Differential involvement of left and right frontoparietal areas in visuospatial planning: An rTMS study

Journal Pre-proof Differential involvement of left and right frontoparietal areas in visuospatial planning: An rTMS study Demis Basso, Chiara Saracini PII:

S0028-3932(19)30302-1

DOI:

https://doi.org/10.1016/j.neuropsychologia.2019.107260

Reference:

NSY 107260

To appear in:

Neuropsychologia

Received Date: 5 April 2019 Revised Date:

4 November 2019

Accepted Date: 8 November 2019

Please cite this article as: Basso, D., Saracini, C., Differential involvement of left and right frontoparietal areas in visuospatial planning: An rTMS study, Neuropsychologia (2019), doi: https://doi.org/10.1016/ j.neuropsychologia.2019.107260. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Demis Basso: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing original draft; Writing - review & editing. Chiara Saracini: Data curation; Investigation; Methodology; Resources; Visualization; Roles/Writing - original draft; Writing - review & editing.

Differential involvement of left and right frontoparietal areas in visuospatial planning: an rTMS study Running Title: Visuospatial planning & rTMS on associative areas

Demis Bassoa,b & Chiara Saracinic,b

a

CESLab, Faculty of Education, Free University of Bozen, Bressanone-Brixen (Italy)

b

The Neuropsychology and Cognitive Neurosciences Research Center (Centro de

Investigación en Neuropsicologia y Neurociencias Cognitivas, CINPSI Neurocog), Universidad Católica del Maule, Talca (Chile) c

Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de

Investigación y Postgrado (VRIP), Universidad Católica del Maule, Talca (Chile)

Corresponding author: Chiara Saracini Avenida San Miguel, 3605 Edificio Parque Tecnológico - Campus San Miguel 3460000 Talca, VII Región del Maule, Chile Email: [email protected], Phone: +56 71 2 633 152 Mobile Phone: +56 9 6668 7548

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Abstract The planning process consists of pre-determining an ordered series of actions to accomplish a goal. Previous research showed that the left prefrontal cortex (PFC) is likely to create the strategy for a plan, while the right PFC could be relevant for its update. These roles for the two PFCs need to be ascertained for visuospatial planning, whether communalities or differences exist with other planning tasks. Moreover, the contribution of the posterior parietal cortex (PPC) to planning still lacks evidence. Online repetitive transcranial magnetic stimulation (1 Hz) was used, and 32 participants were involved in the visuospatial planning task in a within-subject design to inhibit either the frontal or the parietal cortex of either the left or the right hemisphere. The goal consisted of evaluating the contribution of these cortical regions, also controlling for gender, in a computerized version of the travelling salesman problem (TSP), the “Maps” task. The results showed that all the stimulated sites produced significant differences in their involvement, reflected in several parameters (such as initial planning and execution times, strategies and heuristics used), with respect to the control group. The roles for the two PFCs were generally confirmed in all measures except path length, while the contribution of the PPC emerged throughout the measures related to the ongoing execution. We concluded that the results obtained with the TSP paradigm were consistent with results obtained using other tasks used to study the planning process (such as the Tower of London) for the evaluation of PFC contribution. In addition, we showed that the contribution of the PPC to the planning process has probably been underestimated.

Highlights: •

Stimulating the right PFC with rTMS confirmed its role in monitoring and updating visuospatial planning

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Left PFC stimulation affected the initial planning process and increased execution times

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Stimulation of both parietal areas reduced execution times and strategies with changes

1. Introduction Planning has been defined as a cognitive process needed to organize a sequence of thoughts or actions to obtain a goal. According to Cohen (1989), this process consists of a mental

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simulation that includes the extant situation and the desired goals to prepare possible directions to guide action. Planning is involved in a wide range of activities, ranging from the basic finger movements of a pianist (Vilkki & Holst, 1991) to everyday tasks such as deciding which route is better to take to avoid peak traffic during the rush hour (Peruch, Giraudo & Garling, 1989). To compute the best answer and to be able to properly respond to changes in the ongoing situation, our brain constantly compares inner plans of action with real-time feedback from the external environment. While planning can sometimes end before the selected course of action is executed (ballistic plan), monitoring processes are relevant in many situations to detect changes and to adjust the plan to current contingencies (incremental planning). In both cases, the planning process collaborates with a series of sub-processes related to executive functioning, such as working memory, inhibition, and set switching; consequently, the contribution of the associative cortices is central in generating an effective plan to obtain the desired goal. The first studies on planning suggested that frontal regions of the brain were responsible for the planning process (Luria, 1966; Shallice, 1982). Complex problem solving and planning seem to involve the most anterior part of the frontal lobes, the prefrontal cortex (PFC; Baker et al., 1996). Koechlin and colleagues (1999) showed that bilateral regions in the PFC alone are selectively activated when subjects must keep a main goal in mind while performing concurrent (sub)goals. Structured planning tests (such as the Tower of London and the Tower of Hanoi) require a strong contribution by working memory (Viejo-Sobera et al., 2017). Through these tests, it has been shown that activation in the dorsolateral prefrontal cortex (dlPFC) can be attributed to generation and evaluation of abstract sequences of responses and that activation in the regions underlying plan execution may be related to rehearsal of planned sequences of moves (Crescentini, Seyed-Allaei, Vallesi, & Shallice, 2012; Cazalis et al.,

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2003), in addition to response inhibition. These results are consistent with a study by Basso and colleagues (2006), in which repetitive TMS was applied to the left and right hemispheres: a reduction of strategy updates was found, supporting the view of the dlPFC as responsible for plan execution and evaluation. A severe limitation of that study, however, was that both the left and right hemispheres were stimulated simultaneously, preventing any detection of hemispheric differences. Indeed, many studies have suggested lateralized roles for the left and right PFC: the involvement of the left PFC in the planning process seems to be related to flexibility and monitoring processes (Carter et al., 2000; Golden, 1978), while the contribution of the right PFC appears to consist of inhibition and switching processes (Burgess, Veitch, de Lacy Costello, & Shallice, 2000). However, Stuss and Alexander (2007) took a different perspective: they suggested that the distinctions in PFC involvement along the left–right axis might be based on the process required by the task. On the one hand, in the left PFC, those researchers localized criterion-setting processes (namely, strategy production: Cabeza et al., 2003; Fletcher, Shallice, & Dolan, 2000), which are needed to set up and select relevant rules for the given task. On the other hand, the medial and right PFC were thought to be responsible for energization and monitoring/control processes, respectively (see reviews by Stuss, 2011, and Vallesi, 2012). Other scholars have discussed how left-lateralized mid-dlPFC activation may be associated with early processes of internalization, whereas right-lateralized mid-dlPFC activation may reflect subsequent processes of planning (Kaller, Rahm, Spreer, Weiller, & Unterrainer, 2011; but also Nitschke, Ruh, Kappler, Stahl, & Kaller, 2012; Ruh, Rahm, Unterrainer, Weiller, & Kaller, 2012). Kaller and colleagues (2013) applied continuous theta-burst stimulation (cTBS, a particular type of rTMS; see Lowe, Manocchio, Safati, & Hall, 2018 for a review of its effectiveness) to either the left or the right mid-dlPFC, showing an asymmetric effect of cTBS on initial planning time. While inhibition of the left dlPFC was associated

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with reduced planning time, impairment of the right dlPFC was followed by increased initial planning time. Despite the commonly accepted differences in left/right PFC involvement and the general association of planning with the mid-dorsolateral PFC, Nitschke and colleagues (Nitschke, Köstering, Finkel, Weiller, & Kaller 2017) recently conducted an extensive review of literature on the neuroanatomy of planning processes and found that the data did not consistently support the previous assumptions. The authors concluded their review by stating that “adding to the unresolved issue of a possible lateralization of PFC involvement in planning [...], the often assumed association with its mid-dorsolateral part [...] remains to be empirically confirmed by a quantitative examination of activation coordinates across studies”. In the accompanying meta-analysis, the investigators presented the following activation loci identified by their investigation: bilateral involvement of the frontal eye fields, caudate nucleus, anterior insula, inferior parietal lobule, supplementary motor area, precuneus, and mid-dorsolateral and rostrolateral PFC. Lateralized activation was found for the right inferior occipital gyrus, right inferior temporal gyrus, and left posterior cingulate (Nitschke et al., 2017). The rostrolateral PFC (rlPFC) has also been shown by some fNIRS studies to be activated during planning, but its contribution is still debated, although it is probably underestimated (Nitschke, 2017), requiring more and deeper investigation with mixed methodologies and new experimental paradigms and tasks. It is clear now that the planning process is likely to entail a large lateral fronto-parietal network, while the specific contribution of the parts of this network is still a matter of debate. The problem is particularly intricate due to the intrinsic variability of the neurons’ activity in the associative regions (Dosenbach et al., 2006; Parthasarathy et al., 2017) and to the interconnectivity between homologue regions (Kaller et al., 2015), which could increase variability in the results and thereby compromise the consistency of interpretations. In addition to the central role for PFC,

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the literature has agreed on a wider set of areas serving to the planning process, involving not only prefrontal areas but also the parietal cortex. Given the extensive connections between the two associative regions, the dlPFC has been suggested to be responsible for monitoring and manipulating information stored in posterior parietal areas (Dagher, Owen, Boecker, & Brooks, 1999; Petrides, 1994). However, the contribution of the posterior parietal cortex (PPC) is not limited to working memory: these cortical regions are thought to be involved in the construction, control and maintenance of spatial representations (in particular, areas BA7/40: Carpenter, Just, Keller, Eddy, & Thulborn, 1999) and in attentional processing (BA7: Coull & Frith, 1998). Due to its substantial involvement in movement planning and spatial orientation, the PPC, particularly the angular gyrus, has also been shown to be responsible for selecting and planning actions and for priming response selection (Andersen & Cui, 2009; Schiff, Bardi, Basso, & Mapelli, 2011). Thus far, however, the results obtained with the Tower of London (TOL: Shallice, 1982) task have evidenced only a small contribution of the PPC to cognitive planning. Therefore, an open research question would consist of evaluating whether planning requires areas in the PPC (such as the angular gyrus) related to action selection and planning, not simply to visuospatial processing, or whether planning (e.g., use of strategies, optimization) is related only to the frontal cortex. The several tasks used to investigate planning could be considered another source of variability or, to some extent, a solution to the constraint of binding tasks and modalities. The two main tasks are the TOL and the travelling salesman problem (TSP; Cadwallader 1975; MacGregor, Chronicle, & Ormerod. 2004). While the TOL was developed for the evaluation of frontal lobe impairments and is extensively used in the neuropsychological community, the TSP originated in computer science and has mainly been used in cognitive psychology to investigate human performance as a perceptual problem-solving task (MacGregor, & Chu,

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2011). In the TSP, participants must determine the order in which a set of target points (subgoals) must be achieved to optimize the route in terms of time, length and accuracy (that is, including all the subgoals). With respect to the TOL, the TSP differs in several dimensions: the latter requires a limited contribution of working memory and anticipated planning, and the number of problems can potentially be infinite, as can the possible solutions; therefore, the solution could be evaluated onto a continuous scale of measure. Moreover, the task itself includes many versions: there can be either a closed loop or an open path, and navigation can be in free space or limited to a grid (such as the “Manhattan” or “l_1” norm: MacGregor, & Chu, 2011). These features allow experimenters to manipulate several parameters to study visuospatial planning. Indeed, TSP has been regarded as a suitable tool for investigating planning because the task requires participants to generate a strategy, optimizing the order of locations to extract a satisfactory path in a modelling space (Goel & Grafman, 1995). Therefore, it is possible to use visuospatial heuristics and to change the plan during its execution if it is judged no longer convenient to maintain. The Maps task (Basso, Bisiacchi, Cotelli, & Farinello, 2001) is a computerized open path version of the TSP in which participants navigate in a fictitious environment made of regular streets ordered onto horizontal and vertical axes (according to the “Manhattan” norm). This task was previously used to study the relationship between planning and PFC in frontally impaired people and by using TMS in healthy participants (Basso et al., 2001; Basso et al., 2006, Cutini, Di Ferdinando, Basso, Bisiacchi & Zorzi, 2008). The results indicated that changes between heuristics occur quite often in healthy participants, while impaired and TMS-stimulated participants were more likely to follow a plan from the beginning to the end of a trial. The Maps task has shown itself to be useful in the study of visuospatial planning because of several measures that could be extracted through the computerized procedure. Similar to the TOL, the Maps task can separate the initial programming time from the

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execution time. The former interval, from the appearance of the trial to the first action, is thought to entail visual inspection of the trial, definition of a general solution plan and the very first movements. The latter interval, from the first movement to the achievement of the final subgoal, has been a matter of debate. The first theories in computer science (e.g., Vere, 1983) stated that the execution phase did not include additional planning with respect to the planning phase. However, later research in both computer science and neuropsychology (e.g., Georgeff & Lansky, 1987; Owen et al., 1990) reached a consensus that the execution time should be considered to include planning and monitoring in addition to motor execution. In contrast to the TOL, the movements in the Maps task can be evaluated numerically from the comparison of the participant’s performance and the ideal performance (the StepPAO index). Given that participants are moving into a space and an exact number of moves is not indicated to participants, in the Maps task, there could be degrees of difference due to the several trajectories that could be made. In the TOL, the limited number of moves places a heavy weighting on errors, which are often indicative of planning failures. Additionally, with respect to the TOL, a planning index can be computed from the intermediate times and number of movements: the average (meanPI) and the variance (varPI). The former indicates how much the trial has “cost” in terms of additional processes (for example, planning, monitoring, inhibition, switching) with respect to motor execution. The latter indicates whether the effort is constant or variable. Moreover, the strategies and heuristics used by the participants can be extracted from the trajectories, while in the TOL it can only be determined whether the solution was achieved or not. Research using this tool has also reported gender differences (Cazzato, Basso, Cutini, & Bisiacchi, 2010): males appear to produce shorter paths and more online changes in strategy than females. The contribution of visuospatial features in the TSP is high, and this factor has been suggested to account for gender differences. Indeed, a gender-based difference in spatial

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performance has received great attention in the past (starting from the review from Maccoby & Jacklin, 1978) and has been identified in a number of spatial abilities. In particular, mental rotation (Maeda & Yoon, 2013; Hyde, 2007; Voyer, Voyer, & Bryden., 1995) is considered a task in which gender differences consistently occur. Moreover, gender differences have been found quite often in spatial navigation (Moffat, Hampson, & Hatzipantelis, 1998) and wayfinding (Rahman & Koerting, 2008), discrimination of line orientation (Liben & Golbeck, 1986) and Piaget’s water level task (Casey, Nuttall, & Pezaris, 2001), generally showing better performance in male than female participants. Notwithstanding, in the recent literature, strong gender differences have been questioned (for a review, see Hyde, 2016): the goal consisted of disentangling the real cognitive differences from stereotypical and environmentally driven influences. These differences were thought to reflect the involvement of different brain areas in spatial cognition in the two genders, leading to different performance (see, for example, Grön, Wunderlich, Spitzer, Tomczak, & Riepe, 2000; Gur et al., 2000; or, for mental rotation in particular, see Jordan, Wüstenberg, Heinze, Peters, & Jäncke, 2002; Thomsen et al., 2000; Weiss et al., 2003). In the case of visuospatial planning, Unterrainer and colleagues (2004) observed activation of the right dorsolateral PFC (BA 9, 9/46) and inferior PPC (BA 22, 40) in better planners, mainly male subjects. In a later fMRI study with the same TOL task, Unterrainer and colleagues (2005) replicated their finding that, in fact, no gender-specific activation was present if the comparison was made in the same group of good planners, while significant differences appeared if the two groups’ activation patterns were compared. This pattern of results showed that, more than gender, individuals’ performance level could account for different neuronal activation patterns during higher-level cognition. Other studies have already shown that variations in brain activation are more strongly related to performance than to gender (Tagaris et al., 1998; Unterrainer, Wranek, Staffen, Gruber, & Ladurner, 2000) while still observing better performance in

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males than in females at the behavioural level. On the other hand, Boghi and colleagues (2006) showed, in an fMRI recording of TOL tasks at different difficulty levels, that there were different patterns of activation and concluded that gender-specific strategies could exist, with males relying more on visuospatial abilities and females relying more on executive processing.

In the present experiment, we used the Maps task to test current hypotheses on the cortical contribution of associative regions to the planning process. Despite the number of studies showing a differentiated pattern of activation according to lateral stimulation, this functional distinction is still unclear, as Nitschke and colleagues (2017) noted. In a within-subject design, we stimulated either the frontal or the parietal cortex of either the left or the right hemisphere, controlling for gender differences. If findings obtained on the TOL task (Kaller et al., 2011; Kaller et al., 2013) are applicable to the TSP, the left dlPFC should play a role in the creation of the strategy and the management of the heuristics to be used, while the right dlPFC should be related to control and monitoring processes during the execution of the plan. Therefore, on applying inhibitory stimulation to the left dlPFC, we could expect to obtain lengthened paths (higher StepPAO) and increased execution times, given that the production of a feasible strategy would be impaired. If stimulation were applied to the right dlPFC, producing a lack of ongoing monitoring, we would expect reduced execution times and increased path lengths as well as a reduced number of strategy changes (consistent with Basso et al., 2006). Gender differences are also expected: in the study by Cazzato and colleagues (2010), gender differences were found in execution time, strategy selection and the planning index but not in the initial planning time. Therefore, we are more likely to obtain similar performance when stimulating the right dlPFC (which is thought to be responsible for

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these measures) than the left dlPFC (which is thought to be responsible for measures not related to gender). Moreover, in order to evaluate the involvement of PPC and, in particular, of the angular gyrus, rTMS stimulation was performed over either left or right hemisphere, moving anteriorly from the P3 and P4 positions (according to the 10-20 EEG reference system), which were thought to correspond to BA39 (Hertwig, Satrapi & Schönfeldt-Lecuona, 2003). TMS studies of this area have not investigated visuospatial planning; therefore, our hypotheses were borrowed from the literature on other planning/attentional tasks. Due to the contribution of the left PPC in motor attention and preparation and of the right PPC in orienting attention (Corbetta & Shulman, 2002), their inhibitory stimulation could produce similar results. This stimulation could impair the organization and updating of the subgoals’ positions as subjects change their position in the environment. Thus, we expected fewer strategy updates and, in turn, longer paths but faster execution time and lower meanPI and varPI (due to fewer episodes of monitoring and fewer strategy changes – which generally slow performance and cause it to fluctuate in terms of effort). Given that the literature is not convergent with respect to gender differences in PPC contribution (Herrmann et al., 2005; Kucian, Loenneker, Dietrich, Martin, & von Aster, 2005), we adopted an exploratory perspective on this issue.

2. Materials and methods 2.1. Participants Thirty-two healthy participants (mean age: 25.5, SD=5.9) were enrolled in the experiment. The participants comprised 17 men and 15 women with a mean of 16.81 years of education 11

(SD=1.45). The participants were all checked for TMS exclusion criteria (Wasserman, 1998) and gave informed written consent. None of the participants had a history of neurological illness or was on medication at the time of the study. Data from the present investigation were compared with the control condition of the sample used by Basso and colleagues (2006), which consisted of 32 participants (16 males) with the same age (mean=24.5, SD=4.7). In that study, participants completed the same set of Maps task problems as in the present study. The procedure was approved by the Ethics Committee of the Medical Faculty of the University of Tübingen and is in accordance with the Helsinki Declaration of the World Medical Association (http://www.wma.net).

2.2. Materials The main task used in this experiment was the Maps task, which essentially consists of a computerized two-dimensional simulation of an urban environment. On the monitor, streets and buildings are presented in a regular grid, with fixed starting and ending points (located in opposite corners of the grid) and a variable number of intermediate subgoals appearing at the intersections (see Figure 1). Starting at the top-left corner, subjects were asked to move the silhouette (by pressing the arrow keys) to pass over each subgoal and then to reach the final goal, located at the bottom-right corner. Subjects are told to find and use the shortest route in the shortest time. In the present experiment, each trial included seven subgoals plus the final goal.

Figure 1: A trial taken from the Maps task. The square in the upper left corner represents the starting point, and the square in the lower right corner represents the final destination, while the circles at the other intersections represent the subgoals to be achieved before the final one.

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A reaction time task (RT-4) was additionally administered with a twofold goal: to obtain measurement of participants’ visuospatial-motor skills and to calculate the planning index (PI; see further details in the Data Analysis section). A sequence of 25 stimuli measured the individuals’ ability to rapidly press the correct one of four possible arrow keys (4-choice reaction time). A short sound (duration: 150 ms) preceded a visual pre-stimulus (appearing in a range between 200 and 500 ms after the sound), consisting of a human silhouette (the one used in the Maps task), which appeared in the middle of the screen. After 200 to 500 ms, a green circle appeared in one of four positions (up, down, left or right), five ocular degrees away from the silhouette. Participants were required to press the arrow key as fast as possible to move the silhouette towards the circle. A Dantec MagPro stimulator (Skovlunde, Denmark) was used to generate repetitive biphasic magnetic pulses with a 12 cm figure-8-shaped magnetic coil. At the beginning of each TMS 13

session, the individual motor threshold (MT) was determined for the thumb flexor. MT was defined as the minimal intensity of stimulation needed to generate a visible twitch of the thumb flexor muscle in five out of ten stimulations of the motor hotspot (Rossini et al., 1994).

2.3. Procedure Participants were tested in two sessions on two different days separated by one week. In each session, 20 trials of the Maps test were administered separated into two blocks of 10 trials (separated by a pause of 40 minutes). Thus, four blocks were administered, in which the same 20 trials were repeated across the two days. The difficulty of the two sessions was controlled through a) the average number of “steps” to solve the task with the shortest path and b) the number of solutions (that is, how many paths with different trajectory but equal shortest length). For session A: average shortest path= 17.8 steps, average number of solutions: 4.8; for session B: average shortest path= 17.8 steps, average number of solutions: 5.0. Moreover, a series of other tests, administered and randomly distributed between the two days, was applied to obtain further measurements of cognitive functioning. The list included the RT-4 reaction time task, the Corsi Block-Tapping visuospatial memory test (Corsi, 1972), a short version of Raven’s matrices (Arthur & Day, 1994), the Trail Making Test A and B (Reitan, 1958), and the Edinburgh test of hand dominance (Oldfield, 1971). At the beginning of each session, two example trials were presented to familiarize subjects with the Maps task. If participants needed it, a further example was administered so that every participant confirmed to have understood how the task works. Then, the first series of ten trials was administered. At the end of this series, participants were free to rest and to move around in the experimental room to decrease the residual effects of stimulation. After 20 minutes, participants were invited to sit in front of a table for the administration of the paper-and-pencil tests (half in the first session, half in the second session). This part required 14

10 minutes on average, and then participants were asked to sit in front of the computer to perform the second block of 10 Maps trials. In each block, rTMS stimulation was delivered over either frontal areas (F3 or F4) or parietal areas (P3 or P4) at 1 Hz frequency, 100% of the resting MT (mean stimulation: 46.2, standard error (SE): 1.2). Stimulation sites were detected according to the 10-20 international system for electroencephalography (EEG). The sequence of stimulated areas was counterbalanced across subjects, but either the frontal or parietal areas were stimulated in two blocks within the same session. The TMS coil was positioned tangentially to the skull with its centre over the site to be stimulated, with the handle tilted 45 degrees pointing backwards. The coil was held by an experienced experimenter, which controlled for its positioning throughout the block by comparing its position with markers on the head of participants. The stimulation began on average 3 s before each trial (subgoals appeared ranging from 2700 to 3300 ms after that the key was stroked) and ended when the participant reached the final goal.

2.4. Data analysis The computerized Maps test automatically recorded, for each trial, information about the timing and the sequence of subgoals achieved by the participants. In particular, the following measures were collected: initial preplanning time (IPT, that is, the time between the appearance of subgoals and the first keystroke), execution time (that is, the time from the first movement to the achievement of the final goal), the intermediate time and number of “steps” (namely, keystrokes) between every couple of subgoals (an array of 2x8 values), and the order in which subgoals were achieved. From this output, several further calculations were made to obtain variables of interest (for additional details, see Cazzato et al., 2010). The total number of keystrokes produced the StepPAO variable, which consisted of the percentage of “steps” made by the participant 15

above the minimum number of steps required to execute that path. Therefore, the more closely StepPAO approximates zero, the closer the corresponding trajectory is to the optimal solution. Furthermore, the Planning Index was calculated by dividing the intermediate time between each subgoal of the path by the corresponding intermediate number of keystokes. The Planning Index is considered an estimate of the cognitive effort devoted by the subjects to plan and execute each step of the path. This index was created to obtain a series of six measures (one for each subgoal, excluding the first and the last subgoals) weighted by the relative distance of the subgoals in the situation and the participants’ skills in key pressing (the reaction time task). Then, a mean (MeanPI) and a variance (VarPI) of the Planning Index were calculated for each path to summarize the array. The order in which subgoals were collected produced a path that was analysed through the attribution of heuristics and strategies. Four algorithms were run to detect the presence of a heuristic, each one evaluating whether a heuristic could explain the whole path or only part of it. As in previous work, the four heuristics included in the analysis were as follows: a) cluster heuristic: first, all the subgoals are divided into separate regions, and then all subgoals belonging to the same cluster are achieved before the next cluster is entered (Hirtle & Jonides, 1985); b) nearest neighbour heuristic: subgoals are achieved by always moving to the closest subgoal (among those available) from the current position (Barr & Feigenbaum, 1981); c) horizontal direction heuristic: starting from a position on a border, the next subgoal proceeding in the horizontal direction is achieved; in this version of the Maps task, the starting point is in the upper left corner: that is, horizontal direction consisted of proceeding rightward (Cazzato et al., 2010); d) vertical direction heuristic: as with the horizontal direction heuristic, all subgoals are achieved by proceeding on the vertical axis, that is, downward.

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Therefore, the variable Heuristic included four binomial levels and indicated whether each heuristic was used. The variable Heuristic_use could take two values (for each one of the four heuristics): “constant” or “change”, indicating whether that heuristic was attributed to the whole path or only part of it. The variable Strategy was obtained on the basis of heuristic attribution, producing one of these four possible values for each path: 1- “constant” strategy: one or more heuristics used from the beginning to the end of the path; 2- “change” strategy: at least one change of heuristic (strategy in which each heuristic is used for only part of the whole path, but every part of the path is covered by at least one heuristic); 3- “1or2” strategy: either strategy 1 or 2 could explain the subject’s behaviour, and it is not possible to determine which one was used; and 0- no strategy: heuristics used for only part of the path or not at all.

Statistical analyses were performed using the free statistical software R (R Core Team, 2016); the scripts used to analyse the data are reported in the supplementary file. First, an analysis of variance (ANOVA) was run to detect differences due to gender in education and several tests of cognitive functioning. Given that the number of TMS pulses varied according to the duration of both IPT and execution time, another ANOVA was run using Region, Laterality (within-subject factors) and Gender (between-subjects factor): if one or more of these factors were significant, the number of pulses was considered in the subsequent analyses. A loglinear analysis was performed using Region, Laterality, Gender and Strategy (2x2x2x3) to determine whether the stimulation produced a difference among the several conditions

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with respect to the strategies used to solve the task. The level “1or2” was not included in this analysis because of its unclear nature: although it could be defined as a different category with respect to these two, it cannot be attributed to either a constant strategy or a strategy with changes. Another loglinear analysis was performed, similar to the previous one, but using Heuristic_use and Heuristic instead of Strategy (2x2x2x2x4). In order to control for Simpson’s paradox (Simpson, 1951), further analyses were run on a lower number of factors with respect to the loglinear models above indicated. Performance was analysed through a series of mixed-effects regression models (Pinheiro & Bates, 2000) using the lme4 package (Bates, Maechler, Bolker, & Walker, 2014). With mixed-effects modelling, in contrast to traditional regressions, the whole structure of the data can be considered in terms of fixed and random effects, thus improving statistical power. The following measures taken from the Maps task were considered as dependent variables: IPT, Execution time, StepPAO, meanPI and varPI. The five mixed models fitted to the data were all based on the same structure. The 13 fixed effects considered were Region as a two-level factor (Region1 = Frontal and Region2 = Parietal), Laterality as a two-level factor (Laterality1 = Left, Laterality2 = Right), Gender as a two-level factor (Gender1 = Males, Gender2 = Females), Strategy as a four-level factor (Strategy0 = no, Strategy1 = constant, Strategy2 = changes, Strategy3 = 1or2), their interactions, and the five continuous fixedeffect predictors, such as the Reaction Time (RT), Visuospatial Working Memory (vsWM), Logical Intelligence (LIN) and shifting (TMT-BA) – as measured by the 4-choice reaction times task, the Corsi Block-Tapping task, Raven’s matrices and the Trail Making Test, respectively – and the minimum length of the trial (Length). The random effects considered in the model were Trial (to evaluate the contribution of the various stimuli presented) and Participants (to account for individual differences). As in traditional multiple regression, we

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reported the beta value (positive coefficients indicate an increase in values for the dependent variable, whereas negative coefficients indicate a decrease of values; when associated with factors, beta coefficients indicate an adjustment to the intercept, whereas when associated with continuous variables, they indicate an adjustment to the slope), the associated SE, and the t- and p-values obtained through the mixed model analyses.

3. Results The ANOVA using gender as a factor did not show a significant difference in any test of cognitive functioning or education (see Table 1). Table 1: Mean values and associated standard deviations (SD) for education and several cognitive tests separated by gender. On the right, the value of the ANOVA comparison (F, with the degrees of freedom) between genders and the associated probability value (p.).

education number of pulses Corsi Raven Edinburgh TMT-BA RT-4

females mean SD 16.73 1.33 464.10 41.36 6.00 0.93 10.27 1.35 78.89 14.04 37.33 7.69 414.23 85.36

males mean 16.88 459.82 5.94 10.15 77.45 39.82 393.34

SD 1.58 63.47 0.83 1.34 16.34 14.38 74.65

F(1,28) 1.512 1.524 1.194 0.573 0.366 1.489 0.106

p. 0.229 0.234 0.284 0.458 0.550 0.232 0.748

On average, participants received a total of 104.95 TMS pulses (SD=20.65). The ANOVA comparison showed that the number of pulses did not change depending on the conditions (Region or Laterality) or on gender. Strategies: The Region x Laterality x Gender x Strategy (2x2x2x3) loglinear analysis did not achieve the significant threshold: Chi2(2)=4.545, p.=0.103. Further analyses were run separating for gender. The analysis of the group of males showed a significant difference: Chi2(2)=8.134, p=0.018. Inspection of the graph (Figure 2a) indicated that the proportion of participants using no strategy remained constant across the four stimulations, while the 19

proportion using constant strategies was significantly increased with respect to the change strategy in all stimulation conditions except when the left frontal region was stimulated. The analysis of the group of females did not reach the significance threshold: Chi2(2)=0.067, p=0.967. The proportion of participants using no strategy remained constantly below 5%, while the proportion using constant strategies seemed to be higher than the proportion who changed strategy in all conditions (see Figure 2b). In a further analysis, we excluded the nostrategy level and merged the two levels of Laterality: the Region x Strategy (2x2) analysis approached the significant threshold (Chi2(2)=5.798, p.=0.055), indicating a probable difference in the proportion of strategies upon stimulation of the parietal region but not upon stimulation of the frontal region.

Figure 2: The graphs represent the proportion of strategies used, separated for the four stimulated areas (on the x-axis) and the type of strategy: constant (dark grey bars), with changes (grey bars) and no strategy (light grey bars). On the left (a): males; on the right (b): females.

Table 2: On rows, the 4 types of heuristics (R = right, D= down, C = cluster, N= nearest neighbour) are represented separately for the two genders, while in columns, percentages (calculated with respect to the total amount of heuristics used within the session) are separated for Heuristic_use (constant = throughout the whole path; change = for part of it) and the four combinations of stimulated Regions and Laterality. The cluster heuristic is not present for the change heuristic, given that it can be attributed only to the whole path. Data in the “No stimulation” column are taken from the experiment described inBasso et al., 2006. 20

Frontal regions

Parietal regions

Left stimulation

Right stimulation

Left stimulation

Right stimulation

constan t

constan t

constan t

constan t

gend er

heuri stics

Mal es

R

8.0

23.9

14.3

17.1

14.7

16.2

14.0

16.8

5.3

26.2

D

5.1

23.1

4.7

17.9

3.6

16.9

4.8

16.8

3.6

23.7

C

21.9

/

25.7

/

26.6

/

26.5

/

22.6

/

N

5.3

12.8

6.2

14.0

7.7

14.3

7.3

14.0

6.7

11.7

40.2

59.8

50.9

49.1

52.7

47.3

52.5

47.5

38.3

61.7

R

7.3

21.7

8.7

21.2

12.5

17.6

12.0

17.9

5.9

23.6

D

6.7

20.4

9.0

20.8

9.2

19.3

9.3

19.2

10.3

22.1

C

23.6

/

22.4

/

24.4

/

24.4

/

21.6

/

N

6.7

13.7

3.5

14.4

5.8

12.5

6.5

10.7

3.9

12.6

44.3

55.7

43.6

56.4

50.5

49.5

52.2

47.8

41.7

58.3

total

Fem ales

total

change

change

change

No stimulation (control group)

change

con stan t

cha nge

Heuristics: The Region x Laterality x Gender x Heuristic_use x Heuristic (2x2x2x2x3) loglinear analysis did not reach the significance threshold: Chi2(3)=1.744, p.=0.418. Further analyses were performed aggregating the factor Heuristic and separating for the two genders. The analysis mirrored the one performed on Strategy. The male sample showed a significant difference: Chi2(1)= 4.410, p=0.036. By inspection of Table 2, the left frontal site emerged as the only stimulation site for which the pattern of heuristics was similar to that of the controls

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in the literature (Cazzato et al., 2010; Basso et al., 2006), whereas, at the other three sites, the frequency of constant strategies was greater than or equal to that of strategies with changes. This latter result was due to the general reduction in changes of the direction right heuristic. The analysis of the female sample showed no significant difference, with Chi2(1)=0.100; p.=0.752). However, when aggregating Laterality and excluding Heuristic, we found that Region x Heuristic_use was significant (Chi2(1)=11.103, p<0.001), indicating a difference between the stimulation of frontal and parietal regions regarding the amount of constant vs. change strategies: when the frontal lobes were stimulated, a constant strategy was used less (44.0%) than a strategy with changes (56.0%). When stimulation was applied to the parietal lobes, the difference was minimal (51.8% for constant and 48.2% for change strategies). In females, the re-distribution to the constant strategy was present across all the heuristics.

The results obtained from the series of five mixed-effects regression models (IPT, Execution time, StepPAO, meanPI and varPI) are presented in detail in Tables S1-S5 (see supplementary file).

Initial programming time: The first model used the IPT as the dependent variable, which is considered the time needed to visually explore the scene and to program the first movement. The model (Table S1) showed the main effect of Laterality (beta=0.050±0.020, t=2.505, p=0.012) and a significant three-way interaction of Region, Gender and Strategy (beta=0.474±0.164, t=-2.887, p=0.004). Stimulation of the sites in the left hemisphere produced significantly lower IPTs (877.29±23.16) than stimulation of the sites in the right hemisphere (976.40±27.33, Figure 3). In addition, the difference between parietal and frontal stimulation was higher for the 1or2 strategy than for the constant strategy. The difference between genders was higher for the change strategy than for the constant strategy. Furthermore, the

22

three-way interaction showed that while stimulating the parietal regions, the difference between males and females was higher for the 1or2 Strategy (males: 1109.51±219.12; females: 917.35±73.51) than for the constant strategy (males: 1011.21±201.86; females: 1099.21±144.44). Compared to the control group, the stimulation of sites in the left hemisphere produced lower IPT in both genders (males: 1306.91±52.53; females: 1378.42±49.31). Significant random effects of Trial and Participants were found: when we removed the variance associated with these sources from the main model, the goodness of fit of the model improved, resulting in an effect size of r2 =0.52.

Figure 3: Initial planning times for the four stimulation sites and the control group. Dark: left, light: right sites; red: frontal, blue: parietal region; green: control group. Boxplots indicate the median (black line) and quartiles. The width of the shaded area for the violin plots represents the proportion of data located there. Grey points represent participants’ values.

Execution time: This dependent variable represented the time used to execute the task, which includes monitoring, motor planning and execution and, when needed, additional inhibition of the previous plan and re-planning. The model (Table S2) showed a significant effect of region, with execution times lower for parietal than for frontal stimulation. However, the 23

effect was due to a significant difference in males (beta=0.093±0.022, t=4.177, p<0.001: parietal stimulation 5674.11±110.48; frontal stimulation: 6240.91±124.89), not present when stimulating females (6381.78±99.54 vs. 6365.94±105.04). The comparison with the control group showed that stimulations in males produced no difference from the control (6066.82±99.08), while the execution time in females was higher in the control group (7711.27±103.72) than with any of the stimulation sites. The effect of strategies reported a main effect between the constant strategy and both the no-strategy approach (beta=0.121±0.028, t=4.332, p<0.001) and changing strategies (beta=0.027±0.012, t=2.160, p=0.031). The execution time in the former case (6007.09±76.24) was shorter than in the latter (no: 6915.94±276.99; change: 6251±98.21). Finally, the continuous predictors Reaction Time, Length and Visuospatial WM were significant: faster execution was obtained with objectively shorter paths, by people with fast reaction time, and by those with higher WM. Again, random effects of Trial and Participant were found, and the model’s goodness of fit was r2 =0.65.

Figure 4: Execution times for the four stimulation sites and the control group. Dark: left, light: right sites; red: frontal, blue: parietal region; green: control group. Boxplots indicate the median (black line) and quartiles. The width of the shaded area for the violin plots represents the proportion of data located there. Grey points represent participants’ values.

24

StepPAO: The variable represents the length of the path executed by participants compared to the shortest path. The quality of execution (and its planning process) is considered higher when the StepPAO value for that path is closer to zero. The model using StepPAO as the dependent variable (Table S3) showed that strategy was the only significant factor. Indeed, all the levels were different from each other: the best performance was achieved by the 1or2 strategy (beta=-0.0606±0.0124, t=-4.872, p<0.001; 7.36±0.90%), followed by change (beta=-0.0175±0.0067; t=-2.617, p=0.009; 9.87±0.52%) and constant strategies (reference level; 11.33±0.44%). As expected, the no-strategy approach resulted in the worst performance (beta=0.0869±0.0151; t=5.769, p<0.001; 20.94±1.54%). Moreover, a negative relationship between StepPAO and visuospatial WM emerged as significant. Compared to the control sample, a difference was found only when males were stimulated at the frontal sites: the stimulation worsened the performance (frontal stimulation: 0.1115±0.0056; control: 0.0772±0.0035). The model’s goodness of fit for StepPAO was r2 =0.25.

25

Figure 5: StepPAO for the four stimulation sites and the control group. Dark: left, light: right sites; red: frontal, blue: parietal region; green: control group. Boxplots indicate the median (black line) and quartiles. The width of the shaded area for the violin plots represents the proportion of data located there. Grey points represent participants’ values.

Mean of the Planning Index (meanPI): This measure represents the average amount of cognitive effort during the path’s execution; it is thought to include the contribution of intermediate monitoring, planning and inhibition and to exclude that of motor processing. In the analysis of meanPI (Table S4), a main effect for Region was found (beta=0.0608±0.0130, t=-4.662, p<0.001): the Planning Index was higher when stimulating the frontal sites than the parietal sites. Moreover, an interaction of Region with Gender also reached the significance threshold, given that the stimulation of the parietal lobe in males and the frontal lobe in females produced a lower meanPI (0.812±0.013 and 0.812±0.011, respectively) with than the other two conditions (frontal-males: 0.890±0.016; parietalfemales: 0.859±0.011). The stimulation of the parietal lobe in males also produced lower meanPI values than the control (0.894±0.012), while in females, all stimulation sites produced a lower meanPI than the controls (0.997±0.014). Both the 1or2 (beta=0.0661±0.0211, t=3.160, p=0.002; 0.912±0.032) and change strategies 26

(beta=0.0381±0.0110, t=3.349, p<0.001; 0.857±0.012) produced higher meanPI values than a constant strategy (0.818±0.009). In addition, both reaction times and visuospatial WM showed a negative relationship with meanPI. The model’s goodness of fit for meanPI was r2 =0.53.

Figure 6: MeanPI for the four stimulation sites and the control group. Dark: left, light: right sites; red: frontal, blue: parietal region; green: control group. Boxplots indicate the median (black line) and quartiles. The width of the shaded area for the violin plots represents the proportion of data located there. Grey points represent participants’ values.

Variance of the Planning Index (varPI): The last model was run on varPI (Table S5), which indicates how constant the cognitive effort was throughout the execution. Lower values indicate homogeneous effort, while higher values indicate spikes in cognitive work with moments of pure motor execution interposed. As with meanPI, a main effect of Region was found, with the values for parietal stimulation lower than those for the frontal stimulation (beta= -0.338±0.121, t=-2.791, p=0.005; 0.087±0.009 vs. 0.074±0.007). However, in contrast to the case of meanPI, the interaction of Region with Gender was mediated by Laterality (beta=-0.517±0.255, t=-2.025, p=0.043). No difference between the different stimulation sites 27

emerged for females, all of which were lower than the control sample (0.247±0.030). With respect to the control sample (0.158±0.018), males showed reduced varPI values, particularly for the stimulation of the left hemisphere compared to the right, and the difference was more evident with stimulation of the parietal site (left: 0.067±0.014; right: 0.087±0.016) than the frontal sites (left: 0.080±0.010; right: 0.119±0.026). Further significant effects were found for the predictors Reaction time, Length and Visuospatial WM: all of them were negatively associated with varPI. The model’s goodness of fit for varPI was r2 =0.35.

Figure 7: VarPI for the four stimulation sites and the control group. Dark: left, light: right sites; red: frontal, blue: parietal region; green: control group. Boxplots indicate the median (black line) and quartiles. The width of the shaded area for the violin plots represents the proportion of data located there. Grey points represent participants’ values.

4. Discussion The present study aimed to disentangle the contribution of frontal and parietal associative cortices in the left and right hemispheres to visuospatial planning. Inhibitory rTMS, applied to these four areas, has produced a variety of differences with respect to the baseline reported

28

in previous studies (Basso et al., 2006; Cazzato et al., 2010). The results provided partial support for our hypotheses regarding the frontal regions. Indeed, stimulation of the left frontal area was associated with reduced IPT and, in females, reduced execution times but did not affect the choice of strategies or heuristics, confirming that this region could be related to the generation of strategies (Cabeza, Locantore, & Anderson, 2003; Fletcher et al., 2000). The hypothesis of a contribution of the right frontal area on the monitoring of the plan and its possible updates (Stuss, 2011; Vallesi, 2012) was corroborated by the reduction of strategies with changes, but the length of the path was not affected by the stimulation, which reduced execution time in females but not in males. This pattern of results for the frontal areas replicated the pattern produced by stimulation in previous studies (Basso et al., 2006; Cazzato et al., 2010), but we could also see that the stimulation of these regions was positively related to participant’s RTs and negatively related to vsWM span. Moreover, we confirmed that reduced execution times occurred when participants did not change their strategy, while times were slightly increased if changes in strategy occurred and markedly increased if the subjects did not use any strategy. The role of strategy in explaining this difference was also corroborated by the StepPAO variable: the longest path with respect to the optimal one was obtained in the trials not associated with any strategy. However, the use of a change in strategy produced a shorter path than what was achieved if a constant strategy was used, which produced a trade-off between execution time and path length: people who changed strategy required more time but produced better paths than those who chose to continue with the initial strategy. A trade-off between speed and “accuracy” is a common result in tasks where participants are asked to respect both constraints, under the hypothesis of a limited resource capacity (Botto, Basso, Ferrari, & Palladino, 2014; Donkin, Little, & Houpt, 2014). A possible improvement on the present study would consist, therefore, in exploring individual differences in giving priority to a

29

faster execution or to shorter paths, in order to identify possible factors covarying with the performance (e.g., age and motivation) and to reduce variability in results. The stimulation of the parietal sites, which were likely to have included the angular gyri, produced results that mainly, but not completely, corroborated the hypotheses. While the length of the path was not affected by stimulation, stimulation of both parietal areas reduced the number of strategies with changes as well as execution time and the mean of the Planning Index with respect to the frontal stimulation. The variable meanPI is a measure of the cognitive effort required to plan during the execution of a path, while varPI indicates how constant this additional effort is. The impairment of execution time and Planning Index was shown mainly in males, while the effect towards the use of constant strategies was shown in both genders. Previous work on the Maps test (Basso et al., 2006; Cazzato et al., 2010) suggested that males seem to be capable of using more flexible strategies than females and change their current plans more readily. This has been shown to be an advantageous approach, increasing the number of possibilities in the determination of the trajectory and improving the quality of selected plans. On the other hand, females tended not to change the initial plan but rather adhered to it quite closely, confirming results reported in the literature. Plan selection by males is thought to be performed on a perceptual basis (they are thought to use more hetero-centred than ego-centred strategies, interacting between internal aims and elements retrieved from the environment; see Witkin, 1950). Conversely, females used more cognitive-based schemes, elaborated in the first moments of the trial (in fact, female navigation is often referred to as “egocentric” navigation; Lawton, 1994; Ruotolo et al., 2019; Basso, Saracini, Palladino, & Cottini, 2019). In the present study, the stimulation of parietal sites in males produced a reduction in execution time, mainly connected to paths for which a constant heuristic was used, while this difference was not present in females. A tentative explanation could be related to the reduced lateralization of processes in females’ brains

30

compared to males’ (Caspers, et al., 2008; Luders et al., 2004). However, further investigation is needed to find an explanation more reliable than this one. Relatively few studies in the literature have focused on the contribution of regions in the PPC to the planning process (Newman, Carpenter, Varma, & Just, 2003; Unterrainer , 2004; Boghi et al., 2006; Nitschke, 2017), indicating the likely irrelevance of these regions. Notwithstanding, the present results seem to attribute a strong relevance to the parietal areas and, with a certain probability, to the angular gyri. This difference could depend on the task: with respect to the TOL, the TSP has features that could characterize the planning process in different terms. Among other differences, planning does not need to be performed entirely in advance and can be spread among the several subgoals: for example, the contribution of visuospatial working memory is relevant during the execution (for the execution time, stepPAO and PI) but not for the initial planning phase. It follows that the role of ongoing visuospatial skills is greatly increased in the TSP (and, in turn, the Maps task). Thus, an alternative interpretation with respect to the task could consist of attributing to the angular gyrus in the PPC roles related to basic processing, such as visual perception (the “where” pathway: Kravitz, Saleem, Baker, & Mishkin, 2011) and spatial attention (Chambers, Payne, Stokes, & Mattingley, 2004; Schiff et al., 2011). We concluded that several aspects of planning are shared between the TOL and the TSP tasks, particularly related to the contribution of the prefrontal region. The left and right dlPFC are relevant to the planning process across the different tasks, although some differences exist both between the two areas and between the two tasks. Moreover, TMS has evidenced a contribution of the PPC to both qualitative (strategy) and quantitative (execution time and PI) measures. The contribution of the PPC still needs to be clarified, but given the recurrent connections along the fronto-parietal networks, its role in the planning process has probably been underestimated until now.

31

Acknowledgements We would like to thank all the volunteers to our experiment. This study has been conducted without any funding. Authors have no competing interest to declare.

Data Accessibility All R scripts used to obtain the results are reported in the supplementary material file.

Abbreviations BA = Broadmann area dlPFC = dorso-lateral Pre-Frontal Cortex rlPFC = rostro-lateral Pre-Frontal Cortex EEG = Electro-EncephaloGraphy IPT = Initial Planning Time PFC = Pre-Frontal Cortex PPC = Posterior Parietal Cortex RT = Reaction Times rTMS = repetitive Transcranial Magnetic Stimulation SD = Standard Deviation SE = Standard Error TOL = Tower of London TSP = Travelling Salesman Problem vsWM = visuo-spatial Working Memory

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Highlights: •

Stimulating the right PFC with rTMS confirmed its role in monitoring and updating visuospatial planning

•

Left PFC stimulation affected the initial planning process and increased execution times

•

Stimulation of both parietal areas reduced execution times and strategies with changes