Abstract

Little is known about the relation of alpha rhythms and object recognition. Alpha has been generally proposed to be associated with attention and memory and to be particularly important for the mediation of long-distance communication between neuronal populations. However, how these apply to object recognition is still unclear. This study aimed at describing the spatiotemporal dynamics of alpha rhythms while recognizing fragmented images of objects presented for the first time and presented again 24 hr later. Intracranial electroencephalography was performed in six epileptic patients undergoing presurgical evaluation. Time–frequency analysis revealed a strong alpha activity, mainly of the evoked type, propagating from posterior cerebral areas to anterior regions, which was similar whether the objects were recognized or not. Phase coherence analysis, however, showed clear phase synchronization specific for the moment of recognition. Twenty-four hr later, frontal regions displayed stronger alpha activity and more distributed phase synchronization than when images were presented for the first time. In conclusion, alpha amplitude seems to be related to nonspecific mechanism. Phase coherence analysis suggests a communicational role of alpha activity in object recognition, which may be important for the comparison between bottom–up representations and memory templates.

INTRODUCTION

In the past decades, several studies have tried to understand the cerebral electrophysiological mechanisms responsible for object recognition. The focus has been mainly on the induced gamma-band response (iGBR) recorded from scalp EEG. iGBR has been related to coherent perception of visual stimuli and is thought to represent the synchronization of neuronal populations involved in feature binding and comparisons with memory (Bertrand et al., 2013; Hassler, Barreto, & Gruber, 2011; Grützner et al., 2010; Gruber, Müller, & Keil, 2002; Tallon-Baudry & Bertrand, 1999). However, recent evidence attributes a significant association of alpha rhythm to visual perception (Voytek et al., 2010; Freunberger, Klimesch, Griesmayr, Sauseng, & Gruber, 2008). Its specific relation is still unclear, and a number of interpretations have been proposed.

First, many studies measuring alpha amplitude related a reduced alpha intensity, or event-related desynchronization (ERD), to brain activation because of the fact that eye opening, visual stimuli, or increased attention evoked a decrease in alpha amplitude (Klimesch, Fellinger, & Freunberger, 2011; Pfurscheller, 2001). Alternately, an increased alpha event-related synchronization (ERS), observed during retention intervals of working memory and STM tasks, may reflect cortical inhibition for irrelevant stimuli (Sauseng et al., 2005; Busch & Herrmann, 2003; Jensen, Gelfand, Kounios, & Lisman, 2002). At the end of the retention period, an ERD was triggered by the participant's response. On the basis of this result, Klimesch et al. (2011) suggested that ERD might be related to the access and retrieval from memory. Similarly, in object recognition tasks, ERD in posterior brain regions was strongest for recognized objects than for unrecognized stimuli, supporting the link between ERD and retrieval from semantic memory (Freunberger et al., 2008; Vanni, Revonsuo, & Hari, 1997).

However, others argue against this ERD/ERS activation/inhibition hypothesis, by linking an alpha power increase to attentional modulation. Namely, Palva and Palva (2007) questioned whether the ERD recorded after a working memory task's retention interval may simply reflect the end of attentional processes rather than memory activation. Moreover, evoked alpha power has been shown to increase according to working memory load, and at a somatosensory detection task, a parietal ERS has been linked to better and faster performances (Linkenhaer-Hansen, Nikulin, Palva, Ilmoniemi, & Palva, 2004; Jensen et al., 2002). Other evidences of the association between alpha rhythm and attentional top–down modulation come from other techniques, such as alpha phase coherence analyses (Palva & Palva, 2007). For instance, conscious awareness of visual stimuli was found to be dependent on the alpha phase angle (Dugué, Marque, & VanRullen, 2011; Busch, Dubois, & VanRullen, 2009; Matthewson, Gratton, Fabiani, Beck, & Ro, 2009). Moreover, decreased performances at a visual working memory task were also linked to a reduced prestimulus alpha coherence between frontal and posterior brain regions provoked by TMS over pFC (Zanto, Rubens, Thangavel, & Gazzaley, 2011). This supports the idea that the alpha rhythm mediates attentional top–down modulation of prefrontal cortices over occipital regions involved in visual processing.

It has also been suggested that alpha rhythm may act as a means of long-distance communication between brain regions, namely, the frontal and posterior regions, whereas iGBR may represent local communication between neuronal assemblies (von Stein & Sarnthein, 2000). Alpha activity may modulate iGBR in precise regions such that parallel visual processing can efficiently occur (Voytek et al., 2010; Osipova, Hermes, & Jensen, 2008). In relation to this, Bar et al. (2006) proposed a model of object recognition in which visual information is rapidly sent through the magnocellular pathway to prefrontal cortices for rapid integration. From this blurred information processing, the pFC would be able to generate possible candidates of the object's identity by accessing semantic memory and to provide them to the bottom–up processes. This top–down modulation would facilitate recognition by decreasing the number of comparisons between the object's representation and memory templates. Using magnetoencephalography and fMRI, an early alpha phase coherence between OFC and occipital regions and a later phase coherence between OFC and temporal regions during object recognition suggested that the OFC may subserve the function of generating possible candidates to bottom–up processes and may do so by using the alpha rhythm as a way to communicate (Bar et al., 2006).

Alpha rhythm's relation to object recognition is thus difficult to understand as different data analyses were performed with different cognitive tasks in-between studies. Thus, this study generally aimed at describing alpha measures inside a single object recognition task. We used the intracranial EEG (iEEG) technique, which consists in the implantation of subdural grid and/or depth electrodes on the brain of epileptic patients undergoing presurgical evaluation for epileptic foci localization (Figure 1A). This technique has one of the best spatial and temporal resolutions and represents an outstanding opportunity to investigate the spatiotemporal dynamics of alpha rhythms while recognizing objects. Moreover, we decided to use an original task, designed by Doniger et al. (2000), during which fragmented images of objects are presented in an incremental manner such that the object became more and more recognizable (Figure 1B). This task is very unique in the sense that it allows comparison of alpha rhythm at recognition (threshold of recognition, T) to moments before recognition (T − 2 and T − 1) and after recognition (T + 1). We think this task will provide a precise description of alpha in relation to recognition of objects presented for the first time. Moreover, it has been shown that, when fragmented images were presented a second time, recognition happened at prior levels as a consequence of top–down facilitation (Viggiano & Kutas, 2000; Snodgrass & Corwin, 1988). Precise mechanisms about how this happens have not been identified yet. By presenting the same paradigm 24 hr later (Session 2), we aimed at describing alpha rhythm's relation to this top–down facilitation. Analyses were performed using separate as well as total (induced + evoked) time–frequency analyses to assess which response provides the most accurate information about the amplitude of alpha activity triggered by visual stimuli. Evoked analyses provide information about the activity that is phase-locked to stimuli. This type of analysis is known to mask information jittering temporally, commonly termed induced responses, which may be crucial for the understanding of underlying cerebral mechanisms (Tallon-Baudry & Bertrand, 1999). Evoked analyses being more commonly performed to study alpha rhythm, both types of analyses were compared in this study. Moreover, phase coherence analyses were performed to investigate the communicational nature of alpha rhythms.

Figure 1. 

(A) Postimplantation MRIs of a patient implanted with a grid of electrodes. From left to right, sagittal, coronal, and horizontal sections representing an electrode implanted over the occipital cortex. (B) Example of a set of fragmented images at Session 1. The eight images composing each set were presented from the most fragmented image to the complete image. When patients recognized the object, this level was marked as the threshold of recognition (T). Prior levels were marked as T − 1 and T − 2 accordingly, and the next level was marked as T + 1. Images were presented again 24 hr later (Session 2). When images are presented a second time, recognition happens at prior levels. SAG = sagittal; COR = coronal; AXI = axial.

Figure 1. 

(A) Postimplantation MRIs of a patient implanted with a grid of electrodes. From left to right, sagittal, coronal, and horizontal sections representing an electrode implanted over the occipital cortex. (B) Example of a set of fragmented images at Session 1. The eight images composing each set were presented from the most fragmented image to the complete image. When patients recognized the object, this level was marked as the threshold of recognition (T). Prior levels were marked as T − 1 and T − 2 accordingly, and the next level was marked as T + 1. Images were presented again 24 hr later (Session 2). When images are presented a second time, recognition happens at prior levels. SAG = sagittal; COR = coronal; AXI = axial.

We hypothesize the following:

  • 1. 

    If alpha rhythm is more related to access in memory as proposed by Klimesch et al. (2011), total alpha power should be at its lowest level (strongest ERD/lowest ERS) at the moment of recognition (T) compared with other recognition levels because, at T, the observed object has been successfully retrieved from semantic memory. Because of the additional involvement of episodic memory as a top–down facilitation mechanism at Session 2, alpha at T should be more powerful at Session 2 compared with Session 1. However, if alpha rhythm is more related to attentional modulation, it appears logical that its total power would be equivalent across T − 2, T − 1, and T (because participants are equally working hard to make sense out of stimuli between conditions) or may slightly increase at T (because recognition may be more arousing). This pattern would be equivalent at both Sessions 1 and 2.

  • 2. 

    Alpha may mediate communication between top–down areas and regions involved in object processing. According to the model proposed by Bar et al. (2006), the OFC may act as a top–down modulator by generating potential candidates about the object identity, which would be transferred to occipito-temporal areas by means of alpha phase synchronization. From this, it is possible to believe that, as the object gets more and more defined, less potential candidates would have to be transmitted. That may result in a gradual decrease in alpha coherence from T − 2 to T + 1, which would be equivalent at both sessions. However, at Session 2, a higher number of phase coherence between cortical regions, namely with those involved in retrieval from episodic memory, might also be involved. Conversely, one may hypothesize that a match between a memory template and the visual cortical representation of the object at recognition would result in a stronger alpha coherence between regions compared with previous levels. A higher number of phase coherence between other cerebral regions at Session 2 may also apply in this case.

METHODS

Participants

Electrodes were implanted over the brain of six patients (four men, mean age = 27.5 years, SD = 14.2 years) undergoing presurgical evaluation for localization of epileptic foci. All patients were right-handed and were left-hemisphere dominant for language. They all had normal or corrected-to-normal vision. This study was approved by the Centre Hospitalier de l'Université de Montréal Ethics Committee. All patients signed an informed consent form before participating in the study.

Stimuli and Task

Stimuli, taken from a bank of images designed by Snodgrass and Corwin (1988), were line drawings of common objects. Each image of an object was fragmented over eight levels, with level 1 representing the most fragmented version of the object and level 8 representing the image in its complete form (Figure 1B). Each image was presented in such a way that the objects gradually became recognizable. Stimuli appeared on a 17-in. monitor (1280 × 1024 pixels) using E-Prime software (Psychology Software Tools, Inc., Pittsburgh, PA). Images were displayed for 1 sec after which participants were asked if they recognized the object. Interstimuli intervals were variable and long enough to minimize the effect of the verbal response on recordings. Patients were asked at all time to fixate the center of the screen. Between 90 and 150 sets of images were presented depending on the patient's collaboration and level of fatigue. All eight images composing a set were presented even if patients recognized the objects earlier. Images were divided in blocks composed of 30 sets of images. Blocks were administered on different days, depending on the patient's tolerance to the task. Blocks were presented again about 24 hr later, without advising participants that the same fragmented images were going to be presented (Session 2). The order of stimulus presentation was randomized. On the first day of experiment, only new images were presented in blocks. On subsequent days, images seen the day before and new images were presented, such that both Session 1 (new) and Session 2 (old) stimuli could be administered on the same day. This was to control for time and learning effects that could otherwise bias the data.

Localization of Electrodes

Subdural grid and/or strip electrodes were implanted over the brain of patients with up to 124 contact points, strategically localized according to the presurgical evaluation. To avoid contamination of the data by epileptic activity, all selected electrodes in this study were situated away from epileptic zones identified by an expert epileptologist. Localization was performed by first coregistering postimplantation MRIs of all patients onto the Montreal Neurological Institute template and then converting the electrode's Stellate Gridview coordinates to Talairach coordinates. Electrode locations were determined according to the Talairach Daemon atlas (Lancaster et al., 2000). In Figure 3, all electrodes were represented over the right hemisphere for the lateral view of the brain and on the left hemisphere for the medial portion of the brain to facilitate comprehension. It was decided to do so because the activity in both hemispheres was similar. In total, 525 electrodes were analyzed for all patients combined: 80 in the occipital lobe (from two patients), 102 in the parietal lobe (from three patients), 134 in the temporal lobe (from all patients), and 209 in the frontal lobe (from five patients). OFC was defined as in Kringlebach (2005). Twenty-eight electrodes were localized over the OFC.

Data Analysis

Data acquisition was performed with a sampling rate ranging from 200 to 2000 Hz, depending on patients and other undergoing research protocols. Using Brain Vision Analyzer (version 1.0, Brain Products, Gilching, Germany), EEG segments at the moment of recognition were marked as the threshold of recognition (T). Previous levels were marked as T − 1 and T − 2; and the next level, as T + 1 (Figure 1B). All segments containing epileptic activity or a signal amplitude of ±350 μV were rejected because they constituted an obvious artifact. A mean of 92.67% (SD = 12.73%) of trials were artifact free and considered for analysis. Data were filtered from 0.05 to 100 Hz (24 dB/oct). An average reference was applied to the data. Each trial was segmented from −200 to 1000 msec.

Time–Frequency Analysis

Time–frequency analyses were performed over each trial using complex Gaussian Morlet's wavelets, which generate a complex wavelet, w, for different frequencies (σf) and time domain (σt) around a central frequency f0 according to:
formula
with
formula
and
formula

Wavelets were calculated in the frequency range of 6–100 Hz in 2-Hz linear steps. The ratio ff was set to 7, with f corresponding to the central frequency of the wavelet and σf corresponding to its standard deviation. Data were baseline corrected by dividing epochs of −200 to −50 msec. A total power value for each trial at time t and frequency f resulted from this analysis. Time–frequency analysis was also performed over the mean ERP and was subtracted from the total response to result in a purely induced response.

Mean total power values of five time widows were calculated using MATLAB (version 7.9.1.705, The MathWorks, Inc., Natick, MA). The windows comprised all wavelet points from 8.65 to 13.86 Hz (consisting mainly of upper alpha frequency) in the time intervals 0–100, 100–200, 200–400, 400–600, and 600–800 msec (Figure 2B). Mean total power values of each electrode at each window were then plotted over an MRI template according to Talairach coordinates (Figure 2C).

Figure 2. 

Example of a time–frequency graph displaying an induced (A) and total (B) alpha response at Session 1 for the electrode G46. For the total time–frequency analysis, mean power values were calculated in five time windows (0–100, 100–200, 200–400, 400–600, and 600–800 msec) comprised between 8.65 and 13.86 Hz. Mean power values were plotted over brain representations according to their Talairach coordinates (C). Dots represent the location of electrode G46 (Talairach coordinates: 37, −72, 43), and colors represent the mean power values calculated from time windows in (B).

Figure 2. 

Example of a time–frequency graph displaying an induced (A) and total (B) alpha response at Session 1 for the electrode G46. For the total time–frequency analysis, mean power values were calculated in five time windows (0–100, 100–200, 200–400, 400–600, and 600–800 msec) comprised between 8.65 and 13.86 Hz. Mean power values were plotted over brain representations according to their Talairach coordinates (C). Dots represent the location of electrode G46 (Talairach coordinates: 37, −72, 43), and colors represent the mean power values calculated from time windows in (B).

Phase Coherence Analysis

Coherence analyses were performed according to the event-related cross-coherence procedures described in Delorme and Makeig (2004) adapted to MATLAB. It consists in the trial-by-trial measure of coupling between signals of electrode pairs that reveal the degree of synchronization between the two selected electrodes. The obtained value represents the magnitude of cross-coherence centered at the time and frequency of the wavelets and varies from 0 (absence of synchronization) to 1 (perfect synchronization). Electrode pairs compared in the phase coherence analysis were all electrodes implanted in different cerebral lobes of each patient.

Statistical Analyses

Statistical analyses were performed within patients only. A significant finding had to be replicated in more than one patient before being reported to ensure reliability of the results. SPSS (version 19.0.0, IBM Company, Armonk, NY) was used for statistical analyses. A paired t test was applied to compare Session 1 and Session 2 levels at which T happened in each participant. Significance was set to a p value of <.05.

Because variables were not normally distributed, nonparametric analyses were performed. To compare the activity of each electrode to baseline, the Kolmogorov–Smirnov test was applied to each electrode. The number of significant electrodes at Sessions 1 and 2 were compared using Pearson's chi-squared test. p Values < .05 were considered significant for these analyses.

For both amplitude analyses, conditions were compared (T − 2 vs. T − 1 vs. T vs. T + 1) within each electrode using the Friedman test and Wilcoxon signed-rank tests as post hoc analyses. A p value < .008 was used to correct for multiple comparisons between conditions based on Bonferroni correction (post hoc analyses involved six condition comparisons: T − 2 vs. T − 1, T − 2 vs. T, T − 2 vs. T + 1, T − 1 vs. T, T − 1 vs. T + 1, T vs. T + 1). Time interval comparisons were similarly performed but involved a Bonferroni correction of p < .005 for post hoc analyses (10 time interval comparisons: 0–100 vs. 100–200, 0–100 vs. 200–400, 0–100 vs. 400–600, 0–100 vs. 600–800, 100–200 vs. 200–400, 100–200 vs. 400–600, 100–200 vs. 600–800, 200–400 vs. 400–600, 200–400 vs. 600–800, and 400–600 vs. 600–800 msec). Session 1 and Session 2 alpha powers were compared within each electrode using the Wilcoxon signed-rank test, with p < .05.

Phase coherences were tested for significance using the bootstat permutation algorithm described in Delorme and Makeig (2004) adapted to MATLAB. A p value of <.05 was set for significance. An algorithm determined the maximal coherence values and the time point at which it happened. These values were compared between conditions (T − 2 vs. T − 1 vs. T vs. T + 1) using Friedman Test and Wilcoxon signed-rank test (a p value of <.008 was used to correct for multiple comparisons). They were also compared at T between Sessions 1 and 2 using Wilcoxon signed-rank tests. Peak values and their time points were further compared between pairs of regions involved in the coherence (occipital-parietal vs. occipital-temporal vs. occipital–frontal vs. temporal-parietal vs. temporal-frontal vs. parietal-frontal) using Kruskall–Wallis and Mann–Whitney tests as post hoc tests with a p value of <.008 based on Bonferroni correction.

RESULTS

Behavioral Data

A mean of 110.00 (SD = 24.49) sets of images were recognized. Images were recognized at earlier levels at Session 2 (mean T level = 3.78, SD = 1.85) compared with Session 1 (mean T level = 4.79, SD = 1.74; p < .001). T levels of each session are equivalent to what has been previously reported in healthy controls (Doniger et al., 2000; Snodgrass & Corwin, 1988), supporting preserved object recognition abilities.

Spatiotemporal Dynamic of Alpha Rhythm at Times 1 and 2

Significantly fewer electrodes responded more than baseline at Session 2 (mean = 210.35, SD = 76.41) compared with Session 1 (mean = 441.20, SD = 58.92, χ2 (1) = 215.705, p < .001). When comparing induced and total responses, the activity was mostly attributed to an evoked response (Figure 2A compared with Figure 2B).

A temporal propagation of increased alpha activity from posterior to anterior brain regions was observed for all conditions of Sessions 1 and 2. The activity at Session 1 for T is displayed as an example in Figure 3A. Session 2 activity was similar spatially and temporally. First, a very rapid increase in alpha response was seen in the most occipital areas between 0 and 100 msec, after which the activity propagated to parietal and temporal cortices. Frontal regions showed an increase in alpha power at later stages of processing, namely, starting at about 400 msec after stimulus onset, whereas occipital areas showed a reduction in alpha power.

Figure 3. 

(A) Representations of electrodes from which a significant alpha response was recorded and their mean power values for each time window of Session 1 for condition T. (B) Percentage of the alpha response elicited at Session 2 compared with Session 1 at condition T. Each dot represents an electrode. A value of 300% represents an alpha response three times higher at Session 2 than at Session 1. Colors vary according to the scales next to the figures.

Figure 3. 

(A) Representations of electrodes from which a significant alpha response was recorded and their mean power values for each time window of Session 1 for condition T. (B) Percentage of the alpha response elicited at Session 2 compared with Session 1 at condition T. Each dot represents an electrode. A value of 300% represents an alpha response three times higher at Session 2 than at Session 1. Colors vary according to the scales next to the figures.

For both Sessions 1 and 2, alpha responses were not significantly different between levels of recognition (T − 2, T − 1, T, and T + 1). However, at Session 2, the amplitude of the alpha power was superior in frontal regions and inferior in occipital regions than at Session 1 on all conditions (Figure 3B).

Alpha Coherence Analyses

At Session 1, only phase coherences at T reached significance compared with baseline. Magnitudes of coherence were significantly higher at T than at other conditions (Figure 4A; p < .001). At T, primary visual cortices displayed 16 significant phase coherences with the lateral occipital complex (LOC), 13 with parietal cortices, and none with prefrontal areas (Table 1). The LOC showed six alpha phase coherences with parietal electrodes and nine with prefrontal electrodes. Twelve alpha coherences were significant between electrodes implanted over parietal cortices and prefrontal cortices. Of the 28 electrodes implanted over the OFC, none displayed significant phase coherence. Note, however, that the 28 electrodes implanted over the OFC were in three patients with predominantly frontal coverage. No coherence analysis between the OFC and occipital regions was possible.

Figure 4. 

Examples of significant alpha phase coherences at Session 1 between electrodes in the occipital and parietal cortex (A) and at Session 2 between electrodes in the temporal and frontal cortex (B).

Figure 4. 

Examples of significant alpha phase coherences at Session 1 between electrodes in the occipital and parietal cortex (A) and at Session 2 between electrodes in the temporal and frontal cortex (B).

Table 1. 

Number of Significant Alpha Coherence per Regions at T


Session 1
Session 2
LOC
Precuneus
SPL
SFG
MFG
IFG
LOC
Precuneus
SPL
SFG
MFG
IFG
Cuneus 11 
Lingual 12 12 
LOC – – 28 17 23 8a 
Precuneus – – – – – – 
SPL – – – – – – 13 
Total 16 10 17 48 26 45 15 

Session 1
Session 2
LOC
Precuneus
SPL
SFG
MFG
IFG
LOC
Precuneus
SPL
SFG
MFG
IFG
Cuneus 11 
Lingual 12 12 
LOC – – 28 17 23 8a 
Precuneus – – – – – – 
SPL – – – – – – 13 
Total 16 10 17 48 26 45 15 

SPL = superior parietal lobule; SFG = superior frontal gyrus; MFG = middle frontal gyrus; IFG = inferior frontal gyrus.

aIncludes three electrodes as part of the OFC.

At Session 2, the same between-condition pattern was observed; only T phase coherences were significant and of higher magnitude compared with other conditions (Figure 4B; p < .001). Primary visual areas showed significant phase coherence with 17 electrodes implanted over the LOC, 24 with parietal cortices, and 17 with prefrontal cortices (Table 1). LOC displayed 30 significant phase coherences with parietal areas (namely, 28 with electrodes in the superior parietal lobule) and 48 with prefrontal areas. In the OFC, three electrodes showed significant phase coherence with electrodes implanted over the LOC. Twenty-one phase coherences were significant between parietal and prefrontal regions.

Peak values of significant coherences at Session 2 were equivalent to those at Session 1. Moreover, peak values of coherences did not differ across pairs of electrodes of both sessions, that is, no region pair displayed stronger alpha phase synchronization than another. In all significant coherences, timing of peak values varied considerably within regions pairs (for example, in all occipital–temporal significant pairs) and between-regions pairs (for instance, in occipital–temporal vs. occipital–frontal pairs), such that no temporal pattern could be observed. Nevertheless, a higher number of significant phase coherence were found at Session 2 compared with Session 1 for all pairs of regions (p < .001), except for phase coherence between the primary visual areas and the LOC, which remained the same, and between primary visual areas and the precuneus, which decreased nonsignificantly.

DISCUSSION

This study reported an important alpha activity generated by visual stimuli, propagating rapidly from posterior to anterior regions of the brain. This pattern was observed at both Sessions 1 and 2 without any difference between recognition levels. Doing the task 24 hr later involved a higher alpha activity in frontal regions and a decreased activity in occipital regions compared with when images were recognized for the first time. This was related to faster object recognition on Session 2 compared with Session 1. Phase coherence analysis revealed a different pattern. Only coherences at the moment of recognition were significant. Whereas fewer electrodes showed significant power amplitudes at Session 2 compared with Session 1, phase coherence number increased significantly at Session 2. Namely, this increase was strongest between bottom–up and top–down electrode pairs.

Results of this study showed mainly an increased synchronization (an ERS) of alpha activity in all conditions, contrasting previous reports, despite the fact that the response was essentially of the evoked type. However, occipital areas, after the observed 0- to 400-msec ERS, displayed a return-to-baseline alpha power, which could be interpreted as a late ERD. The spatiotemporal dynamic of the increased synchronization was consistent with common visual ERPs found in EEG and cortical activations of the ventral and dorsal visual pathway as revealed by fMRI (Bar et al., 2006). In contrast, the late ERD seems to reflect the end of visual processing in posterior occipital regions as integration at this point may involve more anterior regions, such as the temporal areas for object representation and frontal areas for retrieval in memory. Hence, these findings argue against the ERD/ERS activation/inhibition hypothesis by suggesting the opposite. This may be because of the superior resolution of the iEEG technique, which allowed the precise spatiotemporal measurement of the ERS.

Interestingly, alpha power was equivalent across conditions, suggesting that its amplitude is not specifically related to the binding process responsible for object recognition as opposed to iGBR. If alpha would have been related to successful access to semantic memory happening at recognition, one would have expected to see a specific alpha response at T. That was not the case and contrasts with Freunberger et al. (2008). Our results rather suggest that an increased alpha power may be related to a more general unspecific function, such as attention, rather than successful access to memory. The present task, however, was not specifically designed to assess attentional modulation, nor was it to assess semantic and episodic memory, and thus, must be interpreted with caution.

The fact that Session 2 involved more frontal and less occipital alpha power than Session 1 suggests different top–down mechanisms. At Session 1, it is possible to suggest that patients had to rely more intensely on bottom–up processes happening in occipital regions for perceptual closure of the fragmented images and for the construction of an appropriate cortical representation that could match a memory template in semantic memory. At Session 2, recognition happened faster than at Session 1 suggesting facilitation possibly based on retrieval from episodic memory. Prefrontal cortices are known to play an important role in the retrieval process of episodic memory (Shimamura, 2011; Raposo, Han, & Dobbins, 2009; Christoff & Gabrieli, 2000). A more powerful alpha signal in frontal regions and a lower one in occipital regions at Session 2 compared with Session 1 thus seem consistent with the hypothesis that alpha is involved in top–down facilitation but in a nonspecific manner depending on task demand.

Given that phase coherence was strongly associated with recognition, it seems plausible that alpha may mediate the transfer of information for the comparisons between the cortical representation of the object to be recognized and memory templates such that, when a correspondence occurs, alpha phases align more precisely and recognition happens. This is speculative but relates to the resonance concept proposed by Grossberg (1995) and to Bar et al.'s (2006) model. When seeing again an object that got recognized (such as at T + 1), it would then be possible that top–down facilitation is not needed anymore as bottom–up processes would be now strong enough to do the work on their own (the fresh trace needs to be reactivated without thoroughly searching in memory). Thus, communication between bottom–up and top–down regions might not need to be as strong. Our data support such a hypothesis by the fact that no significant alpha coherence is observed at T + 1. Moreover, alpha phase coherence is more distributed at Session 2, when episodic memory templates may additionally be involved.

Alpha mediating communication between bottom–up and top–down regions supports the model proposed by Bar et al. (2006). However, in this model, the OFC occupied an integral part, which was not the case in the present experiment. Bar et al. (2006) used a repetition priming task, and here, significant coherence between OFC and temporal regions occurred only at Session 2, suggesting that the OFC may be involved in the recognition facilitation of repeated stimuli rather than at the first recognition of objects. However, because no coherence analysis between the OFC and primary visual areas was possible, the involvement of the OFC at the first recognition of objects cannot be totally ruled out. Moreover, we were unable to replicate the temporal dynamics suggested in their study. Instead, we found a high variability in the timing of alpha coherence. This may be explained by the high number of feedforward and feedback projections between cerebral regions, which were more precisely revealed in this study because of the higher temporal and spatial resolution of the iEEG technique.

This study has an important limitation, which is that a high number of statistical analyses without analysis for interactions have been performed over electrodes and no correction for this was applied. Thus, some of the data might have come out significant by chance and must thus be carefully interpreted. Nevertheless, considering that most of the data were highly significant (with p < .001) and the good resolution of the technique, we think that the findings are representative of real alpha activity. Another limitation is that, in the present task, the participants had to provide a verbal answer after the image presentation. This verbal answer (yes/no or the object name) may account for some of the late prefrontal activation reported. Finally, it cannot be ruled out that other factors such as practice, fatigue, and recovery from the surgical procedure could account for some of the data, although we tried presenting both new and old stimuli on the same day to alleviate such an impact.

In conclusion, this study was able to describe precisely alpha activity in response to object recognition. Findings suggest a nonspecific involvement of alpha for top–down modulation. Moreover, alpha may mediate a communicational role by synchronizing its phase between regions so that memory templates can be transferred to bottom–up processes for comparisons. Interactions between alpha rhythm and other frequencies involved in object recognition, such as iGBR, and memory, such as theta rhythm, will help in providing a better understanding of the big picture underlying object recognition.

Acknowledgments

Reprint requests should be sent to Franco Lepore, Centre de Recherche en Neuropsychologie et Cognition, Department of Psychology, Université de Montréal, C.P. 6128 Succ Centre-ville, Montréal, Québec, Canada, H3C 3J7, or via e-mail: franco.lepore@umontreal.ca.

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