Abstract

Recent studies suggest common neural substrates involved in verbal and visual working memory (WM), interpreted as reflecting shared attention-based, short-term retention mechanisms. We used a machine-learning approach to determine more directly the extent to which common neural patterns characterize retention in verbal WM and visual WM. Verbal WM was assessed via a standard delayed probe recognition task for letter sequences of variable length. Visual WM was assessed via a visual array WM task involving the maintenance of variable amounts of visual information in the focus of attention. We trained a classifier to distinguish neural activation patterns associated with high- and low-visual WM load and tested the ability of this classifier to predict verbal WM load (high–low) from their associated neural activation patterns, and vice versa. We observed significant between-task prediction of load effects during WM maintenance, in posterior parietal and superior frontal regions of the dorsal attention network; in contrast, between-task prediction in sensory processing cortices was restricted to the encoding stage. Furthermore, between-task prediction of load effects was strongest in those participants presenting the highest capacity for the visual WM task. This study provides novel evidence for common, attention-based neural patterns supporting verbal and visual WM.

Introduction

There is increasing albeit indirect evidence for shared behavioral and neural mechanisms involved in verbal WM, visual WM, and attention. Behaviorally, verbal and visual WM tasks with stimuli designed to share as few features as possible (spatial visual arrays and word-voice pairings) still show tradeoffs between modalities, with the requirement to retain stimulus sets in both modalities reducing performance in both of them compared with unimodal memory maintenance (Saults and Cowan 2007), and the same is true of nonverbal acoustic and visual tasks (Morey et al. 2011). When 2 stimulus sets are to be retained, there is an initial processing phase in which encoding of materials into WM is vulnerable to feature similarity between the sets, followed by an WM maintenance phase in which there is little or no effect of the inter-set similarity, but during WM maintenance, there is still a tradeoff between the sets compared with control conditions in which 1 set can be ignored (Cowan and Morey 2007). At least the WM maintenance phase therefore appears to fit the profile of an attention-demanding process. For verbal materials, this process can be enhanced with a non-attention-demanding process, covert rehearsal (Camos et al. 2011), but that rehearsal process does not appear to come into play in the retention of spatial arrays of visual objects (Morey and Cowan 2004), which thus must depend on attention during WM maintenance.

Materials and Methods

Participants

Valid data were obtained for 21 right-handed native French-speaking young adults (9 male; mean age: 22.19 years; age range: 18–33) recruited from the university community, with no history of psychological or neurological disorders. The data of 2 participants had to be discarded due to scanner artifacts; 2 additional participants interrupted the study before complete data acquisition. The study was approved by the Ethics Committee of the Faculty of Medicine of the University of Liège and was performed in accordance with the ethical standards described in the Declaration of Helsinki (1964). All participants gave their written informed consent prior to their inclusion in the study.

Visual Array WM Task

Figure 1.

Schematic drawing of the visual array and verbal WM tasks. Note that for the visual array WM task, only the “encoding-only” trials were used for classifier training.

Figure 1.

Schematic drawing of the visual array and verbal WM tasks. Note that for the visual array WM task, only the “encoding-only” trials were used for classifier training.

This task assessed load effects in WM by presenting sequences of 2, 4, or 6 consonant letters sampled without replacement from a pool of 16 different consonants. The letter sequences were presented for 2500 ms on the center of the screen and were organized horizontally. The sequences were then replaced by the sign “*”, indicating that the letter sequences had to be maintained in WM for 5000 ms. After the maintenance interval, a probe letter was shown in 1 of the 2, 4, or 6 possible serial positions indicated by horizontal bars on the center of the screen (see Fig. 1). The participants had to decide within 3000 ms whether the probe letter matched the letter in the indicated serial position in the memory list by pressing the button under their middle finger for “yes” and the button under their index finger for “no.” In 50% of trials, the probe letter did not match the target letter (i.e., a letter not presented in the memory list) or its position (i.e., the letter was part of the memory list but not in the indicated serial position). The probe display was cleared after the participant's response. There were 42 trials for each WM load condition. Finally, a control condition (20 trials) was included, controlling for letter identification, motor response, and decision processes; this condition consisted of the presentation of a sequence containing 2, 4, or 6 times the same vowel A, followed by a 5000-ms delay period indicated by the sign “*” and finishing with a response display showing the same letter in upper or lower case; the participants had to decide whether the case was the same as in the target list by pressing the button under the middle finger for “yes” and by pressing the button under the index for “no.” For all conditions, before the start of a new trial, the sign “!” appeared on the center of the screen during 1000 ms informing the participant about the imminent start of a new trial. The inter-trial interval was of variable duration (random Gaussian distribution centered on a mean duration of 3500 ± 250 ms). The different conditions were administered in pseudo-random order.

fMRI Analyses

Image Preprocessing

Data were preprocessed and analyzed using SPM8 software (Wellcome Department of Imaging Neuroscience, http://www.fil.ion.ucl.ac.uk/spm) implemented in MATLAB (Mathworks, Inc.) for univariate analyses. EPI time series were corrected for motion and distortion with “Realign and Unwarp” (Andersson et al. 2001) using the generated field map together with the FieldMap toolbox (Hutton et al. 2002) provided in SPM8. A mean realigned functional image was then calculated by averaging all the realigned and unwarped functional scans, and the structural T1-image was coregistered to this mean functional image (rigid body transformation optimized to maximize the normalized mutual information between the 2 images). The mapping from subject to MNI space was estimated from the structural image with the “unified segmentation” approach. The warping parameters were then separately applied to the functional and structural images to produce normalized images of resolution 2 × 2 × 2 mm3 and 1 × 1 × 1 mm3, respectively. The scans were screened for motion artifacts, and time series with motion peaks exceeding 3 mm (translation) or 3° (rotation) were discarded. Finally, the warped functional images were spatially smoothed with a Gaussian kernel of 4-mm full-width at half maximum (Schrouff et al. 2012).

Univariate Analyses

Univariate analyses first assessed brain activation levels associated with visual and verbal WM load. For each subject, brain responses were estimated at each voxel, using a general linear model with event-related and epoch-related regressors. For the visual array WM task, 3 regressors modeled the encoding-only trials (1 per load) as zero-duration events; 3 additional regressors also modeled the recognition trials in order to control for variance related to comparison and response processes additionally associated with these trials. For the verbal WM task, the design matrix included 3 regressors, which modeled sustained activity over the entire verbal WM trial as a function of verbal WM load; the epoch-related regressors ranged from the onset of the encoding period until the end of the recognition period; the sensory and motor control condition was modeled implicitly. For each task, boxcar functions representative for each regressor were convolved with the canonical hemodynamic response. The design matrixes also included the realignment parameters to account for any residual movement-related effect. A high pass filter was implemented using a cutoff period of 128 s in order to remove the low-frequency drifts from the time series. Serial autocorrelations were estimated with a restricted maximum likelihood algorithm with an autoregressive model of order 1 (+ white noise). For each design matrix, linear contrasts were defined for the 3 target load conditions. The resulting set of voxel values constituted a map of t statistics [SPM{T}]. For each task, these contrast images, after additional smoothing by 6-mm FHWM, were then entered in a second-level, random effect ANOVA analysis to assess load responsive brain areas. The additional smoothing was implemented in order to reduce noise due to inter-subject differences in anatomical variability and in order to reach a more conventional filter level for group-based univariate analyses $((42+62)=7.21mm)$ (Mikl et al. 2008).

A Priori Locations of Interest

As a rule, for univariate analyses, statistical inferences were performed at the voxel level at P < 0.05 corrected for multiple comparisons across the entire brain volume using Random Field Theory. We in addition focused on a small set of a priori-defined regions-of-interest that have shown interactions between attentional processing and WM in previous studies. These regions included the dorsal attention network with the bilateral posterior IPS [−25, −64, 43; 27, −62, 38], the bilateral superior frontal gyrus [−20, −1, 50; 26, −2, 47] as well as the ventral attention network with the bilateral temporo-parietal junction [−46, −57, 20; 47, −57, 24] and orbito-frontal cortex [−37, 26, −8; 34, 27, −10] (Todd and Marois 2004; Todd et al. 2005; Asplund et al. 2010; Majerus et al. 2012). We also included the left anterior IPS, which has been associated with amodal attentional control processes [−43, −46, 40] (Cowan et al. 2011; Majerus et al. 2012). A small volume correction was applied on a 10-mm radius sphere around these coordinates-of-interest.

For multivariate analyses, 10-mm radius volumes-of-interest were created around these same areas of interest and these volumes-of-interest were then used as masks for the training and test phases, allowing us to determine classification accuracy for these areas of interest. In order to assess the selectivity of the classifications in these areas, we also targeted visual and language processing areas where no between-task predictions were expected, except for shared sensory processing of visual form information during encoding, since the verbal and visual stimuli were both presented in a visual format. These additional volumes-of-interest included the bilateral middle occipital gyrus [−32, −79, 9; 29, −82, 8] (Pessoa et al. 2002) involved in color and basic visual form processing, which may be common to encoding of the square and letter stimuli in the visual and verbal WM conditions. These volumes-of-interest also included the left fusiform [−43, −55, −18] and superior temporal gyri [−58, −36, 10] for orthographic and phonological processing, respectively (McCandliss et al. 2003; Majerus et al. 2010), and which should not be shared between for stimulus encoding in the verbal and visual WM conditions.

Results

Univariate fMRI Analyses

An ANOVA on functional images over the 3 load conditions of the visual array WM task (encoding-only trials) showed a main effect in the dorsal attention network, including the bilateral posterior IPS and the right superior frontal gyrus (see Table 1). These effects were due to significantly higher activation for the 6-load condition relative to the 2-load condition in the bilateral posterior IPS and the bilateral superior frontal gyrus, as well as in the right inferior parietal lobule (see Fig. 2A and Table 1). The 4-load versus 2-load contrast did not lead to significant effects, except for increased activation in a small right posterior IPS area in the 4-load condition.

Table 1

Peak-level activation foci showing overall load-dependent activity in the visual array and verbal WM tasks

Anatomical region ANOVA

BA area No. voxels Left/right x y z SPM {Z}-value No. voxels x y z SPM {Z}-value No. voxels x y z SPM {Z}-value
Visual array
Dorsal attention network
Superior frontal gyrus      −26 −6 54 3.21*
Superior frontal gyrus 24 50 3.23* 107 24 50 3.68*
Intraparietal sulcus (anterior) 40       −36 −42 42 3.18*
Intraparietal sulcus (posterior) 40 −22 −64 42 3.70* 153 −22 −64 42 4.10*
Intraparietal sulcus (posterior) 57 24 −62 52 3.96** 13 22 −60 46 3.43*
Verbal WM
ACC/SMA 6/32 350 −6 60 5.02 369 −6 60 5.27
Posterior cingulate 30           1418 −50 18 4.80
Precentral gyrus 825 −52 46 5.20 1450 −52 46 5.63
Cerebellum VI 745 36 −62 −32 5.21 2271 36 −62 −32 5.46
Lingual gyrus 17 309 −14 −86 5.07  −14 −86 5.47
Insula 13           1428 40 −12 −4 4.82
Middle temporal gyrus 21           1302 −52 −18 −18 4.87
Dorsal attention network
Superior frontal gyrus 27 −24 54 3.65* 132 −24 54 3.63*
Superior frontal gyrus 25 28 54 3.70* 30 56 3.11*
Intraparietal sulcus (anterior) 40 156 −38 −40 38 4.07* 73 −36 −40 40 3.83*
Intraparietal sulcus (posterior) 184 −26 −62 48 3.95* 236 −26 −62 48 4.21*
Intraparietal sulcus (posterior) 26 −62 48 3.45* 26 −62 48 3.51*
Ventral attention network
Orbito-frontal cortex 11/47 2245 −10 54 18 5.07      3993 −10 54 18 5.47
47   −34 32 −14 3.75*      49 −36 32 −14 4.22*
47   28 30 −14 3.73*      71 28 30 −14 4.28*
Temporo-parietal junction 39 604 −50 −64 28 5.15      978 −50 −64 28 5.46
Temporo-parietal junction 39           76 52 −58 26 3.52*
Anatomical region ANOVA

BA area No. voxels Left/right x y z SPM {Z}-value No. voxels x y z SPM {Z}-value No. voxels x y z SPM {Z}-value
Visual array
Dorsal attention network
Superior frontal gyrus      −26 −6 54 3.21*
Superior frontal gyrus 24 50 3.23* 107 24 50 3.68*
Intraparietal sulcus (anterior) 40       −36 −42 42 3.18*
Intraparietal sulcus (posterior) 40 −22 −64 42 3.70* 153 −22 −64 42 4.10*
Intraparietal sulcus (posterior) 57 24 −62 52 3.96** 13 22 −60 46 3.43*
Verbal WM
ACC/SMA 6/32 350 −6 60 5.02 369 −6 60 5.27
Posterior cingulate 30           1418 −50 18 4.80
Precentral gyrus 825 −52 46 5.20 1450 −52 46 5.63
Cerebellum VI 745 36 −62 −32 5.21 2271 36 −62 −32 5.46
Lingual gyrus 17 309 −14 −86 5.07  −14 −86 5.47
Insula 13           1428 40 −12 −4 4.82
Middle temporal gyrus 21           1302 −52 −18 −18 4.87
Dorsal attention network
Superior frontal gyrus 27 −24 54 3.65* 132 −24 54 3.63*
Superior frontal gyrus 25 28 54 3.70* 30 56 3.11*
Intraparietal sulcus (anterior) 40 156 −38 −40 38 4.07* 73 −36 −40 40 3.83*
Intraparietal sulcus (posterior) 184 −26 −62 48 3.95* 236 −26 −62 48 4.21*
Intraparietal sulcus (posterior) 26 −62 48 3.45* 26 −62 48 3.51*
Ventral attention network
Orbito-frontal cortex 11/47 2245 −10 54 18 5.07      3993 −10 54 18 5.47
47   −34 32 −14 3.75*      49 −36 32 −14 4.22*
47   28 30 −14 3.73*      71 28 30 −14 4.28*
Temporo-parietal junction 39 604 −50 −64 28 5.15      978 −50 −64 28 5.46
Temporo-parietal junction 39           76 52 −58 26 3.52*

Note: If not otherwise stated, regions are significant at P < 0.05, with voxel-level FWE corrections for whole-brain volume.

*P < 0.05, small volume corrections, for regions-of-interest; **P < 0.001, uncorrected.

Figure 2.

(A). Brain areas showing load-sensitive activations when comparing the 6-load-versus-the 2-load conditions for the visual array (leftward panel) and verbal WM tasks (rightward panel) with a display threshold of 3 ≤ T ≤ 6 and –6 ≤ T ≤ –3 on 3D template of cortical surface (Van Essen et al. 2001). (B). Common load-sensitive areas in the visual array and verbal WM tasks for the 2-load versus 6-load conditions (null conjunction analysis), with a display threshold of 3 ≤ T ≤ 4 and –4 ≤ T ≤ –3 on a 3D template of cortical surface (Van Essen et al. 2001).

Figure 2.

(A). Brain areas showing load-sensitive activations when comparing the 6-load-versus-the 2-load conditions for the visual array (leftward panel) and verbal WM tasks (rightward panel) with a display threshold of 3 ≤ T ≤ 6 and –6 ≤ T ≤ –3 on 3D template of cortical surface (Van Essen et al. 2001). (B). Common load-sensitive areas in the visual array and verbal WM tasks for the 2-load versus 6-load conditions (null conjunction analysis), with a display threshold of 3 ≤ T ≤ 4 and –4 ≤ T ≤ –3 on a 3D template of cortical surface (Van Essen et al. 2001).

Overall, overlap for the 2-load versus 6-load contrasts in the visual array and WM tasks was most strongly related to the dorsal attention network, and especially for the left posterior IPS. In order to test this overlap statistically, we conducted a conservative null conjunction analysis on the 6 versus 2 load effects in the visual array and verbal WM tasks, confirming statistically significant overlap in the left posterior IPS, and to a lesser extent, in the right posterior IPS as well as the left anterior IPS (see Table 2 and Fig. 2B).

Table 2

Peak-level activation foci showing common load sensitivity for 6-load-versus-2-load conditions in the visual array and verbal WM tasks (null conjunction)

Anatomical region No. voxels Left/right x y z BA area SPM {Z}-value
Intraparietal sulcus (anterior) −38 −38 40 40 3.30*
Intraparietal sulcus (posterior) 155 −22 −64 44 3.75*
Intraparietal sulcus (posterior) 26 −62 48 3.12*
Anatomical region No. voxels Left/right x y z BA area SPM {Z}-value
Intraparietal sulcus (anterior) −38 −38 40 40 3.30*
Intraparietal sulcus (posterior) 155 −22 −64 44 3.75*
Intraparietal sulcus (posterior) 26 −62 48 3.12*

*P < 0.05, small volume corrections for regions-of-interest.

Multivariate fMRI Analyses

Figure 3.

Figure 3.

Region-of-Interest Multivariate Analyses

Figure 4.

Figure 4.

Figure 5.

Figure 5.

In order to determine the statistical significance of the specific sensitivity of regions-of-interest in the dorsal attention network for between-task classification during verbal WM maintenance events relative to the left middle occipital gyrus region, we ran a repeated-measures ANOVA on mean classification accuracies with the factors region (left middle occipital gyrus, left posterior IPS) and verbal WM events; the left posterior IPS was selected here as this region-of-interest was associated with the most robust classification behavior over the entire verbal WM task. For the visual WM-to-verbal WM training-prediction direction, we observed a main effect of region, F1,20 = 8.71, $ηp2=0.30$, P < 0.01, a main effect of event, F11,220 = 13.24, $ηp2=0.40$, P < 0.001, and a significant interaction, F11,220 = 4.23, $ηp2=0.17$, P < 0.001; planned comparisons showed an overall higher classification accuracy for load conditions in the left posterior IPS, and this advantage was particularly strong for the early encoding stage (first 2 events) and the late maintenance phase (events 6 and 7) (P < 0.05, after Bonferroni corrections for multiple comparisons). When running the same analysis for the verbal WM-to-visual WM training-prediction direction, very similar results were observed, with a main effect of region, F1,20 = 18.75, $ηp2=0.48$, P < 0.001, a main effect of event, F11,220 = 12.47, $ηp2=0.38$, P < 0.001, and a significant interaction, F11,220 = 4.84, $ηp2=0.19$, P < 0.001; the superiority of classification accuracy in the left posterior IPS was particularly marked for the encoding and late maintenance stage (events 1, 2, 3, and 6) (P < 0.05, Bonferroni-corrected).

Brain–Behavior Associations

Finally, in order to further show that the robust classification accuracy for the 6-versus-2 load conditions observed in this study reflects the increase of information held in WM, and not merely the increased difficulty or cognitive effort associated with maintaining 6-versus-2 items in WM, we performed a second type of correlation analyses where we examined the direction of the association between overall classification accuracy and visual and verbal WM capacity. If heightened classification accuracy merely reflects the increased difficulty and cognitive effort between the 2 conditions, then participants with “low” k-capacity should show heightened classification accuracy, and this especially in conditions where capacity limits are reached (i.e., the 6-load condition as compared with the 2-load condition). This was tested by averaging individual classification accuracies for the 8 first events of the verbal WM trials (in order not to bias the results by the presence of non-informative end-of-trial events) and by correlating these mean classification accuracies with the maximum k score of the visual WM task and the accuracy score for the verbal WM task (restricted to 6-load trials where performance was most variable). For the visual WM-to-verbal WM prediction direction and for the 6-versus-2 classifiers, we observed a significant positive correlation between mean classification accuracy and maximum k capacity, r = 0.45, P < 0.05, meaning that the higher classification accuracy, the better visual WM capacity; this rules out an interpretation in terms of increased task difficulty as underlying increased between-task classifications. A non-significant positive correlation was also observed with the verbal WM performance score, r = 0.26, P = 0.25. Similar results were observed when conducting the same analyses for the verbal-to-visual prediction direction: both the visual and verbal WM scores showed significant and large positive correlations with mean classification accuracy for 6-versus-2 load classifiers (r = 0.71, P < 0.001, and r = 0.50, P < 0.05, for visual and verbal WM scores, respectively). When running the same analysis on individual classification accuracies for the left posterior IPS region-of-interest, which had yielded the most robust classification pattern in the dorsal attention network, similar results were also observed: For the visual WM-to-verbal WM prediction, the correlation values were r = 0.27, P = 0.24, and r = 0.08, P = 0.72, for visual and verbal WM scores, respectively; for the verbal WM-to-visual WM prediction, the correlation values were as follows: r = 0.46, P < 0.05, and r = 0.21, P = 0.37. In sum, these analyses show that for individuals with lower WM performance, classification accuracy was also reduced, and this was particularly the case for the visual WM scores, which reflect most directly attention-based WM maintenance processes as we will discuss. Note, however, that we need to remain cautious when interpreting the brain–behavior correlations reported here given the relatively small sample size (N = 21) for analyses looking at associations between interindividual differences in neural and behavioral patterns.

Discussion

We show here that neural activation patterns differentiating high- and low-verbal WM load can be predicted by neural patterns dissociating high and low load in a visual array WM task, and vice versa. A region-of-interest approach showed that this was the case more particularly for the bilateral posterior IPS, which had also been identified in univariate conjunction analyses as supporting cross-modal load effects, and for the bilateral superior frontal cortex, which had not been identified in univariate conjunction analyses. These regions define the dorsal attention network. Furthermore, these cross-modal predictions of WM load effects in these regions were observed during maintenance when stimuli were not physically present, as well as during encoding and retrieval. Multivariate analyses also identified cross-modal predictions of load effects in sensory cortices, but these were limited to the encoding and very early maintenance stage. Finally, cross-modal classification accuracy for 6-versus-2 load conditions was highest and most reliable in those participants presenting the highest visual WM capacity.

The present results further imply that the attentional focus has a variable capacity (Cowan 2001; Morrison et al. 2014). Some studies showed that at the moment of retrieval, only the most recent item was associated with enhanced activity or functional connectivity in the posterior parietal cortex, suggesting that only the most recent item is held in the focus of attention (Talmi et al. 2005; Ötzekin et al. 2009, 2010; Nee and Jonides 2011). A more recent study, however, showed that these findings may have been induced by the specific task requirements, with very long encoding lists (up to 12 items) or task cues presented only at the moment of retrieval, which may have made it difficult to efficiently hold and focus attention on the entire memory list (Morrison et al. 2014). Lewis Peacock et al. (2012) also showed that attention can be focused on at least 2 items at once according to their multivariate classification results. Further convergent evidence comes from a behavioral experiment showing that attention in a perceptual search task can be guided by multiple WM items at the same time (Beck et al. 2012). The present study, showing that high (6-item) and low (2-item) attentional load conditions can be reliably distinguished in posterior parietal cortex and that the strength of this distinction varies as a function of scope of attention capacity of the participants, indicates that the capacity of the attentional focus exceeds 1 item and is flexible, that is, it varies between individuals.

The present findings raise the more general question of the functional relevance of shared neural substrates between verbal and visual WM. Although we showed that higher and more reliable between-task classification accuracy was associated with higher scope of attention capacity, variability in classification accuracy was less reliably associated with variability in behavioral verbal WM performance. It may be that visual scope of attention, although involved in the maintenance of information in verbal WM, is not necessarily associated with verbal WM retrieval success. This possibility is supported by the findings of Lewis-Peacock et al. (2012) who showed that WM success did not differ for items actively held in the focus of attention or not, suggesting that recognition based on distributed neural activation patterns in sensory cortex, that is, in activated long-term memory, can be sufficient (see also Riggall and Postle 2012; Emrich et al. 2013; Rahm et al. 2014). A second possibility is that although attentional focalization and refreshing is the only way to maintain nonverbal visual items, verbal items can be maintained via 2 different processes. Verbal information can be maintained either by attentional refreshing of neural representations as for visual information (Raye et al. 2007), or it can be maintained through covert verbal rehearsal, a much less attention-demanding strategy. Camos et al. (2011) showed that for verbal stimuli, participants can be made to use 1 strategy or another. Given 2 possible strategies for verbal WM maintenance, it may have been that participants showing the strongest cross-modal classification accuracies used the attentional strategy more because their higher scope of attention capacity allowed them to do so, whereas those with the weaker cross-modal classifications relied to a larger extent on the verbal rehearsal strategy for the verbal materials.

To conclude, this study provides new evidence for shared, attention-based neural substrates during retention of verbal and visual information in WM, by demonstrating that univariate neural responses associated with verbal and visual WM load not only overlap in the posterior parietal cortex but also that the multivariate neural patterns in a larger part of the dorsal attention network are sufficiently similar to allow for cross-modal predictions of WM load, and this particularly in participants showing the strongest scope of attention capacity. Future studies need to investigate the functional consequences of these findings for the understanding of behavioral WM capacity limitations.

Funding

This work was supported by grants F.R.S.-FNRS N°1.5.056.10 (Fund for Scientific Research FNRS, Belgium) and PAI-IUAP P7/11 (Belgian Science Policy).

Notes

Conflict of Interest: None declared.

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