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Method Article

Online Repetitive Transcranial Magnetic Stimulation of Dorsomedial and Dorsolateral Prefrontal Cortex in Cognition Decision Making, and Cognitive Dissonance

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DOI:

10.3791/67500

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December 5th, 2025

 ,  ,  ,  ,  , 
1Institute for Cognitive Neuroscience, National Research University Higher School of Economics

* These authors contributed equally

In This Article

Summary

This protocol presents a more precise and effective method of repetitive transcranial magnetic stimulation of the dorsolateral and dorsomedial prefrontal cortices during an experimental task using neuronavigation and robotic systems. Based on the results, crucial implications are suggested for future online stimulation research.

Abstract

Transcranial magnetic stimulation (TMS) is a powerful method for revealing causal links between specific brain regions and cognitive functions. While offline TMS has traditionally dominated cognitive neuroscience research, online repetitive TMS (rTMS) presents undoubted advantages, enabling stimulation concurrent with task performance. Leveraging advancements in this stimulation method, the study introduces a novel online rTMS protocol utilizing neuronavigation and robotic systems to precisely target dorsolateral and dorsomedial prefrontal cortices during an experimental procedure. Moreover, the utility of online rTMS in investigating the neural mechanisms underlying cognitive dissonance and neurochronometry of the choice-induced preference change was explored. For that purpose, a protocol for the high-frequency online rTMS was applied during the procedure with a within-subject blocked design and two control conditions. The study leads to principal conclusions for future TMS research, such as the importance of a proper set of time, location, and parameters of the stimulation, electric field modulation, and appropriate control conditions.

Introduction

With reference to modern methods of brain stimulation and neuroimaging techniques, transcranial magnetic stimulation (TMS) is the most suitable method to demonstrate a causal relationship between a region of interest and a cognitive function, compared to neuroimaging techniques such as electroencephalography (EEG), magnetoencephalography, or functional magnetic resonance imaging (fMRI)1,2,3,4. The stimulation allows researchers not only to observe the involvement of brain regions in certain tasks but also to influence them. Additionally, this kind of technique is adequate for precise stimulation of the brain, which allows for better control of experimental conditions. Most previous studies applied offline TMS, stimulating the brain for a certain period of time before a task to produce long-lasting effects of the stimulation. However, the current level of technological development makes it possible to apply not just offline TMS but also online repetitive TMS (rTMS), providing stimulation right at the moment when a participant is performing a task. Online TMS provides short-lasting effects of stimulation compared to the offline TMS effects (i.e., the 15 min effect of stimulation after the 15 min of 1 Hz offline rTMS5,6 compared to just a few hundred milliseconds to a few seconds effect of online rTMS6,7,8). Therefore, unlike the offline TMS, the online TMS technique can be used as a neurochronometry tool due to its temporal resolution. It gives the opportunity to get important information about the regional dynamics of various cognitive processes and to investigate the exact moment of a cognitive process shifting a group of pulses around the stimulus onset. Online rTMS makes it inevitable to raise a call for a transition from offline to online stimulation research.

Two brain regions were the focus of this study: the dlPFC [MNI: -24, 5, 47]9 and dmPFC [MNI: 2, 48, 36]10. The dmPFC is a brain correlate of cognitive dissonance, well-studied both with neuroimaging and stimulation10,11,12,13,14,15. The first findings in the neural mechanisms of cognitive dissonance were made in fMRI research, which disclosed the excitation of the dmPFC in various paradigms, employing the cognitive inconsistency effect11,16,17. However, the causal link between internal conflict resolution and the dmPFC was established only when the TMS and tDCS studies were performed in the cognitive dissonance field10,13. Considering the functions of the dlPFC, previous neuroimaging and stimulation studies have linked left frontal activation with post-decisional preference change, also indicating the important role of the dlPFC in cognitive dissonance reduction9,11,16,18,19,20,21,22. A significant part of the research assumes that left dlPFC is involved in conflict resolution of the post-decisional preference change11,16,18,19.

To stimulate the dLPFC and dmPFC, a more precise and effective method of repetitive transcranial magnetic stimulation using neuronavigation and robotic TMS systems is suggested to target the dlPFC and dmPFC during the experimental task. The MRI-assisted navigation system provides accurate stimulation of the target brain area. It allows the TMS robot to adjust the position of its robotic arm according to the micromovements of the participant’s head during the experiment. The main advantage is the maximization of the rTMS effect as a result of more precise stimulation at a particular period of time during the experimental procedure, controlling its beginning and ending, relying on the stimulus onset and offset. This method can help to stimulate brain areas, reducing the experimenter's assistance during the experiment and, therefore, minimizing the influence of the experimenter's presence on the participant’s performance during the experimental task. The protocol can also be applied to other brain areas and experimental tasks. Last but not least, rTMS was combined with the online stimulation protocol, which allowed for testing a research hypothesis of cognitive dissonance neurochronometry and functions of the related brain regions, such as dlPFC and dmPFC. One of the important parts of cognitive dissonance research is the spreading of alternatives23. It is caused by choice-induced preference change, which occurs when individuals are required to make a hard choice between options that are preferred almost equally. Such a hard choice leads to the inconsistency of the person’s attitudes, arousing cognitive dissonance. To reduce cognitive dissonance, preferences have to be changed, increasing preference for the chosen option and decreasing preference for the rejected option, or the so-called spreading of the alternatives. The important question concerns the precise timing of preference change, whether it occurs during the decision-making process itself or emerges as a consequence of the choice made. The investigation of this concern is becoming feasible due to the development of technology such as TMS, which not only provides observation of decision-making processes as fMRI or EEG do, but also directly influences the cortical activity of the target areas. Further investigation of cognitive dissonance neurochronometry will help understand when the choice-induced preference change really occurs. In line with this objective, a TMS study of the dlPFC and dmPFC roles in the spreading of alternatives following the hard choice would offer a valuable contribution to the understanding of the neural mechanisms of cognitive dissonance and clarify the timing of choice. Using online stimulation instead of offline one, the true time when the spread of alternatives happens in the dmFPC and dlPFC network can be differentiated.

The further described protocol of the rTMS for inhibiting the dlPFC and dmPFC activation must be applied using an MRI-guided neuronavigation system, magnetic stimulator, and robotic arm. The neuronavigation and TMS systems were employed. The MRI-assisted navigation system provides accurate stimulation of the target brain area, allowing the robotic arm to adjust its position according to the micromovements of the participant’s head during the experiment. For this purpose, neuroanatomical MRI must be provided for all subjects of the study. The navigation system requires a specific set of equipment, such as the reference tracker for the participant’s head localization, the calibration plate for tracking a coil, the pointer for setting a group of reference landmarks, and the infrared camera for tracking objects in space. All the equipment, except the camera, has silver reflective reference balls identified by the navigation system, which uses the camera to localize the coordinates of the participant’s head and TMS equipment during the procedure and the experiment. The reference balls' covering is quite fragile and should not be touched to prevent them from damage. Several procedures must be performed to work with the MRI-assisted navigation system effectively: coil and robotic arm calibration, individual MRI scan preparation, and patient registration.

It was hypothesized that there would be no significant preference change for the dlPFC and dmPFC stimulation blocks compared to the control vertex and sham stimulation blocks since the inhibition of the left dlPFC or dmPFC activation should prevent the cognitive dissonance reduction and the following preference change.

Despite the recent advances in making online interventions with rTMS, which opened the ways to investigate the temporal dynamics of brain activity causally related to cognitive processes24,25,26, the field still faces challenges in applying precise and reproducible stimulation, especially during complex cognitive tasks. This study introduces a novel application of neuronavigation and robotic systems in an online rTMS protocol to address these challenges. By integrating the use of structural brain data (and encouraging the further use of functional brain data) and precise robotic positioning, this method significantly enhances the accuracy and consistency of rTMS delivery to dlPFC and dmPFC, regions known to be critical in cognitive dissonance and decision-making27,28. Previous research has predominantly utilized offline TMS10,29,30, limiting the understanding of immediate neural responses during cognitive tasks. Moreover, while prior studies have confirmed the involvement of the dmPFC and dlPFC in cognitive dissonance through both imaging and offline stimulation9,10,13, the precise timing and role of these activations during cognitive dissonance remain poorly understood. Here, online rTMS is leveraged to fill this gap by investigating the neurochronometry of choice-induced preference change, providing insights into the exact moments when cognitive dissonance occurs and is resolved. This study stands apart in its use of a within-subject blocked design that tests the effects of dlPFC and dmPFC stimulation and stimulation within two control conditions to bolster the validity of the results within the same sample and at the same time, which is rarely done (either between-subject protocol are used or two sessions with same subjects are employed; both increase the variability of findings). In summary, the research provides a methodological innovation that enhances the precision and applicability of rTMS in cognitive neuroscience. By directly influencing brain regions during task execution and observing immediate outcomes within the same sample, this study not only addresses significant gaps in the literature but also sets the stage for future research into the neural bases of complex cognitive processes.

Experimental task:

Stimuli:

The stimuli were carefully prepared using the recommendations indicated in the original paper of Izuma and colleagues10. The experimental stimuli consist of 96 abstract images (Figure 1) paired up according to the following criteria: 1) all images should be similar to allow the switch manipulation used in the Choice task to be undetected by participants; 2) all images should be relatively distinct to allow making a conscious choice between the images and to prevent coincident choice due to lack of noticeable differences between the images10 Besides, to be consistent with the criteria for choosing stimuli and matching them in pairs used in the original study10, the stimuli presented in the initial study and shared by the authors were reviewed. The stimuli for the paper were chosen from the public domain free stock photos using keywords from the criteria. According to the authors, the images were matched based on personal impressions. The same principle was used, and during debriefing, participants were asked whether they found images in pairs to be similar and distinguishable. A preliminary validation of the stimuli was not conducted since participants were not expected to rank them in a certain way, as is the case, e.g., with the affective stimuli. However, future researchers need to delve more into the possibilities to validate the stimuli in the same or similar paradigms.

One of the reasons why similar abstract images were chosen as experimental stimuli instead of meaningful original attitudes is that the original attitude is an especially strong and central one. It involves a large degree of relevance and prior commitment, so attitude change is unlikely. According to this important attitude feature, changing strong attitudes in the laboratory will be hard. Furthermore, the experiments with no such strong attitudes have a higher chance of getting a cognitive dissonance.

Behavioral procedure:

This within-subject design study is based on an adopted procedure of Izuma and colleagues’ study10, who applied a modified free-choice paradigm using a “choice-blindness” procedure31,32, where individuals tend to believe that they chose the item that they actually prefer less than the alternative.

During the experiment, participants performed 4 following tasks in a fixed order: (1) the First Rating task, (2) the Choice task, (3) the Second Rating task, and (4) the Memory task (Figure 2). For the First Rating task, participants had to rate how much they liked each of the 96 images using an 8-point Likert scale without time restriction33. Then, they are presented with 48 pairs of images during the Choice task, have to choose one of the images within each pair by clicking on a corresponding rectangle beside the image, perform the Flanker task34, and then confirm their choice by clicking on the image. The Flanker task consists of a sequence of 7 arrows directed to the left or right, randomly to provide the real random directions for all arrows. Participants were instructed to define the direction of the central arrow and press the corresponding keyboard button as soon as possible. The Flanker task aims to distract the participant's attention by clicking on a rectangle instead of a picture in the first stage of choice.

The Choice task has three experimental conditions (Figure 2): switch trials (16 pairs), no-switch trials (16 pairs), and computer trials (16 pairs). All 48 pairs were fixed among the participants, but the programmed script decided which condition each pair was assigned to based on the difference in the ratings for the images within each pair.

When participants choose an image during the switch trials, the images switch positions after the Flanker task so that the top image appears at the bottom and the bottom image appears where the top image was located. The switch trials let control the preference artifact35. It was assumed that participants did not notice this replacement because the abstract images are similar. The Flanker task also aims to divert attention from replacing images. Additionally, participants are allotted only 3 seconds to make a choice before the images disappear, although responses based on memory remain possible thereafter. This condition does not apply to no-switch trials, where participants have to confirm their own choice. During computer trials, the choice is presented by the computer, and participants just have to follow it. The image chosen by the computer is defined based on a participant’s choice in a previous switch trial, i.e., if a participant chose an image with a lower rating in the switch trial, the computer “chooses” an image with a lower rating too. However, if the participant chose an image with a higher rating in a switch trial, the computer “chooses” the highest-rated image. The computer choice condition was used as a control condition, expecting that the preference change effect would not be present since participants would not experience the choice agency10.

The pairs were presented in a specific sequence: no-switch condition pairs are always presented in the 1st, 2nd, 5th, 8th, 11th, 16th, 17th, 20th, 22nd, 28th, 31st, 33d, 39th, 42nd, 43d, and 47th trial, switch condition pairs are always presented in the 3d, 6th, 9th, 12th, 14th, 18th, 21st, 24th, 29th, 32nd, 35th, 37th, 40th, 44th, and 46th trials whereas computer condition pairs always occur in the 4th, 7th, 10th, 13th, 15th, 19th, 23d, 25th, 30th, 34th, 36th, 38th, 41st, 45th, and 48th trials which are shortly after the switch trials. The reason for using such a sequence is that a computer's choice automatically mimics the participant's previous choice.

During the Second Rating task, participants must rate the same 96 images again, but with reminder cues about their previous choice during the Choice task. Each image is accompanied by a reminder of whether the participant or the computer chose this image or not (“You chose this image” or “Вы выбрали эту картинку” in Russian / ”You did not choose this image” or “Вы не выбрали эту картинку” in Russian / ”Computer chosen this image” or “Компьютер выбрал эту картинку” in Russian / ”Computer did not choose this image” or “Компьютер не выбрал эту картинку” in Russian).

To achieve an opportunity to stimulate different brain regions during the Second Rating task, it was decided to divide it into 4 blocks: the left dlPFC, the dmPFC, the vertex, and the sham-dlPFC stimulation. The dlPFC and dmPFC were chosen as brain regions of the most interest for the study, and the sham-dlPFC stimulation and vertex were chosen as the control conditions36. The stimulation target area of the left dlPFC was defined based on Voigt and colleagues' fMRI findings9 with the following coordinates [MNI: −24, 5, 47] since they have found the relative left dlPFC activation for a very similar task and paradigm. The coordinates for the dmPFC were defined based on Izuma and colleagues’ study10 [MNI: 2, 48, 36].

For all stimulation blocks, the online 20 Hz rTMS was applied for 1000 ms from the stimulus onset. Each stimulation block contains 12 pairs of stimuli and has about 2 min of block intervals to change the position of the stimulation coil and provide a safe period according to stimulation safety guidelines37,38. Additionally, if the First Rating task had no time restriction, the Second Rating task had a few time limitations, and each trial lasted from 11 to 13 seconds. First of all, a stimulus was presented just for 1 s with simultaneously applied rTMS during the whole second to cover all suggested starting points of the dlPFC and dmPFC activation. Participants had 4 s to rate the picture using the same 8-point Likert scale33 (from the stimulus onset, plus 3 seconds after that). Then, the fixation cross was presented jittered for 7 to 9 s to prevent participants from getting used to the stimulus onset timing and also to provide a safe inter-trial interval37,38. After completing each of the stimulation blocks, participants answered a questionnaire about how comfortable the stimulation was during this block.

After finishing the Second Rating task, participants also complete the unanticipated memory task, trying to retrieve from memory whether they chose or rejected the same images during the Choice task (computer trials are not included because they do not reflect participants’ choice). They also complete the PANAS questionnaire39, the Preference for Consistency Scale40, and a short post-experimental interview to ensure that participants do not notice the manipulation of their choice during the switch trials of the Choice task.

The task employed in this study is specifically designed to elicit cognitive dissonance by requiring participants to make choices between similarly preferred options, a scenario that triggers inconsistency in preferences and attitudes, a hallmark feature of cognitive dissonance10. The task involves the choice and the second rating task, which allows for disentangling between hypotheses of the cognitive dissonance timing: whether it happens already during the choice or during facing the same alternatives again. This task activates the dorsolateral and dorsomedial prefrontal cortices, regions implicated in decision-making and dissonance resolution. By employing online rTMS, this study not only downregulates these areas, provoking the effect lasting for all the tasks, but also precisely targets the neural activity associated with the options revaluation (second rating task). This protocol, along with the chosen task paradigm, is the most efficient combination, which allows further studies to compare the impact of stimulation during the choice between options and the second rating task, pinpointing the temporal dynamics of the preference change mechanism. The specificity of this experimental design, coupled with the temporal precision of online rTMS, offers a unique and powerful methodology to dissect the neural underpinnings and temporal characteristics of cognitive dissonance in decision-making.

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Protocol

All procedures were approved by the local research ethics committee of the Higher School of Economics (HSE), Moscow, with consent from all participants.

1. Participant recruitment

NOTE: Participants are required to report no history of implanted metallic devices, neurological disorders, or psychiatric conditions. TMS is administered in accordance with the most recent safety recommendations. Prior to participation, all individuals must be thoroughly informed about the nature of the study and provide written informed consent. An entire set of equipment needed to run the online repetitive TMS protocol utilizing neuronavigation and robotic systems for precise targeting of dorsolateral and dorsomedial prefrontal cortices during an experimental procedure was shown (Table of Materials).

  1. Check in the preliminary questionnaire and in the informed consent that participants are right-handed, have no metal, neuro- or cardio stimulators in their head or body, have no hearing problems and migraines, have never had any head traumas, are not pregnant, do not take psychotropic or antihistamine medications, have never had brain or spinal cord surgery and their relatives are not diagnosed with epilepsy.
  2. If participants indicate that they have ever fainted, check that these cases were related solely to physiological reasons. Besides, participants should be informed not to take alcohol or drugs one day before the experiment and not to take coffee or any products and drinks containing caffeine four hours before the start of the experiment.

2. Coil and robotic arm calibration

NOTE: Coil calibration is performed using the calibration plate, whereas the robotic arm does not need any additional equipment to be calibrated. All of them have silver reference balls, which allow the camera to recognize objects in space.

  1. Align the calibration plate with the coil, set the scale on both the calibration plate and coil, and calibrate using the software.
  2. Calibrate the robotic arm force sensor using the associated software. Press the robotic arm surface with different force levels to prevent hard pressure on the participant's head but ensure a snug fit to the head during stimulation.

3. Preparation of the individual MRI scans

NOTE: A major part of the navigation system preparation is preparing the system for individual MRI scans for the patient registration procedure. This can be done even before a participant comes. The main goal is to match the individual brain coordinates with the coordinates in universal coordinate systems, such as MNI and Talairach.

  1. First, make a 3D model of the participant's head using software associated with the TMS system.
  2. Perform the Talairach registration and alignment procedure.
    1. Align each subject's brain to a standardized brain coordinate system based on anatomical landmarks from the Talairach atlas. Manually identify and set 3 benchmarks, namely, the anterior commissure, posterior commissure, and falx cerebri, based on the Talairach atlas.
    2. Identify the anterior commissure as a small circular or oval structure located anterior to the third ventricle and inferior to the rostrum of the corpus callosum on mid-sagittal MRI slices and as a hyperintense dot anterior to the third ventricle on axial slices.
    3. Identify the posterior commissure on mid-sagittal MRI slices as a horizontally oriented structure situated just above the cerebral aqueduct and posterior to the third ventricle. For the falx cerebri select the MRI slice that exhibits maximal bilateral symmetry of deep brain structures and mark it on the interhemispheric fissure.
  3. Adjust the Talairach space grid to the size of the 3D model of the head. The Talairach space will be generated with standardized coordinates not dependent on individual brain differences.
  4. Set the targets of the future stimulation and the required rotation angle according to the predefined MNI coordinates of the study.
  5. Calculate entries for the targets as their corresponding locations on the surface of the participant's head to position the robotic arm during the stimulation.
    NOTE: In this experiment, entries for the vertex control point were manually defined. For marking the vertex target, mark the point located at the intersection of the mid-sagittal and coronal planes that pass through the nasion–inion and interaural line, respectively; or take the vertex coordinates in normalized MNI or Talairach space from prior literature and map the normalized point into the individual MRI space. Software automatically calculates the location in the individual MRI space through affine transformation matrices.
  6. For more accurate tracking of the participant's head location, set MRI landmarks on the 3D model of the participant's head. For this study, landmarks on the left and right ears and the nasion were used, but any other landmarks can be used based on convenience.

4. Patient registration

NOTE: The patient registration procedure is done when the participant arrives.

  1. Provide the consent forms, required information about the experiment, and earplugs for protecting the participant's hearing.
  2. Place the reference tracker on the participant's head. Ensure that it is attached to the participant's head at a point where the robotic arm will not occasionally touch the tracker during the stimulation.
    NOTE: The tracker consists of three silver reflective reference balls that are identified by the navigation system to localize the coordinates of the participant's head during the procedure and the experiment. Conventionally, the tracker is placed on the forehead. However, to be sure that the entry targets of the dlPFC and dmPFC are available for the navigation system and can be reached by the robotic arm, the tracker was placed between the forehead and the right ear (it can be placed near the left ear as well depending on the leading reference plate of the robotic arm).
  3. Fix the reference tracker on the participant's head with the elastic belt tightly enough, but ensure it is comfortable for the participant.
    NOTE: The belt fixation is flexible depending on the participant's head size. The belt was moved down to the forehead area, bent down, and fixed to provide better access to the dmPFC area with the robotic arm. The middle of the belt edge was also tucked to minimize the muscle contractions during stimulation. The strong muscle contractions could prevent participants from seeing the screen with stimuli and may also cause unpleasant sensations.
  4. When the reference tracker is placed on the participant's head, set the real-world landmarks (in this case, ears and the nasion) according to the previously found MRI landmarks using the pointer with a set of reference silver balls. Ensure that the Root Mean Square (RMS) Deviation value is not higher than 5 mm. If the value is higher, set the real-world landmarks again and recalculate the RMS Deviation value.
  5. Supplement these landmarks with a series of additional landmarks by the same pointer to define the participant's head surface. Add these points by creating continuous horizontal and vertical lines uniformly distributed along the head surface. A minimum of 300 additional surface landmarks are required.
    NOTE: After the surface registration, the RMS Deviation value should be less than 2 mm to provide good registration quality. When adding additional points, ensure they are represented adequately on the head surface.
  6. After the registration ends, check that the software correctly registers the position of the pointer on the 3D model by placing it at the head surface (not inside the head or far from its surface).

5. Motor threshold definition

NOTE: Different montages can be chosen to attach the electrodes. For this experiment, the anode electrode was placed on the projection of the first dorsal interosseous (FDI) muscle of the right arm, which was chosen as the target to define the motor threshold. The cathode was placed on the projection of the distal extremity of the proximal phalanx of the index finger, while the ground electrode was placed on the bone of the right arm near the elbow.

  1. Scrub participants' skin and remove remnants of the scrub with alcohol wipes.
  2. Take three clamp electrodes and insert them into the corresponding sockets of the amplifier (anode for the FDI muscle, cathode for the index finger bone, and the ground electrode for the area near the elbow).
  3. Stick three sticky electrodes to the participant's skin. While sticking the electrode to the FDI muscle, ask the participant to tense the FDI muscle by putting all the fingers together and stretching them, whereas to stick the electrode to the index finger bone, ask the participant to bend their finger.
  4. Attach clamp electrodes to sticky electrodes.
  5. Connect the amplifier to the charged battery.
  6. Ensure that the signal from the FDI muscle is not noisy.
    NOTE: The signal is registered with the special software (Table of Materials). It is particularly necessary to ensure a good signal because, otherwise, the motor threshold will be either overstated or understated. The noise amplitude should not exceed 50 µV peak-to-peak; otherwise, try some ways to make the participant's FDI muscle relaxed. If needed, place the participant's head on the pillow or ask the participant to strain his hand for a short time and then relax it.
  7. Set the stimulator intensity to 50% and then adjust it depending on the reaction to pulses, since the range of motor thresholds is considerable.
  8. Find the hand knob area in the precentral gyrus by orienting the coil according to the 3D individual head model. Ensure that the coil is placed tangentially to the head surface and oriented at 45° with respect to the midline. While finding the motor hotspot, make several pulses in the hand knob area and record the points on the head surface where the amplitude of the motor evoked potentials is large.
  9. Repeat the pulses to the points with the highest motor response to check their stability. Increase or decrease the stimulation intensity to find the approximate intensity at which a stable motor response is found only in the potential hotspot.
  10. After choosing the hotspot, orient the coil in the chosen position and make 10 pulses with the intensity at which you choose the motor hotspot. Count the number of times when the amplitude of the motor evoked potential exceeds 50 µV peak-to-peak. Adjust the stimulation intensity to a point where 5 out of 10 pulses of the motor evoked potential amplitude is equal to or higher than 50 µV.

6. Preparation for experimental stimulation

  1. In order to start the experimental stimulation, switch the manual coil to the robotic arm coil and switch the cooling system to ensure the proper work of the robotic arm.
  2. To provide better access to the region of interest and to ensure that the coil does not prevent from seeing the screen, adjust the back of the armchair to make the reclining participant's position. Due to the shift of the participant's head, adjust the navigation system so that it can easily locate the reference tracker and all the required stimulation areas. If needed, the armchair can be lifted or put down to ensure the robotic arm can access the stimulation points.
  3. Before launching the experiment, check the robotic arm's positioning on the participant's head surface. Ensure the participant gradually gets accustomed to the stimulation frequency.
  4. Since the stimulation will be triggered in the experimental software, connect the stimulator and the trigger station with the parallel port.
  5. Activate the software of the station (Table of Materials) to synchronize the software with it and set the stimulation parameters. For the experiment itself, the number of pulses should be set to 20 for the 20 Hz stimulation, and the half-period should be set to 25 ms.
  6. During the training session, start with one pulse and then increase the number of pulses to 5, 10, 15, and finally to 20. After each pulse or train of pulses, ask the participant if the stimulation is comfortable. If the stimulation causes discomfort, stop the experiment and exclude the participant from the final sample.
    NOTE: During the coil alignment, ensure the coil fits closely to the head surface but does not push it hard. If the coil pushes hard or does not remain stable on the head surface, the coil sensitivity settings can be adjusted.
  7. Adjust the screen position to make it fully visible for the participant in the reclining position. The mouse and the keyboard can be put on participants' knees or on the pillow above. Ensure that the stimulation task is organized in randomized blocks with intervals between them when navigating the robotic arm to the required position on the head's surface. During the interval, the participant can have some rest.

7. Data analysis

  1. Choose the dependent variable (here, Likert-scale preference change) and independent variables for the analysis (here, choice (chosen/rejected), rating task (first/second), trial type (switch/no-switch/computer) and stimulation block (dmPFC/dlPFC/sham/vertex)).
  2. Choose the threshold value for statistical significance (here, p-value ≤ .05).
  3. Test the normality of the responses distribution to choose parametric or non-parametric ANOVA statistical analysis.
  4. Conduct ANOVA to test the statistical significance of the preference change depending on the choice status, area of stimulation, target/control condition, presence of manipulation, and sense of agency (choice, rating task, trial type, stimulation factors).
  5. Conduct post-hoc pairwise comparisons to explore whether preference changes are significant within different stimulation blocks, trial types, and choices.
  6. Fit the linear mixed effects model and the ordinary least squares linear regression to test the significance of main effects (choice, rating task, trial type, and stimulation) and interaction effects.

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Results

46 participants were recruited and prepared for the stimulation, but 10 participants had to be excluded from the analysis (8 females and 2 males). Seven participants felt discomfort during the stimulation of the dlPFC and dmPFC because of the strong muscle contractions, 2 participants noticed the manipulation in switch trials during the second rating task, and the data for 1 participant were not recorded due to a technical error. A final sample for the rTMS study consisted of 36 participants (19 males; aged between 18 an...

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Discussion

According to the previous research, it was hypothesized that the inhibition of the left dlPFC or dmPFC activation should disrupt the process of preference change, demonstrating no choice-induced preference change due to the hard choice. Although actual results were inconsistent with the expected ones, several implications are important for future online rTMS research. The study builds upon and extends the findings of Izuma and colleagues10 and Colosio and colleagues13 by no...

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Disclosures

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgements

The article was prepared with the support of the Russian Science Foundation grant № 24-18-00432, https://rscf.ru/project/24-18-00432 and has been carried out using the HSE Automated System of non-invasive brain stimulation with the possibility of synchronous registration of brain activity and registration of eye movements.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Axilum Robotic TMS-Cobot Axilum Robotics, Schiltigheim, France2/1/2002
BrainAmp ExG amplifier - bipolar amplifierBrain Products, GmbH, Germany
BrainVision Recorder 1.21.0004Brain Products, GmbH, Germany
Chair with neckrestMagVenture, Farum, Denmark9016B0081
Electrode wires for surface EMGEBNeuro, Italy6515
Localite TMS Navigator - MR-based software, import data for
morphological MRI (DICOM, NifTi)		Localite, GmbH, Germany10226
Localite TMS Navigator - Navigation patform, Premium editionLocalite, GmbH, Germany21223
MagVenture 24.8 coil tracker,
Geom 1		Localite, GmbH, Germany5221
MagPro X100 MagOption - transcranial magnetic stimulatorMagVenture, Farum, Denmark9016E0731
Nuprep Skin Prep GelWeaver and Company, USA
Plastic non traumatic buttonE.M.S., Bologna, ItalyFIA-PG905/99
Rubber belt – 75 cmE.M.S., Bologna, ItalyFIA-ER-PG905/8
8-shaped coil MC-B65-HO-2MagVenture, Farum, Denmark9016E0462
Sticky tape
Surface Electrodes for EEG/EMGEBNeuro, Italy6515
Trigger Station TS832UBrainTrends ltd, Rome, Italy

References

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