NeuroImage 49 (2010) Contents lists available at ScienceDirect. NeuroImage. journal homepage:

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1 NeuroImage 49 (2010) Contents lists available at ScienceDirect NeuroImage journal homepage: A common functional brain network for autobiographical, episodic, and semantic memory 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, Canada b Rotman Research Institute at Baycrest, Toronto, Ontario, Canada c Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada article info abstract Article history: Received 22 April 2009 Revised 9 August 2009 Accepted 31 August 2009 Available 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 utilized the same pictorial stimuli, but manipulated retrieval demands to extract autobiographical, episodic, or semantic memories. To assess this common network, we first examined the functional connectivity of regions identified by a previous analysis of task-related activity that were active across all three tasks. Three of these regions (left hippocampus, left lingual gyrus, and right caudate nucleus) appeared to share a common pattern of connectivity. This was confirmed in a subsequent functional connectivity analysis using these three regions as seeds. The results of this analysis showed that there was a pattern of functional connectivity 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 anterior and posterior cingulate gyri was positively correlated with the seeds, whereas activity in posterior occipitotemporo-parietal regions was negatively correlated. These findings support the idea that a common neural network underlies the retrieval of declarative memories regardless of memory content. This proposed network consists of increased activity in regions that represent internal processes of memory retrieval and decreased activity in regions that mediate attention to external stimuli Elsevier Inc. All rights reserved. Introduction Despite extensive research in the past several decades, a consensus has yet to be reached as to the neural organization of declarative memory retrieval. Conceptually, declarative memory retrieval has been traditionally dissociated into three types: (1) semantic retrieval, characterized by the conscious recollection of factual information and general knowledge (Tulving, 1972); (2) episodic retrieval, characterized by the conscious recollection of experienced events, which originally included personally relevant events (Tulving, 1972), but today typically pertains to memory for stimuli encoded in the laboratory (i.e., laboratory memory ; see Cabeza and St. Jacques, 2007); and (3) autobiographical retrieval, characterized by the conscious recollection of personally relevant events (Conway and Pleydell-Pearce, 2000). Much of the data from neuropsychological and neuroimaging experiments (e.g., Cipolotti and Maguire, 2003; Gadian et al., 2000; Hirano and Noguchi, 1998; Manns et al., 2003; Nyberg, McIntosh and Tulving, 1998; Vargha-Khadem et al., 1997) support the Corresponding author. Psychology Department, University of Toronto, 100 St. George Street, Toronto, Ontario, Canada M5S 3G3. address: hburian@rotman-baycrest.on.ca (H. Burianova). multiple memory systems view of the organization of declarative memory (Tulving, 1987). This view posits that there are separate memory systems, which specialize in the processing of distinct types of 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 et al., 2002; Tulving and Schacter, 1990; Tulving, 1987). Empirical evidence supporting this view comes primarily from studies showing functional dissociations between episodic, autobiographical, and semantic memory. Neuropsychological studies show that patients with medial temporal lobe lesions are usually impaired on tasks involving autobiographical memory, but not on tasks involving semantic memory (Gadian et al., 2000; Hirano and Noguchi, 1998; Vargha-Khadem et al., 1997), suggesting that the medial temporal lobes (the hippocampus, in particular) engage autobiographical memory exclusively (Tulving et al., 1991; Tulving and Markowitsch, 1998; Vargha-Khadem et al., 1997). Conversely, patients with semantic dementia whose neural damage often involves frontotemporal lobar degeneration (Hodges and Miller, 2001; Neary et al., 1998) are characterized by severe semantic memory loss, whereas their autobiographical memory is relatively spared (Graham et al., 2003; McKinnon et al., 2006; Snowden, Griffiths and Neary, 1994; Westmacott and Moscovitch, 2003). Further neural dissociations /$ see front matter 2009 Elsevier Inc. All rights reserved. doi: /j.neuroimage

2 866 H. Burianova et al. / NeuroImage 49 (2010) have been found in neuroimaging studies that show regional activity in the left inferior prefrontal cortex and left posterior temporal areas related to semantic retrieval (Graham et al., 2003; Vandenberghe et al., 1996; Wiggs, Weisberg and Martin, 1999), left-lateralized activity in the medial temporal and ventromedial frontal regions, temporopolar areas, retrosplenial cingulate cortex, and cerebellum related to autobiographical retrieval (Conway et al., 2002; Gilboa, 2004; Graham et al., 2003; Maguire, 2001), and activity in the right dorsolateral prefrontal areas subserving laboratory episodic retrieval (Duzel et al., 2004; Gilboa, 2004). It is critical to point out, however, that the focus of most of these neuropsychological and neuroimaging studies was largely on differences and functional dissociations of autobiographical, episodic, and semantic memory retrieval. In other words, the experimental paradigms (e.g., using different tasks to test episodic and semantic memory) and statistical methods (e.g., subtracting activity during one memory condition from another, instead of using a neutral baseline) were designed to dissociate neural correlates among the presumably different types of declarative memory. An alternative perspective to the multiple systems view is the unitary system view of the organization of declarative memory, which proposes the idea of a single declarative memory system (Baddley, 1984; Friston, 2002; Kihlstrom, 1984; McIntosh, 1999; Rajah and McIntosh, 2005; Roediger, 1984). Proponents of this view conjecture that a unitary memory system gives rise to all declarative memory retrieval, although memories can vary along a contextual continuum of several dimensions, such as time, space, emotion, or strength of recollection (e.g., Baddley, 1984). This view rests on several theoretical assumptions. Firstly, encoding of to-be-remembered material is almost always contextual (i.e., embedded in already attained knowledge; Baddley, 1984); at retrieval, memories may or may not become decontextualized along one or more of these dimensions (Baddley, 1984; Rajah and McIntosh, 2005; Westmacott and Moscovitch, 2003). Autobiographical and semantic types of memory may thus be conceptualized as the opposite ends of the contextual continuum (Baddley, 1984; Kihlstrom, 1984; Roediger, 1984). Secondly, even if autobiographical memory is at the most detailed end of the contextual continuum, it is not free of factual, semantic information (Gilboa, 2004; Levine et al., 2002); hence, there is considerable overlap among these types of memory, and the boundaries between them are often unclear. Finally, in a similar vein, semantic memory is rarely entirely context-free, but rather contains some contextual and episodic components, although these may be degraded and lack rich detail (Gilboa, 2004; Westmacott and Moscovitch, 2003; Westmacott et al., 2004). Evidence supporting the unitary memory system view consists of both neuroimaging and neuropsychological data that show functional overlap and interdependence of different memory functions (Duncan and Owen, 2000; Kopelman and Kapur, 2001; Manns et al., 2003; Rajah and McIntosh, 2005; Squire and Zola, 1998). These include studies comparing working, episodic, and semantic memory (Nyberg et al., 2002, 2003), working and episodic memory (Braver et al., 2001; Cabeza et al., 2002; Duzel et al., 1999), and semantic and episodic memory (Rajah and McIntosh, 2005; Ryan et al., 2008), all of which reported commonalities in neural activations across the memory tasks. Rajah and McIntosh (2005) provided evidence for the unitary view by modeling separate functional networks for episodic and semantic retrieval tasks and assessing interregional correlations across the two tasks. The results showed no significant differences in the interregional correlations, despite differences in regional activations in the two memory tasks, suggesting the involvement of a single memory system or network. Other data (e.g., Moscovitch, 1992; Seger et al., 2000; Shimamura, 1995) contradict the prefrontal hemispheric differentiation of episodic and semantic retrieval (i.e., activity in the right prefrontal cortex is associated solely with episodic retrieval [Buckner et al., 1998; Cabeza and Nyberg, 2000] and activity in the left prefrontal cortex is solely associated with semantic retrieval [e.g., Goldberg et al., 2007]). In healthy individuals, activity in the right prefrontal cortex was shown to underlie retrieval of novel and creative semantic relations (Dobbins and Wagner, 2005; Seger et al., 2000), whereas activity in the left prefrontal cortex was found to subserve some aspects of episodic remembering (Nolde et al., 1998). This support for the unitary view of declarative retrieval even extends to the hippocampus, traditionally thought to subserve episodic and autobiographical memory only (Tulving et al., 1991; Tulving and Markowitsch, 1998; Vargha-Khadem et al., 1997). For example, there is evidence that hippocampal amnesics, when compared to healthy controls, exhibit impairments in semantic retrieval in addition to profound deficits in episodic and autobiographical retrieval (Kopelman and Kapur, 2001; Manns et al., 2003; Squire and Zola, 1998). Similarly, recent imaging studies of healthy individuals have reported hippocampal activation in both episodic and semantic retrieval (e.g., Burianova and Grady, 2007; Ryan et al., 2008). These data suggest that the hippocampus and the medial temporal cortices are involved in all declarative retrieval. In a previous study, we identified regional activations common to autobiographical, laboratory episodic, and semantic retrieval (Burianova and Grady, 2007). While these results reflect the important neural substrates that are involved in declarative retrieval, they do not provide direct evidence about the functional connectivity of these neural regions. It seems reasonable to assume that memory retrieval, a highly complex cognitive process, would not be localized to discrete brain region(s), but rather would be mediated by the interaction among a number of functionally related neural areas. This idea is not new, as many researchers have argued that it is the activity of distributed neural networks and the interactions among anatomically connected brain regions that directly yield cognitive functions, such as memory (e.g., Finger, 1994; Friston, 1997; McIntosh, 1998, 2000; Mesulam, 1990). In other words, cognitive functions are the emergent properties of the neural interactions (i.e., influences that neural constituents have on one another) among numerous brain areas that comprise a neural network (McIntosh, 1999). An essential aspect of the network approach to the neural organization of cognitive function is the examination of the neural context associated with a specific behaviour (McIntosh, 1998, 1999). Neural context is conceptualized as the activity in a selected brain region that arises as a consequence of modulatory influences from other brain regions (McIntosh, 1998, 1999). Thus, what is important in determining the neural underpinning of a cognitive function is the relation of activity in a brain area with activity of those brain areas with which it is connected. In parallel with this argument is the notion that by measuring only how mean neural activity changes with task in a specific brain area or areas, one might fail to observe relevant interregional functional interactions that occur despite no significant change in the mean activity (e.g., Grady et al., 1998; McIntosh et al., 1994). One approach to quantifying neural interactions is to assess the degree of functional connectivity among brain regions, i.e., the degree to which activity in a specific region correlates or covaries with activity in other areas across the whole brain, thus functioning together as a network (Friston et al., 1993; Friston, 1994; Horwitz et al., 1984). A network is thus defined as a pattern of spatially remote brain regions whose activity levels are correlated, or functionally connected across participants, in order to support a particular behaviour, regardless of whether the average level of activity in any single region of the network is different between the experimental conditions (Friston, 1994; Habib et al., 2003). To statistically study complex neural interactions between different brain structures, the analytical methods must provide a means to quantifying the relation between brain regions, rather than focusing on mean activity differences. Multivariate approaches, such as the partial least squares (PLS) approach to image analysis, enable investigation of functional connectivity of neural regions by calculating the covariance between the activity within selected seed voxels and all other brain voxels

3 H. Burianova et al. / NeuroImage 49 (2010) across the experimental conditions (McIntosh et al., 1996; McIntosh and Gonzalez-Lima, 1994). For instance, Addis et al. (2004a) identified a number of autobiographical regions that differentiated general and specific autobiographical memories from a control baseline using univariate contrasts, but only a subsequent multivariate PLS analysis (Addis et al., 2004b) revealed a shared functional network with a critical connection to the hippocampus. Furthermore, Addis et al. (2007) expanded on these findings by mapping an effective autobiographical network in healthy individuals and epileptic patients. In addition to Addis' work, other researchers have assessed the 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 junction, retrosplenial cortex, and posterior cingulate gyrus. Episodic retrieval of stimuli learned in the laboratory has been linked to a functional network that overlaps to some degree with the autobiographical network, but that also recruits unique brain regions, for instance, 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 in the 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 the autobiographical network, particularly in the lateral temporal gyrus, temporoparietal junction, anterior cingulate gyrus, and ventrolateral prefrontal cortex (Lee et al., 2002; Mummery et al., 1996). However, despite these explorations of the functional and effective connectivity of the neural correlates that underlie declarative memory, no study to date has attempted to identify a single network common to autobiographical, episodic, and semantic retrieval in one experiment and/or analysis. The purpose of this study was to delineate a functional network of spatially distributed neural regions whose activity covaries across the three memory conditions, i.e., to map a common functional declarative retrieval network. To do so, the data were analyzed with seed voxel PLS analysis (Della-Maggiore et al., 2000; McIntosh, 1999; McIntosh et al., 1997; Schreurs et al., 1997) to determine whole-brain patterns of activity correlated with seed regions that showed similar increases of activity across the memory conditions (Burianova and Grady, 2007). We hypothesized that autobiographical, laboratory episodic, and semantic retrieval would recruit a large-scale functional network that underlies the general processes of memory retrieval, such as top-down attentional control, response monitoring, integration of contextual information, working memory manipulation of to-be-retrieved information, and processing of semantic representations. Methods Participants Twelve right-handed, healthy young participants (mean age=27 years; range=21 37 years; 3 males), with at least 16 years of education, took part in the study. All participants signed an informed consent that was approved by ethics boards at Baycrest and Sunnybrook Health Science Centre. Stimuli Experimental stimuli Fifty colour and black and white photographs depicting general, everyday events (e.g., driving or camping), as well as one-time but highly publicized occurrences (e.g., the 9/11 attack on the World Trade Center) were used as visual cues for the experimental retrieval conditions. Control stimuli Five photographs were selected from the set of 50 described above and scrambled using a Matlab script. This ensured that the visual stimulus was rendered meaningless. Procedure The study consisted of one control and three memory retrieval conditions during fmri scanning. Four 14-min runs of 50 trials each were presented to the participants in a counterbalanced order. Trials were randomized within each run. In each trial, an experimental or control stimulus was shown for 4 s. Each experimental stimulus was shown three times during the experiment, but never in sequence or in the same scanning run. Participants were asked to pay attention to the photograph, so that they could successfully answer a subsequently presented question that pertained to the stimulus. After the 4-s presentation of each picture, a question appeared on the screen with three possible answers. Participants had 10 s to respond by pressing 1, 2, or 3 on a number pad. Note that accuracy of memory retrieval was emphasized over speed, and the participants were instructed not to guess. The response period was chosen to provide sufficient time for autobiographical memory retrieval. According to recent electrophysiological evidence, the range of retrieval times for autobiographical memory is between 3 and 9 s, with an average time of 5 s (Conway et al., 2003). After the 10 second response period there was a 1-s inter-trial interval, followed by the next trial. The three memory conditions were as follows: 1. Autobiographical condition, in which the stimulus was followed by a cue designed to elicit a personal memory (e.g., Think of the last time you went camping ). Participants were asked to relive the memory as vividly as possible and subsequently rate the memory according to its vividness (1= very vivid, 2= somewhat vivid, 3= not vivid at all ). 2. Episodic condition, in which the stimulus was followed by a question about the photograph itself (e.g., In the picture, which you have just seen, what is the colour of the tent? ). Participants chose from three answers presented to them (1 or 2 being correct, 3=I don't know). To ensure that this condition did not engage only working or short-term memory, we varied the degree of difficulty of retrieved contextual information; hence, the participants were unaware of which piece of information about the stimulus they would be asked to retrieve. In addition, the presentation of the three experimental conditions was randomized and the temporal lag between subsequent presentations of the same visual stimulus was at least 14 min; hence, our intent was that participants would need to engage episodic memory retrieval from long-term memory storage about the perceptual details of the stimuli, and not solely working memory processes. 3. Semantic condition, in which the stimulus was followed by a factual type of question (e.g., Are there more than 100 camping grounds in Algonquin Park? ). Responses were made in the same fashion as in the episodic condition. In the control condition, the presentation of a scrambled photograph was followed by an arbitrary instruction that was unrelated to the stimulus itself (e.g., Press a key that corresponds to the letter C ). As in the experimental conditions, responses were made by pressing 1, 2, or 3 on a keypad, and the correct key was either 1 or 2. Reaction times for each button press were recorded across all conditions. These behavioural results have been reported elsewhere (Burianova and Grady, 2007). A post-scan interview was administered immediately after the scan session. Participants viewed the 50 photographs again and were asked to describe the autobiographical memory that had been retrieved during the scan in as much detail as possible. Temporal and spatial information as well as the content of the event and

4 868 H. Burianova et al. / NeuroImage 49 (2010) participant's emotion at the time of its occurrence were recorded by the experimenter, to ensure that each autobiographical memory was accompanied by a vivid recollection of a particular episode. fmri data acquisition Anatomical and functional images were collected using a 3T GE scanner 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= , TR=35 ms, TE=6 ms, flip angle=35 ) was acquired for each participant. Brain activation was assessed using the blood oxygenation level-dependent (BOLD) effect (Ogawa et al., 1990) with optimal contrast. For functional imaging, 26 axial slices of 5 mm thickness were obtained, utilizing a T2 -weighted pulse sequence with spiral in-out readout (TR = 2000 ms, TE=30 ms, FOV=20, acquisition matrix= , flip angle=80 ). Visual stimuli were presented using fmri-compatible goggles (Avotec, Inc.) mounted on the head coil. Responses were collected with the Rowland USB Response Box (RURB). fmri data preprocessing Images were reconstructed and motion-corrected utilizing the Analysis of Functional Neuroimages (AFNI; Cox, 1996). The images were spatially co-registered to correct for head motion of the participants by using a 3D Fourier transform interpolation. The peak range of head motion did not exceed 1.2 mm across all participants. To enable group comparisons, each brain scan was spatially normalized, i.e., scaled and warped to match a standard template (the Montreal Neurological Institute [MNI] spiral template) utilizing Statistical Parametric Mapping (SPM99) software. The warping of the brain surface was achieved via a linear transformation with sinc interpolation (i.e., a signal resampling method designed to minimize aliasing in the signal). Lastly, the images were smoothed with a 6 mm Gaussian filter (in SPM), which, acting as a low pass filter, makes the data less noisy by reducing the images' high-frequency components. The voxel size, after preprocessing, was mm. Seed voxel PLS In the seed PLS analysis, we included those trials for the semantic and episodic conditions for which participants made a correct response, and all very vivid and somewhat vivid trials for the autobiographical condition. Seed PLS is a multivariate statistical method utilized in the investigation of the relation of activity in a selected brain region or regions (i.e., a seed voxel) and activity in the rest of the brain across the task conditions (Della-Maggiore et al., 2000; Schreurs et al., 1997; McIntosh, 1999; McIntosh et al., 1997). In other words, seed PLS analysis examines task-related functional connectivity. The selection of the seed voxel(s) can be either datadriven (i.e., determined by previous analyses of the data) or hypothesis-driven (i.e., determined by theoretical assumptions), or both. In our study, the selection of the seed voxels used in the seed PLS analysis was data-driven. In a previous study (Burianova and Grady, 2007), we identified eight brain regions whose activity was significantly increased across all three memory conditions, thus showing activity common to the conditions. To determine whether these regions are also a part of the same functional network, we entered all eight regions into a seed PLS analysis. The analysis revealed a connectivity pattern across the three memory conditions, but not all regions were reliably correlated with this pattern. Therefore, a subsequent analysis was carried out, using the three seed regions that covaried most strongly with the rest of the brain across the three memory conditions the left hippocampus, right caudate nucleus, and left lingual gyrus (see Table 1 for the coordinates). Table 1 Seed voxel regions. Region Hem BA Talairach coordinates Ratio x y z Caudate nucleus R n/a Lingual gyrus L Hippocampus L n/a Abbreviations: Hem=hemisphere; BA=Brodmann's area; R=right; L =left; Ratio=salience/SE ratio from the bootstrap analysis; x coordinate =right/left; y coordinate =anterior/posterior; z coordinate=superior/inferior. This analytical procedure for seed PLS was threefold: firstly, the BOLD values from the selected seed(s) were extracted (i.e., from the peak voxels identified in our previous study), across 8 timepoints after each presentation of the question cue to capture activity during the retrieval phase of the trial. The activity for each seed was averaged across the peak and adjacent timepoints, and then this average measure of seed activity was correlated with activity in all other brain voxels, across all participants, within each condition. Secondly, these correlations were combined into a matrix and decomposed with singular value decomposition (SVD), resulting in a set of latent variables (LVs; mutually orthogonal variables). Each LV consists of a singular image (i.e., brain LV, or the pattern of brain regions that covary in activity with the seed voxel), a singular profile (i.e., seed LV, or the pattern of covariance of the seed voxel and the rest of the brain across the experimental conditions), and a singular value (i.e., the amount of covariance accounted for by each LV). Finally, the significance for each LV is determined by using a permutation test (McIntosh et al., 1996), which involves a random reordering of the data matrix and calculation of a new set of LVs for each reordering. The singular value of each newly permuted LV is compared to the singular value of the original LV, yielding a probability of the number of occurrences that the permuted values exceed the original value. Five hundred permutations were conducted. Because this study used an event-related design, the PLS analysis provided a set of correlated regions (i.e., a map of areas correlated with the seeds) for each of the eight TRs in the analysis. For each TR, a brain score was calculated for each participant that is an index of how strongly that participant shows the particular pattern of brain activity identified for that TR. The brain scores can be used to examine differences in brain activity across conditions, because greater activity in brain areas with positive (or negative) weights on a latent variable will yield positive (or negative) mean scores for a given condition. We calculated the correlation between the brain scores from each significant LV and the seed BOLD values to assess the relation between the whole-brain pattern and activity in the three reference regions. In addition to the permutation test, a second and independent step is to determine the reliability of the saliences (or weights) for the brain voxels characterizing each pattern identified by the LVs. To do this, all saliences for each TR were submitted to a bootstrap estimation of the standard errors (Efron and Tibshirani, 1985), randomly resampling participants, with replacement, and computing the standard error of the saliences after 100 bootstrap samples. Peak voxels with a salience/se ratio N3.0 were considered to be reliable, as this approximates pb0.005 (Sampson et al., 1989). Because PLS uses images in the format developed by the Montreal Neurological Institute (MNI), all coordinates resulting from the PLS analyses were converted from MNI space to Talairach coordinates using the algorithm developed by Brett and colleagues ( Correlation analysis The seed PLS, using the three seeds, resulted in a set of 18 regions whose activity was reliably and strongly correlated with all three, and this set of regions constituted the putative common network. To

5 H. Burianova et al. / NeuroImage 49 (2010) Table 2 Correlations with seed activity. Region Hem BA Talairach coordinates Ratio Cluster x y z Positive correlations Inferior FG L R Medial FG L Anterior CG L 24/ Superior TG L Middle TG L R Temporal pole R Inferior PL (TPJ) L Posterior CG 23/ Negative correlations Middle OG L Precuneus L R Thalamus R n/a Inferior TG L Fusiform gyrus R Inferior PL (SMG) L R Abbreviations: Hem=hemisphere; BA=Brodmann's area; R=right; L =left; Ratio=salience/SE ratio from the bootstrap analysis; Cluster=size of each cluster in number 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. specifically examine the intercorrelations among these regions, we extracted the BOLD signal from them, i.e., from the peak voxel in each cluster (see Table 2 for peak coordinates), and averaged the activity at the timepoint corresponding to the strongest correlation with the seeds and adjacent timepoints. We then calculated interregional correlations within conditions (creating three correlation matrices) and compared these correlation patterns across conditions. We compared the correlation patterns across the conditions by calculating the squared differences in correlations (for each cell of the matrix) between conditions and summing them to obtain a single value for the overall difference in correlations between conditions. The statistical significance of this difference was assessed using a permutation test on the sum of squared differences (McIntosh et al., 1996), which involved a random reordering of the data matrix and calculation of a new set of values for each reordering. Results Behavioural performance Behavioural performance was assessed by comparing the means of the response times across the four conditions (correct trials only), using a repeated-measures ANOVA. The effect of condition was significant, F(3,33) =73.1, pb Pairwise t-tests with Bonferroni corrections for multiple comparisons showed that the response times for autobiographical retrieval (M=6989 ms, SD=1478) differed significantly from that for the control task (M=1925 ms, SD=728) and episodic retrieval (M=4640 ms, SD=1150, both at pb0.01). The difference in reaction time (RT) for autobiographical retrieval and semantic retrieval (M=5858 ms, SD=1366) approached significance (p=.06). Seed PLS The first latent variable yielded by the three-seed PLS analysis accounted for the largest amount of covariance in the data (32%; p b0.002), delineating a group of brain regions whose activity correlated with all three seed regions (the left hippocampus, left lingual gyrus, and right caudate nucleus) during the retrieval of autobiographical, episodic, and semantic memories (see Fig. 1). Positive correlations with the three seed regions were found in temporal cortex (bilaterally in the middle temporal gyrus and superior temporal gyrus), frontal cortex (bilateral inferior frontal gyrus, medial frontal gyrus, and left anterior cingulate gyrus), as well as in the left temporoparietal junction and posterior cingulate gyrus. Negative correlations with the seed regions were found in the left middle occipital gyrus, right thalamus, bilateral precuneus, bilateral fusiform gyrus, and bilateral supramarginal gyrus (see Table 2 for a summary). These patterns of correlation were found across all three memory conditions. Correlation analysis The seed PLS analysis identified a set of regions correlated with the seeds across all three memory conditions. To assess further whether this set of regions represents a common memory retrieval network, we determined if the correlations among all the nodes of this putative network were statistically equivalent across the tasks, as one would expect if the network operates in a similar fashion regardless of the task demands. Fig. 2 shows three correlation matrices representing the 3 seeds and the 18 regions that showed correlated activity with the seeds, both positive and negative. Each matrix shows the visual representation of interregional correlations of activity among the 21 regions, within each memory condition. No significant differences (pn0.05) were found in the overall interregional correlation strengths across the three memory conditions. The correlation matrices indicate that during all memory conditions, most of the regions that positively correlated with the seeds were also correlated positively with one another (denoted in lighter shades of gray), but negatively with regions that show negative correlations with the seeds (denoted in darker shades). Similarly, most of the regions with negative seed correlations correlated positively with each other, but negatively with the regions with positive seed correlations. Despite some apparent interregional differences, there are obvious clusters of strong positive and negative correlations among the voxels that are consistent across the three memory conditions. As a whole, the strength of the overall interregional 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 that is common to autobiographical, episodic, and semantic retrieval. The results of the seed PLS analysis yielded a large-scale network of brain regions that were functionally connected to the left hippocampus, right caudate nucleus, and left lingual gyrus in all memory retrieval conditions. The inferior frontal gyri, medial frontal gyrus, anterior and posterior cingulate gyri, left temporoparietal junction, and a number of temporal areas (bilateral middle temporal gyrus, superior temporal gyrus, and right temporal pole) positively correlated with the seed regions, whereas the left middle occipital gyrus, bilateral precuneus, right thalamus, left inferior temporal gyrus, right fusiform gyrus, and bilateral inferior parietal lobule negatively correlated with the seed regions. These results suggest that these areas, as a whole, comprise a common functional network that subserves the general processes of declarative memory retrieval. We propose that these processes include a multitude of higher-cognitive functions, for instance, working memory, selective attention, error monitoring, response verification, and comprehension of concept representations. We conclude from these results that, together, these cognitive processes and their integration are essential to what is defined as declarative memory retrieval. The three seed regions selected for the functional connectivity analysis have been previously identified as important for memory

6 870 H. Burianova et al. / NeuroImage 49 (2010) 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 s after retrieval 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 to show 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 nucleus at 2 3 s after retrieval onset is shown. Yellow/red = positive correlations with the seeds; blue = negative correlations with the seeds. processing (e.g., Burianova and Grady, 2007). The hippocampus has been implicated as a critical component of declarative retrieval processes (Cabeza et al., 2004; Eldridge et al., 2000; Prince et al., 2005) and relational memory (e.g., memory for the relations between items and context [e.g., Davachi and Wagner, 2002], or between objects and features of a scene [e.g., Ryan and Cohen, 2004]). The hippocampus also may play a role in non-declarative processes, such as working memory and object organization (Seeck et al., 1995) and nonmnemonic processes, such as local feature extraction in form perception (Barense et al., 2005; Beason-Held et al., 1998) and visual discrimination of scenes (Davies et al., 2004). The left lingual gyrus has been linked to working memory maintenance of visuospatial information (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 and memory, particularly in regard to feedback processing and the ability to shift a cognitive set in response to the environment (Packard and Knowlton, 2002). The three seed regions were found to positively covary with a number of frontal, temporal, and parietal areas. These areas of functional connectivity broadly overlap with the common memoryrelated brain regions, which were found in our previous analysis (Burianova and Grady, 2007), although the exact locations are not identical. The inferior frontal gyrus has been implicated in declarative and working memory retrieval (Nyberg et al., 2002), as well as in response inhibition and selection control (Aron et al., 2004; Brass et al., 2005; Liddle et al., 2001), and top-down attentional control (Banich et al., 2000; Dove et al., 2008). The medial prefrontal areas are involved in social functions and self-referential processes in both semantic and episodic memory retrieval (Addis et al., 2004a,b; Gilboa et al., 2004; Levine et al., 2004). The functional connectivity of these frontal areas with the three seed regions suggests involvement of the frontostriatal and hippocampo-frontal pathways. The frontrostriatal pathway 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 in attentional 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 interactions (Smith et al., 2004). The hippocampo-frontal pathway, involving the left hippocampus, has been linked to the retrieval of contextual details (Svoboda et al., 2006), despite some researchers' arguing its sole involvement in autobiographical memory retrieval (e.g., Addis et al., 2007). The common prefrontal link of the frontostriatal and hippocampo-frontal pathways allows for an indirect effect of the caudate nucleus on the hippocampus and vice versa. Some researchers speculate that the hippocampus and caudate nucleus functionally interact to jointly mediate behavioural output during performance (Dagher et al., 2001; Packard and Knowlton, 2002). We propose that the frontal nodes of the common retrieval network subserve topdown attentional, inhibitory, and monitoring functions, as well as working memory manipulation and maintenance of information necessary for a successful declarative recollection of memories that relate to the self and others. The middle and superior temporal gyri were involved in our common network, and have been linked to the processing of personal

7 H. Burianova et al. / NeuroImage 49 (2010) Fig. 2. Correlations among peak voxels of the common functional network. The three correlation matrices show similarities in regional interrelations across the three memory conditions. Correlation values are indicated by shades of gray (positive correlations are denoted in lighter shades; negative correlations are denoted in darker shades). Values on the vertical 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. and general semantic representations (e.g., Martin and Chao, 2001), which are essential in the retrieval of all declarative memory. Martin and Chao (2001) further note a temporo-frontal pathway, connecting the middle and superior temporal areas with the inferior prefrontal cortex, enabling the retrieval, monitoring, and manipulation of semantic representations. Levine et al. (1999) found impaired autonoetic processing (due, hypothetically, to deficits in self-regulation) in patient ML, whose traumatic brain injury led to a disconnection of the temporal and frontal lobes, in the right hemisphere. The functional relevance of the temporopolar area is much less clear but its functional connectivity with the hippocampus, middle and superior temporal gyri, and the temporoparietal junction suggests its involvement in multimodal perceptual analysis (Olson et al., 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 that both 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 comprehending someone else's state of mind; Arzy et al., 2006; Moriguchi et al., 2006), and conceptual knowledge (Graham et al., 2003). Our finding that these temporal and parietal regions are nodes of the common retrieval network suggests that these areas participate in the processing of necessary semantic representations, as well as the convergence of relevant semantic, perceptual, and others' state of mind information of which declarative memories are composed. The anterior and posterior cingulate gyri are known to be bidirectionally connected to one another via the cingulum bundle (Goldman-Rakic, 1988; Vogt et al., 1979) and indirectly via the medial temporal lobes (Morris et al., 1999), with functional connections to the temporal, frontal, and parietal lobes. Thus, they are speculated to be involved in a variety of cognitive and emotional processes (e.g., Bush et al., 1998; Critchley et al., 2003), including self-referential

8 872 H. Burianova et al. / NeuroImage 49 (2010) (Gusnard, 2005; Gusnard et al., 2001) and visuospatial processing (e.g., Rosenbaum et al., 2004). Although the anterior part of the cingulate gyrus is often linked to uniquely autobiographical processes (Svoboda et al., 2006), other researchers found it active in both episodic and semantic processes (e.g., Mummery et al., 1996). Johnson et al. (2006) linked both the anterior and posterior cingulate gyri to self-reflection, albeit linking the anterior cingulate cortex to instrumental self-reflection (i.e., directed towards the inner self) and the posterior cingulate cortex to experiential self-reflection (i.e., directed towards the self as it relates, externally, to others). The involvement of the anterior and posterior cingulate gyri in the common retrieval network emphasizes the critical role of intrinsically driven and selfrelated processes in declarative memory search and retrieval. Thus, it should come as no surprise that these regions overlap with the default-mode regions whose activity decreases during demanding external tasks, but increases during self-referential processing and monitoring of the internal environment (e.g., Gusnard, 2005). The anterior and posterior cingulate gyri are two frequently delineated nodes 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 functionally connected brain areas that negatively correlated with the left hippocampus, lingual gyrus, and right caudate nucleus, yet positively correlated with one another. These areas included the left middle occipital gyrus, bilateral precuneus, right thalamus, left inferior temporal gyrus, right fusiform gyrus, and bilateral inferior parietal lobule. Given the similarity between these regions and attentional networks proposed by Corbetta et al. (2000, 2008), these negatively correlating regions are likely involved in attention to external stimuli. In a recent study, Fu et al. (2005) found a network of posterior brain areas, including the middle occipital gyrus, precuneus, fusiform gyrus, and inferior parietal lobes, that was involved in externally driven early visuospatial attentional modulation, as well as in later perceptual feedback to the primary visual areas. In the paradigm used here, the processing of memory search is internally driven and our analysis focused only on the period of retrieval, not on the period of cue presentation. Thus, when the common network is recruited for this internal process (i.e., an increase in activity), activity in the externally driven occipito-temporo-parietal network is reduced, or maybe even suppressed (i.e., a decrease in activity). It is likely that the externally driven network would be more active if memory were more directly cued by an external stimulus, consistent with the idea of Ciaramelli et al. (2008) that different parts of the parietal cortex mediate attention to retrieval cues in memory search. In conclusion, the results of this study yielded a large-scale functional network of frontal, temporal, and parietal areas, as well as the anterior and posterior cingulate gyri, in line with the unitary systems view that suggests a single declarative memory system (Baddley, 1984; Friston, 2002; Kihlstrom, 1984; McIntosh, 1999; Rajah and McIntosh, 2005; Roediger, 1984). The functional connections within this network did not significantly differ across the memory conditions, indicating its importance in all types of declarative retrieval. This neural overlap of autobiographical, laboratory episodic, and semantic retrieval suggests that all declarative memory retrieval engages a set of functional processes, mediated by the use of this common network, and provides further support for the notion of a unitary memory system. One implication of this unitary system is that no declarative memory retrieval is ever purely semantic or autobiographical, for example, but necessarily involves overlapping processes and multiple types of content, both contextual and contextfree. The regions that we found to be part of the common network suggest that some of these overlapping cognitive processes that are involved in declarative memory are likely to include top-down attentional, inhibitory, and monitoring processes, working memory manipulation, maintenance of information, and convergence of semantic and self-referential information. Activity in this network during memory retrieval may be facilitated by down-regulation of an occipito-temporo-parietal network of posterior brain areas, which supports attentional modulation in response to external stimuli. Acknowledgments We thank the staff at Sunnybrook Health Science Centre for their assistance in this experiment. This work was funded by the Canadian Institutes of Health Research (grant MOP 14036). C.L. Grady is also supported by the Canada Research Chairs program, the Ontario Research Fund, and the Canadian Foundation for Innovation. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi: /j.neuroimage References Addis, D.R., Moscovitch, M., Crawley, A.P., McAndrews, M.P., 2004a. Recollective qualities modulate hippocampal activation during autobiographical memory retrieval. Hippocampus 14, Addis, D.R., McIntosh, A.R., Moscovitch, M., Crawley, A.P., McAndrews, M.P., 2004b. 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