Neurobiology of Learning and Memory 92 (2009) 99–105

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Neurobiology of Learning and Memory

journal homepage: www.elsevier .com/ locate/ynlme

Grey-matter differences related to true and false recognition of emotionallycharged stimuli – A voxel based morphometry study

Artur Marchewka a,*, Katarzyna Jednoróg a, Anna Nowicka a, André Brechmann b, Anna Grabowska a,c

a Nencki Institute of Experimental Biology, Department of Neurophysiology, Laboratory of Psychophysiology, 3, Pasteur Street, 02-093 Warsaw, Polandb Non-Invasive Brain Imaging, Leibniz Institute for Neurobiology, D-39118 Magdeburg, Germanyc Warsaw School of Social Sciences and Humanities, 03-815 Warsaw, Poland

a r t i c l e i n f o a b s t r a c t

Article history:Received 12 December 2008Revised 27 February 2009Accepted 7 March 2009Available online 16 March 2009

Keywords:MemoryMagnetic resonance imagingEmotion

1074-7427/$ - see front matter � 2009 Elsevier Inc. Adoi:10.1016/j.nlm.2009.03.003

* Corresponding author. Fax: +48 22 822 5342.E-mail address: a.marchewka@nencki.gov.pl (A. M

The issue concerning the neuronal basis of true and false recognition is still a subject of extensive debate.In the present study voxel based morphometry (VBM) was used to examine structural brain correlates ofthese processes. Since several studies indicate that emotional content facilitates false recognition wedecided to use emotional stimuli. Behavioral measures, i.e., true and false recognition rates were usedas covariants in VBM analyses. VBM results indicated that the true recognition correlated positively withgrey-matter (GM) density in bilateral amygdala, anterior cingulate and middle temporal gyrus, i.e., brainregions, involved in the memory of emotional material, as revealed by fMRI results. False recognition cor-related negatively with GM density in prefrontal areas (BA47 and BA9), supporting the role of the pre-frontal cortex in monitoring retrieval and limiting the probability of false recognition. Thus our VBMfindings (i) point to the brain structures critical for correct and false emotional memory and (ii) disclosestructural differences between the neural bases of these two types of memory.

� 2009 Elsevier Inc. All rights reserved.

1. Introduction

Since the early study of Bartlett (1932) it has been recognizedthat memory is not a literal reproduction of the past, but is rathera reconstructive process in which bits and pieces of informationfrom different sources are pulled together. Studies of memorywould not be possible if memory was perfect. Its imperfections,however, are not restricted to simply forgetting; also includedare distortions and illusions such as false recognition. This occurswhen subjects incorrectly claim that a novel item has beenencountered previously (Schacter & Slotnick, 2004). Though, therehave been an increasing number of studies examining the func-tional brain correlates of recognition memory using positron emis-sion tomography (PET) and functional magnetic resonance imaging(fMRI), the neural basis of false recognition and true recognition isstill not fully understood.

An early PET block design study (Schacter, Reiman, et al., 1996)on auditory material showed that true (studied words) and false(non-studied associated words) recognition contrasted with thebaseline fixation condition, activated similar regions, the dorsolat-eral prefrontal cortex, medial parietal, and medial temporal re-gions. Direct comparison indicated greater activation for truethan false recognition in the left temporal region. Activations re-lated to true recognition in this study were interpreted as a sensory

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archewka).

signature of memory processes. A subsequent event-related fMRIexperiment (Schacter, Buckner, Koutstaal, Dale, & Rosen, 1997) re-ported that there were common areas of activations when true(old-hit) and false recognition (related-false alarm) were comparedto a fixation baseline control. However, no activations were foundwhen true recognition was compared to false recognition. AnotherfMRI study (Cabeza, Rao, Wagner, Mayer, & Schacter, 2001) usingword lists documented that the parahippocampal gyrus was moreactive when true recognition (old-hit) was compared with falserecognition (related-false alarm). It was concluded that this activa-tion reflected true recognition-related contextual memory sincethe parahippocampal gyrus has been associated with contextualprocessing (see also, Bar & Aminoff, 2003). Structures involved inboth true and false recognition, when compared directly, includedthe prefrontal cortex, parietal cortex and hippocampus. Those re-gions were often reported as related to veridical old/new recogni-tion memory (Slotnick, Moo, Segal, & Hart, 2003; Wheeler &Buckner, 2003). Orbitofrontal cortex and cerebellar regions weremore active for false than for true items.

The aforementioned findings were obtained for verbal stimuli.In 2004 Slotnick & Schacter using abstract shapes as stimuli at-tempted to show a sensory signature that distinguishes true fromfalse memory. Both true (old-hits > new-correct rejections) andfalse recognition (related-false alarms > new-correct rejection)activated a large number of brain regions – prefrontal cortex, pari-etal cortex, hippocampus and late visual processing areas (BA19,BA37), showing overlap with regions activated by words. Analysis

100 A. Marchewka et al. / Neurobiology of Learning and Memory 92 (2009) 99–105

revealed that only early visual processing regions (BA17, BA18)showed greater activation when true recognition (old-hit) wascompared to false recognition (related-false alarm). This resultwas therefore in support of the authors’ sensory reactivationhypothesis. The activity in early visual processing regions, how-ever, did not reflect a neural correlate of conscious recognitionfor sensory information but was a manifestation of repetitionpriming (i.e., non-conscious, implicit memory). On the other hand,false recognition contrasted with true recognition led to activationmainly in the left temporal cortex, including Wernicke’s area(BA22). The activity found in language processing areas was in linewith a previous study reporting bias toward verbal retrieval strat-egies associated with false memory (Schooler, Gerhard, & Loftus,1986).

The above mentioned studies (see Schacter & Slotnick, 2004 forreview) which used rather simple visual stimuli like words or geo-metrical shapes were inconclusive concerning the neuronal basisof recognition. False recognition-related activations were foundin various structures, parahippocampal gyrus (Schacter, Reiman,et al., 1996), orbitofrontal cortex and cerebellar regions (Cabezaet al., 2001) and temporal cortex (Slotnick & Schacter, 2004). Acti-vations related to true recognition have been reported in the lefttemporal region (Schacter, Reiman, et al., 1996), parahippocampalgyrus (Cabeza et al., 2001) and early visual processing regions(Slotnick & Schacter, 2004). An additional shortcoming of studiesdone so far is that these true–false differences depend on the for-mat of the recognition test as well as material used as stimuli.Moreover, different contrasts/comparisons lead to distinct patternsof activations of various brain regions.

To avoid complications resulting from direct comparison ofexperimental conditions, in the present study, we used voxel basedmorphometry (VBM) to investigate the issue of structures involvedin true and false recognition. The VBM method, which was firstlyintroduced to show anatomical differences between healthy sub-ject and patients, and now, thanks to the greater precision of datapreprocessing, became a popular tool to study structural correlatesof higher functions in healthy populations (Ashburner & Friston,2000, 2005; Ridgway et al., 2008). In general the value of VBM isthat it permits the comprehensive measurement of differencesthroughout the entire brain. VBM technique allows also directmeasurement of GM density that reflects anatomical correlates ofcognitive parameters (Draganski & May, 2008; Maguire, Nannery,& Spiers, 2006).

Thus, we employed VBM in order to find structural brain corre-lates of behavioral measures of false and true recognition. In addi-tion, the use of the VBM method in this field was justified by thenotion that the subject’s age influences the false recognition ratethus suggesting plausible brain structural changes, mainly in theprefrontal cortex (Dennis, Kim, & Cabeza, 2008; Paz-Alonso, Ghetti,Donohue, Goodman, & Bunge, 2008). Also in our recent directedforgetting study we showed significant correlation between GMvolume of prefrontal cortex and hippocampus with behavioralmeasure of the ability to overcome the inhibitory impact of the‘‘forget” instruction (Nowicka, Jednoróg, Marchewka, & Brech-mann, in press).

True and false recognition rates were used as covariants in VBManalyses, i.e., we were interested whether an increase in such rateswas accompanied by an increase or a decrease in GM density.Based on several recent studies (El Sharkawy, Groth, Vetter, Ber-aldi, & Fast, 2008; Marchewka et al., 2008; Otgaar, Candel, & Merc-kelbach, 2008) we decided to use emotionally charged pictures(both emotionally negative and positive) as stimuli in order tomaximize the false recognition rate. In our previous study usingpictorial material (photographs taken from IAPS, Lang, Bradley, &Cuthbert, 2008) we reported that emotionally negative pictures in-creased the false recognition rate in comparison to emotionally

neutral pictures (Marchewka et al., 2008). Similarly, using verbalmaterial El Sharkawy et al. (2008) reported that critical lures asso-ciated with negative words lists elicited significantly more falsememories than those associated with neutral word lists (but seePesta, Murphy, & Sanders, 2001). Additionally, when listening tonarratives concerning a negative event, children elicited more falsememories than for a neutral event (Otgaar et al., 2008). Thus, itseems that enhanced false recognition rate for emotional itemshas been observed regardless of stimulus modality (visual, audi-tory) or type of processed information (verbal, non-verbal). Inter-estingly, some psychological studies even point out that falserecognition is always related to emotions (Drivdahl, Zaragoza, &Learned, 2009; Laney & Loftus, 2008).

Based on evidence suggesting age-related involvement of pre-frontal areas in false recognition (Dennis et al., 2008; Paz-Alonsoet al., 2008), findings of neuropsychological studies in patientswith prefrontal cortex damage (Schacter, Curran, Galluccio, Mil-berg, & Bates, 1996), and our previous fMRI results (Marchewkaet al., 2008), we expected changes in GM density in the prefrontalcortex related to false recognition rate. Furthermore, we predictedtrue recognition-related differences in GM density in brain regionsof limbic lobe and prefrontal cortex since these areas have beenfound to be associated functionally with recognition of emotionallycharged stimuli (Kensinger, Garoff-Eaton, & Schacter, 2007; see Bu-chanan, 2007 for review).

2. Materials and procedures

2.1. Participants

Twenty healthy, right-handed subjects (eight women) partici-pated in the study. The average age was 24.2 (SD = 0.67). Theywere students in their final year at the University of Magdeburg.All subjects gave their written informed consent to the study,which was approved by the Ethical Committee of the Universityof Magdeburg.

2.2. Stimuli and procedure

Images from the International Affective Picture System (Lang,Bradley & Cuthbert, 2008) were used as stimuli. These were photo-graphs of people, faces, landscapes, animals and objects whichwere initially sorted according to the norms into emotionally neg-ative and emotionally positive. In the first part of the study 100pictures were shown, 50 emotionally negative (mean va-lence = 2.90, SD = 0.85; mean arousal = 5.62, SD = 0.91; mean dom-inance = 3.95, SD = 0.76) and 50 emotionally positive (meanvalence = 7.00, SD = 0.8; mean arousal = 4.34, SD = 1.01; meandominance = 6.24, SD = 0.57). In the second part, 240 pictures wereshown, those displayed previously plus 140 new images (70 emo-tionally negative – mean valence = 2.91, SD = 1.02; mean arou-sal = 5.64, SD = 0.97; mean dominance = 3.91, SD = 0.79, and 70emotionally positive – mean valence = 6.97, SD = 0.66; mean arou-sal = 4.50, SD = 0.92; mean dominance = 6.1, SD = 0.52). Stimuliwere fully counterbalanced between the two study phases in re-spect of their content, valence, arousal and dominance.

During the first part of the study, the subject’s task was to an-swer the question whether a presented picture was emotionallynegative or positive. In the second part of the study, which tookplace after a 20 min break with an interfering task, the participantsunderwent a recognition test. All the original images were pre-sented again, mixed pseudo randomly with new ones. Subjectshad to categorize each picture as old or new. The stimuli were dis-played for approximately 1 sec using Presentation software (http://

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www.neurobs.com). On the same day, the subjects underwent theMRI scanning procedure.

2.3. MRI parameters and data analysis

Imaging was performed with a neuro-optimized GE 1.5 T SigmaHorizon LX MRscanner. T1-weighted high-resolution anatomical3D was obtained by using a spoiled GRASS 8 sequence (TR = 24 ms,TE = 8 ms, FOV = 250 x 250 mm). 3D images were then interpolatedto the 256 slices (voxel size 1x1x1) using Brain Voyager software.The MRI data were analyzed using VBM implemented in StatisticalParametric Mapping (SPM5, Wellcome Department of ImagingNeuroscience, London, UK) run on MATLAB 7.1 (Mathworks, Na-tick, MA, USA). The preliminaries, pre-processing and statisticswere performed according to published guidelines for reportingVBM results (Ridgway et al., 2008). First, data were displayed inSPM5 and reasonable alignment of the images with the T1 tem-plate was checked. The next step was the unified segmentation(Ashburner & Friston, 2005) of GM and white matter (WM). Theunmodulated normalized data were used. Finally, data weresmoothed with a 10 mm full-width, half-maximum (FWHM)Gaussian kernel. Statistical analysis was conducted using one-sam-ple T-test. An absolute threshold masking value of 0.05 was se-lected. We performed two separate analyses for the twocovariants: false and true recognition rate. The age of the subjectwas also used as a covariant since the age may influence grey mat-ter distribution. Data with more than five significant voxels are re-porter at P < 0.05, FWE corrected after usage of small volumecorrection – SWC (Worsley et al., 1996). Correlation between GM

Fig. 1. Differences in GM density in the right inferior frontal gyrus related to false recognsuperimposed regions where GM density correlated negatively with false recognition ra

density and false or true recognition rate was established bytwo-tail Pearson correlation test.

3. Results

3.1. Behavioral results

False recognition rates (new items recognized as old) rangedfrom 5% to 32% (mean = 15.5, SD = 7.42). There were no significantdifferences in false recognition rate, as measured by T-tests, eitherbetween positive and negative stimuli (mean = 16.77, SD = 8.89;mean = 14.27, SD = 8.55, respectively) or between men and women(mean = 16.2, SD = 6.58; mean = 14.5, SD = 8.92, respectively). Inaddition, Pearson correlation analysis for positive and negativestimuli revealed significant positive correlation (r = 0.513 atP < 0.05). True recognition rate (correctly recognized old items)ranged from 46% to 92% (mean = 78.3, SD = 12.1). Similarly, therewere no significant differences in true recognition rate, as mea-sured by T-test, either between positive and negative stimuli(mean = 77.29, SD = 14.63; mean = 79.28, SD = 11.40, respectively)or between men and women (mean = 78.08, SD = 13.67;mean = 78.6, SD = 10.03, respectively). Additionally, Pearson corre-lation analysis for positive and negative stimuli revealed signifi-cant strong positive correlation (r = 0.780 at P < 0.01). Due to lackof significant differences as well as positive correlation in recogni-tion rate between positive and negative stimuli, we averaged posi-tive and negative recognition rates to obtain one covariant for falseand one for true recognition in the VBM analyses.

ition rate. (a) Sagittal and coronal views showing normalized T1 single subject withte; (b) GM density as a function of false recognition rate.

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3.2. VBM results

The whole brain VBM analyses based on regression model withfalse recognition rate as a covariant revealed that there were twostructures where GM density correlated negatively with false rec-ognition (i.e., the higher the GM density the lower the false recog-nition rate). The first was the right inferior frontal gyrus (BA 47;x = 42, y = 24, z = 2; T = 6.26; number of voxels = 34; correlationcoefficient two-tail r = �0.828 at P < 0.001, see Fig. 1). The otherstructure was the left dorsolateral prefrontal cortex (DLPFC, BA9;x = �2, y = 52, z = 26; T = 5.57; number of voxels = 101; correlationcoefficient two-tail r = �0.795 at P < 0.001). No structure wasfound where GM density correlated positively with the false recog-nition rate. Separate region of interest analyses based on Brod-mann’s areas using WFU_pick atlas (Maldjian, Laurienti, Kraft, &Burdette, 2003) for other part of prefrontal cortex did not showany significant changes in GM related to false recognition.

The whole brain VBM analyses with the true recognition rate asa covariant pointed to structures where GM density correlated pos-itively with true recognition (i.e., the higher GM density the higherthe true recognition rate). These included the right amygdala(x = 30, y = 0, z = �16; T = 3.73; number of voxels = 48; correlationcoefficient two-tail r = 0.657 at P < 0.001, see Fig. 2), the left amyg-dala (x = �30, y = �2, z = �16; T = 3.51; number of voxels = 43; cor-relation coefficient two-tail r = 0.627 at P < 0.001), the left anteriorcingulate (BA 32; x = �10, y = 32, z = 22; T = 3.73; number of vox-els = 52; correlation coefficient two-tail r = 0.682 at P < 0.001)and the right middle temporal gyrus (BA21; x = 42, y = �4,

Fig. 2. Differences in GM density in the right amygdala related to true recognitionsuperimposed regions where GM density correlated positively with true recognition rat

z = �28; T = 3.51; number of voxels = 32; correlation coefficienttwo-tail r = 0.617 at P < 0.001). Region of interest analysis for hip-pocampus and other structures of the limbic lobe and prefrontalcortex did not revealed any significant changes of GM density re-lated to true recognition. All significant GM density changes re-lated to false and true recognition are depicted in Fig. 3. No othersignificant results were obtained.

In addition, to explore positive and negative emotional effectswe performed a region of interest (ROI) analysis of amygdala withmore liberal statistical threshold (at P < 0.005, uncorrected), sepa-rately for negatively and positively charged items. It revealedhighly positive correlation (left amygdala, correlation coefficienttwo-tail r = 0.655, P < 0.001; right amygdala, r = 0.686, P < 0.001)between true recognition rate of emotionally negative stimuliand GM density. In case of emotionally positive stimuli, analysisshowed weaker positive correlation (left amygdala, r = 0.534,P < 0.05; right amygdala, r = 0.495, P < 0.05) than for emotionallynegative stimuli.

4. Discussion

We used the VBM method to study GM differences related tofalse and true recognition of emotionally charged stimuli. Up todate, a relatively small number of VBM papers have been pub-lished. Moreover, most of them were aimed at investigatingchanges in brain structure related to diseases or disorders such anarcolepsy, migraine, Alzheimer, Parkinson and dyslexia. VBM

rate. (a) Sagittal and coronal views showing normalized T1 single subject withe; (b) GM density as a function of true recognition rate.

Fig. 3. Differences in GM density related to false and true recognition rate superimposed on render single subject T1 image.

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experiments on healthy subject showed that the concentration ofGM in the hippocampus of healthy humans correlates positivelywith spatial learning (Bohbot, Lerch, Thorndycraft, Iaria, & Zijden-bos, 2007) and with the g-factor of intelligence (Colom, Jung, & Ha-ier, 2006). It has also been reported that the GM density correlatesnegatively with some deficits such as developmental dyscalculia(Rotzer et al., 2008). To our knowledge this is the first study whichemployed VBM technique to study recognition processes inhealthy subjects. We showed that the GM density correlates nega-tively with the false recognition rate of emotionally charged mate-rial in the right inferior prefrontal cortex (BA47, IFC) and the leftdorsolateral prefrontal cortex (BA9, DLPFC). In other words thosesubjects who committed less false recognition errors had greaterGM density in those structures. The IFC and the DLPFC have beenimplicated, as revealed by fMRI and PET, in various aspects of hu-man behavior, memory processes (Blumenfeld & Ranganath, 2006),monitoring functions (Gallo, Kensinger, & Schacter, 2006), decep-tion (Spence, Kaylor-Hughes, Farrow, & Wilkinson, 2008) and deci-sion making (Sakagami & Pan, 2007). In our previous fMRI study onfalse recognition, using different set of stimuli and different groupof subjects, we found activations in the right inferior frontal cortex(Marchewka et al., 2008), the same region for which GM densitychanges were observed in the present study. In line many neuro-imaging memory recognition studies also implicated various partsof prefrontal cortex as related to false recognition (Kensinger et al.,2007; Schacter, Curran, et al., 1996; Schacter et al., 1997; Slotnick &Schacter, 2004). In general, the prefrontal structures are attributedto monitoring processes that regulate memory accuracy, such assearching specific information and resulting decision processes(Gallo et al., 2006). Patients with prefrontal cortex damage often

show elevated false recognition to new events (e.g., Verfaellie,Rapcsak, Keane, & Alexander, 2004) and sometimes create imagi-nary stories about their past (i.e., confabulation; Burgess & Shallice,1996). Consequently, current findings of decreased GM density inprefrontal structures, correlating negatively with false recognitionrate, might reflect weaker monitoring functions which result ingreater vulnerability to false recognition under high monitoringdemands.

Our study revealed that the true recognition rate of emotionallycharged stimuli correlated positively with the GM density in amyg-dala, bilaterally, the left anterior cingulate (BA32) and the rightmiddle temporal gyrus (BA21). The role of amygdala in processingand memory of emotional events has been implicated by manystudies (see Sergerie, Chochol, & Armony, 2008 and Costafreda,Brammer, David, & Fu, 2008 for recent reviews). Costafreda et al.(2008) reported that in 385 neuroimaging studies emotional stim-uli were associated with higher probability of amygdala activitythan neutral stimuli. Those results are in line with earlier modelpostulating that the human amygdala acts as a ‘‘relevance detec-tor”, involved in processing of biologically relevant stimuli, regard-less of their valence (Sander, Grafman, & Zalla, 2003). As pointedout by Ritchey, Dolcos, and Cabeza (2008) it is thanks to the mod-ulatory influence of amygdala on medial temporal lobe that emo-tional episodic memories, due to arousal-driven enhancements,improve consolidation compared to neutral memories. Amygdalaactivity during the processing of emotional items correlates withthe subsequent memory of those stimuli (Cahill et al., 1996).

In normal subjects, amygdala activity has been found to correlatewith individual differences in recollection persistence for emotional(but not for neutral) pictures (Ritchey et al., 2008) and it is related to

104 A. Marchewka et al. / Neurobiology of Learning and Memory 92 (2009) 99–105

successful true, but not false, recognition of emotionally charged(negative) items (Kensinger et al., 2007). In line, patients with bilat-eral amygdala damage failed to show enhanced memory for emo-tionally arousing material (Adolphs, Cahill, Schul, & Babinsky,1997) and severity of amygdala pathology could predict memoryperformance for emotional items. Similarly, in our current studywe obtained a positive correlation between recognition accuracyof emotionally positive and negative items and GM density in theamygdala in healthy subjects. However, one might doubt if our datacould specifically be associated with the emotional nature of thestimuli because we did not include neutral items in this study. Tryingto relate to this issue, we performed separate ROI analyses for posi-tively and negatively charged stimuli based on the results of previ-ous studies showing a specific role of amygdala for negative (fear,anger) emotions (Schafe & LeDoux, 2004). We obtained correlationbetween true recognition and GM density for both negative and po-sitive stimuli; however the effect was stronger for the former. Thiscorroborates with studies implicating the there is higher probabilityof amygdala activation for emotionally negative stimuli (for reviewsee, Costafreda et al., 2008).

Our study revealed that not only the GM density in amygdalabut also GM in left anterior cingulate and right middle temporalgyrus correlates with correct recognition. In line with this finding,patients who received an anterior cingulotomy exhibited a generalimpairment in their emotion recognition accuracy compared tohealthy controls (Ridout et al., 2007). The authors argued thatthe observed deficit in emotion recognition accuracy may be a con-sequence of impaired attentional control, due to surgical lesions tothe anterior cingulate cortex. Findings in subjects with focal dam-age to the medial prefrontal cortex, with maximal overlap in thedorsal anterior cingulate cortex revealed that this structure is crit-ical for monitoring memory performance. Specifically, it generatesaccurate recall confidence and feeling-of-knowing judgments(Modirrousta & Fellows, 2008). As revealed by VBM, lower recogni-tion accuracy for negative facial expressions, observed in dementiapatients, correlated with regional changes in GM tissue content inthe right middle temporal gyrus (BA21) and right lateral inferiortemporal gyrus (BA20) (Rosen et al., 2006). These data suggeststhat regions in the right temporal network are important for visualprocessing of negative emotions.

On the behavioral level we showed that there were no signifi-cant differences in false and true recognition rates between emo-tionally negative and positive material. These findings may implythat it is not valence which influences recognition memory, butemotionality per se, probably due to plausible semantic cohesive-ness of emotional items (McNeely, Dywan, & Segalowitz, 2004).

In conclusion, we have found brain structures where GM den-sity correlates with behavioral measure of true and false recogni-tion. Our findings point to specific brain areas that can serve asmarkers of recognition memory for emotional pictorial materialto the extent that GM density might reflect certain predispositionsor experience induced reorganizations. However, a potential meth-odological shortcoming of our study is the fact that we cannot dis-tinguish whether GM differences related to true recognition reflecteffective encoding processes or if they mirror successful retrieval.Moreover, having no neutral comparison condition, we cannotexplicitly say whether the observed GM correlates are specific onlyfor emotionally charged material. Further investigation includingnon-emotional control pictures could clarify this matter.

Acknowledgments

We would like to thank Claus Tempelmann for technical help inMRI experiments. The study was supported by the Polish Ministryof Science and Higher Education grant (N 106 042 034) and Centerfor Advanced Imaging (BMBF 01GO0505).

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