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W4293247072 abstract "Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Current theory and empirical studies suggest that humans segment continuous experiences into events based on the mismatch between predicted and actual sensory inputs; detection of these ‘event boundaries’ evokes transient neural responses. However, boundaries can also occur at transitions between internal mental states, without relevant external input changes. To what extent do such ‘internal boundaries’ share neural response properties with externally driven boundaries? We conducted an fMRI experiment where subjects watched a series of short movies and then verbally recalled the movies, unprompted, in the order of their choosing. During recall, transitions between movies thus constituted major boundaries between internal mental contexts, generated purely by subjects’ unguided thoughts. Following the offset of each recalled movie, we observed stereotyped spatial activation patterns in the default mode network, especially the posterior medial cortex, consistent across different movie contents and even across the different tasks of movie watching and recall. Surprisingly, the between-movie boundary patterns did not resemble patterns at boundaries between events within a movie. Thus, major transitions between mental contexts elicit neural phenomena shared across internal and external modes and distinct from within-context event boundary detection, potentially reflecting a cognitive state related to the flushing and reconfiguration of situation models. Editor's evaluation This paper provides convincing evidence that internally generated event boundaries occurring at abrupt shifts in mental state evoke similar neural responses as those triggered by a change in sensory input. Given that much past work has linked the detection of event boundaries to the discrepancy between prediction and input, these new findings are significant and anticipated to spur much future research on event boundaries in the absence of external change. This innovative and methodologically rigorous study will be of interest to cognitive neuroscientists working on topics broadly related to memory, event segmentation, and mental context. https://doi.org/10.7554/eLife.73693.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Humans perceive and remember continuous experiences as discrete events (Brunec et al., 2018; Clewett et al., 2019; Shin and DuBrow, 2021; Zacks, 2020). Studies of event segmentation have shown that when participants attend to external information (e.g., watch a video), (1) boundaries between events are detected when mismatches arise between predicted and actual sensory input (Zacks et al., 2007; Zacks et al., 2011), and (2) boundary detection evokes transient neural responses in a consistent set of brain areas (Reagh et al., 2020; Speer et al., 2007; Zacks et al., 2001). Among these areas is the default mode network (DMN; Buckner and DiNicola, 2019) proposed to be involved in representing complex mental models of events (Ranganath and Ritchey, 2012; Ritchey and Cooper, 2020). However, a substantial portion of human cognition is internally driven (Hasselmo, 1995; Honey et al., 2018), and such spontaneous production of thoughts and actions is also punctuated by mental context transitions (Christoff et al., 2016; Mildner and Tamir, 2019; Smallwood and Schooler, 2015; Tseng and Poppenk, 2020). What manner of brain activity marks boundaries between mental contexts when they are internally generated? Are the brain responses at internal boundaries similar to those at external boundaries? Here, we used naturalistic movie viewing and free spoken recall with fMRI to characterize neural activity at boundaries between internally generated mental contexts (Figure 1A). Subjects watched 10 short movies (encoding phase), then verbally recounted the movies in any order, in their own words (recall phase). The transitions between recalled movies were determined purely by subjects’ internal mentation; no external cues prompted the recall onset or offset of each movie. Moreover, the unguided spoken recall allowed us to identify the exact moments of context transitions and explicitly track shifts in the contents of thoughts (Chen et al., 2017; Sripada and Taxali, 2020), which was not possible in prior studies using silent rest (Karapanagiotidis et al., 2020; Tseng and Poppenk, 2020). At these internal boundaries between recalled movies, we observed transient, highly generalizable and fine-grained activation patterns throughout the DMN, consistent across diverse movie contents and similar to those at external between-movie boundaries during encoding. Moreover, these between-movie boundary patterns were not merely stronger versions of within-movie ‘event boundaries,’ but instead manifested as a distinct type of neural transition. We propose that these cortical patterns reflect a cognitive state related to the major flushing and reconfiguration of mental context (DuBrow et al., 2017; Manning et al., 2016). Figure 1 with 3 supplements see all Download asset Open asset Experimental procedures and univariate responses. (A) In the encoding phase, subjects watched 10 short movies approximately 2–8 min long. Each movie started with a 6 s title scene. In the free spoken recall phase, subjects verbally recounted each movie plot in as much detail as possible regardless of the order of presentation. After recalling one movie, subjects spontaneously proceeded to the next movie, and the transitions between movies were considered as internally driven boundaries. Red arrows indicate the boundaries (onsets and offsets) between watched or recalled movies. Black arrows indicate the non-boundary moments (middle) of each watched or recalled movie. (B) Whole-brain maps of unthresholded mean activation (blood oxygen level-dependent [BOLD] signals z-scored across all volumes within a scanning run) following between-movie boundaries during recall (4.5–19.5 s from the offset of each movie). Blue areas indicate regions with lower-than-average activation, where the average activation of a scanning run was z = 0. Likewise, red areas indicate regions with higher-than-average activation. White outlines indicate areas that showed significantly lower or higher activation following between-movie boundaries compared to non-boundary periods (false discovery rate-corrected q < 0.05; minimum surface area = 16 mm2). The non-boundary periods were defined as the middle 15 s of each recalled movie, shifted forward by 4.5 s. Changes in whole-brain univariate responses across time around the boundaries are shown in Figure 1—video 1 (recall phase) and Figure 1—video 2 (encoding phase). Results We first examined whether internally driven boundaries evoke changes in blood oxygen level-dependent (BOLD) signals during recall. We observed transient changes in activation at the boundaries between recalled movies in widespread cortical regions (Figure 1—video 1; see Figure 1—figure supplement 1 for activation time courses). A whole-brain analysis with multiple comparisons correction revealed that the mean activation of boundary periods (15 s following the offset of each movie) was generally lower than that of non-boundary periods (middle 15 s within each movie) in multiple areas, including the motor, auditory, and inferior parietal cortices, although a smaller number of regions showed higher activation during non-boundary periods (Figure 1B). Next, we tested whether there were neural activation patterns specific to internally driven boundaries and consistent across different movies. We performed a whole-brain pattern similarity analysis on the recall data to identify regions where (1) boundary period activation patterns were positively correlated across different recalled movies (Figure 2A, blue arrow a > 0), and (2) this correlation was higher at boundaries than at non-boundaries (Figure 2A, blue arrows a > b). We observed a consistent boundary pattern, that is, whenever participants transitioned from talking about one movie to the next, in several cortical parcels (Schaefer et al., 2018), including the DMN and auditory/motor areas (Figure 2B). Thus, the boundary patterns within the recall phase were likely to be driven by both shared low-level sensory/motor factors (e.g., breaks in recall speech generation) as well as cognitive states (e.g., memory retrieval) at recall boundaries. No cortical parcel showed significantly negative correlations between boundary patterns or greater correlations in the non-boundary compared to boundary conditions. Figure 2 with 2 supplements see all Download asset Open asset Consistent activation patterns associated with between-movie boundaries. (A) Schematic of the pattern similarity analysis. Boundary patterns were defined as the mean pattern averaged across 15 s following the offset of each watched or recalled movie. Non-boundary patterns were defined as the mean pattern averaged across 15 s in the middle of each watched or recalled movie. For each subject and cortical parcel (Schaefer et al., 2018; 200 parcels per hemisphere), we computed pairwise between-movie pattern similarity (Pearson correlation), separately for boundary patterns and non-boundary patterns measured during recall (a and b, blue arrows). We also computed between-movie and between-phase (encoding-recall) pattern similarity, again separately for boundary and non-boundary patterns (c and d, red arrows). The time windows for both boundary and non-boundary periods were shifted forward by 4.5 s to account for the hemodynamic response delay. (B) Whole-brain t statistic map of cortical parcels that showed consistent between-movie boundary patterns during recall. These parcels displayed significantly greater between-movie pattern similarity in the boundary condition compared to the non-boundary condition during recall. The map was masked by parcels that showed significantly positive between-movie pattern similarity in the boundary condition during recall. Both effects were Bonferroni corrected across parcels (p<0.05). (C) Whole-brain t statistic map of cortical parcels that showed consistent between-movie boundary patterns across encoding and recall. These parcels displayed significantly greater between-movie and between-phase pattern similarity in the boundary condition compared to the non-boundary condition. The map was masked by parcels that showed significantly positive between-movie and between-phase pattern similarity in the boundary condition. Both effects were Bonferroni corrected across parcels (p<0.05). To what extent is the internally driven boundary pattern, measured during recall, similar to patterns observed at boundaries during encoding? To test this, we again computed between-movie pattern similarity for all cortical parcels in the brain, but now across the encoding and recall phases (Figure 2A, red arrows). We found that DMN areas showed a consistent boundary pattern across task phases (encoding and recall) and across movies (Figure 2C). Again, no cortical parcel showed negative correlations between boundary patterns or greater correlations in the non-boundary condition. Among the DMN areas, the posterior medial cortex (PMC) showed the most consistent boundary patterns; thus, we next examined the phenomenon in more detail specifically in PMC. Figure 3A and C visualize the high and consistently positive correlations of PMC boundary patterns across different movies both within the recall phase (recall offset vs. recall offset, t(14) = 11.82, p<0.001, Cohen’s dz = 3.05, 95% confidence interval (CI) = [0.28,0.41]) and even between experimental phases (recall offset vs. encoding offset, t(14) = 14.54, p<0.001, Cohen’s dz = 3.75, 95% CI = [0.28,0.38]). No such correlation was present between non-boundary patterns (t(14)s < 1, ps > 0.3). Individual subjects’ activation maps visualize the similarity between boundary patterns during encoding and recall (Figure 3B, Figure 3—figure supplement 1). We observed similar results in the lateral parietal DMN subregion (angular gyrus; Figure 3—figure supplement 2), as well as using shorter (4.5 s) time windows of boundary and non-boundary periods (Figure 2—figure supplement 1, Figure 3—figure supplement 3). Figure 3 with 4 supplements see all Download asset Open asset Boundary pattern in the posterior medial cortex (PMC). (A) PMC activation pattern similarity (Pearson correlation) between the 10 movie stimuli (M1–10), conditions (offset = boundary, middle = non-boundary), and experimental phases (encoding, recall), averaged across all subjects. The boundary pattern of a movie was defined as the mean pattern averaged across the 15 s window following the offset of the movie. The non-boundary pattern was defined as the mean pattern averaged across the 15 s window in the middle of a movie. The time windows for both boundary and non-boundary patterns were shifted forward by 4.5 s to account for the hemodynamic response delay. PMC regions of interest (ROIs) are shown as white areas on the inflated surface of a template brain. (B) Subject-specific mean activation patterns associated with between-movie boundaries during encoding (left) and recall (right). The boundary patterns were averaged across all movies and then z-scored across vertices within the PMC ROI mask, separately for each experimental phase. PMC (demarcated by black outlines) of four example subjects (S1–4) are shown on the medial surface of the right hemisphere of the fsaverage6 template brain. (C) Within-phase (recall-recall) and between-phase (encoding-recall) pattern similarity across different movies, computed separately for the boundary (offset) and non-boundary (middle) patterns in PMC. Bar graphs show the mean across subjects. Circles represent individual subjects. Error bars show SEM across subjects. ***p<.001. (D) Time-point-by-time-point PMC pattern similarity across the encoding phase and recall phase activation patterns around between-movie boundaries, averaged across all subjects. The time series of activation patterns were locked to either the onset (left) or the offset (right) of each movie. During encoding, the onset of a movie and the offset of the preceding movie were separated by a 6 s title scene. During recall, onsets and offsets of recalled movies were separated by, on average, a 9.3 s pause (boundaries concatenated across subjects, SD = 16.8 s). Dotted lines on the left and right panels indicate the mean offset times of the preceding movies and the mean onset times of the following movies, respectively. Note that in this figure zero corresponds to the true stimulus/behavior time, with no shifting for hemodynamic response delay. Areas outlined by black lines indicate correlations significantly different from zero after multiple comparisons correction (Bonferroni corrected p<0.05). Time–time correlations within each experimental phase can be found in Figure 3—figure supplement 4. Figure 3—source data 1 Source data for Figure 3. https://cdn.elifesciences.org/articles/73693/elife-73693-fig3-data1-v2.xlsx Download elife-73693-fig3-data1-v2.xlsx Thus far, we tested boundary responses following offsets, based on prior findings that post-stimulus neural responses contribute to memory formation (Ben-Yakov et al., 2013; Ben-Yakov and Dudai, 2011; Medvedeva et al., 2021). However, other studies also reported neural responses specific to the onset of an episode (Bulkin et al., 2020; Fox et al., 2005; Wen et al., 2020). Is the generalized boundary pattern evoked by the onset of a movie, rather than the offset? We examined this question by comparing the temporal emergence of the generalized boundary pattern following movie offsets versus onsets (Figure 3D); note that the offset of a movie was temporally separated from the onset of the following movie during both encoding and recall (see Figure 1A). Specifically, we extracted the mean time series of PMC activation patterns around between-movie boundaries, time-locked to either the onset or offset of each watched or recalled movie. We then computed between-phase (encoding-recall) pattern similarity across the individual time points of the activation pattern time series. We found that significantly positive between-phase correlations emerged well before the encoding and recall onsets (Figure 3D, left panel), starting from 4.5 s following the offsets of the preceding watched or recalled movie (Figure 3D, right panel). Thus, boundary patterns were not exclusively triggered by movie onsets; it is likely that offset responses significantly contributed to the boundary patterns. We focused our analyses up to this point on transitions between movies because they provided clear boundaries between mental contexts during recall. However, event boundaries in naturalistic movie stimuli are often defined as transitions between scenes within a movie (Baldassano et al., 2017; Chen et al., 2017; Zacks et al., 2010). In prior work, it has been shown that for within-movie event boundaries neural responses scale positively with human judgments of the ‘strength’ of scene transitions (Ben-Yakov and Henson, 2018). Thus, we hypothesized that boundaries between movies (i.e., between mental contexts) would manifest as stronger versions of within-movie boundaries with qualitatively similar patterns; in other words, boundary patterns would generalize across different scales of boundaries. To test this idea, we first confirmed that there were consistent within-movie event boundary patterns in PMC during encoding; within-movie boundary patterns were more similar to each other than to non-boundary patterns (Figure 4—figure supplement 1). We then tested whether this within-movie boundary pattern resembled the between-movie boundary pattern by measuring the correlation between (1) the mean between-movie boundary pattern during recall and (2) the mean within-movie event boundary pattern during encoding (Figure 4). Surprisingly, the two were negatively correlated (t(14) = 5.10, p<0.001, Cohen’s dz = 1.32, 95% CI = [–0.34, –0.14]), in contrast to the strong positive correlation across encoding and recall between-movie boundary patterns (t(14) = 25.02, p<.001, Cohen’s dz = 6.46, 95% CI = [0.67,0.79]). The within-movie event boundary pattern was also negatively correlated with the encoding phase between-movie boundary pattern (t(14) = 7.31, p<0.001, Cohen’s dz = 1.89, 95% CI = [–0.44, –0.24]). Within-movie and between-movie boundary patterns did not resemble each other, regardless of the specific time windows used to define the boundary periods (Figure 4—figure supplement 2). These results suggest that the between-movie boundary pattern may reflect a cognitive state qualitatively different from the state elicited by within-movie event boundaries during movie watching. Figure 4 with 2 supplements see all Download asset Open asset Comparing between-movie and within-movie boundary patterns in the posterior medial cortex (PMC). (A) Schematic of the analysis. For each subject, we created the template PMC activation pattern associated with between-movie boundaries by averaging activation patterns following the offset of each between-movie boundary (orange bars), separately for encoding and recall phases. Likewise, the template within-movie event boundary pattern was created by averaging the activation patterns following the offset of each within-movie boundary during encoding (green bars). We then measured the similarity (Pearson correlation) between the mean between-movie boundary patterns during encoding and recall (a, orange arrow). We also measured the similarity between the mean within-movie boundary pattern during encoding and the mean between-movie boundary pattern during recall (b, green arrow). For both between- and within-movie boundaries, boundary periods were 15 s long, shifted forward by 4.5 s. (B) Pattern similarity between template boundary patterns. The orange bar shows the mean correlation across the between-movie boundary patterns during encoding and recall. The green bar shows the mean correlation across the between-movie boundary pattern during recall and the within-movie boundary pattern during encoding. Circles represent individual subjects. Error bars show SEM across subjects. ***p<0.001 against zero. Figure 4—source data 1 Source data for Figure 4. https://cdn.elifesciences.org/articles/73693/elife-73693-fig4-data1-v2.xlsx Download elife-73693-fig4-data1-v2.xlsx Is the generalized between-movie boundary pattern driven by shared low-level perceptual or motoric factors rather than cognitive states? First, shared visual features at between-movie boundaries (i.e., black screen) cannot explain the transient, boundary-specific similarity between encoding and recall phases because visual input was identical across boundary and non-boundary periods during recall (i.e., a fixation dot on black background). Indeed, encoding boundary patterns were more similar to recall boundary patterns than to recall non-boundary patterns in DMN areas, suggesting a limited contribution of shared visual input to the generalized boundary pattern (Figure 2—figure supplement 2). Likewise, the absence of verbal responses at boundaries cannot explain the boundary pattern generalized across encoding and recall phases as no speech was generated throughout the entire encoding phase. Moreover, PMC boundary patterns showed positive between-phase pattern correlations (t(14) = 3.94, p=0.003, Cohen’s dz = 1.25, 95% CI = [0.1,0.36]) greater than those of non-boundary patterns (t(14) = 3.22, p=0.011, Cohen’s dz = 1.02, 95% CI of the difference = [0.06,0.36]) even when restricted to boundaries without pauses between recalled movies. We also ruled out the possibility that silence during movie title scenes and pauses at recall boundaries drove the generalized boundary pattern in PMC; the recall boundary pattern was not correlated with the pattern associated with silent periods during encoding (t(14) = 1.93, p=0.074, Cohen’s dz = 0.498, 95% CI = [–0.19,0.01]), whereas the auditory cortex showed a positive correlation between the two (t(14) = 10.31, p<0.001, Cohen’s dz = 2.66, 95% CI = [0.3,0.45]) (Figure 5). Likewise, the movies’ audio amplitudes modulated the time course of similarity between the recall boundary pattern and the encoding data in the auditory cortex, but not in PMC (Figure 5—figure supplements 1 and 2). Figure 5 with 2 supplements see all Download asset Open asset Examining the effects of silence on the generalized boundary pattern. For each subject, we computed a Pearson correlation between the mean activation pattern of the moments of silence during encoding (blue bars) and the mean activation pattern of between-movie boundaries during recall (orange bar) in the posterior medial cortex (PMC) and the auditory cortex (AUD). The moments of silence near between-movie boundaries (i.e., within the first 45 s of each movie) during encoding were excluded from the analysis. PMC and AUD regions of interest are shown as white areas on the inflated surface of template brains. Gray bars on the right panel indicate the mean pattern similarity across subjects. Circles represent individual subjects. Error bars show SEM across subjects. ***p<0.001 against zero. Figure 5—source data 1 Source data for Figure 5. https://cdn.elifesciences.org/articles/73693/elife-73693-fig5-data1-v2.xlsx Download elife-73693-fig5-data1-v2.xlsx Discussion This study investigated brain responses to internally generated boundaries between mental contexts during continuous and unguided memory recall of naturalistic narratives. We found that internally driven mental context boundaries evoke generalized neural activation patterns in core posterior-medial areas of the DMN (Ritchey and Cooper, 2020). These cortical patterns were similar to those observed at major boundaries between externally driven contexts (different audiovisual movies), suggesting that they reflect a general cognitive state associated with mental context transitions. However, these between-context patterns were distinct from within-context event boundary detection signals. The highly similar neural activation patterns for internally- and externally driven boundaries observed in this study demonstrate event segmentation without changes in external input. This finding diverges from the currently dominant empirical and theoretical perspectives on event segmentation; in most studies, event boundaries are defined or manipulated by changes in perceptual or spatiotemporal features (e.g., Chen et al., 2017; DuBrow and Davachi, 2013; Radvansky and Copeland, 2006), and boundary detection is posited to occur when those changes mismatch our expectations of the current situation (Zacks et al., 2007; Zacks et al., 2011). This prediction error framework successfully explains various phenomena related to event perception and memory organization (see Zacks, 2020 for a review); however, evidence has also shown that predicted changes in external features can create boundaries and have similar behavioral effects (Pettijohn and Radvansky, 2016; Schapiro et al., 2013). To resolve the discrepancy, an alternative theoretical framework has recently proposed that boundaries are perceived when the probability distribution of inferred current situations, rather than observed external features per se, changes from the previous time point (Shin and DuBrow, 2021). According to this account, event segmentation can occur when there is no perceptual change or when transitions are already predicted, which may explain the boundary-related neural responses at self-generated transitions between memories during recall in our study. The boundary pattern that generalized across internally and externally driven boundaries was most strongly observed in the DMN, in line with earlier findings implicating the DMN in mental context transitions (Baldassano et al., 2017; Crittenden et al., 2015; Smith et al., 2018). Prior studies have shown that the DMN responds to external context transitions, including experimental task switching (Crittenden et al., 2015; Smith et al., 2018) as well as event boundaries in movie clips (Reagh et al., 2020; Speer et al., 2007). Considering these findings and the widely known involvement of the DMN in internally oriented cognition (e.g., Addis et al., 2007; Andrews-Hanna et al., 2010; Christoff et al., 2009) together, it has been suggested that the DMN integrates both internal and external information to represent and maintain an abstract mental model of the current situation or state (Stawarczyk et al., 2021; Yeshurun et al., 2021); located furthest away from sensorimotor areas (Smallwood et al., 2021), the DMN integrates information across different modalities (Bonnici et al., 2016; Ramanan et al., 2018) and over long timescales (Chang et al., 2021; Hasson et al., 2015). Supporting this idea, neural activation patterns in subregions of the DMN, especially PMC, tend to persist for extended periods of time during naturalistic movie watching, and transitions between these persistent neural states coincide with perceived event boundaries (Baldassano et al., 2017; Geerligs et al., 2021). Our study extends this finding by identifying a transient, boundary-induced phenomenon, which is a unique and independent state represented in the DMN. That is, at major event boundaries, a temporary boundary state may exist in between the neural patterns representing the two events, rather than one event pattern switching directly to the next. Although the boundary-related PMC activation patterns were consistent across internally and externally driven boundaries, they did not generalize across within- and between-movie boundaries. Relatedly, a recent human neurophysiological study (Zheng et al., 2022) reported that medial temporal cortex neurons distinguished within- and between-movie boundaries while subjects were watching short video clips; some neurons responded only to between-movie boundaries, whereas a separate group of neurons responded to both types of boundaries. These findings may be in line with the view that event boundaries have a hierarchical structure, with different brain areas along the information pathway reflecting different levels of boundaries, from fine-grained sensory transitions to coarse-grained situational transitions (Baldassano et al., 2017; Chang et al., 2021; Geerligs et al., 2021). However, it is still puzzling that within- and between-movie boundaries in our study produced qualitatively distinct neural patterns within a highest-order area (PMC), even though both categories consisted of prominent boundaries between situations spanning tens of seconds to several minutes. What are the crucial differences between the two levels of boundaries? One important factor might be the presence or absence of inter-event connections. Even the most salient within-movie boundaries still demand some integration of information across events as the events are semantically or causally related, and ultimately constitute a single coherent narrative (Lee and Chen, 2021; Song et al., 2021b). In contrast, an entire cluster of related events, or the narrative as a whole, might be completely ‘flushed’ at between-movie boundaries; this difference could induce distinct cognitive states at the two levels of boundaries, giving rise to different PMC patterns. What is the cognitive state that is generalized across internal- and external boundaries between completely different contexts, but distinct from the state evoked by boundaries within the same context? W" @default.
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W4293247072 title "Author response: A generalized cortical activity pattern at internally generated mental context boundaries during unguided narrative recall" @default.
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