NeuroImage 49 (2010) 865–874

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A common functional brain network for autobiographical, episodic, and semanticmemory retrieval

Hana Burianova a,b,⁎, Anthony R. McIntosh a,b, Cheryl L. Grady a,b,c

a Department of Psychology, University of Toronto, Toronto, Ontario, Canadab Rotman Research Institute at Baycrest, Toronto, Ontario, Canadac Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada

⁎ Corresponding author. Psychology Department, UGeorge Street, Toronto, Ontario, Canada M5S 3G3.

E-mail address: hburian@rotman-baycrest.on.ca (H.

1053-8119/$ – see front matter © 2009 Elsevier Inc. Adoi:10.1016/j.neuroimage.2009.08.066

a b s t r a c t

a r t i c l e i n f o

Article history:Received 22 April 2009Revised 9 August 2009Accepted 31 August 2009Available online 8 September 2009

The objective of this study was to delineate a common functional network that underlies autobiographical,episodic, and semantic memory retrieval. We conducted an event-related fMRI study in which we utilizedthe same pictorial stimuli, but manipulated retrieval demands to extract autobiographical, episodic, orsemantic memories. To assess this common network, we first examined the functional connectivity ofregions identified by a previous analysis of task-related activity that were active across all three tasks. Threeof these regions (left hippocampus, left lingual gyrus, and right caudate nucleus) appeared to share acommon pattern of connectivity. This was confirmed in a subsequent functional connectivity analysis usingthese three regions as seeds. The results of this analysis showed that there was a pattern of functionalconnectivity that characterized all three seeds and that was common across the three retrieval conditions.Activity in inferior frontal and middle temporal cortex bilaterally, left temporoparietal junction, and anteriorand posterior cingulate gyri was positively correlated with the seeds, whereas activity in posterior occipito-temporo-parietal regions was negatively correlated. These findings support the idea that a common neuralnetwork underlies the retrieval of declarative memories regardless of memory content. This proposednetwork consists of increased activity in regions that represent internal processes of memory retrieval anddecreased activity in regions that mediate attention to external stimuli.

© 2009 Elsevier Inc. All rights reserved.

Introduction

Despite extensive research in the past several decades, a consensushas yet to be reached as to the neural organization of declarativememory retrieval. Conceptually, declarative memory retrieval hasbeen traditionally dissociated into three types: (1) semantic retrieval,characterized by the conscious recollection of factual information andgeneral knowledge (Tulving, 1972); (2) episodic retrieval, character-ized by the conscious recollection of experienced events, whichoriginally included personally relevant events (Tulving, 1972), buttoday typically pertains to memory for stimuli encoded in thelaboratory (i.e., ”laboratory memory”; see Cabeza and St. Jacques,2007); and (3) autobiographical retrieval, characterized by theconscious recollection of personally relevant events (Conway andPleydell-Pearce, 2000). Much of the data from neuropsychological andneuroimaging experiments (e.g., Cipolotti and Maguire, 2003; Gadianet al., 2000; Hirano and Noguchi, 1998; Manns et al., 2003; Nyberg,McIntosh and Tulving, 1998; Vargha-Khadem et al., 1997) support the

niversity of Toronto, 100 St.

Burianova).

ll rights reserved.

multiple memory systems view of the organization of declarativememory (Tulving, 1987). This view posits that there are separatememory systems, which specialize in the processing of distinct typesof information and recruit functionally independent neural networks,each mediating a specific memory function (Cabeza and Nyberg,2000; Gabrieli, 1998; Nyberg, McIntosh and Tulving, 1998; Nyberg etal., 2002; Tulving and Schacter, 1990; Tulving, 1987). Empiricalevidence supporting this view comes primarily from studies showingfunctional dissociations between episodic, autobiographical, andsemantic memory. Neuropsychological studies show that patientswith medial temporal lobe lesions are usually impaired on tasksinvolving autobiographical memory, but not on tasks involvingsemantic memory (Gadian et al., 2000; Hirano and Noguchi, 1998;Vargha-Khadem et al., 1997), suggesting that the medial temporallobes (the hippocampus, in particular) engage autobiographicalmemory exclusively (Tulving et al., 1991; Tulving and Markowitsch,1998; Vargha-Khadem et al., 1997). Conversely, patients withsemantic dementia whose neural damage often involves frontotem-poral lobar degeneration (Hodges andMiller, 2001; Neary et al., 1998)are characterized by severe semantic memory loss, whereas theirautobiographical memory is relatively spared (Graham et al., 2003;McKinnon et al., 2006; Snowden, Griffiths and Neary, 1994;Westmacott and Moscovitch, 2003). Further neural dissociations

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have been found in neuroimaging studies that show regional activityin the left inferior prefrontal cortex and left posterior temporal areasrelated to semantic retrieval (Graham et al., 2003; Vandenberghe etal., 1996; Wiggs, Weisberg and Martin, 1999), left-lateralized activityin the medial temporal and ventromedial frontal regions, tempor-opolar areas, retrosplenial cingulate cortex, and cerebellum related toautobiographical retrieval (Conway et al., 2002; Gilboa, 2004; Grahamet al., 2003; Maguire, 2001), and activity in the right dorsolateralprefrontal areas subserving “laboratory” episodic retrieval (Duzel etal., 2004; Gilboa, 2004). It is critical to point out, however, that thefocus of most of these neuropsychological and neuroimaging studieswas largely on differences and functional dissociations of autobio-graphical, episodic, and semantic memory retrieval. In other words,the experimental paradigms (e.g., using different tasks to test episodicand semantic memory) and statistical methods (e.g., subtractingactivity during onememory condition from another, instead of using aneutral baseline) were designed to dissociate neural correlates amongthe presumably different types of declarative memory.

An alternative perspective to the multiple systems view is theunitary system view of the organization of declarativememory, whichproposes the idea of a single declarative memory system (Baddley,1984; Friston, 2002; Kihlstrom, 1984; McIntosh, 1999; Rajah andMcIntosh, 2005; Roediger, 1984). Proponents of this view conjecturethat a unitary memory system gives rise to all declarative memoryretrieval, although memories can vary along a contextual continuumof several dimensions, such as time, space, emotion, or strength ofrecollection (e.g., Baddley, 1984). This view rests on severaltheoretical assumptions. Firstly, encoding of to-be-rememberedmaterial is almost always contextual (i.e., embedded in alreadyattained knowledge; Baddley, 1984); at retrieval, memories may ormay not become decontextualized along one or more of thesedimensions (Baddley, 1984; Rajah and McIntosh, 2005; Westmacottand Moscovitch, 2003). Autobiographical and semantic types ofmemory may thus be conceptualized as the opposite ends of thecontextual continuum (Baddley, 1984; Kihlstrom, 1984; Roediger,1984). Secondly, even if autobiographical memory is at the mostdetailed end of the contextual continuum, it is not free of factual,semantic information (Gilboa, 2004; Levine et al., 2002); hence, thereis considerable overlap among these ‘types’ of memory, and theboundaries between them are often unclear. Finally, in a similar vein,semantic memory is rarely entirely context-free, but rather containssome contextual and episodic components, although these may bedegraded and lack rich detail (Gilboa, 2004; Westmacott andMoscovitch, 2003; Westmacott et al., 2004).

Evidence supporting the unitary memory system view consists ofboth neuroimaging and neuropsychological data that show functionaloverlap and interdependence of different memory functions (Duncanand Owen, 2000; Kopelman and Kapur, 2001; Manns et al., 2003;Rajah and McIntosh, 2005; Squire and Zola, 1998). These includestudies comparing working, episodic, and semantic memory (Nyberget al., 2002, 2003), working and episodic memory (Braver et al., 2001;Cabeza et al., 2002; Duzel et al., 1999), and semantic and episodicmemory (Rajah and McIntosh, 2005; Ryan et al., 2008), all of whichreported commonalities in neural activations across the memorytasks. Rajah and McIntosh (2005) provided evidence for the unitaryview by modeling separate functional networks for episodic andsemantic retrieval tasks and assessing interregional correlationsacross the two tasks. The results showed no significant differencesin the interregional correlations, despite differences in regionalactivations in the two memory tasks, suggesting the involvement ofa single memory system or network. Other data (e.g., Moscovitch,1992; Seger et al., 2000; Shimamura, 1995) contradict the prefrontalhemispheric differentiation of episodic and semantic retrieval (i.e.,activity in the right prefrontal cortex is associated solely with episodicretrieval [Buckner et al., 1998; Cabeza and Nyberg, 2000] and activityin the left prefrontal cortex is solely associatedwith semantic retrieval

[e.g., Goldberg et al., 2007]). In healthy individuals, activity in the rightprefrontal cortex was shown to underlie retrieval of novel andcreative semantic relations (Dobbins and Wagner, 2005; Seger et al.,2000), whereas activity in the left prefrontal cortex was found tosubserve some aspects of episodic remembering (Nolde et al., 1998).

This support for the unitary view of declarative retrieval evenextends to the hippocampus, traditionally thought to subserveepisodic and autobiographical memory only (Tulving et al., 1991;Tulving and Markowitsch, 1998; Vargha-Khadem et al., 1997). Forexample, there is evidence that hippocampal amnesics, whencompared to healthy controls, exhibit impairments in semanticretrieval in addition to profound deficits in episodic and autobio-graphical retrieval (Kopelman and Kapur, 2001; Manns et al., 2003;Squire and Zola, 1998). Similarly, recent imaging studies of healthyindividuals have reported hippocampal activation in both episodicand semantic retrieval (e.g., Burianova and Grady, 2007; Ryan et al.,2008). These data suggest that the hippocampus and the medialtemporal cortices are involved in all declarative retrieval.

In a previous study, we identified regional activations common toautobiographical, “laboratory” episodic, and semantic retrieval (Bur-ianova and Grady, 2007). While these results reflect the importantneural substrates that are involved in declarative retrieval, they do notprovide direct evidence about the functional connectivity of theseneural regions. It seems reasonable to assume that memory retrieval,a highly complex cognitive process, would not be localized to discretebrain region(s), but rather would be mediated by the interactionamong a number of functionally related neural areas. This idea is notnew, as many researchers have argued that it is the activity ofdistributed neural networks and the interactions among anatomicallyconnected brain regions that directly yield cognitive functions, such asmemory (e.g., Finger, 1994; Friston, 1997; McIntosh, 1998, 2000;Mesulam, 1990). In other words, cognitive functions are the emergentproperties of the neural interactions (i.e., influences that neuralconstituents have on one another) among numerous brain areas thatcomprise a neural network (McIntosh, 1999). An essential aspect ofthe network approach to the neural organization of cognitive functionis the examination of the neural context associated with a specificbehaviour (McIntosh, 1998, 1999). Neural context is conceptualizedas the activity in a selected brain region that arises as a consequence ofmodulatory influences from other brain regions (McIntosh, 1998,1999). Thus, what is important in determining the neural underpin-ning of a cognitive function is the relation of activity in a brain areawith activity of those brain areas with which it is connected. Inparallel with this argument is the notion that by measuring only howmean neural activity changes with task in a specific brain area orareas, one might fail to observe relevant interregional functionalinteractions that occur despite no significant change in the meanactivity (e.g., Grady et al., 1998; McIntosh et al., 1994).

One approach to quantifying neural interactions is to assess thedegree of functional connectivity among brain regions, i.e., the degreeto which activity in a specific region correlates or covaries withactivity in other areas across the whole brain, thus functioningtogether as a network (Friston et al., 1993; Friston, 1994; Horwitz etal., 1984). A network is thus defined as a pattern of spatially remotebrain regions whose activity levels are correlated, or functionallyconnected across participants, in order to support a particularbehaviour, regardless of whether the average level of activity in anysingle region of the network is different between the experimentalconditions (Friston, 1994; Habib et al., 2003). To statistically studycomplex neural interactions between different brain structures, theanalytical methods must provide a means to quantifying the relationbetween brain regions, rather than focusing on mean activitydifferences. Multivariate approaches, such as the partial least squares(PLS) approach to image analysis, enable investigation of functionalconnectivity of neural regions by calculating the covariance betweenthe activity within selected seed voxels and all other brain voxels

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across the experimental conditions (McIntosh et al., 1996; McIntoshand Gonzalez-Lima, 1994). For instance, Addis et al. (2004a) identifieda number of autobiographical regions that differentiated general andspecific autobiographical memories from a control baseline usingunivariate contrasts, but only a subsequent multivariate PLS analysis(Addis et al., 2004b) revealed a shared functional network with acritical connection to the hippocampus. Furthermore, Addis et al.(2007) expanded on these findings by mapping an effectiveautobiographical network in healthy individuals and epilepticpatients. In addition to Addis' work, other researchers have assessedthe autobiographical network (Levine et al., 2004; Maguire et al.2001), pinpointing the interplay of frontal (medial, middle, inferior,and superior frontal gyri) and temporal areas (temporal pole,hippocampus, and parahippocampal gyrus), temporoparietal junc-tion, retrosplenial cortex, and posterior cingulate gyrus. Episodicretrieval of stimuli learned in the laboratory has been linked to afunctional network that overlaps to some degree with the autobio-graphical network, but that also recruits unique brain regions, forinstance, the insula, occipito-temporal cortex, posterior parietal areas,and precuneus (e.g., Nyberg et al., 2002; for a review see Gilboa et al.,2004). Semantic memory processes have been generally reflected inthe inter-correlation of activity in a number of temporal regions(inferior, middle, and superior temporal gyrus; Martin and Chao,2001; Vandenberghe et al., 1996), as well as in an overlap with theautobiographical network, particularly in the lateral temporal gyrus,temporoparietal junction, anterior cingulate gyrus, and ventrolateralprefrontal cortex (Lee et al., 2002; Mummery et al., 1996). However,despite these explorations of the functional and effective connectivityof the neural correlates that underlie declarative memory, no study todate has attempted to identify a single network common toautobiographical, episodic, and semantic retrieval in one experimentand/or analysis.

The purpose of this study was to delineate a functional network ofspatially distributed neural regions whose activity covaries across thethree memory conditions, i.e., to map a common functionaldeclarative retrieval network. To do so, the data were analyzedwith seed voxel PLS analysis (Della-Maggiore et al., 2000; McIntosh,1999; McIntosh et al., 1997; Schreurs et al., 1997) to determinewhole-brain patterns of activity correlated with seed regions thatshowed similar increases of activity across the memory conditions(Burianova and Grady, 2007). We hypothesized that autobiograph-ical, “laboratory” episodic, and semantic retrieval would recruit alarge-scale functional network that underlies the general processes ofmemory retrieval, such as top-down attentional control, responsemonitoring, integration of contextual information, working memorymanipulation of to-be-retrieved information, and processing ofsemantic representations.

Methods

Participants

Twelve right-handed, healthy young participants (mean age=27years; range=21–37 years; 3 males), with at least 16 years ofeducation, took part in the study. All participants signed an informedconsent that was approved by ethics boards at Baycrest andSunnybrook Health Science Centre.

Stimuli

Experimental stimuliFifty colour and black and white photographs depicting general,

everyday events (e.g., driving or camping), as well as one-time buthighly publicized occurrences (e.g., the 9/11 attack on the WorldTrade Center) were used as visual cues for the experimental retrievalconditions.

Control stimuliFive photographs were selected from the set of 50 described above

and scrambled using a Matlab script. This ensured that the visualstimulus was rendered meaningless.

Procedure

The study consisted of one control and three memory retrievalconditions during fMRI scanning. Four 14-min runs of 50 trials eachwere presented to the participants in a counterbalanced order. Trialswere randomized within each run. In each trial, an experimental orcontrol stimulus was shown for 4 s. Each experimental stimulus wasshown three times during the experiment, but never in sequence or inthe same scanning run. Participants were asked to pay attention to thephotograph, so that they could successfully answer a subsequentlypresented question that pertained to the stimulus. After the 4-spresentation of each picture, a question appeared on the screen withthree possible answers.

Participants had 10 s to respond by pressing 1, 2, or 3 on a numberpad. Note that accuracy of memory retrieval was emphasized overspeed, and the participants were instructed not to guess. The responseperiod was chosen to provide sufficient time for autobiographicalmemory retrieval. According to recent electrophysiological evidence,the range of retrieval times for autobiographical memory is between 3and 9 s, with an average time of 5 s (Conway et al., 2003). After the 10second response period there was a 1-s inter-trial interval, followedby the next trial. The three memory conditions were as follows:

1. Autobiographical condition, in which the stimulus was followed bya cue designed to elicit a personal memory (e.g., “Think of the lasttime you went camping”). Participants were asked to relive thememory as vividly as possible and subsequently rate the memoryaccording to its vividness (1=“very vivid,” 2=“somewhat vivid,”3=“not vivid at all”).

2. Episodic condition, in which the stimulus was followed by aquestion about the photograph itself (e.g., “In the picture, whichyou have just seen, what is the colour of the tent?”). Participantschose from three answers presented to them (1 or 2 being correct,3=I don't know). To ensure that this condition did not engage onlyworking or short-term memory, we varied the degree of difficultyof retrieved contextual information; hence, the participants wereunaware of which piece of information about the stimulus theywould be asked to retrieve. In addition, the presentation of thethree experimental conditions was randomized and the temporallag between subsequent presentations of the same visual stimuluswas at least 14 min; hence, our intent was that participants wouldneed to engage episodic memory retrieval from long-termmemorystorage about the perceptual details of the stimuli, and not solelyworking memory processes.

3. Semantic condition, in which the stimulus was followed by afactual type of question (e.g., “Are there more than 100 campinggrounds in Algonquin Park?”). Responses were made in the samefashion as in the episodic condition.

In the control condition, the presentation of a scrambledphotograph was followed by an arbitrary instruction that wasunrelated to the stimulus itself (e.g., “Press a key that correspondsto the letter ‘C’”). As in the experimental conditions, responses weremade by pressing 1, 2, or 3 on a keypad, and the correct key was either1 or 2. Reaction times for each button press were recorded across allconditions. These behavioural results have been reported elsewhere(Burianova and Grady, 2007).

A post-scan interview was administered immediately after thescan session. Participants viewed the 50 photographs again and wereasked to describe the autobiographical memory that had beenretrieved during the scan in as much detail as possible. Temporaland spatial information as well as the content of the event and

Table 1Seed voxel regions.

Region Hem BA Talairach coordinates Ratio

x y z

Caudate nucleus R n/a 12 15 −4 6.2Lingual gyrus L 18 −4 −70 3 4.9Hippocampus L n/a −28 −20 −16 6.9

Abbreviations: Hem=hemisphere; BA=Brodmann's area; R=right; L=left;Ratio=salience/SE ratio from the bootstrap analysis; x coordinate=right/left; ycoordinate=anterior/posterior; z coordinate=superior/inferior.

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participant's emotion at the time of its occurrence were recorded bythe experimenter, to ensure that each autobiographical memory wasaccompanied by a vivid recollection of a particular episode.

fMRI data acquisition

Anatomical and functional images were collected using a 3T GEscanner with a standard head coil. A standard, high resolution, T1-weighted volumetric anatomical MRI (124 axial slices, 1.4 mm thick,FOV=22 cm, acquisition matrix=256×256×124, TR=35 ms,TE=6 ms, flip angle=35°) was acquired for each participant. Brainactivation was assessed using the blood oxygenation level-dependent(BOLD) effect (Ogawa et al., 1990)with optimal contrast. For functionalimaging, 26 axial slices of 5 mm thickness were obtained, utilizing aT2⁎-weighted pulse sequence with spiral in-out readout (TR=2000 ms, TE=30 ms, FOV=20, acquisition matrix=64×64×26, flipangle=80°).

Visual stimuli were presented using fMRI-compatible goggles(Avotec, Inc.) mounted on the head coil. Responses were collectedwith the Rowland USB Response Box (RURB).

fMRI data preprocessing

Images were reconstructed and motion-corrected utilizing theAnalysis of Functional Neuroimages (AFNI; Cox, 1996). The imageswere spatially co-registered to correct for head motion of theparticipants by using a 3D Fourier transform interpolation. The peakrange of headmotion did not exceed 1.2mm across all participants. Toenable group comparisons, each brain scan was spatially normalized,i.e., scaled and warped to match a standard template (the MontrealNeurological Institute [MNI] spiral template) utilizing StatisticalParametric Mapping (SPM99) software. The warping of the brainsurface was achieved via a linear transformation with sinc interpo-lation (i.e., a signal resampling method designed to minimize aliasingin the signal). Lastly, the images were smoothed with a 6 mmGaussian filter (in SPM), which, acting as a low pass filter, makes thedata less noisy by reducing the images' high-frequency components.The voxel size, after preprocessing, was 4×4×4 mm.

Seed voxel PLS

In the seed PLS analysis, we included those trials for the semanticand episodic conditions for which participants made a correctresponse, and all “very vivid” and “somewhat vivid” trials for theautobiographical condition. Seed PLS is a multivariate statisticalmethod utilized in the investigation of the relation of activity in aselected brain region or regions (i.e., a seed voxel) and activity in therest of the brain across the task conditions (Della-Maggiore et al.,2000; Schreurs et al., 1997; McIntosh, 1999; McIntosh et al., 1997). Inother words, seed PLS analysis examines task-related functionalconnectivity. The selection of the seed voxel(s) can be either data-driven (i.e., determined by previous analyses of the data) orhypothesis-driven (i.e., determined by theoretical assumptions), orboth. In our study, the selection of the seed voxels used in the seed PLSanalysis was data-driven. In a previous study (Burianova and Grady,2007), we identified eight brain regions whose activity wassignificantly increased across all three memory conditions, thusshowing activity common to the conditions. To determine whetherthese regions are also a part of the same functional network, weentered all eight regions into a seed PLS analysis. The analysis revealeda connectivity pattern across the three memory conditions, but not allregions were reliably correlated with this pattern. Therefore, asubsequent analysis was carried out, using the three seed regionsthat covaried most strongly with the rest of the brain across the threememory conditions—the left hippocampus, right caudate nucleus, andleft lingual gyrus (see Table 1 for the coordinates).

This analytical procedure for seed PLS was threefold: firstly, theBOLD values from the selected seed(s) were extracted (i.e., from thepeak voxels identified in our previous study), across 8 timepoints aftereach presentation of the question cue to capture activity during theretrieval phase of the trial. The activity for each seed was averagedacross the peak and adjacent timepoints, and then this averagemeasure of seed activity was correlated with activity in all other brainvoxels, across all participants, within each condition. Secondly, thesecorrelations were combined into a matrix and decomposed withsingular value decomposition (SVD), resulting in a set of latentvariables (LVs; mutually orthogonal variables). Each LV consists of asingular image (i.e., “brain LV,” or the pattern of brain regions thatcovary in activitywith the seed voxel), a singular profile (i.e., “seed LV,”or the pattern of covariance of the seed voxel and the rest of the brainacross the experimental conditions), and a singular value (i.e., theamount of covariance accounted for by each LV). Finally, thesignificance for each LV is determined by using a permutation test(McIntosh et al., 1996), which involves a random reordering of thedatamatrix and calculation of a new set of LVs for each reordering. Thesingular value of each newly permuted LV is compared to the singularvalue of the original LV, yielding a probability of the number ofoccurrences that the permuted values exceed the original value. Fivehundred permutations were conducted. Because this study used anevent-related design, the PLS analysis provided a set of correlatedregions (i.e., a map of areas correlated with the seeds) for each of theeight TRs in the analysis. For each TR, a “brain score”was calculated foreach participant that is an index of how strongly that participantshows the particular pattern of brain activity identified for that TR. Thebrain scores can be used to examine differences in brain activity acrossconditions, because greater activity in brain areas with positive (ornegative) weights on a latent variable will yield positive (or negative)mean scores for a given condition. We calculated the correlationbetween the brain scores from each significant LV and the seed BOLDvalues to assess the relation between the whole-brain pattern andactivity in the three reference regions. In addition to the permutationtest, a second and independent step is to determine the reliability ofthe saliences (or weights) for the brain voxels characterizing eachpattern identified by the LVs. To do this, all saliences for each TR weresubmitted to a bootstrap estimation of the standard errors (Efron andTibshirani, 1985), randomly resampling participants, with replace-ment, and computing the standard error of the saliences after 100bootstrap samples. Peak voxels with a salience/SE ratio N3.0 wereconsidered to be reliable, as this approximates pb0.005 (Sampson etal., 1989). Because PLS uses images in the format developed by theMontreal Neurological Institute (MNI), all coordinates resulting fromthe PLS analyses were converted from MNI space to Talairachcoordinates using the algorithm developed by Brett and colleagues(www.mrc-cbu.cam.ac.uk/Imaging/Common/mnispance.shtml).

Correlation analysis

The seed PLS, using the three seeds, resulted in a set of 18 regionswhose activity was reliably and strongly correlated with all three, andthis set of regions constituted the putative common network. To

Table 2Correlations with seed activity.

Region Hem BA Talairach coordinates Ratio Cluster

x y z

Positive correlationsInferior FG L 47 −36 38 −9 6.8 50

R 47 48 23 −11 5.6 29Medial FG L 10 −4 50 −6 6.9 25Anterior CG L 24/32 −8 35 9 6.6 30Superior TG L 22 −55 −53 21 5.9 35Middle TG L 21 −48 −20 −2 6.5 74

R 21 48 −20 −2 7.7 83Temporal pole R 22 51 8 −4 7.1 70Inferior PL (TPJ) L 40 −63 −49 32 7.0 25Posterior CG 23/31 0 −61 21 7.3 74

Negative correlationsMiddle OG L 19 −40 −81 15 −5.4 29Precuneus L 7 −8 −79 45 −7.2 44

R 7 12 −83 45 −6.9 95Thalamus R n/a 4 −15 15 −7.5 18Inferior TG L 37 −55 −51 −8 −6.5 52Fusiform gyrus R 20 40 −28 −19 −8.6 80Inferior PL (SMG) L 40 −36 −52 39 −9.8 100

R 40 32 −53 36 −9.3 34

Abbreviations: Hem=hemisphere; BA=Brodmann's area; R=right; L=left;Ratio=salience/SE ratio from the bootstrap analysis; Cluster=size of each cluster innumber of voxels; FG=frontal gyrus; CG=cingulate gyrus; TG=temporal gyrus;OG=occipital gyrus; PL=parietal lobule; TPJ= temporoparietal junction;SMG=supramarginal gyrus; x coordinate=right/left; y coordinate=anterior/posterior; z coordinate=superior/inferior.

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specifically examine the intercorrelations among these regions, weextracted the BOLD signal from them, i.e., from the peak voxel in eachcluster (see Table 2 for peak coordinates), and averaged the activity atthe timepoint corresponding to the strongest correlation with theseeds and adjacent timepoints. We then calculated interregionalcorrelations within conditions (creating three 21×21 correlationmatrices) and compared these correlation patterns across conditions.We compared the correlation patterns across the conditions bycalculating the squared differences in correlations (for each cell of thematrix) between conditions and summing them to obtain a singlevalue for the overall difference in correlations between conditions.The statistical significance of this difference was assessed using apermutation test on the sum of squared differences (McIntosh et al.,1996), which involved a random reordering of the data matrix andcalculation of a new set of values for each reordering.

Results

Behavioural performance

Behavioural performance was assessed by comparing themeans ofthe response times across the four conditions (correct trials only),using a repeated-measures ANOVA. The effect of condition wassignificant, F(3,33)=73.1, pb0.001. Pairwise t-tests with Bonferronicorrections for multiple comparisons showed that the response timesfor autobiographical retrieval (M=6989 ms, SD=1478) differedsignificantly from that for the control task (M=1925 ms, SD=728)and episodic retrieval (M=4640 ms, SD=1150, both at pb0.01). Thedifference in reaction time (RT) for autobiographical retrieval andsemantic retrieval (M=5858ms, SD=1366) approached significance(p=.06).

Seed PLS

The first latent variable yielded by the three-seed PLS analysisaccounted for the largest amount of covariance in the data (32%;pb0.002), delineating a group of brain regions whose activity

correlated with all three seed regions (the left hippocampus, leftlingual gyrus, and right caudate nucleus) during the retrieval ofautobiographical, episodic, and semanticmemories (see Fig. 1). Positivecorrelationswith the three seed regions were found in temporal cortex(bilaterally in themiddle temporal gyrus and superior temporal gyrus),frontal cortex (bilateral inferior frontal gyrus, medial frontal gyrus, andleft anterior cingulate gyrus), as well as in the left temporoparietaljunction and posterior cingulate gyrus. Negative correlations with theseed regions were found in the left middle occipital gyrus, rightthalamus, bilateral precuneus, bilateral fusiform gyrus, and bilateralsupramarginal gyrus (see Table 2 for a summary). These patterns ofcorrelation were found across all three memory conditions.

Correlation analysis

The seed PLS analysis identified a set of regions correlated with theseeds across all three memory conditions. To assess further whetherthis set of regions represents a common memory retrieval network,we determined if the correlations among all the nodes of this putativenetwork were statistically equivalent across the tasks, as one wouldexpect if the network operates in a similar fashion regardless of thetask demands. Fig. 2 shows three correlation matrices representingthe 3 seeds and the 18 regions that showed correlated activity withthe seeds, both positive and negative. Each matrix shows the visualrepresentation of interregional correlations of activity among the 21regions, within each memory condition. No significant differences(pN0.05) were found in the overall interregional correlation strengthsacross the threememory conditions. The correlationmatrices indicatethat during all memory conditions, most of the regions that positivelycorrelated with the seeds were also correlated positively with oneanother (denoted in lighter shades of gray), but negatively withregions that show negative correlations with the seeds (denoted indarker shades). Similarly, most of the regions with negative seedcorrelations correlated positively with each other, but negatively withthe regions with positive seed correlations. Despite some apparentinterregional differences, there are obvious clusters of strong positiveand negative correlations among the voxels that are consistent acrossthe three memory conditions. As a whole, the strength of the overallinterregional correlations did not differ across the conditions,consistent with the idea of a common functional memory network.

Discussion

The purpose of this study was to identify a functional network thatis common to autobiographical, episodic, and semantic retrieval. Theresults of the seed PLS analysis yielded a large-scale network of brainregions that were functionally connected to the left hippocampus,right caudate nucleus, and left lingual gyrus in all memory retrievalconditions. The inferior frontal gyri, medial frontal gyrus, anterior andposterior cingulate gyri, left temporoparietal junction, and a numberof temporal areas (bilateral middle temporal gyrus, superior temporalgyrus, and right temporal pole) positively correlated with the seedregions, whereas the left middle occipital gyrus, bilateral precuneus,right thalamus, left inferior temporal gyrus, right fusiform gyrus, andbilateral inferior parietal lobule negatively correlated with the seedregions. These results suggest that these areas, as a whole, comprise acommon functional network that subserves the general processes ofdeclarative memory retrieval. We propose that these processesinclude a multitude of higher-cognitive functions, for instance,working memory, selective attention, error monitoring, responseverification, and comprehension of concept representations. Weconclude from these results that, together, these cognitive processesand their integration are essential to what is defined as ‘declarativememory retrieval.’

The three seed regions selected for the functional connectivityanalysis have been previously identified as important for memory

Fig. 1. Seed PLS results: common functional network. (a) A pattern of correlated activity at 6–8 s after retrieval onset is shown. (b) A pattern of correlated activity at 10–12 s afterretrieval onset is shown. (c) Correlations of activity in the three seeds with the brain scores (summary measures of whole-brain activity in each subject per condition) are plotted toshow how activity in the seeds correlates with activity in the entire network. The error bars represent confidence intervals based on the bootstrap. (d) Activity in the caudate nucleusat 2–3 s after retrieval onset is shown. Yellow/red=positive correlations with the seeds; blue=negative correlations with the seeds.

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processing (e.g., Burianova and Grady, 2007). The hippocampus hasbeen implicated as a critical component of declarative retrievalprocesses (Cabeza et al., 2004; Eldridge et al., 2000; Prince et al., 2005)and relational memory (e.g., memory for the relations between itemsand context [e.g., Davachi andWagner, 2002], or between objects andfeatures of a scene [e.g., Ryan and Cohen, 2004]). The hippocampusalso may play a role in non-declarative processes, such as workingmemory and object organization (Seeck et al., 1995) and non-mnemonic processes, such as local feature extraction in formperception (Barense et al., 2005; Beason-Held et al., 1998) and visualdiscrimination of scenes (Davies et al., 2004). The left lingual gyrushas been linked to working memory maintenance of visuospatialinformation (Ragland et al., 2002), imagery (Kosslyn et al., 1993;Mazard et al., 2005), and crossmodal attention (Ferber et al., 2007).The caudate nucleus also was found to be involved in learning andmemory, particularly in regard to feedback processing and the abilityto shift a cognitive set in response to the environment (Packard andKnowlton, 2002).

The three seed regions were found to positively covary with anumber of frontal, temporal, and parietal areas. These areas offunctional connectivity broadly overlap with the common memory-related brain regions, which were found in our previous analysis(Burianova and Grady, 2007), although the exact locations are notidentical. The inferior frontal gyrus has been implicated in declarativeand working memory retrieval (Nyberg et al., 2002), as well as inresponse inhibition and selection control (Aron et al., 2004; Brass etal., 2005; Liddle et al., 2001), and top-down attentional control(Banich et al., 2000; Dove et al., 2008). Themedial prefrontal areas are

involved in social functions and self-referential processes in bothsemantic and episodic memory retrieval (Addis et al., 2004a,b; Gilboaet al., 2004; Levine et al., 2004). The functional connectivity of thesefrontal areas with the three seed regions suggests involvement of thefrontostriatal and hippocampo-frontal pathways. The frontrostriatalpathway has been implicated in abstract reasoning (Melrose et al.,2007), working memory (Ashby et al., 2005; Gazzaley et al., 2004),and internal object representations (Kraut et al., 2002), as well as inattentional control and cognitive planning and execution of a set-shift(Monchi et al., 2006), due to its predominant dopamine dependency(Chudasama and Robbins, 2006) and dopamine–glutamate interac-tions (Smith et al., 2004). The hippocampo-frontal pathway, involvingthe left hippocampus, has been linked to the retrieval of contextualdetails (Svoboda et al., 2006), despite some researchers' arguing itssole involvement in autobiographical memory retrieval (e.g., Addis etal., 2007). The common prefrontal link of the frontostriatal andhippocampo-frontal pathways allows for an indirect effect of thecaudate nucleus on the hippocampus and vice versa. Some research-ers speculate that the hippocampus and caudate nucleus functionallyinteract to jointly mediate behavioural output during performance(Dagher et al., 2001; Packard and Knowlton, 2002). We propose thatthe frontal nodes of the common retrieval network subserve top-down attentional, inhibitory, and monitoring functions, as well asworking memory manipulation and maintenance of informationnecessary for a successful declarative recollection of memories thatrelate to the self and others.

The middle and superior temporal gyri were involved in ourcommon network, and have been linked to the processing of personal

Fig. 2. Correlations among peak voxels of the common functional network. The three correlation matrices show similarities in regional interrelations across the three memoryconditions. Correlation values are indicated by shades of gray (positive correlations are denoted in lighter shades; negative correlations are denoted in darker shades). Values on thevertical and horizontal axes correspond to the region numbers in the attached legend. The matrix is symmetrical about the main diagonal, which corresponds to perfect correlation(+1) of each voxel with itself. STG=superior temporal gyrus; CN=caudate nucleus; IPL=inferior parietal lobule; IFG=inferior frontal gyrus; pCG=posterior cingulate gyrus;aCG=anterior cingulate gyrus; LG=lingual gyrus; MTG=middle temporal gyrus; med FG=medial frontal gyrus; HIPP=hippocampus; MOG=middle occipital gyrus;PCU=precuneus; TH=thalamus; ITG=inferior temporal gyrus.

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and general semantic representations (e.g., Martin and Chao, 2001),which are essential in the retrieval of all declarative memory. Martinand Chao (2001) further note a temporo-frontal pathway, connectingthe middle and superior temporal areas with the inferior prefrontalcortex, enabling the retrieval, monitoring, and manipulation ofsemantic representations. Levine et al. (1999) found impairedautonoetic processing (due, hypothetically, to deficits in self-regula-tion) in patient ML, whose traumatic brain injury led to adisconnection of the temporal and frontal lobes, in the righthemisphere. The functional relevance of the temporopolar area ismuch less clear but its functional connectivity with the hippocampus,middle and superior temporal gyri, and the temporoparietal junctionsuggests its involvement in multimodal perceptual analysis (Olson etal., 2007), as the temporal pole is speculated to be a convergence area,which integrates diverse streams of information into a unified whole(Markowitsch et al., 1985). Other researchers have suggested thatboth the temporal pole and the left temporoparietal junction are

critical in the encoding of personal memories (Nakamura and Kubota,1996), multisensory processing of body and self (Arzy et al., 2006),retrieval of theory-of-mind information (i.e., when comprehendingsomeone else's state of mind; Arzy et al., 2006; Moriguchi et al., 2006),and conceptual knowledge (Graham et al., 2003). Our finding thatthese temporal and parietal regions are nodes of the commonretrieval network suggests that these areas participate in theprocessing of necessary semantic representations, as well as theconvergence of relevant semantic, perceptual, and others' state ofmind information of which declarative memories are composed.

The anterior and posterior cingulate gyri are known to bebidirectionally connected to one another via the cingulum bundle(Goldman-Rakic, 1988; Vogt et al., 1979) and indirectly via the medialtemporal lobes (Morris et al., 1999), with functional connections tothe temporal, frontal, and parietal lobes. Thus, they are speculated tobe involved in a variety of cognitive and emotional processes (e.g.,Bush et al., 1998; Critchley et al., 2003), including self-referential

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(Gusnard, 2005; Gusnard et al., 2001) and visuospatial processing(e.g., Rosenbaum et al., 2004). Although the anterior part of thecingulate gyrus is often linked to uniquely autobiographical processes(Svoboda et al., 2006), other researchers found it active in bothepisodic and semantic processes (e.g., Mummery et al., 1996). Johnsonet al. (2006) linked both the anterior and posterior cingulate gyri toself-reflection, albeit linking the anterior cingulate cortex to instru-mental self-reflection (i.e., directed towards the inner self) and theposterior cingulate cortex to experiential self-reflection (i.e., directedtowards the self as it relates, externally, to others). The involvement ofthe anterior and posterior cingulate gyri in the common retrievalnetwork emphasizes the critical role of intrinsically driven and self-related processes in declarative memory search and retrieval. Thus, itshould come as no surprise that these regions overlap with the‘default-mode’ regions whose activity decreases during demandingexternal tasks, but increases during self-referential processing andmonitoring of the internal environment (e.g., Gusnard, 2005). Theanterior and posterior cingulate gyri are two frequently delineatednodes of this self-referential default-mode network (e.g., Fox et al.,2005; Greicius and Menon, 2004; Gusnard et al., 2001; Toro et al.,2008).

Finally, the connectivity analysis yielded a number of functionallyconnected brain areas that negatively correlated with the lefthippocampus, lingual gyrus, and right caudate nucleus, yet positivelycorrelated with one another. These areas included the left middleoccipital gyrus, bilateral precuneus, right thalamus, left inferiortemporal gyrus, right fusiform gyrus, and bilateral inferior parietallobule. Given the similarity between these regions and attentionalnetworks proposed by Corbetta et al. (2000, 2008), these negativelycorrelating regions are likely involved in attention to external stimuli.In a recent study, Fu et al. (2005) found a network of posterior brainareas, including the middle occipital gyrus, precuneus, fusiform gyrus,and inferior parietal lobes, that was involved in externally driven earlyvisuospatial attentional modulation, as well as in later perceptualfeedback to the primary visual areas. In the paradigm used here, theprocessing of memory search is internally driven and our analysisfocused only on the period of retrieval, not on the period of cuepresentation. Thus, when the common network is recruited for thisinternal process (i.e., an increase in activity), activity in the externallydriven occipito-temporo-parietal network is reduced, or maybe evensuppressed (i.e., a decrease in activity). It is likely that the externallydriven network would be more active if memory were more directlycued by an external stimulus, consistent with the idea of Ciaramelli etal. (2008) that different parts of the parietal cortex mediate attentionto retrieval cues in memory search.

In conclusion, the results of this study yielded a large-scalefunctional network of frontal, temporal, and parietal areas, as well asthe anterior and posterior cingulate gyri, in line with the unitarysystems view that suggests a single declarative memory system(Baddley, 1984; Friston, 2002; Kihlstrom, 1984;McIntosh, 1999; Rajahand McIntosh, 2005; Roediger, 1984). The functional connectionswithin this network did not significantly differ across the memoryconditions, indicating its importance in all types of declarativeretrieval. This neural overlap of autobiographical, “laboratory”episodic, and semantic retrieval suggests that all declarative memoryretrieval engages a set of functional processes, mediated by the use ofthis commonnetwork, and provides further support for the notion of aunitary memory system. One implication of this unitary system is thatno declarative memory retrieval is ever purely “semantic” or“autobiographical,” for example, but necessarily involves overlappingprocesses and multiple types of content, both contextual and context-free. The regions that we found to be part of the common networksuggest that some of these overlapping cognitive processes that areinvolved in declarative memory are likely to include top-downattentional, inhibitory, and monitoring processes, working memorymanipulation, maintenance of information, and convergence of

semantic and self-referential information. Activity in this networkduring memory retrieval may be facilitated by down-regulation of anoccipito-temporo-parietal network of posterior brain areas, whichsupports attentional modulation in response to external stimuli.

Acknowledgments

We thank the staff at Sunnybrook Health Science Centre for theirassistance in this experiment. This work was funded by the CanadianInstitutes of Health Research (grant MOP 14036). C.L. Grady is alsosupported by the Canada Research Chairs program, the OntarioResearch Fund, and the Canadian Foundation for Innovation.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.neuroimage.2009.08.066.

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