDITION × PAIR (F5/70 = 2.98, p b .05). Significant differences (by post-hoc Scheffé tests; p b .01) are marked (⁎) in Fig. 4. In general, in both frequency bands there are differences in phase-locking between objects and controls in the relevant time-windows that are apparent in POIs only. Objects show enhanced alpha and theta phase-locking compared to controls. We also performed additional analyses including all electrode pairs (nPAIRS = 300). The results of the one-sample t-tests are depicted for two time-windows in Fig. 5. Upper alpha phase-locking is stronger for objects compared to controls in a right-sided anterior-to-posterior network within 2.0–2.5 s. Theta shows a more global phase-locking – stronger for objects than controls – in both time-windows. Correlation analysis for upper alpha phase-locking As is apparent from Fig. 3 upper alpha PLV shows decoupling at the beginning of each stimulus train. We did a correlation analyses to evaluate the specificity of the underlying upper alpha network by comparing pre-stimulus upper alpha PLVs with post-stimulus PLVs over 10 consecutive timesamples. For a similar approach see Yamagishi et al. (2003). We assumed that the stronger pre-stimulus PLV evolves from a large, global network unspecific for stimulus processing which would be reflected in high correlations between PLVs of electrode pairs. The decoupling or desynchronization probably reflects the formation of a specific network relevant for stimulus processing. Thus, for three time-intervals (−0.5–0 s, 0.5–1.0 s, 2.0–2.5 s) and for each subject we calculated the correlations between PLVs of the 50 POIs. The number of samples for the correlations was 10 as represented by 10 timesamples in each time-interval (time resolution = 50 ms, interval = 500 ms). For every single subject we revealed a 50 × 50 matrix involving all POI-to-POI correlations. We evaluated the number of significant (p b .05) positive and negative correlations and averaged across subjects. Finally, to estimate differences of the frequencies of significant correlations
between the conditions and time-intervals we calculated an ANOVA with factors CONDITION (objects, controls), TIME (−0.5–0, 0.5–1.0, 2.0–2.5 s) and COEFFICIENT (positive and negative correlation coefficients). The ANOVA yielded significant effects for factors COEFFICIENT (F1/49 = 436.02, p b .001) and TIME (F2/98 = 8.42, p b .01) as well as significant interactions for CONDITION × TIME (F2/98 = 3.2; p b .05), COEFFICIENT × TIME (F2/98 = 64.17, p b .001) and CONDITION × COEFFICIENT × TIME (F2/98 = 46.02, p b .001). Posthoc tests showed that negative correlations increased with time and positive correlation decreased with time. This is in accordance with our assumption that a global, unspecific network sharpens to a specific network important for visual semantic processing. Motor activity In Fig. 6 the topography of ERD for objects and controls is shown for a time-window where subjects were responding to objects (at about 2.75 s). In both conditions, ERD exhibits maximal values over occipital regions. These findings suggest strongly that upper alpha activity is not influenced by the motor response. Discussion The obtained findings are consistent with the hypothesis that upper alpha oscillations play a specific role during the access and retrieval of semantic information. We could replicate earlier findings (e.g. Klimesch et al.,1994; Klimesch, 1997; Vanni et al.,1997) showing that the extent of ERD is related to semantic information and not e.g., to task difficulty. In the present study, the recognition of real objects is related to a larger extent of power suppression than those of meaningless objects, although the former task may be considered easier than the latter task. Most importantly, however, we observed a significant increase in long-range phase-coherence during that time period, in which objects are recognized. This effect was primarily found in a frequency range of 10–12 Hz. Although we did not use individually adjusted alpha bands (to reduce the complexity of phase analyses) this frequency range can be considered the upper alpha band in young healthy adults (e.g., Klimesch, 1999). We assume that phase coupling between frontal and posterior regions reflects an interactive top-down control process
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during the access of semantic information in LTM. This process can also be described in terms of a search process that results in a rapid selection of ‘entry structures’ into LTM. The suggested interpretation is well in line with previous studies. As an example, Schack et al. (2003) investigated alpha phase-coherence during the encoding of spoken words (four lists containing either 25 concrete or 25 abstract nouns were presented in a block design). The topographical pattern of phase coupling showed large differences between concrete and abstract words already in a very early time-window of 100–200 ms. Because mean pronunciation duration was around 760 ms, the observed differences in phase coupling cannot reflect processes that are related to the full comprehension of the presented words. Instead, it may be argued that these early phase coupling differences reflect differences in access processes between abstract and concrete words. The fact that the speed of traveling alpha waves was faster for abstract than concrete words suggests in addition that the search area for abstract words (which are less numerous than concrete words) can be narrowed down faster than that for concrete words. Phase coupling may also be interpreted to reflect binding. It has been suggested that the neural representation of an object is reflected by a neural assembly that is dynamically formed through the synchronization of oscillations (Varela et al., 2001). As proposed in classical binding studies (Singer et al., 1997; Von der Malsburg, 1995; Gray and Singer, 1989; Rodriguez et al., 1999) synchronization of high frequency gamma oscillations (N 25 Hz) establishes the formation of a coherent percept. However, recent work on visual encoding suggests also a specific role for alpha oscillations in object recognition (Mima et al., 2001; Sewards and Sewards 1999; Vanni et al., 1997). Sewards and Sewards (1999) suggest that static visual objects are bound together through synchronized firing at slow oscillations such as alpha. Mima et al. (2001) used distorted and non-distorted images of objects and found that correctly identified objects elicited enhanced interhemispheric alpha coherence when compared with distorted objects. This inter-hemispheric coupling is probably a process related to early semantic encoding. Unfortunately, we could not replicate the findings by Mima et al. (2001) which could be due to task differences. As can be seen in Fig. 3 we found a more right-hemispheric network. However, alpha phase coupling may serve as a global mechanism integrating relevant brain areas over large distances, whereas higher frequencies like gamma may be involved in short-distance communication serving more as local processes for sensory integration (Von Stein et al., 2000). Von Stein et al. (2000) found that the activation of a memory trace of previously learned stimuli elicited pronounced alpha coupling between brain areas. The obtained data may also be interpreted in regard to “visual primary consciousness” which describes the processes underlying the self-report to visual stimulation (Babiloni et al., 2007). In a recent study it has been shown that high alpha amplitudes are associated with conscious perception of cues in a spatial attention paradigm (Babiloni et al., 2006). In a followup study Babiloni et al. (2007) showed – using repetitive transcranial-magnetic stimulation (rTMS) – that especially parietal areas are involved in primary visual consciousness. It could be possible that differences between objects and controls are apparent because of processes underlying primary visual consciousness.
Whereas alpha seems to be more important as a guiding mechanism between stimulus information and already stored information in semantic long-term memory, one could speculate that theta might play a prominent role in achieving new memory traces. In our task we found stronger theta power and phase-coherence for objects compared to controls within 2.0–2.5 s. In a later time-window (3.0–4.0 s) the theta effect reverses indicating that objects now show reduced theta amplitudes and phase-coherence when compared with controls. As can be seen in Fig. 2 there is a clear theta–alpha dissociation which means that theta shows opposite effects than alpha. This traditional response was found in animal and human studies and varies with memory demands (Pennekamp et al., 1994; Gevins et al., 1997; Klimesch et al., 1999; Makeig et al., 2004; Gomarus et al., 2006). Klimesch (1999) outlines that the theta–alpha relationship is important for episodic and semantic encoding, respectively. This is in accordance with recent findings showing that frontal-midline theta is associated with context updating (Makeig et al., 2004; Sauseng et al., 2007). It is noteworthy to mention that coherency measures can be influenced by several factors such as volume conduction or selection of reference site. In a study by Trujillo et al. (2005) it could be shown that gamma-coherence can be affected by eye-movements recorded from the nose-reference. To rule out such effects we used a linked-earlobe reference. Furthermore, it has also been shown that the application of an average reference yield quite similar results as a linkedearlobe reference (see Nunez et al., 1999). It can also be argued that differences between controls and objects are provoked by motor-related activity because responses to controls appeared mainly during or even after level 1 presentation, whereas responses to objects were made in level 2 and in level 1. Pfurtscheller (1992) showed that event-related desynchronization (ERD) is elicited over motor-cortical regions contralateral to the moved hand, whereas event-related synchronization can be observed on ipsilateral regions. Importantly, motor-related ERS/ERD effects are highly topographically specific over sensory-motor areas (e.g. electrode C3 and C4). To control for motor-related effects we calculated the ERS/ERD values (see Pfurtscheller, 2001) and plotted them as topographical maps (cf. Fig. 6). Note that the ERD is located in posterior regions only which can hardly be interpreted as a motor-related effect that would show up in more frontal and central regions. In conclusion, the described findings suggest a prominent role for alpha during the access to semantic LTM and document that in contrast to traditional views, alpha activity may not be associated with ‘nil-working’ or ‘idling’ (Pfurtscheller and Lopes da Silva, 1999). In contrast, alpha may play a selective and important role for the (coordinated) timing of neural processes during top-down control on LTM (Klimesch et al., 2007). Other research groups have emphasized an important role of alpha for attentional mechanisms and consciousness (Palva and Palva, 2007; VanRullen and Koch, 2003). Acknowledgments This research was supported by a grant from the Deutsche Forschungsgemeinschaft (DFG), project number DFG-KL1856/ 1-1 of Forschergruppe 448. P.S. is recipient of an APART fellowship of the Austrian Academy of Sciences.
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Contents lists available at ScienceDirect
NeuroImage j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y n i m g
Alpha phase coupling reflects object recognition Roman Freunberger, Wolfgang Klimesch ⁎, Birgit Griesmayr, Paul Sauseng, Walter Gruber Department of Physiological Psychology, University of Salzburg, Austria
a r t i c l e
i n f o
Article history: Received 29 February 2008 Revised 25 April 2008 Accepted 11 May 2008 Available online 21 May 2008
a b s t r a c t In the present study, we investigate the role of upper alpha oscillations for semantic access and retrieval processes. In each of a series of trials, subjects were presented trains of distorted pictures (with decreasing levels of degradation), and were asked to respond as quickly as possible when they recognize the meaning of the picture. The results show that during the time-window of picture recognition, upper alpha power decreased but inter-areal phase synchronization increased as compared to meaningless control pictures. We assume that synchronous alpha oscillations – particularly during a decrease in alpha power – reflect topographically specific neural network activity that is related to the access of semantic information in LTM. © 2008 Elsevier Inc. All rights reserved.
Introduction In contrast to most other frequency domains in the human EEG, alpha oscillations exhibit event-related decreases as well as increases in power (event-related desynchronization/synchronization or ERD/ERS; cf. Pfurtscheller and Klimesch,1991). A variety of studies indicate that ERS is elicited in situations, where subjects withhold or control the execution of a response and is obtained over sites that probably are under, or exert top-down control (Klimesch et al., 1999; Jensen et al., 2002; Schack and Klimesch, 2002; Busch and Herrmann, 2003; Cooper et al., 2003; Herrmann et al., 2004; Sauseng et al., 2005a,b). Most interestingly, ERD – considered for a long time ‘the typical alpha response’ – may not simply be interpreted as an obligatory response to stimulation as the following example documents. In a perceptually demanding target detection task with letters (presented for only 67 ms and followed by a pattern mask), no ERD was observed for good performers (Hanslmayr et al., 2005). Klimesch et al. (2007) reviewed a variety of studies, indicating that the onset of ERD always is associated with the onset of a retrieval process not only in cognitive but motor memory tasks (e.g., Neuper et al., 1999) as well. During retrieval a pronounced ERD can be observed, whose magnitude appears to be related to the extent of semantic integration/activation. As an example, in a ‘selective retrieval’ task by Sauseng et al. (2002, 2005b) subjects had first to learn verbal
⁎ Corresponding author. University of Salzburg, Department of Physiological Psychology, Institute of Psychology, Hellbrunnerstr. 34, A-5020 Salzburg, Austria. Fax: +43 662 8044 5126. E-mail address: [email protected] (W. Klimesch). 1053-8119/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2008.05.020
labels for each of 8 visual patterns. Then, the verbal labels were presented and subjects had to retrieve the respective visual pattern. The results showed ERS during retrieval attempts but ERD when information is actually retrieved. In addition, it was found that the onset of ERD was significantly correlated with subsequent retrieval performance. Finally, it should be emphasized that these findings – relating alpha to semantic retrieval – are highly frequency specific and can be observed only (or at least primarily) in the upper alpha frequency range (for reviews see e.g., Klimesch, 1999 and Klimesch et al., 2007). In the present study we want to investigate the question, whether during access and/or retrieval of semantic information, upper alpha phase coupling can be observed over task relevant brain areas. In an attempt to prolong the time period of access and retrieval processes, we used trains of pictures with different levels of distortions. In each trial, subjects viewed a train of 4 pictures (with the highest level of distortion for the first picture and nearly no distortion for the fourth picture). To vary the extent of semantic information we used meaningless pictures as controls. Trials with pictures of objects were intermixed with control trials where no object could be recognized. We assume that in every trial subjects build up expectancies about the forthcoming pictures in a top-down manner. As proposed by Klimesch et al. (2007) large amplitude alpha serves as a guiding mechanism enabling the access to a long-term memory trace. Thereby, frontal sites are thought to exhibit top-down control on the access to long-term memory located in more posterior brain sites (Sauseng et al., 2005a,b; Schack et al., 2005) that are also relevant for conscious perception (Babiloni et al., 2006, 2007). Thus, we expect selective effects of long-range phase-coherence between long-
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term-memory-related posterior sites and frontal sites involved in top-down processes and, hence, we selectively focus on long-range frontal–posterior electrode pairs for the analysis of phase-coherence. Because theta oscillations (4–6 Hz) have been associated with context updating (Makeig et al., 2004; Sauseng et al., 2007) and considered as relevant for episodic and semantic encoding (Klimesch, 1999) we would expect higher theta phase coupling in the object condition compared to controls. Materials and methods Participants 24 subjects participated in this experiment (14 females, 10 males) with a mean age of 25.0 years (SD = 4.7). All except one subject were right-handed and they did not report any neurological or mental diseases. Subjects participated in the experiment after giving written informed consent. After examination of eye and movement artifacts 9 subjects had to be excluded which resulted in a sample of 15 subjects for data analyses.
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Task A pool of 200 pictures was used for the task involving 150 real objects (vehicles, buildings, plants, animals) and 50 control objects that were made by smearing of real objects. Stimuli covered a visual angle of 6.22° × 4.15°. We only used pictures that were dominated by one clearly visible object centered in the middle of the picture (Fig. 1). One trial consisted of a series of 4 images of one picture varying in its degree of distortion from level 4 (maximum distortion) to level 1 (minimum distortion). Pre-testing ensured that objects could be reliably recognized within level 2. For each level, stimuli were presented for 1.0 s immediately followed by the next level image. Subjects had to respond as quickly as they could recognize the object by pressing a button in the real objects’ series. In the control series or when they were not able to recognize an object they pressed a second button, respectively. Since it has been demonstrated that physical stimulus parameters such as the spatial frequency of the stimuli can modulate alpha activity (e.g. Fründ et al., 2007), we tried to keep these parameters as constant across conditions as possible.
Fig. 1. The experimental procedure is shown. Each image was presented for 1 s and the subjects had to indicate – by pressing a respective button – whether they recognized an object or not. The trains of images were presented from high to low levels of distortion. It was assured that subjects can recognize an object within level 2.
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EEG recording EEG-signals were recorded using a 32-channel biosignal amplifier system (SynAmps, Neuroscan Inc.) and data were sampled at 1000 Hz. Signals were referenced to linked-earlobe electrodes and acquired within a bandwidth of 0.15–70 Hz. To prevent for influences of net current a notch filter was set at 50 Hz. 31 Ag– AgCl-electrodes were mounted using an EasyCap on the following positions: Fp1, Fp2, F7, F3, Fz, F4, F8, Fc3, Fcz, Fc4, T3, C3, Cz, C4, T4, Cp5, Cp1, Cp2, Cp6, T5, P3, Pz, P4, T6, Po7, Po3, Po4, Po8, O1, Oz, O2. Impedances were kept below 8 kΩ. To control for vertical eye artifacts a bipolar EOG-channel was used. Epochs containing eye or muscle artifacts were rejected. Data were epoched for controls and objects separately from −0.5 to 4.0 s to the onset of level 4. For data analysis, only epochs where subjects responded correctly within level 2 were included. To overcome possible timing artifacts due to the presentation software, every single trial was inspected. Trials including a jitter between the presented pictures were rejected. On average we obtained 41.8 (SD = 14.5) trials for objects and 31.6 (SD = 7.5) trials for controls. Total power analyses For power analysis, a complex Morlet wavelet transformation as implemented in BrainVision Analyzer 1.05 (Megis Software,
Munich) was applied to the selected epochs. The obtained power estimates were averaged over epochs. In order to establish a good time-frequency resolution we used a Morlet parameter c = 10 for frequencies from 2 to 20 Hz and c = 7 for frequencies from 30 to 70 Hz. The obtained data were then averaged for eight topographical regions: left frontal (Fp1, F7, F3, Fc3), right frontal (Fp2, F8, F4, Fc4), midline central (Fz, Fcz, Cz), left centro-temporal (T3, C3, Cp5, Cp1), right centro-temporal (T4, C4, Cp6, Cp2), left parieto-occipital (T5, P3, Po3, Po7, O1), right parieto-occipital (T6, P4, Po4, Po8, O2) and midline parieto-occipital (Pz, Oz). Five frequency bands were defined for statistical analyses: theta (3.67–6.50 Hz), lower alpha (6.42– 9.75 Hz), upper alpha (9.17–13.00 Hz), lower gamma (25.71– 54.83 Hz) and higher gamma (58.29–78.33 Hz). Analysis of phase-coherence For single-trial analyses of phase-coherence (or phase coupling) between electrodes we used the PLV measure (Lachaux et al., 1999) as implemented in BESA 5.1.8 (Megis Software, Munich). PLV values were calculated for frequencies from 2 to 20 Hz (1 Hz steps) with a time resolution of 50 ms. Values were Fisher-z transformed to establish normalization of coherence coefficients and averaged for the upper alpha (10–12 Hz) and theta (4–6 Hz) frequency bands. We selected
Fig. 2. Plots for alpha and theta total power. A) The line graphs show the power curves for upper alpha (10–12 Hz), for the right centro-temporal (RCT) region-of-interest and for theta (4–6 Hz) for the midline central region (MC). The x-axis represents the time course starting from −0.5 to 4.0 s averaged over 500 ms time-windows, respectively (t1 = −0.5–0, t2 = 0–0.5, t3 = 0.5–1.0, t4 = 1.0–1.5, t5 = 1.5–2.0, t6 = 2.0–2.5, t7 = 2.5–3.0, t8 = 3.0–3.5, t9 = 3.5–4.0 s). Black lines represent objects and grey lines controls. The shaded areas indicate significant differences between objects and controls. For upper alpha significant differences were found within 2.5–3.0 s and for theta within 2.0–2.5 and 3.0–4.0 s. B) Time-frequency images for electrode Cp2 for objects (left) and controls (right) are shown, separately. ‘L4–L1’ at the top of the TF-plots indicates the presentation of the different levels from level 4 to level 1. Note the stronger upper alpha suppression for objects within level 2. The reaction-time histograms show responses from all trials and all subjects. For objects only trials with responses within level 2 were selected to establish an equal number of trials in both conditions.
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these bands because power effects were found only within these two frequency bands (see Results section). Statistical analyses For power estimates three-way ANOVAs with factors CONDITION (Objects, Controls), TIME (9 subsequent 500 ms timewindows from −0.5 to 4.5 s) and TOPOGRAPHY (LF, RF, LCP, RCP, MC, LCT, RCT, LPO, RPO, MPO) were calculated for each of the five frequency bands (see above). For estimation of Fisher-z transformed PLVs we defined 50 pairs-of-interest (POI, see Fig. 3A) including long-range connections between frontal and posterior electrodes only. The remaining 250 pairs were defined as non-task-relevant (NonPOI) and were used for control analyses. For a similar approach see Gerloff et al. (1998). POIs were selected by two criteria: (1) the electrode pair must involve a frontal electrode and a site from parietal, parieto-occipital or occipital recording regions and (2) the electrode pair must be considered as “long-range”, thus, no neighboring electrodes are selected for POIs. This was also done to control for possible influences of volume conduction (see Nunez et al., 1997). We calculated a three-way ANOVA including factors CONDITION (objects, controls), PAIRS (POIs, NonPOIs) and TIME (t1 = 1100–1250 ms, t2 = 1300–1450 ms, t3 = 1500–1650 ms, t4 = 2100–2250 ms, t5 = 2300–2450 ms, t6 = 2500–2650 ms). These time-windows were selected for the following reasons: (1) we expected PLV differences within level 2 (2.0–3.0 s) because within this level, object recognition should take place, (2) total power analyses revealed differences within level 2 and (3) visual inspection of the peaks of PLV curves suggests differences within these narrow time-windows. (see Fig. 3C). For post-hoc comparisons of significant interactions Scheffé tests were applied to the data (p b .01). To test for PLV differences between conditions across all electrode pairs in an additional analysis we selected 500 ms time-windows starting from −0.5 to 4.0 s relative to the onset of level 4 and calculated one-way t-tests of significant differences between objects and controls (test value = 0) for all possible electrode combinations (nPAIRS = 300).
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were made in level 3 and 4. See Fig. 3B for visualization of response accuracy. In Fig. 2B reaction-time histograms are plotted. Mean reaction-time for objects within level 2 was 2.6 s (SD=0.22 s, relative to the onset of level 4) and for controls 3.4 s (SD=0.38 s). Total power For power estimates, the three-way interaction CONDITION × TIME× TOPOGRAPHY reached significance for theta (F56/784 = 2.38, p b .05) and upper alpha (F56/784 = 2.46, p b .05). Post-hoc Scheffé tests (p b .05) showed that objects elicited significantly pronounced upper alpha amplitudes in the late time-window between 3.5 and 4.0 s in regions LT, RT, POL, POR and POC. In region RCT upper alpha amplitudes were decreased for objects as compared to controls in the time-window of 2.5–3.0 s (cf. Figs. 2A,B).
Motor activity To control for motor-related effects we calculated ERD/ERS values according to the formula: ERD/ERS = Test − Reference/ Reference ⁎ 100, where the reference-interval was set between − 400 and − 200 ms to stimulus-onset and the testinterval involved the whole post-stimulus train of 4.0 s. Motor-related effects would be characterized by a stronger ERD contralateral and ERS ipsilateral to hand movements topographically located in motor areas (cf. Pfurtscheller, 1992). Results Behavioral data On the average, overall performance was 91.3% (SD = 5.2) in the object condition. More precisely, subjects correctly identified 48.8% (SD = 14.5) in level 1, 37.87% (SD = 12.7) in level 2, 4.5% (SD = 5.1) in level 3 and 0.1% (0.2) in level 4 as real pictures. For controls overall performance was 84.6% (SD = 8.4). The percentage of correctly recognized controls in level 1 and level 2 was 76.1% (SD = 14.7) and 8.6% (SD = 10.9), respectively. No decisions
Fig. 3. In A) the selected electrodes – encircled by black lines – that were used for subsequent phase-locking (PLV) analyses are depicted. In the PLV analyses frontal-toposterior long-range connections were included only. B) Mean percentage hit rate is depicted from level 4 to level 1 (L4–L1). Vertical lines represent the standard error of mean. C) The time-courses of upper alpha and theta PLVs (Fisher-z transformed) are plotted. Black lines represent objects and dotted black lines controls. The marked arrays represent the six time-windows used for statistical analyses (t1–t6). Note that there is a clear difference in phase-locking between objects and controls within level 2, apparent in both frequency bands.
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Fig. 4. Fisher-z transformed PLVs are plotted for theta and upper alpha and for POIs and NonPOIs, separately. The x-coordinate represents the time-intervals selected for statistical analyses (see also Fig. 3C, t1 = 1100–1250 ms, t2 = 1300–1450 ms, t3 = 1500–1650 ms, t4 = 2100–2250 ms, t5 = 2300–2450 ms, t6 = 2500–2650 ms). Significant differences (p b .01) between objects and controls are represented by “ ⁎ ”. There are no differences in PLVs for NonPOIs.
For theta, control pictures are characterized by enhanced amplitudes within 3.0 to 4.0 s in all topographical regions. In the midline central region objects showed higher amplitudes compared to controls between 2.0 and 2.5 s (Fig. 2A).
PLV In Fig. 4 (see also Fig. 3C) the results of the three-way ANOVAs are depicted for upper alpha and theta. For theta the ANOVA
Fig. 5. In additional analyses all pairs (n = 300) were included and one-sample t-tests were calculated between objects and controls. Two time-windows are plotted (2.0–2.5 and 2.5– 3.0 s). The results are shown for theta and upper alpha, red lines indicate that objects show significant (p b .01) stronger phase-locking compared to controls, blue lines indicate that controls show increased phase-locking compared to objects.
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Fig. 6. Topographical ERD/ERS (event-related desynchronization/synchronization) maps for objects and controls for the upper alpha range. Red indicates ERS and blue ERD. Strongest ERD was found in occipital regions.
yielded significant main effects for CONDITION (F1/14 = 6.17, p b .05) as well as significant interactions for TIME × CONDITION (F5/70 = 2.97, p b .05), CONDITION × PAIR (F1/14 = 21.49, p b .001) and TIME× CONDITION × PAIR (F5/70 = 4.23, p b .05). For upper alpha we found a significant main effect for PAIR (F1/14 = 15.15, p b .01) and significant interactions between factors CONDITION × PAIR (F3/42 = 11.65, p b .01) and TIME × CONDITION × PAIR (F5/70 = 2.98, p b .05). Significant differences (by post-hoc Scheffé tests; p b .01) are marked (⁎) in Fig. 4. In general, in both frequency bands there are differences in phase-locking between objects and controls in the relevant time-windows that are apparent in POIs only. Objects show enhanced alpha and theta phase-locking compared to controls. We also performed additional analyses including all electrode pairs (nPAIRS = 300). The results of the one-sample t-tests are depicted for two time-windows in Fig. 5. Upper alpha phase-locking is stronger for objects compared to controls in a right-sided anterior-to-posterior network within 2.0–2.5 s. Theta shows a more global phase-locking – stronger for objects than controls – in both time-windows. Correlation analysis for upper alpha phase-locking As is apparent from Fig. 3 upper alpha PLV shows decoupling at the beginning of each stimulus train. We did a correlation analyses to evaluate the specificity of the underlying upper alpha network by comparing pre-stimulus upper alpha PLVs with post-stimulus PLVs over 10 consecutive timesamples. For a similar approach see Yamagishi et al. (2003). We assumed that the stronger pre-stimulus PLV evolves from a large, global network unspecific for stimulus processing which would be reflected in high correlations between PLVs of electrode pairs. The decoupling or desynchronization probably reflects the formation of a specific network relevant for stimulus processing. Thus, for three time-intervals (−0.5–0 s, 0.5–1.0 s, 2.0–2.5 s) and for each subject we calculated the correlations between PLVs of the 50 POIs. The number of samples for the correlations was 10 as represented by 10 timesamples in each time-interval (time resolution = 50 ms, interval = 500 ms). For every single subject we revealed a 50 × 50 matrix involving all POI-to-POI correlations. We evaluated the number of significant (p b .05) positive and negative correlations and averaged across subjects. Finally, to estimate differences of the frequencies of significant correlations
between the conditions and time-intervals we calculated an ANOVA with factors CONDITION (objects, controls), TIME (−0.5–0, 0.5–1.0, 2.0–2.5 s) and COEFFICIENT (positive and negative correlation coefficients). The ANOVA yielded significant effects for factors COEFFICIENT (F1/49 = 436.02, p b .001) and TIME (F2/98 = 8.42, p b .01) as well as significant interactions for CONDITION × TIME (F2/98 = 3.2; p b .05), COEFFICIENT × TIME (F2/98 = 64.17, p b .001) and CONDITION × COEFFICIENT × TIME (F2/98 = 46.02, p b .001). Posthoc tests showed that negative correlations increased with time and positive correlation decreased with time. This is in accordance with our assumption that a global, unspecific network sharpens to a specific network important for visual semantic processing. Motor activity In Fig. 6 the topography of ERD for objects and controls is shown for a time-window where subjects were responding to objects (at about 2.75 s). In both conditions, ERD exhibits maximal values over occipital regions. These findings suggest strongly that upper alpha activity is not influenced by the motor response. Discussion The obtained findings are consistent with the hypothesis that upper alpha oscillations play a specific role during the access and retrieval of semantic information. We could replicate earlier findings (e.g. Klimesch et al.,1994; Klimesch, 1997; Vanni et al.,1997) showing that the extent of ERD is related to semantic information and not e.g., to task difficulty. In the present study, the recognition of real objects is related to a larger extent of power suppression than those of meaningless objects, although the former task may be considered easier than the latter task. Most importantly, however, we observed a significant increase in long-range phase-coherence during that time period, in which objects are recognized. This effect was primarily found in a frequency range of 10–12 Hz. Although we did not use individually adjusted alpha bands (to reduce the complexity of phase analyses) this frequency range can be considered the upper alpha band in young healthy adults (e.g., Klimesch, 1999). We assume that phase coupling between frontal and posterior regions reflects an interactive top-down control process
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during the access of semantic information in LTM. This process can also be described in terms of a search process that results in a rapid selection of ‘entry structures’ into LTM. The suggested interpretation is well in line with previous studies. As an example, Schack et al. (2003) investigated alpha phase-coherence during the encoding of spoken words (four lists containing either 25 concrete or 25 abstract nouns were presented in a block design). The topographical pattern of phase coupling showed large differences between concrete and abstract words already in a very early time-window of 100–200 ms. Because mean pronunciation duration was around 760 ms, the observed differences in phase coupling cannot reflect processes that are related to the full comprehension of the presented words. Instead, it may be argued that these early phase coupling differences reflect differences in access processes between abstract and concrete words. The fact that the speed of traveling alpha waves was faster for abstract than concrete words suggests in addition that the search area for abstract words (which are less numerous than concrete words) can be narrowed down faster than that for concrete words. Phase coupling may also be interpreted to reflect binding. It has been suggested that the neural representation of an object is reflected by a neural assembly that is dynamically formed through the synchronization of oscillations (Varela et al., 2001). As proposed in classical binding studies (Singer et al., 1997; Von der Malsburg, 1995; Gray and Singer, 1989; Rodriguez et al., 1999) synchronization of high frequency gamma oscillations (N 25 Hz) establishes the formation of a coherent percept. However, recent work on visual encoding suggests also a specific role for alpha oscillations in object recognition (Mima et al., 2001; Sewards and Sewards 1999; Vanni et al., 1997). Sewards and Sewards (1999) suggest that static visual objects are bound together through synchronized firing at slow oscillations such as alpha. Mima et al. (2001) used distorted and non-distorted images of objects and found that correctly identified objects elicited enhanced interhemispheric alpha coherence when compared with distorted objects. This inter-hemispheric coupling is probably a process related to early semantic encoding. Unfortunately, we could not replicate the findings by Mima et al. (2001) which could be due to task differences. As can be seen in Fig. 3 we found a more right-hemispheric network. However, alpha phase coupling may serve as a global mechanism integrating relevant brain areas over large distances, whereas higher frequencies like gamma may be involved in short-distance communication serving more as local processes for sensory integration (Von Stein et al., 2000). Von Stein et al. (2000) found that the activation of a memory trace of previously learned stimuli elicited pronounced alpha coupling between brain areas. The obtained data may also be interpreted in regard to “visual primary consciousness” which describes the processes underlying the self-report to visual stimulation (Babiloni et al., 2007). In a recent study it has been shown that high alpha amplitudes are associated with conscious perception of cues in a spatial attention paradigm (Babiloni et al., 2006). In a followup study Babiloni et al. (2007) showed – using repetitive transcranial-magnetic stimulation (rTMS) – that especially parietal areas are involved in primary visual consciousness. It could be possible that differences between objects and controls are apparent because of processes underlying primary visual consciousness.
Whereas alpha seems to be more important as a guiding mechanism between stimulus information and already stored information in semantic long-term memory, one could speculate that theta might play a prominent role in achieving new memory traces. In our task we found stronger theta power and phase-coherence for objects compared to controls within 2.0–2.5 s. In a later time-window (3.0–4.0 s) the theta effect reverses indicating that objects now show reduced theta amplitudes and phase-coherence when compared with controls. As can be seen in Fig. 2 there is a clear theta–alpha dissociation which means that theta shows opposite effects than alpha. This traditional response was found in animal and human studies and varies with memory demands (Pennekamp et al., 1994; Gevins et al., 1997; Klimesch et al., 1999; Makeig et al., 2004; Gomarus et al., 2006). Klimesch (1999) outlines that the theta–alpha relationship is important for episodic and semantic encoding, respectively. This is in accordance with recent findings showing that frontal-midline theta is associated with context updating (Makeig et al., 2004; Sauseng et al., 2007). It is noteworthy to mention that coherency measures can be influenced by several factors such as volume conduction or selection of reference site. In a study by Trujillo et al. (2005) it could be shown that gamma-coherence can be affected by eye-movements recorded from the nose-reference. To rule out such effects we used a linked-earlobe reference. Furthermore, it has also been shown that the application of an average reference yield quite similar results as a linkedearlobe reference (see Nunez et al., 1999). It can also be argued that differences between controls and objects are provoked by motor-related activity because responses to controls appeared mainly during or even after level 1 presentation, whereas responses to objects were made in level 2 and in level 1. Pfurtscheller (1992) showed that event-related desynchronization (ERD) is elicited over motor-cortical regions contralateral to the moved hand, whereas event-related synchronization can be observed on ipsilateral regions. Importantly, motor-related ERS/ERD effects are highly topographically specific over sensory-motor areas (e.g. electrode C3 and C4). To control for motor-related effects we calculated the ERS/ERD values (see Pfurtscheller, 2001) and plotted them as topographical maps (cf. Fig. 6). Note that the ERD is located in posterior regions only which can hardly be interpreted as a motor-related effect that would show up in more frontal and central regions. In conclusion, the described findings suggest a prominent role for alpha during the access to semantic LTM and document that in contrast to traditional views, alpha activity may not be associated with ‘nil-working’ or ‘idling’ (Pfurtscheller and Lopes da Silva, 1999). In contrast, alpha may play a selective and important role for the (coordinated) timing of neural processes during top-down control on LTM (Klimesch et al., 2007). Other research groups have emphasized an important role of alpha for attentional mechanisms and consciousness (Palva and Palva, 2007; VanRullen and Koch, 2003). Acknowledgments This research was supported by a grant from the Deutsche Forschungsgemeinschaft (DFG), project number DFG-KL1856/ 1-1 of Forschergruppe 448. P.S. is recipient of an APART fellowship of the Austrian Academy of Sciences.
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